CN118972494B - An intelligent voice outbound calling platform - Google Patents

The invention relates to the technical field of automatic dialing, in particular to an intelligent voice outbound platform which comprises a network monitoring module, a cost calculating module, a geographic matching module, a time optimizing module, a selection adjusting module and a call encrypting module. According to the invention, by correlating the intelligent voice outbound success rate with the cost and carrying out cost benefit analysis, the resource allocation is more accurate, the service quality and the customer satisfaction are improved, the service is more personalized and the response speed is faster by matching the geographic position and the demand of the customer, the dialing strategy is adjusted to adapt to the service condition and the congestion condition of a network channel, the call quality and the cost efficiency are further optimized, the network is dynamically adjusted to capture the optimal balance point of the cost and the performance, in the continuously changing network environment, the user can always obtain the optimal service experience, the enterprise operation cost is reduced, the user experience is enhanced by customizing the communication mode, and the revolutionary influence is generated on the operation of the call center.

Intelligent voice outbound platform

Technical Field

The invention relates to the technical field of automatic dialing, in particular to an intelligent voice outbound platform.

Background

The technical field of automatic dialing is focused on starting telephone calls by utilizing an automatic platform, is applied to call centers, customer service centers and marketing activities, and aims to improve the working efficiency and reduce the requirement of manually dialing the telephone. The automatic dialing platform can automatically dial telephone numbers according to a preset list, and transfer the call to a real operator or play a prerecorded message after the call is connected. May be further integrated with speech recognition platforms, artificial intelligence, and machine learning algorithms to optimize response policies, analyze call content, and provide personalized services. Developments in automatic dialing technology also include complex algorithms to manage call timing, optimize call allocation, and comply with legal regulations, such as preventing frequent nonsensical calls.

The intelligent voice outbound platform is a platform integrating an automatic dialing technology and a voice processing technology, aims at automating a telephone call process and providing highly interactive customer service experience, effectively manages batch outbound activities by using an automatic dialing function, and integrates voice recognition and natural language processing technologies at the same time, so that the platform can understand and respond to voice input of customers. The intelligent voice outbound platform is widely applied to the fields of customer support, marketing popularization, information broadcasting and the like, can remarkably improve communication efficiency, reduces enterprise operation cost, and enhances user experience by providing a customized communication mode. The popularity of the platform is a revolutionary improvement to call center operation, enabling businesses to maintain good customer relationships while maintaining high efficiency.

Although the existing automatic dialing technology can manage batch calls and improve efficiency, the existing automatic dialing technology lacks adaptability to network states and deep analysis on cost effectiveness, and in actual operation, particularly in environments with complex or unstable network conditions, service quality is different and customer experience difference is large. For example, auto-dialing platforms still blindly initiate calls when the network is congested, resulting in high call failure rates and inefficient resource utilization, especially deadly in the customer service and marketing areas. The prior art does not fully utilize geographical and time data to optimize dialing policies, resulting in the inability to provide personalized services according to the actual needs and location of customers, which is a disadvantage in a strong market competition.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides an intelligent voice outbound platform.

In order to achieve the above purpose, the present invention adopts the following technical scheme that an intelligent voice outbound platform includes:

The network monitoring module is used for monitoring the delay of a plurality of network channels, recording the packet loss rate and the data transmission speed, analyzing and recording performance indexes and generating network performance data;

The cost calculation module calculates the cost of the differentiated intelligent voice outbound channel by adopting the network performance data, correlates the intelligent voice outbound success rate with the cost and outputs a cost benefit analysis result;

The geographic matching module performs matching analysis of the geographic position and the intelligent voice outbound network state according to the cost benefit analysis result and the client geographic position and the real-time changing requirement of the user, and outputs the target network configuration;

the time optimization module adopts the target network configuration, adjusts the time and user selection of the intelligent voice outbound according to the network channel use condition and the congestion condition of the differentiated time period, and constructs an adjustment dialing strategy;

The selection adjustment module is used for optimizing the selection of the intelligent voice outbound network based on the adjustment dialing strategy, capturing the optimal balance point of the intelligent voice outbound cost and the performance through the dynamic adjustment network, and outputting the network selection strategy;

And the call encryption module distributes an encryption key for the intelligent voice outbound call according to the network selection strategy, verifies the data security of the call through the use of the real-time management key, and outputs an outbound encryption verification result.

As a further aspect of the present invention, the network performance data includes delay indexes, packet loss rates, and data transmission speeds of differentiated channels, the cost benefit analysis results include operation costs, call success rates, and cost comparison analysis results of channels, the target network configuration includes optimal channel selection and priority setting, the adjustment dialing policy includes dialing time and channel selection policy adjusted differently according to network congestion conditions and time periods, the network selection policy includes optimized network routing and connection priorities, and the outbound encryption verification results include encryption key management and verification records, key validity, and encryption strength verification of intelligent voice outbound.

As a further aspect of the present invention, the network monitoring module includes:

the network monitoring submodule monitors the delay of the multi-network channel, circularly captures delay data of the differential network channel, records the delay data by combining with a timestamp of the intelligent voice outbound, analyzes the change trend of the data and generates delay monitoring data;

the data recording sub-module extracts key information from the delay monitoring data, and identifies the use record of the network channel by combining the packet loss rate and the data transmission speed of the network channel to generate a network transmission record;

And the performance analysis submodule utilizes the network transmission record to analyze the data of the service condition of the differentiated network channel, and evaluates the network performance in a multi-dimensional way to obtain network performance data.

As a further aspect of the present invention, the cost calculation module includes:

The cost accounting submodule calculates the operation cost of the differential intelligent voice outbound channel based on the network performance data, calculates the operation cost of the intelligent voice outbound channel according to the influence of network quality on cost fluctuation, and generates channel cost data;

The success rate analysis submodule analyzes the success rate of the intelligent voice outbound by utilizing the channel cost data, associates the call success rate with the differentiated channel cost, analyzes the relationship between the cost and the success rate, and outputs cost success rate association data;

And the benefit computing sub-module extracts key indexes from the cost success rate associated data, evaluates the optimal ratio of the cost to the success rate, and formulates a cost benefit balance strategy to obtain a cost benefit analysis result.

As a further aspect of the present invention, the geographic matching module includes:

The position positioning sub-module is used for analyzing regional characteristics and user distribution conditions based on the cost-benefit analysis result and combining geographic position information of differentiated users, evaluating influence factors of geographic positions on intelligent voice outbound calls and generating geographic analysis data;

The demand analysis submodule utilizes the geographic analysis data, adopts a clustering algorithm to identify a geographic area associated with intelligent voice outbound demand, dynamically matches the demand of a user in real time, and outputs a demand matching result;

and the network configuration sub-module performs configuration optimization of the intelligent voice outbound network according to the requirement matching result, analyzes optimal network channel selection, and checks that the geographic position is optimally matched with the network state to obtain target network configuration.

As a further aspect of the present invention, the formula of the clustering algorithm is as follows:

Wherein, As the sum of the squares of the total distances,For the sampleIs a sum of the number of (c),For clusteringIs used in the number of (a) and (b),In order to indicate the variable(s),As the weight coefficient of the light-emitting diode,As the data of the geographic location of the object,As a center of the cluster,For the intensity of the user's demand,In order to be a center of the required intensity,In order to be time sensitive, the time sensitive,In the event of a time-sensitive centre,In the case of a geographical distance,In order for the intensity of demand to be a distance,Is a time sensitive distance.

As a further aspect of the present invention, the time optimization module includes:

The time analysis submodule analyzes network channel use data of different time periods based on the target network configuration, records network activity and user behavior patterns in each time period, identifies peaks and valleys of network loads and generates the time use data;

The network condition evaluation submodule evaluates the congestion condition of the network by utilizing the time use data, identifies a load period and a channel, records the congestion condition of the network by real-time monitoring and outputs a congestion evaluation result;

and the policy making sub-module adjusts the time setting and the user selection parameters of the intelligent voice outbound according to the congestion evaluation result and combining the conversation habit and the network state of the user, optimizes the dialing time and the channel allocation, and checks the quality and the efficiency of the intelligent voice outbound to obtain an adjusted dialing policy.

As a further aspect of the present invention, the selection adjustment module includes:

the policy analysis submodule analyzes the relevance of the intelligent voice outbound behavior and the dialing policy by adopting a decision tree algorithm based on the adjustment dialing policy, evaluates the adaptability and efficiency of the policy, analyzes the adjustment requirement of the policy and generates policy optimization data;

the network adjustment submodule utilizes the strategy optimization data to implement dynamic adjustment of network configuration, adjusts channel allocation and network routing according to real-time network performance feedback, optimizes intelligent voice outbound communication quality and outputs a network optimization result;

And the balance point positioning sub-module extracts key performance indexes from the network optimization result, performs comparison analysis with the cost data, analyzes the optimal balance point of the cost benefit, verifies the cost and performance optimization of the intelligent voice outbound, and obtains a network selection strategy.

As a further aspect of the present invention, the decision tree algorithm has the following formula:

Wherein, Is the non-purity of the kene,For belonging to category in nodeIs a function of the probability of (1),As a total number of categories,For the set of attributes that are additionally referenced,Is a set of attributesMiddle (f)The value of the individual attribute(s),Is a set of attributesIs used for the average value of (a),Is the firstThe weight of the individual attributes is determined,As the weight coefficient of the light-emitting diode,Is a set of attributesIs a number of (3).

As a further aspect of the present invention, the call encryption module includes:

the key distribution submodule dynamically distributes an encryption key for the intelligent voice outbound call based on the network selection strategy, and audits the authority of the use of the encryption key to generate encrypted key data;

the key monitoring submodule utilizes the encrypted key data to implement real-time management and monitoring of the key, update and verify the validity of the key, avoid key leakage and reuse and output a key management result;

And the encryption examination sub-module performs encryption verification on the intelligent voice outbound call through the key management result, verifies the integrity of encryption implementation, verifies the security and privacy protection of call data, and obtains an outbound encryption verification result.

Compared with the prior art, the invention has the advantages and positive effects that:

In the invention, the delay of the multi-network channel, the packet loss rate and the data transmission speed are monitored, the optimization brought by analyzing the network performance data is remarkable, the intelligent voice outbound success rate is correlated with the cost, and the cost benefit analysis is carried out, so that the resource allocation is more accurate, and the service quality and the customer satisfaction are improved. By matching the geographic position and the demand of the client and combining the optimized network configuration, the service is more personalized and the response speed is faster. The dialing strategy is adjusted to adapt to the use condition and the congestion condition of the network channel, so that the call quality and the cost efficiency are further optimized. The network is dynamically adjusted to capture the optimal balance point of cost and performance, and the real-time adjustment mechanism ensures that in a continuously-changing network environment, a user can always obtain the optimal service experience, so that the enterprise operation cost is reduced, the user experience is enhanced through a customized communication mode, and revolutionary influence is generated on the operation of a call center.

Drawings

FIG. 1 is a platform flow diagram of the present invention;

FIG. 2 is a schematic view of a platform frame according to the present invention;

FIG. 3 is a flow chart of a network monitoring module according to the present invention;

FIG. 4 is a flow chart of a costing module of the present invention;

FIG. 5 is a flowchart of a geographic matching module of the present invention;

FIG. 6 is a flow chart of a time optimization module of the present invention;

FIG. 7 is a flow chart of a selective adjustment module according to the present invention;

fig. 8 is a flow chart of a call encryption module according to the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Referring to fig. 1 to 2, an intelligent voice outbound platform includes:

The network monitoring module monitors the delay of a plurality of network channels, records the packet loss rate and the data transmission speed, analyzes and records the performance index, identifies the use record of the network channels, evaluates the network performance in a multi-dimensional way and generates network performance data;

the cost calculation module calculates the cost of the differentiated intelligent voice outbound channel by adopting network performance data, correlates the intelligent voice outbound success rate with the cost, evaluates the optimal ratio of the cost to the success rate, and outputs a cost benefit analysis result;

the geographic matching module performs matching analysis of the geographic position and the intelligent voice outbound network state according to the cost benefit analysis result and the client geographic position and the real-time change requirement of the user, and outputs the target network configuration;

the time optimization module adopts target network configuration, adjusts the time and user selection of intelligent voice outbound according to the network channel use condition and congestion condition of the differentiated time period, optimizes dialing time and channel allocation, and constructs an adjustment dialing strategy;

The selection adjustment module is used for optimizing the selection of the intelligent voice outbound network based on the adjustment dialing strategy, capturing the optimal balance point of the intelligent voice outbound cost and the performance through the dynamic adjustment network, and outputting the network selection strategy;

The call encryption module distributes encryption keys for intelligent voice outbound calls according to a network selection strategy, avoids key leakage and repeated use by managing the use of the keys in real time, checks the data security of the calls, and outputs outbound encryption verification results.

The network performance data comprises delay indexes, packet loss rates and data transmission speeds of differentiated channels, the cost benefit analysis results comprise operation cost, conversation success rate and cost comparison analysis results of the channels, the target network configuration comprises optimal channel selection and priority setting, the adjustment dialing strategy comprises dialing time and channel selection strategy which are adjusted in a differentiated mode according to network congestion conditions and time periods, the network selection strategy comprises optimized network routing and connection priorities, and the outbound encryption verification results comprise encryption key management and verification records, key validity and encryption strength verification of intelligent voice outbound.

Referring to fig. 2 and 3, the network monitoring module includes:

The network monitoring submodule monitors the delay of the multi-network channel, circularly captures delay data of the differential network channel, records the delay data by combining with a timestamp of the intelligent voice outbound, analyzes the change trend of the data, and generates an execution flow of the delay monitoring data as follows;

The network monitoring submodule monitors the delay of the multiple network channels, ensures the continuity and the efficiency of network communication, circularly captures the delay data of each network channel, and records the delay data by combining with the timestamp of the intelligent voice outbound platform. By analyzing the data, the change trend of the network delay and the potential network bottleneck can be identified, and a network manager is helped to adjust the network configuration or enhance the network capacity in time. The future network delay problem can be predicted through long-term monitoring and data analysis, a scientific basis is provided for network maintenance, delay monitoring data are generated, and the following formula is adopted:

Wherein, Representing the delay monitor data and,Represent the firstThe delay of the individual network channels is determined,For the number of network channels,Is the corresponding timestamp.

The data recording sub-module extracts key information from the delay monitoring data, and identifies the use record of the network channel by combining the packet loss rate and the data transmission speed of the network channel, and the execution flow of generating the network transmission record is as follows;

The data recording sub-module extracts key information from the delay monitoring data, and combines the packet loss rate and the data transmission speed of the network channels to identify the service condition of each network channel, record and analyze the performance indexes of the network, such as delay, packet loss rate and throughput, so as to identify the stability and efficiency of the network. Through the data, the performance of the network under different conditions can be better understood, demonstration data are provided for network optimization, network transmission records are generated, and the formula is adopted:

Wherein, Representing a record of the network transmission,Is the firstThe delay data of the individual channels is used,For the number of channels to be used,Is the packet loss rate of the packet,Is the data transmission speed.

The performance analysis submodule utilizes network transmission records to analyze data of the service conditions of the differentiated network channels, and the performance of the network is estimated in a multi-dimensional manner, so that the execution flow of the obtained network performance data is as follows;

The performance analysis submodule utilizes network transmission records to carry out deep analysis on the service conditions of different network channels, evaluates the network performance in a multi-dimensional mode, and analyzes performance data of each channel, such as delay, packet loss rate and throughput, so as to identify strengths and weaknesses of the network performance. Through multi-dimensional evaluation, the performance of the network in different scenes can be revealed, the target network configuration of an administrator is helped, the overall performance of the network is improved, network performance data is obtained, and the following formula is adopted:

Wherein, Representing the network performance data and,Is the number of channels that are to be formed,AndRespectively represent the firstDelay, packet loss rate, and data transmission speed of each channel.

Referring to fig. 2 and 4, the cost calculation module includes:

The cost accounting submodule calculates the operation cost of the differential intelligent voice outbound channel based on the network performance data, calculates the operation cost of the intelligent voice outbound channel by referring to the influence of the network quality on the cost fluctuation, and generates an execution flow of the channel cost data as follows;

The cost accounting submodule carries out detailed accounting on the operation cost of the intelligent voice outbound channel based on the network performance data, evaluates the performance of different network channels, including delay, packet loss rate and data transmission speed, and how factors influence the operation cost. By analyzing the relationship between network quality and cost, it is possible to identify channels that are cost-increasing due to inefficient network performance. The relationship between investment and potential cost savings required to maintain a high quality network is also calculated, helping a decision maker to formulate a more cost effective network operation strategy, generate channel cost data, and employ the formula:

Wherein, Representing the running cost of the intelligent voice outbound channel,Is the number of channels that are to be formed,Is the firstThe unit operation cost of the individual channels,Is the firstNetwork quality index of each channel.

The success rate analysis submodule analyzes the success rate of the intelligent voice outbound by utilizing the channel cost data, associates the call success rate with the differentiated channel cost, analyzes the relation between the cost and the success rate, and outputs the execution flow of the cost success rate association data as follows;

The success rate analysis submodule analyzes the success rate of the intelligent voice outbound by utilizing the channel cost data, explores the relation between the call success rate and the differentiated channel cost, and can identify the channel with the highest and lowest cost efficiency by combining the cost of the channel with the call success rate so as to provide data support for optimizing resource allocation and cost management. Helping to reveal how to improve overall success rate by improving network performance or adjusting resource allocation, outputting cost success rate associated data, adopting the formula:

Wherein, Represents the cost-success rate-related data,Is the number of channels that are to be formed,Is the firstThe success rate of the individual channels is determined,Is the corresponding running cost.

The benefit computing sub-module extracts key indexes from the cost success rate associated data, evaluates the optimal ratio of the cost to the success rate, and formulates a cost benefit balance strategy to obtain an execution flow of a cost benefit analysis result as follows;

the benefit computing sub-module extracts key indexes from the cost success rate associated data, evaluates the optimal ratio of the cost to the success rate, and uses the data to formulate a cost benefit balancing strategy for maximizing return on investment. By comparing the cost-benefit ratios of different channels, the optimal resource allocation and budget allocation strategy can be recommended to improve the overall operation efficiency and customer satisfaction, and the cost-benefit analysis result is obtained by adopting the following formula:

Wherein, The results of the cost-benefit analysis are presented,AndRespectively represent the firstThe benefit of the success rate of the individual channels and the corresponding costs.

Referring to fig. 2 and 5, the geographic matching module includes:

The location positioning sub-module is used for analyzing regional characteristics and user distribution conditions based on cost-effectiveness analysis results and combining geographic location information of differentiated users, evaluating influence factors of geographic locations on intelligent voice outbound calls, and generating execution flow of geographic analysis data as follows;

The position positioning sub-module is based on a cost-benefit analysis result, and is used for evaluating influence factors of geographic positions on intelligent voice outbound calls by combining geographic position information of differentiated users, further analyzing regional characteristics and user distribution conditions, and identifying how a specific geographic position affects call success rate and cost benefit. By analysis, insight can be provided as to how to adjust the service policies to suit the needs of a particular area. The decision maker is helped to understand the specific influence of regional factors on service efficiency, more targeted resource allocation and service optimization are promoted, geographic analysis data are generated, and the formula is adopted as follows:

Wherein, Representing the data of the geographical analysis,Is the number of areas to be treated,Is the firstThe geographical location identity of the individual areas,Is the distribution density of users at the geographic location,Is cost-effective data related to the region.

The demand analysis sub-module utilizes geographic analysis data, adopts a clustering algorithm to identify a geographic area associated with intelligent voice outbound demand, dynamically matches the demand of a user in real time, and outputs the execution flow of a demand matching result as follows;

The demand analysis sub-module utilizes the geographic analysis data, adopts a clustering algorithm to identify geographic areas closely related to intelligent voice outbound demands, classifies the geographic areas according to demand characteristics, and realizes dynamic matching of user demands. The method is helpful for optimizing service allocation and ensuring that resources are utilized most effectively geographically. By tracking and analyzing the geographical distribution of the user demands in real time, more accurate service adjustment suggestions can be provided, and demand matching results can be output.

The formula of the clustering algorithm is as follows:

Wherein, As the sum of the squares of the total distances,For the sampleIs a sum of the number of (c),For clusteringIs used in the number of (a) and (b),In order to indicate the variable(s),As the weight coefficient of the light-emitting diode,As the data of the geographic location of the object,As a center of the cluster,For the intensity of the user's demand,In order to be a center of the required intensity,In order to be time sensitive, the time sensitive,In the event of a time-sensitive centre,In the case of a geographical distance,In order for the intensity of demand to be a distance,Is a time sensitive distance.

The execution flow is as follows:

selecting geographic location data Intensity of user demandAnd time sensitivityDetermining the number of clusters as a feature of a set of sample pointsInitializing a cluster centerCenter of required intensityAnd a time sensitive centerFor each sample point, calculating and distributing the sample points to the nearest clustering center, and based on the weighted comprehensive distance, weighting coefficientsIs determined by an optimization algorithm such as grid search to ensure that the importance of each feature is properly reflected, updatedUpdating the center point of each clusterSo thatAnd (3) minimizing, repeating the process until the clustering centers are not changed or reach the preset iteration times, outputting the demand category of each geographic area and the distance between each geographic area and each clustering center, and matching the user demands and the geographic positions in real time.

The network configuration sub-module performs configuration optimization of the intelligent voice outbound network according to the requirement matching result, analyzes optimal network channel selection, and checks that the geographic position is optimally matched with the network state, so that the execution flow of target network configuration is obtained as follows;

And the network configuration sub-module performs configuration optimization of the intelligent voice outbound network according to the demand matching result, analyzes optimal network channel selection and ensures optimal matching of the geographic position and the network state. By evaluating the performance and cost effectiveness of each network channel and the association with the geographic location, the most suitable network configuration strategy can be recommended, which is helpful for improving the call quality and reducing the operation cost, and the target network configuration is obtained by adopting the following formula:

Wherein, Representing the result of the configuration of the target network,Is the number of channels that are to be formed,Is the firstThe quality of service score for each channel,Is the performance to cost-effectiveness ratio of the channel.

Referring to fig. 2 and 6, the time optimization module includes:

The time analysis sub-module analyzes the network channel use data in the differentiated time periods based on the target network configuration, records the network activity and the user behavior mode in each time period, identifies the peak value and the valley of the network load, and generates the execution flow of the time use data as follows;

The time analysis sub-module is based on the target network configuration, and further analyzes the network channel use data in the differentiated time periods, continuously records the network activity and the user behavior patterns in each time period, and provides detailed data to identify peaks and valleys of the network load. The time period analysis is helpful for understanding the performance of network channels in a specific time period, and can predict and prepare necessary resource allocation to cope with expected high load, generate time usage data, and adopts the following formula:

Wherein, The time-of-use data is represented,Is the total amount of hours that will be needed,Is the firstActivity level within one hour of each other,Is user behavior data within the same time period.

The network condition evaluation submodule evaluates the congestion condition of the network by utilizing time use data, identifies a load period and a channel, records the congestion condition of the network by real-time monitoring, and outputs the execution flow of the congestion evaluation result as follows;

The network condition evaluation sub-module utilizes time usage data to accurately evaluate the congestion condition of the network, and identifies the load peak time and related channels by monitoring and recording the congestion condition of the network in real time, thereby being beneficial to timely finding and solving the network congestion problem, ensuring the smoothness of a communication channel, outputting the congestion evaluation result, and adopting the following formula:

Wherein, The congestion evaluation result is indicated as such,Is the number of time periods that are to be counted,Is a time period ofThe network load within the network is such that,Is the channel capacity over a period of time.

The policy making sub-module adjusts the time setting and user selection parameters of the intelligent voice outbound according to the congestion evaluation result and combining the call habit and the network state of the user, optimizes the dialing time and channel allocation, and checks the quality and efficiency of the intelligent voice outbound to obtain an execution flow of adjusting the dialing policy as follows;

The policy making sub-module adjusts the time setting and the user selection parameters of the intelligent voice outbound according to the congestion evaluation result and combining the conversation habit and the network state of the user, and improves the quality and the efficiency of the intelligent voice outbound by optimizing the dialing time and the channel allocation. Through policy adjustment, the call attempt in the congestion period can be reduced, the user experience and the overall responsiveness of the service are improved, the dialing policy is adjusted, and the following formula is adopted:

Wherein, Indicating that the adjusted dialing strategy is to be used,Is a channelIs used in the number of (a) and (b),Is the optimized dialing opportunity parameter,Is a channel allocation parameter.

Referring to fig. 2 and 7, the selection adjustment module includes:

The strategy analysis sub-module is used for analyzing the relevance of the intelligent voice outbound behavior and the dialing strategy based on the adjustment dialing strategy by adopting a decision tree algorithm, evaluating the adaptability and the efficiency of the strategy, analyzing the adjustment requirement of the strategy and generating the execution flow of strategy optimization data, wherein the execution flow is as follows;

The policy analysis sub-module is used for analyzing the relevance of the intelligent voice outbound behavior and the dialing policy by adopting a decision tree algorithm based on the adjustment of the dialing policy, analyzing the intelligent voice outbound data by using the decision tree algorithm, and identifying the influence of different dialing policies on the success rate and the response time. And (3) evaluating the adaptability and efficiency of the existing dialing strategy through analysis, determining whether strategy adjustment is needed, and generating strategy optimization data.

The formula of the decision tree algorithm is as follows:

Wherein, Is the non-purity of the kene,For belonging to category in nodeIs a function of the probability of (1),As a total number of categories,For the set of attributes that are additionally referenced,Is a set of attributesMiddle (f)The value of the individual attribute(s),Is a set of attributesIs used for the average value of (a),Is the firstThe weight of the individual attributes is determined,As the weight coefficient of the light-emitting diode,Is a set of attributesIs a number of (3).

The execution flow is as follows:

determining current node, selecting partition attribute, calculating each category Probability in current nodeObtaining the original Indonesia, for the additional attribute setCalculate each attributeRelative to the average valueAnd multiplying by a weightSumming the deviations to obtain a weighted sum of the dispersion of the attributes using coefficientsTo balance the influence of the non-purity of the genii and the dispersity of the attribute, and calculateValue, determining weight coefficientIs determined by cross-validation or AIC (red pool information content) criteria to ensure generalization ability and accuracy of the model, and integrally calculates the node-improved genie unreliability.

The network adjustment submodule utilizes strategy optimization data to implement dynamic adjustment of network configuration, adjusts channel allocation and network routing according to real-time network performance feedback, optimizes intelligent voice outbound communication quality, and outputs the execution flow of a network optimization result as follows;

The network adjustment submodule utilizes strategy optimization data to implement dynamic adjustment of network configuration, and dynamically adjusts channel allocation and network routing according to real-time network performance feedback. The adjustment aims at optimizing the communication quality of the intelligent voice outbound and ensures the high efficiency and stability of data transmission. By continuously monitoring the network state and adjusting the network configuration, outputting a network optimization result, and adopting the formula:

Wherein, A network optimization score is represented and,Is an indexIs a sum of the number of (c),Is an index of the performance of the network,Is an index of the delay to be used,AndIs a weight factor.

The balance point positioning sub-module extracts key performance indexes from the network optimization result, performs comparison analysis with cost data, analyzes the optimal balance point of cost benefit, verifies the cost and performance optimization of intelligent voice outbound, and obtains the execution flow of a network selection strategy as follows;

The balance point positioning sub-module extracts key performance indexes from the network optimization result, compares and analyzes the key performance indexes with the cost data, the platform analyzes the optimal balance point of the cost benefit to verify the cost and performance optimization of the intelligent voice outbound, the platform can find the optimal operation point between the cost and the performance, the intelligent voice outbound platform is enabled to operate in the most economical and effective state to obtain a network selection strategy,

Wherein, Representing a point of cost-effective balance,Is the performance index of the product, is the performance index,Is an index of the cost, and is used for the production of the high-quality steel,Is a parameter under considerationIs a sum of (3).

Referring to fig. 2 and 8, the call encryption module includes:

The key distribution submodule dynamically distributes an encryption key for the intelligent voice outbound call based on a network selection strategy, and audits the authority of the encryption key to use, and the execution flow of generating the encrypted key data is as follows;

The key distribution submodule dynamically distributes an encryption key for the intelligent voice outbound call based on a network selection strategy, the platform determines proper key types and lengths according to the network selection strategy so as to adapt to security requirements under different network conditions, dynamically generates the encryption key, audits the authority of key use, ensures that the key is only used by authorized users and devices, and generates encrypted key data.

The key monitoring submodule utilizes the encrypted key data to implement real-time management and monitoring of the key, update and verify the validity of the key, avoid key leakage and reuse, and output the execution flow of the key management result as follows;

The key monitoring submodule utilizes the encrypted key data to implement real-time management and monitoring of the key, and the platform continuously updates and verifies the validity of the key to ensure that the key is not illegally copied or revealed. By monitoring the use condition of the key in real time, the platform can discover any abnormal activity in time, avoid the risks of key leakage and repeated use, output a key management result, and adopt the formula as follows:

Wherein, Representing the monitored state of the key,Is the firstThe weight of the individual keys is determined,Is an indication function of the display,Is a key that is used to store a key,Is the validity time of the keyIs the total number of keys monitored.

The encryption examination sub-module performs encryption verification on the intelligent voice outbound call through the key management result, verifies the integrity of encryption implementation, verifies the safety and privacy protection of call data, and obtains the execution flow of the outbound encryption verification result as follows;

The encryption examination sub-module performs encryption verification on the intelligent voice outbound call through the key management result, and the platform checks the integrity of encryption implementation to ensure that the encryption process of each call meets the safety standard. The platform also can verify the security and privacy protection of the call data, confirm that the data is not tampered or revealed in the transmission process, and obtain the outbound encryption verification result through a series of verification.

The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.