A smart two-way scoring and management system for group insurance agents and channels

Abstract

The application discloses a kind of group insurance business personnel and channel intelligent two-way scoring and management system, specifically related to digital management technical field, including two-way scoring module, through the two-way evaluation interface of business personnel mobile terminal and channel management system deployment, real-time acquisition business personnel resource support score to channel and channel service ability score to business personnel, introduce Nash equilibrium game algorithm to eliminate malicious scoring deviation, and obtain two-way scoring data based on time decay factor and data normalization, and according to target business and multi-modal data acquisition module constructs two-way mapping relationship;The application is analyzed and processed by two-way scoring data and business operation layer data, interactive communication layer data, external environment data and compliance risk control layer data, and the business personnel-channel matching degree is outputted and intelligent strategy is triggered, the automatic intelligent matching of cooperative relationship is realized, the matching efficiency and accuracy are improved, and resource efficient configuration is promoted.

Description

Technical Field

[0001] This invention relates to the field of digital management technology, and more specifically, to an intelligent two-way scoring and management system for group insurance agents and channels. Background Technology

[0002] In group insurance business, the cooperative relationship between agents and channels plays a crucial role in business development and performance improvement; a good cooperative relationship can expand business coverage, improve customer service quality, enhance market competitiveness, and promote business innovation.

[0003] Traditional salesperson and channel management methods include recruitment and training, business plan development, and daily business supervision. Recruitment and training, based on business development needs, involve recruiting salespeople and screening partner channels through recruitment websites, talent markets, and internal referrals. In the business plan development step, management formulates annual, quarterly, and monthly business plans based on market conditions and company goals, clearly defining sales targets, target customer groups, and business expansion areas for salespeople and channels. Daily business supervision involves understanding the business progress of salespeople and channels through regular meetings, work reports, and on-site visits.

[0004] However, in actual use, it still has some shortcomings, such as low efficiency. In traditional group insurance business, the cooperation between agents and channels relies on manual experience matching, which is inefficient and lacks dynamic adjustment; there is a lack of two-way evaluation mechanism, and the cooperation effect between agents and channels cannot be quantified, resulting in uneven resource allocation and inconsistent cooperation quality; and insufficient dynamic management capabilities. Data such as agent performance, channel compliance scores, and customer feedback are scattered in different systems, making it difficult to integrate and analyze, resulting in a lack of dynamic learning capabilities and the inability to automatically optimize based on real-time data.

[0005] Therefore, there is an urgent need to provide an intelligent two-way scoring and management system for group insurance agents and channels to solve the problems of low efficiency, lack of two-way evaluation mechanism, and insufficient dynamic management capabilities in the existing agent and channel management methods. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent two-way scoring and management system for group insurance agents and channels, which solves the problems mentioned in the background art through the following solutions.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent two-way scoring and management system for group insurance agents and channels, comprising:

[0008] The two-way scoring module collects in real time the salesperson's resource support score for the channel and the channel's service capability score for the salesperson through the two-way evaluation interface deployed on the salesperson's mobile terminal and the channel management system. It introduces the Nash equilibrium game algorithm to eliminate malicious scoring bias, and obtains two-way scoring data based on the time decay factor and data normalization. It also constructs a two-way mapping relationship between the target business and the multimodal data acquisition module.

[0009] The multimodal data acquisition module connects to the insurance company's core business system, mobile sales terminals, and third-party credit reporting platforms through a distributed data bus. It collects target business-related data covering the entire business chain in real time, uses edge computing nodes for data cleaning and spatiotemporal alignment, and uses homomorphic encryption technology to protect the privacy of sensitive fields, thus building a basic data lake.

[0010] The indicator calculation module, based on the basic data lake of the multimodal data acquisition module, calculates the data influence coefficients of the business operation layer, the interaction and communication layer, the external environment, and the compliance and risk control layer through nonlinear coupling modeling, and constructs a two-way scoring core feature system.

[0011] The scoring data processing module uses graph database to construct a salesperson-channel scoring relationship network based on the bidirectional scoring data obtained from the bidirectional scoring module, employs LSTM model to predict scoring trends, extracts time series features, and calculates bidirectional scoring weight factors.

[0012] The matching degree calculation module non-linearly couples the data influence coefficients of the business operation layer, the interaction and communication layer, the external environment, and the compliance and risk control layer obtained by the indicator calculation module with the bidirectional scoring weight factors obtained by the scoring data processing module, and outputs the matching degree between the salesperson and the channel.

[0013] The salesperson and channel management module compares all salesperson-channel matching scores obtained from the matching score calculation module with the matching score, finds the three salesperson-channel matching scores with the highest scores, and obtains the corresponding salespersons based on the matching score mapping relationship, triggers the matching score strategy, and generates business strategy results.

[0014] The data interaction module transmits the corresponding salesperson information, target business-related data, and business strategy results to the information terminal, providing reference data for making adjustment measures.

[0015] Preferably, the two-way rating data includes salesperson rating data of the channel and channel rating data of the salesperson;

[0016] Salesperson's rating of the channel includes resource support rating (S1), settlement timeliness rating (S2), exception handling cooperation rating (S3), and system integration maturity rating (S4). Channel's rating of the salesperson includes solution professionalism rating (C1), response timeliness rating (C2), compliance rating (C3), and cooperation stability rating (C4).

[0017] Preferably, the basic data lake includes business operation layer data, interaction and communication layer data, external environment layer data, and compliance and risk control layer data;

[0018] The business operation layer data includes: effective task volume, denoted as Q; task processing time, denoted as T; operation error rate, denoted as E; process automation rate, denoted as A; interaction and communication layer data includes: total communication process time, denoted as D; average response delay, denoted as L; number of communication interruptions, denoted as B; emergency demand response rate, denoted as R; external environment layer data includes: market expense ratio, denoted as M; policy keyword hit rate, denoted as P; competitor activity intensity, denoted as C; data stale days, denoted as t; compliance and risk control layer data includes: number of abnormal operations, denoted as F; number of compliance inspection defects, denoted as D; risk handling timeliness, denoted as H.

[0019] Preferably, the data impact coefficient of the business operation layer represents the business scale efficiency through the effective task volume, the processing time and error rate constitute the efficiency loss factor, the process automation rate is used as the gain adjustment term, the task volume strengthens the scale effect in an exponential form, the error rate acts on the processing time in a multiplicative form, and the efficiency loss caused by errors is quantified as the "equivalent extension" of processing time. The automation rate is constrained by the upper limit of the gain with a saturation function to achieve dimensional normalization and nonlinear coupling, which is used to quantify the balance between the efficiency and quality of business personnel execution, and to build the capability profile in intelligent matching.

[0020] Preferably, the data impact coefficient of the interactive communication layer measures service input with the total duration of the communication process, uses response delay as a timeliness penalty, and adjusts emergency response efficiency by exponentially decaying the number of interruptions, thereby eliminating the conflict between the dimensions of communication duration and response speed, realizing a dynamic balance assessment of service quality, and is used to identify high-response quality salespersons.

[0021] Preferably, the external environment data impact coefficient is used to construct a competitive pressure baseline by the ratio of market costs to policy regulation, the intensity of competitor activities is used to form an environmental inhibition factor with exponential decay, and the time decay function dynamically weakens the impact of outdated policies, thereby achieving multi-dimensional coupling of external risks and being used to evaluate channel resource allocation strategies.

[0022] Preferably, the compliance risk control layer data impact coefficient is characterized by the geometric mean of abnormal operations and compliance defects to represent the risk exposure intensity, and the handling timeliness is constrained by an inverse proportional function to limit the risk diffusion speed, thereby achieving a dynamic balance between risk identification and control effectiveness.

[0023] Preferably, the two-way scoring weight factor is constructed by the geometric mean of the salesperson's score for the channel and the channel's score for the salesperson, and an exponential decay function of the score difference is introduced to suppress game bias, so as to achieve dynamic balance and dimension normalization of the two-way evaluation, which is used to quantify the compatibility of cooperation between the two parties.

[0024] Preferably, the salesperson-channel matching degree reflects basic service capabilities through the geometric mean of business operation efficiency and interactive communication quality. External environmental pressure is converted into a competitive gain coefficient through a Sigmoid function, and compliance risk forms a non-linear decay constraint with the inverse square. The two-way scoring weight superimposed with a hyperbolic tangent function strengthens the advantages of high-trust cooperation, realizing multi-dimensional coupling of business capabilities, environmental adaptability, risk control and subjective evaluation, which is used to quantify the value of dynamic adaptation.

[0025] Preferably, the matching degree strategy is triggered as follows:

[0026] When the salesperson-channel matching degree is the highest among the three salesperson-channel matching degrees, the corresponding salesperson and the corresponding channel will trigger a smart contract to extend the cooperation period, simultaneously increase resource quotas, allocate high-value product agency rights, open access to the core customer pool, be featured at the top of the internal resource platform, and participate in joint marketing activities with high-end customers.

[0027] When the salesperson-channel matching degree is not the top three salesperson-channel matching degrees, customized training courses are pushed to the corresponding salesperson and corresponding channel, an improvement progress report is generated weekly, and the improvement results data is updated and sent to the multimodal data acquisition module.

[0028] The technical effects and advantages of this invention are as follows:

[0029] This invention analyzes and processes bidirectional scoring data and data from the business operation layer, interactive communication layer, external environment layer, and compliance and risk control layer. The two together output the salesperson-channel matching degree and trigger intelligent strategies to achieve automated intelligent matching of cooperative relationships, improve matching efficiency and accuracy, and promote efficient resource allocation.

[0030] This invention collects in real time the resource support ratings of salespersons to channels and the service capability ratings of salespersons from channels through a two-way evaluation interface. Based on a graph database, it constructs a rating relationship network and calculates the two-way rating weight factors to form a quantitative evaluation system covering multiple dimensions such as resource support, settlement timeliness, solution professionalism, and compliance. This transforms the cooperation effect between the two parties into measurable data indicators, achieves dynamic balance and dimension unification of evaluation, and promotes transparent traceability and fair comparison of cooperation quality.

[0031] This invention not only collects and analyzes two-way evaluation data, but also connects to multi-source systems to build a basic data lake that includes business operations, interactive communication, external environment, and compliance risk control data. It achieves non-linear coupling of multi-dimensional data and two-way scoring weights, integrates full-chain data and performs real-time analysis and modeling, realizes dynamic evaluation of cooperation status and adaptive adjustment of strategies, and improves the real-time and scientific nature of management decisions. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] As attached Figure 1 The intelligent two-way scoring and management system for group insurance agents and channels, as shown, includes:

[0035] The two-way scoring module collects in real time the resource support scores of salespersons and the service capability scores of salespersons from the channel through a two-way evaluation interface deployed on the salesperson's mobile terminal and the channel management system. It introduces the Nash equilibrium game algorithm to eliminate malicious scoring bias, and obtains two-way scoring data based on the time decay factor and data normalization. It also constructs a two-way mapping relationship between the target business and the multimodal data acquisition module.

[0036] In this embodiment, it should be specifically noted that the two-way scoring data includes salesperson's scoring data on the channel and the channel's scoring data on the salesperson.

[0037] In this embodiment, it should be specifically noted that the salesperson's rating data for the channel includes resource support rating (S1), settlement timeliness rating (S2), exception handling cooperation rating (S3), and system integration maturity rating (S4). The channel's rating data for the salesperson includes solution professionalism rating (C1), response timeliness rating (C2), compliance rating (C3), and cooperation stability rating (C4).

[0038] The multimodal data acquisition module connects to the insurance company's core business system, mobile sales terminals, and third-party credit reporting platforms through a distributed data bus. It collects target business-related data covering the entire business chain in real time, uses edge computing nodes for data cleaning and spatiotemporal alignment, and uses homomorphic encryption technology to protect the privacy of sensitive fields, thus building a basic data lake.

[0039] In this embodiment, it should be specifically noted that the basic data lake includes business operation layer data, interaction and communication layer data, external environment layer data, and compliance and risk control layer data.

[0040] In this embodiment, it should be specifically noted that the business operation layer data includes the effective task volume, denoted as Q; task processing time, denoted as T; operation error rate, denoted as E; process automation rate, denoted as A; interaction and communication layer data includes the total communication process time, denoted as D; average response delay, denoted as L; number of communication interruptions, denoted as B; emergency demand response rate, denoted as R; external environment layer data includes the market expense ratio, denoted as M; policy keyword hit rate, denoted as P; competitor activity intensity, denoted as C; and data staleness days, denoted as t; compliance and risk control layer data includes the number of abnormal operations, denoted as F; the number of compliance inspection defects, denoted as D; and risk handling timeliness, denoted as H.

[0041] In this embodiment, it should be specifically noted that the effective task volume is obtained in real time through the RESTful API interface of the insurance company's CRM system, which retrieves the number of policies marked as "completed" by the salesperson in the system. Triggers are deployed in the MySQL database to monitor policy status change events, record operation timestamps and operator IDs, and execute an ETL job daily at midnight to directly collect and statistically analyze the effective task volume.

[0042] In this embodiment, it should be specifically explained that the task processing time is calculated by embedding a probe in the workflow engine of the OA system to record the task creation timestamp and completion timestamp, as follows:

[0043] ,

[0044] Where t start Indicates the task creation timestamp, t end This indicates the task completion timestamp.

[0045] In this embodiment, it should be specifically noted that the operation error rate is calculated by capturing business system error events through the audit log analysis system and using a log collector to capture error codes in real time.

[0046] In this embodiment, it should be specifically noted that the process automation rate is obtained by calling the API in the management platform to obtain the automated task execution records, connecting to the API of the task scheduling system, and calculating the proportion of automated tasks.

[0047] In this embodiment, it should be specifically noted that the total duration of the communication process is calculated by parsing the chat record metadata through the OpenAPI of the integrated communication platform, removing gaps of more than 5 minutes, recording them as the effective communication time, and using a time window function.

[0048] In this embodiment, it should be specifically explained that the average response delay is achieved by deploying a message queue listener in the customer service system to record the time of the customer's inquiry and the time of the first response, calculating the time difference and excluding non-working hours. Specifically:

[0049] ,

[0050] Where j represents the index of the query records, m represents the total number of query records, and tr j tq represents the time of the first human response to the j-th query record. j This indicates the time the customer asked the question in the j-th query record.

[0051] In this embodiment, it should be specifically noted that the number of communication interruptions is obtained by analyzing the detailed call records of the voice call system, filtering abnormal termination codes, and directly collecting abnormal disconnection events by calling the Webhook interface in the video conferencing system.

[0052] In this embodiment, it should be specifically noted that the emergency demand response rate is calculated by tracking overdue work orders after marking tasks with an emergency level in the work order system. Specifically:

[0053] ,

[0054] Where OTC represents the number of overdue work orders, and TUT represents the total number of urgent work orders.

[0055] In this embodiment, it should be specifically noted that the market expense ratio is obtained by exporting the market activity expense details through the OracleEBS module connected to the financial system, and calculating the total monthly expense by dividing the monthly market expense by the total monthly expense.

[0056] In this embodiment, it is specifically noted that the policy keyword hit rate and data staleness days are obtained by deploying a web crawler to periodically crawl, using the BERT model to extract text features, matching the target business keyword library, dividing the number of matched keywords by the total number of keywords, and recording the number of days from the policy release date to the present, i.e., the data staleness days.

[0057] In this embodiment, it should be specifically noted that the intensity of competitor activities is obtained by crawling marketing activity announcements from competitor websites or official accounts using the Scrapy framework, classifying activity types using an NLP model, and directly counting the number of competitor activities per month.

[0058] In this embodiment, it should be specifically noted that the number of abnormal operations is monitored by configuring policy rules in the database auditing system to monitor UPDATE operations on sensitive tables. Unauthorized access is identified using the RBAC permission model, and the data is directly collected.

[0059] In this embodiment, it should be specifically noted that the number of compliance inspection defects is directly collected by querying the failed items through the GraphQL interface by connecting to the inspection result database.

[0060] In this embodiment, it should be specifically noted that the risk handling timeliness is obtained by recording the risk warning trigger time and the handling completion time in the risk control system, and by calculating the risk handling time difference using Splunk.

[0061] The indicator calculation module, based on the basic data lake of the multimodal data acquisition module, calculates the data influence coefficients of the business operation layer, the interaction and communication layer, the external environment layer, and the compliance and risk control layer through nonlinear coupling modeling, and constructs a two-way scoring core feature system.

[0062] In this embodiment, it should be specifically explained that the data impact coefficient of the business operation layer represents the business scale efficiency through the effective task volume. Processing time and error rate constitute efficiency loss factors, and process automation rate serves as a gain adjustment term. The task volume amplifies the scale effect exponentially, and the error rate acts on the processing time multiplicatively, quantifying the efficiency loss caused by errors as an "equivalent extension" of processing time. The automation rate constrains the upper limit of gain with a saturation function, achieving dimensional normalization and nonlinear coupling. This is used to quantify the balance between the efficiency and quality of business personnel execution and to construct capability profiles in intelligent matching. Specifically:

[0063] ,

[0064] Where Q represents the effective number of tasks, T represents the task processing time, E represents the operation error rate, and A represents the process automation rate.

[0065] In this embodiment, it should be specifically noted that the data influence coefficient of the interactive communication layer measures service investment based on the total duration of the communication process, uses response delay as a timeliness penalty, and adjusts emergency response efficiency by exponentially decaying the number of interruptions. This eliminates the conflict between the dimensions of communication duration and response speed, achieving a dynamic balance assessment of service quality, and is used to identify high-response quality salespersons. Specifically:

[0066] ,

[0067] Where D represents the total duration of the communication process, L represents the average response delay, B represents the number of communication interruptions, and R represents the emergency response rate.

[0068] In this embodiment, it should be specifically noted that the external environment data impact coefficient is constructed by using the ratio of market expenses to policy regulation to form a competitive pressure baseline. The intensity of competitor activities decays exponentially to form an environmental inhibition factor. The time decay function dynamically weakens the impact of outdated policies, achieving multi-dimensional coupling of external risks. This is used to evaluate channel resource allocation strategies, specifically:

[0069] ,

[0070] Where M represents the proportion of market expenses, P represents the hit rate of policy keywords, C represents the intensity of competitor activities, and t represents the number of days of outdated data.

[0071] In this embodiment, it should be specifically noted that the data impact coefficient of the compliance risk control layer represents the risk exposure intensity by the geometric mean of abnormal operations and compliance defects, and the handling timeliness constrains the risk diffusion speed through an inverse proportional function, thereby achieving a dynamic balance between risk identification and control effectiveness. Specifically:

[0072] ,

[0073] Where F represents the number of abnormal operations, D represents the number of compliance inspection defects, and H represents the timeliness of risk handling.

[0074] The scoring data processing module uses a graph database to construct a salesperson-channel scoring relationship network based on the bidirectional scoring data obtained from the bidirectional scoring module. It then uses an LSTM model to predict scoring trends, extracts time series features, and calculates bidirectional scoring weight factors.

[0075] In this embodiment, it should be specifically explained that the two-way scoring weight factor is constructed by using the geometric mean of the salesperson's rating of the channel and the channel's rating of the salesperson. An exponential decay function of the scoring difference is introduced to suppress game bias, thereby achieving dynamic balance and dimensional normalization of the two-way evaluation. This is used to quantify the compatibility of cooperation between the two parties. Specifically:

[0076] ,

[0077] in

[0078] ,

[0079] in

[0080] ,

[0081] Where Ss represents the salesperson's overall rating of the channel, Sc represents the channel's overall rating of the salesperson, S1 represents the resource support rating, S2 represents the settlement timeliness rating, S3 represents the cooperation rating in handling exceptions, S4 represents the system integration maturity rating, C1 represents the solution professionalism rating, C2 represents the response timeliness rating, C3 represents the compliance rating, and C4 represents the cooperation stability rating.

[0082] The matching degree calculation module non-linearly couples the data influence coefficients of the business operation layer, the interaction and communication layer, the external environment, and the compliance and risk control layer obtained from the indicator calculation module with the bidirectional scoring weight factors obtained from the scoring data processing module, and outputs the salesperson-channel matching degree.

[0083] In this embodiment, it should be specifically explained that the salesperson-channel matching degree reflects basic service capabilities through the geometric mean of business operation efficiency and interactive communication quality. External environmental pressure is converted into a competitive gain coefficient via a Sigmoid function, and compliance risk forms a non-linear decay constraint with the inverse square. The superposition of bidirectional scoring weights and a hyperbolic tangent function strengthens the advantages of high-trust cooperation, realizing a multi-dimensional coupling of business capabilities, environmental adaptability, risk control, and subjective evaluation, used to quantify the value of dynamic adaptation. Specifically:

[0084] ,

[0085] Where Co represents the data influence coefficient of the business operation layer, Cc represents the data influence coefficient of the interaction and communication layer, Ce represents the data influence coefficient of the external environment layer, Cr represents the data influence coefficient of the compliance and risk control layer, and W represents the two-way scoring weight factor.

[0086] The salesperson and channel management module compares all salesperson-channel matching scores obtained from the matching score calculation module with the matching score, finds the three salesperson-channel matching scores with the highest scores, and obtains the corresponding salespersons based on the matching score mapping relationship, triggers the matching score strategy, and generates business strategy results.

[0087] In this embodiment, it should be specifically explained that the matching degree strategy triggering is as follows:

[0088] When the salesperson-channel matching degree is the highest among the three salesperson-channel matching degrees, the corresponding salesperson and the corresponding channel will trigger a smart contract to extend the cooperation period, simultaneously increase resource quotas, allocate high-value product agency rights, open access to the core customer pool, be featured at the top of the internal resource platform, and participate in joint marketing activities with high-end customers.

[0089] When the salesperson-channel matching degree is not the top three salesperson-channel matching degrees, customized training courses are pushed to the corresponding salesperson and corresponding channel, an improvement progress report is generated weekly, and the improvement results data is updated and sent to the multimodal data acquisition module.

[0090] The data interaction module transmits the corresponding salesperson information, target business-related data, and business strategy results to the information terminal, providing reference data for making adjustment measures.

[0091] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0092] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A two-way intelligent scoring and management system for group insurance agents and channels, characterized in that, The method comprises the following steps: A two-way scoring module, through a two-way evaluation interface deployed on a mobile terminal of a business agent and a channel management system, collects in real time a resource support score of the business agent on the channel and a service ability score of the channel on the business agent, introduces a Nash equilibrium game algorithm to eliminate malicious scoring bias, obtains two-way scoring data based on a time decay factor and data normalization, and constructs a two-way mapping relationship according to a target business and a multi-modal data acquisition module; A multi-modal data acquisition module, through a distributed data bus, connects an insurance company core business system, a mobile business terminal and a third-party credit investigation platform, collects in real time target business related data covering the whole business chain, performs data cleaning and time-space alignment by using an edge computing node, performs privacy protection processing on sensitive fields by using a homomorphic encryption technology, and constructs a basic data lake; An index calculation module, based on the basic data lake of the multi-modal data acquisition module, calculates a business operation layer data influence coefficient, an interactive communication layer data influence coefficient, an external environment data influence coefficient and a compliance and risk control layer data influence coefficient by using a nonlinear coupling modeling, and constructs a two-way scoring core feature system; A scoring data processing module, based on the two-way scoring module, uses a graph database to construct a business agent-channel scoring relationship network, uses an LSTM model to predict a scoring trend, extracts time series features, and calculates a two-way scoring weight factor; A matching degree calculation module, by nonlinear coupling of the business operation layer data influence coefficient, the interactive communication layer data influence coefficient, the external environment data influence coefficient and the compliance and risk control layer data influence coefficient obtained by the index calculation module and the two-way scoring weight factor obtained by the scoring data processing module, outputs a business agent-channel matching degree; A business agent and channel management module compares all business agent-channel matching degrees obtained by the matching degree calculation module, obtains the top three business agent-channel matching degrees, and according to the business agent-channel matching degree mapping relationship, obtains corresponding business agents, triggers a matching degree strategy, and generates a business strategy result; A data interaction module transmits corresponding business agent information, target business related data and a business strategy result to an information terminal, and provides reference data for taking adjustment measures.

2. The intelligent bidirectional scoring and management system for a group insurance agent and a channel according to claim 1, characterized in that: The two-way scoring data comprises business agent-channel scoring data and channel-business agent scoring data; The business agent-channel scoring data comprises a resource support score S1, a settlement timeliness score S2, an abnormality processing cooperation degree score S3, a system docking maturity score S4, a channel-business agent scoring data comprises a scheme professionalism score C1, a response timeliness score C2, a compliance score C3 and a cooperation stability score C4.

3. The intelligent bidirectional scoring and management system for a group insurance agent and a channel according to claim 1, characterized in that: The basic data lake comprises business operation layer data, interactive communication layer data, external environment layer data and compliance and risk control layer data; The business operation layer data comprises an effective task amount Q, a task processing time length T, an operation error rate E and a process automation rate A; The interactive communication layer data comprises a communication process total time length D, an average response delay L and a communication interruption number B. Emergency demand response rate, denoted as R; external environment layer data including market cost proportion, denoted as M; policy keyword hit rate, denoted as P; competitor activity intensity; denoted as C, data obsolescence days, denoted as t; compliance risk control layer data including abnormal operation times, denoted as F; compliance inspection defect number, denoted as D; risk disposal timeliness, denoted as H.

4. The intelligent bidirectional scoring and management system for group insurance business personnel and channels according to claim 3, characterized in that: the business operation layer data influence coefficient is specifically: ， wherein Q represents effective task quantity, T represents task processing time length, E represents operation error rate, and A represents process automation rate.

5. The intelligent bidirectional scoring and management system for group insurance business personnel and channels according to claim 3, characterized in that: the interactive communication layer data influence coefficient is specifically: ， wherein D represents total communication process time length, L represents average response delay, B represents communication interruption times, and R represents emergency demand response rate.

6. The intelligent bidirectional scoring and management system for group insurance business personnel and channels according to claim 3, characterized in that: the external environment data influence coefficient is specifically: ， wherein M represents market cost proportion, P represents policy keyword hit rate, C represents competitor activity intensity, and t represents data obsolescence days.

7. The intelligent bidirectional scoring and management system for group insurance business personnel and channels according to claim 3, characterized in that: the compliance risk control layer data influence coefficient is specifically: ， wherein F represents abnormal operation times, D represents compliance inspection defect number, and H represents risk disposal timeliness.

8. The intelligent bidirectional scoring and management system for group insurance business personnel and channels according to claim 3, characterized in that: the bidirectional scoring weight factor is specifically: ， wherein ， wherein ， wherein Ss represents business personnel's comprehensive score on channels, Sc represents channels' comprehensive score on business personnel, S1 represents resource support score, S2 represents settlement timeliness score, S3 represents abnormal processing cooperation degree score, S4 represents system interfacing maturity score, C1 represents scheme professionalism score, C2 represents response timeliness score, C3 represents compliance score, and C4 represents cooperation stability score.

9. The intelligent bidirectional scoring and management system for group insurance business personnel and channels according to claim 1, characterized in that: the business personnel-channel matching degree is specifically: ， wherein Co represents business operation layer data influence coefficient, Cc represents interactive communication layer data influence coefficient, Ce represents external environment data influence coefficient, Cr represents compliance risk control layer data influence coefficient, and W represents bidirectional scoring weight factor.

10. The intelligent bidirectional scoring and management system for a banding agent and a channel according to claim 1, wherein: the matching degree strategy triggering is specifically: when the business personnel-channel matching degree is the maximum of the three business personnel-channel matching degrees, the business of the corresponding business personnel and the corresponding channel triggers the intelligent contract to extend the cooperation period, synchronously improves the resource quota, allocates high-value product agency rights, opens core customer pool access rights, tops the recommendation on the internal resource platform, and participates in high-end customer joint marketing activities. When the business- channel matching degree is not the top three business- channel matching degrees, the corresponding business- channel matching degree is not the top three business- channel matching degrees, the corresponding business- channel matching degree is not the top three business- channel matching degrees, the corresponding business- channel matching degree is not the top three business- channel matching degrees, the corresponding business- channel matching degree is not the top three business- channel matching degrees, the corresponding business- channel matching degree is not the top three business- channel matching degrees, the corresponding business- channel matching degree is not the top three business- channel matching degrees, the corresponding business- channel matching degree is not the top three business- channel matching degrees, the corresponding

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