Insurance operation process whole-link analysis method and device, equipment and medium

Abstract

The application relates to the field of intelligent decision-making, and discloses an insurance operation process full-link analysis method and device, electronic equipment and a storage medium. The method comprises the following steps: defining a standard operation process of a service personnel; training a pre-trained hidden Markov model by using a marked text to obtain a trained hidden Markov model; training a pre-trained long short-term memory model by using a hidden state to obtain a trained long short-term memory model; inputting a small-stage distribution into a pre-trained language representation model to train the pre-trained language representation model by using the small-stage distribution, and obtaining a trained language representation model; analyzing a service operation process corresponding to a current interaction record by using the trained hidden Markov model, the trained long short-term memory model and the trained language representation model; and updating a question and answer knowledge base by using the service operation process to obtain an updated knowledge base. The application can finely manage an insurance operation process of a service personnel to a user.

Description

Technical Field

[0001] The present invention relates to the fields of intelligent decision-making and financial technology, and in particular to a method, device, electronic device and computer-readable storage medium for full-link analysis of insurance operation processes. Background Art

[0002] As a highly competitive field, the insurance industry has significant complexity and professional characteristics in its products, which requires agents to have certain professional knowledge and skills. At the same time, the purchase decision of insurance products usually requires users to think carefully, so agents need to have in-depth communication and exchanges with users to understand their needs and concerns and provide targeted products and services.

[0003] In order to improve the efficiency and effectiveness of agent operations, insurance companies need to conduct refined management of operating processes and analysis of user demands. Refined management can better organize and manage operating processes and improve work efficiency. At the same time, it can also better analyze and optimize operating processes and improve agent operation results. Analysis of user demands can help agents better understand user needs and expectations, provide products and services that better meet user needs, and improve user satisfaction and purchase intention. At the same time, through analysis of user demands, operations personnel can also better understand market trends and competition, and provide support for the company to formulate more effective application strategies and plans. Therefore, refined management and analysis of user demands are important means to improve the efficiency and effectiveness of agent operations in the insurance field, and are also one of the key factors for insurance companies to enhance their competitiveness.

[0004] In the existing technology, there is a lack of an effective method to finely manage the insurance operation process of agents for users. Summary of the Invention

[0005] The present invention provides a method, device, electronic device and computer-readable storage medium for analyzing the entire chain of insurance operation processes, the main purpose of which is to finely manage the insurance operation processes of agents for users.

[0006] To achieve the above objectives, the present invention provides a method for analyzing the entire insurance process chain, comprising:

[0007] Collecting historical interaction records between agents and historical users, converting the historical interaction records into historical interaction texts, and performing text preprocessing on the historical interaction texts to obtain preprocessed texts;

[0008] defining a standard operating procedure for the agent, annotating the pre-processed text using the standard operating procedure to obtain annotated text, and constructing a question-and-answer knowledge base between the agent and the historical user using the pre-processed text;

[0009] Constructing a pre-trained hidden Markov model, a pre-trained long-short-term memory model, and a pre-trained language representation model between the agent and the historical user;

[0010] Using the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model, and obtaining a hidden state of the trained hidden Markov model with respect to the annotated text;

[0011] Training the pre-trained long short-term memory model through the hidden state to obtain a trained long short-term memory model, and obtaining a small stage distribution of the trained long short-term memory model with respect to the hidden state;

[0012] Inputting the small-stage distribution into the pre-trained language representation model to train the pre-trained language representation model using the small-stage distribution to obtain a trained language representation model;

[0013] Obtaining a current interaction record between the agent and the current user, and analyzing the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long short-term memory model, and the trained language representation model;

[0014] The question-and-answer knowledge base is updated through the agent operation process to obtain an updated knowledge base, and the agent operation process and the updated knowledge base are used to determine the full-link analysis result of the operation process between the agent and the current user.

[0015] In order to solve the above problems, the present invention further provides a device for analyzing the entire chain of insurance operation processes, the device comprising:

[0016] A text preprocessing module is used to collect historical interaction records between agents and historical users, convert the historical interaction records into historical interaction texts, and perform text preprocessing on the historical interaction texts to obtain preprocessed texts;

[0017] a knowledge base construction module, configured to define a standard operating procedure for the agent, annotate the pre-processed text using the standard operating procedure to obtain annotated text, and construct a question-and-answer knowledge base between the agent and the historical user using the pre-processed text;

[0018] A model building module, used to build a pre-trained hidden Markov model, a pre-trained long short-term memory model and a pre-trained language representation model between the agent and the historical user;

[0019] a state acquisition module, configured to train the pre-trained hidden Markov model using the annotated text to obtain a trained hidden Markov model, and obtain a hidden state of the trained hidden Markov model with respect to the annotated text;

[0020] a stage acquisition module, configured to train the pre-trained long short-term memory model using the hidden state to obtain a trained long short-term memory model, and obtain a small stage distribution of the trained long short-term memory model with respect to the hidden state;

[0021] A language training module, configured to input the small-stage distribution into the pre-trained language representation model, so as to train the pre-trained language representation model using the small-stage distribution to obtain a trained language representation model;

[0022] a process analysis module for obtaining a current interaction record between the agent and the current user, and analyzing the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long-short-term memory model, and the trained language representation model;

[0023] The full-link analysis module is used to update the question-and-answer knowledge base through the agent operation process to obtain an updated knowledge base, and use the agent operation process and the updated knowledge base to determine the full-link analysis result of the operation process between the agent and the current user.

[0024] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0025] at least one processor; and,

[0026] a memory communicatively connected to the at least one processor; wherein,

[0027] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to implement the above-mentioned full-link analysis method of the insurance operation process.

[0028] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one computer program. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned full-link analysis method of the insurance operation process.

[0029] It can be seen that the embodiment of the present invention defines the standard operating procedures of the agent to divide the agent operating procedures into macro stages and specific small stages from the dual dimensions of online and offline, and deeply analyzes the user's demands for each small stage. Furthermore, the embodiment of the present invention uses the preprocessed text to construct a question-and-answer knowledge base between the agent and the historical user to provide users with seamless and efficient question answers. This strategy strengthens the control over the agent's operating procedures and improves the user experience. The embodiment of the present invention uses the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model for efficiently identifying the large stages of the operating process and providing a macro perspective. The embodiment of the present invention uses the hidden state to train the pre-trained long short-term memory model to use LSTM to go into details. , accurately capturing small stage changes in the operation process, and complementing the aforementioned hidden Markov model, together constructing a more complete and more accurate agent operation process stage identification system. The embodiment of the present invention distributes the pre-trained language representation model through the small stage to obtain a trained language representation model for utilizing the bidirectional encoding mechanism of BERT and the sequence modeling capability of CRF, so that the combined model can effectively process long texts and complex sentence structures. Even when faced with user demand expressions that have never appeared before, it can still be recognized based on the existing learning model and has strong generalization capabilities. The embodiment of the present invention updates the question-and-answer knowledge base through the agent operation process for regular review of the content of the knowledge base to ensure the accuracy and timeliness of the information. At the same time, a feedback mechanism is established to collect feedback from users and agent teams on the use of the knowledge base and continuously optimize the content. Therefore, the full-link analysis method, device, electronic device and computer-readable storage medium of the insurance operation process proposed in the embodiment of the present invention can finely manage the insurance operation process of agents for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of the application environment of the full-link analysis method for insurance operation processes provided by one embodiment of the present invention;

[0031] Figure 2 A schematic diagram of a process flow for a full-link analysis method for an insurance operation process according to an embodiment of the present invention;

[0032] Figure 3 A schematic diagram of the modules of a device for analyzing the entire insurance operation process according to an embodiment of the present invention;

[0033] Figure 4 A schematic diagram of the internal structure of an electronic device for implementing a full-link analysis method for insurance operation processes according to an embodiment of the present invention;

[0034] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] The embodiment of the present invention provides a full-link analysis method for insurance operation process, which can be applied in Figure 1 In an application environment, wherein the client communicates with the server through a network, the server can collect historical interaction records between the agent and historical users through the client, convert the historical interaction records into historical interaction texts, perform text preprocessing on the historical interaction texts to obtain preprocessed texts; define the standard operating procedures of the agent, use the standard operating procedures to annotate the preprocessed texts to obtain annotated texts, and use the preprocessed texts to build a question-and-answer knowledge base between the agent and the historical users; build a pretrained hidden Markov model, a pretrained long-short term memory model and a pretrained language representation model between the agent and the historical users; use the annotated texts to train the pretrained hidden Markov model to obtain a trained hidden Markov model, and obtain the hidden state of the trained hidden Markov model with respect to the annotated texts; The pre-trained long-short-term memory model is trained through the hidden state to obtain a trained long-short-term memory model, and the small-stage distribution of the trained long-short-term memory model with respect to the hidden state is obtained; the small-stage distribution is input into the pre-trained language representation model to train the pre-trained language representation model through the small-stage distribution to obtain a trained language representation model; the current interaction record between the agent and the current user is obtained, and based on the question-and-answer knowledge base, the agent operation process corresponding to the current interaction record is analyzed through the trained hidden Markov model, the trained long-short-term memory model and the trained language representation model; the question-and-answer knowledge base is updated through the agent operation process to obtain an updated knowledge base, and the full-link analysis result of the operation process between the agent and the current user is determined by using the agent operation process and the updated knowledge base.

[0037] Reference Figure 2 FIG. 1 is a flow chart of a method for analyzing the entire insurance operation process according to an embodiment of the present invention. In this embodiment of the present invention, the method for analyzing the entire insurance operation process includes the following steps S1-S5:

[0038] S1. Collect historical interaction records between agents and historical users, convert the historical interaction records into historical interaction texts, and perform text preprocessing on the historical interaction texts to obtain preprocessed texts.

[0039] In an embodiment of the present invention, the historical interaction records refer to historical call records between humans and AI robots, online interaction records, etc. (voice, text data) in the insurance industry.

[0040] Optionally, the process of converting the historical interaction records into historical interaction texts may use ASR (automatic speech recognition) to convert the insurance voice call records into texts, while the historical interaction records that are originally texts do not need to be converted.

[0041] Optionally, the process of performing text preprocessing on the historical interactive text includes removing stop words and special characters, correcting spelling errors, restoring stems, etc.

[0042] S2. Define a standard operating procedure for the agent, use the standard operating procedure to annotate the preprocessed text to obtain annotated text, and use the preprocessed text to construct a question-and-answer knowledge base between the agent and the historical user.

[0043] The embodiment of the present invention defines a standard operating procedure for the agent to divide the agent operating procedure into macroscopic stages and specific sub-stages from both online and offline dimensions, and conducts in-depth analysis of user demands for each sub-stage.

[0044] The standard operating procedure refers to the response that the agent needs to make based on the user's inquiry or text after the agent has a conversation with the user.

[0045] In one embodiment of the present invention, the definition of the standard operating procedures of the agent includes: dividing the agent's operating procedure stages; wherein the operating procedure stages include an opening remarks stage, a demand exploration stage, a product introduction stage, an objection handling stage, a result facilitation stage, and a closing remarks stage; subdividing the operating procedure stages to obtain subdivided process stages; and using the operating procedure stages and the subdivided process stages as the standard operating procedures of the agent.

[0046] Exemplarily, the division of the agent's work process stages and the subdivision of the work process stages to obtain the subdivided process stages include: defining a standard agent work process stage and subdivided small stages, such as opening remarks (agent self-introduction, confirmation of user identity), demand exploration (asking users about specific needs and questions), product introduction (product recommendation, detailed introduction of product or service features and advantages), objection handling (answering user concerns, membership rules consultation, claims process consultation, preferential consultation), transaction facilitation (making purchase suggestions, quoting, and attempting to close a deal), closing remarks (thanking the user for their time, providing follow-up contact information, and making appointments for services), etc.

[0047] Optionally, the process of marking the pre-processed text using the standard operating procedure refers to identifying the texts in the pre-processed text that belong to the same major stage or minor stage based on the standard operating procedure. For example, the pre-processed text between the agent and the user includes 1 to 5, of which 1 to 2 belong to the major stage of opening remarks, 1 belongs to the minor stage of agent self-introduction, and 2 belongs to the minor stage of confirming user identity.

[0048] Furthermore, the embodiment of the present invention uses the preprocessed text to build a question-and-answer knowledge base between the agent and the historical user to provide users with seamless and efficient question answers. This strategy strengthens the control over the agent's operating process and improves the user experience.

[0049] Among them, the question and answer knowledge base is mainly composed of keywords in the preprocessed text, user intentions and agent responses corresponding to user intentions (agent responses refer to the insurance operation processes made by the agents, such as sending product information, arranging follow-up words, etc.).

[0050] S3. Construct a pre-trained hidden Markov model, a pre-trained long short-term memory model, and a pre-trained language representation model between the agent and the historical user.

[0051] In an embodiment of the present invention, the pre-trained hidden Markov model refers to a model composed of an untrained hidden Markov model, the pre-trained long short-term memory model refers to a model composed of an untrained multi-layer stacked LSTM network, and the pre-trained language representation model is composed of an untrained BERT model, a CRF (conditional random field) layer and an intent recognition module, wherein the CRF (conditional random field) layer and the intent recognition module serve as the two output ends of the BERT model, and the intent recognition module is composed of a fully connected layer and a softmax layer.

[0052] S4. Using the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model, and obtaining a hidden state of the trained hidden Markov model with respect to the annotated text.

[0053] The embodiment of the present invention uses the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model, which is used to efficiently identify the major stages of the work process and provide a macro perspective.

[0054] In one embodiment of the present invention, the method of using the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model includes: obtaining the real large stage distribution in the annotated text; extracting keywords and phrases in the annotated text; generating observation data of the annotated text using the keywords and phrases; calculating the state transition probability between each real large stage distribution in the real large stage distribution in the pre-trained hidden Markov model; calculating the observation probability of the observation data under the real large stage distribution in the pre-trained hidden Markov model; analyzing the virtual large stage distribution corresponding to the observation data based on the state transition probability and the observation probability; calculating the loss function value between the real large stage distribution and the virtual large stage distribution; and training the parameters to be trained of the pre-trained hidden Markov model based on the loss function value to obtain a trained hidden Markov model.

[0055] The observation data refers to a sequence consisting of the keywords and the phrases.

[0056] Optionally, the process of analyzing the virtual large stage distribution corresponding to the observation data based on the state transition probability and the observation probability refers to: obtaining the state transition probability P1(1, 2) between 1 real large stage distribution and 2 real large stage distributions, obtaining the observation probability P2(1, 3) between 1 real large stage distribution and 3 observation data, and calculating P3(2, 3) based on P1(1, 2) and P2(1, 3), which is generally calculated through the extended formula Bayesian probability, prior probability and posterior probability of the Markov chain.

[0057] Optionally, the process of training the parameters to be trained of the pre-trained hidden Markov model based on the loss function value can be implemented by a gradient descent algorithm.

[0058] Furthermore, in an embodiment of the present invention, the hidden state refers to a virtual large-scale distribution corresponding to the annotated text output by the trained hidden Markov model.

[0059] S5. Train the pre-trained long short-term memory model through the hidden state to obtain a trained long short-term memory model, and obtain the small stage distribution of the trained long short-term memory model with respect to the hidden state.

[0060] The embodiment of the present invention trains the pre-trained long short-term memory model through the hidden state, so as to use LSTM to delve into details and accurately capture small stage changes in the operation process. The two complement each other with the aforementioned hidden Markov model to jointly build a more complete and accurate agent operation process stage identification system.

[0061] In one embodiment of the present invention, the pre-trained long short-term memory model is trained through the hidden state to obtain a trained long short-term memory model, including: obtaining an input layer, a multi-layer long short-term memory layer and a fully connected layer in the pre-trained long short-term memory model; obtaining an input gate, an output gate and a forget gate of the multi-layer long short-term memory layer; after receiving the hidden state through the input end, extracting an initial feature vector of the hidden state using the input gate, the output gate and the forget gate; identifying a long-term dependency of the initial feature vector using the multi-layer long short-term memory layer; converting the initial feature vector into a final feature vector using the multi-layer long short-term memory layer according to the long-term dependency; obtaining a real small stage distribution in the annotated text; calculating a probability distribution of the final feature vector with respect to the real small stage distribution using the fully connected layer; identifying a virtual small stage distribution of the hidden state through the probability distribution; calculating a loss value between the virtual small stage distribution and the real small stage distribution; and optimizing the parameters to be optimized in the pre-trained long short-term memory model according to the loss value to obtain a trained long short-term memory model.

[0062] Among them, the long-term dependency relationship refers to the implicit relationship identified by the stacked multi-layer LSTM, which is represented by the final feature vector. For example, in the scenario where the LSTM of the first layer and the LSTM of the second layer are stacked, the initial feature vector is converted into the final feature vector by the connection relationship between the LSTM of the first layer and the LSTM of the second layer. During the conversion process, the long-term dependency relationship of the identified initial feature vector will be blended into the vector to obtain the final feature vector. Therefore, the final feature vector evolves from the long-term dependency relationship and represents the long-term dependency relationship.

[0063] Furthermore, in an embodiment of the present invention, the small-stage distribution refers to a virtual small-stage distribution corresponding to the hidden state output by the trained long short-term memory model.

[0064] S6. Input the small-stage distribution into the pre-trained language representation model, so as to train the pre-trained language representation model through the small-stage distribution to obtain a trained language representation model.

[0065] The embodiment of the present invention obtains a trained language representation model by training the pre-trained language representation model through the small-stage distribution, so as to utilize the bidirectional encoding mechanism of BERT and the sequence modeling capability of CRF, so that the combined model can effectively process long texts and complex sentence structures. Even when faced with user demand expressions that have never appeared before, it can still be recognized based on the existing learning model, and has strong generalization ability.

[0066] In one embodiment of the present invention, the pre-trained language representation model is trained through the small-stage distribution to obtain a trained language representation model, including: obtaining the BERT module, entity recognition module and intent recognition module in the pre-trained language representation model; using the BERT module to output the semantic vector of the small-stage distribution; using the entity recognition module to identify the key entities of the semantic vector; using the intent recognition module to identify the user intent corresponding to the semantic vector; according to the key entity, using a preset multi-task learning framework to optimize the first network parameters between the BERT module and the entity recognition module to obtain optimized first network parameters; according to the user intent, using the multi-task learning framework to optimize the second network parameters between the BERT module and the intent recognition module to obtain optimized second network parameters; determining the trained language representation model corresponding to the pre-trained language representation model through the optimized first network parameters and the optimized second network parameters.

[0067] Among them, the first network parameters and the second network parameters both include weights and biases, the first network parameters between the BERT module and the entity recognition module are equivalent to the network parameters that only include the BERT module and the entity recognition module, and the second network parameters between the BERT module and the intent recognition module are equivalent to the network parameters that only include the BERT module and the intent recognition module.

[0068] Optionally, the process of determining the trained language representation model corresponding to the pre-trained language representation model through the optimized first network parameters and the optimized second network parameters refers to taking the language representation model including the optimized first network parameters and the optimized second network parameters as the trained language representation model.

[0069] S7. Obtain the current interaction record between the agent and the current user, and analyze the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long-short-term memory model, and the trained language representation model.

[0070] In one embodiment of the present invention, the agent operation process corresponding to the current interaction record is analyzed based on the question and answer knowledge base through the trained hidden Markov model, the trained long short-term memory model and the trained language representation model, including: identifying the first-stage distribution corresponding to the current interaction record through the trained hidden Markov model; outputting the second-stage distribution corresponding to the first-stage distribution through the trained long short-term memory model; extracting the text key entities and text intent of the second-stage distribution using the trained language representation model; querying the question and answer record corresponding to the text key entity in the question and answer knowledge base; and querying the agent operation process corresponding to the text intent in the question and answer record.

[0071] Among them, the text key entity refers to the user text keywords, such as insurance type, insurance amount, terms, etc., the text intention refers to the intention of the user text, such as consultation, quotation, complaint, purchase, etc., and the question and answer record refers to the conversation record related to the text key entity (insurance type, insurance amount, terms, etc.).

[0072] S8. Update the question-and-answer knowledge base through the agent operation process to obtain an updated knowledge base, and use the agent operation process and the updated knowledge base to determine a full-link analysis result of the operation process between the agent and the current user.

[0073] The embodiment of the present invention updates the question-and-answer knowledge base through the agent operation process to regularly review the content of the knowledge base to ensure the accuracy and timeliness of the information. At the same time, a feedback mechanism is established to collect feedback from users and the agent team on the use of the knowledge base and continuously optimize the content.

[0074] Among them, the seat operation process refers to the seat response, and the seat response refers to the insurance operation process made by the seat, such as sending product information, arranging subsequent follow-up words, etc.

[0075] In one embodiment of the present invention, updating the question and answer knowledge base through the agent operation process to obtain an updated knowledge base includes: obtaining feedback information of the current user on the agent operation process; determining whether the feedback information is negative information; when the feedback information is negative information, querying the current user's required operation process; correcting the agent operation process to the required operation process to obtain a corrected operation process; and using the corrected operation process to complete the update of the question and answer knowledge base to obtain an updated knowledge base.

[0076] Among them, the feedback information refers to the user's satisfaction survey on the seat operation process collected in the questionnaire initiated to the user after the question and answer session is completed; the negative information refers to dissatisfaction information; and the required operation process refers to the seat operation process required by the current user determined by human experts.

[0077] It can be seen that the embodiment of the present invention defines the standard operating procedures of the agent to divide the agent operating procedures into macro stages and specific small stages from the dual dimensions of online and offline, and deeply analyzes the user's demands for each small stage. Furthermore, the embodiment of the present invention uses the preprocessed text to construct a question-and-answer knowledge base between the agent and the historical user to provide users with seamless and efficient question answers. This strategy strengthens the control over the agent's operating procedures and improves the user experience. The embodiment of the present invention uses the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model for efficiently identifying the large stages of the operating process and providing a macro perspective. The embodiment of the present invention uses the hidden state to train the pre-trained long short-term memory model to use LSTM to go into details. , accurately capturing small stage changes in the operation process, and complementing the aforementioned hidden Markov model, together constructing a more complete and more accurate agent operation process stage identification system. The embodiment of the present invention distributes the pre-trained language representation model through the small stage to obtain a trained language representation model for utilizing the bidirectional encoding mechanism of BERT and the sequence modeling capability of CRF, so that the combined model can effectively process long texts and complex sentence structures. Even when faced with user demand expressions that have never appeared before, it can still be recognized based on the existing learning model and has strong generalization capabilities. The embodiment of the present invention updates the question-and-answer knowledge base through the agent operation process for regular review of the content of the knowledge base to ensure the accuracy and timeliness of the information. At the same time, a feedback mechanism is established to collect feedback from users and agent teams on the use of the knowledge base and continuously optimize the content. Therefore, the full-link analysis method, device, electronic device and computer-readable storage medium of the insurance operation process proposed in the embodiment of the present invention can finely manage the insurance operation process of agents for users.

[0078] like Figure 3 The figure shows a functional module diagram of the full-link analysis device for the insurance operation process of the present invention.

[0079] The insurance operation process full-link analysis device 100 described in the present invention can be installed in an electronic device. According to the functions to be implemented, the insurance operation process full-link analysis device can include a text preprocessing module 101, a knowledge base construction module 102, a model construction module 103, a state acquisition module 104, a stage acquisition module 105, a language training module 106, a process analysis module 107 and a full-link analysis module 108. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform fixed functions, which are stored in the memory of the electronic device.

[0080] In this embodiment, the functions of each module / unit are as follows:

[0081] The text preprocessing module 101 is used to collect historical interaction records between agents and historical users, convert the historical interaction records into historical interaction texts, and perform text preprocessing on the historical interaction texts to obtain preprocessed texts;

[0082] The knowledge base construction module 102 is used to define the standard operating procedures of the agent, annotate the pre-processed text using the standard operating procedures to obtain annotated text, and construct a question-and-answer knowledge base between the agent and the historical user using the pre-processed text;

[0083] The model building module 103 is used to build a pre-trained hidden Markov model, a pre-trained long short-term memory model and a pre-trained language representation model between the agent and the historical user;

[0084] The state acquisition module 104 is configured to train the pre-trained hidden Markov model using the annotated text to obtain a trained hidden Markov model, and obtain a hidden state of the trained hidden Markov model with respect to the annotated text;

[0085] The stage acquisition module 105 is used to train the pre-trained long short-term memory model through the hidden state to obtain a trained long short-term memory model, and obtain the small stage distribution of the trained long short-term memory model with respect to the hidden state;

[0086] The language training module 106 is configured to input the small-stage distribution into the pre-trained language representation model, so as to train the pre-trained language representation model using the small-stage distribution to obtain a trained language representation model;

[0087] The process analysis module 107 is configured to obtain the current interaction record between the agent and the current user, and analyze the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long short-term memory model, and the trained language representation model;

[0088] The full-link analysis module 108 is used to update the question-and-answer knowledge base through the agent operation process to obtain an updated knowledge base, and use the agent operation process and the updated knowledge base to determine the full-link analysis result of the operation process between the agent and the current user.

[0089] Optionally, the knowledge base building module 102 defines the standard operating procedures of the agents, including:

[0090] Divide the agent's work process into stages; wherein the work process stages include opening remarks stage, demand exploration stage, product introduction stage, objection handling stage, result facilitation stage and closing remarks stage;

[0091] Subdividing the operation process phases to obtain subdivided process phases;

[0092] The operation process stage and the subdivided process stage are used as the standard operation process of the seat staff.

[0093] When the model building module 103 trains the hidden Markov model, it includes:

[0094] Obtaining the true large-scale phase distribution in the annotated text;

[0095] Extracting keywords and phrases from the annotated text;

[0096] generating observation data of the annotated text using the keywords and the phrases;

[0097] calculating, in the pre-trained hidden Markov model, a state transition probability between each of the true large stage distributions;

[0098] calculating the observation probability of the observation data under the true large-stage distribution in the pre-trained hidden Markov model;

[0099] Analyzing the virtual large-stage distribution corresponding to the observation data according to the state transition probability and the observation probability;

[0100] Calculating a loss function value between the real large stage distribution and the virtual large stage distribution;

[0101] Based on the loss function value, the parameters to be trained of the pre-trained hidden Markov model are trained to obtain a trained hidden Markov model.

[0102] Accordingly, when the model building module 103 trains the long short-term memory model, it includes:

[0103] Obtaining the input layer, multi-layer long short-term memory layer, and fully connected layer in the pre-trained long short-term memory model;

[0104] Obtaining the input gate, output gate, and forget gate of the multi-layer long short-term memory layer;

[0105] After receiving the hidden state through the input end, extracting an initial feature vector of the hidden state using the input gate, the output gate, and the forget gate;

[0106] Identifying long-term dependencies of the initial feature vectors using the multi-layer long short-term memory layer;

[0107] According to the long-term dependency, the initial feature vector is converted into a final feature vector using the multi-layer long short-term memory layer;

[0108] Obtain the true small-stage distribution in the annotated text;

[0109] Calculating the probability distribution of the final feature vector with respect to the true small stage distribution using the fully connected layer;

[0110] identifying a virtual small-stage distribution of the hidden state by the probability distribution;

[0111] Calculating a loss value between the virtual small stage distribution and the real small stage distribution;

[0112] According to the loss value, the parameters to be optimized in the pre-trained long short-term memory model are optimized to obtain a trained long short-term memory model.

[0113] Accordingly, when the model building module 103 trains the language representation model, it includes:

[0114] Obtain the BERT module, entity recognition module, and intent recognition module in the pre-trained language representation model;

[0115] Outputting the semantic vector of the small-stage distribution using the BERT module;

[0116] Identifying key entities of the semantic vector using the entity recognition module;

[0117] Identifying the user intent corresponding to the semantic vector using the intent recognition module;

[0118] According to the key entity, optimizing first network parameters between the BERT module and the entity recognition module using a preset multi-task learning framework to obtain optimized first network parameters;

[0119] According to the user intent, optimizing second network parameters between the BERT module and the intent recognition module using the multi-task learning framework to obtain optimized second network parameters;

[0120] A trained language representation model corresponding to the pre-trained language representation model is determined by using the optimized first network parameters and the optimized second network parameters.

[0121] Reliably, the process analysis module 107 is specifically used to:

[0122] Identifying the first-stage distribution corresponding to the current interaction record by using the trained hidden Markov model;

[0123] Outputting a second-stage distribution corresponding to the first-stage distribution through the trained long short-term memory model;

[0124] Extracting the key text entities and text intents distributed in the second stage using the trained language representation model;

[0125] Querying the question and answer records corresponding to the key text entities in the question and answer knowledge base;

[0126] The agent operation process corresponding to the text intent is searched in the question and answer record.

[0127] The full-link analysis module 108 is specifically configured to:

[0128] Obtaining feedback from the current user on the agent's work process;

[0129] Determining whether the feedback information is negative information;

[0130] When the feedback information is negative, querying the required operation process of the current user;

[0131] Modifying the seat operation process to the required operation process to obtain a modified operation process;

[0132] The question-and-answer knowledge base is updated using the correction process to obtain an updated knowledge base.

[0133] like Figure 4 , which is a structural diagram of an electronic device 1 for implementing a full-link analysis method for insurance operation processes according to the present invention.

[0134] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an insurance operation process full-link analysis program.

[0135] Among them, the processor 10 can be composed of an integrated circuit in some embodiments, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits packaged with the same function or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device 1, and uses various interfaces and lines to connect the various components of the entire electronic device 1. It executes various functions and processes data of the electronic device 1 by running or executing programs or modules stored in the memory 11 (for example, executing a full-link analysis program for an insurance operation process, etc.), and calling data stored in the memory 11.

[0136] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Furthermore, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 can not only be used to store application software and various types of data installed on the electronic device 1, such as the code of the full-link analysis program of the insurance operation process, but can also be used to temporarily store data that has been output or is to be output.

[0137] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0138] The communication interface 13 is used for communication between the above-mentioned electronic device 1 and other devices, including a network interface and an employee interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices 1. The employee interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the employee interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual employee interface.

[0139] Figure 4 Only the electronic device 1 having components is shown, and it can be understood by those skilled in the art that Figure 4 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0140] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering the various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0141] It should be understood that the embodiment is for illustrative purposes only and the scope of the patent invention is not limited to this structure.

[0142] The insurance operation process full-link analysis program stored in the memory 11 of the electronic device 1 is a combination of multiple computer programs. When running in the processor 10, it can achieve the following:

[0143] Collecting historical interaction records between agents and historical users, converting the historical interaction records into historical interaction texts, and performing text preprocessing on the historical interaction texts to obtain preprocessed texts;

[0144] defining a standard operating procedure for the agent, annotating the pre-processed text using the standard operating procedure to obtain annotated text, and constructing a question-and-answer knowledge base between the agent and the historical user using the pre-processed text;

[0145] Constructing a pre-trained hidden Markov model, a pre-trained long-short-term memory model, and a pre-trained language representation model between the agent and the historical user;

[0146] Using the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model, and obtaining a hidden state of the trained hidden Markov model with respect to the annotated text;

[0147] Training the pre-trained long short-term memory model through the hidden state to obtain a trained long short-term memory model, and obtaining a small stage distribution of the trained long short-term memory model with respect to the hidden state;

[0148] Inputting the small-stage distribution into the pre-trained language representation model to train the pre-trained language representation model using the small-stage distribution to obtain a trained language representation model;

[0149] Obtaining a current interaction record between the agent and the current user, and analyzing the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long short-term memory model, and the trained language representation model;

[0150] The question-and-answer knowledge base is updated through the agent operation process to obtain an updated knowledge base, and the agent operation process and the updated knowledge base are used to determine the full-link analysis result of the operation process between the agent and the current user.

[0151] Specifically, the specific implementation method of the processor 10 for the above computer program can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0152] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0153] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by the processor of the electronic device 1, the computer program can achieve:

[0154] Collecting historical interaction records between agents and historical users, converting the historical interaction records into historical interaction texts, and performing text preprocessing on the historical interaction texts to obtain preprocessed texts;

[0155] defining a standard operating procedure for the agent, annotating the pre-processed text using the standard operating procedure to obtain annotated text, and constructing a question-and-answer knowledge base between the agent and the historical user using the pre-processed text;

[0156] Constructing a pre-trained hidden Markov model, a pre-trained long-short-term memory model, and a pre-trained language representation model between the agent and the historical user;

[0157] Using the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model, and obtaining a hidden state of the trained hidden Markov model with respect to the annotated text;

[0158] Training the pre-trained long short-term memory model through the hidden state to obtain a trained long short-term memory model, and obtaining a small stage distribution of the trained long short-term memory model with respect to the hidden state;

[0159] Inputting the small-stage distribution into the pre-trained language representation model to train the pre-trained language representation model using the small-stage distribution to obtain a trained language representation model;

[0160] Obtaining a current interaction record between the agent and the current user, and analyzing the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long short-term memory model, and the trained language representation model;

[0161] The question-and-answer knowledge base is updated through the agent operation process to obtain an updated knowledge base, and the agent operation process and the updated knowledge base are used to determine the full-link analysis result of the operation process between the agent and the current user.

[0162] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0163] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0164] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0165] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0166] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0167] Embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0168] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

[0170] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0171] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. If any software tools or components other than those of the company appear in the application embodiments, they are merely used for illustration and do not represent actual use. Although the present invention has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above-mentioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A full-link analysis method for insurance operation processes, characterized by: The method comprises: Collecting historical interaction records between agents and historical users, converting the historical interaction records into historical interaction texts, and performing text preprocessing on the historical interaction texts to obtain preprocessed texts; defining a standard operating procedure for the agent, annotating the pre-processed text using the standard operating procedure to obtain annotated text, and constructing a question-and-answer knowledge base between the agent and the historical user using the pre-processed text; Constructing a pre-trained hidden Markov model, a pre-trained long-short-term memory model, and a pre-trained language representation model between the agent and the historical user; Using the annotated text to train the pre-trained hidden Markov model to obtain a trained hidden Markov model, and obtaining a hidden state of the trained hidden Markov model with respect to the annotated text; Training the pre-trained long short-term memory model through the hidden state to obtain a trained long short-term memory model, and obtaining a small stage distribution of the trained long short-term memory model with respect to the hidden state; Inputting the small-stage distribution into the pre-trained language representation model to train the pre-trained language representation model using the small-stage distribution to obtain a trained language representation model; Obtaining a current interaction record between the agent and the current user, and analyzing the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long short-term memory model, and the trained language representation model; The question-and-answer knowledge base is updated through the agent operation process to obtain an updated knowledge base, and the agent operation process and the updated knowledge base are used to determine the full-link analysis result of the operation process between the agent and the current user.

2. The full-link analysis method for insurance operation process according to claim 1, characterized in that: The standard operating procedures for defining the agents include: Divide the agent's work process into stages; wherein the work process stages include opening remarks stage, demand exploration stage, product introduction stage, objection handling stage, result facilitation stage and closing remarks stage; Subdividing the operation process phases to obtain subdivided process phases; The operation process stage and the subdivided process stage are used as the standard operation process of the seat staff.

3. The insurance operation process full-link analysis method according to claim 1, characterized in that: The method of training the pre-trained hidden Markov model using the annotated text to obtain a trained hidden Markov model includes: Obtaining the true large-scale phase distribution in the annotated text; Extracting keywords and phrases from the annotated text; generating observation data of the annotated text using the keywords and the phrases; calculating, in the pre-trained hidden Markov model, a state transition probability between each of the true large stage distributions; calculating the observation probability of the observation data under the true large-stage distribution in the pre-trained hidden Markov model; Analyzing the virtual large-stage distribution corresponding to the observation data according to the state transition probability and the observation probability; Calculating a loss function value between the real large stage distribution and the virtual large stage distribution; Based on the loss function value, the parameters to be trained of the pre-trained hidden Markov model are trained to obtain a trained hidden Markov model.

4. The insurance operation process full-link analysis method according to claim 1, characterized in that: The pre-trained long short-term memory model is trained by the hidden state to obtain a trained long short-term memory model, comprising: Obtaining the input layer, multi-layer long short-term memory layer, and fully connected layer in the pre-trained long short-term memory model; Obtaining the input gate, output gate, and forget gate of the multi-layer long short-term memory layer; After receiving the hidden state through the input end, extracting the initial feature vector of the hidden state using the input gate, the output gate and the forget gate; Identifying long-term dependencies of the initial feature vectors using the multi-layer long short-term memory layer; According to the long-term dependency, the initial feature vector is converted into a final feature vector using the multi-layer long short-term memory layer; Obtain the true small-stage distribution in the annotated text; Calculating the probability distribution of the final feature vector with respect to the true small stage distribution using the fully connected layer; identifying a virtual small-stage distribution of the hidden state by the probability distribution; Calculating a loss value between the virtual small stage distribution and the real small stage distribution; According to the loss value, the parameters to be optimized in the pre-trained long short-term memory model are optimized to obtain a trained long short-term memory model.

5. The insurance operation process full-link analysis method according to claim 1, characterized in that: The pre-trained language representation model is trained by the small-stage distributed training to obtain a trained language representation model, including: Obtain the BERT module, entity recognition module, and intent recognition module in the pre-trained language representation model; Outputting the semantic vector of the small-stage distribution using the BERT module; Identifying key entities of the semantic vector using the entity recognition module; Identifying the user intent corresponding to the semantic vector using the intent recognition module; According to the key entity, optimizing first network parameters between the BERT module and the entity recognition module using a preset multi-task learning framework to obtain optimized first network parameters; According to the user intent, optimizing second network parameters between the BERT module and the intent recognition module using the multi-task learning framework to obtain optimized second network parameters; A trained language representation model corresponding to the pre-trained language representation model is determined by using the optimized first network parameters and the optimized second network parameters.

6. The insurance operation process full-link analysis method according to claim 1, characterized in that: The analyzing, based on the question-answer knowledge base, the agent operation process corresponding to the current interaction record by using the trained hidden Markov model, the trained long short-term memory model, and the trained language representation model includes: Identifying the first-stage distribution corresponding to the current interaction record by using the trained hidden Markov model; Outputting a second-stage distribution corresponding to the first-stage distribution through the trained long short-term memory model; Extracting the key text entities and text intents distributed in the second stage using the trained language representation model; Querying the question and answer records corresponding to the key text entities in the question and answer knowledge base; The agent operation process corresponding to the text intent is searched in the question and answer record.

7. The insurance operation process full-link analysis method according to claim 1, characterized in that: The updating of the question-and-answer knowledge base through the agent operation process to obtain an updated knowledge base includes: Obtaining feedback from the current user on the agent's work process; Determining whether the feedback information is negative information; When the feedback information is negative, querying the required operation process of the current user; Modifying the seat operation process to the required operation process to obtain a modified operation process; The question-and-answer knowledge base is updated using the correction process to obtain an updated knowledge base.

8. A device for analyzing the entire chain of insurance operation processes, characterized in that: The device comprises: A text preprocessing module is used to collect historical interaction records between agents and historical users, convert the historical interaction records into historical interaction texts, and perform text preprocessing on the historical interaction texts to obtain preprocessed texts; a knowledge base construction module, configured to define a standard operating procedure for the agent, annotate the pre-processed text using the standard operating procedure to obtain annotated text, and construct a question-and-answer knowledge base between the agent and the historical user using the pre-processed text; A model building module, configured to build a pre-trained hidden Markov model, a pre-trained long short-term memory model, and a pre-trained language representation model between the agent and the historical user; a state acquisition module, configured to train the pre-trained hidden Markov model using the annotated text to obtain a trained hidden Markov model, and obtain a hidden state of the trained hidden Markov model with respect to the annotated text; a stage acquisition module, configured to train the pre-trained long short-term memory model using the hidden state to obtain a trained long short-term memory model, and obtain a small stage distribution of the trained long short-term memory model with respect to the hidden state; A language training module, configured to input the small-stage distribution into the pre-trained language representation model, so as to train the pre-trained language representation model using the small-stage distribution to obtain a trained language representation model; a process analysis module for obtaining a current interaction record between the agent and the current user, and analyzing the agent operation process corresponding to the current interaction record based on the question-answer knowledge base using the trained hidden Markov model, the trained long-short-term memory model, and the trained language representation model; The full-link analysis module is used to update the question-and-answer knowledge base through the agent operation process to obtain an updated knowledge base, and use the agent operation process and the updated knowledge base to determine the full-link analysis result of the operation process between the agent and the current user.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the full-link analysis method of the insurance operation process as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the full-link analysis method of the insurance operation process as described in any one of claims 1 to 7.

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