An insurance recommendation method and device, equipment and medium

Abstract

The application discloses an insurance recommendation method, device, equipment and medium, comprising: inputting physiological data, behavior data and environment data of a user into a risk assessment module of a target recommendation model, outputting potential risk events of the user and risk scores corresponding to the risk events through the risk assessment module, inputting an insurance knowledge graph, each risk event and the risk scores corresponding to the risk events into a first recommendation module of the target recommendation model, outputting recommended insurance products for the corresponding risk events through the first recommendation module, inputting economic status, living status of the user and the recommended insurance products for each risk event into a second recommendation module of the target recommendation model, and outputting a target insurance plan recommended for the corresponding insurance products through the second recommendation module. The application can be applied to a home-based care scene in the field of finance and insurance, realizes that the insurance recommendation is adapted to the actual situation of the user, and improves the accuracy and reliability of the insurance recommendation.

Description

Technical Field

[0001] This invention relates to the fields of data processing technology and financial insurance, and in particular to an insurance recommendation method, apparatus, device and medium. Background Technology

[0002] In recent years, with the continuous improvement of residents' risk awareness and insurance demand, insurance products have been widely used in areas such as old-age security and health care. Traditional insurance recommendation models mainly rely on users' limited knowledge and the experience of insurance agents to select insurance products. This approach has inherent flaws such as strong subjectivity and limited coverage. With the deepening development of artificial intelligence technology in the financial and insurance field, algorithm-based intelligent insurance recommendation systems have gradually emerged. These systems analyze users' basic information (such as age, income, and occupation) and static health data (such as medical history and family history of hereditary diseases), combined with the terms and coverage of insurance products, to achieve automated recommendations. For example, in home-based elderly care scenarios, product recommendations are made based on static tag data such as the elderly person's age and medical history. However, this recommendation method still has certain limitations in terms of the applicability and accuracy of the recommendations to users.

[0003] Therefore, improving the accuracy of insurance recommendations has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides an insurance recommendation method, apparatus, device, and medium to address the problem of improving the accuracy of insurance recommendations.

[0005] An insurance recommendation method includes: Acquire users' physiological data, behavioral data, and environmental data of their surroundings; The physiological data, behavioral data, and environmental data are input into the risk assessment module of the target recommendation model, and the risk assessment module outputs the user's potential risk events and the risk scores of the corresponding risk events. Obtain an insurance knowledge graph, input the insurance knowledge graph, each risk event, and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and output the insurance products recommended for the corresponding risk event through the first recommendation module; The economic and living conditions of the user are obtained. The economic and living conditions, along with the insurance products recommended for the user for each risk event, are input into the second recommendation module of the target recommendation model. The second recommendation module then outputs the target insurance plan recommended for the corresponding insurance product.

[0006] An insurance recommendation device includes: The data acquisition module is used to acquire the user's physiological data, behavioral data, and environmental data of the environment in which the user is located; The risk event assessment module is used to input the physiological data, the behavioral data, and the environmental data into the risk assessment module of the target recommendation model, and output the user's potential risk events and the risk scores of the corresponding risk events through the risk assessment module. The first recommendation module is used to obtain an insurance knowledge graph, input the insurance knowledge graph, each risk event and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and output the insurance products recommended for the corresponding risk event through the first recommendation module. The second recommendation module is used to obtain the user's economic status and living status, input the economic status, the living status, and the insurance products recommended for each risk event into the second recommendation module of the target recommendation model, and output the target insurance plan recommended for the corresponding insurance products through the second recommendation module.

[0007] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described insurance recommendation method.

[0008] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described insurance recommendation method.

[0009] The aforementioned insurance recommendation method, device, computer equipment, and storage medium input the user's physiological data, behavioral data, and environmental data into the risk assessment module of the target recommendation model, outputting the user's potential risk events and corresponding risk scores. The insurance knowledge graph, each risk event, and its corresponding risk score are input into the first recommendation module of the target recommendation model, outputting recommended insurance products for the corresponding risk events. The user's economic status, living conditions, and the recommended insurance products for each risk event are input into the second recommendation module of the target recommendation model, outputting a target insurance plan recommended for the corresponding insurance products.

[0010] Among them, the target recommendation model identifies potential risk events and risk scores of users based on user physiological data, behavioral data, and environmental data, realizing multi-dimensional and dynamic risk assessment. Based on risk events and risk scores, combined with insurance knowledge graphs, it recommends insurance products for users and recommends corresponding insurance plans based on users' economic status and life status. This makes the recommended products adapt to users' dynamic needs and actual situation, improving the accuracy of insurance recommendations. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of an application environment for an insurance recommendation method according to an embodiment of the present invention; Figure 2 This is a flowchart of an insurance recommendation method according to an embodiment of the present invention; Figure 3 This is another flowchart of the insurance recommendation method in one embodiment of the present invention; Figure 4 This is another flowchart of the insurance recommendation method in one embodiment of the present invention; Figure 5 This is another flowchart of the insurance recommendation method in one embodiment of the present invention; Figure 6 This is a schematic diagram of an insurance recommendation device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0013] 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, not all, of the embodiments of the present invention. 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.

[0014] The insurance recommendation method provided in this embodiment of the invention can be applied to, for example... Figure 1 The application environment is shown. Specifically, this insurance recommendation method is applied in the home-based elderly care scenario within the financial insurance field. The insurance recommendation system includes, for example, [details omitted]. Figure 1 The diagram illustrates a client and server that communicate over a network to address the issue of improving the accuracy of insurance recommendations. The client, also known as the user terminal, is the program that provides local services to the customer, corresponding to the server. The client can be installed on, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0015] For example, in the home-based elderly care scenario within the financial insurance sector, the insurance recommendation method of this invention can be used to predict potential risk events (sudden myocardial infarction, fall, and heatstroke) and corresponding risk scores for users based on collected user physiological data (such as heart rate, blood pressure, and blood oxygen), behavioral data (such as stride length and gait), and environmental data (such as air humidity, temperature, and ground friction coefficient). Based on an insurance knowledge graph, the user's economic status (income level, asset status), and living conditions (family structure, daily living patterns), affordable and suitable insurance products and corresponding target insurance plans can be recommended, thus improving the accuracy of insurance recommendations.

[0016] In one embodiment, such as Figure 2 As shown, an insurance recommendation method is provided, which is applied to... Figure 1 Taking the server in the example, the following steps are included: Step S201: Obtain the user's physiological data, behavioral data, and environmental data of the environment in which the user is located.

[0017] Step S202: Input physiological data, behavioral data, and environmental data into the risk assessment module of the target recommendation model, and output the user's potential risk events and the risk scores of the corresponding risk events through the risk assessment module.

[0018] In this embodiment, physiological data can refer to data characterizing human vital signs and biological indicators, such as heart rate, blood pressure, and blood oxygen. Behavioral data can refer to data characterizing an individual's daily activities and habits, such as stride length and gait. Environmental data can refer to data characterizing information about the human body's surrounding environment, such as air humidity, temperature, and ground friction coefficient. The target recommendation model can refer to a deep learning model that has been trained for insurance recommendations. This model, after training, can recommend corresponding insurance products and insurance plans based on the user's physiological, behavioral, and environmental data. Risk events can refer to sudden health or safety threats that may occur based on the user's physiological, behavioral, and environmental data, such as sudden myocardial infarction risk events, fall risk events, and heatstroke risk events. Risk scores can be scores characterizing the degree of risk of risk events. The risk assessment module can refer to a module that predicts potential risk events and risk scores based on physiological, behavioral, and environmental data.

[0019] Specifically, the system collects physiological data such as heart rate, blood pressure, and blood oxygen, behavioral data such as stride length and gait, and environmental data such as air humidity, temperature, and ground friction coefficient. The physiological data is input into the physiological risk assessment module of the target recommendation model, which outputs the user's potential physiological risk events and corresponding risk scores. The behavioral data is input into the behavioral risk assessment module of the target recommendation model, which outputs the user's potential behavioral risk events and corresponding risk scores. The environmental data is input into the environmental risk assessment module of the target recommendation model, which outputs the user's potential environmental risk events and corresponding risk scores.

[0020] Physiological risk events can refer to sudden health threats predicted based on physiological data, such as sudden myocardial infarction risk events, stroke risk events, and arrhythmia risk events. Behavioral risk events can refer to accidental injuries or health threats predicted based on behavioral data, such as fall risk events and sports injury risk events. Environmental risk events can refer to health or safety threats predicted based on environmental data, such as heatstroke risk events and respiratory system risk events. The physiological risk assessment module can refer to a module that predicts potential physiological risk events, the behavioral risk assessment module can refer to a module that predicts potential behavioral risk events, and the environmental risk assessment module can refer to a module that predicts potential environmental risk events.

[0021] Step S203: Obtain the insurance knowledge graph, input the insurance knowledge graph, each risk event and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and output the insurance products recommended for the corresponding risk event through the first recommendation module.

[0022] In this embodiment, the insurance knowledge graph can refer to a pre-set knowledge graph containing the relationship between risk events and corresponding insurance products, and the first recommendation module can refer to a module that recommends insurance products based on the insurance knowledge graph, risk events, and risk scores.

[0023] Specifically, the insurance knowledge graph, risk events, and corresponding risk scores are input into the first recommendation module. Based on the risk events and risk scores, the first recommendation module matches the insurance products corresponding to the risk events in the insurance knowledge graph.

[0024] Optionally, after the risk assessment module outputs the user's potential risk events and corresponding risk scores, the risk scores of the user's potential risk events and corresponding risk events can be displayed on the front end to obtain the intervention measures taken by the user for all risk events. Based on the intervention measures, the risk scores of the user's potential risk events and corresponding risk events are adjusted to obtain updated risk events and updated risk scores of corresponding updated risk events. The insurance knowledge graph, each updated risk event, and the updated risk scores of corresponding updated risk events are input into the first recommendation module of the target recommendation model. The first recommendation module outputs the insurance products recommended for the user's corresponding updated risk events.

[0025] Intervention measures can refer to actions or behavioral adjustments taken by users to reduce the probability of risk events occurring. Updated risk events can refer to sudden health or safety threats that may still occur after intervention measures are taken. Updated risk scores can refer to scores that characterize the risk level of updated risk events.

[0026] Step S204: Obtain the user's economic status and living status, input the economic status, living status, and the insurance products recommended for each risk event into the second recommendation module of the target recommendation model, and output the target insurance plan recommended for the corresponding insurance products through the second recommendation module.

[0027] In this embodiment, economic status can refer to the user's income level and asset status, living status can refer to the user's family structure and daily living pattern, the target insurance plan can refer to the insurance plan of the corresponding insurance product predicted based on the user's economic status and living status, and the second recommendation module can refer to the module that predicts the target insurance plan of the insurance product based on the economic status and living status.

[0028] Specifically, the economic status, living conditions, and recommended insurance products for each risk event are input into the second recommendation module of the target recommendation model, and the second recommendation module outputs the target insurance plan recommended for the corresponding insurance product.

[0029] In this embodiment, a target recommendation model is used to identify potential risk events and risk scores of users based on user physiological data, behavioral data, and environmental data, achieving multi-dimensional and dynamic risk assessment. Based on risk events and risk scores, combined with an insurance knowledge graph, insurance products are recommended for users. Based on the user's economic status and life status, corresponding insurance plans are recommended for insurance products, making the recommended products adaptable to the user's dynamic needs and actual situation, thus improving the accuracy of insurance recommendations.

[0030] In one embodiment, such as Figure 3As shown, an insurance recommendation method is provided. In step S203 above, the insurance knowledge graph, each risk event, and the risk score of the corresponding risk event are input into the first recommendation module of the target recommendation model. The first recommendation module outputs the insurance products recommended for the corresponding risk events, including the following steps: Step S301: Input the insurance knowledge graph, each risk event, and the risk score of the corresponding risk event into the first recommendation module. For any risk event, the first recommendation module matches the risk event with the first type of node in the insurance knowledge graph to determine the first type of matching node that matches the risk event.

[0031] Step S302: Determine the second-type neighbor nodes that are directly connected to the first-type matching nodes, and the triggering conditions between each second-type neighbor node and the first-type matching node.

[0032] Step S303: Based on the risk score of the risk event and the triggering conditions between each second-type neighbor node and the first-type matching node, determine the second-type matching node that matches the risk score of the risk event from all second-type neighbor nodes, and determine that the insurance product represented by the second-type matching node is the insurance product corresponding to the risk event.

[0033] In this embodiment, the first type of node can refer to a node representing a risk event, such as myocardial infarction, fall, heatstroke, etc., and the second type of node can refer to a node representing an insurance product, such as critical illness insurance, accident insurance, etc. The first type of matching node can refer to a node that matches a specific risk event, and the second type of neighbor node can refer to a node that is directly connected to the first type of matching node. The triggering condition can refer to the relationship between the first type of matching node and the second type of neighbor node. For example, when the risk score of the risk event represented by the first type of matching node meets a specific threshold, the insurance product represented by the second type of neighbor node is determined to be the insurance product recommended for the risk event.

[0034] Specifically, for any risk event, the first recommendation module matches the risk event with the first type of nodes in the insurance knowledge graph based on a similarity algorithm, determines the first type of matching node that matches the risk event, determines the second type of neighbor nodes of the first type of matching node, determines the triggering conditions between the first type of matching node and each second type of neighbor node, determines the triggering conditions that satisfy the risk score of the risk event from all triggering conditions, determines the second type of neighbor node and second type of matching node corresponding to the triggering condition, and identifies the insurance product represented by the second type of matching node as the insurance product corresponding to the risk event.

[0035] In this embodiment, a target recommendation model is used to structurally associate and match risk events and risk scores with an insurance knowledge graph, recommending corresponding insurance products for each risk event. This improves the efficiency and accuracy of insurance product recommendations.

[0036] In one embodiment, such as Figure 4 As shown, an insurance recommendation method is provided. In step S204 above, economic status, living status, and the insurance products recommended for each risk event are input into the second recommendation module of the target recommendation model. The second recommendation module outputs the target insurance plan recommended for the corresponding insurance product, including the following steps: Step S401: Input the economic status, living status, and each insurance product recommended to the user into the second recommendation module. For any insurance product, the second recommendation module generates a preliminary insurance plan based on the economic status and living status.

[0037] Step S402: Perform a suitability assessment on each preliminary insurance plan, obtain the assessment results, and determine the target insurance plan from all preliminary insurance plans based on the assessment results.

[0038] In this embodiment, the preliminary insurance plan can refer to the insurance product plan predicted based on the user's economic and living conditions, the evaluation result can refer to the result of the suitability evaluation of the preliminary insurance plan, and the target insurance plan can refer to the insurance plan selected from the preliminary insurance plan based on the evaluation result.

[0039] Specifically, for any insurance product, the second recommendation module generates a preliminary insurance plan for that insurance product, such as the coverage amount and payment method. A risk assessment is performed on each preliminary insurance plan. The assessment can be an assessment of the user's economic suitability, the coverage scope, and the suitability for their lifestyle. The assessment results are obtained, and based on the assessment results, the optimal plan is determined as the target insurance plan from all the preliminary insurance plans.

[0040] In this embodiment, a target recommendation model is used to generate preliminary insurance product plans based on the user's economic and lifestyle status, and then filters these plans to obtain the target insurance plan. This achieves personalized, affordable, and practically tailored target insurance plan recommendations, improving the accuracy of insurance recommendations.

[0041] In one embodiment, such as Figure 5 As shown, an insurance recommendation method is provided, which, before obtaining the user's physiological data, behavioral data, and environmental data of the environment in step S201 above, further includes the following steps: Step S501: Use a smart wearable device to detect physiological data, use a camera to capture behavioral data, and use an environmental sensor to detect environmental data.

[0042] Step S502: Use a preset encryption algorithm to encrypt the physiological data, behavioral data, and environmental data to obtain ciphertext form of the physiological data, behavioral data, and environmental data.

[0043] In this embodiment, the preset encryption algorithm can refer to a pre-set algorithm for encrypting data, such as a symmetric encryption algorithm, an asymmetric encryption algorithm, etc.

[0044] Specifically, smart wearable devices such as smartwatches can be used to collect users' physiological data, cameras and other imaging devices can be used to capture users' behavioral data, and sensors can be used to collect environmental data. Based on a preset encryption algorithm, the collected physiological, behavioral, and environmental data are encrypted to obtain ciphertext physiological, behavioral, and environmental data. When acquiring users' physiological, behavioral, and environmental data, the corresponding decryption algorithm for the preset encryption algorithm is obtained. The decryption algorithm is used to decrypt the ciphertext physiological, behavioral, and environmental data to obtain physiological, behavioral, and environmental data.

[0045] In this embodiment, by encrypting the collected physiological, behavioral, and environmental data and decrypting them when making insurance recommendations, data security and user privacy are protected, thereby improving the reliability of insurance recommendations.

[0046] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0047] In one embodiment, an insurance recommendation device is provided, which corresponds one-to-one with the insurance recommendation method described in the above embodiments. For example... Figure 6 As shown, the insurance recommendation device includes a data acquisition module 61, a risk event assessment module 62, a first recommendation module 63, and a second recommendation module 64. Detailed descriptions of each functional module are as follows: Data acquisition module 61 is used to acquire the user's physiological data, behavioral data, and environmental data of the environment in which the user is located; The risk event assessment module 62 is used to input the physiological data, the behavioral data and the environmental data into the risk assessment module of the target recommendation model, and output the user's potential risk events and the risk scores of the corresponding risk events through the risk assessment module. The first recommendation module 63 is used to obtain an insurance knowledge graph, input the insurance knowledge graph, each risk event and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and output the insurance products recommended for the corresponding risk event through the first recommendation module. The second recommendation module 64 is used to obtain the user's economic status and living status, input the economic status, the living status, and the insurance products recommended for each risk event into the second recommendation module of the target recommendation model, and output the target insurance plan recommended for the corresponding insurance products through the second recommendation module.

[0048] Optionally, the aforementioned risk event assessment module 62 includes: The physiological assessment unit is used to input the physiological data into the physiological risk assessment module of the target recommendation model, and output the user's potential physiological risk events and the risk scores of the corresponding physiological risk events through the physiological risk assessment module. The behavior assessment unit is used to input the behavior data into the behavior risk assessment module of the target recommendation model, and output the user's potential behavior risk events and the risk scores of the corresponding behavior risk events through the behavior risk assessment module. The environmental assessment unit is used to input the environmental data into the environmental risk assessment module of the target recommendation model, and output the user's potential environmental risk events and the risk scores of the corresponding environmental risk events through the environmental risk assessment module.

[0049] Optionally, the aforementioned first recommendation module 63 includes: The first matching unit is used to input the insurance knowledge graph, each risk event and the risk score of the corresponding risk event into the first recommendation module. For any risk event, the first recommendation module matches the risk event with the first type of node in the insurance knowledge graph to determine the first type of matching node that matches the risk event. The determining unit is used to determine the second type of neighboring nodes that are directly connected to the first type of matching nodes, and the triggering conditions between each second type of neighboring node and the first type of matching node; The second matching unit is used to determine, based on the risk score of the risk event and the triggering conditions between each second-type neighbor node and the first-type matching node, a second-type matching node that matches the risk score of the risk event from all second-type neighbor nodes, and to determine that the insurance product represented by the second-type matching node is the insurance product corresponding to the risk event.

[0050] Optionally, the second recommendation module 64 mentioned above includes: The preliminary generation unit is used to input the economic status, the living status, and each insurance product recommended to the user into the second recommendation module, and for any insurance product, the second recommendation module generates a preliminary insurance plan for the insurance product based on the economic status and the living status. The screening unit is used to perform a suitability assessment on each preliminary insurance plan, obtain the assessment results, and determine the target insurance plan from all preliminary insurance plans based on the assessment results.

[0051] Optionally, the insurance recommendation device further includes: The display module is used to display the user's potential risk events and the risk scores of the corresponding risk events on the front end. The measures acquisition module is used to acquire the intervention measures taken by the user for all risk events; The adjustment module is used to adjust the potential risk events of the user and the risk scores of the corresponding risk events according to the intervention measures, so as to obtain updated risk events and updated risk scores of the corresponding updated risk events; The aforementioned first recommendation module 63 also includes: The third recommendation unit is used to input the insurance knowledge graph, each updated risk event, and the updated risk score of the corresponding updated risk event into the first recommendation module of the target recommendation model, and output the insurance products recommended for the user corresponding to the updated risk event through the first recommendation module.

[0052] Optionally, the insurance recommendation device further includes: The data acquisition module is used to detect the physiological data using a smart wearable device, capture the behavioral data using a camera, and detect the environmental data using an environmental sensor. An encryption module is used to encrypt the physiological data, behavioral data, and environmental data using a preset encryption algorithm to obtain ciphertext physiological data, behavioral data, and environmental data.

[0053] Optionally, the data acquisition module 61 mentioned above includes: An algorithm acquisition unit is used to acquire the decryption algorithm corresponding to the preset encryption algorithm; The decryption unit is used to decrypt the ciphertext-form physiological data, behavioral data, and environmental data using the decryption algorithm to obtain the physiological data, behavioral data, and environmental data.

[0054] Specific limitations regarding the insurance recommendation device can be found in the limitations of the insurance recommendation method described above, and will not be repeated here. Each module in the aforementioned insurance recommendation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0055] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores the user's physiological data, behavioral data, and environmental data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements an insurance recommendation method.

[0056] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the insurance recommendation method described in the above embodiment, for example... Figure 2 As shown in S201-S204, or Figures 3 to 5 As shown, to avoid repetition, it will not be described again here. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in this embodiment of the insurance recommendation device, for example, Figure 6 The functions of the data acquisition module 61, risk event assessment module 62, first recommendation module 63, and second recommendation module 64 shown are not described again here to avoid duplication.

[0057] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the insurance recommendation method described in the above embodiment, for example... Figure 2 As shown in S201-S204, or Figures 3 to 5 As shown, to avoid repetition, it will not be described again here. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in this embodiment of the insurance recommendation device, for example, Figure 6The functions of the data acquisition module 61, risk event assessment module 62, first recommendation module 63, and second recommendation module 64 shown are not described again here to avoid duplication.

[0058] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0059] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to 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.

[0060] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An insurance recommendation method, characterized in that, include: Acquire users' physiological data, behavioral data, and environmental data of their surroundings; The physiological data, behavioral data, and environmental data are input into the risk assessment module of the target recommendation model, and the risk assessment module outputs the user's potential risk events and the risk scores of the corresponding risk events. Obtain an insurance knowledge graph, input the insurance knowledge graph, each risk event, and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and output the insurance products recommended for the corresponding risk event through the first recommendation module; The user's economic and living conditions are obtained, and the economic and living conditions, along with the insurance products recommended for each risk event, are input into the second recommendation module of the target recommendation model. The second recommendation module then outputs the target insurance plan recommended for the corresponding insurance product.

2. The insurance recommendation method according to claim 1, characterized in that, The step of inputting the physiological data, behavioral data, and environmental data into the risk assessment module of the target recommendation model, and outputting the user's potential risk events and corresponding risk scores through the risk assessment module, includes: The physiological data is input into the physiological risk assessment module of the target recommendation model, and the physiological risk assessment module outputs the user's potential physiological risk events and the risk scores of the corresponding physiological risk events. The behavioral data is input into the behavioral risk assessment module of the target recommendation model, and the behavioral risk assessment module outputs the user's potential behavioral risk events and the risk scores of the corresponding behavioral risk events. The environmental data is input into the environmental risk assessment module of the target recommendation model, and the environmental risk assessment module outputs the user's potential environmental risk events and the risk scores of the corresponding environmental risk events.

3. The insurance recommendation method according to claim 1, characterized in that, The step of inputting the insurance knowledge graph, each risk event, and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and outputting the insurance product recommended for the corresponding risk event through the first recommendation module, includes: The insurance knowledge graph, each risk event, and the risk score of the corresponding risk event are input into the first recommendation module. For any risk event, the first recommendation module matches the risk event with the first type of node in the insurance knowledge graph to determine the first type of matching node that matches the risk event. Determine the second type of neighboring nodes that are directly connected to the first type of matching nodes, and the triggering conditions between each second type of neighboring node and the first type of matching node; Based on the risk score of the risk event and the triggering conditions between each second-type neighbor node and the first-type matching node, a second-type matching node that matches the risk score of the risk event is determined from all second-type neighbor nodes, and the insurance product represented by the second-type matching node is determined to be the insurance product corresponding to the risk event.

4. The insurance recommendation method according to claim 1, characterized in that, The step of inputting the economic status, the living status, and the recommended insurance products for each risk event into the second recommendation module of the target recommendation model, and outputting the target insurance plan recommended for the corresponding insurance products through the second recommendation module, includes: The economic status, the living status, and each insurance product recommended to the user are input into the second recommendation module. For any insurance product, the second recommendation module generates a preliminary insurance plan for the insurance product based on the economic status and the living status. A suitability assessment is performed on each preliminary insurance plan to obtain the assessment results. Based on the assessment results, the target insurance plan is determined from all the preliminary insurance plans.

5. The insurance recommendation method according to claim 1, characterized in that, After the risk assessment module outputs the user's potential risk events and the corresponding risk scores for those events, the method further includes: The user's potential risk events and the corresponding risk scores for those events will be displayed on the front end. Obtain the intervention measures taken by the user for all risk events; Based on the intervention measures, the potential risk events of the user and the risk scores of the corresponding risk events are adjusted to obtain updated risk events and updated risk scores of the corresponding updated risk events; The step of inputting the insurance knowledge graph, each risk event, and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and outputting the insurance product recommended for the user corresponding to the risk event through the first recommendation module, further includes: The insurance knowledge graph, each updated risk event, and the updated risk score of the corresponding updated risk event are input into the first recommendation module of the target recommendation model. The first recommendation module then outputs the insurance products recommended for the user corresponding to the updated risk event.

6. The insurance recommendation method according to claim 1, characterized in that, Before acquiring the user's physiological data, behavioral data, and environmental data of their environment, the method further includes: The physiological data is detected using smart wearable devices, the behavioral data is captured using imaging devices, and the environmental data is detected using environmental sensors. The physiological data, behavioral data, and environmental data are encrypted using a preset encryption algorithm to obtain ciphertext physiological data, behavioral data, and environmental data.

7. The insurance recommendation method according to claim 6, characterized in that, The acquisition of the user's physiological data, behavioral data, and environmental data of the environment includes: Obtain the decryption algorithm corresponding to the preset encryption algorithm; The aforementioned decryption algorithm is used to decrypt the ciphertext-formatted physiological data, behavioral data, and environmental data to obtain the physiological data, behavioral data, and environmental data.

8. An insurance recommendation device, characterized in that, include: The data acquisition module is used to acquire the user's physiological data, behavioral data, and environmental data of the environment in which the user is located; The risk event assessment module is used to input the physiological data, the behavioral data, and the environmental data into the risk assessment module of the target recommendation model, and output the user's potential risk events and the risk scores of the corresponding risk events through the risk assessment module. The first recommendation module is used to obtain an insurance knowledge graph, input the insurance knowledge graph, each risk event and the risk score of the corresponding risk event into the first recommendation module of the target recommendation model, and output the insurance products recommended for the corresponding risk event through the first recommendation module. The second recommendation module is used to obtain the user's economic status and living status, input the economic status, the living status, and the insurance products recommended for each risk event into the second recommendation module of the target recommendation model, and output the target insurance plan recommended for the corresponding insurance products through the second recommendation module.

9. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the insurance recommendation method 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 insurance recommendation method as described in any one of claims 1 to 7.

Images