Data construction methods, apparatus, computer equipment and storage media

Abstract

This application belongs to the fields of artificial intelligence and fintech, and relates to a data construction method, including: receiving a user-triggered request to construct a blacklist matching algorithm; receiving a target subject matching algorithm selected by the user from a subject matching algorithm selection interface; receiving a target product domain object selected by the user from a product domain object configuration page; generating a target blacklist identifier based on the target blacklist type information input by the user; and constructing a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object. This application also provides a data construction device, computer equipment, and storage medium. Furthermore, this application relates to blockchain technology, and the blacklist matching algorithm can be stored in the blockchain. This application can be applied to blacklist matching scenarios in the financial field, enabling the rapid construction of target blacklist matching algorithms that meet user needs, thus improving the construction efficiency of target blacklist matching algorithms.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence development technology and financial technology, and in particular to data construction methods, devices, computer equipment and storage media. Background Technology

[0002] For property insurance risk control systems, blacklisting is a core risk control rule. It involves identifying relevant entities in the underwriting process, such as natural persons (policyholders or insured persons), corporate organizations (insurance companies, brokerage firms, and agent companies), addresses (the address of the insured or the policyholder's address), and mobile phone numbers. This is combined with information about the insurance products to be verified, such as technology products, market products, types of insurance, and liabilities. The system matches the blacklist data; if a match is found, the underwriting verification is rejected. Blacklist control effectively blocks malicious insurance applications from abnormal customers, reduces malicious claims, and combats insurance fraud, making it a crucial risk control measure for protecting the interests of insurance companies.

[0003] Typically, property insurance risk control systems develop customized blacklist matching algorithms for each type of blacklist. However, as the number of blacklist types increases, the iterative maintenance of these matching algorithms becomes complex. With customized development, the input and output parameters of the matching algorithms differ for each type. Therefore, for each new type of blacklist, developers need to rebuild the matching algorithm accordingly, resulting in low efficiency in algorithm development. Summary of the Invention

[0004] The purpose of this application is to propose a data construction method, apparatus, computer device, and storage medium to solve the technical problem that the existing blacklist matching algorithm requires developers to rebuild the blacklist matching algorithm corresponding to the new type of blacklist each time, resulting in low construction efficiency of the blacklist matching algorithm.

[0005] To address the aforementioned technical problems, this application provides a data construction method, employing the following technical solution:

[0006] Determine whether a user-triggered request to build a blacklist matching algorithm has been received;

[0007] If so, a preset subject matching algorithm selection interface is displayed; wherein, the subject matching algorithm selection interface includes multiple subject matching algorithms;

[0008] Receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface;

[0009] Display a preset product domain object configuration page; wherein, the product domain object configuration page includes a variety of product domain objects;

[0010] Receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page;

[0011] Obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information;

[0012] Based on the target subject matching algorithm and the target product domain object, a target blacklist matching algorithm corresponding to the target blacklist identifier is constructed.

[0013] Furthermore, after the step of constructing a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object, the method further includes:

[0014] Determine whether an insurance verification request triggered by the business system has been received; wherein, the insurance verification request carries a specified blacklist identifier and insurance product information;

[0015] If so, retrieve the first blacklist matching algorithm that matches the specified blacklist identifier from the preset algorithm data table;

[0016] Based on the subject matching information contained in the first blacklist matching algorithm, the specified subject parameters corresponding to the subject matching information are obtained from the insurance product information;

[0017] Based on the specified product domain objects contained in the first blacklist matching algorithm, obtain the first product domain object information corresponding to the specified product domain object from the insured product information;

[0018] Invoke the preset target blacklist database;

[0019] Based on the specified subject parameters, information is queried in the target blacklist database, and information on the second product domain object corresponding to the specified product domain object is obtained from the target blacklist database.

[0020] The information of the first product domain object and the information of the second product domain object are analyzed, and the insurance verification request is processed accordingly based on the analysis results.

[0021] Furthermore, the step of invoking the preset target blacklist database specifically includes:

[0022] Obtain all pre-built blacklist databases;

[0023] Filter out the specified blacklist database that matches the specified blacklist identifier from all the blacklist databases;

[0024] Use the specified blacklist as the target blacklist.

[0025] Furthermore, the step of analyzing the first product domain object information and the second product domain object information, and processing the insurance verification request accordingly based on the analysis results, specifically includes:

[0026] Determine whether the first product domain object information is the same as the second product domain object information;

[0027] If the first product domain object information is the same as the second product domain object information, then generate the first verification information of successful blacklist matching and restrict the response to the insurance verification request;

[0028] If the first product domain object information is different from the second product domain object information, a second verification message indicating unsuccessful blacklist matching is generated, and the insurance verification request is responded to normally.

[0029] Furthermore, after the step of constructing a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object, the method further includes:

[0030] Determine whether an adjustment request for the target blacklist matching algorithm has been received from a specified user;

[0031] If so, receive the adjustment operation input by the specified user that corresponds to the target blacklist matching algorithm;

[0032] Obtain the adjustment information corresponding to the adjustment operation;

[0033] Based on the adjustment information, the target blacklist matching algorithm is adjusted to obtain the corresponding second blacklist matching algorithm.

[0034] The second blacklist matching algorithm is stored.

[0035] Furthermore, the step of storing the second blacklist matching algorithm specifically includes:

[0036] Obtain the first version number of the target blacklist matching algorithm;

[0037] A second version number of the second blacklist matching algorithm is generated based on the first version number;

[0038] Obtain the storage information corresponding to the target blacklist matching algorithm;

[0039] The second blacklist matching algorithm is stored based on the stored information and the second version number.

[0040] Furthermore, after the step of constructing a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object, the method further includes:

[0041] Create a data association relationship between the target blacklist identifier and the target blacklist matching algorithm;

[0042] Obtain the preset algorithm data table;

[0043] Based on the data association, the target blacklist identifier and the target blacklist matching algorithm are stored in the algorithm data table.

[0044] To address the aforementioned technical problems, this application also provides a data construction apparatus, which employs the following technical solution:

[0045] The first judgment module is used to determine whether a user-triggered request to build a blacklist matching algorithm has been received;

[0046] The first display module is used to display a preset subject matching algorithm selection interface if the condition is met; wherein, the subject matching algorithm selection interface includes multiple subject matching algorithms;

[0047] The first receiving module is configured to receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface;

[0048] The second display module is used to display a preset product domain object configuration page; wherein, the product domain object configuration page includes a variety of product domain objects;

[0049] The second receiving module is used to receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page;

[0050] The generation module is used to obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information;

[0051] The construction module is used to construct a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object.

[0052] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0053] Determine whether a user-triggered request to build a blacklist matching algorithm has been received;

[0054] If so, a preset subject matching algorithm selection interface is displayed; wherein, the subject matching algorithm selection interface includes multiple subject matching algorithms;

[0055] Receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface;

[0056] Display a preset product domain object configuration page; wherein, the product domain object configuration page includes a variety of product domain objects;

[0057] Receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page;

[0058] Obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information;

[0059] Based on the target subject matching algorithm and the target product domain object, a target blacklist matching algorithm corresponding to the target blacklist identifier is constructed.

[0060] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0061] Determine whether a user-triggered request to build a blacklist matching algorithm has been received;

[0062] If so, a preset subject matching algorithm selection interface is displayed; wherein, the subject matching algorithm selection interface includes multiple subject matching algorithms;

[0063] Receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface;

[0064] Display a preset product domain object configuration page; wherein, the product domain object configuration page includes a variety of product domain objects;

[0065] Receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page;

[0066] Obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information;

[0067] Based on the target subject matching algorithm and the target product domain object, a target blacklist matching algorithm corresponding to the target blacklist identifier is constructed.

[0068] Compared with the prior art, the embodiments of this application have the following main advantages:

[0069] Upon receiving an insurance verification request triggered by a business system, carrying a specified blacklist identifier and insurance product information, this embodiment intelligently retrieves a first blacklist matching algorithm from a preset algorithm data table that matches the specified blacklist identifier. Then, based on the first blacklist matching algorithm, it retrieves corresponding first product domain object information from the insurance product information. Furthermore, based on the specified subject parameters contained in the insurance product information, it retrieves corresponding second product domain object information from the target blacklist database. Subsequently, by analyzing the first and second product domain object information and processing the insurance verification request accordingly based on the analysis results, it achieves automatic and rapid blacklist interception and detection of insurance product information. This improves the standardization and accuracy of insurance verification request processing, effectively intercepting malicious insurance applications from abnormal customers, and helps reduce the occurrence of malicious claims and insurance fraud. Attached Figure Description

[0070] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0072] Figure 2 A flowchart of an embodiment of the data construction method according to this application;

[0073] Figure 3 This is a schematic diagram of a structure of an embodiment of the data construction apparatus according to this application;

[0074] Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0076] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0077] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0078] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0079] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0080] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0081] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0082] It should be noted that the data construction method provided in the embodiments of this application is generally executed by a server / terminal device, and correspondingly, the data construction device is generally located in the server / terminal device.

[0083] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0084] Continue to refer to Figure 2 A flowchart illustrating an embodiment of the data construction method according to this application is shown. The order of steps in the flowchart can be changed, and some steps can be omitted, depending on different needs. The data construction method provided in this application embodiment can be applied to any scenario requiring the construction of a blacklist matching algorithm, and thus can be applied to products in these scenarios, such as the construction of a blacklist matching algorithm in the financial insurance field. The data construction method includes the following steps:

[0085] Step S201: Determine whether a user-triggered request to build a blacklist matching algorithm has been received.

[0086] In this embodiment, the data construction method runs on an electronic device (e.g., Figure 1 The server / terminal device shown can obtain the construction request via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future-developed wireless connection methods. The execution entity of the data construction method can specifically be the property insurance risk control system built into the electronic device. The aforementioned construction request for the blacklist matching algorithm is a construction request triggered by the user clicking the algorithm construction button on the algorithm construction page. The aforementioned algorithm construction page is a pre-created page used to generate the blacklist matching algorithm.

[0087] Step S202: If yes, display a preset subject matching algorithm selection interface; wherein the subject matching algorithm selection interface includes multiple subject matching algorithms.

[0088] In this embodiment, the aforementioned subject matching algorithm selection interface is a pre-built page containing various subject matching algorithms that can be selected. The aforementioned subject matching algorithms are pre-developed based on the actual business needs of the blacklisted subjects. For example, for the blacklist requirement of individual financial natural person customers, a natural person subject matching algorithm based on ID number + ID type will be established. Specifically, the subject matching algorithms include: ID number + ID type matching; name + ID number + ID type matching; organization keyword matching; province + city + district matching; province + city + district + detailed address matching; mobile phone number matching, etc.

[0089] Step S203: Receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface.

[0090] In this embodiment, the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface can be determined by capturing the user's selection operation for the desired target subject matching algorithm in the subject matching algorithm selection interface.

[0091] Step S204: Display a preset product domain object configuration page; wherein the product domain object configuration page includes a variety of product domain objects.

[0092] In this embodiment, the aforementioned product domain object configuration page is a pre-built page containing various selectable product domain objects. These product domain objects are constructed based on the insurance products required for the blacklist in actual business needs, resulting in domain objects for verification products. Specifically, product domain objects include market products, technology products, insurance types, liabilities, product categories, etc.

[0093] Step S205: Receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page.

[0094] In this embodiment, the target product domain object selected by the user from all the product domain objects included in the product domain object configuration page can be determined by capturing the user's selection operation for the desired target product domain object in the product domain object configuration page.

[0095] Step S206: Obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information.

[0096] In this embodiment, the aforementioned target blacklist type information is information input by the user based on actual business usage needs, used to identify the business type of the generated target blacklist matching algorithm. This target blacklist type information can be used as the target blacklist identifier. For example, the target blacklist type information may include types categorized by business perspective, such as group finance, group opinion, individual finance, and internet app, and may also include types categorized by list subject, such as natural persons, enterprises / organizations, addresses, and mobile phone numbers.

[0097] Step S207: Based on the target subject matching algorithm and the target product domain object, construct a target blacklist matching algorithm corresponding to the target blacklist identifier.

[0098] In this embodiment, a function engine with rule algorithm construction capabilities can be invoked to perform rule algorithm construction processing on the target subject matching algorithm and the target product domain object, thereby constructing a target blacklist matching algorithm corresponding to the target blacklist identifier. For example, if the target subject matching algorithm includes customer ID number + ID type, the target product domain object includes insurance type + liability, and the target blacklist identifier is such as an individual customer's name, then the constructed target blacklist matching algorithm will be an individual customer name blacklist matching algorithm containing the algorithm content of customer ID number + ID type + insurance type + liability.

[0099] This application receives a target subject matching algorithm selected by the user from all subject matching algorithms included in the subject matching algorithm selection interface, and a target product domain object selected by the user from all product domain objects included in the product domain object configuration page. Based on the target blacklist type information input by the user, a target blacklist identifier is generated. Then, based on the target subject matching algorithm and the target product domain object, a target blacklist matching algorithm corresponding to the target blacklist identifier is quickly constructed. Users only need to select information according to their personal needs in the subject matching algorithm selection interface and product domain object configuration page to quickly and accurately construct a target blacklist matching algorithm that meets their requirements. This improves the efficiency of target blacklist matching algorithm construction and facilitates the subsequent use of the target blacklist matching algorithm to effectively intercept malicious insurance applications from abnormal customers, thus helping to avoid malicious claims and insurance fraud.

[0100] In some alternative implementations, after step S207, the electronic device may further perform the following steps:

[0101] Determine whether an insurance verification request triggered by the business system has been received; wherein the insurance verification request carries a specified blacklist identifier and insurance product information.

[0102] In this embodiment, in the application scenario of the insurance field, the above-mentioned business system can specifically be an insurance system, such as a car insurance business system, a life insurance business system, an accident insurance business system, and so on.

[0103] If so, obtain the first blacklist matching algorithm that matches the specified blacklist identifier from the preset algorithm data table.

[0104] In this embodiment, the aforementioned algorithm data table is a pre-built database storing blacklist matching algorithms corresponding to various blacklist types, with each blacklist matching algorithm indexed by its blacklist identifier. Each blacklist matching algorithm consists of subject matching information and product domain objects. For example, if the specified blacklist identifier is "Tuancai individual customer," then the specified blacklist identifier corresponds to the Tuancai individual customer blacklist matching algorithm, and the content of the Tuancai individual customer blacklist matching algorithm includes ID number + ID type + technology product / market product. Specifically, the blacklist matching algorithms stored in the algorithm data table may include multiple algorithms, and the algorithm content of these multiple blacklist matching algorithms may include: customer name + ID number + ID type + technology product / market product; customer ID number + ID type + technology product / market product; customer ID number + ID type + insurance type + liability; customer name + technology product / market product; customer name + insurance type + liability; organization ID number + ID type + technology product / market product; organization ID number + ID type + insurance type + liability; organization name (keyword included) + ID type + insurance type + liability; policyholder address + province + city + district + insurance type + liability; policyholder address + province + city + district + insurance type + street detailed address + liability, etc.

[0105] Based on the subject matching information contained in the first blacklist matching algorithm, the specified subject parameters corresponding to the subject matching information are obtained from the insurance product information.

[0106] In this embodiment, the subject matching information contained in the first blacklist matching algorithm can be obtained, and then the information of the insured product information can be extracted based on the subject matching information to obtain the specified subject parameters corresponding to the subject matching information from the insured product information.

[0107] Based on the specified product domain objects contained in the first blacklist matching algorithm, the first product domain object information corresponding to the specified product domain object is obtained from the insured product information.

[0108] In this embodiment, the specified product domain object contained in the first blacklist matching algorithm can be obtained, and then the information of the insured product information can be extracted based on the specified product domain object to obtain the first product domain object information corresponding to the specified product domain object from the insured product information.

[0109] Call the preset target blacklist database.

[0110] In this embodiment, the specific implementation process of calling the preset target blacklist database will be described in more detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0111] Based on the specified subject parameters, information is queried in the target blacklist database to obtain information on the second product domain object corresponding to the specified product domain object.

[0112] In this embodiment, the target blacklist database is searched and matched using the specified subject parameters to obtain product domain object information that matches the specified product domain object from the target blacklist database, thereby obtaining the aforementioned second product domain object information.

[0113] The information of the first product domain object and the information of the second product domain object are analyzed, and the insurance verification request is processed accordingly based on the analysis results.

[0114] In this embodiment, the specific implementation process of analyzing the first product domain object information and the second product domain object information and processing the insurance verification request accordingly based on the analysis results will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0115] This application determines whether an insurance verification request triggered by a business system has been received. If so, it retrieves a first blacklist matching algorithm that matches the specified blacklist identifier from a preset algorithm data table. Then, based on the subject matching information contained in the first blacklist matching algorithm, it retrieves the specified subject parameter corresponding to the subject matching information from the insurance product information. Next, based on the specified product domain object contained in the first blacklist matching algorithm, it retrieves the first product domain object information corresponding to the specified product domain object from the insurance product information. Subsequently, it calls a preset target blacklist library. Further, it performs an information query on the target blacklist library based on the specified subject parameter and retrieves the second product domain object information corresponding to the specified product domain object from the target blacklist library. Finally, it analyzes the first product domain object information and the second product domain object information and processes the insurance verification request accordingly based on the analysis results. Upon receiving an insurance verification request triggered by a business system, carrying a specified blacklist identifier and insurance product information, this application intelligently retrieves a first blacklist matching algorithm from a preset algorithm data table that matches the specified blacklist identifier. Then, based on the first blacklist matching algorithm, it retrieves corresponding first product domain object information from the insurance product information. Furthermore, based on the specified subject parameters contained in the insurance product information, it retrieves corresponding second product domain object information from the target blacklist database. Subsequently, by analyzing the first and second product domain object information and processing the insurance verification request accordingly based on the analysis results, this application achieves automatic and rapid blacklist interception and detection of insurance product information. This improves the standardization and accuracy of insurance verification request processing, effectively intercepting malicious insurance applications from abnormal customers, and helps reduce the occurrence of malicious claims and insurance fraud.

[0116] In some optional implementations of this embodiment, the step of calling the preset target blacklist database includes the following steps:

[0117] Retrieve all pre-built blacklist libraries.

[0118] In this embodiment, a blacklist library corresponding to each blacklist type is pre-built, and each blacklist library uses the blacklist type of the blacklist library as identification information so that the corresponding blacklist library can be quickly called in the future using the blacklist type.

[0119] Select the specified blacklist database that matches the specified blacklist identifier from all the blacklist databases.

[0120] In this embodiment, a specified blacklist type matching the specified blacklist type can be determined from all blacklist types by matching a specified blacklist identifier with the blacklist types in all blacklist databases. Then, the blacklist database corresponding to the specified blacklist type can be filtered from all blacklist databases to obtain the specified blacklist database.

[0121] Use the specified blacklist as the target blacklist.

[0122] This application obtains all pre-built blacklist databases; then filters out a specified blacklist database that matches a specified blacklist identifier from all the blacklist databases; subsequently, the specified blacklist database is used as the target blacklist database. This application improves the efficiency of obtaining the target blacklist database by filtering all the blacklist databases using a specified blacklist identifier to select the specified blacklist database that matches the specified blacklist identifier and using it as the target blacklist database. This allows subsequent processing of the insurance product information in the insurance verification request using only the target blacklist database, instead of using all blacklist databases, thus effectively reducing the workload of blacklist interception and detection processing and significantly improving the processing efficiency of blacklist interception and detection processing.

[0123] In some optional implementations, the analysis of the first product domain object information and the second product domain object information, and the corresponding processing of the insurance verification request based on the analysis results, includes the following steps:

[0124] Determine whether the information of the first product domain object is the same as the information of the second product domain object.

[0125] In this embodiment, a similarity calculation algorithm can be used to calculate the similarity between the first product domain object information and the second product domain object information to obtain the corresponding similarity. If the obtained similarity is 100%, it is determined that the first product domain object information and the second product domain object information are the same; otherwise, it is determined that the first product domain object information and the second product domain object information are different.

[0126] If the first product domain object information is the same as the second product domain object information, then the first verification information of the successful blacklist match is generated, and the response to the insurance verification request is restricted.

[0127] In this embodiment, if the first product domain object information is detected to be the same as the second product domain object information, it indicates that the target blacklist database contains information that is the same as the first product domain object information. That is, the first product domain object information belongs to malicious insurance data that needs to be intercepted. As a result, first verification information of successful blacklist matching will be generated, and the response to the insurance verification request will be restricted.

[0128] If the first product domain object information is different from the second product domain object information, a second verification message indicating unsuccessful blacklist matching is generated, and the insurance verification request is responded to normally.

[0129] In this embodiment, if the first product domain object information is detected to be different from the second product domain object information, it indicates that the target blacklist database does not record information that is the same as the first product domain object information. That is, the first product domain object information does not belong to malicious insurance data that needs to be intercepted. As a result, a second verification message indicating that the blacklist match is unsuccessful will be generated, and the insurance verification request will be responded to normally.

[0130] This application determines whether the first product domain object information and the second product domain object information are the same. If they are the same, a first verification message indicating a successful blacklist match is generated, and the response to the insurance verification request is restricted. If they are different, a second verification message indicating an unsuccessful blacklist match is generated, and the insurance verification request is responded to normally. This application performs similarity analysis on the first and second product domain object information, and based on the results, accurately determines whether the insurance verification request is a malicious application. It then processes the application accordingly based on the determination results, improving the standardization and accuracy of processing insurance verification requests. This effectively intercepts malicious applications from abnormal customers, helping to reduce malicious claims and insurance fraud.

[0131] In some alternative implementations, after step S207, the electronic device may further perform the following steps:

[0132] Determine whether a request to adjust the matching algorithm for the target blacklist has been received from a specified user.

[0133] In this embodiment, the designated user can be a system maintenance personnel. The designated user can trigger an adjustment request for the target blacklist matching algorithm by clicking the adjustment button on the preset algorithm configuration page.

[0134] If so, receive the adjustment operation input by the specified user that corresponds to the target blacklist matching algorithm.

[0135] In this embodiment, the above-mentioned adjustment operation can be the adjustment operation of the target blacklist matching algorithm performed by a specified user on the above-mentioned algorithm configuration page. The adjustment operation may include operations such as adding data, deleting data, replacing data, and changing data.

[0136] Obtain the adjustment information corresponding to the adjustment operation.

[0137] In this embodiment, the aforementioned adjustment information may be data generated by a specified user after performing an adjustment operation, such as added data information, deleted data information, replaced data information, changed data information, etc.

[0138] Based on the adjustment information, the target blacklist matching algorithm is adjusted to obtain the corresponding second blacklist matching algorithm.

[0139] In this embodiment, the target blacklist matching algorithm can be adjusted according to the adjustment information input by the specified user to obtain a second blacklist matching algorithm that meets the adjustment requirements of the specified user.

[0140] The second blacklist matching algorithm is stored.

[0141] In this embodiment, the specific implementation process of storing the second blacklist matching algorithm will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0142] This application determines whether an adjustment request for the target blacklist matching algorithm triggered by a specified user has been received. If so, it receives the adjustment operation corresponding to the target blacklist matching algorithm input by the specified user; then, it obtains the adjustment information corresponding to the adjustment operation; subsequently, it adjusts the target blacklist matching algorithm based on the adjustment information to obtain a corresponding second blacklist matching algorithm; and then stores the second blacklist matching algorithm. After constructing the target blacklist matching algorithm corresponding to the target blacklist identifier, this application can further adjust the target blacklist matching algorithm based on the adjustment information corresponding to the adjustment operation input by the specified user to obtain a second blacklist matching algorithm that meets the actual needs of the specified user. This achieves intelligent maintenance of the blacklist matching algorithm, ensures the accuracy of the generated second blacklist matching algorithm, and improves the user experience for the specified user.

[0143] In some optional implementations of this embodiment, storing the second blacklist matching algorithm includes the following steps:

[0144] Obtain the first version number of the target blacklist matching algorithm.

[0145] In this embodiment, the version information of the target blacklist matching algorithm can be obtained, and then the first version number of the target blacklist matching algorithm can be queried based on the version information.

[0146] The second version number of the second blacklist matching algorithm is generated based on the first version number.

[0147] In this embodiment, the first version number of the target blacklist matching algorithm can be incremented by 1 to obtain the processed first version number, and this processed first version number can be used as the second version number of the second blacklist matching algorithm.

[0148] Obtain the storage information corresponding to the target blacklist matching algorithm.

[0149] In this embodiment, the aforementioned storage information is the storage location information of the target blacklist matching algorithm.

[0150] The second blacklist matching algorithm is stored based on the stored information and the second version number.

[0151] In this embodiment, the second blacklist matching algorithm can be labeled with a second version number to obtain the labeled second blacklist matching algorithm. Then, based on this storage information, the labeled second blacklist matching algorithm and the target blacklist matching algorithm are stored together.

[0152] This application obtains a first version number of the target blacklist matching algorithm; then generates a second version number of the second blacklist matching algorithm based on the first version number; subsequently obtains storage information corresponding to the target blacklist matching algorithm; and finally stores the second blacklist matching algorithm based on the storage information and the second version number. This application generates the second version number of the second blacklist matching algorithm based on the first version number of the target blacklist matching algorithm, and then stores the second blacklist matching algorithm based on the storage information of the target blacklist matching algorithm. This facilitates rapid backtracking of the blacklist matching algorithm based on the use of the version number, improving the storage intelligence of the blacklist matching algorithm.

[0153] In some optional implementations of this embodiment, after step S207, the electronic device may further perform the following steps:

[0154] Create a data association between the target blacklist identifier and the target blacklist matching algorithm.

[0155] In this embodiment, the target blacklist identifier can be used as an index identifier for the target blacklist matching algorithm to create a data association between the target blacklist identifier and the target blacklist matching algorithm.

[0156] Obtain the preset algorithm data table.

[0157] In this embodiment, the algorithm data table is a pre-built data table used to store blacklist matching algorithms.

[0158] Based on the data association, the target blacklist identifier and the target blacklist matching algorithm are stored in the algorithm data table.

[0159] In this embodiment, based on the data association, the target blacklist identifier can be used as the index identifier of the target blacklist matching algorithm in the algorithm data table, and then the target blacklist matching algorithm can be stored in the algorithm data table.

[0160] This application establishes a data association between the target blacklist identifier and the target blacklist matching algorithm; then obtains a preset algorithm data table; subsequently, based on the data association, the target blacklist identifier and the target blacklist matching algorithm are stored in the algorithm data table. This application achieves intelligent storage of target blacklist matching algorithms by establishing a data association between the target blacklist identifier and the target blacklist matching algorithm, and then storing the target blacklist identifier and the target blacklist matching algorithm in the algorithm data table based on this data association. This facilitates the rapid retrieval of the corresponding target blacklist matching algorithm from the algorithm data table based on the use of the target blacklist identifier, thereby improving the efficiency of blacklist matching algorithm retrieval.

[0161] 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.

[0162] It should be emphasized that, in order to further ensure the privacy and security of the aforementioned target blacklist matching algorithm, the aforementioned target blacklist matching algorithm can also be stored in a node of a blockchain.

[0163] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0164] The embodiments of this application 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 obtain optimal results.

[0165] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0166] 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 instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0167] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0168] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a data construction apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0169] like Figure 3 As shown, the data construction device 300 described in this embodiment includes: a first judgment module 301, a first judgment module 302, a first display module 303, a second display module 304, a second receiving module 305, a generation module 306, and a construction module 307. Wherein:

[0170] The first judgment module 301 is used to determine whether a user-triggered request to build a blacklist matching algorithm has been received;

[0171] The first display module 302 is used to display a preset subject matching algorithm selection interface if the condition is met; wherein, the subject matching algorithm selection interface includes multiple subject matching algorithms;

[0172] The first receiving module 303 is used to receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface;

[0173] The second display module 304 is used to display a preset product domain object configuration page; wherein, the product domain object configuration page includes a variety of product domain objects;

[0174] The second receiving module 305 is used to receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page;

[0175] The generation module 306 is used to obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information;

[0176] The construction module 307 is used to construct a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object.

[0177] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data construction method in the aforementioned embodiments, and will not be repeated here.

[0178] In some optional implementations of this embodiment, the data construction apparatus further includes:

[0179] The second judgment module is used to determine whether an insurance verification request triggered by the business system has been received; wherein, the insurance verification request carries a specified blacklist identifier and insurance product information;

[0180] The first acquisition module is used to, if so, acquire a first blacklist matching algorithm that matches the specified blacklist identifier from a preset algorithm data table;

[0181] The second acquisition module is used to acquire a specified subject parameter corresponding to the subject matching information from the insurance product information based on the subject matching information contained in the first blacklist matching algorithm.

[0182] The third acquisition module is used to acquire first product domain object information corresponding to the specified product domain object from the insured product information based on the specified product domain object contained in the first blacklist matching algorithm.

[0183] The calling module is used to invoke a preset target blacklist database;

[0184] The query module is used to query the target blacklist database based on the specified subject parameters and obtain the second product domain object information corresponding to the specified product domain object from the target blacklist database.

[0185] The processing module is used to analyze the first product domain object information and the second product domain object information, and to process the insurance verification request accordingly based on the analysis results.

[0186] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data construction method in the aforementioned embodiments, and will not be repeated here.

[0187] In some optional implementations of this embodiment, the calling module includes:

[0188] The first submodule is used to retrieve all pre-built blacklist databases;

[0189] The filtering submodule is used to filter out the specified blacklist database that matches the specified blacklist identifier from all the blacklist databases.

[0190] The determination submodule is used to use the specified blacklist library as the target blacklist library.

[0191] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data construction method in the aforementioned implementation method, and will not be repeated here.

[0192] In some optional implementations of this embodiment, the processing module includes:

[0193] The judgment submodule is used to determine whether the first product domain object information is the same as the second product domain object information;

[0194] The first processing submodule is used to generate first verification information for successful blacklist matching if the first product domain object information is the same as the second product domain object information, and to restrict the response to the insurance verification request.

[0195] The second processing submodule is used to generate second verification information indicating unsuccessful blacklist matching if the first product domain object information is different from the second product domain object information, and to respond normally to the insurance verification request.

[0196] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data construction method in the aforementioned embodiments, and will not be repeated here.

[0197] In some optional implementations of this embodiment, the data construction apparatus further includes:

[0198] The third judgment module is used to determine whether an adjustment request for the target blacklist matching algorithm triggered by a specified user has been received;

[0199] The third receiving module is used to receive, if yes, the adjustment operation input by the specified user corresponding to the target blacklist matching algorithm;

[0200] The fourth acquisition module is used to acquire the adjustment information corresponding to the adjustment operation;

[0201] An adjustment module is used to adjust the target blacklist matching algorithm based on the adjustment information to obtain a corresponding second blacklist matching algorithm.

[0202] The first storage module is used to store the second blacklist matching algorithm.

[0203] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data construction method in the aforementioned embodiments, and will not be repeated here.

[0204] In some optional implementations of this embodiment, the first storage module includes:

[0205] The second acquisition submodule is used to acquire the first version number of the target blacklist matching algorithm;

[0206] A generation submodule is used to generate a second version number of the second blacklist matching algorithm based on the first version number;

[0207] The third acquisition submodule is used to acquire the storage information corresponding to the target blacklist matching algorithm;

[0208] The storage submodule is used to store the second blacklist matching algorithm based on the storage information and the second version number.

[0209] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data construction method in the aforementioned embodiments, and will not be repeated here.

[0210] In some optional implementations of this embodiment, the data construction apparatus further includes:

[0211] A creation module is used to create a data association relationship between the target blacklist identifier and the target blacklist matching algorithm;

[0212] The fifth acquisition module is used to acquire a preset algorithm data table;

[0213] The second storage module is used to store the target blacklist identifier and the target blacklist matching algorithm into the algorithm data table based on the data association relationship.

[0214] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data construction method in the aforementioned embodiments, and will not be repeated here.

[0215] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference] for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0216] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0217] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0218] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for data construction methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0219] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the data construction method.

[0220] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0221] Compared with the prior art, the embodiments of this application have the following main advantages:

[0222] In this embodiment, upon receiving an insurance verification request triggered by the business system carrying a specified blacklist identifier and insurance product information, the system intelligently retrieves a first blacklist matching algorithm from a preset algorithm data table that matches the specified blacklist identifier. Then, based on the first blacklist matching algorithm, it retrieves corresponding first product domain object information from the insurance product information. Furthermore, based on the specified subject parameters contained in the insurance product information, it retrieves corresponding second product domain object information from the target blacklist database. Subsequently, by analyzing the first and second product domain object information and processing the insurance verification request accordingly based on the analysis results, the system automatically and quickly completes the blacklist interception and detection processing of insurance product information. This improves the standardization and accuracy of processing insurance verification requests, effectively intercepting malicious insurance applications from abnormal customers, and helps reduce the occurrence of malicious claims and insurance fraud.

[0223] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the data construction method described above.

[0224] Compared with the prior art, the embodiments of this application have the following main advantages:

[0225] In this embodiment, upon receiving an insurance verification request triggered by the business system carrying a specified blacklist identifier and insurance product information, the system intelligently retrieves a first blacklist matching algorithm from a preset algorithm data table that matches the specified blacklist identifier. Then, based on the first blacklist matching algorithm, it retrieves corresponding first product domain object information from the insurance product information. Furthermore, based on the specified subject parameters contained in the insurance product information, it retrieves corresponding second product domain object information from the target blacklist database. Subsequently, by analyzing the first and second product domain object information and processing the insurance verification request accordingly based on the analysis results, the system automatically and quickly completes the blacklist interception and detection processing of insurance product information. This improves the standardization and accuracy of processing insurance verification requests, effectively intercepting malicious insurance applications from abnormal customers, and helps reduce the occurrence of malicious claims and insurance fraud.

[0226] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0227] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A data construction method, characterized by, Includes the following steps: Determine whether a user-triggered request to build a blacklist matching algorithm has been received; If so, a preset subject matching algorithm selection interface is displayed; wherein, the subject matching algorithm selection interface includes multiple subject matching algorithms; Receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface; Display a preset product domain object configuration page; wherein, the product domain object configuration page includes a variety of product domain objects; Receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page; Obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information; Based on the target subject matching algorithm and the target product domain object, a target blacklist matching algorithm corresponding to the target blacklist identifier is constructed. Specifically, by calling a functional engine with rule algorithm construction capabilities, the target subject matching algorithm and the target product domain object are processed to construct a target blacklist matching algorithm corresponding to the target blacklist identifier. The step of constructing a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object further includes: Determine whether an insurance verification request triggered by the business system has been received; wherein, the insurance verification request carries a specified blacklist identifier and insurance product information; If so, retrieve the first blacklist matching algorithm that matches the specified blacklist identifier from the preset algorithm data table; Based on the subject matching information contained in the first blacklist matching algorithm, the specified subject parameters corresponding to the subject matching information are obtained from the insurance product information; Based on the specified product domain objects contained in the first blacklist matching algorithm, obtain the first product domain object information corresponding to the specified product domain object from the insured product information; Call the preset target blacklist database; Based on the specified subject parameters, information is queried in the target blacklist database, and information on the second product domain object corresponding to the specified product domain object is obtained from the target blacklist database. The information of the first product domain object and the information of the second product domain object are analyzed, and the insurance verification request is processed accordingly based on the analysis results.

2. The data construction method according to claim 1, characterized in that, The step of calling the preset target blacklist database specifically includes: Retrieve all pre-built blacklist databases; Filter out the specified blacklist database that matches the specified blacklist identifier from all the blacklist databases; Use the specified blacklist as the target blacklist.

3. The data construction method according to claim 1, characterized in that, The step of analyzing the first product domain object information and the second product domain object information, and processing the insurance verification request accordingly based on the analysis results, specifically includes: Determine whether the first product domain object information is the same as the second product domain object information; If the first product domain object information is the same as the second product domain object information, then generate the first verification information of successful blacklist matching and restrict the response to the insurance verification request; If the first product domain object information is different from the second product domain object information, a second verification message indicating unsuccessful blacklist matching is generated, and the insurance verification request is responded to normally.

4. The data construction method according to claim 1, characterized in that, After the step of constructing a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object, the method further includes: Determine whether an adjustment request for the target blacklist matching algorithm has been received from a specified user; If so, receive the adjustment operation input by the specified user that corresponds to the target blacklist matching algorithm; Obtain the adjustment information corresponding to the adjustment operation; Based on the adjustment information, the target blacklist matching algorithm is adjusted to obtain the corresponding second blacklist matching algorithm. The second blacklist matching algorithm is stored.

5. The data construction method according to claim 4, characterized in that, The step of storing the second blacklist matching algorithm specifically includes: Obtain the first version number of the target blacklist matching algorithm; A second version number of the second blacklist matching algorithm is generated based on the first version number; Obtain the storage information corresponding to the target blacklist matching algorithm; The second blacklist matching algorithm is stored based on the stored information and the second version number.

6. The data construction method according to claim 1, characterized in that, After the step of constructing a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object, the method further includes: Create a data association relationship between the target blacklist identifier and the target blacklist matching algorithm; Obtain the preset algorithm data table; Based on the data association, the target blacklist identifier and the target blacklist matching algorithm are stored in the algorithm data table.

7. A data construction apparatus, characterized in that, include: The first judgment module is used to determine whether a user-triggered request to build a blacklist matching algorithm has been received; The first display module is used to display a preset subject matching algorithm selection interface if the condition is met; wherein, the subject matching algorithm selection interface includes multiple subject matching algorithms; The first receiving module is configured to receive the target subject matching algorithm selected by the user from all the subject matching algorithms included in the subject matching algorithm selection interface; The second display module is used to display a preset product domain object configuration page; wherein, the product domain object configuration page includes a variety of product domain objects; The second receiving module is used to receive the target product domain object selected by the user from all the product domain objects contained in the product domain object configuration page; The generation module is used to obtain the target blacklist type information input by the user, and generate a corresponding target blacklist identifier based on the target blacklist type information; The construction module is used to construct a target blacklist matching algorithm corresponding to the target blacklist identifier based on the target subject matching algorithm and the target product domain object; Specifically, by calling a functional engine with rule algorithm construction capabilities, the target subject matching algorithm and the target product domain object are processed to construct a target blacklist matching algorithm corresponding to the target blacklist identifier. The data construction device also includes: The second judgment module is used to determine whether an insurance verification request triggered by the business system has been received; wherein, the insurance verification request carries a specified blacklist identifier and insurance product information; The first acquisition module is used to, if so, acquire a first blacklist matching algorithm that matches the specified blacklist identifier from a preset algorithm data table; The second acquisition module is used to acquire a specified subject parameter corresponding to the subject matching information from the insurance product information based on the subject matching information contained in the first blacklist matching algorithm. The third acquisition module is used to acquire first product domain object information corresponding to the specified product domain object from the insured product information based on the specified product domain object contained in the first blacklist matching algorithm. The calling module is used to invoke a preset target blacklist database; The query module is used to perform information query on the target blacklist database based on the specified subject parameters, and obtain the second product domain object information corresponding to the specified product domain object from the target blacklist database; The processing module is used to analyze the first product domain object information and the second product domain object information, and to process the insurance verification request accordingly based on the analysis results.

8. A computer device comprising a memory and a processor, the memory storing computer-readable instructions, wherein the processor, when executing the computer-readable instructions, implements the steps of the data construction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data construction method as described in any one of claims 1 to 6.

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