Data updating method and device, equipment and storage medium

Abstract

The application relates to the technical field of data processing, is suitable for the medical and health field and the financial field, and provides a data updating method, device and equipment and a storage medium, the method comprising the following steps: constructing a first database comprising a plurality of first insurance policy data; selecting a plurality of second insurance policy data from the first database according to a set period and a set number; acquiring target insurance policy data matched with each second insurance policy data from a second database; updating the matched second insurance policy data by using each target insurance policy data, changing the processing state identifier of the updated second insurance policy data from unprocessed to processed; migrating the second insurance policy data with the changed identifier to the first database as target insurance policy data, and removing the first insurance policy data corresponding to the target insurance policy data from the first database. The application aims to improve the updating efficiency of data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data updating method, apparatus, device and storage medium. Background Technology

[0002] With the continuous development of the healthcare and insurance industries, the medical service rights system has emerged. This system provides medical-related services to customers of health insurance and other insurance products, such as providing medical treatment certificates and verifying performance, greatly improving the convenience and efficiency of medical services. However, as a later-developed system, the data in the medical service rights system is incomplete, often leading to situations such as missing certificates and failed performance verification when providing medical-related services to customers.

[0003] To address these issues, it's necessary to query earlier data sources via the interface to extract relevant medical service data and update it in the medical service rights system to supplement and improve the missing data. However, this approach is easily limited by system service load and interface performance, leading to problems such as interface response timeouts and uncontrollable update processes, resulting in very low data update efficiency. Summary of the Invention

[0004] The main objective of this application is to provide a data updating method, apparatus, device, and storage medium, which aims to improve the efficiency of data updating.

[0005] Firstly, this application provides a data updating method, including: Construct a first database that includes multiple first policy data, where the processing status identifiers of the first policy data include processed and unprocessed; According to the set cycle and set quantity, multiple second policy data are selected from the first database, and the processing status of the second policy data is marked as unprocessed; Retrieve target policy data that matches each of the second policy data from the second database, wherein the second database stores multiple policy data that match the first policy data; The matching second policy data is updated using the target policy data, and the processing status flag of the updated second policy data is changed from unprocessed to processed. The second policy data after the change of identifier is migrated to the first database as the target policy data, and the first policy data corresponding to the target policy data is removed from the first database.

[0006] Secondly, this application also provides a data updating device, the data updating device comprising: The construction module is used to construct a first database that includes multiple first policy data, wherein the processing status identifiers of the first policy data include processed and unprocessed; The selection module is used to select multiple second policy data from the first database according to a set period and a set quantity, and the processing status of the second policy data is marked as unprocessed; The acquisition module is used to acquire target policy data that matches each of the second policy data from the second database, wherein the second database stores multiple policy data that match the first policy data; The update module is used to update the matching second policy data using the target policy data, and change the processing status identifier of the updated second policy data from unprocessed to processed. The migration module is used to migrate the second policy data after the change of identifier to the first database as the target policy data, and to remove the first policy data corresponding to the target policy data from the first database.

[0007] Thirdly, this application also provides a computer device, the computer device including a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the data update method as described above.

[0008] Fourthly, this application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the data update method described above.

[0009] This application provides a data update method, apparatus, device, and storage medium. First, a first database containing multiple first policy data is constructed to facilitate rapid access to the first policy data in the first database. Second, the processing speed and frequency of the first policy data are controlled by setting a period and a quantity, ensuring data update efficiency while avoiding excessive load due to large-scale data selection. Then, target policy data matching each second policy data is obtained, and each second policy data is updated using the target policy data, ensuring the comprehensiveness and accuracy of the data update. Finally, by migrating the second policy data with changed identifiers to the first database as target policy data and removing the corresponding original first policy data from the first database, the overall data quality of the first database is effectively improved. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the 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.

[0011] Figure 1 This is a flowchart illustrating the steps of a data update method provided in an embodiment of this application. Figure 2 for Figure 1 A flowchart illustrating the sub-steps of the data update method in the diagram; Figure 3 A schematic diagram illustrating a scenario for implementing the data update method provided in this embodiment; Figure 4 A schematic block diagram of a data update device provided in an embodiment of this application; Figure 5 for Figure 4 A schematic block diagram of a submodule of the data update device in the diagram; Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of this application.

[0012] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0015] This application provides a data update method, apparatus, device, and storage medium. The data update method can be applied to a terminal device or a server. The terminal device can be an electronic device such as a mobile phone, tablet computer, laptop computer, desktop computer, personal digital assistant, or wearable device. The server can be a single server or a server cluster composed of multiple servers. The following explanation uses the application of this data update method to a server as an example.

[0016] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0017] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a data update method provided in an embodiment of this application.

[0018] like Figure 1 As shown, the data update method includes steps S101 to S105.

[0019] Step S101: Construct a first database that includes multiple first policy data.

[0020] The first policy data can be extracted from the policies corresponding to some types of insurance from multiple policies. The specific types of insurance can be adjusted according to the actual scenario. For example, in the scenario of providing medical-related services using the medical service rights system, the first policy data can be the policy data corresponding to the types of insurance (such as medical insurance, health insurance, etc.) related to medical service rights extracted from multiple policies provided by financial insurance institutions.

[0021] Specifically, when constructing the first database, raw policy data can be extracted in batches from the insurance company's policy management platform through a preset data interface. This raw policy data is then standardized according to a preset format to extract various key fields, forming a structured set of first policy data. This facilitates more efficient querying and analysis of the first policy data in the future. At the same time, a processing status identifier is added to each piece of first policy data to represent its processing status. The processing status identifier includes "processed" and "unprocessed," so that the first policy data in different processing statuses can be distinguished and processed in the future.

[0022] In one embodiment, constructing a first database including multiple first policy data includes: obtaining a temporary data table for storing the first policy data; converting the multiple first policy data to be updated according to the data storage format of the temporary data table to obtain a target temporary data table; and storing the target temporary data table in a preset database to establish the first database.

[0023] For example, referring to Table 1 below, Table 1 is a temporary data table provided in an embodiment of this application. The data storage format of the temporary data table is as follows: Table 1

[0024] Each field in this temporary data table has a corresponding field in the original policy data. Based on the temporary data table, the original policy data corresponding to fields such as "pol_no" and "cert_no" in the first policy data to be updated can be converted according to the set types such as varchar(50) and datetime to ensure that the data format matches the temporary data table, thereby generating the target temporary data table.

[0025] Understandably, by storing the target temporary data table in a preset database, the scattered and uniformly formatted first policy data can be centrally managed. When the first policy data needs to be updated, the data can be efficiently selected directly from the first database without having to search and integrate from multiple different data sources or files with different formats, thus effectively improving the efficiency and accuracy of data processing.

[0026] It should be noted that, in order to further ensure the privacy and security of the aforementioned first policy data and related information, the aforementioned first policy data and related information can also be stored in a blockchain node. The technical solution of this application can also be applied to adding other data files stored on the blockchain. The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0027] Step S102: Select multiple second policy data from the first database according to the set period and set quantity.

[0028] The set period and set quantity are used to control the frequency and batch size of data selected from the primary database. In some cases, both the set period and set quantity can be set to smaller values ​​to select multiple second policy data from the primary database in small, frequent batches. In other cases, the set period and set quantity can be set to larger values ​​to reduce the frequency of selection operations and obtain more data at once. In still other cases, the two can be combined: during nighttime hours when system load is low, the set period can be appropriately extended and the set quantity increased to efficiently complete data selection using idle resources; while during peak business hours, to avoid impacting core business, the set period can be shortened and the set quantity reduced to distribute database pressure through high-frequency, small-batch processing; and so on. Therefore, the set period and set quantity can be flexibly adjusted according to actual needs and system load to avoid excessive database pressure or data backlog and congestion, ensuring stable system operation.

[0029] Furthermore, during the selection process from the first database, unprocessed policy data is filtered out as second policy data by processing status identifiers. This ensures that subsequent data update operations are performed only on the second policy data that meets the criteria, avoiding duplicate selection of already processed data and improving the accuracy and relevance of data processing.

[0030] In one embodiment, the set quantity includes a first set quantity and a second set quantity, where the first set quantity is greater than the second set quantity. The first and second set quantities are used to limit the amount of data selected from the first database. Figure 2 As shown, step S102 includes sub-steps S1021 to S1022.

[0031] Sub-step S1021: Using a preset distributed scheduling engine, select multiple first policy data from the first database as policy data to be consumed according to a set period and a first set quantity, and send each policy data to be consumed to the message queue.

[0032] The preset distributed scheduling engine supports distributed timed task scheduling, which is used to trigger data selection tasks at set intervals. The first preset quantity is used to limit the number of first policy data selected from the first database each time, so as to control the access pressure on the first database and the load on the message queue of a single data selection operation, effectively reducing the memory pressure on the first database and subsequent systems.

[0033] For example, referring to Table 2, which shows the scheduling task configuration parameters corresponding to the distributed scheduling engine in this embodiment of the application, the details are as follows: Table 2

[0034] Accordingly, the Structured Query Language (SQL) executed by the distributed scheduling engine is as follows: select* fromtransient_pol_deal whereis_deal='N' limit=#{num} order bycreated_date desc Specifically, during the selection process, the distributed scheduling engine uses a first set number as the maximum number of records for each query. By executing SQL, it selects the first policy data marked as unprocessed from multiple first policy data in the first database as the policy data to be consumed. It then serializes these policy data to be consumed into a specific format (such as JSON) and sends them to a message queue (MQ, Message Queue).

[0035] Sub-step S1022: Using a preset distributed middleware, select multiple policy data to be consumed from the message queue as the second policy data according to the second set quantity.

[0036] The preset distributed middleware is used to consume and distribute messages in the message queue, while the second preset quantity is used to limit the amount of policy data to be consumed each time the distributed middleware consumes policy data to be consumed from the message queue.

[0037] Understandably, by combining a distributed scheduling engine and distributed middleware, not only is self-production and self-consumption of data achieved, but peak shaving and valley filling of data processing are also realized, avoiding abnormal fluctuations in service CPU and memory caused by large-scale business processing occupying service threads and memory.

[0038] In one embodiment, selecting multiple first policy data from a first database as policy data to be consumed according to a set period and a first set quantity, and sending each policy data to be consumed to a message queue, includes: at the beginning of each set period, obtaining the number of policy data to be processed in the message queue; if the number of policy data is less than or equal to a preset threshold, selecting multiple first policy data from the first database as new policy data to be consumed according to the first set quantity, and sending each new policy data to the message queue; if the number of policy data is greater than the preset threshold, pausing the selection of first policy data from the first database as policy data to be consumed.

[0039] The preset threshold is used to measure the current data load pressure on the message queue, preventing system performance degradation or excessive resource consumption due to excessive message backlog. The specific value of the preset threshold can be determined comprehensively based on factors such as the processing capacity of the consumption queue and the subsequent consumption speed in the actual business scenario. For example, the preset threshold can be set to a smaller value to avoid excessive data backlog and ensure stable system operation.

[0040] It is understood that in this embodiment, multiple second policy data are selected under the condition of meeting the set time period. However, second policy data is not selected from the first database every time the set time period is reached. That is to say, the set time period is a necessary but not sufficient condition for selecting multiple second policy data from the first database. When the number of policies in the message queue is close to or reaches the preset threshold, even if the time has entered a new set time period, the system will pause data selection to prioritize the processing efficiency of the existing data in the queue. Subsequent data will be added after the queue pressure is relieved. In this way, it can effectively avoid continuously pushing data into the message queue when the processing capacity is limited, and avoid unnecessary consumption of system resources caused by frequent selection operations.

[0041] For example, assuming a preset threshold of 0, at the beginning of each set period, the system first checks whether there is any pending consumption policy data in the message queue. If not (i.e., the number of policies is 0), new pending consumption policy data is selected from the first database according to a first set number and sent to the message queue. If there is already pending data in the message queue (the number of policies is greater than 0), new pending consumption policy data is not selected from the database until the pending consumption data in the message queue is consumed and processed by the downstream distributed middleware until the number drops below the threshold, at which point the system resumes pulling pending consumption data from the first database.

[0042] Understandably, determining whether to select new policy data based on the number of pending consumption policy data in the message queue can achieve a balance between the selection and consumption of pending consumption data. This prevents message queue congestion caused by the data generation speed being much faster than the consumption speed, and also avoids resource waste caused by frequent data reading from the database.

[0043] In one embodiment, obtaining the number of insurance policies pending consumption data in the message queue includes: detecting a preset third database to determine whether a counting marker exists in the third database; wherein the third database is used to store the counting marker, and the counting marker is used to represent the number of insurance policies pending consumption data in the message queue; if it exists, the counting marker is obtained and its value is determined; if it does not exist, a counting marker is generated according to a first preset quantity and stored in the third database.

[0044] The pre-defined third database, such as Redis (Remote Dictionary Server), is used to record and maintain counting flags related to the number of pending policy data in the message queue. These counting flags represent the number of pending policy data in the message queue. In other words, when the number of pending policy data in the message queue increases or decreases, the counting flags are adjusted accordingly to ensure that the counting flags stored in the third database always remain consistent with the actual amount of pending policy data in the message queue.

[0045] Understandably, using counters to reflect the real-time changes in the number of pending insurance policy data in the message queue allows for faster and more accurate control over the start and stop of data selection, thus helping to improve data update efficiency.

[0046] In one embodiment, a preset distributed middleware is used to select multiple policy data to be consumed from the message queue as second policy data according to a second predetermined number. This includes: using the preset distributed middleware to listen to the message queue; when the message queue receives any policy data to be consumed, each consumption thread pulls the policy data to be consumed from the message queue to obtain multiple second policy data.

[0047] The distributed middleware includes multiple consumer threads, with a number equivalent to the second set number. Each consumer thread is used to independently and in parallel pull policy data to be consumed from the message queue.

[0048] For example, refer to Figure 3 , Figure 3 This is a schematic diagram illustrating a scenario for implementing the data update method provided in this embodiment. Assume the distributed middleware includes 8 consumer threads. Figure 3 As can be seen, the distributed middleware, acting as an MQ consumer, will immediately respond and begin retrieving data from the message queue once a new policy to be consumed enters the queue. It is important to emphasize that each consumer thread in the distributed middleware operates independently during data retrieval. Therefore, even if one thread experiences a brief processing delay or an exception, the other consumer threads can continue to function normally, effectively improving the overall stability of the data retrieval process.

[0049] Understandably, by utilizing a distributed middleware with multiple consumer threads for data retrieval, the time required to obtain batch second policy data from the message queue is effectively shortened, the data throughput of the entire data update process is improved, and thus the efficiency of data update is enhanced.

[0050] Step S103: Obtain the target policy data that matches the data of each second policy from the second database. The second database stores multiple policy data that match the first policy data. This policy data can be generated and pre-stored based on historical business records and may include basic policy information, policy identifiers, and coverage terms.

[0051] Specifically, the determination of the target policy data that matches each second policy data can be obtained by fuzzy matching the second policy data with the first policy data in the second database using preset matching rules and algorithms, or it can be obtained by precise matching based on certain special identifiers contained in the second policy data with the identifiers of the first policy data in the second database. For example, when the policy number carried in the second policy data is "PA202510270101", the system will search all the first policy data in the second database, filter out the record with the same policy number "PA202510270101", and determine it as the target policy data that matches the second policy data.

[0052] It is important to emphasize that the second database can include multiple sub-databases. These sub-databases can be divided according to business type or time dimension to facilitate more efficient data retrieval and matching. For example, there could be a product center database, a policy center database, and a customer center database. Different sub-databases store policy data related to specific business scenarios, allowing for quick location of the corresponding sub-database based on the business attributes of the second policy data when retrieving target policy data, thus improving data retrieval efficiency.

[0053] In one embodiment, obtaining target policy data matching each second policy data from a second database includes: obtaining policy identifiers for each second policy data, whereby the policy identifier includes at least one of policy number, sub-policy number, and customer number; obtaining multiple policy data matching each second policy data from the second database based on the policy identifiers for each second policy data; and performing deduplication processing on the multiple policy data corresponding to each second policy data to obtain the target policy data corresponding to each second policy data.

[0054] For example, refer to Figure 3 Assume the second database includes a product center database, a policy center database, and a customer center database. Assume the policy identifier for the second policy data includes the policy number and the customer number. The process of obtaining the target policy data that matches the second policy data is as follows: First, the retrieval interface of the product center database is called, passing in the policy number and customer number of the second policy data, to query whether a policy data record containing that policy number exists in the product center database. Next, the retrieval interface of the policy center database is called again with the same policy number, performing the same query operation. Subsequently, the retrieval interface of the customer center database is called again to perform a search, verifying the correctness of fields such as the customer number. If the product center database returns one matching record, the policy center database returns two matching records (one of which is a duplicate of the record returned by the product center database), and the customer center database returns no matching record, then all matching records obtained from the three sub-databases are aggregated. These records are then deduplicated, removing duplicate policy data, ultimately resulting in two unique policy data records. These two records are identified as the target policy data corresponding to the second policy data.

[0055] Understandably, matching data using policy identifiers allows for more accurate location of policy data in the second database that is associated with the second policy data, thus improving the comprehensiveness and accuracy of subsequent data updates. Furthermore, deduplication of the matching results effectively prevents duplicate data from impacting subsequent update processes, ensuring the uniqueness and accuracy of the target policy data.

[0056] Step S104: Update the matching second policy data using the target policy data, and change the processing status flag of the updated second policy data from unprocessed to processed.

[0057] Specifically, based on preset field mapping relationships and update rules, key information from the target policy data, such as policyholder information, insured amount, insurance period, and payment method, can be accurately synchronized to the corresponding fields in the second policy data. For example, after comparing the second policy data with the corresponding target policy data, if it is determined that a certain medical benefit is missing in the second policy data, the specific content of that medical benefit from the target policy data will be completely filled into the corresponding field of the second policy data. Similarly, if a discrepancy is found between the policyholder's contact number in the second policy data and the target policy data, the contact number from the target policy data will be used as the latest data to overwrite the corresponding part in the second policy data. Furthermore, the processing status of the updated second policy data will be changed from unprocessed to processed to avoid repeated updates to the same policy data in the future.

[0058] Step S105: Migrate the second policy data after the change of identifier to the first database as the target policy data, and remove the first policy data corresponding to the target policy data from the first database.

[0059] Specifically, after updating the second policy data and changing its processing status identifier, it is migrated to the first database to facilitate subsequent use of the first database to support various business operations, such as policy inquiries, providing medical treatment certificates, and fulfillment verification. Simultaneously, to avoid data redundancy and confusion, the original first policy data corresponding to the target policy data is completely removed from the first database, ensuring that only the latest and valid policy data is retained, thereby improving the overall data quality of the first database.

[0060] The data update method provided in the above embodiments first constructs a first database containing multiple first policy data to facilitate rapid access to the first policy data in the first database. Second, it controls the processing speed and frequency of the first policy data by setting a period and a quantity, ensuring data update efficiency while avoiding excessive load due to large-scale data selection. Then, it obtains target policy data that matches each second policy data and uses the target policy data to update each second policy data, ensuring the comprehensiveness and accuracy of the data update. Finally, it migrates the second policy data with changed identifiers to the first database as target policy data and removes the corresponding original first policy data from the first database, effectively improving the overall data quality of the first database.

[0061] Please refer to Figure 4 , Figure 4 This is a schematic block diagram of a data update device provided in an embodiment of this application.

[0062] like Figure 4 As shown, the data update device 200 includes: Module 201 is used to construct a first database that includes multiple first policy data, and the processing status identifiers of the first policy data include processed and unprocessed; The selection module 202 is used to select multiple second policy data from the first database according to a set period and a set quantity. The processing status of the second policy data is marked as unprocessed. The acquisition module 203 is used to acquire target policy data that matches each second policy data from the second database. The second database stores policy data that matches multiple first policy data. The update module 204 is used to update the matching second policy data using the target policy data, and change the processing status flag of the updated second policy data from unprocessed to processed. The migration module 205 is used to migrate the second policy data after the change of the identifier to the first database as the target policy data, and remove the first policy data corresponding to the target policy data from the first database.

[0063] In one embodiment, the building module 201 is further configured to: Obtain a temporary data table for storing the first policy data; convert the format of multiple first policy data to be updated according to the data storage format of the temporary data table to obtain the target temporary data table; store the target temporary data table in a preset database to establish the first database.

[0064] In one embodiment, the set quantity includes a first set quantity and a second set quantity, wherein the first set quantity is greater than the second set quantity, such as... Figure 5 As shown, module 202 includes: The first selection submodule 2021 is used to select multiple first policy data from the first database as policy data to be consumed according to a set period and a first set number using a preset distributed scheduling engine, and send each policy data to be consumed to a message queue.

[0065] The second selection submodule 2022 is used to select multiple policy data to be consumed from the message queue as the second policy data by using a preset distributed middleware according to a second set quantity.

[0066] In one embodiment, the first selection submodule 2011 is further configured to: At the beginning of each set period, the number of pending consumption policy data in the message queue is obtained; if the number of policies is less than or equal to a preset threshold, multiple first policy data are selected from the first database as new pending consumption policy data according to the first set number, and each new pending consumption policy data is sent to the message queue; if the number of policies is greater than the preset threshold, the selection of first policy data from the first database as pending consumption policy data is paused.

[0067] In one embodiment, the first selection submodule 2011 is further configured to: The system checks a preset third database to determine if a counting marker exists in the third database. The third database is used to store the counting marker, which represents the number of insurance policies in the message queue that are pending consumption. If the counting marker exists, it is retrieved and its value is determined. If the counting marker does not exist, it is generated according to a first preset quantity and stored in the third database.

[0068] In one embodiment, the second selection submodule 2012 is further configured to: The message queue is monitored using a pre-defined distributed middleware, which includes a second set number of consumer threads. When the message queue receives any policy data to be consumed, each consumer thread pulls the policy data to be consumed from the message queue to obtain multiple second policy data.

[0069] In one embodiment, the acquisition module 203 is further configured to: Obtain the policy identifier for each second policy data, which includes at least one of policy number, sub-policy number, and customer number; based on the policy identifier for each second policy data, retrieve multiple policy data that match each second policy data from the second database; perform deduplication processing on the multiple policy data corresponding to each second policy data to obtain the target policy data corresponding to each second policy data.

[0070] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the above-described device and its modules and units can be referred to the corresponding processes in the aforementioned data update method embodiments, and will not be repeated here.

[0071] The apparatus provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 6 It runs on the computer device shown.

[0072] Please see Figure 6 , Figure 6 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0073] like Figure 6 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory may include a storage medium and internal memory, and the storage medium may be non-volatile or volatile.

[0074] The storage medium may store the operating system and computer programs. The computer programs include program instructions that, when executed, cause the processor to perform any data update method.

[0075] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0076] Internal memory provides an environment for the execution of computer programs stored in the storage medium. When the computer program is executed by the processor, it enables the processor to perform any data update method.

[0077] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0078] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0079] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Construct a first database that includes multiple first policy data, and the processing status identifiers of the first policy data include processed and unprocessed; According to the set cycle and set quantity, select multiple second policy data from the first database, and mark the processing status of the second policy data as unprocessed; Retrieve target policy data that matches each second policy data from the second database, which stores policy data that matches multiple first policy data. The matching second policy data is updated using the target policy data, and the processing status of the updated second policy data is changed from unprocessed to processed. The second policy data after the change of identifier is migrated to the first database as the target policy data, and the first policy data corresponding to the target policy data is removed from the first database.

[0080] In one embodiment, the processor, when constructing a first database comprising multiple first policy data, is configured to: Obtain the temporary data table used to store the first policy data; Based on the data storage format of the temporary data table, the format of multiple first policy data to be updated is converted to obtain the target temporary data table; Store the target temporary data table into a preset database to establish the first database.

[0081] In one embodiment, the set quantity includes a first set quantity and a second set quantity, wherein the first set quantity is greater than the second set quantity. When the processor selects multiple second policy data from the first database according to a set period and a set quantity, it is used to: Using a pre-defined distributed scheduling engine, multiple first policy data are selected from the first database as policy data to be consumed according to a set period and a first set number, and each policy data to be consumed is sent to a message queue; wherein, the processing status of the policy data to be consumed is marked as unprocessed. Using a pre-defined distributed middleware, multiple policy data to be consumed are selected from the message queue as the second policy data according to a second set quantity.

[0082] In one embodiment, when the processor selects multiple first policy data from a first database as policy data to be consumed according to a set period and a first set quantity, and sends each policy data to be consumed to a message queue, it is configured to: At the beginning of each set period, obtain the number of insurance policies in the message queue that are pending consumption data; If the number of policies is less than or equal to a preset threshold, then select multiple first policy data from the first database as new policy data to be consumed according to the first set number, and send each new policy data to be consumed to the message queue. If the number of policies exceeds a preset threshold, the selection of the first policy data from the first database as the policy data to be consumed will be paused.

[0083] In one embodiment, when the processor retrieves the number of pending consumption policy data in the message queue, it is configured to: The pre-defined third database is checked to determine whether a counting marker exists in the third database; the third database is used to store the counting marker, which represents the number of insurance policies in the message queue that are pending consumption. If it exists, retrieve the counter tag and determine its value; If it does not exist, a count tag is generated according to the first set quantity, and the count tag is stored in the third database.

[0084] In one embodiment, when the processor selects multiple policy data to be consumed from the message queue as second policy data according to a second predetermined number using a preset distributed middleware, it is configured to: The message queue is monitored using a pre-defined distributed middleware, which includes a second set number of consumer threads. When the message queue receives any policy data to be consumed, each consumption thread pulls the policy data to be consumed from the message queue to obtain multiple second policy data.

[0085] In one embodiment, when the processor retrieves target policy data matching each second policy data from the second database, it is configured to: Obtain the policy identifier for each second policy data. The policy identifier includes at least one of the following: policy number, sub-policy number, and customer number. Based on the policy identifier of each second policy data, retrieve multiple policy data that match each second policy data from the second database; The duplicate policy data corresponding to each second policy data is deduplicated to obtain the target policy data corresponding to each second policy data.

[0086] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the computer device described above can be referred to the corresponding process in the aforementioned data update method embodiments, and will not be repeated here.

[0087] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0088] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to various embodiments of the data update method of this application.

[0089] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0090] Furthermore, the computer's usable storage medium may primarily include a stored program area and a stored data area. The stored program area may store the operating system, applications required for at least one function, etc.; the stored data area may store data created based on the use of blockchain nodes, etc. 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. A blockchain is essentially a decentralized database, a chain of data blocks linked 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 may include a blockchain underlying platform, a platform product service layer, and an application service layer, etc.

[0091] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0092] It should also be understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, herein, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0093] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0094] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above descriptions are merely specific implementations of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data update method, characterized in that, include: Construct a first database that includes multiple first policy data, where the processing status identifiers of the first policy data include processed and unprocessed; According to the set cycle and set quantity, multiple second policy data are selected from the first database, and the processing status of the second policy data is marked as unprocessed; Retrieve target policy data that matches each of the second policy data from the second database, wherein the second database stores multiple policy data that match the first policy data; The matching second policy data is updated using the target policy data, and the processing status flag of the updated second policy data is changed from unprocessed to processed. The second policy data after the change of identifier is migrated to the first database as the target policy data, and the first policy data corresponding to the target policy data is removed from the first database.

2. The data update method as described in claim 1, characterized in that, The set quantity includes a first set quantity and a second set quantity, wherein the first set quantity is greater than the second set quantity; The step of selecting multiple second policy data from the first database according to a set period and a set quantity includes: Using a preset distributed scheduling engine, multiple first policy data are selected from the first database as policy data to be consumed according to a set period and the first set quantity, and each policy data to be consumed is sent to a message queue; wherein, the processing status of the policy data to be consumed is marked as unprocessed. Using a pre-defined distributed middleware, multiple policy data to be consumed are selected from the message queue as the second policy data according to the second set quantity.

3. The data update method as described in claim 2, characterized in that, The step of selecting multiple first policy data from the first database as policy data to be consumed according to a set period and the first set quantity, and sending each of the policy data to be consumed to a message queue, includes: At the beginning of each set period, obtain the number of insurance policies in the message queue that are pending consumption. If the number of insurance policies is less than or equal to a preset threshold, then multiple first insurance policy data are selected from the first database as new insurance policy data to be consumed according to the first set number, and each of the new insurance policy data to be consumed is sent to the message queue; If the number of insurance policies exceeds the preset threshold, the selection of the first insurance policy data from the first database as the insurance policy data to be consumed will be paused.

4. The data update method as described in claim 3, characterized in that, The number of policies for which the pending consumption policy data in the message queue is obtained includes: A preset third database is checked to determine whether a counting marker exists in the third database; wherein, the third database is used to store the counting marker, and the counting marker is used to represent the number of insurance policies in the message queue that are pending consumption. If it exists, then obtain the counting tag and determine the value of the counting tag; If it does not exist, the counting marker is generated according to the first set quantity, and the counting marker is stored in the third database.

5. The data update method as described in claim 2, characterized in that, The step of using a preset distributed middleware to select multiple pending insurance policy data from the message queue as the second set number of insurance policy data includes: The message queue is monitored using a pre-defined distributed middleware, which includes the second set number of consumer threads. When the message queue receives any of the policy data to be consumed, each of the consumption threads pulls the policy data to be consumed from the message queue to obtain multiple second policy data.

6. The data update method as described in claim 1, characterized in that, The construction of the first database, which includes multiple first policy data, includes: Obtain the temporary data table used to store the first policy data; Based on the data storage format of the temporary data table, the format of multiple first policy data to be updated is converted to obtain the target temporary data table; The target temporary data table is stored in a preset database to establish the first database.

7. The data update method according to any one of claims 1-6, characterized in that, The step of obtaining target policy data that matches the data of each of the second policies from the second database includes: Obtain the policy identifier for each of the second policy data, wherein the policy identifier includes at least one of the policy number, sub-policy number, and customer number; Based on the policy identifier of each of the second policy data, multiple policy data that match each of the second policy data are obtained from the second database; The multiple policy data corresponding to each of the second policy data are deduplicated to obtain the target policy data corresponding to each of the second policy data.

8. A data update device, characterized in that, The data update device includes: The construction module is used to construct a first database that includes multiple first policy data, wherein the processing status identifiers of the first policy data include processed and unprocessed; The selection module is used to select multiple second policy data from the first database according to a set period and a set quantity, and the processing status of the second policy data is marked as unprocessed; The acquisition module is used to acquire target policy data that matches each of the second policy data from the second database, wherein the second database stores multiple policy data that match the first policy data; The update module is used to update the matching second policy data using the target policy data, and change the processing status identifier of the updated second policy data from unprocessed to processed. The migration module is used to migrate the second policy data after the change of identifier to the first database as the target policy data, and to remove the first policy data corresponding to the target policy data from the first database.

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 by the processor, wherein when the computer program is executed by the processor, it implements the data update method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the data update method as described in any one of claims 1 to 7.

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