Data processing method and device, electronic equipment and computer readable storage medium
By acquiring data modification request information from the system, preprocessing it, and using a server cluster for multi-threaded concurrent modification, with the core server correcting in case of anomalies, the system solves the problems of low efficiency and stability when modifying large amounts of data, achieving fast and stable data processing.
Patent Information
- Application Number
- CN202511221293.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-28
- Publication Date
- 2025-11-14
Smart Images
Figure CN120950190A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of this application relate to, but are not limited to, the field of data processing, and particularly to a data processing method, apparatus, electronic device, and computer-readable storage medium. Background Technology
[0002] With the continuous promotion of smart healthcare and the improvement of insurance systems, various software systems are increasingly being used to store and manage relevant data. For example, during the insurance period, if the policyholder discovers errors or omissions in their application, or if unforeseen circumstances cause discrepancies between the information recorded on the signed insurance bill and the actual situation, they can apply for a policy amendment. Similarly, in the field of smart healthcare, changes to patient information can be modified through the system. However, when making large-scale modifications to system data, system maintenance personnel currently need to manually verify and modify each piece of data. This increases their workload, leads to slower real-time system response, and negatively impacts the user experience. Summary of the Invention
[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.
[0004] To address the problems mentioned in the background section, this application provides a data processing method, apparatus, electronic device, and computer-readable storage medium that can quickly modify system data in batches, reduce the workload of system maintenance personnel, improve system response speed, and provide users with a better user experience.
[0005] In a first aspect, embodiments of this application provide a data processing method, including:
[0006] Obtain data modification request information, wherein the data modification request information is used to indicate that business information should be modified;
[0007] Add the data modification request information to the preset list of items to be modified;
[0008] Each of the data modification request messages in the list to be modified is preprocessed to obtain data preprocessing request messages;
[0009] Each server in the preset server cluster is controlled to perform multi-threaded concurrent modification processing based on the data preprocessing request information to obtain data modification result information;
[0010] If the data modification result indicates that the modification of the business information is abnormal, determine the data modification abnormality sub-information from the data modification result information;
[0011] The target business information is determined by modifying the abnormal sub-information based on the data, and the target business information is modified using the core server in the server cluster.
[0012] Secondly, embodiments of this application also provide a data processing apparatus, the apparatus comprising:
[0013] An acquisition unit is used to acquire data modification request information, wherein the data modification request information is used to indicate that business information should be modified.
[0014] An adding unit is used to add the data modification request information to a preset list of items to be modified;
[0015] The preprocessing unit is used to preprocess each of the data modification request information in the list to be modified to obtain data preprocessing request information;
[0016] The first modification unit is used to control each server in the preset server cluster to perform multi-threaded concurrent modification processing according to each of the data preprocessing request information, and obtain data modification result information.
[0017] A filtering unit is used to determine data modification anomaly sub-information from the data modification result information when the data modification result indicates that the modification of the business information is abnormal;
[0018] The second modification unit is used to determine the target business information by modifying the abnormal sub-information based on the data, and to modify the target business information using the core server in the server cluster.
[0019] Thirdly, embodiments of this application also provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data processing method described in the first aspect above.
[0020] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for performing the data processing method described in the first aspect above.
[0021] The data processing method according to the embodiments provided in this application has at least the following beneficial effects: During data processing, firstly, data modification request information is obtained, wherein the data modification request information is used to indicate modifications to business information; then, the data modification request information is added to a preset list to be modified; next, each data modification request information in the list to be modified is preprocessed to obtain data preprocessing request information; then, each server in a preset server cluster is controlled to perform multi-threaded concurrent modification processing according to each data preprocessing request information to obtain data modification result information; if the data modification result indicates an anomaly in the modification of business information, data modification anomaly sub-information is determined from the data modification result information; finally, the target business information is determined based on the data modification anomaly sub-information, and the core server in the server cluster is used to modify the target business information. Through the above technical solution, controlling each server in the server cluster to perform multi-threaded concurrent modification processing according to each data preprocessing request information enables rapid batch modification of system data, reducing the workload of system maintenance personnel, improving system response speed, and providing users with a good user experience; furthermore, the core server in the server cluster can be used to modify the target business information, making system data modification more stable and reliable. Attached Figure Description
[0022] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.
[0023] Figure 1 This is a schematic flowchart of a data processing method provided in one embodiment of this application;
[0024] Figure 2 yes Figure 1 A schematic diagram of a specific implementation of step S300;
[0025] Figure 3 yes Figure 1 A schematic diagram of a specific implementation of step S400;
[0026] Figure 4 yes Figure 1 A schematic diagram of a specific implementation of step S500;
[0027] Figure 5 yes Figure 1 A schematic diagram of a specific implementation method of step S600;
[0028] Figure 6 This is a flowchart illustrating a data processing method provided in another embodiment of this application;
[0029] Figure 7 Is it completed? Figure 3 A flowchart illustrating a specific implementation method following step S430;
[0030] Figure 8 This is a schematic diagram of a data processing apparatus provided in one embodiment of this application;
[0031] Figure 9 This is a schematic diagram of an electronic device provided in one embodiment of this application. Detailed Implementation
[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0033] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0034] It should be noted that, 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 belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0035] 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.
[0036] AI is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce new intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information processes of human consciousness and thought. Furthermore, artificial intelligence utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results—the theories, methods, technologies, and application systems available for use.
[0037] 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.
[0038] Artificial intelligence, or AI, is the theory, method, technology, and application system that uses 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.
[0039] The servers involved in artificial intelligence technology can be standalone servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.
[0040] This application provides a data processing method, apparatus, electronic device, and computer-readable storage medium. In the data processing process, firstly, data modification request information is acquired, which indicates modifications to business information. Next, the data modification request information is added to a preset list to be modified. Then, each data modification request in the list to be modified is preprocessed to obtain data preprocessing request information. Next, each server in a preset server cluster is controlled to perform multi-threaded concurrent modification processing based on each data preprocessing request, resulting in data modification result information. If the data modification result indicates an anomaly in the business information modification, data modification anomaly sub-information is determined from the data modification result information. Finally, the target business information is determined based on the data modification anomaly sub-information, and the core server in the server cluster is used to modify the target business information. Through this technical solution, controlling each server in the server cluster to perform multi-threaded concurrent modification processing based on each data preprocessing request can quickly perform batch modifications of system data, reducing the workload of system maintenance personnel, improving system response speed, and providing users with a better user experience. Furthermore, utilizing the core server in the server cluster to modify the target business information makes system data modification more stable and reliable.
[0041] The data processing method provided in this application relates to the field of data processing technology. The data processing method provided in this application can be applied to a terminal or a server, and can also be software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.
[0042] 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.
[0043] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.
[0044] The embodiments of this application will be further described below with reference to the accompanying drawings.
[0045] like Figure 1 As shown, Figure 1 This is a schematic flowchart of a data processing method provided in one embodiment of this application. The data processing method includes the following steps:
[0046] Step S100: Obtain data modification request information, wherein the data modification request information is used to indicate that business information should be modified.
[0047] The data processing method provided in this application first obtains data modification request information during the data processing process. The data modification request information is used to indicate the modification of business information. When the data modification request information is obtained, it can be added to a pre-set list to be modified to prepare for subsequent data processing.
[0048] It is worth noting that the data modification request information in this application embodiment is used to instruct modifications to business information, that is, to modify relevant data in the business system. The data modification request information is used to indicate the specific data that needs to be modified. For example, in the field of financial insurance business, modifying system data may include policy information, policy status information, claims data, customer relationship data, system parameters and rules data, financial and settlement data, etc. Among them, policy information may include the identity information of the insured / beneficiary, the address and value assessment parameters of the insured object, payment method and bank account; claims data may include the compensation amount and medical diagnosis code. Alternatively, in the field of smart healthcare, modifying system data may include patient treatment data, hospital operation data, IoT device and real-time monitoring data, and scientific research data; among them, patient treatment data may include electronic health record data, diagnosis and treatment records, and examination and testing data; hospital operation data may include resource scheduling information, material management data, and financial settlement information.
[0049] It is worth noting that all data modification request information is obtained in accordance with relevant laws, regulations, and standards. Furthermore, when this application embodiment needs to obtain sensitive personal information of users, it will obtain the user's individual permission or consent through pop-ups or redirection to a confirmation page. Only after explicitly obtaining the user's individual permission or consent will it obtain the necessary user-related data required for the normal operation of this application embodiment.
[0050] Step S200: Add the data modification request information to the preset list of items to be modified.
[0051] The data processing method provided in this application embodiment, after obtaining data modification request information, wherein the data modification request information is used to indicate the modification of business information; then the data modification request information can be added to a pre-set list to be modified, and subsequently, each data modification request information in the modification list can be preprocessed to obtain data preprocessing request information, and then the servers in the server cluster can perform data modification processing according to the data modification request information.
[0052] It is worth noting that data modification request information is added to a pre-defined list of items to be modified, in preparation for subsequent data modification processing. This list can be a data storage table.
[0053] Step S300: Preprocess each data modification request information in the list to be modified to obtain data preprocessing request information.
[0054] The data processing method provided in this application embodiment can preprocess each data modification request in the pre-set list to be modified after adding the data modification request information to the list to be modified, thereby obtaining data preprocessing request information to prepare for subsequent data modification.
[0055] It is worth noting that in the process of preprocessing the data modification request information in the list to be modified, the data modification request information in the list to be modified is first cleaned to obtain data request cleaned information; then, the data request cleaned information is converted to obtain data request converted information; finally, the data request converted information is standardized to obtain data preprocessed request information, which is prepared for subsequent data modification requests.
[0056] like Figure 2 As shown, the process of preprocessing each data modification request information in the list to be modified to obtain data preprocessing request information may include the following steps:
[0057] Step S310: Perform data cleaning on the data modification request information in the list to be modified to obtain data request cleaning information;
[0058] Step S320: Perform data type conversion processing on the data request cleaning information to obtain data request conversion information.
[0059] Step S330: Standardize the data request transformation information to obtain the data preprocessing request information.
[0060] For steps S310 to S330, firstly, the data modification request information in the list to be modified is cleaned to obtain data request cleaned information; then, the data request cleaned information is converted to obtain data request converted information; finally, the data request converted information is standardized to preprocess the data request information, thus preparing it for subsequent data request modifications.
[0061] It is worth noting that data cleaning of the data modification request information in the modification list can remove interfering information, making subsequent data modification processing more accurate and improving the accuracy of data modification. Furthermore, data type conversion of the cleaned data request information yields transformed data request information, facilitating subsequent data modification processing. Standardizing the transformed data request information allows servers in the server cluster to recognize and process the preprocessed data request information, enabling them to modify system data based on the corresponding preprocessed data request information, thus simplifying and accelerating system data modification.
[0062] Step S400: Control each server in the preset server cluster to perform multi-threaded concurrent modification processing based on each data preprocessing request information, and obtain data modification result information.
[0063] The data processing method provided in this application, after preprocessing the data modification request information of each data in the modification list to obtain data preprocessing request information, can control each server in the pre-set server cluster to perform multi-threaded concurrent modification processing according to each data preprocessing request information, thereby obtaining data modification result information; by controlling each server in the server cluster to perform multi-threaded concurrent modification processing according to each data preprocessing request information in the above manner, the system data modification efficiency can be greatly improved, and the effect of batch modification can be achieved.
[0064] For example, in a financial insurance business system, the insurance system is built and operated based on a server cluster, which includes three servers. During large-scale data modification, the obtained data preprocessing request information is distributed to the three servers, and subsequently, the three servers can be directly used to modify the data according to the corresponding data preprocessing request information. Alternatively, in the field of smart healthcare, a smart healthcare system can also be built and operated based on a server cluster, which includes four servers. During large-scale data modification, the obtained data preprocessing request information is distributed to the four servers, and subsequently, the four servers can be directly used to modify the data according to the corresponding data preprocessing request information, thus significantly improving the efficiency of system data modification. It is worth noting that during the process of modifying system data using each server in the server cluster, the servers do not affect each other and each performs data modification independently.
[0065] like Figure 3As shown, controlling each server in a pre-defined server cluster to perform multi-threaded concurrent modification processing based on various data preprocessing request information to obtain data modification result information may include the following steps:
[0066] Step S410: Determine the data ownership information from each data preprocessing request message;
[0067] Step S420: Match the data attribution information with the tag information carried by each server in the server cluster to determine the target server;
[0068] Step S430: Using the target server, modify the corresponding business information according to the data preprocessing request information to obtain the data modification result information.
[0069] For steps S410 to S430, in the process of controlling each server in the preset server cluster to perform multi-threaded concurrent modification processing based on each data preprocessing request information to obtain data modification result information, firstly, the data ownership information is determined from each data preprocessing request information; then, the data ownership information is matched with the tag information carried by each server in the server cluster to determine the target server; finally, the target server is used to modify the corresponding business information according to the data preprocessing request information to obtain data modification result information. Through the above technical solution, controlling each server in the server cluster to perform multi-threaded concurrent modification processing based on each data preprocessing request information can significantly improve the system's data modification efficiency and achieve batch modification effects.
[0070] It is worth noting that determining data ownership information from each data preprocessing request prepares for the subsequent allocation and processing of data preprocessing requests. The data ownership information is then matched with the tagging information carried by each server in the server cluster to obtain the target server. Finally, the target server can be used to modify the corresponding business information according to the data preprocessing request information, resulting in the data modification result. This technical solution makes the entire system's data modification process more stable and reliable.
[0071] It is worth noting that each data preprocessing request corresponds to business information. Therefore, once the target server is determined, the corresponding business information can be modified based on the data preprocessing request information allocated to it. The whole process is safe and reliable, and it can also greatly speed up the system data modification efficiency.
[0072] Step S500: If the data modification result indicates that there is an anomaly in the modification of business information, determine the data modification anomaly sub-information from the data modification result information.
[0073] The data processing method provided in this application embodiment controls each server in a preset server cluster to perform multi-threaded concurrent modification processing based on various data preprocessing request information. After obtaining the data modification result information, if the data modification result indicates that there is an anomaly in the modification of business information, the abnormal data modification sub-information can be determined from the data modification result information to prepare for subsequent modifications, so that the modification of system data can be more stable, safe and reliable.
[0074] It's worth noting that the data modification result indicates an anomaly in the business information modification. This means that an error occurred during the modification of the corresponding business information based on the data preprocessing request information, and the information was not accurately modified according to the request. In such cases, the data modification anomaly sub-information can be identified from the data modification result information, preparing for the re-modification of the anomalous business information.
[0075] like Figure 4 As shown, when the data modification result indicates an anomaly in the modification of business information, determining the data modification anomaly sub-information from the data modification result information may include the following steps:
[0076] Step S510: If the data modification result indicates that there is an anomaly in the modification of business information, the data modification result information is split into multiple data modification feedback information, wherein each data modification feedback information includes data modification marker information.
[0077] Step S520: Based on the data modification marker information, determine the data modification anomaly sub-information from multiple data modification feedback information.
[0078] The data processing method provided in this application, when the data modification result indicates that there is an anomaly in the modification of business information, determines the abnormal data modification sub-information from the data modification result information. First, the data modification result information is split to obtain multiple data modification feedback information, wherein each data modification feedback information includes data modification marker information. Then, based on the data modification marker information, the abnormal data modification sub-information is determined from the multiple data modification feedback information to prepare for the re-modification of the abnormal business information.
[0079] For example, if the data modification result indicates that the business information modification is abnormal, the data modification result information can be split into multiple data modification feedback information. Each data modification feedback information contains data modification marker information. For example, a data modification marker information of 1 indicates that the data modification is normal; a data modification marker information of 0 indicates that the data modification has an abnormal situation. Therefore, based on the data modification marker information, the abnormal data modification sub-information can be easily and conveniently determined from the multiple data modification feedback information.
[0080] Step S600: Determine the target business information based on the data modification anomaly sub-information, and use the core server in the server cluster to modify the target business information.
[0081] The data processing method provided in this application, when the data modification result indicates that there is an anomaly in the modification of business information, determines the data modification anomaly sub-information from the data modification result information; and determines the target business information based on the data modification anomaly sub-information, and uses the core server in the server cluster to modify the target business information. By modifying the target business information based on the core server in the server cluster, the reliability of data modification is ensured, and the stability and reliability of system data modification are improved.
[0082] For example, in the financial insurance business, when modifying large amounts of data in an insurance business system, each server in a server cluster first performs parallel batch modification of the corresponding business information, which can significantly accelerate the efficiency of system data modification. However, some system data may encounter anomalies during the modification process, requiring the core server to reprocess the previously modified target business information to improve the reliability and stability of data modification in the insurance business system. Similarly, in the smart healthcare industry, when modifying large amounts of data in a smart healthcare system, each server in a server cluster can also perform parallel batch modification of the corresponding business information, thus significantly accelerating the efficiency of system data modification. However, if some system data encounters anomalies during the modification process, the core server must also reprocess the previously modified target business information to improve the reliability and stability of data modification in the smart healthcare system.
[0083] like Figure 5 As shown, modifying target business information using the core server in a server cluster can include the following steps:
[0084] Step S610: Determine the corresponding data preprocessing request information based on the target business information;
[0085] Step S620: Modify the target business information according to the data preprocessing request information through the core server.
[0086] For steps S610 to S620, during the process of modifying the target business information using the core server in the server cluster, the corresponding data preprocessing request information is determined based on the target business information. Then, the core server can be used to modify the target business information based on the data preprocessing request information. Since the core server is more reliable than ordinary servers, using the core server to re-modify the previously modified abnormal business information can greatly improve the reliability of data modification.
[0087] It is worth noting that each target business information corresponds to a data preprocessing request. Therefore, the corresponding data preprocessing request can be determined based on the target business information, in order to prepare for subsequent data modifications.
[0088] like Figure 6 As shown, the data modification request information includes user attribute information. The data processing method in this embodiment of the application further includes the following steps:
[0089] Step S710: If the user attribute information indicates that the user is within the preset priority level range, add a priority level mark to the corresponding data modification request information.
[0090] Step S720: Select a core server from the server cluster according to the priority level marker, and control the core server to modify the corresponding business information according to the data modification request information.
[0091] For steps S710 to S720, during the data processing, if the user attribute information indicates that the user is within the pre-set priority level range, a priority level mark can be added to the corresponding data modification request information. Subsequently, the core server can be selected directly from the server cluster based on the priority level mark, and the core server can modify the corresponding business information according to the data modification request information. This allows for rapid modification of high-priority business information, making data modification more flexible.
[0092] For example, in an insurance business system, if the user attribute information in a data modification request indicates that the user falls within a preset priority level, a priority level marker can be added to the corresponding data modification request. Subsequently, the core server can directly modify this business information, improving the flexibility of data modification and satisfying the needs of higher-priority users more quickly. Alternatively, in a smart healthcare system, if the user attribute information in a data modification request indicates that the user falls within a preset priority level, a priority level marker can also be added to the corresponding data modification request. Subsequently, the core server can also directly modify this business information, ensuring the needs of higher-priority users are met.
[0093] like Figure 7 As shown, controlling each server in the preset server cluster to perform multi-threaded concurrent modification processing based on various data preprocessing request information, and obtaining the data modification result information, can include the following steps:
[0094] Step S440: Compress the data modification result information to obtain a compressed package of modification results;
[0095] Step S450: Add the current time information to the modified result compressed package;
[0096] Step S460: Send the modified result compressed package back to the preset monitoring terminal.
[0097] For steps S440 to S460, each server in the preset server cluster is controlled to perform multi-threaded concurrent modification processing according to each data preprocessing request information. After obtaining the data modification result information, the data modification result information can be compressed to obtain a modification result compressed package. Then, the current time information is added to the modification result compressed package. Finally, the modification result compressed package can be fed back to the preset monitoring terminal to inform the system maintenance personnel of the data modification status.
[0098] It is worth noting that the current time information should be added to the modified result compressed package to distinguish data modification operations performed at different times, facilitating subsequent system maintenance and review. The monitoring terminal can be a mobile phone, computer, or tablet, etc.
[0099] In addition, such as Figure 8 As shown, one embodiment of this application also provides a data processing apparatus 10, the apparatus comprising:
[0100] The acquisition unit 100 is used to acquire data modification request information, wherein the data modification request information is used to indicate that business information should be modified.
[0101] Add unit 200, used to add data modification request information to a preset list of items to be modified;
[0102] The preprocessing unit 300 is used to preprocess each data modification request information in the list to be modified to obtain data preprocessing request information.
[0103] The first modification unit 400 is used to control each server in the preset server cluster to perform multi-threaded concurrent modification processing according to each data preprocessing request information, and obtain data modification result information.
[0104] The filtering unit 500 is used to determine the abnormal data modification sub-information from the data modification result information when the data modification result indicates that there is an anomaly in the modification of business information.
[0105] The second modification unit 600 is used to determine the target business information based on the data modification anomaly sub-information, and to modify the target business information using the core server in the server cluster.
[0106] It should be noted that during data processing, the process first involves acquiring data modification request information, which indicates modifications to business information. Next, these requests are added to a pre-defined list of requests to be modified. Then, each modification request in the list is pre-processed to obtain pre-processed requests. Following this, each server in the pre-defined server cluster is controlled to perform multi-threaded concurrent modification processing based on these pre-processed requests, resulting in modified data. If the modified data indicates an anomaly in the business information modification, an anomaly sub-information is identified from the modified data result information. Finally, the target business information is determined based on the anomaly sub-information, and the core server in the server cluster is used to modify it. This technical solution, by controlling each server in the server cluster to perform multi-threaded concurrent modification processing based on the pre-processed requests, enables rapid batch modification of system data, reducing the workload of system maintenance personnel, improving system response speed, and providing a better user experience. Furthermore, utilizing the core server in the server cluster to modify the target business information ensures more stable and reliable data modification.
[0107] The specific implementation of the data processing device 10 is basically the same as the specific embodiment of the data processing method described above, and will not be repeated here.
[0108] In addition, such as Figure 9As shown, one embodiment of this application also provides an electronic device 700, which includes: a memory 720, a processor 710, and a computer program stored on the memory 720 and executable on the processor 710.
[0109] The processor 710 and memory 720 can be connected via a bus or other means.
[0110] The non-transient software program and instructions required to implement the data processing method of the above embodiments are stored in the memory 720. When executed by the processor 710, the data processing method of each of the above embodiments is executed.
[0111] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0112] Furthermore, one embodiment of this application provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor 710 or a controller, for example, by a processor 710 in the above-described device embodiment, causing the processor 710 to perform the data processing method in the above-described embodiment.
[0113] The above embodiments can be used in combination, and modules with the same name in different embodiments may be the same or different.
[0114] The foregoing has described specific embodiments of this application; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0115] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and computer-readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0116] The apparatus, device, computer-readable storage medium and method provided in the embodiments of this application are corresponding. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be described again here.
[0117] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.
[0118] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, Atmel AT91 SAM, Microchip PIC18F26K20, and Silicon Labs C8051 F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.
[0119] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.
[0120] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing the embodiments of this application, the functions of each unit can be implemented in one or more software and / or hardware.
[0121] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0122] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0125] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0126] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0127] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0128] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. 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 apparatus that includes said element.
[0129] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.
[0130] The embodiments of this application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.
[0131] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0132] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.
Claims
1. A data processing method, characterized in that, include: Obtain data modification request information, wherein the data modification request information is used to indicate that business information should be modified; Add the data modification request information to the preset list of items to be modified; Each of the data modification request messages in the list to be modified is preprocessed to obtain data preprocessing request messages; Each server in the preset server cluster is controlled to perform multi-threaded concurrent modification processing based on the data preprocessing request information to obtain data modification result information; If the data modification result indicates that the modification of the business information is abnormal, determine the data modification abnormality sub-information from the data modification result information; The target business information is determined by modifying the abnormal sub-information based on the data, and the target business information is modified using the core server in the server cluster.
2. The data processing method according to claim 1, characterized in that, The step of preprocessing each of the data modification request information in the list to be modified to obtain data preprocessing request information includes: Data cleaning is performed on the data modification request information in the list to be modified to obtain data request cleaning information; The data request cleaning information is subjected to data type conversion processing to obtain data request conversion information; The data request transformation information is standardized to obtain the data preprocessing request information.
3. The data processing method according to claim 1, characterized in that, Each server in the pre-defined server cluster performs multi-threaded concurrent modification processing based on the various data preprocessing request information to obtain data modification result information, including: Determine data ownership information from each of the aforementioned data preprocessing request messages; The data attribution information is matched with the tag information carried by each server in the server cluster to determine the target server; Using the target server, the corresponding business information is modified according to the data preprocessing request information to obtain the data modification result information.
4. The data processing method according to claim 1, characterized in that, When the data modification result indicates that the modification of the business information is abnormal, the step of determining the abnormal data modification sub-information from the data modification result information includes: When the data modification result indicates that the modification of the business information is abnormal, the data modification result information is split into multiple data modification feedback information, wherein each data modification feedback information includes data modification marker information; Based on the data modification marker information, the data modification anomaly sub-information is determined from multiple data modification feedback information.
5. The data processing method according to claim 1, characterized in that, The modification of the target business information using the core server in the server cluster includes: The corresponding data preprocessing request information is determined based on the target business information; The core server modifies the target business information based on the data preprocessing request information.
6. The data processing method according to claim 1, characterized in that, The data modification request information includes user attribute information, and the method further includes: When the user attribute information indicates that the user is within a preset priority level range, a priority level marker is added to the corresponding data modification request information; Based on the priority level marker, a core server is selected from the server cluster, and the core server is controlled to modify the corresponding business information according to the data modification request information.
7. The data processing method according to claim 1, characterized in that, After each server in the pre-defined server cluster performs multi-threaded concurrent modification processing based on the data preprocessing request information to obtain the data modification result information, the method further includes: The data modification result information is compressed to obtain a compressed package of modification results; Add the current time information to the modified result compressed package; The modified result compressed package is sent back to the preset monitoring terminal.
8. A data processing apparatus, characterized in that, The device includes: An acquisition unit is used to acquire data modification request information, wherein the data modification request information is used to indicate that business information should be modified. An adding unit is used to add the data modification request information to a preset list of items to be modified; The preprocessing unit is used to preprocess each of the data modification request information in the list to be modified to obtain data preprocessing request information; The first modification unit is used to control each server in the preset server cluster to perform multi-threaded concurrent modification processing according to each of the data preprocessing request information, and obtain data modification result information. A filtering unit is used to determine data modification anomaly sub-information from the data modification result information when the data modification result indicates that the modification of the business information is abnormal; The second modification unit is used to determine the target business information by modifying the abnormal sub-information based on the data, and to modify the target business information using the core server in the server cluster.
9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the data processing method as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the data processing method according to any one of claims 1 to 7.