Request response method, data processing method, device, equipment, medium and program product

Abstract

The invention provides a request response method which can be applied to the technical field of distributed services and the technical field of financial science and technology. The request response method comprises the following steps: in response to a user request for acquiring product information of a target product, reading an nth batch of extracted data related to business analysis of an nth time period from a cache database, the nth batch of extracted data is data extracted from target total product data stored in a business database through a first micro-service and stored in a cache database, and the target total product data is data generated by updating the nth batch of data of the business database in the nth time period through the first micro-service, the business database is a persistent storage database, and n is a positive integer; calling an early warning rule pre-constructed for the target product from a rule base; processing the nth batch of extracted data based on an early warning rule, and generating early warning information of the target product in the nth time period; and sending the early warning information to the terminal equipment, thereby enabling the terminal equipment to display an early warning label for the target product in a display interface based on the early warning information. The invention further provides a request response device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the fields of distributed service technology and financial technology, and specifically to a request response method, apparatus, equipment, medium and program product, as well as a data processing method, apparatus, equipment, medium and program product. Background Art

[0002] As financial products in financial institutions become more diversified, the number of product combinations recommended to customers is increasing.

[0003] In the process of implementing the present disclosure, it was found that there are currently no perfect early warning monitoring measures for various types of products. Most customers log in to the product page through terminal devices to obtain basic information, and users further infer the risk level of the product based on this basic information, which makes it impossible to achieve automated, visual and precise early warning monitoring display; moreover, conventional early warning methods in the prior art mostly perform simple monitoring of product indicators on the service end of the institution's business system, and then submit requests through manual customer service. Not only is the monitoring not accurate enough, but there is also no method for rapid visual monitoring and display on the front end. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a request response method, apparatus, device, medium and program product, as well as a data processing method, apparatus, device, medium and program product.

[0005] According to a first aspect of the present disclosure, a request response method is provided, which is applied to a second microservice, and includes: in response to a user request for obtaining product information of a target product, reading an nth batch of extracted data related to a business analysis of an nth time period from a cache database; processing the nth batch of extracted data based on an early warning rule to generate early warning information of the target product in the nth time period; and sending the early warning information to a terminal device, so that the terminal device displays an early warning label for the target product in a display interface based on the early warning information.

[0006] A second aspect of the present disclosure provides a request response device, including: a reading module, used to respond to a user request for obtaining product information of a target product, and read the nth batch of extracted data related to the business analysis of the nth time period from a cache database; a calling module, used to call a pre-built early warning rule for the target product from a rule library; a processing module, used to process the nth batch of extracted data based on the early warning rule, and generate early warning information for the target product in the nth time period; a first sending module, used to send the early warning information to a terminal device, so that the terminal device displays the early warning label for the target product in a display interface based on the early warning information.

[0007] According to a third aspect of the present disclosure, a data processing method is provided, which is applied to a first microservice, including: obtaining initial full product data for a target product from a business database; updating the second product initial data based on the first product update data to generate second product update data; generating target full product data based on the first product update data and the second product update data to complete a complete update of an nth batch of the business database in an nth time period; extracting an nth batch of extracted data related to business analysis processing in an nth time period from the target full product data, and storing the nth batch of extracted data in a cache database, so that a second microservice reads the nth batch of extracted data from the cache database, and processes the nth batch of extracted data based on a warning rule pre-built for the target product to generate warning information for the target product in the nth time period.

[0008] The fourth aspect of the present disclosure provides a data processing device, including: a second acquisition module, used to acquire initial full product data for a target product from a business database, wherein the initial full product data includes first product update data whose nth batch update has been completed in the nth time period, and second product initial data whose nth batch update has not yet been completed in the nth time period; a second generation module, used to generate target full product data based on the first product update data and the second product update data, so as to complete the nth batch complete update of the business database in the nth time period; an extraction module, used to extract the nth batch of extracted data related to the business analysis processing of the nth time period from the target full product data, and store the nth batch of extracted data in a cache database, so that the second microservice reads the nth batch of extracted data from the cache database, and processes the nth batch of extracted data based on the warning rules pre-built for the target product, and generates warning information for the target product in the nth time period.

[0009] A fifth aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0010] The sixth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0011] The seventh aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0012] According to the embodiment of the present disclosure, the data updated by the first microservice in full is stored in a persistent database, which is convenient for the persistent storage of the target full product data and ensures the security of data storage; the nth batch of extracted data extracted from the target full product data is stored in a cache database, which improves the rate at which the second microservice reads data compared to directly reading the nth batch of extracted data from the persistent database, and further improves the timeliness of generating early warning information for each product. Secondly, through the distributed service composed of the first microservice and the second microservice, the data update and early warning functions are separated, which improves the overall processing efficiency of the system compared to the same server executing multiple tasks. Based on the above characteristics, by displaying the early warning information in the form of a label in the visual interface, the display efficiency of the early warning label is further improved, the real-time display of the early warning label is realized, the user waiting time is reduced, the user's operability and experience are improved, and the user is convenient to monitor the target product. Thus, the automation, precision and visualization of early warning monitoring for the target product are realized. It can be seen that after storing the fully updated data of the first microservice in a persistent database based on business needs, the data needed for the day is separated and stored separately in the cache, which facilitates the second service to quickly perform data warning processing, eliminates the operations of data extraction and preprocessing, and realizes rapid visual warning display on the front end. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0014] Figure 1 The application scenario diagram of the request response method or data processing method according to the embodiment of the present disclosure is schematically shown;

[0015] Figure 2 A flowchart of a request response method according to an embodiment of the present disclosure is schematically shown;

[0016] Figure 3 The following schematically shows a system architecture diagram of a request response method according to an embodiment of the present disclosure;

[0017] Figure 4A The product display interface according to the embodiment of the present disclosure is schematically shown;

[0018] Figure 4B The display interface of the warning label corresponding to the product according to the embodiment of the present disclosure is schematically shown;

[0019] Figure 5 The diagram schematically shows an interaction diagram between the second microservice and the third microservice according to an embodiment of the present disclosure;

[0020] Figure 6 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown;

[0021] Figure 7 The structure block diagram of the request response device according to the embodiment of the present disclosure is schematically shown;

[0022] Figure 8 A structural block diagram schematically shows a data processing device according to an embodiment of the present disclosure; and

[0023] Fig. 9 A block diagram of an electronic device suitable for implementing a request response method or a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0024] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0025] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0026] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0027] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0028] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0029] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0030] The request response method, data processing method, request response device, and data processing device in the embodiments of the present disclosure can be used in the field of distributed service technology and the field of financial technology, and can also be used in any field other than the field of distributed service technology and the field of financial technology. The application field of the request response method, data processing method, request response device, and data processing device in the embodiments of the present disclosure is not limited.

[0031] Figure 1 An application scenario diagram of a request response method or a data processing method according to an embodiment of the present disclosure is schematically shown.

[0032] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first microservice 101 , a second microservice 102 , a third microservice 103 , a business database 104 , and a cache database 105 .

[0033] The first microservice 101 can obtain product information from the business database 104, or pre-process the product data in the business database 104 and send it to the cache database 105. For example, extract some product information from the business database 104 and send it to the cache database 105. The first microservice 101 is mainly used to pre-process product information, including classification and data extraction. The second microservice 102 can obtain product information from the business database 104, or obtain product information from the cache database 105. The second microservice 102 is mainly used to analyze the data cached by the first microservice 101 to the cache database 105 and generate warning information. The third microservice 103 can obtain product information from the business database 104, or obtain product information from the cache database 105. The third microservice 103 is mainly used to check and update the warning label generated by the second microservice 102.

[0034] The business database 104 is used to store data that needs to be persistently stored, and the cache database 105 is used to temporarily store data that needs to be obtained in a timely manner. The business database 104 and the cache database 105 can interact with each other, and the business database 104 and the cache database 105 can also interact with the first microservice 101, the second microservice 102, and the third microservice 103 respectively.

[0035] The first microservice 101, the second microservice 102, and the third microservice 103 may be servers that provide various services, such as a background management server that provides support for websites browsed by users using terminal devices (only as an example). The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, messages, or data obtained or generated according to user requests) to the terminal device.

[0036] It should be noted that the request response method provided in the embodiment of the present disclosure can generally be executed by the second microservice 102. Accordingly, the request response device provided in the embodiment of the present disclosure can generally be set in the second microservice 102. The request response method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the second microservice 102 and can communicate with the first microservice 101, the third microservice 103, the business database 104, and the cache database 105. Correspondingly, the request response device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the second microservice 102 and can communicate with the first microservice 101, the third microservice 103, the business database 104, and the cache database 105.

[0037] Similarly, the data processing method provided in the embodiment of the present disclosure can generally be executed by the first microservice 101. Accordingly, the data processing device provided in the embodiment of the present disclosure can generally be set in the first microservice 101. The data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the first microservice 101 and can communicate with the second microservice 102, the third microservice 103, the business database 104, and the cache database 105. Accordingly, the data processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the first microservice 101 and can communicate with the second microservice 102, the third microservice 103, the business database 104, and the cache database 105.

[0038] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0039] The following will be based on Figure 1 The scene described by Figure 2~Figure 5 The message calling and message sending methods of the disclosed embodiment are described in detail.

[0040] Figure 2 The flowchart of the request response method according to the embodiment of the present disclosure is schematically shown. Figure 3 The system architecture diagram of the request response method according to the embodiment of the present disclosure is schematically shown. Figure 2 and Figure 3 Provide detailed explanation.

[0041] like Figure 2 As shown, the request response method of this embodiment includes operations S210 to S240, and the request response method can be executed by the second microservice.

[0042] In operation S210, in response to a user request for obtaining product information of a target product, an nth batch of extracted data related to business analysis in an nth time period is read from a cache database, wherein the nth batch of extracted data is data extracted from target full product data stored in a business database by a first microservice and stored in the cache database, the target full product data is data generated by the first microservice when updating the business database for the nth batch of data in an nth time period, the business database is a persistent storage database, and n is a positive integer.

[0043] According to an embodiment of the present disclosure, the target product is displayed on the user terminal through a visual interface, and the user's corresponding request is obtained according to the user's operation of clicking the target product on the visual interface.

[0044] According to the embodiments of the present disclosure, the cache database is used to temporarily store frequently accessed data. The data is usually stored in memory with fast read and write speeds. The data in the cache database can be deleted regularly to free up storage space. The business database is a persistent storage database used for long-term data storage. The data is written to a storage medium (such as a hard disk, solid-state drive, etc.), and the data will not be lost in the event of a system restart or power outage. The access speed of the cache database is much faster than that of a business database that reads data from a disk.

[0045] According to the embodiments of the present disclosure, the time period is divided from the perspective of time. This time period can be a pre-set, regular time interval. For example, one hour, one day, one week, one month, etc. Assuming that the time period is divided by day, when n is equal to 1, the first time period is the first day, the second time period is the second day, and so on.

[0046] According to the embodiments of the present disclosure, batches are used to represent the division of data update operations. Batches can be divided based on data volume, business logic, or other factors. For example, within a time period (a day), due to the large amount of data, the business database needs to be updated multiple times, and each update is a batch.

[0047] According to an embodiment of the present disclosure, the nth time period corresponds to the nth batch, that is, one time period corresponds to one batch. In other words, the business database is updated only once in one time period. As shown in Table 1, if the business database is updated only once in one day, the first day is the first batch, and the third day is the third batch.

[0048] Table 1

[0049]

[0050] According to the embodiment of the present disclosure, the target full product data is all the updated data of the product within n time periods. As shown in Table 1, the current processing cycle is the sixth day, so the data of the product from the first day to the sixth day is the target full product data. Figure 3As shown, the target full product data (product information) is stored in the business database so that this part of the data can be persistently stored. Furthermore, the data extracted in the nth batch can be data that must be used to generate warning information, or it can be data that is used more frequently in response to user requests. The first microservice extracts part of the data (for example, the data of the nth batch) from the business database and stores the extracted data of the nth batch in the cache database so that the nth batch of extracted data can be quickly read from the cache database. Among them, the data extracted in the nth batch can be data corresponding to any time period or any batch in the target full product data. For example, it can be data on the fifth and sixth days, data on the third and second days, data from the fourth to sixth days, and so on.

[0051] According to an embodiment of the present disclosure, after the first microservice stores the nth batch of extracted data in the cache database, the second microservice can read the nth batch of extracted data related to the target product from the cache database according to the user request, such as Figure 3 As shown, the server obtains data from the database. Figure 3 In the example, the database type is not limited, and it can be either a business database or a cache database. The execution subject of the server is not limited to the first microservice, the second microservice, or the third microservice. The second microservice can directly read the relevant data from the cache database, which is faster and more efficient than reading the relevant data directly from the business database.

[0052] In operation S220 , a pre-built warning rule for a target product is called from a rule library.

[0053] According to the embodiments of the present disclosure, for each or each type of product, a correspondence between a product and an early warning rule is pre-built and stored in a rule base. When a target product requested by a user is identified, the early warning rule corresponding to the product or the type of product is directly called from the rule base.

[0054] In operation S230, the nth batch of extracted data is processed based on the warning rule to generate warning information of the target product in the nth time period.

[0055] According to the embodiments of the present disclosure, Figure 3 As shown, the database includes a business database for storing persistent data and a cache database for storing temporary data. The server includes a first microservice for preprocessing product data information, a second microservice for performing early warning analysis on the data, and a third microservice for verifying the early warning information. Figure 3In the data preprocessing stage shown, the first microservice processes the nth batch of extracted data based on the warning rule. The processing method may be to determine whether the nth batch of extracted data meets the conditions in the warning rule based on the product name or product type and in combination with the warning rule. Alternatively, the warning rule corresponding to the nth batch of extracted data is matched from the warning rule based on the product name or product type.

[0056] In operation S240, the warning information is sent to the terminal device, so that the terminal device displays a warning label for the target product in a display interface based on the warning information.

[0057] like Figure 3 As shown, the first microservice is used to obtain data from the database (cache database) and pre-process the data, which can be specifically to update the business database in the nth time period and extract the nth batch of extracted data from the target full product data stored in the business database. The second microservice is used to perform data analysis based on the nth batch of extracted data to generate warning information, and send the warning information to the terminal device. The third microservice is used to verify the label displayed on the terminal device interface, and modify the warning label to the correct label if the verification is inconsistent. The terminal device generates and displays the warning label based on the received warning information, and the terminal device can also obtain the user operation behavior (such as the user clicks the warning label or the user does not click the label) based on the interaction with the user to determine whether to continue the warning reminder or stop the warning reminder. The terminal device sends the user's non-click behavior to the second or third microservice for risk assessment, and stores the assessment information in the business database or cache database.

[0058] Figure 4A The product display interface according to the embodiment of the present disclosure is schematically shown; Figure 4B The display interface of the warning label corresponding to the product according to the embodiment of the present disclosure is schematically shown.

[0059] According to the embodiments of the present disclosure, Figure 4A As shown in FIG. 1 , the user can click on the target product (e.g., product 1) through the visual display interface of the terminal device to request detailed information of the target product (wherein the detailed information includes the warning information of the product). When the user clicks on the target product, the second microservice performs the above operations S210 to S230 to generate the warning information corresponding to the target product, so that the terminal device generates a warning label according to the warning information and displays it on the screen such as Figure 4B In the display interface shown. Figure 4B In the table, the Product 1 column is used to display the product name, the Product Risk Characterization Information column is used to display the detailed information of the product, and the Warning A column is used to display the warning label.

[0060] According to the embodiment of the present disclosure, the data updated by the first microservice in full is stored in a persistent database, which is convenient for the persistent storage of the target full product data and ensures the security of data storage; the nth batch of extracted data extracted from the target full product data is stored in a cache database, which improves the rate at which the second microservice reads data compared to directly reading the nth batch of extracted data from the persistent database, and further improves the timeliness of generating early warning information for each product. Secondly, through the distributed service composed of the first microservice and the second microservice, the data update and early warning functions are separated, which improves the overall processing efficiency of the system compared to the same server executing multiple tasks. Based on the above characteristics, by displaying the early warning information in the form of a label in the visual interface, the display efficiency of the early warning label is further improved, the real-time display of the early warning label is realized, the user waiting time is reduced, the user's operability and experience are improved, and the user is convenient to monitor the target product. Thus, the automation, precision and visualization of early warning monitoring for the target product are realized. It can be seen that after storing the fully updated data of the first microservice in a persistent database based on business needs, the data needed for the day is separated and stored separately in the cache, which facilitates the second service to quickly perform data warning processing, eliminates the operations of data extraction and preprocessing, and realizes rapid visual warning display on the front end.

[0061] According to an embodiment of the present disclosure, the target full product data includes first product update data and second product update data, the first product update data includes n groups of product risk characterization information corresponding to n time periods of the target product, and the second product update data includes n groups of average risk characterization information corresponding to n time periods of the target product. According to an embodiment of the present disclosure, in the financial field, the target product may be a financial product, and the product risk characterization information is used to characterize the probability of the target product incurring risks during the transaction process. Specifically, the risk of the product can be characterized by indicators such as product yield, product safety, and product circulation cycle. The average risk characterization information is obtained by calculating the average value based on the time period and the product risk characterization information.

[0062] According to the embodiments of the present disclosure, due to the special requirements of financial services, timely or high-frequency data updates are required. Therefore, the time period can be divided into days, one day as one time period, and the first product update data and the second product update data can be divided into days, one day as a group of first product update data and second product update data. According to actual needs, a time period can also be set to a few hours, which will not be repeated here.

[0063] According to an embodiment of the present disclosure, Table 1 exemplarily shows the target full product data. For example, the first product update data may be the annual yield of the product, and the second product update data may be the average yield. The first product update data is calculated based on the daily yield of the product. The first product update data may also be the product safety factor, and the second product update data may be the average safety factor.

[0064] As shown in Table 1, n is equal to 6, and the first column is n groups of product risk characterization information for n time periods, and each time period is a group. The second column is the first product update data. The third column is the second product update data. Tables 1 and 1 are exemplary data provided for ease of understanding, and the data and time periods in the embodiments of the present disclosure are not limited thereto.

[0065] According to an embodiment of the present disclosure, the nth batch of extracted data includes n-m+1 groups of product risk characterization information from the mth time period to the nth time period extracted from the first product update data, and includes the nth group of average risk characterization information corresponding to the nth time period extracted from the second product update data, where m is a positive integer and m<n.

[0066] According to an embodiment of the present disclosure, m<n, therefore, at least 2 groups of product risk characterization information are obtained.

[0067] Exemplarily, taking m=3 as an example, the nth batch of extracted data includes: 4 groups of product risk characterization information from the 3rd time period to the 6th time period extracted from the first product update data, namely, the product risk characterization information of 2.190%, 2.008%, 1.825%, and 1.825% on the third day, the fourth day, the fifth day, and the sixth day, and the 6th group of average risk characterization information of 1.886% corresponding to the 6th time period extracted from the second product update data.

[0068] Exemplarily, taking m=5 as an example, the nth batch of extracted data includes: 2 groups of product risk characterization information from the 5th time period to the 6th time period extracted from the first product update data, namely, the product risk characterization information of 1.825% and 1.825% on the fifth and sixth days, and the 6th group of average risk characterization information 1.886% corresponding to the 6th time period extracted from the second product update data.

[0069] According to the embodiments of the present disclosure, for financial services, the risk information of financial products needs to be continuously updated at a high frequency so that users can obtain the latest product-related data in real time. This places certain requirements on the reading rate and calculation rate of the system. By extracting part of the necessary data for calculating the second product update data from the target full product data, compared with the real-time update of the target full product data in the database every time it is updated, the amount of resources and time consumed by the pre-processing of unnecessary large amounts of data are reduced, and the data reading rate can also be accelerated. By directly calling the nth batch of extracted data stored in the cache database in advance, data separation from the target full product data in the business database is achieved, the data processing efficiency is improved, and the front-end display efficiency is indirectly improved.

[0070] According to an embodiment of the present disclosure, the early warning rule includes a first sub-rule for verifying a trend of changes in product risk characterization and a second sub-rule for verifying a degree of product risk characterization.

[0071] According to an embodiment of the present disclosure, the first sub-rule may be: if the nth group of product risk characterization information in the nth time period is greater than the n-1th group of product risk characterization information in the n-1th time period, the product risk characterization change trend is an increasing trend; if the nth group of product risk characterization information in the nth time period is less than the n-1th group of product risk characterization information in the n-1th time period, the product risk characterization change trend is a decreasing trend. As shown in Table 1, the product risk characterization information on the second day is lower than that on the first day, and the product risk characterization change trend is a decreasing trend; the product risk characterization information on the third day is higher than that on the second day, and the product risk characterization change trend is an increasing trend.

[0072] According to an embodiment of the present disclosure, the second sub-rule can be obtained by comparing the average risk characterization information with the preset risk characterization information threshold. The second sub-rule can be: if the average risk characterization information of the nth group in the nth time period is greater than the preset risk characterization information threshold, the product risk level is low; if the average risk characterization information of the nth group in the nth time period is less than or equal to the preset risk characterization information threshold, the product risk level is high.

[0073] According to the embodiments of the present disclosure, by setting verification rules for verifying the changing trend of product risk characterization and verifying the degree of product risk characterization, data analysis and processing are performed based on these two rules. The generated risk identification results can more comprehensively analyze the risk changing trend and risk degree of the product, evaluate the product risk more comprehensively and objectively, and obtain more accurate early warning information.

[0074] According to an embodiment of the present disclosure, processing the nth batch of extracted data based on the early warning rule includes steps 11 to 13.

[0075] In step 11, based on the first sub-rule, the numerical change trend of the risk characterization information of the n-m+1 groups of products is judged to generate a first judgment result for characterizing the risk characterization change trend of the target product.

[0076] For example, as shown in Table 1, when m=4, the numerical change trend of the risk characterization information of the 4th, 5th, and 6th groups of products is judged based on the first sub-rule, and the risk characterization change trend of the target product on the fourth, fifth, and sixth days is generated. Specifically, the change trend of the 4th day compared with the 3rd day is decreasing; when m=5, the change trend of the 5th day compared with the 4th day is decreasing.

[0077] In step 12, the numerical value of the average risk characterization information of the nth group is judged based on the second sub-rule to generate a second judgment result for characterizing the risk characterization degree of the target product.

[0078] For example, assuming that the preset risk characterization information threshold is 1.989%, as shown in Table 1, the value of the average risk characterization information of the nth group (1.886%) is less than the preset risk characterization information threshold, and the second judgment result is that the target product has a high risk level on the sixth day.

[0079] In step 13, based on the first discrimination result and the second discrimination result, early warning information of the target product in the nth time period is generated.

[0080] According to an embodiment of the present disclosure, step 13 includes steps 131 to 133.

[0081] In step 131, when the first discrimination result characterizes that the risk characterization change trend of the target product is a decreasing trend and the second discrimination result characterizes that the risk characterization degree of the target product meets a first preset numerical range, early warning trigger information is generated.

[0082] According to an embodiment of the present disclosure, the first preset numerical range can be a fixed numerical range preset according to the product type, or a fixed numerical range calculated according to historical business data within a preset time range. Exemplarily, as shown in Table 1, the first judgment result is: the product risk characterization information on the second day shows a decreasing trend compared with the first day; assuming that the first preset numerical range is 1.712%~1.856%, the risk characterization degree (average risk characterization information) of the target product on the second day meets the first preset numerical range, and the early warning trigger information is generated.

[0083] According to an embodiment of the present disclosure, the first preset numerical range may also be a dynamic numerical range calculated in real time by combining multiple parameters. The first preset numerical range may be: average risk characterization information of the nth group < reference risk characterization information of the target product + warning threshold.

[0084] According to an embodiment of the present disclosure, the target product reference risk characterization information can characterize the expected risk characterization information for the target product. The corresponding target product reference risk characterization information can be preset according to different product types, or the target product reference risk characterization information can be calculated based on the historical data of the target product. For example, the target product reference risk characterization information can be the expected return value of the product, the expected safety value of the product, or the expected flow cycle value of the product.

[0085] According to the embodiments of the present disclosure, the risk level of the product is determined for different product attributes and product types, and the warning threshold of the product is determined according to the risk level. For example, the risk level of the product is divided into 5 levels, R1 (cautious), R2 (robust), R3 (balanced), R4 (aggressive), and R5 (aggressive). The warning threshold of R1 is 0.1%, the warning threshold of R2 is 0.2%, the warning threshold of R3 is 0.3%, the warning threshold of R4 is 0.4%, and the warning threshold of R5 is 0.5%. The above is only for exemplary purposes and does not represent real data.

[0086] Exemplarily, the reference risk characterization information of the target product is set to 2%, which means that the expected return or expected safety factor of the product is 2%, and the risk level is set to R1, combined with the data in Table 1.

[0087] Day 1: 2.373%>2%+0.1%; no judgment is required on the first day.

[0088] The second day: 1.734%<2%+0.1%; if the first judgment result of the second day is decreasing; and the second judgment result is that the risk characterization degree of the target product meets the first preset numerical range, the first warning trigger information is generated.

[0089] The third day: 1.886%<2%+0.1%; the first judgment result on the third day is decreasing; no warning trigger information is generated.

[0090] Day 4: 1.917<2%+0.1%; if the first judgment result on the fourth day is decreasing; and the second judgment result is that the risk characterization degree of the target product meets the first preset numerical range, the second warning trigger information is generated.

[0091] Day 5: 1.898%<2%+0.1%; if the first judgment result on the fifth day is decreasing, and the second judgment result is that the risk characterization degree of the target product meets the first preset numerical range, the third warning trigger information is generated.

[0092] In step 132, when the warning trigger information is generated a predetermined number of times in succession, a first level warning information is generated.

[0093] According to the embodiments of the present disclosure, the predetermined number of times can be set based on experience or can be customized by the user in the terminal device. For example, if the predetermined number of times is 2, then after two warning trigger messages are generated consecutively, the first level warning message is generated. After that, the first level warning message is sent to the terminal device so that the terminal device displays the warning label for the target product in the display interface based on the warning message, for example Figure 4B Warning A shown in .

[0094] According to the above example, if two warning trigger messages are generated consecutively on the fourth and fifth days, a first-level warning message is generated.

[0095] In step 133, when the second discrimination result indicates that the risk characterization degree of the target product meets the second preset numerical range, a second level warning information is generated.

[0096] Or further, when the first discrimination result characterizes that the risk characterization change trend of the target product is a decreasing trend, and the second discrimination result characterizes that the risk characterization degree of the target product meets the second preset numerical range, the second level warning information is generated.

[0097] Among them, the first level warning information is used to indicate that the risk level of the target product is a lower risk level, and the second level warning information is used to indicate that the risk level of the target product is a higher risk level.

[0098] According to an embodiment of the present disclosure, the second preset numerical range may be a fixed numerical range preset according to the product type, or a fixed numerical range calculated according to historical business data within a preset time range. The determination method is the same as that of the first preset numerical range, which will not be repeated here.

[0099] According to an embodiment of the present disclosure, the second preset numerical range may also be a dynamic numerical range calculated in real time by combining multiple parameters. The second preset numerical range is: average risk characterization information of the nth group < reference risk characterization information of the target product - warning threshold.

[0100] According to the above example, combined with the data in Table 1.

[0101] Day 6: 1.886% < 2% - 0.1%; the second judgment result on the sixth day is that the risk characterization degree of the target product meets the second preset numerical range, and the second level warning information is directly generated. After that, the second level warning information is sent to the terminal device, so that the terminal device displays the warning label for the target product in the display interface based on the warning information.

[0102] According to an embodiment of the present disclosure, the warning label displayed in the display interface includes a first label (warning A) displayed based on the first level warning information, and a second label (warning B) displayed based on the second level warning information, and the first label and the second label have different label styles. For example, the first label is displayed in orange and the second label is displayed in red.

[0103] According to the embodiments of the present disclosure, by setting different discrimination conditions, different levels of early warning information are generated based on risk information under different circumstances, thereby realizing a hierarchical and refined early warning strategy, facilitating the provision of early warning prompts of different degrees to users, improving the accuracy of early warnings, and improving user experience.

[0104] Figure 5 The diagram schematically shows an interaction diagram between the second microservice and the third microservice according to an embodiment of the present disclosure.

[0105] like Figure 5 As shown, after sending the warning information to the terminal device, the method further includes: storing the warning information in a cache database. The third microservice executes steps 21 to 23.

[0106] In step 21, the latest label display information for the target product in the display interface is obtained.

[0107] According to an embodiment of the present disclosure, the latest label display information may be the latest real label displayed on the front-end interface after a display instruction is issued to the front-end, for example Figure 4B Alert A corresponding to product 1 is shown.

[0108] In step 22, the latest label display information is checked for consistency with the warning information in the cache database.

[0109] When the amount of data to be processed is large, there may be a situation where the actual warning label does not match the correct label information. For example, when the warning conditions are met, the warning label should be displayed, but the warning label is not actually displayed in the display column of the product, or the first warning information should be displayed, but the second warning information is displayed in the display column of the product. Therefore, it is necessary to verify the label display status.

[0110] Specifically, the latest warning information related to the product (for example, first-level warning, second-level warning, or no warning) can be obtained from the cache database; and the latest label display information of the target product can be analyzed, such as based on the current label style, to determine the warning level of the current product. For example, if the label is displayed in orange, it corresponds to the first-level warning. After that, the latest label display information is checked for consistency with the warning information in the cache database to determine whether the latest label display information is consistent with the warning information.

[0111] In step 23, when the consistency check fails, a label modification instruction is sent to the terminal device, so that the terminal device modifies the warning label in the display interface.

[0112] According to an embodiment of the present disclosure, if the latest label display information is consistent with the warning information, there is no need to update the latest label display information; if the verification is inconsistent, the terminal device is instructed to modify the latest label display style according to the latest warning information to ensure that the warning label displayed in the interface is the correct latest label.

[0113] According to the embodiments of the present disclosure, by having the third microservice perform data verification, the data verification and early warning functions are separated, which improves the overall processing efficiency of the system compared to the same server performing multiple tasks. Based on the above features, by timely updating the label information, the accuracy of the real-time display of the early warning label is achieved, and the precision and visualization of the early warning monitoring of the target product are further improved. At the same time, storing the early warning information in the cache database can also improve the reading rate and calculation rate of the third microservice.

[0114] According to an embodiment of the present disclosure, after sending the warning information to the terminal device, the user can view the warning label through the visual display interface on the terminal device and operate the warning label. Figure 3 As shown, the user clicks the warning label to confirm the risk and cancels the prompt information of the warning label, or the user does not confirm to cancel the prompt information of the warning label.

[0115] According to an embodiment of the present disclosure, the second microservice can obtain the user's operation behavior, and determine whether to continue to generate warning information or stop sending warning information based on the operation behavior. For example, when the user cancels the warning prompt according to manual operation, it means that the user has known the warning information, and the warning information is stopped. When the user's operation to cancel the warning prompt is not received, it means that the user ignored or did not see the warning information, and the warning information can be risk assessed to determine whether to continue to remind the customer or stop reminding in other ways.

[0116] According to the embodiments of the present disclosure, through the display of the visual interface and the interaction with the user, the methods of early warning prompts and early warning cancellation are enriched, the user's operability is improved, and it is convenient for the user to view product information and early warning information in real time.

[0117] According to an embodiment of the present disclosure, after generating the warning information, in addition to sending the warning label to the terminal device, a warning call can be made directly based on the warning information, warning level and user's communication information, and the customer can continue to be reminded or stop the reminder based on the content of the call with the user.

[0118] Figure 6The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0119] like Figure 6 As shown, the data processing method includes operations S610 to S640, which are performed by the first microservice.

[0120] In operation S610, initial full product data for the target product is obtained from the business database, wherein the initial full product data includes first product update data whose nth batch update has been completed in the nth time period, and second product initial data whose nth batch update has not been completed in the nth time period, wherein the business database is a persistent storage database.

[0121] For example, as shown in Table 1, the update data of the first product that has completed the first batch update in the first time period is 2.373%, and the initial data of the second product that has not completed the first batch update in the first time period is 0. The update data of the first product that has completed the second batch update in the second time period is 1.095%, and the initial data of the second product that has not completed the second batch update in the second time period is 2.373%. The update data of the first product that has completed the third batch update in the third time period is 2.190%, and the initial data of the second product that has not completed the third batch update in the third time period is 1.734%.

[0122] In operation S620, the second product initial data is updated based on the first product update data to generate second product update data.

[0123] According to an embodiment of the present disclosure, the second product initial data of the mth batch in the mth time period is updated based on the first product update data of m batches updated in m time periods to generate the second product update data.

[0124] In operation S630, target full product data is generated based on the first product update data and the second product update data to complete a complete update of the nth batch of the business database in the nth time period.

[0125] According to an embodiment of the present disclosure, after the updated first product update data and the second product initial data are obtained according to calculation, the complete update of n batches of data corresponding to n time periods is completed to obtain the target full product data.

[0126] In operation S640, the nth batch of extracted data related to the business analysis processing of the nth time period is extracted from the target full product data, and the nth batch of extracted data is stored in the cache database, so that the second microservice reads the nth batch of extracted data from the cache database, and processes the nth batch of extracted data based on the warning rules pre-built for the target product, and generates warning information for the target product in the nth time period.

[0127] According to the embodiment of the present disclosure, the data updated by the first microservice in full is stored in a persistent database, which is convenient for the persistent storage of the target full product data and ensures the security of data storage; the nth batch of extracted data extracted from the target full product data is stored in a cache database, which improves the rate at which the second microservice reads data compared to directly reading the nth batch of extracted data from the persistent database, and further improves the timeliness of generating early warning information for each product. Secondly, through the distributed service composed of the first microservice and the second microservice, the data update and early warning functions are separated, which improves the overall processing efficiency of the system compared to the same server executing multiple tasks. Based on the above characteristics, by displaying the early warning information in the form of a label in the visual interface, the display efficiency of the early warning label is further improved, the real-time display of the early warning label is realized, the user waiting time is reduced, the user's operability and experience are improved, and the user is convenient to monitor the target product. Thus, the automation, precision and visualization of early warning monitoring for the target product are realized. It can be seen that after storing the fully updated data of the first microservice in a persistent database based on business needs, the data needed for the day is separated and stored separately in the cache, which facilitates the second service to quickly perform data warning processing, eliminates the operations of data extraction and preprocessing, and realizes rapid visual warning display on the front end.

[0128] According to an embodiment of the present disclosure, the first product update data includes n groups of product risk characterization information of the target product corresponding to n time periods, and the second product update data includes n groups of average risk characterization information of the target product corresponding to n time periods; extracting the nth batch of extracted data related to the business analysis processing of the nth time period from the target full product data includes: extracting n-m+1 groups of product risk characterization information from the mth time period to the nth time period from the first product update data, m is a positive integer and m<n; extracting the nth group of average risk characterization information corresponding to the nth time period from the second product update data, wherein the n-m+1 groups of product risk characterization information and the nth group of average risk characterization information are used as the nth batch of extracted data.

[0129] According to the embodiments of the present disclosure, by extracting the first product update data and the second product update data, it can be used to identify the product risk change trend and risk level, and only a small amount of necessary data needs to be extracted to achieve the purpose of early warning identification, thereby improving data processing efficiency. Compared with the full amount of data as the basis, the amount of data calculation is reduced.

[0130] According to an embodiment of the present disclosure, updating the second product initial data based on the first product update data includes: calculating the numerical mean of n groups of product risk characterization information, generating the nth group of average risk characterization information corresponding to the nth time period, so as to complete the update of the second product initial data.

[0131] Specifically, the average value of the first product update data of n batches of updates completed in n time periods is calculated, and the average value is used as the second product update data of the nth batch in the nth time period. Exemplarily, as shown in Table 1, the first product update data of the first batch of updates completed in the first time period is 2.373%, and the first product update data of the second batch of updates completed in the second time period is 1.095%, then the second product update data of the second batch in the second time period is (2.373%+1.095%) / 2=1.734%. Exemplarily, as shown in Table 1, the first product update data of the first batch of updates completed in the first time period is 2.373%, the first product update data of the second batch of updates completed in the second time period is 1.095%, and the first product update data of the third batch of updates completed in the third time period is 2.190%, then the second product update data of the third batch in the third time period is (2.373%+1.095%+2.190) / 3=1.886%.

[0132] According to the embodiments of the present disclosure, due to the real-time requirements of financial products for data updates, by obtaining the mean, it is possible to objectively reflect the information represented over a long period of time as a whole to a certain extent, thereby improving the reference value of the data. By updating the data, the product information is updated to the latest data, and the early warning analysis based on the latest data is more accurate and objective.

[0133] Based on the above request response method, the present disclosure also provides a request response device. Figure 7 The device is described in detail.

[0134] Figure 7 The structural block diagram of the request response device according to an embodiment of the present disclosure is schematically shown.

[0135] like Figure 7 As shown, the request response device 700 of this embodiment includes a reading module 710 , a calling module 720 , a processing module 730 and a first sending module 740 .

[0136] The reading module 710 is used to respond to a user request for obtaining product information of a target product, and read the nth batch of extracted data related to the business analysis of the nth time period from the cache database, wherein the nth batch of extracted data is extracted from the target full product data stored in the business database by the first microservice and stored in the cache database. The target full product data is: the data generated by the first microservice updating the business database for the nth batch of data in the nth time period, the business database is a persistent storage database, and n is a positive integer. In one embodiment, the reading module 710 can be used to perform the operation S210 described above, which is not repeated here. The calling module 720 is used to call the pre-built warning rules for the target product from the rule library. In one embodiment, the calling module 720 can be used to perform the operation S220 described above, which is not repeated here. The processing module 730 is used to process the nth batch of extracted data based on the warning rule to generate warning information of the target product in the nth time period. In one embodiment, the processing module 730 can be used to perform the operation S230 described above, which is not repeated here. The first sending module 740 is used to send warning information to the terminal device, so that the terminal device displays a warning label for the target product in the display interface based on the warning information. In one embodiment, the first sending module 740 can be used to perform the operation S240 described above, which will not be repeated here.

[0137] According to an embodiment of the present disclosure, the processing module 730 includes a first discrimination submodule and a second discrimination submodule.

[0138] The first discrimination submodule is used to discriminate the numerical change trend of the risk characterization information of the n-m+1 groups of products based on the first sub-rule, and generate a first discrimination result for characterizing the risk characterization change trend of the target product. The second discrimination submodule is used to discriminate the numerical value of the average risk characterization information of the nth group based on the second sub-rule, and generate a second discrimination result for characterizing the risk characterization degree of the target product; the generation submodule is used to generate the warning information of the target product in the nth time period according to the first discrimination result and the second discrimination result.

[0139] According to an embodiment of the present disclosure, the generation submodule includes: a first generation unit, a second generation unit and a third generation unit.

[0140] The first generating unit is used to generate early warning trigger information when the risk characterization change trend of the target product characterized by the first discrimination result is a decreasing trend and the risk characterization degree of the target product characterized by the second discrimination result satisfies a first preset numerical range.

[0141] The second generating unit is used to generate the first level warning information when the warning trigger information is generated continuously for a predetermined number of times.

[0142] The third generating unit is used to generate second-level warning information when the risk characterization change trend of the target product represented by the first judgment result is a decreasing trend and the risk characterization degree of the target product represented by the second judgment result satisfies a second preset numerical range; wherein the warning label displayed in the display interface includes a first label based on the first-level warning information and a second label based on the second-level warning information, and the first label and the second label have different label styles.

[0143] According to an embodiment of the present disclosure, the request response device further includes: a storage module, a first acquisition module, a checking module and a second sending module.

[0144] A storage module is used to store the warning information in a cache database; the third microservice performs the following operations: a first acquisition module is used to obtain the latest label display information for the target product in the display interface; a verification module is used to perform consistency verification on the latest label display information and the warning information in the cache database; a second sending module is used to send a label modification instruction to the terminal device if the consistency verification fails, so that the terminal device can modify the warning label in the display interface.

[0145] Based on the above data processing method, the present disclosure also provides a data processing device. Figure 8 The device is described in detail.

[0146] Figure 8 The structure block diagram of a data processing device according to an embodiment of the present disclosure is schematically shown.

[0147] like Figure 8 As shown, the data processing device 800 of this embodiment includes a second acquisition module 810 , a first generation module 820 , a second generation module 830 and an extraction module 840 .

[0148] The second acquisition module 810 is used to acquire the initial full product data for the target product from the business database, wherein the initial full product data includes the first product update data that has completed the nth batch update in the nth time period, and the second product initial data that has not completed the nth batch update in the nth time period, wherein the business database is a persistent storage database. In one embodiment, the second acquisition module 810 can be used to perform the operation S610 described above, which will not be repeated here.

[0149] The first generating module 820 is used to update the second product initial data based on the first product update data to generate the second product update data. In one embodiment, the first generating module 820 can be used to perform the operation S620 described above, which will not be described in detail here.

[0150] The second generation module 830 is used to generate the target full product data based on the first product update data and the second product update data to complete the complete update of the nth batch of the business database in the nth time period. In one embodiment, the second generation module 830 can be used to perform the operation S630 described above, which will not be repeated here.

[0151] The extraction module 840 is used to extract the nth batch of extracted data related to the business analysis processing of the nth time period from the target full product data, and store the nth batch of extracted data in the cache database, so that the second microservice reads the nth batch of extracted data from the cache database, and processes the nth batch of extracted data based on the pre-built warning rules for the target product to generate warning information for the target product in the nth time period. In one embodiment, the extraction module 840 can be used to perform the operation S640 described above, which will not be repeated here.

[0152] According to an embodiment of the present disclosure, the extraction module 840 includes: a first extraction submodule and a second extraction submodule.

[0153] The first extraction submodule is used to extract n-m+1 groups of product risk characterization information from the mth time period to the nth time period from the first product update data, where m is a positive integer and m<n. The second extraction submodule is used to extract the nth group of average risk characterization information corresponding to the nth time period from the second product update data, wherein the n-m+1 groups of product risk characterization information and the nth group of average risk characterization information are used as the nth batch of extracted data.

[0154] According to an embodiment of the present disclosure, the first generating module 820 includes: a calculating submodule.

[0155] The calculation submodule is used to calculate the numerical mean of n groups of product risk characterization information, generate the nth group of average risk characterization information corresponding to the nth time period, so as to complete the update of the initial data of the second product.

[0156] According to an embodiment of the present disclosure, any multiple modules among the reading module 710, the calling module 720, the processing module 730, the first sending module 740, the second acquiring module 810, the first generating module 820, the second generating module 830 and the extracting module 840 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the reading module 710, the calling module 720, the processing module 730, the first sending module 740, the second acquisition module 810, the first generating module 820, the second generating module 830 and the extraction module 840 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the reading module 710, the calling module 720, the processing module 730, the first sending module 740, the second acquisition module 810, the first generating module 820, the second generating module 830 and the extraction module 840 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0157] Figure 8 A block diagram of an electronic device suitable for implementing a request response method or a data processing method according to an embodiment of the present disclosure is schematically shown.

[0158] like Figure 8 As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0159] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0160] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the input / output (I / O) interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output (I / O) interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that the computer program read therefrom is installed into the storage portion 808 as needed.

[0161] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0162] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 802 and / or RAM 803 described above and / or one or more memories other than ROM 802 and RAM 803.

[0163] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the request response method and data processing method provided by the embodiment of the present disclosure.

[0164] The above functions defined in the system / device of the embodiment of the present disclosure are executed when the computer program is executed by the processor 801. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0165] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0166] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0167] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0168] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0169] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0170] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A request response method, characterized in that: The method is applied to a second microservice, and the method includes: In response to a user request for obtaining product information of a target product, an nth batch of extracted data related to business analysis in an nth time period is read from a cache database, wherein the nth batch of extracted data is data extracted from target full product data stored in a business database by a first microservice and stored in the cache database, the target full product data is data generated by the first microservice performing an nth batch of data update on the business database in the nth time period, the business database is a persistent storage database, and n is a positive integer; Calling pre-built warning rules for the target product from a rule library; Processing the nth batch of extracted data based on the warning rule to generate warning information of the target product in the nth time period; The warning information is sent to a terminal device, so that the terminal device displays a warning label for the target product in a display interface based on the warning information.

2. The method according to claim 1, characterized in that: The target full product data includes first product update data and second product update data, the first product update data includes n groups of product risk characterization information corresponding to n time periods of the target product, and the second product update data includes n groups of average risk characterization information corresponding to n time periods of the target product; The nth batch of extracted data includes n-m+1 groups of product risk characterization information from the mth time period to the nth time period extracted from the first product update data, and includes the nth group of average risk characterization information corresponding to the nth time period extracted from the second product update data, where m is a positive integer and m<n.

3. The method according to claim 2, characterized in that The early warning rule includes a first sub-rule for verifying the changing trend of the product risk characterization, and a second sub-rule for verifying the degree of the product risk characterization; Processing the nth batch of extracted data based on the early warning rule includes: Based on the first sub-rule, the numerical change trend of the risk characterization information of the n-m+1 groups of products is judged to generate a first judgment result for characterizing the risk characterization change trend of the target product; Based on the second sub-rule, the numerical value of the nth group of average risk characterization information is judged to generate a second judgment result for characterizing the risk characterization degree of the target product; According to the first discrimination result and the second discrimination result, early warning information of the target product in the nth time period is generated.

4. The method according to claim 3, characterized in that Generating the warning information of the target product in the nth time period according to the first discrimination result and the second discrimination result includes: When the first discrimination result indicates that the risk characterization change trend of the target product is a decreasing trend, and the second discrimination result indicates that the risk characterization degree of the target product meets a first preset numerical range, generating early warning trigger information; When the warning trigger information is generated a predetermined number of times in succession, a first level warning information is generated; When the first discrimination result indicates that the risk characterization change trend of the target product is a decreasing trend, and the second discrimination result indicates that the risk characterization degree of the target product meets a second preset numerical range, generating a second level warning information; Among them, the warning label displayed in the display interface includes a first label displayed based on the first level warning information, and a second label displayed based on the second level warning information, and the label styles of the first label and the second label are different.

5. The method according to claim 4, characterized in that The first preset value range is: the average risk characterization information of the nth group < the reference risk characterization information of the target product + the warning threshold value; The second preset value range is: the average risk characterization information of the nth group < the reference risk characterization information of the target product - the warning threshold value; The warning threshold is determined according to the product type of the target product.

6. The method according to claim 1, characterized in that After sending the warning information to the terminal device, the method further includes: Storing the warning information in a cache database; The third microservice performs the following operations: Obtaining the latest label display information for the target product in the display interface; Performing consistency check between the latest label display information and the warning information in the cache database; In the case where the consistency check fails, a label modification instruction is sent to the terminal device, so that the terminal device modifies the warning label in the display interface.

7. A data processing method, characterized in that: The method is applied to a first microservice, and the method includes: Acquire initial full product data for the target product from a business database, wherein the initial full product data includes first product update data that has completed the nth batch update in the nth time period, and second product initial data that has not completed the nth batch update in the nth time period, wherein the business database is a persistent storage database; updating the second product initial data based on the first product update data to generate second product update data; Based on the first product update data and the second product update data, generating target full product data to complete the complete update of the nth batch of the business database in the nth time period; An nth batch of extracted data related to the business analysis processing in the nth time period is extracted from the target full product data, and the nth batch of extracted data is stored in a cache database, so that the second microservice reads the nth batch of extracted data from the cache database, and processes the nth batch of extracted data based on the warning rules pre-built for the target product, and generates warning information of the target product in the nth time period.

8. The method according to claim 7, characterized in that The first product update data includes n groups of product risk characterization information of the target product corresponding to n time periods, and the second product update data includes n groups of average risk characterization information of the target product corresponding to n time periods; Extracting the nth batch of extracted data related to the business analysis process of the nth time period from the target full amount of product data includes: Extracting n-m+1 groups of product risk characterization information from the mth time period to the nth time period from the first product update data, where m is a positive integer and m<n; The nth group of average risk characterization information corresponding to the nth time period is extracted from the second product update data, wherein the n-m+1 groups of product risk characterization information and the nth group of average risk characterization information are used as the nth batch of extracted data.

9. The method according to claim 8, characterized in that Updating the second product initial data based on the first product update data includes: Calculate the numerical mean of the n groups of product risk characterization information to generate the nth group of average risk characterization information corresponding to the nth time period to complete the update of the second product initial data.

10. A request response device, characterized in that: The device comprises: a reading module, configured to respond to a user request for obtaining product information of a target product, and read an nth batch of extracted data related to business analysis in an nth time period from a cache database, wherein the nth batch of extracted data is data extracted from target full product data stored in a business database by a first microservice and stored in the cache database, the target full product data is data generated by the first microservice updating the business database by an nth batch of data in the nth time period, the business database is a persistent storage database, and n is a positive integer; A calling module, used to call the pre-built warning rules for the target product from the rule library; A processing module, used for processing the nth batch of extracted data based on the warning rule to generate warning information of the target product in the nth time period; The first sending module is used to send the warning information to the terminal device, so that the terminal device displays a warning label for the target product in a display interface based on the warning information.

11. A data processing device, characterized in that: The device comprises: A second acquisition module is used to acquire initial full product data for a target product from a business database, wherein the initial full product data includes first product update data that has completed an n-th batch update in an n-th time period, and second product initial data that has not completed an n-th batch update in an n-th time period, wherein the business database is a persistent storage database; A first generating module, configured to update the second product initial data based on the first product update data to generate second product update data; A second generating module, configured to generate target full product data based on the first product update data and the second product update data, so as to complete a complete update of an nth batch of the business database in an nth time period; An extraction module is used to extract the nth batch of extracted data related to the business analysis processing in the nth time period from the target full product data, and store the nth batch of extracted data in a cache database, so that the second microservice reads the nth batch of extracted data from the cache database, and processes the nth batch of extracted data based on the warning rules pre-built for the target product, and generates warning information of the target product in the nth time period.

12. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 9.

13. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

14. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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