Code pre-reading system and algorithm module execution method

Abstract

The invention relates to a code pre-application system, and the system comprises a data service module which is used for providing data related to the use habit of a user, and the data comprises an industry identifier; the algorithm module is used for constructing an industry user code pre-application model according to the data related to the use habits of the users, and calculating execution time of code pre-application for different users in different industries based on the industry user code pre-application model; and the timed task module is used for receiving the execution time from the algorithm module and regularly initiating a code application task according to the execution time. The invention further relates to a method for executing the algorithm module in the code pre-reading system, a computer readable storage medium and a computer program product.

Description

Technical Field

[0001] The present application relates to the field of two-dimensional codes, and more specifically, to a pre-registration code system, a method for executing an algorithm module in the pre-registration code system, a computer-readable storage medium, and a computer program product. Background Art

[0002] QR codes are inseparable from our daily lives and are indispensable in payment, travel, campus, coupon collection, login, etc. Especially in payment and travel scenarios, there are higher requirements for the speed, fault tolerance, and concurrency of QR code application.

[0003] In the existing code application (i.e., applying for a QR code) solution, one method is that the background will go to the corresponding industry system to apply for a code only when there is an online code application request; another method is that the client initiates a code application request when it is connected to the Internet, and it falls into the client cache for user use.

[0004] However, the above existing code application solutions all have certain defects. On the one hand, if you only rely on online code application, when the industry system is down or inaccessible, users will not be able to use the QR code at all, which will reduce the reliability of the entire system. On the other hand, if you simply rely on the QR code cache obtained by the client online (also called "offline code" or "offline QR code") to complete the purpose of pre-application code, due to the uncontrollable user behavior, there may be a large number of code application requests in a short period of time, which will greatly increase the pressure on the industry system, the concurrent pressure of the code application background, and the cache pressure. Moreover, most QR codes are valid for less than 24 hours. Simply relying on the QR code cache obtained by the client online will cause users to request the industry system background to obtain the QR code when they use it for the first time every day, which will not achieve the effect of improving system reliability. Summary of the invention

[0005] The inventor of this application realized that if we can do a good job of analyzing user usage habits and establish a suitable industry user pre-application code model, and pre-apply for codes at different times for different users in different industries (that is, apply for QR codes in advance before the client requests), it will not only improve the user experience, but also disperse the request pressure on the background, which will be of great help in improving the overall performance of the system.

[0006] According to one aspect of the present application, a pre-code application system is provided, the system comprising: a data service module for providing user code application data and QR code configuration data related to user usage habits, the user code application data comprising an industry identifier; an algorithm module for constructing an industry user pre-code application model based on the user code application data and the QR code configuration data, and calculating the execution time for pre-code application for different users in different industries based on the industry user pre-code application model; and a timed task module for receiving the execution time from the algorithm module, and initiating a code application task based on the execution time.

[0007] As a supplement or replacement for the above solution, the above pre-application code system also includes: a server, wherein the scheduled task module initiates a code application task to the server according to the execution time.

[0008] As a supplement or replacement for the above-mentioned scheme, in the above-mentioned pre-code application system, the server is configured to: after receiving the code application task, apply for codes to the industry system in batches; and after receiving the code application result from the industry system, cache the code application result locally for user access.

[0009] As a supplement or replacement for the above solution, in the above pre-application code system, the server is further configured to: provide user application code data and QR code configuration data to the data service module.

[0010] As a supplement or replacement for the above solution, in the above pre-application code system, the server is a code issuing platform.

[0011] As a supplement or replacement for the above solution, in the above pre-code application system, the user code application data also includes the number of times and time of user code application per day, and the QR code configuration data includes the validity period of the QR code and the maximum number of times the QR code can be used.

[0012] As a supplement or replacement for the above-mentioned scheme, in the above-mentioned pre-application code system, the algorithm module is configured to: determine an industry identification data set; for a specific industry in the industry identification data set, construct the industry user pre-application code model, and predict the golden application code time period for the specific industry in the next change cycle based on the industry user pre-application code model; determine a set of scheduled task time points for the specific industry based on the predicted golden application code time period; and determine the execution time of the pre-application codes of different users in the specific industry based on the scheduled task time point set.

[0013] As a supplement or replacement for the above solution, in the above pre-application code system, the industry identification data set includes: the industry identification corresponding to the QR code whose validity period is within a preset time range.

[0014] As a supplement or alternative to the above solution, in the above pre-application code system, the preset time range is 6 to 24 hours.

[0015] As a supplement or replacement for the above solution, in the above pre-application code system, the industry user pre-application code model is constructed as follows:

[0016]

[0017] as well as

[0018]

[0019] in,

[0020] For Industry B i The minimum value of the predicted value of the golden application time on the nth day; For Industry B i The maximum value of the predicted value of the golden application time on the nth day; For Industry B i The minimum value of the statistical value of the golden application time on the nth day; For Industry B i The maximum value of the statistical value of the golden application time on the nth day; are the autoregressive coefficients; θ1, θ2…θ q is the moving average coefficient; p is the number of autoregressive terms, q is the number of moving average terms; e n-q Refers to distractors; For Industry B i The validity period of the QR code (or certificate validity period); μ min , μ max is a constant term; For Industry B i The maximum number of times a QR code can be used. For Industry B i The average number of times a person uses the QR code on the npth day.

[0021] As a supplement or replacement for the above solution, in the above pre-application code system, as well as

[0022] in, Indicates industry B i The industry code application time when the code application volume reaches P% on the nth day, where P is the preset value.

[0023] As a supplement or replacement for the above solution, in the above pre-code system, the algorithm module is configured to: obtain a preset scheduled task time point set SetT = {T1, T2, ..., T l}, l represents the number of preset time points of scheduled tasks; and according to as well as Select the time point for industry B from the preset scheduled task time point set SetT i A collection of scheduled task time points m means for industry B i The number of scheduled task time points, where m is less than or equal to 1.

[0024] As a supplement or replacement for the above solution, in the above pre-application code system, the algorithm module is configured to: determine industry B i The active user set in the last change cycle; and by performing a hash operation on the user identifiers in the active user set and the scheduled task time point set The number of scheduled task time points m in the modulus is taken to determine the industry B i The pre-application code time for each active user.

[0025] As a supplement or alternative to the above solution, in the above pre-application code system, the change cycle is 7 days.

[0026] According to another aspect of the present application, a method for executing an algorithm module in a pre-code system is provided, the method comprising: receiving user code application data and QR code configuration data related to user usage habits, the user code application data comprising an industry identifier; constructing an industry user pre-code model according to the user code application data and the QR code configuration data, and calculating the execution time of pre-code application for different users in different industries based on the industry user pre-code model; and sending the execution time so that a timing task module of the pre-code system can initiate a code application task according to the execution time.

[0027] As a supplement or replacement for the above solution, in the above method, the user code application data also includes the number of times and time of user code application per day, and the QR code configuration data includes the validity period of the QR code and the maximum number of times the QR code can be used.

[0028] As a supplement or replacement for the above-mentioned scheme, in the above-mentioned method, an industry user pre-application code model is constructed according to the user code application data and the QR code configuration data, and the execution time of pre-application code for different users in different industries is calculated based on the industry user pre-application code model, including: determining an industry identification data set; for a specific industry in the industry identification data set, constructing the industry user pre-application code model, and predicting the golden code application time period of the specific industry in the next change cycle according to the industry user pre-application code model; determining a set of scheduled task time points for the specific industry according to the predicted golden code application time period; and determining the execution time of pre-application codes for different users in the specific industry based on the scheduled task time point set.

[0029] As a supplement or replacement for the above solution, in the above method, the industry identification data set includes: industry identifications corresponding to QR codes whose validity periods are within a preset time range.

[0030] As a supplement or alternative to the above solution, in the above method, the preset time range is 6 to 24 hours.

[0031] As a supplement or replacement for the above solution, in the above method, the industry user pre-application code model is constructed as follows:

[0032]

[0033] as well as

[0034]

[0035]

[0036] in,

[0037] For Industry B i The minimum value of the predicted value of the golden application time on the nth day; For Industry B i The maximum value of the predicted value of the golden application time on the nth day; For Industry B i The minimum value of the statistical value of the golden application time on the nth day; For Industry B i The maximum value of the statistical value of the golden application time on the nth day; are the autoregressive coefficients; θ1, θ2…θ q is the moving average coefficient; p is the number of autoregressive terms, q is the number of moving average terms; e n-q Refers to distractors; For Industry B iThe validity period of the QR code (or certificate validity period); μ min , μ max is a constant term; For Industry B i The maximum number of times a QR code can be used. For Industry B i The average number of times a person uses the QR code on the npth day.

[0038] As a supplement or replacement for the above solution, in the above method, as well as

[0039] in, Indicates industry B i The industry code application time when the code application volume reaches P% on the nth day.

[0040] As a supplement or replacement for the above solution, in the above method, according to the predicted golden code application time period, determining the time point set of scheduled tasks for the specific industry includes: obtaining a preset time point set of scheduled tasks SetT = {T1, T2, ..., T l}, 1 represents the number of preset time points of scheduled tasks; and as well as Select the time point for industry B from the preset scheduled task time point set SetT i A collection of scheduled task time points m means for industry B i The number of scheduled task time points, where m is less than or equal to 1.

[0041] As a supplement or replacement for the above solution, in the above method, based on the time point set of the scheduled task, determining the execution time of the pre-application code of different users in the specific industry includes: determining industry B i The active user set in the last change cycle; and by performing a hash operation on the user identifiers in the active user set and the scheduled task time point set The number of scheduled task time points m in the modulus is taken to determine the industry B i The pre-application code time for each active user.

[0042] As a supplement or alternative to the above solution, in the above method, the change cycle is 7 days.

[0043] According to another aspect of the present application, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium. When the instructions are executed by a processor, the processor executes the method as described above.

[0044] According to another aspect of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0045] The pre-code application scheme of the embodiment of the present application builds a suitable industry user pre-code application model based on the user's code application data (including but not limited to the number of daily code applications, time, and industry identification of the user as of the day) and combined with the QR code configuration data (including but not limited to the validity period and maximum number of uses corresponding to different QR codes). Users in different industries go to different industry systems at a reasonable time to pre-apply for codes (and cache them), which can greatly improve the reliability and high concurrency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and other objects and advantages of the present application will become more fully apparent from the following detailed description in conjunction with the accompanying drawings, wherein the same or similar elements are represented by the same reference numerals.

[0047] Figure 1 A schematic diagram of the structure of a pre-application code system according to an embodiment of the present application is shown;

[0048] Figure 2 A schematic flow chart showing a method executed by an algorithm module in a pre-registration code system according to an embodiment of the present application is shown; and

[0049] Figure 3 A schematic diagram of the process of the pre-application code service according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] For the purpose of brevity and illustration, the principles of the present application are described herein mainly with reference to its exemplary embodiments. However, those skilled in the art will readily recognize that the same principles are equally applicable to all types of methods and systems for generating and managing reply codes, and that the same or similar principles may be implemented therein, without departing from the true spirit and scope of the present application.

[0051] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0052] Figure 1 FIG. 1 shows a schematic diagram of the structure of a pre-registration code system 1000 according to an embodiment of the present application. Figure 1 As shown, the pre-application code system 1000 includes the following modules:

[0053] A data service module 110, which is used to provide user code application data and QR code configuration data related to user usage habits, wherein the user code application data includes an industry identifier;

[0054] An algorithm module 120, which is used to construct an industry user pre-code application model according to the user code application data and the two-dimensional code configuration data, and calculate the execution time of pre-code application for different users in different industries based on the industry user pre-code application model; and

[0055] The timing task module 130 is used to receive the execution time from the algorithm module 120 and initiate the code application task according to the execution time.

[0056] The pre-application code system 1000 of the above embodiment provides a system based on an industry user pre-application code model, which can effectively reduce system risks and improve system performance.

[0057] In the context of this application, the "pre-application code" aims to generate and provide QR codes in advance by predicting user needs, thereby improving service efficiency and user experience. In traditional QR code generation systems, QR codes are generally requested only when users need them, which may cause system response delays or service unavailability during peak hours. The pre-application code system of this application generates QR codes in advance by predicting user needs, ensuring that users can obtain them immediately when they need them.

[0058] The term "user code application data" refers to the relevant data generated by the user when using the QR code service, which is associated with the user's usage habits. In one embodiment, the user code application data includes an industry identifier, such as ID1 for the banking industry and ID2 for third-party payment institutions. It should be noted that the above is only used as an example and is not a limitation of the content of this application.

[0059] In one embodiment, the user code application data also includes the number of times and time that the user applies for a code every day. Here, the so-called "daily code application time" refers to the specific time point or time period when the user requests to generate a QR code.

[0060] In one or more embodiments, the user code application data may also include but is not limited to:

[0061] User identity information: a user’s unique identifier, such as a user ID or account information;

[0062] Code application frequency: the number of times a user requests to generate a QR code within a certain period of time;

[0063] Request location: The geographic location where the user requested the QR code, which may include GPS coordinates or location name;

[0064] Business scenarios: application scenarios where users request QR codes, such as payment, check-in, identity verification, etc.

[0065] User behavior patterns: user behavior habits when using QR code services, such as preferred time and location for applying for codes;

[0066] Historical code application records: The user’s past code application records, including successful and failed code application attempts.

[0067] The term "QR code configuration data" refers to the configuration data related to the QR code obtained after the user applies for the code, which is used to define the characteristics and / or generation rules of the QR code.

[0068] In one embodiment, the QR code configuration data includes the validity period of the QR code (i.e., the length of time from the generation of the QR code to its expiration) and the maximum number of times the QR code can be used (i.e., the maximum number of times the QR code can be scanned and used within the validity period). In one or more embodiments, the QR code configuration data may also include the following:

[0069] Coding type: the coding standard of the QR code, such as QR Code, Data Matrix, etc.

[0070] Size and resolution: The physical size and image resolution of the QR code;

[0071] Error correction capability: The error correction level of the QR code, which affects its readability when damaged;

[0072] Security settings: security features of the QR code, such as encrypted information, digital signature, etc.

[0073] Content information: data contained in the QR code or the data link it points to, such as URL, payment information, etc.

[0074] The pre-application code system 1000 of the present application can better understand user needs, effectively reduce system risks, improve system performance, and enhance user satisfaction by effectively managing and analyzing user application code data and QR code configuration data.

[0075] The term "industry user pre-application code model" refers to a model based on data analysis and machine learning technology to predict the user's demand for QR code application at a specific time for a specific industry. Based on this "industry user pre-application code model", QR codes can be applied for in advance and cached (for example, cached according to the validity period of the QR code), thereby optimizing the code application process and improving service efficiency and user experience.

[0076] although Figure 1 Not shown, in one embodiment, the above-mentioned pre-application code system 1000 may also include: a server, wherein the timed task module 130 initiates the application code task to the server according to the execution time. In the context of this application, the server-side refers to the part of the system responsible for processing, storing and retrieving data in the client-server architecture. The server is usually composed of servers, which are powerful computers or computer clusters that run various services and applications to respond to client requests.

[0077] In one embodiment, the server is a code issuing platform. The server can be configured to: after receiving the code application task from the timed task module 130, apply for codes to the industry system in batches; and after receiving the code application result from the industry system, cache the code application result locally for user access.

[0078] In the context of this application, industry systems refer to information systems designed specifically for a specific industry or field. These systems typically include a series of software applications, databases, hardware resources, and network infrastructure that work together to support industry-specific QR code generation processes.

[0079] In one embodiment, the server can also be configured to provide the data service module with user code application data and QR code configuration data. In other words, the data service module can obtain relevant data from the server that is useful for building an industry user pre-application code model. In this way, the industry user pre-application code model can be continuously updated and iterated based on the latest data, with higher accuracy.

[0080] In addition, in order to ensure the accuracy of data provided by the data service module, the data service module can also obtain relevant data from credible authoritative organizations.

[0081] In one embodiment, the algorithm module 120 is configured to perform the following steps: determine an industry identification data set; construct the industry user pre-application code model for a specific industry in the industry identification data set, and predict the golden application code time period for the specific industry in the next change cycle based on the industry user pre-application code model; determine a set of scheduled task time points for the specific industry based on the predicted golden application code time period; and determine the execution time of the pre-application codes of different users in the specific industry based on the scheduled task time point set.

[0082] The term "golden time" is used to describe the time of day that is most suitable for QR code application. This time can be determined based on user behavior analysis, system load, business peak and other factors, with the goal of optimizing the QR code generation and distribution process and improving service efficiency and user experience.

[0083] In one embodiment, the industry identification data set includes: industry identifications corresponding to QR codes whose validity periods are within a preset time range (eg, 6 to 24 hours).

[0084] In one or more embodiments, the industry user pre-application code model is constructed as follows:

[0085]

[0086] as well as

[0087]

[0088] in,

[0089] For Industry B i The minimum value of the predicted value of the golden application time on the nth day; For Industry B i The maximum value of the predicted value of the golden application time on the nth day; For Industry B i The minimum value of the statistical value of the golden application time on the nth day; For Industry B i The maximum value of the statistical value of the golden application time on the nth day; are the autoregressive coefficients; θ1, θ2…θ q is the moving average coefficient; p is the number of autoregressive terms in the AR model (i.e., autoregressive model); q is the number of moving average terms in the MA model (i.e., moving average model); en-q Refers to distractors; For Industry B i The validity period of the QR code (or certificate validity period); μ min , μ max is a constant term; For Industry B i The maximum number of times a QR code can be used. For Industry B i The average number of times a person uses the QR code on the npth day.

[0090] In the context of this application, an autoregressive model is a time series model that assumes that the future values ​​of a time series can be predicted by a weighted average of its past values. This model is based on a key assumption: data points in the past can be used to predict future data points. The moving average model focuses on the relationship between the current value of the time series and a series of prediction errors (or random disturbances). The moving average model assumes that the current value of the time series can be represented by a constant plus the weighted average of the prediction errors of the previous periods.

[0091] In one or more embodiments, the industry user pre-application code model is generated based on the combination of the AR model (i.e., autoregressive model) and the MA model (i.e., moving average model), which takes into account both the autoregressive characteristics and the moving average characteristics of the time series, and can more comprehensively capture the dynamic characteristics of the time series.

[0092] It is understandable that after constructing the industry user pre-application code model, the algorithm module 120 can determine the coefficients by fitting and predict the golden application code time period of the next change cycle (for example, the next 7 days) of each industry system.

[0093] In one embodiment, the predicted golden application period is based on as well as The two are used to determine.

[0094] In the above formula, Indicates industry B i The industry code application time when the code application volume reaches P% on the nth day, where P is the preset value (which can be adjusted according to business needs). min , μ max is a constant term (and a negative value).

[0095] Thus, industry B i The predicted value of the golden application period Can be determined as:

[0096]

[0097] In one embodiment, the algorithm module 120 is configured to: obtain a preset scheduled task time point set SetT={T1, T2, ..., T l}, 1 represents the number of preset scheduled task time points (assuming that 7 days is a change cycle, and there are 12 scheduled task time points per day, then 84 scheduled task time points can be obtained, that is, 1=84); and according to as well as Select the time point for industry B from the preset scheduled task time point set SetT i A collection of scheduled task time points m means for industry B i The number of scheduled task time points (i.e., the number of scheduled task execution time points that meet the conditions), where m is less than or equal to l.

[0098] In one embodiment, the algorithm module 120 is further configured to: determine industry B i The active user set in the last change cycle; and by performing a hash operation on the user identifiers in the active user set and the scheduled task time point set The number of scheduled task time points m in the modulus is taken to determine the industry B i The pre-application code time for each active user.

[0099] In one embodiment, Industry B i The active user set in the last change cycle is defined as the set of users who have used the system for more than D days in the last change cycle. This set can be obtained through statistics. For example, the user set

[0100] In the above embodiment, the algorithm module 120 is configured to perform a hash operation on the user identifiers in the active user set and to calculate the time point set of the scheduled task. The number of scheduled task time points m in the modulus is taken to determine the industry B i The pre-application code time of each active user in In this way, the pre-application code requests of users in the same industry can be made as evenly distributed as possible, reducing the pressure on the system.

[0101] refer to Figure 2 , which shows a flow chart of a method 2000 executed by an algorithm module in a pre-registration code system according to an embodiment of the present application. Figure 2 As shown, the method 2000 includes the following steps:

[0102] In step S210, receiving user code application data and QR code configuration data related to the user's usage habits, wherein the user code application data includes an industry identifier;

[0103] In step S220, an industry user pre-application code model is constructed according to the user application code data and the two-dimensional code configuration data, and the execution time of pre-application code for different users in different industries is calculated based on the industry user pre-application code model; and

[0104] In step S230, the execution time is sent so that the scheduled task module of the pre-code application system can initiate the code application task according to the execution time.

[0105] In one or more embodiments, the pre-application code system may include an algorithm module for constructing an industry user pre-application code model based on user code application data and QR code configuration data, and calculating the execution time for pre-application codes for different users in different industries based on the industry user pre-application code model.

[0106] In the context of this application, the "pre-application code" aims to generate and provide QR codes in advance by predicting user needs, thereby improving service efficiency and user experience. In traditional QR code generation systems, QR codes are generally requested only when users need them, which may cause system response delays or service unavailability during peak hours. The pre-application code system of this application generates QR codes in advance by predicting user needs, ensuring that users can obtain them immediately when they need them.

[0107] The term "user code application data" refers to the relevant data generated by the user when using the QR code service, which is associated with the user's usage habits. In one embodiment, the user code application data includes an industry identifier, such as ID1 for the banking industry and ID2 for third-party payment institutions. It should be noted that the above is only used as an example and is not a limitation of the content of this application.

[0108] In one embodiment, the user code application data also includes the number of times and time that the user applies for a code every day. Here, the so-called "daily code application time" refers to the specific time point or time period when the user requests to generate a QR code.

[0109] In one or more embodiments, user code application data may also include, but is not limited to: user identity information (a user's unique identifier, such as a user ID or account information); code application frequency (the number of times a user requests the generation of a QR code within a certain period of time); code application location (the geographic location where the user requests the QR code, which may include GPS coordinates or a location name); business scenarios (application scenarios in which users request QR codes, such as payment, check-in, and identity authentication); user behavior patterns (users' behavioral habits when using QR code services, such as preferred code application time and location); historical code application records (users' past code application records, including successful and failed code application attempts), etc.

[0110] The term "QR code configuration data" refers to the configuration data related to the QR code obtained after the user applies for the code, which is used to define the characteristics and / or generation rules of the QR code.

[0111] In one embodiment, the QR code configuration data includes the validity period of the QR code (i.e., the length of time from the generation to the expiration of the QR code) and the maximum number of uses of the QR code (i.e., the maximum number of times the QR code can be scanned and used within the validity period). In one or more embodiments, the QR code configuration data may also include the following: encoding type (encoding standard of the QR code, such as QR Code, Data Matrix, etc.); size and resolution (physical size and image resolution of the QR code); error correction capability (error correction level of the QR code, which affects its readability when damaged); security settings (security features of the QR code, such as encryption information, digital signature, etc.); content information (data contained in the QR code or data link pointed to, such as URL, payment information, etc.), etc.

[0112] The method 2000 executed by the algorithm module in the pre-code application system of the embodiment of the present application can better understand user needs, effectively reduce system risks, improve system performance, and enhance user satisfaction by effectively managing and analyzing user code application data and QR code configuration data.

[0113] In step S220, an industry user pre-application code model is constructed based on the user code application data and the QR code configuration data, and the execution time of pre-application code for different users in different industries is calculated based on the industry user pre-application code model. In one embodiment, step S220 includes: determining an industry identification data set; constructing the industry user pre-application code model for a specific industry in the industry identification data set, and predicting the golden code application time period for the specific industry in the next change cycle based on the industry user pre-application code model; determining a set of scheduled task time points for the specific industry based on the predicted golden code application time period; and determining the execution time of pre-application codes for different users in the specific industry based on the set of scheduled task time points.

[0114] In one embodiment, the industry identification data set includes: industry identifications corresponding to QR codes whose validity periods are within a preset time range (eg, 6 to 24 hours).

[0115] The term "industry user pre-application code model" refers to a model based on data analysis and machine learning technology to predict the user's demand for QR code application at a specific time for a specific industry. Based on this "industry user pre-application code model", QR codes can be applied for in advance and cached (for example, cached according to the validity period of the QR code), thereby optimizing the code application process and improving service efficiency and user experience.

[0116] In one or more embodiments, the industry user pre-application code model is constructed as follows:

[0117]

[0118] as well as

[0119]

[0120] in,

[0121] For Industry B i The minimum value of the predicted value of the golden application time on the nth day; For Industry B i The maximum value of the predicted value of the golden application time on the nth day; For Industry B i The minimum value of the statistical value of the golden application time on the nth day; For Industry B i The maximum value of the statistical value of the golden application time on the nth day; are the autoregressive coefficients; θ1, θ2…θ q is the moving average coefficient; p is the number of autoregressive terms in the AR model (i.e., autoregressive model); q is the number of moving average terms in the MA model (i.e., moving average model); e n-q Refers to distractors; For Industry B i The validity period of the QR code (or certificate validity period); μ min , μ max is a constant term; For Industry B i The maximum number of times a QR code can be used. For Industry B i The average number of times a person uses the QR code on the npth day.

[0122] The term "golden time" is used to describe the time of day that is most suitable for QR code application. This time can be determined based on user behavior analysis, system load, business peak and other factors, with the goal of optimizing the QR code generation and distribution process and improving service efficiency and user experience.

[0123] In the context of this application, an autoregressive model is a time series model that assumes that the future values ​​of a time series can be predicted by a weighted average of its past values. This model is based on a key assumption: data points in the past can be used to predict future data points. The moving average model focuses on the relationship between the current value of the time series and a series of prediction errors (or random disturbances). The moving average model assumes that the current value of the time series can be represented by a constant plus the weighted average of the prediction errors of the previous periods.

[0124] In one or more embodiments, the industry user pre-application code model is generated based on the combination of the AR model (i.e., autoregressive model) and the MA model (i.e., moving average model), which takes into account both the autoregressive characteristics and the moving average characteristics of the time series, and can more comprehensively capture the dynamic characteristics of the time series.

[0125] It can be understood that after constructing the industry user pre-application code model, the algorithm module can determine the coefficients by fitting and predict the golden application code time period of the next change cycle (for example, the next 7 days) of each industry system.

[0126] In one embodiment, according to the predicted golden code application time period, determining the time point set of scheduled tasks for the specific industry includes: obtaining a preset time point set of scheduled tasks SetT = {T1, T2, ..., T l}, l represents the number of preset time points of scheduled tasks; and according to as well as Select the time point for industry B from the preset scheduled task time point set SetT i A collection of scheduled task time points m means for industry B i The number of scheduled task time points, where m is less than or equal to l.

[0127] In one embodiment, based on the time point set of the scheduled task, determining the execution time of the pre-application code of different users in the specific industry includes: determining the execution time of the pre-application code of the industry B i The active user set in the last change cycle; and by performing a hash operation on the user identifiers in the active user set and the scheduled task time point set The number of scheduled task time points m in the modulus is taken to determine the industry B i The pre-application code time for each active user.

[0128] According to another aspect of the present application, a computer-readable storage medium is provided, in which instructions are stored. When the instructions are executed by a processor, the processor executes the method 2000 executed by the algorithm module in the pre-registration code system as described above. The computer-readable medium referred to in the present application includes various types of computer storage media, which can be any available medium that can be accessed by a general or special computer. For example, the computer-readable medium can include RAM, ROM, EPROM, EEPROM, etc. 2 PROM, register, hard disk, removable disk, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or can be used to carry or store the desired program code unit in the form of instruction or data structure and can be accessed by general or special computer, or general or special processor. As used herein, the disk usually copies data magnetically, while the dish uses laser to optically copy data. The above combination should also be included in the protection scope of computer-readable medium. Exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative solution, the storage medium can be integrated into the processor. The processor and the storage medium can reside in the ASIC. The ASIC can reside in the user terminal. In an alternative solution, the processor and the storage medium can reside in the user terminal as discrete components.

[0129] According to another aspect of the present application, a computer program product is provided. The product includes a computer program, and when the computer program is executed by a processor, the method 2000 executed by the algorithm module in the pre-registration code system as described above is implemented.

[0130] refer to Figure 3 , which shows a schematic diagram of the process of the pre-application code service according to an embodiment of the present application. Figure 3As shown, the algorithm module sends a request to the data service module to obtain the calculation data, and receives the corresponding calculation data from the data service module. In one embodiment, the calculation data may include the user code application data and the QR code configuration data mentioned above. Based on the calculation data, the algorithm module calculates the industry code application time and provides it to the timing task module. In one embodiment, the algorithm module can first construct an industry user pre-application code model based on the calculation data, and then obtain the industry code application time. After receiving the industry code application time from the algorithm module, the timing task module regularly initiates the code application task to the code issuance platform (rather than waiting for the client to initiate a request), so that the code issuance platform applies for codes to different industry systems in batches, caches the response code application results received from the industry system (for example, caches according to the validity period of the QR code), and waits for the execution of the next timing task. In addition, the code issuance platform also provides the user code application data and QR code configuration data to the data service module. In this way, the industry user pre-application code model can be continuously updated and iterated based on the latest data, with higher accuracy.

[0131] In summary, the pre-code application scheme of the embodiment of the present application builds a suitable industry user pre-code application model based on the user's code application data (including but not limited to the number of daily code applications, time, and industry identification of the user as of that day) and combined with the QR code configuration data (including but not limited to the validity period and maximum number of uses corresponding to different QR codes). Users in different industries go to different industry systems at a reasonable time to pre-apply for codes (and cache them), which can greatly improve the reliability and high concurrency of the system.

[0132] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Those skilled in the art can think of other feasible changes or substitutions based on the technical scope disclosed in the present application, and such changes or substitutions are all included in the protection scope of the present application. In the absence of conflict, the implementation modes of the present application and the features in the implementation modes can also be combined with each other. The protection scope of the present application shall be subject to the description of the claims.

Claims

1. A pre-registration code system, characterized in that: The system comprises: A data service module, used to provide user code application data and QR code configuration data related to user usage habits, wherein the user code application data includes industry identification; An algorithm module, used to construct an industry user pre-application code model according to the user application code data and the two-dimensional code configuration data, and calculate the execution time of pre-application code for different users in different industries based on the industry user pre-application code model; and The timing task module is used to receive the execution time from the algorithm module and initiate the code application task according to the execution time.

2. The pre-registration code system according to claim 1, further comprising: The server side, wherein the scheduled task module initiates a code application task to the server side according to the execution time.

3. The pre-registration code system as claimed in claim 2, wherein: The server is configured as follows: After receiving the code application task, apply for codes to the industry system in batches; and After receiving the code application result from the industry system, the code application result is cached locally for user access.

4. The pre-registration code system as claimed in claim 3, wherein: The server is also configured as follows: Provide user code application data and QR code configuration data to the data service module.

5. The pre-registration code system according to any one of claims 2 to 4, wherein: The server is a code issuing platform.

6. The pre-registration code system according to claim 1, wherein: The user code application data also includes the number of times and time of the user's daily code application, and the QR code configuration data includes the validity period of the QR code and the maximum number of times the QR code is used.

7. The pre-registration code system according to claim 6, wherein: The algorithm module is configured as follows: Identify industry identification data sets; For a specific industry in the industry identification data set, construct a user pre-application code model for the industry, and predict the golden application code time period for the specific industry in the next change cycle based on the user pre-application code model for the industry; According to the predicted golden application period, determine the time point set of scheduled tasks for the specific industry; as well as Based on the scheduled task time point set, the execution time of the pre-application code for different users in the specific industry is determined.

8. The pre-registration code system according to claim 7, wherein: The industry identification data set includes: industry identifications corresponding to QR codes whose validity periods are within a preset time range.

9. The pre-registration code system as claimed in claim 8, wherein: The preset time range is 6 to 24 hours.

10. The pre-registration code system according to claim 7, wherein: The industry user pre-application code model is constructed as follows: as well as in, For Industry B i The minimum value of the predicted value of the golden application time on the nth day; For Industry B i The maximum value of the predicted value of the golden application time on the nth day; For Industry B i The minimum value of the statistical value of the golden application time on the nth day; For Industry B i The maximum value of the statistical value of the golden application time on the nth day; are the autoregressive coefficients; θ1, θ2…θ q is the moving average coefficient; p is the number of autoregressive terms, q is the number of moving average terms; e n-q Refers to distractors; For Industry B i The validity period of the QR code; μ min , μ max is a constant term; For Industry B i The maximum number of times a QR code can be used. For Industry B i The average number of times a person uses the QR code on the npth day.

11. The pre-registration code system according to claim 10, wherein: as well as in, Indicates industry B i The industry code application time when the code application volume reaches P% on the nth day.

12. The pre-registration code system according to claim 10 or 11, wherein: The algorithm module is configured as follows: Get the preset scheduled task time point set SetT = {T1, T2, ..., T l }, l represents the number of preset time points of scheduled tasks; as well as according to as well as Select the time point for industry B from the preset scheduled task time point set SetT i A collection of scheduled task time points m means for industry B i The number of scheduled task time points, where m is less than or equal to l.

13. The pre-registration code system according to claim 12, wherein: The algorithm module is configured as follows: Determine Industry B i The set of active users during the last change cycle; and By performing a hash operation on the user IDs in the active user set and The number of scheduled task time points m in the modulus is taken to determine the industry B i The pre-application code time for each active user.

14. The pre-registration code system according to claim 7, wherein: The change period is 7 days.

15. A method for executing an algorithm module in a pre-registration code system, characterized in that: The method comprises: Receiving user code application data and QR code configuration data related to user usage habits, wherein the user code application data includes an industry identifier; Constructing an industry user pre-code application model according to the user code application data and the QR code configuration data, and calculating the execution time of pre-code application for different users in different industries based on the industry user pre-code application model; and The execution time is sent so that the timing task module of the pre-code application system can initiate the code application task according to the execution time.

16. The method of claim 15, wherein: The user code application data also includes the number of times and time of the user's daily code application, and the QR code configuration data includes the validity period of the QR code and the maximum number of times the QR code is used.

17. The method of claim 16, wherein: Building an industry user pre-application code model according to the user application code data and the QR code configuration data, and calculating the execution time of pre-application codes for different users in different industries based on the industry user pre-application code model includes: Identify industry identification data sets; For a specific industry in the industry identification data set, construct a user pre-application code model for the industry, and predict the golden application code time period for the specific industry in the next change cycle based on the user pre-application code model for the industry; Determine a set of scheduled task time points for the specific industry based on the predicted golden application time period; and Based on the scheduled task time point set, the execution time of the pre-application code for different users in the specific industry is determined.

18. The method of claim 17, wherein: The industry identification data set includes: industry identifications corresponding to QR codes whose validity periods are within a preset time range.

19. The method of claim 18, wherein: The preset time range is 6 to 24 hours.

20. The method of claim 17, wherein: The industry user pre-application code model is constructed as follows: as well as in, For Industry B i The minimum value of the predicted value of the golden application time on the nth day; For Industry B i The maximum value of the predicted value of the golden application time on the nth day; For Industry B i The minimum value of the statistical value of the golden application time on the nth day; For Industry B i The maximum value of the statistical value of the golden application time on the nth day; are the autoregressive coefficients; θ1, θ2…θ q is the moving average coefficient; p is the number of autoregressive terms, q is the number of moving average terms; e n-q Refers to distractors; For Industry B i The validity period of the QR code; μ min , μ max is a constant term; For Industry B i The maximum number of times a QR code can be used. For Industry B i The average number of times a person uses the QR code on the npth day.

21. The method of claim 20, wherein: as well as in, Indicates industry B i The industry code application time when the code application volume reaches P% on the nth day.

22. The method of claim 20 or 21, wherein: According to the predicted golden application time period, the set of scheduled task time points for the specific industry is determined to include: Get the preset scheduled task time point set SetT = {T1, T2, ..., T l }, l represents the number of preset time points of scheduled tasks; and according to as well as Select the time point for industry B from the preset scheduled task time point set SetT i A collection of scheduled task time points m means for industry B i The number of scheduled task time points, where m is less than or equal to l.

23. The method of claim 22, wherein: Based on the scheduled task time point set, determining the execution time of the pre-application code for different users in the specific industry includes: Determine Industry B i The set of active users during the last change cycle; and By performing a hash operation on the user IDs in the active user set and The number of scheduled task time points m in the modulus is taken to determine the industry B i The pre-application code time for each active user.

24. The method of claim 17, wherein: The change period is 7 days.

25. A computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, characterized in that: When the instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 15 to 24.

26. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 15 to 24 is implemented.

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