Electronic signature distribution method and device, equipment and medium

Abstract

The invention discloses an electronic signature distribution method and device, equipment and a medium, and the method comprises the steps: obtaining the number of historical electronic signatures in a preset time period, analyzing the number of historical electronic signatures according to a time factor and a space factor which affect the usage amount of the electronic signatures, and determining the change trend of the number of electronic signatures in the preset time period; based on the change trend of the number of the electronic signatures in the preset time period, predicting the electronic signature demand by using a pre-trained prediction model to obtain the future number of the electronic signatures in the future time period, and based on the future number of the electronic signatures and the stepped charging rules of the CA manufacturers, adopting different distribution strategies for the CA manufacturers. It can be seen that the future number of the electronic signatures in the future time period can be predicted through the pre-trained prediction model according to the historical number of the electronic signatures, and then the number of the electronic signatures can be automatically allocated to each CA manufacturer according to the stepped charging rule of each CA manufacturer, so that low cost and good experience are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and in particular to a method, device, equipment and medium for allocating electronic signatures. Background Art

[0002] An electronic signature refers to a person or organization that generates, sends, and receives electronic documents such as text, images, audio, and video on the Internet or other electronic information systems, and adds a signature to them. Electronic signatures can verify the identity of the signer, express the signing opinion, and ensure that the document has not been tampered with or forged.

[0003] At present, many group companies carry out various businesses (for example, banking, credit card, insurance, securities, etc.). In order to facilitate system maintenance and management, these group companies all use electronic signatures, and this electronic signature technology requires a manufacturer with an electronic CA license to issue a CA certificate. The existing solution is to cooperate with multiple CA manufacturers and connect them in a master-slave mode, but this solution cannot automatically allocate the number of electronic signatures to each CA manufacturer, resulting in high CA fees. Therefore, how to automatically allocate the number of electronic signatures to each CA manufacturer and reduce the cost of electronic signatures has become an urgent problem to be solved. Summary of the invention

[0004] Based on this, it is necessary to address the above technical issues. The embodiments of the present invention provide a method, device, equipment and medium for allocating electronic signatures to solve the problem that the existing technology cannot automatically allocate the number of electronic signatures to each CA manufacturer, thereby resulting in very high CA fees.

[0005] A first aspect of an embodiment of the present application provides a method for distributing an electronic signature, the method comprising: Get the number of historical electronic signatures within a preset time period; Analyze the number of historical electronic signatures based on time factors and space factors that affect the use of electronic signatures, and determine the change trend of the number of electronic signatures within a preset time period; Based on the changing trend of the number of electronic signatures in a preset time period, the demand for electronic signatures is predicted using a pre-trained prediction model to obtain the future number of electronic signatures in a future period; Based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer, different allocation strategies are adopted for each CA manufacturer.

[0006] A second aspect of an embodiment of the present application provides an electronic signature distribution device, the electronic signature distribution device comprising: An acquisition module is used to obtain the number of historical electronic signatures within a preset time period; A determination module, configured to analyze the number of historical electronic signatures according to time factors and space factors that affect the usage of electronic signatures, and determine a change trend of the number of electronic signatures within a preset time period; A prediction module, used to predict the demand for electronic signatures based on the changing trend of the number of electronic signatures in a preset time period using a pre-trained prediction model, and obtain the future number of electronic signatures in a future period; The allocation module is used to adopt different allocation strategies for each CA manufacturer based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer.

[0007] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for distributing electronic signatures as described in the first aspect is implemented.

[0008] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the electronic signature distribution method as described in the first aspect is implemented.

[0009] In summary, the present invention provides a method, device, equipment and medium for distributing electronic signatures, which obtains the number of historical electronic signatures within a preset time period, analyzes the number of historical electronic signatures according to the time factors and space factors that affect the use of electronic signatures, determines the changing trend of the number of electronic signatures within the preset time period, and uses a pre-trained prediction model to predict the demand for electronic signatures based on the changing trend of the number of electronic signatures within the preset time period, and obtains the future number of electronic signatures in the future time period. Based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer, different allocation strategies are adopted for each CA manufacturer. It can be seen that the present application can predict the future number of electronic signatures in the future time period according to the pre-trained prediction model based on the number of historical electronic signatures, and then according to the tiered charging rules of each CA manufacturer, the number of electronic signatures can be automatically allocated to each CA manufacturer, thereby ensuring low costs and good experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1This is an application environment diagram of an electronic signature distribution method provided by an embodiment of the present invention; Figure 2 It is a flowchart of a method for distributing an electronic signature provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of a change trend in a method for distributing electronic signatures provided by an embodiment of the present invention; Figure 4 It is a structural schematic diagram of an electronic signature distribution device provided by one embodiment of the present invention; Figure 5 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term “if” may be interpreted as “when” or “uponce” or “in response to determining”, depending on the context. Similarly, the phrases “if it is determined” or “if matched to [described condition or event]” may be interpreted as meaning “upon determination” or “in response to determination” or “uponce matched to [described condition or event]” or “in response to matching to [described condition or event]”, depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] See also Figure 1 , is an application environment diagram of an electronic signature distribution method provided by an embodiment of the present invention. An electronic signature distribution method provided by an embodiment of the present invention can be applied in Figure 1 In the application environment, the client communicates with the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs) and other computer devices. The client here is replaced by a business system. The server can be implemented with an independent server or a server cluster composed of multiple servers. Financial data, such as loan information and deposit information, can be uploaded and downloaded through the server.

[0021] See also Figure 2 , is a flow chart of a method for distributing an electronic signature provided by an embodiment of the present invention, such as Figure 2 As shown, the method for distributing the electronic signature can be implemented through the following steps.

[0022] S201: Obtain the number of historical electronic signatures within a preset time period.

[0023] In step S201, the preset time period can be determined according to the allocation requirements, for example, it can be one month, one week, fifteen days, three days, etc. Specifically, signature data can be collected from the electronic signature system, relevant database or business system, and these signature data usually include the timestamp of the signature, the signer information and the signature content, etc. The time period to be queried is specified, for example: January 1, 2023 to December 31, 2023. Then, the collected data is filtered using a query language (such as SQL) or a programming language (such as Python), the signature data within the time period is extracted, and the filtered records are counted to obtain the number of historical electronic signatures within the preset time period.

[0024] Through the above steps and examples, the historical number of electronic signatures within a preset time period can be effectively obtained, and these data can be used for further analysis and decision-making to further improve the accuracy of subsequent electronic signature quantity allocation.

[0025] S202: Analyze the number of historical electronic signatures based on time factors and space factors that affect the usage of electronic signatures, and determine the change trend of the number of electronic signatures within a preset time period.

[0026] In step S202, there are many factors that affect the usage of electronic signatures, such as spatial factors such as technology, users, market, platform, and time factors. According to the time factors and spatial factors that affect the usage of electronic signatures, the number of historical electronic signatures is analyzed to obtain the change trend of the number of electronic signatures within a preset time period. The change trend can reflect the increase or decrease of the number of each electronic signature within the preset time period, such as Figure 3 As shown, the trend of change can be used to predict the future number of electronic signatures in future time periods.

[0027] In one embodiment of the invention, determining a change trend of the number of electronic signatures within a preset time period includes: Preprocessing the number of historical electronic signatures according to time factors and space factors that affect the usage of electronic signatures to obtain the number of historical electronic signatures after preprocessing, wherein the preprocessing includes standardization, outlier removal, and missing value filling; Based on a preset time series method, the pre-processed historical electronic signature quantity is analyzed to determine the change trend of the electronic signature quantity within a preset time period.

[0028] Specifically, the time factors of this embodiment generally include different time points within a preset time period, the length of the time period, seasonal changes, etc. These factors can affect the usage of electronic signatures, because the user's signing needs may change over time. Spatial factors generally include different geographical locations, industry fields, user groups, etc. These factors can affect the usage of electronic signatures, because the signing needs of different regions, industries and user groups may be different. According to the time factors and spatial factors that affect the usage of electronic signatures, the number of historical electronic signatures is preprocessed to obtain the number of historical electronic signatures after preprocessing, wherein the standardization process is to scale the data so that it falls into a small specific interval for subsequent data analysis. For the number of historical electronic signatures, the standardization process can help eliminate the order of magnitude differences between different time periods or different data sources; an outlier refers to a value that is significantly different from other data in the data set. For the number of historical electronic signatures, an outlier may be caused by system errors, data entry errors, or special events. Eliminating outliers can improve the accuracy and reliability of data analysis; missing values ​​refer to values ​​that are missing or unavailable in a data set. For the number of historical electronic signatures, missing values ​​may be caused by system failure, data loss, or data not being recorded. Filling in missing values ​​can improve data integrity and availability.

[0029] For example, suppose there is a data set of historical electronic signature quantities, containing the following data: Date / Number of Signatures: 2023-01-01 / 100; 2023-01-02 / 200; 2023-01-03 / -; 2023-01-04 / 500; 2023-01-05 / 1000; 2023-01-06 / 2000. Using the Z-score standardization method, the data is converted into a distribution with a mean of 0 and a standard deviation of 1. Then, using a statistical method, values ​​that deviate from the mean by more than two standard deviations are considered outliers. In this case, the number of signatures of 2000 on 2023-01-06 is an outlier and should be removed. Finally, the mean filling method is used to fill the missing value of 2023-01-03 with (100+200+500+1000) / 4=450. After preprocessing, the resulting data set is as follows: Date / number of signatures (after standardization): 2023-01-01 / -0.707; 2023-01-02 / -0.354; 2023-01-03 / 0.000 (filled value); 2023-01-04 / 1.061; 2023-01-05 / 1.772. Through preprocessing, more accurate, reliable and usable data sets can be obtained, providing strong support for subsequent data analysis.

[0030] In this embodiment, after the preprocessed number of historical electronic signatures, the preset time series method may include multiple methods, such as moving average, exponential smoothing, autoregressive model (AR), autoregressive moving average model (ARMA), seasonal autoregressive integrated moving average model (SARIMA), etc. By analyzing the preprocessed number of historical electronic signatures through the preset time series method, and then determining the change trend of the number of electronic signatures within the preset time period, it is helpful to better understand the characteristics of the data, so that timely information support can be better provided for future allocation decisions.

[0031] S203: Based on the changing trend of the number of electronic signatures within a preset time period, the electronic signature demand is predicted using a pre-trained prediction model to obtain the future number of electronic signatures in a future period.

[0032] In step S203, a suitable prediction model is selected according to the changing trend of the number of electronic signatures within a preset time period. Commonly used prediction models include machine learning models (such as support vector machines, random forests, etc.) and deep learning models (such as recurrent neural networks, long short-term memory networks, etc.). The trained prediction model is applied to data prediction for future time periods to obtain a predicted value of the number of electronic signatures in the future time period. The prediction results are then analyzed to evaluate the accuracy and reliability of the prediction model, which helps enterprises or individuals better plan the use and management of electronic signatures and improve business efficiency and security.

[0033] In one embodiment of the invention, the prediction model is trained in the following manner: Acquire a training data set, wherein the training data set includes a plurality of historical electronic signature data, and the historical electronic signature data includes the number of historical electronic signatures within a preset time period; Inputting the training data set into the machine learning model to be trained, and iteratively training the machine learning model to be trained using the gradient descent method; When the loss function of the machine learning model to be trained tends to converge, the training is stopped to obtain the trained prediction model, wherein the loss function is a mean square error loss function.

[0034] Specifically, a training data set is obtained, and the training set is stored in a hive table for subsequent model training and prediction. Hive is a data warehouse tool based on Hadoop, which is used for data extraction, transformation, and loading. It is a mechanism that can store, query, and analyze large-scale data stored in Hadoop. The hive data warehouse tool can map structured data files to a database table, and provides SQL query functions, which can convert SQL statements into MapReduce tasks for execution. The advantage of Hive is that it has a low learning cost, and can implement fast MapReduce statistics through similar SQL statements, making MapReduce simpler without having to develop a dedicated MapReduce application. Hive is very suitable for statistical analysis of data warehouses. The above training data set includes multiple historical electronic signature data, and the historical electronic signature data includes the number of historical electronic signatures within a preset time period.

[0035] After obtaining the above training set, the training set is input into the machine learning model to be trained, and the machine learning model to be trained is iteratively trained using the gradient descent method. The machine learning model to be trained can include two basic algorithm blocks: TCN algorithm block (Temporal Convolutional Network, which is composed of dilated, causal 1D convolutional layers with the same input and output lengths) and LSTM algorithm block (Long short-term memory, a time-recurrent neural network), and the remaining network structure is composed of MLP (Multi-Layer Perceptron). The above training set can be input into the machine learning model to be trained, and the machine learning model to be trained is iteratively trained using the gradient descent method. Among them, the gradient descent method is a first-order optimization algorithm, also commonly known as the steepest descent method. To use the gradient descent method to find the local minimum of a function, it is necessary to iteratively search for a specified step distance point in the opposite direction of the gradient (or approximate gradient) corresponding to the current point on the function. The loss function of the model can be set to the MSE loss function (Mean Square Error), which is a regression loss function. Then, when the loss function of the above-mentioned machine learning model to be trained tends to converge, the training can be stopped to obtain a trained prediction model so that the prediction model can obtain more accurate prediction results.

[0036] In one embodiment of the invention, obtaining the future number of electronic signatures in a future period includes: Extracting a characteristic value of the number of historical electronic signatures based on a change trend of the number of electronic signatures within a preset time period; The characteristic value is input into a pre-trained prediction model to predict the demand for electronic signatures, so as to obtain the future number of electronic signatures in a future period.

[0037] Specifically, according to the changing trend of the number of electronic signatures in a preset time period, the characteristic values ​​of the number of historical electronic signatures are extracted, and then the characteristic values ​​are input into the pre-trained prediction model to predict the demand for electronic signatures, so as to obtain the future number of electronic signatures in the future period. This application evaluates the accuracy of the prediction results by using indicators such as the root mean square error RMSE. If the prediction result is inaccurate, you can consider increasing the characteristic value, adjusting the parameters of the LSTM model (such as the number of hidden layers, the number of neurons in the hidden layer, the learning rate, etc.), or using ensemble learning methods (such as bagging, boosting, etc.) to improve the prediction performance.

[0038] In this embodiment, the regularity and trend of changes in the number of electronic signatures are captured through a pre-trained prediction model, so that a more accurate prediction of the demand for electronic signatures in future time periods can be made, so as to quickly and accurately obtain the future number of electronic signatures in future time periods, thereby better allocating and configuring resources.

[0039] S204: Based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer, adopt different allocation strategies for each CA manufacturer.

[0040] In step S204, after the future number of electronic signatures, the latest tiered charging rules are obtained from the official website, product manual or customer service department of each CA manufacturer. These rules usually include unit prices, preferential policies, value-added service fees, etc. under different usage quantities. The charging rules of each CA manufacturer are compared and analyzed to find the most cost-effective manufacturer under different usage quantities. Then, based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer, different allocation strategies are adopted for each CA manufacturer to automatically allocate traffic to each CA manufacturer to ensure low costs and good experience. If there are problems with the allocation strategy later, the automatic diversion strategy can be adjusted at any time to provide system support for the flexible adjustment of the business direction of financial institutions.

[0041] In an embodiment of the invention, different allocation strategies are adopted for each CA vendor, including: Based on the tiered charging rules of each CA vendor, determine the weight of the number of electronic signatures allocated to each CA vendor; A weight is allocated based on the future number of electronic signatures and the number of electronic signatures of each CA manufacturer, and a corresponding number of electronic signatures is allocated to each CA manufacturer according to a preset time period.

[0042] Specifically, based on the tiered charging rules of each CA manufacturer, a judgment matrix between CA manufacturers is constructed, which represents the relative advantages and disadvantages of each manufacturer on the indicator. The weight vector of each judgment matrix is ​​calculated using the calculation methods of the hierarchical analysis method, such as the eigenvector method, the geometric mean method, etc., to determine the weight distribution of the number of electronic signatures of each CA manufacturer. By distributing weights based on the future number of electronic signatures and the number of electronic signatures of each CA manufacturer, the corresponding number of electronic signatures is allocated to each CA manufacturer according to the preset time period, so as to realize automatic distribution of traffic to each CA manufacturer and ensure low cost and good experience.

[0043] In an embodiment of the invention, before determining the weights of the number of electronic signatures of each CA vendor, the following steps are included: Based on the tiered charging rules of each CA vendor, determine the electronic signature quantity thresholds and corresponding switching conditions for different tiers; Determine whether the number of electronic signatures of the first CA manufacturer meets the electronic signature number threshold; If the number of electronic signatures of the first CA manufacturer meets the electronic signature number threshold, it will automatically switch to the second CA manufacturer and update the corresponding electronic signature configuration information until the electronic signature configuration information of all CA manufacturers is completed.

[0044] Specifically, an interface monitoring module is designed in advance to detect the status of each CA manufacturer's interface in real time, including key indicators such as availability and response time. When it is detected that a CA manufacturer's interface is unavailable, a switching mechanism is immediately triggered to record and update the signature volume data of each CA manufacturer in real time. Based on the tiered charging rules of each CA manufacturer, the electronic signature quantity thresholds and corresponding switching conditions of different tiers are determined. When the signature volume of a CA manufacturer reaches the set threshold, the switching mechanism is triggered, and then the optimal CA manufacturer is automatically selected according to the interface status, signature volume statistics and tiered quotation rules. Then, when the switching conditions are met, it is automatically switched to the new CA manufacturer, and the corresponding configuration and status information is updated, that is, it is determined whether the number of electronic signatures of the first CA manufacturer meets the electronic signature quantity threshold. If the number of electronic signatures of the first CA manufacturer meets the electronic signature quantity threshold, the switching mechanism is triggered, and it is automatically switched to the second CA manufacturer, and the corresponding electronic signature configuration information is updated, wherein the electronic signature configuration information involves specific parameters for setting and using electronic signatures, and the specific parameters are quantity, time, etc., and so on, until the electronic signature configuration information of all CA manufacturers is completed, so as to allocate weights to the number of electronic signatures of each CA manufacturer in the future.

[0045] In this embodiment, the electronic signature capability has high availability and will not cause business unavailability due to a failure of a CA manufacturer. In particular, the scenarios involving electronic signatures in the financial industry are all important business scenarios, and the general service time is 7*24 hours. It can automatically switch the number of electronic signatures according to the CA manufacturer's situation when there is no one on duty, so that the business will not be perceived and the user experience is smooth.

[0046] In summary, the present invention provides a method, device, equipment and medium for distributing electronic signatures, which obtains the number of historical electronic signatures within a preset time period, analyzes the number of historical electronic signatures according to the time factors and space factors that affect the use of electronic signatures, determines the changing trend of the number of electronic signatures within the preset time period, and uses a pre-trained prediction model to predict the demand for electronic signatures based on the changing trend of the number of electronic signatures within the preset time period, and obtains the future number of electronic signatures in the future time period. Based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer, different allocation strategies are adopted for each CA manufacturer. It can be seen that the present application can predict the future number of electronic signatures in the future time period according to the pre-trained prediction model based on the number of historical electronic signatures, and then according to the tiered charging rules of each CA manufacturer, the number of electronic signatures can be automatically allocated to each CA manufacturer, thereby ensuring low costs and good experience.

[0047] See also Figure 4 , Figure 4 Schematic diagram of the structure of the electronic signature distribution device provided by the embodiment of the present invention. In this embodiment, the terminal includes various units for executing Figure 2 For details, please refer to the steps in the corresponding embodiment. Figure 2 as well as Figure 2 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 4 The electronic signature distribution device 40 includes: an acquisition module 41, a determination module 42, a prediction module 43, and a distribution module 44.

[0048] An acquisition module 41 is used to acquire the number of historical electronic signatures within a preset time period; A determination module 42, configured to analyze the number of historical electronic signatures according to time factors and space factors that affect the usage of electronic signatures, and determine a change trend of the number of electronic signatures within a preset time period; A prediction module 43 is used to predict the demand for electronic signatures based on the change trend of the number of electronic signatures in a preset time period using a pre-trained prediction model to obtain the future number of electronic signatures in a future period; The allocation module 44 is used to adopt different allocation strategies for each CA manufacturer based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer.

[0049] Optionally, the determination module 42 is specifically configured to: Preprocessing the number of historical electronic signatures according to time factors and space factors that affect the usage of electronic signatures to obtain the number of historical electronic signatures after preprocessing, wherein the preprocessing includes standardization, outlier removal, and missing value filling; Based on a preset time series method, the pre-processed historical electronic signature quantity is analyzed to determine the change trend of the electronic signature quantity within a preset time period.

[0050] Optionally, the prediction module 43 is specifically used for: Extracting a characteristic value of the number of historical electronic signatures based on a change trend of the number of electronic signatures within a preset time period; The characteristic value is input into a pre-trained prediction model to predict the demand for electronic signatures, so as to obtain the future number of electronic signatures in a future period.

[0051] Optionally, the prediction module 43 is further used for: Acquire a training data set, wherein the training data set includes a plurality of historical electronic signature data, and the historical electronic signature data includes the number of historical electronic signatures within a preset time period; Inputting the training data set into the machine learning model to be trained, and iteratively training the machine learning model to be trained using the gradient descent method; When the loss function of the machine learning model to be trained tends to converge, the training is stopped to obtain the trained prediction model, wherein the loss function is a mean square error loss function.

[0052] Optionally, the allocation module 44 is specifically used for: Based on the tiered charging rules of each CA vendor, determine the weight of the number of electronic signatures allocated to each CA vendor; A weight is allocated based on the future number of electronic signatures and the number of electronic signatures of each CA manufacturer, and a corresponding number of electronic signatures is allocated to each CA manufacturer according to a preset time period.

[0053] Optionally, the allocation module 44 is further used for: Based on the tiered charging rules of each CA vendor, determine the electronic signature quantity thresholds and corresponding switching conditions for different tiers; Determine whether the number of electronic signatures of the first CA manufacturer meets the electronic signature number threshold; If the number of electronic signatures of the first CA manufacturer meets the electronic signature number threshold, it will automatically switch to the second CA manufacturer and update the corresponding electronic signature configuration information until the electronic signature configuration information of all CA manufacturers is completed.

[0054] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0055] Figure 5 Schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 5 As shown, the computer device of this embodiment includes: at least one processor ( Figure 5 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned electronic signature distribution method embodiments are implemented.

[0056] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 5 These are merely examples of computer devices and do not constitute limitations on the computer devices. The computer devices may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, they may also include a network interface, a display screen, and an input system.

[0057] In one embodiment, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor in a computer device, the computer device can perform the steps of any embodiment of a method for distributing an electronic signature disclosed in the present invention, which will not be repeated here. The computer-readable storage medium can be non-volatile or volatile.

[0058] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0059] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0060] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0061] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0062] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for distributing an electronic signature, characterized in that: include: Get the number of historical electronic signatures within a preset time period; Analyze the number of historical electronic signatures based on time factors and space factors that affect the use of electronic signatures, and determine the change trend of the number of electronic signatures within a preset time period; Based on the changing trend of the number of electronic signatures in a preset time period, the demand for electronic signatures is predicted using a pre-trained prediction model to obtain the future number of electronic signatures in a future period; Based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer, different allocation strategies are adopted for each CA manufacturer.

2. The method for distributing an electronic signature according to claim 1, characterized in that: According to the time factors and space factors that affect the usage of electronic signatures, the number of historical electronic signatures is analyzed to determine the change trend of the number of electronic signatures within a preset time period, including: Preprocessing the number of historical electronic signatures according to time factors and space factors that affect the usage of electronic signatures to obtain the number of historical electronic signatures after preprocessing, wherein the preprocessing includes standardization, outlier removal, and missing value filling; Based on a preset time series method, the pre-processed historical electronic signature quantity is analyzed to determine the change trend of the electronic signature quantity within a preset time period.

3. The method for distributing an electronic signature according to claim 1, wherein: Based on the future number of electronic signatures and the tiered charging rules of each CA vendor, different allocation strategies are adopted for each CA vendor, including: Based on the tiered charging rules of each CA vendor, determine the weight of the number of electronic signatures allocated to each CA vendor; A weight is allocated based on the future number of electronic signatures and the number of electronic signatures of each CA manufacturer, and a corresponding number of electronic signatures is allocated to each CA manufacturer according to a preset time period.

4. The method for distributing an electronic signature according to claim 3, characterized in that: Before determining the weights of the number of electronic signatures of each CA manufacturer, the following steps are included: Based on the tiered charging rules of each CA vendor, determine the electronic signature quantity thresholds and corresponding switching conditions for different tiers; Determine whether the number of electronic signatures of the first CA manufacturer meets the electronic signature number threshold; If the number of electronic signatures of the first CA manufacturer meets the electronic signature number threshold, it will automatically switch to the second CA manufacturer and update the corresponding electronic signature configuration information until the electronic signature configuration information of all CA manufacturers is completed.

5. The method for distributing an electronic signature according to claim 1, wherein: Based on the changing trend of the number of electronic signatures in a preset time period, the pre-trained prediction model is used to predict the demand for electronic signatures to obtain the future number of electronic signatures in a future period, including: Extracting a characteristic value of the number of historical electronic signatures based on a change trend of the number of electronic signatures within a preset time period; The characteristic value is input into a pre-trained prediction model to predict the demand for electronic signatures, so as to obtain the future number of electronic signatures in a future period.

6. The method for distributing an electronic signature according to claim 1, wherein: The prediction model is trained as follows: Acquire a training data set, wherein the training data set includes a plurality of historical electronic signature data, and the historical electronic signature data includes the number of historical electronic signatures within a preset time period; Inputting the training data set into the machine learning model to be trained, and iteratively training the machine learning model to be trained using the gradient descent method; When the loss function of the machine learning model to be trained tends to converge, the training is stopped to obtain the trained prediction model, wherein the loss function is a mean square error loss function.

7. An electronic signature distribution device, characterized in that: include: An acquisition module is used to obtain the number of historical electronic signatures within a preset time period; A determination module, configured to analyze the number of historical electronic signatures according to time factors and space factors that affect the usage of electronic signatures, and determine a change trend of the number of electronic signatures within a preset time period; A prediction module, used to predict the demand for electronic signatures based on the changing trend of the number of electronic signatures in a preset time period using a pre-trained prediction model, and obtain the future number of electronic signatures in a future period; The allocation module is used to adopt different allocation strategies for each CA manufacturer based on the future number of electronic signatures and the tiered charging rules of each CA manufacturer.

8. The electronic signature distribution device according to claim 1, characterized in that: The determination module also includes: Preprocessing the number of historical electronic signatures according to time factors and space factors that affect the usage of electronic signatures to obtain the number of historical electronic signatures after preprocessing, wherein the preprocessing includes standardization, outlier removal, and missing value filling; Based on a preset time series method, the pre-processed historical electronic signature quantity is analyzed to determine the change trend of the electronic signature quantity within a preset time period.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for distributing an electronic signature as claimed in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the electronic signature distribution method according to any one of claims 1 to 6 is implemented.

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