Electronic invoice data aggregation and management platform based on cloud service

Abstract

The invention belongs to the technical field of electronic invoice management, and particularly relates to an electronic invoice data aggregation and management platform based on cloud service. According to the method, the access strategy is adjusted by obtaining the system regulation and control access data, the platform can optimize the system access performance of the electronic invoice platform in a corresponding optimization mode, efficient operation under different load conditions is ensured, and the system access performance of the electronic invoice platform is improved by predicting the system access requirements of the user and regulating and controlling the access data. Adjustment can be automatically carried out when the load is too high, system response delay or collapse caused by excessive access is avoided, system resources can be adjusted in real time according to the current situation, stable operation of a platform is ensured, user requirements can be predicted in advance, a system access path can be optimized, the retrieval experience and the use experience of a user are remarkably improved, and the user experience is improved. And particularly, in a high-access-volume scene, smooth access speed and high response capability can be kept.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic invoice management, and in particular relates to an electronic invoice data aggregation and management platform based on cloud services. Background Art

[0002] With the rapid development of e-commerce and the Internet, enterprises have an increasing demand for invoice management. The traditional paper invoice management method has many disadvantages, such as high management cost, low query efficiency, easy loss and forgery, etc. In order to deal with these problems, electronic invoices came into being. As a product of the information age, electronic invoices not only have legal effect and tax validity, but also can significantly reduce corporate expenses, optimize management tasks, and improve efficiency and competitiveness.

[0003] Under the policy guidance of the State Administration of Taxation, the application of electronic invoices is becoming more and more widespread, and the demand for enterprises to build their own electronic invoice platforms has also increased. Electronic invoice platforms can realize the automated management of invoices, simplify the operation process, and improve work efficiency. However, as large enterprises concentrate most of their invoicing business on electronic invoices, although the application of electronic invoices has achieved certain results, the existing electronic invoice management platforms still have some shortcomings. For example, some platforms cannot achieve comprehensive aggregation and management of invoice data, making it difficult for enterprises to obtain complete invoice information; some platforms have bottlenecks in data processing and storage, and cannot cope with the challenges of high concurrency, strong interaction and massive data storage; in addition, some platforms still have deficiencies in invoice verification and anti-counterfeiting, which easily leads to problems such as invoice forgery and reuse. Summary of the invention

[0004] The purpose of the present invention is to provide an electronic invoice data aggregation and management platform based on cloud services, which can effectively solve the performance bottleneck problem that the electronic invoice system may encounter during high-concurrency access, and realize the functions of data aggregation, precise retrieval and system access optimization.

[0005] The technical solution adopted by the present invention is as follows: An electronic invoice data aggregation and management platform based on cloud services, including an electronic invoice retrieval module, a predictive retrieval module, a predictive access module, and an access optimization module; The electronic invoice search module is used to obtain the current search information of the user currently accessing the electronic invoice data, and to construct a first time period to obtain the first search data of the user accessing the electronic invoice data within the first time period; A predictive search module, used to obtain search focus information of users accessing electronic invoice data based on the first search data and the current search information, obtain historical search information of users accessing electronic invoice data in the current period, obtain search preference information based on the historical search information, and obtain predictive search information based on the search focus information and the search preference information; A predicted access module, used to obtain the system historical access and usage data of the electronic invoice data in the current period, and obtain the system predicted access and usage data corresponding to the predicted retrieval information; The access optimization module is used to obtain the system planned access usage data of the predicted retrieval information based on the system historical access usage data and the system predicted access usage data, and to determine whether the system planned access usage data meets the first preset condition. If not, the system regulated access usage data is obtained based on the system planned access usage data, and the corresponding optimization method is obtained based on the system regulated access usage data to optimize the system access of the platform.

[0006] In a preferred embodiment, the electronic invoice retrieval module includes a current retrieval unit, a time period end unit, a first duration unit, a time period start unit, a first time period unit and a first retrieval unit; A current search unit, used to obtain the current search information of the electronic invoice data currently accessed by the user; The time period end unit is used to obtain the time of the current search information and mark it as the time period end time; A first duration unit, used to obtain a first duration; A time period start unit, used for obtaining a time period start time according to the first time period and the time period end time; A first time period unit, used to obtain a first time period according to a time period end time and a time period start time; The first retrieval unit is used to obtain multiple retrieval information of users accessing electronic invoice data within a first period of time and summarize them into first retrieval data.

[0007] In a preferred embodiment, the prediction retrieval module includes a retrieval feature unit, a first matrix unit, a current matrix unit, a focus matrix unit, a focus threshold unit and a retrieval focus judgment unit; A retrieval feature unit, used to obtain corresponding multiple retrieval feature information according to the first retrieval data; A first matrix unit, used for obtaining a first search feature matrix according to a plurality of search feature information; The current matrix unit is used to obtain corresponding multiple search feature types according to the current search information and construct the current search feature matrix; A focus matrix unit, used for obtaining a search focus matrix according to the first search feature matrix and the current search feature matrix; A focus threshold unit, used to obtain a search focus threshold; A search focus judgment unit, used to judge whether each element in the search focus matrix exceeds a corresponding search focus threshold; If there is a search feature type corresponding to the element exceeding the corresponding preset emphasis threshold in each element of the search emphasis matrix, the search feature type corresponding to the element exceeding the corresponding preset emphasis threshold is marked as search emphasis information.

[0008] In a preferred embodiment, the prediction retrieval module further includes a history retrieval unit, a feature type unit, a retrieval times unit, a retrieval threshold unit and a preference judgment unit; A historical retrieval unit, used to obtain historical retrieval information of users accessing electronic invoice data during the current period; A feature type unit, used to obtain corresponding multiple search feature types according to historical search information; A search times unit, used to obtain the search times of each search feature type; A search threshold unit, used to obtain a search threshold number of times; A preference judgment unit, used to judge whether the number of searches for each search feature type exceeds a search threshold number; If the number of searches for each search feature type exceeds the search threshold, the search feature type that exceeds the search threshold is marked as search preference information; If the number of searches for each search feature type does not exceed the search threshold number, then sort the multiple search feature types in descending order according to the number of searches to obtain a search feature type sorting table, obtain the number of search preference feature types, and select the search feature types that meet the number of search preference feature types from the search feature type sorting table in descending order and mark them as search preference information.

[0009] In a preferred embodiment, the prediction and retrieval module further includes a focus category unit, a preference category unit and a prediction and retrieval unit; A focus category unit, used to obtain a corresponding search feature category according to the search focus information, and mark it as a search feature focus category; A preference category unit, used to obtain a corresponding search feature category according to the search preference information, and mark it as a search feature preference category; The predictive retrieval unit is used to summarize the retrieval feature emphasis categories and the retrieval feature preference categories, retain the repeated retrieval feature categories, and mark them as predictive retrieval information.

[0010] In a preferred embodiment, the predicted access module includes a historical use unit, a first use unit and a predicted use unit; A historical usage unit, used to obtain the usage rates of multiple system resources for accessing electronic invoice data in the current period, and to aggregate the multiple system resource usage rates into system historical access usage data; A first usage unit is used to obtain a plurality of historical system resource usage rates corresponding to each search feature type in the predicted search information from the system historical access usage data, and mark the historical system resource usage rate corresponding to each search feature type as a first system resource usage rate; The predicted usage unit is used to obtain system predicted access usage data corresponding to the predicted retrieval information according to a plurality of first system resource usage rates corresponding to each retrieval feature type.

[0011] In a preferred embodiment, the access optimization module further includes an evaluation end unit, an evaluation start unit, an evaluation period unit, an evaluation system unit, a predicted access use unit, and a system planned access use unit; An evaluation end unit, used to obtain the acquisition time of the system predicted access usage data and mark it as the evaluation end time; An evaluation start unit is used to obtain the evaluation duration, and obtain the evaluation start time according to the evaluation duration and the end time of the evaluation period; An evaluation period unit, used to obtain the evaluation period according to the evaluation end time and the evaluation start time; An evaluation system unit, used to obtain the historical system resource usage rate corresponding to each search feature type in the system historical access usage data and the system predicted access usage data during the evaluation period, and mark it as the evaluation system resource usage rate; A predicted access usage unit, used to obtain a corresponding system predicted access usage rate according to the system predicted access usage data; The system planned access usage unit is used to obtain the system planned access usage rate according to multiple evaluated system resource usage rates and system predicted access usage rates, and mark it as system planned access usage data.

[0012] In a preferred embodiment, the access optimization module further includes a usage threshold unit, a usage judgment unit and a usage regulation unit; A usage threshold unit is used to obtain a system planned access usage threshold rate; A usage judgment unit is used to judge whether the system planned access usage rate exceeds the system planned access usage threshold; If the system planned access usage rate exceeds the system planned access usage threshold, the system planned access usage rate is determined to be overloaded and marked as the system excess access usage rate; The control and utilization unit is used to obtain system control and utilization data according to the system excess access utilization rate.

[0013] In a preferred solution, the access optimization module further includes an optimization table unit, an interval comparison unit and an optimization method unit; An optimization table unit, used to obtain a platform optimization table, wherein the platform optimization table includes a plurality of system control access usage interval values ​​and a platform optimization mode corresponding to each system control access usage interval value; An interval comparison unit, used for obtaining a corresponding target system regulation access usage interval value according to the system regulation access usage rate; The optimization mode unit is used to obtain the corresponding platform optimization mode from the platform optimization table according to the target system control access usage interval value.

[0014] And, the electronic invoice data aggregation and management terminal based on cloud services, including: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the cloud service-based electronic invoice data aggregation and management platform.

[0015] The technical effects achieved by the present invention are: The present invention adjusts the access strategy by obtaining system regulation access data. The platform will adopt corresponding optimization methods to optimize the system access performance of the electronic invoice platform to ensure efficient operation under different load conditions. By predicting the user's system access needs and regulating the access data, it can automatically adjust when the load is too high to avoid system response delays or crashes due to excessive access. It can adjust system resources in real time according to the current situation to ensure the stable operation of the platform. It can predict user needs in advance and optimize the system access path. The user's retrieval experience and usage experience are significantly improved, especially in high-access scenarios, and can maintain smooth access speed and high responsiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a system module diagram provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0020] Secondly, the present invention is described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the schematic diagrams are only examples and should not limit the scope of protection of the present invention.

[0021] Please refer to the attached Figure 1 As shown, a cloud service-based electronic invoice data aggregation and management platform is provided, including an electronic invoice retrieval module, a predictive retrieval module, a predictive access module and an access optimization module; The electronic invoice search module is used to obtain the current search information of the user currently accessing the electronic invoice data, and to construct a first time period to obtain the first search data of the user accessing the electronic invoice data within the first time period; A predictive search module, used to obtain search focus information of users accessing electronic invoice data based on the first search data and the current search information, obtain historical search information of users accessing electronic invoice data in the current period, obtain search preference information based on the historical search information, and obtain predictive search information based on the search focus information and the search preference information; A predicted access module, used to obtain the system historical access and usage data of the electronic invoice data in the current period, and obtain the system predicted access and usage data corresponding to the predicted retrieval information; The access optimization module is used to obtain the system planned access usage data of the predicted retrieval information based on the system historical access usage data and the system predicted access usage data, and to determine whether the system planned access usage data meets the first preset condition. If not, the system regulated access usage data is obtained based on the system planned access usage data, and the corresponding optimization method is obtained based on the system regulated access usage data to optimize the system access of the platform.

[0022] As mentioned above, the electronic invoice retrieval module first obtains the retrieval information related to the electronic invoice data that the user is currently accessing, and uses this to construct a specific time period (the first time period). In the first time period, all retrieval behaviors of the user accessing the electronic invoice data are collected and the corresponding first retrieval data is generated. The predictive retrieval module obtains the focus of the user's access to the electronic invoice data in different time periods based on the user's first retrieval data and current retrieval information, that is, the retrieval focus information. At the same time, by calling the retrieval data of the user's historical time period, the retrieval preference information is extracted, that is, the content that the user tends to retrieve in similar situations or time periods. Combining the user's current retrieval focus information with the historical preference information, the predictive retrieval information is generated, that is, the data that the user may access or retrieve next is predicted. The predictive access module analyzes the usage of the electronic invoice system in similar historical time periods based on the system's historical access data in the current time period, and determines the platform's operating performance under different loads. Then, the corresponding system predicted access data is generated based on the predicted retrieval information, predicting that in the current time period, The access optimization module compares the system predicted access data with the system historical access data to generate system planned access data and predicts the resource usage of the platform in the current time period. If the system planned access data exceeds the first preset load or usage conditions (such as excessive traffic, insufficient resource allocation, etc.), the automatic control mechanism will be started to adjust the access strategy by obtaining the system controlled access data. The platform will adopt the corresponding optimization method to optimize the system access performance of the electronic invoice platform to ensure efficient operation under different load conditions. By predicting the user's system access needs and regulating the access data, it can automatically adjust when the load is too high to avoid system response delays or crashes due to excessive access. It can adjust system resources in real time according to the current situation to ensure the stable operation of the platform. It can predict user needs in advance and optimize the system access path. The user's retrieval experience and usage experience are significantly improved, especially in high-access scenarios, and can maintain smooth access speed and high responsiveness.

[0023] In a specific embodiment, the electronic invoice retrieval module includes a current retrieval unit, a time period end unit, a first duration unit, a time period start unit, a first time period unit and a first retrieval unit; A current search unit, used to obtain the current search information of the electronic invoice data currently accessed by the user; The time period end unit is used to obtain the time of the current search information and mark it as the time period end time; A first duration unit, used to obtain a first duration; A time period start unit, used for obtaining a time period start time according to the first time period and the time period end time; A first time period unit, used to obtain a first time period according to a time period end time and a time period start time; The first retrieval unit is used to obtain multiple retrieval information of users accessing electronic invoice data within a first period of time and summarize them into first retrieval data.

[0024] As mentioned above, the current search unit is responsible for obtaining the current search information of the user's current access to the electronic invoice data in real time, that is, the specific data search request that the user is performing. By recording each search behavior of the user, the user's attention to a certain type of data is tracked. When a user's search operation ends, the time when the search behavior ends is recorded through the time period end unit, and it is marked as the time period end time. This timestamp is used to determine the duration of the user's search operation. The first time period unit is used to define a fixed time period length, called the first time period, which can be dynamically set according to business needs (such as 5 minutes or 1 hour) and is used to divide the time range of user behavior analysis. According to the time period end time and the first time period, the corresponding time period start time is calculated through the time period start unit. This time marks the starting point of a series of search behaviors of the user within a certain period of time, that is, the start time of the first time period is equal to the end time of the time period minus the first duration. The first time period unit is responsible for using the end time of the time period and the start time of the time period to obtain a specific time interval, called the first time period. During this time period, all search behaviors of the user are recorded. During the first time period, the search information of multiple electronic invoice data conducted by the user is obtained through the first search unit, and the search information is summarized to generate the first search data. By dividing a specific time period and recording the search information of the user within the time period, the search behavior of the user in different time periods can be fully understood, which helps to refine the analysis of the user's interests and search focus information within a specific time period, and provide a basis for platform optimization.

[0025] In a specific embodiment, the prediction retrieval module includes a retrieval feature unit, a first matrix unit, a current matrix unit, a focus matrix unit, a focus threshold unit and a retrieval focus judgment unit; A retrieval feature unit, used to obtain corresponding multiple retrieval feature information according to the first retrieval data, wherein the retrieval feature information includes multiple retrieval feature types, and the retrieval feature types include invoice issuance date, invoice tax rate, and invoice title; A first matrix unit, used for obtaining a first search feature matrix according to the plurality of search feature information, wherein the first search feature matrix is ​​a row matrix, and each element in the first search feature matrix is ​​a search vector value of each search feature type; A current matrix unit is used to obtain a plurality of corresponding search feature types according to the current search information, and construct a current search feature matrix, wherein the current search feature matrix is ​​a matrix having one row with the first search feature matrix, and each element in the current search feature matrix is ​​a search vector value for each search feature type; A focus matrix unit, used for obtaining a search focus matrix according to the first search feature matrix and the current search feature matrix; A focus threshold unit, used to obtain a search focus threshold; A search focus judgment unit, used to judge whether each element in the search focus matrix exceeds a corresponding search focus threshold; If there is a search feature type corresponding to the element exceeding the corresponding preset emphasis threshold in each element of the search emphasis matrix, the search feature type corresponding to the element exceeding the corresponding preset emphasis threshold is marked as search emphasis information.

[0026] As mentioned above, the retrieval feature unit is responsible for extracting multiple retrieval feature information related to the retrieval behavior from the first retrieval data. This information may include specific attributes of the invoice, such as the invoice issuance date, invoice tax rate, customer name and invoice header, etc. Each retrieval feature type reflects the specific attributes of the invoice data that the user pays attention to during the time period. The first matrix unit will generate a retrieval vector value for each retrieval feature type extracted from the retrieval feature information, that is, the user's retrieval intensity on a certain feature. The retrieval vector values ​​of multiple retrieval feature types will form a first retrieval feature matrix, which is a row matrix, indicating the user's attention to different invoice features during a specific time period. The current matrix unit extracts the corresponding retrieval feature information according to the user's current retrieval behavior, and constructs a current retrieval feature matrix that is consistent with the format of the first retrieval feature matrix. The focus matrix unit calculates the first retrieval feature matrix and the current retrieval feature matrix. The calculation formula of the retrieval focus matrix is ​​C=Y*D, where C represents the retrieval focus matrix , Y represents the first search feature matrix, D represents the current search feature matrix, and a search focus matrix is ​​generated to reflect the changes in the user's current search behavior compared with the historical behavior. Each matrix element represents the degree of change in the user's search focus on the feature. The focus threshold unit is responsible for setting the search focus threshold of each search feature. The search focus judgment unit determines whether the user's behavior on certain search features exceeds the preset focus threshold by comparing each element in the search focus matrix with the search focus threshold. If the focus matrix element value corresponding to a certain search feature exceeds the focus threshold of the feature, the search feature is marked as search focus information, indicating that the feature is focused on by the user in the current search. By analyzing the difference between the user's current search behavior and the historical search behavior, the invoice features that the user is currently more concerned about can be dynamically identified. For example, the user may pay special attention to a certain invoice tax rate or invoice data within a specific date range in the current search, and automatically mark these focus information to provide a basis for subsequent search optimization.

[0027] In a specific embodiment, the prediction retrieval module further includes a history retrieval unit, a feature type unit, a retrieval times unit, a retrieval threshold unit, and a preference judgment unit; A historical retrieval unit, used to obtain historical retrieval information of users accessing electronic invoice data during the current period; A feature type unit, used to obtain corresponding multiple search feature types according to historical search information; A search times unit, used to obtain the search times of each search feature type; A search threshold unit, used to obtain a search threshold number of times; A preference judgment unit, used to judge whether the number of searches for each search feature type exceeds a search threshold number; If the number of searches for each search feature type exceeds the search threshold, the search feature type that exceeds the search threshold is marked as search preference information; If the number of searches for each search feature type does not exceed the search threshold number, then sort the multiple search feature types in descending order according to the number of searches to obtain a search feature type sorting table, obtain the number of search preference feature types, and select the search feature types that meet the number of search preference feature types from the search feature type sorting table in descending order and mark them as search preference information.

[0028] As mentioned above, the historical search unit is responsible for obtaining the historical search information of the user in the current time period. By analyzing the user's past search behavior in the time period, the user's electronic invoice data access records at different times can be obtained. The feature type unit extracts the corresponding multiple search feature types based on the historical search information, such as the invoice issuance date, invoice amount, invoice type, etc. Each search feature type represents a certain invoice attribute that the user is concerned about. The search frequency unit is responsible for counting the frequency of occurrence of each search feature type in the historical search information, that is, the number of times the user visits certain features. By calculating the user's search frequency for each feature type in the current time period, the user's attention to different features can be quantified. The search threshold unit is responsible for setting a search threshold number, that is, when the search number of a feature type exceeds the threshold, it will be considered that the user has a clear preference for the feature. This threshold can be dynamically adjusted according to business needs to ensure the accuracy of preference identification. The preference judgment unit compares the search number of each search feature type with the search threshold number to determine which feature types have a search number exceeding the threshold. For feature types that exceed the threshold, they are marked as retrieval preference information, and it is considered that the user has a significant preference for this feature. If the number of retrievals of all feature types does not exceed the threshold, they are sorted according to the number of retrievals of each feature type to generate a retrieval feature type sorting table. Then, according to the set number of retrieval preference feature types, the feature types with the highest number of retrievals are selected from the sorting table in turn and marked as retrieval preference information. Through statistics and analysis of user retrieval times, the user's significant preference in electronic invoice data retrieval can be accurately identified. For example, if a user frequently searches for a specific invoice issuance date or invoice amount, these preference information can be identified in advance to provide support for subsequent personalized recommendations and service optimization. Not only can significant preferences be judged by setting a retrieval number threshold, but also feature types with high user attention can be automatically screened out according to the sorting algorithm when no feature type exceeds the threshold. This dual judgment mechanism can flexibly adapt to the retrieval behavior of different users, whether it is a user who frequently searches for a certain type of feature or a user who disperses and searches for multiple features, their preferences can be accurately captured.

[0029] In a specific embodiment, the prediction and retrieval module further includes a focus category unit, a preference category unit and a prediction and retrieval unit; A focus category unit, used to obtain a corresponding search feature category according to the search focus information, and mark it as a search feature focus category; A preference category unit, used to obtain a corresponding search feature category according to the search preference information, and mark it as a search feature preference category; The predictive retrieval unit is used to summarize the retrieval feature emphasis categories and the retrieval feature preference categories, retain the repeated retrieval feature categories, and mark them as predictive retrieval information.

[0030] As mentioned above, the focus category unit is responsible for extracting the types of search features that the user is currently focusing on from the search focus information. These feature categories may be specific invoice attributes (such as the date of the invoice, the tax rate, etc.) that the user is currently focusing on, as analyzed by the focus matrix. The extracted feature categories will be marked as search feature focus categories, indicating that they are the features that the user focuses on in the current search behavior. The preference category unit obtains the search feature categories that the user frequently searches for in the historical search behavior based on the search preference information. These preference feature categories represent the significant focus points shown by the user over a long period of time. These feature categories are marked as search feature preference categories. The prediction search unit The feature focus types and the search feature preference types are summarized, and the repeated search feature types that appear in both are retained. These repeated feature types represent the features that the user currently focuses on, and are also the preference features in historical searches, so they have higher predictive value. Finally, these repeated search feature types are marked as predicted search information, indicating specific invoice features that the user may continue to pay attention to in the future. This can more accurately predict the user's future search needs. Those feature types that appear in both the current focus information and the historical preference information represent the user's continued focus in the near and long term, and more targeted personalized services can be provided based on this information.

[0031] In a specific embodiment, the predicted access module includes a historical use unit, a first use unit and a predicted use unit; A historical usage unit, used to obtain the usage rates of multiple system resources for accessing electronic invoice data in the current period, and to aggregate the multiple system resource usage rates into system historical access usage data; A first usage unit is used to obtain a plurality of historical system resource usage rates corresponding to each search feature type in the predicted search information from the system historical access usage data, and mark the historical system resource usage rate corresponding to each search feature type as a first system resource usage rate; The predicted usage unit is used to obtain system predicted access usage data corresponding to the predicted retrieval information according to a plurality of first system resource usage rates corresponding to each retrieval feature type.

[0032] As mentioned above, the historical usage unit is responsible for collecting system resource usage information related to electronic invoice data in the current period, including the usage of multiple resources such as CPU, memory, network bandwidth, etc. The collected multiple system resource usage data will be summarized to form a complete system historical access usage data. The first usage unit extracts the historical system resource usage related to each search feature type in the predicted retrieval information from the summarized system historical access usage data. For example, queries for certain specific invoice feature types may rely more on network bandwidth or storage resources. By screening out multiple historical resource usages corresponding to these search feature types and marking them as first system resource usages, the predicted usage unit predicts future system resource usage based on the multiple first system resource usages corresponding to each search feature type, and generates system predicted access usage data. The calculation formula for the system predicted access usage data is: , where F represents the system predicted access usage rate, that is, the system predicted access usage data, k represents the number of the search feature type in the predicted search information, k=1,2,3…m, i represents the number of the first system resource usage rates of the search feature type in the predicted search information, where i=1,2,3…n, It is represented as the first system resource usage rate of the ith retrieval feature type in the predicted retrieval information, It is expressed as the system resource usage rate of the kth search feature type in the predicted retrieval information. Based on the trend of historical data and combined with the current retrieval needs, it reasonably infers the future load and demand on different resources, thereby providing a reference for optimization and resource allocation. It can extract the historical system resource usage corresponding to each search feature type and make accurate predictions based on the predicted retrieval information.

[0033] In a specific embodiment, the access optimization module further includes an evaluation end unit, an evaluation start unit, an evaluation period unit, an evaluation system unit, a predicted access use unit, and a system planned access use unit; An evaluation end unit, used to obtain the acquisition time of the system predicted access usage data and mark it as the evaluation end time; An evaluation start unit is used to obtain the evaluation duration, and obtain the evaluation start time according to the evaluation duration and the end time of the evaluation period; An evaluation period unit, used to obtain the evaluation period according to the evaluation end time and the evaluation start time; An evaluation system unit, used to obtain the historical system resource usage rate corresponding to each search feature type in the system historical access usage data and the system predicted access usage data during the evaluation period, and mark it as the evaluation system resource usage rate; A predicted access usage unit, used to obtain a corresponding system predicted access usage rate according to the system predicted access usage data; The system planned access usage unit is used to obtain the system planned access usage rate according to multiple evaluated system resource usage rates and system predicted access usage rates, and mark it as system planned access usage data.

[0034] As mentioned above, the evaluation end unit is responsible for obtaining the acquisition time of the system predicted access usage data and marking it as the evaluation end time. This time point marks the end of the evaluation process. The evaluation start unit is used to determine the time range of the evaluation process. It obtains the evaluation duration (i.e., the length of time required for the evaluation) and combines it with the evaluation end time to calculate the evaluation start time. According to the evaluation start time and the evaluation end time, the evaluation period unit is responsible for obtaining the evaluation period. The evaluation system unit extracts the historical system resource usage rate related to each retrieval feature type in the system predicted access usage data from the system historical access usage data to generate the evaluation system resource usage rate. In order to understand the actual usage of system resources in the past evaluation period, the predicted access usage unit uses the system predicted access usage data to calculate the system predicted access usage rate. The calculation formula of the system predicted access usage data is: , where G represents the system planned access utilization rate, F represents the system predicted access utilization rate, k represents the number of the search feature type in the predicted search information, k=1,2,3…m, i represents the number of the first system resource utilization rates of the search feature type in the predicted search information, where i=1,2,3…n, represents the first system resource usage rate of the ith retrieval feature type in the predicted retrieval information, and g represents the serial numbers of multiple evaluation system resource usage rates, g=1,2,3…r, It is represented as the g-th evaluation system resource utilization rate. The system planned access utilization unit integrates multiple evaluation system resource utilization rates and system predicted access utilization rates to generate the system planned access utilization rate. This data will guide the platform in future resource allocation and usage decisions, ensure efficient use of resources, and generate accurate evaluation system resource utilization rates. It provides data support for resource planning, can effectively avoid waste or shortage of resources, and ensure stable operation of the platform during peak periods.

[0035] In a specific embodiment, the access optimization module further includes a usage threshold unit, a usage judgment unit, and a usage regulation unit; A usage threshold unit is used to obtain a system planned access usage threshold rate; A usage judgment unit is used to judge whether the system planned access usage rate exceeds the system planned access threshold value rate; If the system planned access usage rate exceeds the system planned access threshold value rate, the system planned access usage rate is determined to be overloaded and marked as the system excess access usage rate; The control and utilization unit is used to obtain system control and utilization data according to the system excess access utilization rate.

[0036] As mentioned above, the threshold unit obtains the system planned access usage threshold rate, which refers to the maximum access usage rate that the system can withstand under normal planned operation. This threshold is used to judge the current load of the system to ensure that the system operates within a controllable range. The usage judgment unit is responsible for real-time monitoring of the system planned access usage rate and comparing it with the pre-set system planned access usage threshold rate. If the system planned access usage rate exceeds the system planned access usage threshold rate, the unit will determine that the system is in an "overload" state and mark this state as the system excess access usage rate. Once it is determined that there is an excess access usage rate, the control usage unit will intervene to obtain the system control access usage data based on the system excess access usage rate. The calculation formula for the system control access usage data is: , where T represents the system control access utilization rate, that is, the system control access utilization data, G represents the system planned access utilization rate, Z represents the system planned access utilization threshold rate, and a represents the system access utilization compensation coefficient. This data will be used to formulate control measures to balance the load of the platform and ensure that the platform can still operate stably under high load conditions.

[0037] In a specific embodiment, the access optimization module further includes an optimization table unit, an interval comparison unit, and an optimization method unit; An optimization table unit, used to obtain a platform optimization table, wherein the platform optimization table includes a plurality of system control access usage interval values ​​and a platform optimization mode corresponding to each system control access usage interval value; An interval comparison unit, used for obtaining a corresponding target system regulation access usage interval value according to the system regulation access usage rate; The optimization mode unit is used to obtain the corresponding platform optimization mode from the platform optimization table according to the target system control access usage interval value.

[0038] As mentioned above, the optimization table unit is responsible for generating and maintaining the platform optimization table, which contains multiple system regulation access usage interval values ​​and the platform optimization method corresponding to each interval value. These data provide a reference basis for subsequent access optimization, ensuring that under different access usage conditions, regulation can be performed according to the corresponding optimization method. The interval comparison unit determines the corresponding target system regulation access usage interval value based on the current system regulation access usage rate, and judges which optimization interval the current platform load belongs to by comparing the current usage rate with the interval value in the platform optimization table. Once the target system regulation access usage interval value is determined, the optimization method unit will obtain the corresponding platform optimization method from the platform optimization table. The optimization method may include traffic restriction, load balancing, resource allocation adjustment and other measures to cope with the current usage status and ensure that the platform can run efficiently. This dynamic adjustment capability enables the platform to maintain efficient performance under various load conditions.

[0039] And, the electronic invoice data aggregation and management terminal based on cloud services, including: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the cloud service-based electronic invoice data aggregation and management platform.

[0040] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. The electronic invoice data aggregation and management platform based on cloud services is characterized by: It includes an electronic invoice retrieval module, a predictive retrieval module, a predictive access module and an access optimization module; The electronic invoice search module is used to obtain the current search information of the user currently accessing the electronic invoice data, and to construct a first time period to obtain the first search data of the user accessing the electronic invoice data within the first time period; A predictive search module, used to obtain search focus information of users accessing electronic invoice data based on the first search data and the current search information, obtain historical search information of users accessing electronic invoice data in the current period, obtain search preference information based on the historical search information, and obtain predictive search information based on the search focus information and the search preference information; A predicted access module, used to obtain the system historical access and usage data of the electronic invoice data in the current period, and obtain the system predicted access and usage data corresponding to the predicted retrieval information; The access optimization module is used to obtain the system planned access usage data of the predicted retrieval information based on the system historical access usage data and the system predicted access usage data, and to determine whether the system planned access usage data meets the first preset condition. If not, the system regulated access usage data is obtained based on the system planned access usage data, and the corresponding optimization method is obtained based on the system regulated access usage data to optimize the system access of the platform.

2. The cloud-based electronic invoice data aggregation and management platform according to claim 1, characterized in that: The electronic invoice retrieval module includes a current retrieval unit, a time period end unit, a first duration unit, a time period start unit, a first time period unit and a first retrieval unit; A current search unit, used to obtain the current search information of the electronic invoice data currently accessed by the user; The time period end unit is used to obtain the time of the current search information and mark it as the time period end time; A first duration unit, used to obtain a first duration; A time period start unit, used for obtaining a time period start time according to the first time period and the time period end time; A first time period unit, used to obtain a first time period according to a time period end time and a time period start time; The first retrieval unit is used to obtain multiple retrieval information of users accessing electronic invoice data within a first period of time and summarize them into first retrieval data.

3. The cloud-based electronic invoice data aggregation and management platform according to claim 1, characterized in that: The prediction retrieval module includes a retrieval feature unit, a first matrix unit, a current matrix unit, a focus matrix unit, a focus threshold unit and a retrieval focus judgment unit; A retrieval feature unit, used to obtain corresponding multiple retrieval feature information according to the first retrieval data; A first matrix unit, used for obtaining a first search feature matrix according to a plurality of search feature information; The current matrix unit is used to obtain corresponding multiple search feature types according to the current search information and construct the current search feature matrix; A focus matrix unit, used for obtaining a search focus matrix according to the first search feature matrix and the current search feature matrix; A focus threshold unit, used to obtain a search focus threshold; A search focus judgment unit, used to judge whether each element in the search focus matrix exceeds a corresponding search focus threshold; If there is a search feature type corresponding to the element exceeding the corresponding preset emphasis threshold in each element of the search emphasis matrix, the search feature type corresponding to the element exceeding the corresponding preset emphasis threshold is marked as search emphasis information.

4. The cloud-based electronic invoice data aggregation and management platform according to claim 3 is characterized in that: The prediction retrieval module also includes a history retrieval unit, a feature type unit, a retrieval number unit, a retrieval threshold unit and a preference judgment unit; A historical retrieval unit, used to obtain historical retrieval information of users accessing electronic invoice data during the current period; A feature type unit, used to obtain corresponding multiple search feature types according to historical search information; A search times unit, used to obtain the search times of each search feature type; A search threshold unit, used to obtain a search threshold number of times; A preference judgment unit, used to judge whether the number of searches for each search feature type exceeds a search threshold number; If the number of searches for each search feature type exceeds the search threshold, the search feature type that exceeds the search threshold is marked as search preference information; If the number of searches for each search feature type does not exceed the search threshold number, then sort the multiple search feature types in descending order according to the number of searches to obtain a search feature type sorting table, obtain the number of search preference feature types, and select the search feature types that meet the number of search preference feature types from the search feature type sorting table in descending order and mark them as search preference information.

5. The cloud-based electronic invoice data aggregation and management platform according to claim 4, characterized in that: The prediction and retrieval module also includes a focus category unit, a preference category unit and a prediction and retrieval unit; A focus category unit, used to obtain a corresponding search feature category according to the search focus information, and mark it as a search feature focus category; A preference category unit, used to obtain a corresponding search feature category according to the search preference information, and mark it as a search feature preference category; The predictive retrieval unit is used to summarize the retrieval feature emphasis categories and the retrieval feature preference categories, retain the repeated retrieval feature categories, and mark them as predictive retrieval information.

6. The cloud-based electronic invoice data aggregation and management platform according to claim 1, characterized in that: The predicted access module includes a historical use unit, a first use unit and a predicted use unit; A historical usage unit, used to obtain the usage rates of multiple system resources for accessing electronic invoice data in the current period, and to aggregate the multiple system resource usage rates into system historical access usage data; A first usage unit is used to obtain a plurality of historical system resource usage rates corresponding to each search feature type in the predicted search information from the system historical access usage data, and mark the historical system resource usage rate corresponding to each search feature type as a first system resource usage rate; The predicted usage unit is used to obtain system predicted access usage data corresponding to the predicted retrieval information according to a plurality of first system resource usage rates corresponding to each retrieval feature type.

7. The cloud-based electronic invoice data aggregation and management platform according to claim 6, characterized in that: The access optimization module also includes an evaluation end unit, an evaluation start unit, an evaluation period unit, an evaluation system unit, a predicted access use unit, and a system planned access use unit; An evaluation end unit, used to obtain the acquisition time of the system predicted access usage data and mark it as the evaluation end time; An evaluation start unit is used to obtain the evaluation duration, and obtain the evaluation start time according to the evaluation duration and the end time of the evaluation period; An evaluation period unit, used to obtain the evaluation period according to the evaluation end time and the evaluation start time; An evaluation system unit, used to obtain the historical system resource usage rate corresponding to each search feature type in the system historical access usage data and the system predicted access usage data during the evaluation period, and mark it as the evaluation system resource usage rate; A predicted access usage unit, used to obtain a corresponding system predicted access usage rate according to the system predicted access usage data; The system planned access usage unit is used to obtain the system planned access usage rate according to multiple evaluated system resource usage rates and system predicted access usage rates, and mark it as system planned access usage data.

8. The cloud-based electronic invoice data aggregation and management platform according to claim 7, characterized in that: The access optimization module also includes a usage threshold unit, a usage judgment unit and a usage regulation unit; A usage threshold unit is used to obtain a system planned access usage threshold rate; A usage judgment unit is used to judge whether the system planned access usage rate exceeds the system planned access usage threshold; If the system planned access usage rate exceeds the system planned access usage threshold, the system planned access usage rate is determined to be overloaded and marked as the system excess access usage rate; The control and utilization unit is used to obtain system control and utilization data according to the system excess access utilization rate.

9. The cloud-based electronic invoice data aggregation and management platform according to claim 8, characterized in that: The access optimization module also includes an optimization table unit, an interval comparison unit and an optimization method unit; An optimization table unit, used to obtain a platform optimization table, wherein the platform optimization table includes a plurality of system control access usage interval values ​​and a platform optimization mode corresponding to each system control access usage interval value; An interval comparison unit, used for obtaining a corresponding target system regulation access usage interval value according to the system regulation access usage rate; The optimization mode unit is used to obtain the corresponding platform optimization mode from the platform optimization table according to the target system control access usage interval value.

10. The electronic invoice data aggregation and management terminal based on cloud services is characterized by: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the cloud service-based electronic invoice data aggregation and management platform as described in any one of claims 1 to 8.

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