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CN119225973A - Electronic invoice data aggregation and management platform based on cloud services - Google Patents

Electronic invoice data aggregation and management platform based on cloud services Download PDF

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Publication number
CN119225973A
CN119225973A CN202411423753.3A CN202411423753A CN119225973A CN 119225973 A CN119225973 A CN 119225973A CN 202411423753 A CN202411423753 A CN 202411423753A CN 119225973 A CN119225973 A CN 119225973A
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search
access
retrieval
information
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张学锋
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Shanghai Sifang Qidian Information Technology Co ltd
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Shanghai Sifang Qidian Information Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/04—Billing or invoicing

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  • General Business, Economics & Management (AREA)
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  • Marketing (AREA)
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  • General Engineering & Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

本发明属于电子发票管理技术领域，具体涉及基于云服务的电子发票数据聚合与管理平台。该发明，通过获取系统调控访问数据调整访问策略，平台会采用对应的优化方式来优化电子发票平台的系统访问性能，保证在不同负载条件下的高效运作，通过预测用户的系统访问需求并进行访问数据的调控，能够在负载过高时自动进行调整，避免因访问过量导致系统响应延迟或崩溃，能够根据当前情况实时调整系统资源，确保平台的稳定运行，能够提前预测用户需求并优化系统访问路径，用户的检索体验和使用体验得到显著提升，尤其是在高访问量场景下，能够保持流畅的访问速度和高响应能力。

The present invention belongs to the field of electronic invoice management technology, and specifically relates to an electronic invoice data aggregation and management platform based on cloud services. The invention, by obtaining system regulation access data to adjust access strategies, the platform will adopt corresponding optimization methods to optimize the system access performance of the electronic invoice platform, ensure efficient operation under different load conditions, and automatically adjust when the load is too high by predicting the user's system access needs and regulating access data 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, predict user needs in advance and optimize system access paths, and significantly improve the user's retrieval experience and usage experience, especially in high-access scenarios, it can maintain smooth access speed and high responsiveness.

Description

Cloud service-based electronic invoice data aggregation and management platform
Technical Field
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.
Background
With the rapid growth of electronic commerce and the internet, enterprises are increasingly demanding invoice management. The traditional paper invoice management mode has a plurality of defects such as high management cost, low inquiry efficiency, easy loss and counterfeiting and the like. To address these issues, electronic invoices have been developed. As a product of the informatization age, the electronic invoice not only has legal effectiveness and tax effectiveness, but also can obviously reduce the expense of enterprises, optimize management tasks and improve the efficiency and competitiveness.
Under the guidance of the policies of the national tax administration, the application of electronic invoices is more and more widespread, and the demands of enterprises for self-building electronic invoice platforms are also increased. The electronic invoice platform can realize automatic management of invoices, simplify operation flow and improve work efficiency. However, as large enterprises focus most of billing businesses on electronic invoices, although the application of electronic invoices has achieved a certain result, the existing electronic invoice management platform still has some drawbacks. For example, part of platforms cannot realize comprehensive aggregation and management of invoice data, so that enterprises are difficult to acquire complete invoice information, part of platforms have bottlenecks in data processing and storage, cannot cope with challenges of high concurrency, strong interaction and mass data storage, and part of platforms still have defects in invoice verification and anti-counterfeiting, so that problems of invoice counterfeiting, repeated use and the like are easily caused.
Disclosure of Invention
The invention aims to provide an electronic invoice data aggregation and management platform based on cloud service, which can effectively solve the performance bottleneck problem possibly encountered by an electronic invoice system during high concurrency access and realize the functions of data aggregation, accurate retrieval and system access optimization.
The technical scheme adopted by the invention is as follows:
the cloud service-based electronic invoice data aggregation and management platform comprises an electronic invoice retrieval module, a prediction access module and an access optimization module;
The electronic invoice retrieval module is used for acquiring current retrieval information of the electronic invoice data accessed by the user currently, constructing a first period of time and acquiring first retrieval data of the electronic invoice data accessed by the user in the first period of time;
The prediction retrieval module is used for acquiring retrieval emphasis information of the user accessing the electronic invoice data according to the first retrieval data and the current retrieval information, acquiring historical retrieval information of the user accessing the electronic invoice data in the current period, acquiring retrieval preference information according to the historical retrieval information, and acquiring prediction retrieval information according to the retrieval emphasis information and the retrieval preference information;
The prediction access module is used for acquiring system historical access use data of the electronic invoice data in the current period and acquiring system prediction access use data corresponding to the prediction retrieval information;
The access optimization module is used for acquiring system planning access use data of the prediction search information according to the system historical access use data and the system prediction access use data, judging whether the system planning access use data meets a first preset condition, acquiring system regulation access use data according to the system planning access use data if the system planning access use data does not meet the first preset condition, and acquiring corresponding optimization mode optimization system access of the platform according to the system regulation access use data.
In a preferred scheme, the electronic invoice search module comprises a current search unit, a time period ending unit, a first time period unit, a time period starting unit, a first time period unit and a first search unit;
the current retrieval unit is used for acquiring current retrieval information of the electronic invoice data accessed by the user currently;
the time period ending unit is used for acquiring the time of the current retrieval information and marking the time period ending time;
The first time length unit is used for acquiring a first time length;
a period starting unit for acquiring a period starting time according to the first time length and the period ending time;
The first time period unit is used for acquiring a first time period according to the time period ending time and the time period starting time;
the first retrieval unit is used for acquiring a plurality of pieces of retrieval information of the electronic invoice data accessed by the user in the first period of time and summarizing the retrieval information into first retrieval data.
In a preferred scheme, the prediction retrieval module comprises a retrieval feature unit, a first matrix unit, a current matrix unit, a emphasis threshold unit and a retrieval emphasis judging unit;
the retrieval feature unit is used for acquiring a plurality of corresponding retrieval feature information according to the first retrieval data;
The first matrix unit is used for acquiring a first retrieval feature matrix according to the plurality of retrieval feature information;
The current matrix unit is used for acquiring a plurality of corresponding retrieval feature types according to the current retrieval information and constructing a current retrieval feature matrix;
The emphasis matrix unit is used for acquiring a retrieval emphasis matrix according to the first retrieval feature matrix and the current retrieval feature matrix;
The emphasis threshold unit is used for acquiring a retrieval emphasis threshold;
The search emphasis judging unit is used for judging whether each element in the search emphasis matrix exceeds a corresponding search emphasis threshold value;
If each element of the search emphasis matrix exceeds the corresponding preset emphasis threshold, marking the search feature types corresponding to the elements exceeding the corresponding preset emphasis threshold as search emphasis information.
In a preferred scheme, the prediction searching module further comprises a history searching unit, a characteristic type unit, a searching frequency unit, a searching threshold unit and a preference judging unit;
the history retrieval unit is used for acquiring history retrieval information of the electronic invoice data accessed by the user in the current period;
The characteristic type unit is used for acquiring a plurality of corresponding retrieval characteristic types according to the history retrieval information;
The search frequency unit is used for acquiring the search frequency of each search feature type;
The retrieval threshold unit is used for acquiring retrieval threshold times;
a preference judging unit for judging whether the search times of each search feature category exceeds the search threshold times;
If the search times of each search feature type exceeds the search threshold times, marking the search feature type exceeding the search threshold times as search preference information;
If the search times of each search feature type do not exceed the search threshold times, a plurality of search feature types are ranked according to the search times in a sequence from high to low to obtain a search feature type ranking table, the number of search preference feature types is obtained, and the search feature types which accord with the number of search preference feature types are sequentially selected from the search feature type ranking table in the sequence from high to low and marked as search preference information.
In a preferred scheme, the prediction retrieval module further comprises a emphasis category unit, a preference category unit and a prediction retrieval unit;
The emphasis type unit is used for acquiring corresponding retrieval feature types according to the retrieval emphasis information and marking the retrieval feature types as retrieval feature emphasis types;
a preference category unit, configured to obtain a corresponding search feature category according to the search preference information, and mark the search feature category as a search feature preference category;
and the prediction searching unit is used for summarizing the search feature emphasis types and the search feature preference types, reserving repeated search feature types and marking the repeated search feature types as prediction searching information.
In a preferred scheme, the prediction access module comprises a history use unit, a first use unit and a prediction use unit;
The historical use unit is used for acquiring a plurality of system resource utilization rates of the electronic invoice data accessed in the current period and summarizing the plurality of system resource utilization rates into system historical access use data;
a first usage unit, configured to obtain, from the system history access usage data, a plurality of historical system resource usage rates corresponding to each search feature type in the predicted search information, and mark the historical system resource usage rate corresponding to each search feature type as a first system resource usage rate;
and the prediction using unit is used for acquiring system prediction access using data corresponding to the prediction retrieval information according to a plurality of first system resource using rates corresponding to each retrieval feature type.
In a preferred scheme, the access optimization module further comprises an assessment ending unit, an assessment starting unit, an assessment period unit, an assessment system unit, a predicted access use unit and a system planning access use unit;
the evaluation ending unit is used for acquiring the acquisition time of the system prediction access use data and marking the acquisition time as the evaluation ending time;
The evaluation starting unit is used for acquiring the evaluation duration and acquiring the evaluation starting time according to the evaluation duration and the ending time of the evaluation period;
an evaluation period unit for acquiring an evaluation period according to the evaluation end time and the evaluation start time;
The evaluation system unit is used for acquiring historical system resource utilization rate corresponding to each retrieval characteristic type in the system prediction access use data in the system history access use data in the evaluation period, and marking the historical system resource utilization rate as the evaluation system resource utilization rate;
The predicted access use unit is used for acquiring corresponding system predicted access use rate according to the system predicted access use data;
And the system plan access use unit is used for acquiring the system plan access use rate according to the plurality of estimated system resource use rates and the system forecast access use rate and marking the system plan access use rate as system plan access use data.
In a preferred scheme, the access optimization module further comprises a use threshold unit, a use judgment unit and a regulation and control use unit;
a usage threshold unit for acquiring a system plan access usage threshold rate;
A use judgment unit for judging whether the system plan access use rate exceeds a system plan access use threshold;
If the system plan access utilization rate exceeds the system plan access utilization threshold, judging that the system plan access utilization rate is overload and marking the system plan access utilization rate as the system excess access utilization rate;
The regulation and control use unit is used for acquiring system regulation and control access use data according to the system excess access use rate.
In a preferred scheme, the access optimization module further comprises an optimization table unit, an interval comparison unit and an optimization mode unit;
The optimizing table unit is used for acquiring a platform optimizing table, wherein the platform optimizing table comprises a plurality of system regulation access use interval values and platform optimizing modes corresponding to each system regulation access use interval value;
the interval comparison unit is used for acquiring a corresponding target system regulation access use interval value according to the system regulation access use rate;
and the optimization mode unit is used for acquiring a corresponding platform optimization mode from the platform optimization table according to the target system regulation access use interval value.
And an electronic invoice data aggregation and management terminal based on cloud service, comprising:
One or more processors;
A storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the cloud service-based electronic invoice data aggregation and management platform.
The invention has the technical effects that:
According to the invention, the system access performance of the electronic invoice platform is optimized by acquiring the system regulation access data to adjust the access strategy, the platform can adopt a corresponding optimization mode to ensure the efficient operation under different load conditions, the system response delay or collapse caused by excessive access can be avoided by predicting the system access requirement of a user and regulating the access data, the system resource can be adjusted in real time according to the current situation, the stable operation of the platform is ensured, the user requirement can be predicted in advance and the system access path is optimized, the search experience and the use experience of the user are obviously improved, and especially under a high access quantity scene, the smooth access speed and the high response capability can be maintained.
Drawings
FIG. 1 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Further, the present invention will be described in detail with reference to the drawings, which are only examples for convenience of illustration, and should not limit the scope of the present invention.
Referring to fig. 1, an electronic invoice data aggregation and management platform based on cloud service is provided, which comprises an electronic invoice retrieval module, a prediction access module and an access optimization module;
The electronic invoice retrieval module is used for acquiring current retrieval information of the electronic invoice data accessed by the user currently, constructing a first period of time and acquiring first retrieval data of the electronic invoice data accessed by the user in the first period of time;
The prediction retrieval module is used for acquiring retrieval emphasis information of the user accessing the electronic invoice data according to the first retrieval data and the current retrieval information, acquiring historical retrieval information of the user accessing the electronic invoice data in the current period, acquiring retrieval preference information according to the historical retrieval information, and acquiring prediction retrieval information according to the retrieval emphasis information and the retrieval preference information;
The prediction access module is used for acquiring system historical access use data of the electronic invoice data in the current period and acquiring system prediction access use data corresponding to the prediction retrieval information;
The access optimization module is used for acquiring system planning access use data of the prediction search information according to the system historical access use data and the system prediction access use data, judging whether the system planning access use data meets a first preset condition, acquiring system regulation access use data according to the system planning access use data if the system planning access use data does not meet the first preset condition, and acquiring corresponding optimization mode optimization system access of the platform according to the system regulation access use data.
The electronic invoice retrieval module firstly acquires retrieval information related to electronic invoice data currently accessed by a user, and constructs a specific time period (first time period) according to the retrieval information, in the first time period, all retrieval behaviors of the user for accessing the electronic invoice data are collected, corresponding first retrieval data are generated, the prediction retrieval module obtains side points of the user for accessing the electronic invoice data in different time periods according to the first retrieval data of the user and the current retrieval information, namely retrieval side information, and simultaneously, retrieval preference information is extracted by calling the retrieval data of a user history time period, namely content which is prone to be retrieved by the user in a similar situation or time period, the current retrieval side information of the user is combined with the history preference information, prediction retrieval information is generated, namely data which can be accessed or retrieved by the user in the next time period is predicted, the prediction access module analyzes the use condition of an electronic invoice system in the history similar time period according to the system history access data, judges the running performance of a platform under different loads according to the prediction retrieval information, predicts the use condition of the corresponding system prediction access data in the current time period, the user for accessing the electronic invoice data in the current time period is predicted, and the system is adjusted in advance, and resources are distributed according to the prediction access condition of the prediction access information, if the system access condition is not adjusted, the system access condition is adjusted, and the system access resource is automatically is distributed in advance, compared with the system access condition is adjusted, and the system access condition is automatically is better than the system access condition is adjusted, if the system access condition is adjusted, the system access condition is accessed is adjusted, the platform can optimize the system access performance of the electronic invoice platform by adopting a corresponding optimization mode, ensures the efficient operation under different load conditions, can automatically adjust when the load is too high by predicting the system access requirement of a user and regulating and controlling access data, avoids system response delay or breakdown caused by excessive access, can adjust system resources in real time according to the current situation, ensures the stable operation of the platform, can predict the user requirement in advance and optimize the system access path, and obviously improves the retrieval experience and the use experience of the user, and can keep smooth access speed and high response capability especially under a high access amount scene.
In a specific embodiment, the electronic invoice search module includes a current search unit, a period end unit, a first period unit, a period start unit, a first period unit, and a first search unit;
the current retrieval unit is used for acquiring current retrieval information of the electronic invoice data accessed by the user currently;
the time period ending unit is used for acquiring the time of the current retrieval information and marking the time period ending time;
The first time length unit is used for acquiring a first time length;
a period starting unit for acquiring a period starting time according to the first time length and the period ending time;
The first time period unit is used for acquiring a first time period according to the time period ending time and the time period starting time;
the first retrieval unit is used for acquiring a plurality of pieces of retrieval information of the electronic invoice data accessed by the user in the first period of time and summarizing the retrieval information into first retrieval data.
The current search unit is responsible for acquiring current search information of the current access electronic invoice data of the user, namely, a specific data search request which is carried out by the user in real time, tracking the attention degree of the user to certain data by recording each search action of the user, recording the time of ending the search action by a time period ending unit when the one search operation of the user is ended, marking the time as the time period ending time, wherein the time stamp is used for determining the duration of the search operation of the user, the first time period unit is used for defining a fixed time period length, called first time period, the time period can be dynamically set (such as 5 minutes or 1 hour) according to service requirements, the time range for analyzing the user action is divided, the corresponding time period starting time is calculated by the time period starting unit according to the time period ending time and the first time period, namely, the starting time of the first time period is equal to the time period ending time of the first time period, the first time period unit is responsible for acquiring a specific time period, called the first time period, recording all search actions of the user are recorded, the search actions of the user are acquired in the first time period, the first time period is not used for acquiring the specific information, the search information is not fully known by the user, the search platform is fully known by the search information, the search information is acquired by the first time period, the user interest is not being fully acquired by the user, and the search information is fully known by the user, and the user is fully known by the search information.
In a specific embodiment, the prediction retrieval module comprises a retrieval feature unit, a first matrix unit, a current matrix unit, a emphasis threshold unit and a retrieval emphasis judging unit;
the system comprises a search feature unit, a search feature processing unit and a search processing unit, wherein the search feature unit is used for acquiring a plurality of corresponding search feature information according to first search data, the search feature information comprises a plurality of search feature types, and the search feature types comprise invoice issuing date, invoice tax rate and invoice head-up;
The first matrix unit is used for acquiring a first retrieval feature matrix according to the plurality of retrieval feature information, wherein the first retrieval feature matrix is a row of matrix, and each element in the first retrieval feature matrix is a retrieval vector value of each retrieval feature type;
the current matrix unit is used for acquiring a plurality of corresponding search feature types according to the current search information and constructing a current search feature matrix, wherein the current search feature matrix is a row of matrix with the first search feature matrix, and each element in the current search feature matrix is a search vector value of each search feature type;
The emphasis matrix unit is used for acquiring a retrieval emphasis matrix according to the first retrieval feature matrix and the current retrieval feature matrix;
The emphasis threshold unit is used for acquiring a retrieval emphasis threshold;
The search emphasis judging unit is used for judging whether each element in the search emphasis matrix exceeds a corresponding search emphasis threshold value;
If each element of the search emphasis matrix exceeds the corresponding preset emphasis threshold, marking the search feature types corresponding to the elements exceeding the corresponding preset emphasis threshold as search emphasis information.
The above-mentioned search feature unit is responsible for extracting a plurality of search feature information related to search behavior from the first search data, where the information may include specific attributes of an invoice, such as invoice date, invoice tax rate, customer name, invoice header, etc., each search feature type reflects specific attributes of invoice data focused by a user in the time period, the first matrix unit generates a search vector value for each search feature type extracted from the search feature information, that is, search strength of the user on a certain feature, the search vector values of the plurality of search feature types form a first search feature matrix, where the matrix is a row of matrices representing focuses of the user on different invoice features in the specific time period, and the current matrix unit extracts corresponding search feature information according to the current search behavior of the user, and constructing a current search feature matrix consistent with the first search feature matrix format, wherein the emphasis matrix unit calculates the first search feature matrix and the current search feature matrix, the calculation formula of the search emphasis matrix is C=Y×D, wherein C is represented as a search emphasis matrix, Y is represented as the first search feature matrix, D is represented as the current search feature matrix, a search emphasis matrix is generated to reflect the change of the current search behavior of the user compared with the change in the historical behavior, each matrix element represents the search emphasis change degree of the user on the feature, the emphasis threshold unit is responsible for setting the search emphasis threshold of each search feature, the search emphasis judging unit judges whether the behavior of the user on certain search features exceeds the preset emphasis threshold by comparing each element in the search emphasis matrix with the search emphasis threshold, if the emphasis matrix element value corresponding to a certain retrieval feature exceeds the emphasis threshold value of the feature, marking the retrieval feature as retrieval emphasis information, representing that the feature is focused by a user in the current retrieval, and dynamically identifying the invoice feature which is focused by the user at present by analyzing the difference between the current retrieval behavior and the historical retrieval behavior of the user, for example, the user may focus on the invoice data in a certain invoice tax rate or a specific date range in the current retrieval, and automatically marking the emphasis information, thereby providing basis for the optimization of the subsequent retrieval.
In a specific embodiment, the prediction search module further includes a history search unit, a feature type unit, a search number unit, a search threshold unit, and a preference judgment unit;
the history retrieval unit is used for acquiring history retrieval information of the electronic invoice data accessed by the user in the current period;
The characteristic type unit is used for acquiring a plurality of corresponding retrieval characteristic types according to the history retrieval information;
The search frequency unit is used for acquiring the search frequency of each search feature type;
The retrieval threshold unit is used for acquiring retrieval threshold times;
a preference judging unit for judging whether the search times of each search feature category exceeds the search threshold times;
If the search times of each search feature type exceeds the search threshold times, marking the search feature type exceeding the search threshold times as search preference information;
If the search times of each search feature type do not exceed the search threshold times, a plurality of search feature types are ranked according to the search times in a sequence from high to low to obtain a search feature type ranking table, the number of search preference feature types is obtained, and the search feature types which accord with the number of search preference feature types are sequentially selected from the search feature type ranking table in the sequence from high to low and marked as search preference information.
The history searching unit is responsible for acquiring the history searching information of the user in the current period, the electronic invoice data access records of the user in different time can be obtained by analyzing the searching actions of the user in the past in the current period, the characteristic type unit extracts a plurality of corresponding searching characteristic types, such as the issuing date of an invoice, the invoice amount, the invoice type and the like, according to the history searching information, each searching characteristic type represents a certain invoice attribute focused by the user, the searching frequency unit is responsible for counting the occurrence frequency of each searching characteristic type in the history searching information, namely the frequency of the user accessing certain characteristics, the degree of focusing of the user on different characteristics can be quantified by calculating the searching frequency of the user on each characteristic type in the current period, the searching threshold unit is responsible for setting a searching threshold frequency, namely when the searching frequency of a certain characteristic type exceeds the threshold, the user is considered to have obvious preference for the feature, the threshold value can be dynamically adjusted according to service requirements, accuracy of preference identification is ensured, a preference judging unit judges which feature types have the search times exceeding the threshold value by comparing the search times of each search feature type with the search threshold times, the feature types exceeding the threshold value are marked as search preference information, the user is considered to have obvious preference for the feature, if the search times of all feature types do not exceed the threshold value, the feature types are ranked according to the search times of each feature type to generate a search feature type ranking table, then the feature type with the highest search times is sequentially selected from the ranking table according to the set search preference feature type number and marked as search preference information, the method and the system can accurately identify the remarkable preference of the user in the electronic invoice data retrieval, for example, if the user frequently retrieves the specific invoice issuing date or invoice amount, the preference information can be identified in advance, support is provided for subsequent personalized recommendation and service optimization, the remarkable preference can be judged through setting a retrieval frequency threshold, and under the condition that no feature types exceed the threshold, the feature types with higher user attention degree can be automatically selected according to a sorting algorithm, and the dual judgment mechanism can flexibly adapt to the retrieval behaviors of different users, and can accurately capture the preference of the users whether the users frequently retrieve certain types of features or the users who retrieve a plurality of features in a scattered manner.
In a specific embodiment, the prediction retrieval module further includes a emphasis category unit, a preference category unit, and a prediction retrieval unit;
The emphasis type unit is used for acquiring corresponding retrieval feature types according to the retrieval emphasis information and marking the retrieval feature types as retrieval feature emphasis types;
a preference category unit, configured to obtain a corresponding search feature category according to the search preference information, and mark the search feature category as a search feature preference category;
and the prediction searching unit is used for summarizing the search feature emphasis types and the search feature preference types, reserving repeated search feature types and marking the repeated search feature types as prediction searching information.
The emphasis type unit is responsible for extracting the retrieval feature types of the current focus of the user from the retrieval emphasis information, the feature types may be specific invoice attributes (such as date and tax rate of invoices) of the current focus of the user, which are analyzed through the emphasis matrix, the extracted feature types are marked as retrieval feature emphasis types, which represent the features of the focus of the user in the current retrieval behavior, the preference type unit obtains the retrieval feature types of the focus of the user in the historical retrieval behavior according to retrieval preference information, which represent significant focus points of the user in a longer time period, marks the feature types as retrieval feature preference types, the prediction retrieval unit gathers the retrieval feature emphasis types and the retrieval feature preference types, and keeps repeated retrieval feature types appearing in both types, the repeated feature types represent the features of the current focus of the user, and are also preference features in the historical retrieval, so that the characteristics have higher prediction value, finally, the repeated retrieval feature types are marked as prediction retrieval information, which represent the specific features of the user possibly continuously focuses in the future, the preference feature types can be more accurately predicted in the future, the invoice types can appear in the current focus of the user in the past, and the service preference information can be continuously provided in the future according to the history information, and the preference information can continuously appear in the future according to the history of the user.
In a specific embodiment, the predictive access module includes a history use unit, a first use unit, and a predictive use unit;
The historical use unit is used for acquiring a plurality of system resource utilization rates of the electronic invoice data accessed in the current period and summarizing the plurality of system resource utilization rates into system historical access use data;
a first usage unit, configured to obtain, from the system history access usage data, a plurality of historical system resource usage rates corresponding to each search feature type in the predicted search information, and mark the historical system resource usage rate corresponding to each search feature type as a first system resource usage rate;
and the prediction using unit is used for acquiring system prediction access using data corresponding to the prediction retrieval information according to a plurality of first system resource using rates corresponding to each retrieval feature type.
The above-mentioned history usage unit is responsible for collecting the usage information of system resources related to the electronic invoice data, including the usage of multiple resources such as CPU, memory, network bandwidth, etc., in the current period, the collected multiple system resource usage data will be summarized to form a complete system history access usage data, the first usage unit extracts the history system resource usage related to each retrieval feature type in the predicted retrieval information from the summarized system history access usage data, for example, the inquiry of some specific invoice feature types may depend more on network bandwidth, or stores the resources, screens out multiple history resource usage corresponding to these retrieval feature types and marks it as the first system resource usage, the prediction usage unit predicts the future system resource usage according to multiple first system resource usage corresponding to each retrieval feature type, generates the system prediction access usage data, and the calculation formula of the system prediction access usage data is thatWhere F is a system predicted access usage rate, i.e., system predicted access usage data, k is a number of a search feature type in the predicted search information, k=1, 2, 3..m, i is a number of a plurality of first system resource usage rates of the search feature type in the predicted search information, where i=1, 2, 3..n,An ith first system resource usage rate expressed as a search feature class in the predicted search information,The method is characterized in that the method is used for predicting the system resource utilization rate of the kth retrieval feature type in the retrieval information, reasonably presuming the load and the demand of different resources in the future by combining the current retrieval demand based on the trend of historical data, so that references are provided for optimization and resource allocation, the historical system resource utilization condition corresponding to each retrieval feature type can be extracted, and the accurate prediction can be carried out by combining the prediction retrieval information.
In a specific embodiment, the access optimization module further comprises an assessment ending unit, an assessment starting unit, an assessment period unit, an assessment system unit, a predicted access usage unit and a system plan access usage unit;
the evaluation ending unit is used for acquiring the acquisition time of the system prediction access use data and marking the acquisition time as the evaluation ending time;
The evaluation starting unit is used for acquiring the evaluation duration and acquiring the evaluation starting time according to the evaluation duration and the ending time of the evaluation period;
an evaluation period unit for acquiring an evaluation period according to the evaluation end time and the evaluation start time;
The evaluation system unit is used for acquiring historical system resource utilization rate corresponding to each retrieval characteristic type in the system prediction access use data in the system history access use data in the evaluation period, and marking the historical system resource utilization rate as the evaluation system resource utilization rate;
The predicted access use unit is used for acquiring corresponding system predicted access use rate according to the system predicted access use data;
And the system plan access use unit is used for acquiring the system plan access use rate according to the plurality of estimated system resource use rates and the system forecast access use rate and marking the system plan access use rate as system plan access use data.
The above-mentioned, the assessment ending unit is responsible for obtaining the acquisition time of the system forecast access usage data, and marks it as the assessment ending time, this time point marks the end of the assessment process, the assessment starting unit is used for determining the time range of the assessment process, it calculates the assessment starting time by obtaining the assessment duration (i.e. the time length required for assessment) and combining with the assessment ending time, the assessment period unit is responsible for obtaining the assessment period according to the assessment starting time and the assessment ending time, the assessment system unit extracts the historical system resource usage rate related to each retrieval feature category in the system forecast access usage data from the system historical access usage data, generates the assessment system resource usage rate, in order to understand the actual usage situation of the system resource in the past assessment period, the forecast access usage unit utilizes the system forecast access usage data, calculates the system forecast access usage rate, the calculation formula of the system forecast access usage data isWhere G denotes a system plan access usage rate, F denotes a system predicted access usage rate, k denotes a number of a search feature type in the predicted search information, k=1, 2, 3..m, i denotes a number of a plurality of first system resource usage rates of the search feature type in the predicted search information, where i=1, 2, 3..n,The i-th first system resource usage rate expressed as the kind of retrieval feature in the predictive retrieval information, g expressed as a number of a plurality of estimated system resource usage rates, g=1, 2, 3..r,The system plan access utilization unit synthesizes a plurality of evaluation system resource utilization rates and system prediction access utilization rates, generates the system plan access utilization rate, and the data can guide a platform to allocate and use decisions in the future, ensure the efficient use of resources, generate accurate evaluation system resource utilization rate, provide data support for resource planning, effectively avoid the waste or shortage of resources and ensure the stable operation of the platform in peak periods.
In a specific embodiment, the access optimization module further comprises a use threshold unit, a use judgment unit and a regulation use unit;
a usage threshold unit for acquiring a system plan access usage threshold rate;
the use judging unit is used for judging whether the system plan access use rate exceeds the system plan access threshold value rate;
If the system plan access utilization rate exceeds the system plan access threshold value rate, judging that the system plan access utilization rate is overload and marking the system plan access utilization rate as the system excess access utilization rate;
The regulation and control use unit is used for acquiring system regulation and control access use data according to the system excess access use rate.
The threshold unit obtains the system plan access use threshold rate, the threshold rate refers to the maximum access use rate that the system can bear under the normal plan operation condition, the threshold is used for judging the current load condition of the system, the system is ensured to operate within a controllable range, the use judging unit is responsible for monitoring the system plan access use rate in real time and comparing with the preset system plan access use threshold rate, if the system plan access use rate exceeds the system plan access use threshold rate, the unit judges the system to be in an overload state and marks the state as the system overage access use rate, the regulation and control use unit intervenes once judging that the overage access use rate exists, the system regulation and control access use data is obtained based on the system overage access use rate, and the calculation formula of the system regulation and control access use data is thatIn the formula, T is expressed as system regulation access utilization rate, namely system regulation access utilization data, G is expressed as system plan access utilization rate, Z is expressed as system plan access utilization threshold rate, a is expressed as system access utilization compensation coefficient, and the data is used for making regulation measures to balance the load of a platform and ensure that the platform can still stably run under the condition of high load.
In a specific embodiment, the access optimization module further comprises an optimization table unit, an interval comparison unit and an optimization mode unit;
The optimizing table unit is used for acquiring a platform optimizing table, wherein the platform optimizing table comprises a plurality of system regulation access use interval values and platform optimizing modes corresponding to each system regulation access use interval value;
the interval comparison unit is used for acquiring a corresponding target system regulation access use interval value according to the system regulation access use rate;
and the optimization mode unit is used for acquiring a corresponding platform optimization mode from the platform optimization table according to the target system regulation access use interval value.
The optimization table unit is responsible for generating and maintaining a platform optimization table, the table comprises a plurality of system regulation access use interval values and a platform optimization mode corresponding to each interval value, the data provide reference basis for subsequent access optimization, the regulation and control can be carried out according to the corresponding optimization mode under different access use rate conditions, the interval comparison unit determines the corresponding target system regulation access use interval value according to the current system regulation access use rate, the current use rate is compared with the interval value in the platform optimization table to judge which optimization interval the current load condition of the platform belongs to, once the target system regulation access use interval value is determined, the optimization mode unit obtains the corresponding platform optimization mode from the platform optimization table, the optimization mode possibly comprises measures such as flow limitation, load balancing, resource allocation adjustment and the like, so that the platform can be ensured to operate efficiently under various load conditions, and the capability of dynamic adjustment enables the platform to maintain high efficiency performance under various load conditions.
And an electronic invoice data aggregation and management terminal based on cloud service, comprising:
One or more processors;
A storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the cloud service-based electronic invoice data aggregation and management platform.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.

Claims (10)

1.基于云服务的电子发票数据聚合与管理平台，其特征在于，包括电子发票检索模块、预测检索模块、预测访问模块和访问优化模块；1. An electronic invoice data aggregation and management platform based on cloud services, characterized by comprising an electronic invoice retrieval module, a predictive retrieval module, a predictive access module and an access optimization module; 电子发票检索模块，用于获取用户当前访问电子发票数据的当前检索信息，并构建第一时段，获取第一时段内的用户访问电子发票数据的第一检索数据；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; 预测检索模块，用于根据第一检索数据以及当前检索信息，获取用户访问电子发票数据的检索侧重信息，获取当前时段用户访问电子发票数据的历史检索信息，根据历史检索信息获取检索偏好信息，根据检索侧重信息以及检索偏好信息获取预测检索信息；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; 预测访问模块，用于获取当前时段电子发票数据的系统历史访问使用数据，并获取预测检索信息对应的系统预测访问使用数据；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; 访问优化模块，用于根据系统历史访问使用数据以及系统预测访问使用数据获取预测检索信息的系统计划访问使用数据，判断系统计划访问使用数据是否符合第一预设条件，若不符合，则根据系统计划访问使用数据获取系统调控访问使用数据，根据系统调控访问使用数据获取对应的优化方式优化平台的系统访问。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.根据权利要求1所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述电子发票检索模块包括当前检索单元、时段结束单元、第一时长单元、时段开始单元、第一时段单元和第一检索单元；2. The electronic invoice data aggregation and management platform based on cloud services according to claim 1, characterized in that the electronic invoice retrieval module comprises 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; 当前检索单元，用于获取用户当前访问电子发票数据的当前检索信息；A current search unit, used to obtain the current search information of the electronic invoice data currently accessed by the user; 时段结束单元，用于获取当前检索信息的时间，并标记为时段结束时间；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; 第一时长单元，用于获取第一时长；A first duration unit, used to obtain a first duration; 时段开始单元，用于根据第一时长以及时段结束时间获取时段开始时间；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; 第一时段单元，用于根据时段结束时间以及时段开始时间获取第一时段；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; 第一检索单元，用于获取第一时段内的用户访问电子发票数据的多个检索信息，并汇总为第一检索数据。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.根据权利要求1所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述预测检索模块包括检索特征单元、第一矩阵单元、当前矩阵单元、侧重矩阵单元、侧重阈值单元和检索侧重判断单元；3. The electronic invoice data aggregation and management platform based on cloud services according to claim 1, characterized in that the prediction and retrieval module comprises 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; 检索特征单元，用于根据第一检索数据获取对应的多个检索特征信息；A retrieval feature unit, used to obtain corresponding multiple retrieval feature information according to the first retrieval data; 第一矩阵单元，用于根据多个检索特征信息获取第一检索特征矩阵；A first matrix unit, used for obtaining a first search feature matrix according to a plurality of search feature information; 当前矩阵单元，用于根据当前检索信息获取对应的多个检索特征种类，并构建当前检索特征矩阵；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; 侧重矩阵单元，用于根据第一检索特征矩阵以及当前检索特征矩阵获取检索侧重矩阵；A focus matrix unit, used for obtaining a search focus matrix according to the first search feature matrix and the current search feature matrix; 侧重阈值单元，用于获取检索侧重阈值；A focus threshold unit, used to obtain a search focus threshold; 检索侧重判断单元，用于判断检索侧重矩阵中每一个元素是否超过对应的检索侧重阈值；A search focus judgment unit, used to judge whether each element in the search focus matrix exceeds a corresponding search focus threshold; 若检索侧重矩阵的每一个元素中存在超过对应的预设侧重阈值，则将超过对应的预设侧重阈值的元素所对应的检索特征种类标记为检索侧重信息。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.根据权利要求3所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述预测检索模块还包括历史检索单元、特征种类单元、检索次数单元、检索阈值单元和偏好判断单元；4. The electronic invoice data aggregation and management platform based on cloud services according to claim 3 is characterized in that the prediction and retrieval module further comprises a historical retrieval unit, a feature type unit, a retrieval number unit, a retrieval threshold unit and a preference judgment unit; 历史检索单元，用于获取当前时段用户访问电子发票数据的历史检索信息；A historical retrieval unit, used to obtain historical retrieval information of users accessing electronic invoice data during the current period; 特征种类单元，用于根据历史检索信息获取对应的多个检索特征种类；A feature type unit, used to obtain corresponding multiple search feature types according to historical search information; 检索次数单元，用于获取每个检索特征种类的检索次数；A search times unit, used to obtain the search times of each search feature type; 检索阈值单元，用于获取检索阈值次数；A search threshold unit, used to obtain a search threshold number of times; 偏好判断单元，用于判断每个检索特征种类的检索次数是否超过检索阈值次数；A preference judgment unit, used to judge whether the number of searches for each search feature type exceeds a search threshold number; 若每个检索特征种类的检索次数存在超过检索阈值次数，则将超过检索阈值次数的检索特征种类标记为检索偏好信息；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; 若每个检索特征种类的检索次数均不存在超过检索阈值次数，则根据检索次数将多个检索特征种类按照从高到低的顺序进行排序得到检索特征种类排序表，获取检索偏好特征种类数量，从检索特征种类排序表中按照从高到低的顺序依次选取符合检索偏好特征种类数量的检索特征种类并标记为检索偏好信息。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.根据权利要求4所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述预测检索模块还包括侧重种类单元、偏好种类单元和预测检索单元；5. The electronic invoice data aggregation and management platform based on cloud services according to claim 4, characterized in that the prediction and retrieval module further comprises a focus category unit, a preference category unit and a prediction and retrieval unit; 侧重种类单元，用于根据检索侧重信息获取对应的检索特征种类，并标记为检索特征侧重种类；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; 偏好种类单元，用于根据检索偏好信息获取对应的检索特征种类，并标记为检索特征偏好种类；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; 预测检索单元，用于将检索特征侧重种类与检索特征偏好种类进行汇总，保留重复的检索特征种类，并标记为预测检索信息。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.根据权利要求1所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述预测访问模块包括历史使用单元、第一使用单元和预测使用单元；6. The electronic invoice data aggregation and management platform based on cloud services 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; 历史使用单元，用于获取当前时段访问电子发票数据的多个系统资源使用率，并将多个系统资源使用率汇总为系统历史访问使用数据；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; 第一使用单元，用于从系统历史访问使用数据中获取与预测检索信息中每个检索特征种类所对应的多个历史系统资源使用率，并将每个检索特征种类所对应的历史系统资源使用率标记为第一系统资源使用率；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; 预测使用单元，用于根据每个检索特征种类所对应的多个第一系统资源使用率获取预测检索信息对应的系统预测访问使用数据。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.根据权利要求6所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述访问优化模块还包括评估结束单元、评估开始单元、评估时段单元、评估系统单元、预测访问使用单元和系统计划访问使用单元；7. The electronic invoice data aggregation and management platform based on cloud services according to claim 6, characterized in that the access optimization module further comprises 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; 评估结束单元，用于获取系统预测访问使用数据的获取时间，并标记为评估结束时间；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; 评估开始单元，用于获取评估时长，根据评估时长以及评估时段的结束时间获取评估开始时间；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; 评估时段单元，用于根据评估结束时间以及评估开始时间的获取评估时段；An evaluation period unit, used to obtain the evaluation period according to the evaluation end time and the evaluation start time; 评估系统单元，用于获取评估时段内系统历史访问使用数据中与系统预测访问使用数据中每个检索特征种类所对应的历史系统资源使用率，并标记为评估系统资源使用率；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; 预测访问使用单元，用于根据系统预测访问使用数据获取对应的系统预测访问使用率；A predicted access usage unit, used to obtain a corresponding system predicted access usage rate according to the system predicted access usage data; 系统计划访问使用单元，用于根据多个评估系统资源使用率以及系统预测访问使用率获取系统计划访问使用率，并标记为系统计划访问使用数据。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.根据权利要求7所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述访问优化模块还包括使用阈值单元、使用判断单元和调控使用单元；8. The electronic invoice data aggregation and management platform based on cloud services according to claim 7, characterized in that the access optimization module further comprises a usage threshold unit, a usage judgment unit and a usage regulation unit; 使用阈值单元，用于获取系统计划访问使用阈值率；A usage threshold unit is used to obtain a system planned access usage threshold rate; 使用判断单元，用于判断系统计划访问使用率是否超过系统计划访问使用阈值；A usage judgment unit is used to judge whether the system planned access usage rate exceeds the system planned access usage threshold; 若系统计划访问使用率超过系统计划访问使用阈值，则判定系统计划访问使用率为过负荷，并标记为系统超额访问使用率；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; 调控使用单元，用于根据系统超额访问使用率获取系统调控访问使用数据。The control and utilization unit is used to obtain system control and utilization data according to the system excess access utilization rate. 9.根据权利要求8所述的基于云服务的电子发票数据聚合与管理平台，其特征在于，所述访问优化模块还包括优化表单元、区间对比单元和优化方式单元；9. The electronic invoice data aggregation and management platform based on cloud services according to claim 8, characterized in that the access optimization module further comprises an optimization table unit, an interval comparison unit and an optimization method unit; 优化表单元，用于获取平台优化表，其中，平台优化表包括多个系统调控访问使用区间值以及每个系统调控访问使用区间值所对应平台优化方式；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; 区间对比单元，用于根据系统调控访问使用率获取对应的目标系统调控访问使用区间值；An interval comparison unit, used for obtaining a corresponding target system regulation access usage interval value according to the system regulation access usage rate; 优化方式单元，用于根据目标系统调控访问使用区间值从平台优化表中获取对应的平台优化方式。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.基于云服务的电子发票数据聚合与管理终端，其特征在于，包括：10. The electronic invoice data aggregation and management terminal based on cloud services is characterized by including: 一个或多个处理器；one or more processors; 存储装置，其上存储有一个或多个程序；a storage device having one or more programs stored thereon; 当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现执行权利要求1至8中任一项所述基于云服务的电子发票数据聚合与管理平台。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.
CN202411423753.3A 2024-10-12 2024-10-12 Electronic invoice data aggregation and management platform based on cloud services Pending CN119225973A (en)

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