Electronic official document application performance evaluation method based on data magic cube

Abstract

The invention discloses an electronic official document application performance evaluation method based on a data magic cube. The method comprises the following steps: constructing a multi-level electronic official document performance initial evaluation index system based on an evaluation object; the multi-level electronic official document performance initial evaluation index system comprises official document circulation performance, user use performance and system application performance; constructing a data magic cube framework, collecting operation data of the electronic official document system, and processing the operation data through the data magic cube framework; and extracting data in the data magic cube, and performing quantitative evaluation on the electronic official document application efficiency in combination with the electronic official document efficiency initial evaluation index system. Through a multi-dimensional data analysis technology, intelligent analysis in multiple aspects such as document circulation efficiency, user use efficiency and system use efficiency is realized. Multi-dimensional cross analysis is carried out based on the data magic cube, and the user is helped to insight the document processing efficiency from different perspectives. Intelligent optimization can be carried out based on historical data, and the future official document processing trend is predicted, so that a scientific basis is provided for management decision making.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and particularly relates to a method for evaluating the application effectiveness of electronic official documents based on a data cube. Background Art

[0002] With the rapid development of science and technology, as a product of the combination of information technology and official document business, the electronic official document system reduces the printing, storage, transportation, etc. of paper official documents, and at the same time, through automated and intelligent official document processing processes, reduces labor costs, optimizes the quality of official documents, and significantly improves the official document processing efficiency. Electronic official documents are based on the management systems and relevant office standard specifications of relevant units, combined with the corresponding human resources situation, to determine the construction goals of the electronic official document system and carry out the design work of the electronic official document system, so as to implement the construction of the electronic official document system. On the basis of clarifying the goals and requirements, a set of scientific and reasonable evaluation system for the application effectiveness of electronic official documents is determined, so as to comprehensively evaluate the effectiveness of electronic official documents while re-examining the construction achievements of the electronic official document system to ensure that the application is consistent with the plan. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a method for evaluating the application effectiveness of electronic official documents based on a data cube to solve the problems existing in the above prior art.

[0004] To achieve the above object, the present invention provides a method for evaluating the application effectiveness of electronic official documents based on a data cube, including:

[0005] Constructing a multi-level initial evaluation index system for the effectiveness of electronic official documents based on the evaluation object; the multi-level initial evaluation index system for the effectiveness of electronic official documents includes official document circulation effectiveness, user usage effectiveness, and system application effectiveness;

[0006] Constructing a data cube architecture, collecting the operation data of the electronic official document system, and processing the operation data through the data cube architecture;

[0007] Extracting the data in the data cube, and quantitatively evaluating the application effectiveness of the electronic official document in combination with the initial evaluation index system for the effectiveness of the electronic official document.

[0008] Optionally, the official document circulation effectiveness includes, but is not limited to, official document elements, the quantity of incoming and outgoing official documents, and working timeliness;

[0009] The user usage effectiveness includes, but is not limited to, workload, work complexity, and processing error scores;

[0010] The system application effectiveness includes, but is not limited to, system stability, system security, system fluency, and system satisfaction.

[0011] Optionally, the operation data includes structured data and unstructured data; the structured data is business scenario data, and the unstructured data is electronic official document data.

[0012] Optionally, the data cube classifies and manages data from three dimensions: data source, data organization, and data security, including:

[0013] Classify the operation data into internal transfer data, superior-subordinate exchange data, and parallel department exchange data according to the data source; summarize the operation data to form an efficiency evaluation data set; establish a data classification management mechanism to perform security management on the operation data.

[0014] Optionally, before performing quantitative evaluation, screen the initial evaluation index system for multi-level electronic official document efficiency and assign weights. The process includes: obtaining the index scores of the initial evaluation index system for electronic official document efficiency by using the expert scoring method, and selecting the indicators with a cumulative contribution rate not lower than the preset value as important indicators; assigning weights to each important indicator based on the expert scores.

[0015] Optionally, the process of assigning weights includes: making pairwise comparison judgments on the lower-level nodes of each relevant node in the initial evaluation index system for multi-level electronic official document efficiency according to the importance scale to obtain a judgment matrix, and performing consistency check on the judgment matrix; calculating the maximum eigenvalue and the corresponding eigenvector of each judgment matrix, and performing normalization processing to obtain the weight vector of each node, and correcting the weight vector of each node through a wavelet neural network to obtain the final weight of each node.

[0016] Optionally, calculate the average value of all expert evaluation values as the final evaluation value of the indicator, and calculate the comprehensive score of the application efficiency of the electronic official document based on the node value, the final weight, and the final evaluation value to achieve quantitative evaluation.

[0017] The present invention provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the above method.

[0018] The present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0019] The present invention provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0020] Compared with the prior art, the present invention has the following advantages and technical effects:

[0021] The electronic official document efficiency evaluation system based on the data cube disclosed by the present invention realizes intelligent analysis in multiple aspects such as official document circulation efficiency, user usage efficiency, and system usage efficiency through multi-dimensional data analysis technology. The core advantage of the data cube lies in its powerful data modeling ability, which supports multi-dimensional cross-analysis and helps users gain insights into official document processing efficiency from different perspectives. The system intuitively displays key indicators of official document circulation through visualization analysis tools such as dynamic heat maps, trend prediction curves, and correlation matrices, facilitating the optimization of the approval process and improving the overall office efficiency. In addition, the system has an adaptive learning ability, which can perform intelligent optimization based on historical data and predict future official document processing trends, thereby providing a scientific basis for management decisions. Brief Description of the Drawings

[0022] The drawings forming a part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0023] Figure 1 It is the efficiency evaluation system architecture of the embodiment of the present invention. Detailed Embodiments

[0024] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0025] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0026] Embodiment 1

[0027] As Figure 1 shown, in this embodiment, an electronic official document application efficiency evaluation method based on the data cube is provided, including:

[0028] Constructing a multi-level initial evaluation index system for electronic official document efficiency based on the evaluation object; the multi-level initial evaluation index system for electronic official document efficiency includes official document circulation efficiency, user usage efficiency, and system application efficiency;

[0029] Furthermore, the official document circulation efficiency includes, but is not limited to, official document elements, the quantity of incoming and outgoing documents, and work timeliness; the user usage efficiency includes, but is not limited to, workload, work complexity, and processing error scores; the system application efficiency includes, but is not limited to, system stability, system security, system fluency, and system satisfaction.

[0030] Specifically, the construction of the evaluation index system for the application effectiveness of the e-official document system has the characteristics of being interdisciplinary. The basic theoretical methods involved come from various different disciplines, showing strong domain intersection. Generally speaking, a wider range of indicators and more perspectives of indicators are conducive to improving the accuracy of effectiveness evaluation. The evaluation index system should comprehensively reflect the various target requirements of the system to be evaluated, and should be as scientific, reasonable, in line with the actual situation as possible, and acceptable to relevant personnel.

[0031] The core purpose of constructing a complete, effective and reliable evaluation index system for the application effectiveness of e-official documents is to objectively and comprehensively evaluate the application effectiveness level of e-official documents. It needs to meet certain standards and principles, and be able to comprehensively reflect the overall evaluation indicators and various characterization parameters of the operation effectiveness of e-official documents in each agency. Therefore, it is very necessary to conduct systematic research and reasonable construction on the evaluation index system for the application effectiveness of e-official documents to achieve a scientific and reliable comprehensive evaluation of the application effectiveness of e-official documents. When actually designing the index system, the following five basic principles should be followed:

[0032] 1. Systematic principle. The index system should have a clear hierarchical structure, with the same-level indicators being independent of each other, and there should be a certain logical correlation between the upper and lower-level indicators.

[0033] 2. Specificity principle. The indicators should be clear and specific, and can be described for easy understanding.

[0034] 3. Quantifiability principle. The indicators should be quantified as numerical values as much as possible to facilitate measurement, comparison and quantitative evaluation.

[0035] 4. Accessibility principle. The index data should be convenient to obtain.

[0036] 5. Dynamic principle. Each indicator changes dynamically according to the actual work progress, focusing on the application effectiveness of e-official documents and evaluation indicators, and dynamically reflecting the effectiveness of the e-official document system stage by stage.

[0037] To comprehensively improve the scientific level of management, it is necessary to introduce a scientific management model that follows objective laws and pursues objective performance. For electronic official documents, this model requires the construction of a reasonable and complete effectiveness evaluation index system. The whole-cycle management of the electronic official document system includes processes such as submission for approval, signature and seal, delivery, and archiving, which are characterized by a large amount of data, a variety of user types, and strict standards and specifications. The application effectiveness evaluation of electronic official documents involves multiple disciplinary fields such as mathematics, computer science, management science, and systems engineering, and highly relies on a stable and reliable information infrastructure. Therefore, the modeling of its application effectiveness evaluation faces difficulties such as multi-dimensions, multi-levels, and high complexity. By adopting three perspectives of "business - user - system" to perform dimensionality reduction and deconstruction on the problem of electronic official document effectiveness evaluation, analyzing the factors of electronic official document effectiveness evaluation from multiple angles, mapping complex problems to different planes, designing a quantitative algorithm for the capabilities of evaluation objects, and based on the concept of combining subjective and objective weights configuration, constructing a multi-level three-dimensional structure effectiveness evaluation index from three perspectives, realizing the "quantitative - qualitative" combined application effectiveness evaluation of electronic official documents.

[0038] 1. Document circulation effectiveness (Document, D): Based on indicators such as the quantity of official documents processed by the electronic official document system per unit time, the corresponding processing time for each step, and the quality of official document processing, construct an evaluation model for the circulation effectiveness of electronic official documents to comprehensively evaluate the circulation effectiveness of electronic official documents.

[0039] 2. User usage effectiveness (User, U): Determine the basic indicators based on the processing workload, work complexity, work timeliness, etc. of personnel in each position, and further process to obtain statistical indicators such as the satisfaction rate, error rate, and delay rate of official document processing.

[0040] 3. System application effectiveness (System, S): Determine the indicators based on the stability, security, fluency, and satisfaction of the electronic official document system.

[0041] According to the current progress of the application of electronic official documents, fully considering the business characteristics of electronic official documents and focusing on the current situation of user usage of electronic official documents, construct a multi-level three-dimensional electronic official document application effectiveness evaluation index system with the structure of "object - ability - attribute - index". The effectiveness evaluation index system maps the overall evaluation object to the document circulation effectiveness (D), user usage effectiveness (U), and system application effectiveness (S) from perspectives such as business, user, and system, and divides detailed indicators based on functional attributes, ultimately realizing the scientific construction of the overall application effectiveness DUS model of electronic official documents.

[0042] Based on the influencing factors of electronic official documents and combined with the application efficiency goals, it is proposed that the evaluation of the application efficiency verification system can adopt the index system in Table 1. The application efficiency indicators are composed of 3 first-level indicators, 10 second-level indicators, and 50 third-level indicators. The first-level indicators are divided into three aspects: official document circulation efficiency, user usage efficiency, and system application efficiency, comprehensively evaluating the efficiency of the electronic official document system.

[0043] The weight of an indicator represents its relative importance in the overall evaluation. The weight settings of indicators at each level are determined comprehensively based on subjective and objective factors such as the importance of the indicators and the implementation stage of digital reform, and can be adjusted regularly according to the actual situation.

[0044] The evaluation index system for the efficiency of electronic official documents is shown in Table 1:

[0045] Table 1

[0046]

[0047]

[0048]

[0049] Build a data cube architecture, collect the operation data of the electronic official document system, and process the operation data through the data cube architecture;

[0050] Furthermore, the operation data includes structured data and unstructured data; the structured data is business scenario data, and the unstructured data is electronic official document file data.

[0051] Furthermore, the data cube classifies and manages data from three dimensions: data source, data organization, and data security, including:

[0052] Classify the operation data into internal circulation data, superior-subordinate exchange data, and parallel department exchange data according to the data source; summarize the operation data to form an efficiency evaluation data set; establish a data classification management mechanism to manage the security of the operation data;

[0053] After clarifying the evaluation object, based on the existing metadata of the electronic official document system, sort out the complete set of evaluable data in the electronic official document system, and determine the scope and boundary of the evaluation data as needed from the complete set of data according to business scenarios such as electronic official document circulation, approval, and supervision, and around the efficiency evaluation goal of electronic official documents.

[0054] The structured data mainly includes electronic official document layout metadata, electronic official document drafting metadata, electronic official document sending and handling metadata, electronic official document receiving and handling metadata, electronic official document sorting and archiving metadata, and electronic official document storage metadata, etc. The unstructured data mainly includes types such as the streaming files and layout files of the electronic official document text and attachments.

[0055] The metadata of the electronic document layout include: copy number, confidentiality level and confidentiality period, urgency, issuing agency logo, document number, issuer, title, main recipient agency, main text, attachment description, no main text description, issuing agency signature, issuer signature, date of writing, seal, notes, attachments, copy recipient agencies, number of copies printed, handling instructions, handling unit, contact person, telephone number, printing and issuing agency and printing date, page number, etc.

[0056] Metadata for electronic document drafting includes: ① Content information, primarily including text, graphics, images, audio, and video information. ② Drafting information, primarily including drafter information, drafting time, and confidentiality information. ③ Review information, primarily including reviewer information, review opinions, review time, editor information, modification content, and modification time. ④ Approval and issuance information, primarily including reviewer information, instruction content, and approval and issuance time.

[0057] Metadata for electronic document issuance includes: ① Review information, which primarily includes reviewer information, review time, and review result records. ② Registration information, which primarily includes document number information and distribution range information. ③ Issuance information, which primarily includes issuer information, issuance time, issuance result records, electronic seal affixing time, and electronic seal information. ④ Delivery information, which primarily includes issuing authority information, sender information, and delivery time.

[0058] Metadata for handling electronic official documents includes: ① Receipt information. This mainly includes information about the receiving authority, the recipient, and the receipt time. ② Review information. This mainly includes information about the reviewer, the review time, and records of the review results. ③ Handling information. This mainly includes proposed handling opinions, information about the approving person, handling opinions, and the handling time, as well as information about the person handling the document, handling opinions, and records of the handling results. ④ Approval (circulation) information. This mainly includes information about the person transmitting the document, information about the approval (circulation) instructions, and the approval (circulation) time. ⑤ Request information. This mainly includes information about the person requesting the document, the request time, and records of the request results. ⑥ Reply information. This mainly includes information about the responder, the reply time, and records of the reply results.

[0059] Metadata for electronic document archiving includes: ① File number. This primarily includes the archive number, archive category code, archiving year, retention period, category code, and document number. ② Retention period. This includes permanent and periodic retention. The periodic retention period is typically 50 years. ③ Transfer and receipt information. This primarily includes information about the transferor, transfer date, recipient, and receipt date.

[0060] The metadata of electronic official documents includes: ① Access information. It mainly includes access approval records, information of accessors, and access time. ② Copying and output information. It mainly includes copying and output approval records, information of copying and output responsible persons, number of copies and time of copying and output, as well as file identifiers of copying and output. ③ Change information. It mainly includes change contents such as classification level, confidentiality period, storage period, main scope, etc., as well as change time, information of change reviewers, and information of change approvers. ④ Revocation and invalidation information. It mainly includes information of the organs that decide to revoke and invalidate official documents, reasons for revocation and invalidation, and time of revocation and invalidation. ⑤ Deletion information. It mainly includes information of the organs that decide to delete official documents, deletion result information, and deletion time.

[0061] The storage metadata of electronic official documents includes computer file name, computer file format, computer file size, file association, associated object, and association relationship.

[0062] For the selection of structured data, the evaluation objects that can be mainly considered include:

[0063] ① Related to circulation: used for evaluating the time used in the whole life cycle of electronic official documents and the saved time, whether the post settings are reasonable, analysis of the location of bottlenecks, etc.

[0064] ② Related to approval: used for evaluating the time used for approval by various roles and the approval time, office efficiency, etc.

[0065] ③ Related to the quantity of electronic official documents: used for evaluating the usage of electronic official documents by each unit and the online rate, etc.

[0066] ④ Related to distribution of documents for reading: used for evaluating the total number of documents for reading entered and the number of forwarded copies of documents for reading, etc.

[0067] For the selection of unstructured data, the evaluation objects that can be mainly considered include:

[0068] ① Streaming files of electronic official documents: used for evaluating the analysis of the content theme field of electronic official documents, application scope, etc.;

[0069] ② Layout files of electronic official documents: used for evaluating the total data volume of electronic official documents and the storage situation, etc.

[0070] Based on the data of the electronic official document system, combined with data such as abstracts and keywords formed after the documents are processed, supplemented by data such as the length of service of system acquisition posts, personnel business capabilities, and system operation and maintenance, electronic official document application effectiveness evaluation data resources are formed to provide data services for applications.

[0071] In this embodiment, the data sources cover structured, semi-structured and unstructured data, forming a complete data ecosystem. Structured data mainly includes document number, circulation time, processing node, processor, approval results, etc.; semi-structured data involves document metadata in XML and JSON formats, such as approval opinions, modification records, attachment information, etc.; unstructured data includes document text, scanned copies, audio and video recordings, etc. In order to ensure the integrity, accuracy and consistency of the data, the system introduces the ETL (Extract-Transform-Load) process to extract, clean, transform and load different types of data to adapt them to the multi-dimensional analysis model of the Data Cube.

[0072] Since the electronic official document effectiveness evaluation and verification system needs to display evaluation data in real time and perform a large number of query operations, which puts a lot of pressure on the database, it is necessary to build an electronic official document evaluation system database. Relying on the results of the electronic official document system construction, through data induction and extraction, and using integrated governance and other means, the data required for the model is selected to provide data support for the evaluation model and demonstration and verification system.

[0073] Among them, the system database adopts a distributed storage architecture, combining relational databases (such as PostgreSQL, MySQL) and NoSQL databases (such as MongoDB, HBase) to meet the storage requirements of different data types. Structured data is stored in relational databases to support transaction consistency and efficient queries; semi-structured data is stored in NoSQL databases, using the document storage model to optimize query efficiency; unstructured data is stored in distributed file systems (such as HDFS, MinIO) or object storage, and the retrieval capability is improved through indexing engines (such as Elasticsearch). The system uses RESTful API to interact with the database, supporting real-time queries, document flow path tracking, approval status updates and other functions. Through the data cube engine, the system can efficiently construct data cubes, support multi-dimensional analysis, and provide decision support for optimizing document processes.

[0074] The data resources for the performance evaluation of electronic official documents applications are divided according to three dimensions: data source, data organization, and data security, forming a performance evaluation data cube.

[0075] From the perspective of data sources, it mainly includes internal circulation data, data exchanged between superiors and subordinates, and data exchanged between parallel departments.

[0076] From the perspective of data organization, it mainly includes data preparation (pre-processing of data extracted from the electronic document system production library and unstructured data), data aggregation (compilation and aggregation of collected data according to objects to form performance evaluation data sets), and data application (forming performance evaluation data services according to evaluation indicators).

[0077] The data preprocessing stage covers multiple steps such as data cleaning, transformation, standardization, and augmentation to ensure data quality and model stability. First, data cleaning includes deduplication, outlier detection, format standardization, and missing value filling to improve data reliability. Second, data transformation uses methods such as normalization, standardization, and time series transformation to make different types of data consistent. In terms of standardization, official document time formats, approval opinion texts, etc. need to be converted into a unified format for the data analysis module to parse. In addition, for text and image data, natural language processing (NLP) techniques are used for word segmentation, sentiment analysis, or OCR is used to identify key fields for subsequent analysis.

[0078] From the perspective of data security, a "three-color" data classification management is established, classifying data into three categories: red data, yellow data, and green data. Among them, red data is classified as confidential content data; yellow data is internal sensitive data; green data is static data that can be made public.

[0079] The above different types of data are extracted using different methods, and corresponding data extraction rules are set. Structured data is extracted through SQL queries, index optimization, and batch processing methods, including key fields such as official document circulation records, approver information, and timestamps; semi-structured data relies on pattern matching, JSON / XML parsing, and regular expression rules for extraction, such as approval opinions, process status changes, etc.; unstructured data is parsed using technologies such as NLP, OCR, and speech recognition. For example, keywords and themes are extracted from the official document text, or text content is extracted from scanned documents. By setting up a rule library and an intelligent learning mechanism, the data extraction strategy can be dynamically optimized to ensure the accuracy and efficiency of data extraction.

[0080] (1) Extraction rules for red data - confidential content data

[0081] First, role-based access control (RBAC) and the principle of least privilege (PoLP) are adopted to ensure that only authorized users can extract the corresponding data. In addition, during the data extraction process, sensitive fields are desensitized, such as using hash algorithms, partial masking, or data encryption to reduce the risk of leakage. All confidential data is stored using advanced encryption technologies such as AES or RSA and is isolated and managed through an independent security server. At the same time, the system introduces audit logs to record every data extraction operation and combines anomaly detection algorithms to promptly detect and prevent unauthorized access. To further improve security, some confidential data is only allowed to be extracted within a specific time window and is combined with an automatic destruction or recycling mechanism to prevent leakage risks caused by long-term storage.

[0082] (2) Extraction rules for yellow data - internal sensitive data

[0083] Internal sensitive data usually contains key information such as enterprise operations, policy formulation, and approval processes, and strict control over data extraction permissions is required. To ensure data security and compliance, a hierarchical authorization mechanism is adopted, and different access levels are set according to data sensitivity, such as for internal use only, access by specific departments, or exclusive permissions for senior executives. In addition, combined with dynamic permission control technology, permissions are adjusted in real time based on the identity, device, network environment, etc. of the visitor. For example, external network access may only be able to obtain some desensitized data. To track the usage of data, invisible digital watermarks are embedded when extracting sensitive data to ensure that once a leak occurs, the data source can be traced. Some data will also be automatically blurred. For example, financial information may only show a range rather than specific amounts to reduce risks. At the same time, all data accesses need to record logs, and combined with artificial intelligence to analyze abnormal access behaviors, triggering security alerts or automatically blocking suspicious operations in real time.

[0084] (3) Extraction Rules for Green Data - Public Static Data

[0085] Public static data can be provided for external query and use, but it is still necessary to ensure the integrity and anti-tampering ability of the data. When storing public data, standardized formats such as JSON, XML, and CSV are adopted to improve data readability and compatibility and support external access through RESTful APIs. At the same time, to prevent data from being tampered with, hash verification technology (such as SHA256) is introduced, or a blockchain deposit and evidence mechanism is adopted to ensure that any changes can be traced. For public data with high access volume, cache optimization strategies such as CDN or Redis are used to improve data query efficiency and reduce server load. In addition, to prevent malicious crawling and abuse, the API access interface has a frequency limit (Rate Limit) to ensure reasonable use of resources. For official documents or policy documents that require version management, a historical version query function is provided to ensure that users can obtain the latest and past version information to enhance data transparency and traceability.

[0086] Extract the data in the data cube and quantitatively evaluate the application effectiveness of electronic official documents in combination with the initial evaluation index system for the effectiveness of electronic official documents.

[0087] Furthermore, before conducting quantitative evaluation, the initial evaluation index system for the effectiveness of multi-level electronic official documents is screened and weighted. The process includes: obtaining the index scores of the initial evaluation index system for the effectiveness of electronic official documents using the expert scoring method, and selecting the indicators with a cumulative contribution rate not lower than the preset value as important indicators; assigning weights to each important indicator based on expert scores.

[0088] Furthermore, the process of assigning weights includes: making pairwise comparison judgments on the lower-level nodes of each relevant node in the initial evaluation index system of multi-level e-government document effectiveness according to the importance scale to obtain a judgment matrix, and conducting consistency check on the judgment matrix; calculating the maximum eigenvalue and the corresponding eigenvector of each judgment matrix, and performing normalization processing to obtain the weight vector of each node, and correcting the weight vector of each node through a wavelet neural network to obtain the final weight of each node.

[0089] Furthermore, calculate the average value of all experts' evaluation values as the final evaluation value of the index, and calculate the comprehensive score of the e-government document application effectiveness based on the node value, the final weight, and the final evaluation value to achieve quantitative evaluation.

[0090] (1) Index screening

[0091] The indexes screened by experts are the bottom-level nodes in the index system. Assume that S experts score the importance of all N indexes, and choose 1 - 10 to represent the importance degree of the indexes. Among them, 10 means very important and 1 means completely unimportant. Denote the importance score of the j-th index by the i-th expert as P j 。

[0092] For the i-th expert, the contribution rate of the j-th index is

[0093]

[0094] For all S experts, the contribution rate of the j-th index is

[0095]

[0096] Arrange the contribution rates of all indexes in descending order to form a set {q i , ……q N}. Following the principle that the cumulative contribution rate of indexes is not less than 60%, screen and obtain m main indexes, where m satisfies On this basis, an effectiveness evaluation index system can be obtained, and m main indexes are retained.

[0097] (2) Index evaluation

[0098] The DUS effectiveness evaluation indexes are divided into non-quantifiable indexes and quantifiable indexes, and different types of indexes have different normalization methods. Hereinafter, uniformly mark the evaluation of the j-th index by the i-th expert as

[0099] For non-quantifiable indexes, is the grade vector; for quantifiable indexes is the specific value.

[0100] Non - quantifiable line indicators cannot directly show their performance advantages or disadvantages through numerical values. Therefore, five levels of excellent, good, medium, poor, and very poor are set for description, and a scoring vector is set.

[0101] L=(1, 0.75, 0.5, 0.25, 0) T (3)

[0102] If it is excellent, then If it is good And so on.

[0103] Quantifiable indicators can be further divided into larger - the - better type, smaller - the - better type, and interval - optimal type indicators according to their attribute characteristics. Denote the upper and lower bounds of the quantifiable indicators as and

[0104] Larger - the - better type indicators are those for which the larger the value, the better. Its normalization formula is

[0105]

[0106] Smaller - the - better type indicators are those for which the smaller the value, the better. Its normalization formula is

[0107]

[0108] Interval - optimal type indicators are those for which the performance value is best within a specific range. Denote the upper and lower bounds of the optimal value range of the indicator as

[0109] The upper and lower bounds of the range are respectively denoted as and Its normalization formula is

[0110]

[0111] For all S experts, the evaluation value of the j - th indicator is

[0112]

[0113] (3) Index weight

[0114] Weight calculation is carried out for all middle - layer nodes and top - layer nodes in the indicator system whose number of lower - layer nodes exceeds 2. Each expert makes pairwise comparison and judgment on all lower - layer nodes of the relevant nodes according to the importance scale of 1 - 9. Denote the judgment matrix of the i - th expert for the e - th middle - layer or top - layer node as

[0115]

[0116] In the formula, a mn(m, n = 1, 2, ……, r) represents the importance of the m-th index relative to the n-th index, taking natural numbers from 1 to 9 or their reciprocals, and a mn = 1 / a nm a mm = 1

[0117] First, perform a consistency check on each judgment matrix. For the judgment matrices that fail the consistency check, the experts need to be informed to modify them until the consistency check is passed.

[0118] Then, calculate the maximum eigenvalue and the corresponding eigenvector Normalize the eigenvector to obtain the weight vector of all the lower-level nodes corresponding to the e-th middle-level or top-level node.

[0119] Obviously, the judgments of the experts are subjective, so they are not exactly the same. The final weight vector w should be obtained by comprehensively considering the judgments of all experts. e .

[0120] Take the average weight of the experts as the ideal solution, the weights of other experts as the input, and correct the weights of the indicators with large differences. Since the wavelet neural network has good function approximation ability and correction ability, in this embodiment, the weights are obtained through the wavelet neural network.

[0121] The wavelet neural network includes an input layer, a hidden layer, and an output layer. The input layer has S neurons, corresponding to S experts. The number of neurons in the hidden layer changes with the number of input experts, and the output layer has 1 neuron. X z is the z-th (z = 1, 2, …… S) input sample of the input layer. X z is the z-th (z = 1, 2, …… S) input sample of the input layer, that is, the set of judgment weights of the z-th expert for r lower-level nodes, that is

[0122]

[0123] In the formula, y is the output value, that is, the set of judgment weights after synthesis.

[0124] y = {w1, w2, … w r} (10)

[0125] The calculation formula of the hidden layer is

[0126]

[0127] In the formula: w zj is the weight value connecting the z-th node of the input layer and the j-th node of the hidden layer; w jis the weight connecting the hidden layer node j and the output layer node; a j and b j are respectively the scaling and translation scales of the hidden layer node j.

[0128] In this embodiment, the wavelet basis function adopts the Morlet wavelet function, and the wavelet function expression of node j is

[0129]

[0130] Then the output weight Finally, the weights of each index under a certain judgment node are output.

[0131] (4) Index estimation

[0132] Based on the index evaluation and the system weights, the values of each node can be calculated layer by layer from bottom to top. Assume that the e-th middle layer node has r lower layer nodes, and their node values are respectively denoted as x e1 , x e2 , ’x er , and the corresponding weight vector w e =(w e1 , w e2 ,, w er ) T , then the value of this middle layer node is

[0133]

[0134] In particular, if these r lower layer nodes are all bottom layer index nodes, then the value of this middle layer node can be directly calculated using the evaluation results of each index, and there is:

[0135]

[0136] Finally, all node values c1, c 2， c3 can be obtained. On this basis, using the weight vector w e =(w 1, w2, w3) T , the DUS performance evaluation result can be obtained:

[0137]

[0138] This embodiment provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the above method.

[0139] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0140] This embodiment provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the above method.

[0141] The above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the technical field of the present application within the technical scope disclosed by the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the application effectiveness of electronic documents based on Data Cube, characterized in that: The following steps are involved: Constructing a multi-level initial evaluation index system for electronic official document effectiveness based on the evaluation object; the multi-level initial evaluation index system for electronic official document effectiveness includes official document circulation efficiency, user use efficiency, and system application efficiency; Constructing a data cube architecture, collecting the operating data of the electronic document system, and processing the operating data through the data cube architecture; The data in the data cube is extracted, and the application efficiency of electronic documents is quantitatively evaluated in combination with the initial evaluation index system of electronic document efficiency.

2. The electronic document application efficiency evaluation method based on the data cube according to claim 1 is characterized in that: The document circulation efficiency mentioned above includes but is not limited to document elements, document sending and receiving volume and work time efficiency; The user usage efficiency includes but is not limited to workload, work complexity and processing error score; The system application performance includes but is not limited to system stability, system security, system fluency, and system satisfaction.

3. The electronic document application efficiency evaluation method based on the data cube according to claim 1 is characterized in that: The operation data includes structured data and unstructured data; the structured data is business scenario data, and the unstructured data is electronic document data.

4. The electronic document application efficiency evaluation method based on the data cube according to claim 1 is characterized in that: The Data Cube classifies and manages data from three dimensions: data source, data organization, and data security, including: According to the data source, the operation data is divided into internal circulation data, upper and lower level exchange data and parallel department exchange data; the operation data is aggregated to form an efficiency evaluation data set; and a data classification management mechanism is established to manage the operation data securely.

5. The method for evaluating the application effectiveness of electronic official documents based on a data cube according to claim 1, characterized in that Before conducting a quantitative assessment, the multi-level electronic document efficiency initial evaluation index system is screened and weights are assigned. The process includes: using the expert scoring method to obtain the indicator scores of the electronic document efficiency initial evaluation index system, selecting indicators with a cumulative contribution rate not lower than the preset value as important indicators; and assigning weights to each important indicator based on the expert scoring.

6. The electronic document application efficiency evaluation method based on the data cube according to claim 5 is characterized in that: The weight allocation process includes: comparing and judging the lower-level nodes of each relevant node in the multi-level electronic document effectiveness initial evaluation index system pairwise according to the importance scale to obtain a judgment matrix, and performing a consistency check on the judgment matrix; calculating the maximum eigenvalue and corresponding eigenvector of each judgment matrix, and performing normalization processing to obtain the weight vector of each node; correcting the weight vector of each node through a wavelet neural network to obtain the final weight of each node.

7. The electronic document application efficiency evaluation method based on the data cube according to claim 6 is characterized in that: The average of all expert evaluation values is calculated as the final evaluation value of the indicator. The comprehensive score of the electronic document application efficiency is calculated based on the node value, final weight and final evaluation value to achieve quantitative evaluation.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-7.

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