Enterprise information management method and management system based on digitization

Abstract

The invention discloses an enterprise information management method and management system based on digitization, which solves the problems of fragmentation and non-structuring of information by digitizing enterprise information, realizes unified integration and management of data, is convenient for storage, retrieval and analysis of digital information, remarkably improves the information processing efficiency and accuracy, and is suitable for popularization and application. A spatial domain data matrix is constructed, a spatial distribution structure of information is formed, internal relation of the information is revealed, time domain features are extracted from the matrix, and a time domain data sequence is generated, so that enterprises can grasp dynamic change of the information, insight potential problems and opportunities and calculate management indexes as important indexes for evaluating information management efficiency and quality; quantitative reference is provided for a management layer, objective evaluation and continuous improvement of information management are promoted through application of management indexes, intelligent and automatic monitoring and management are achieved, management efficiency is improved, error risks are reduced, and a solid guarantee is provided for stable operation of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise information management, and particularly to a digital-based enterprise information management method and management system. Background Art

[0002] In the current digital age, enterprise information management faces unprecedented challenges and opportunities. Traditional information management methods often rely on manual recording and processing of information, which is not only inefficient but also error-prone and unable to meet the rapidly changing information management needs of modern enterprises. With the rapid development of information technology, enterprises have begun to explore converting information into digital form to improve the efficiency and accuracy of information management.

[0003] However, simply digitizing information is not sufficient to solve all problems. Enterprise information is complex and diverse, including multiple aspects such as financial data, production data, sales data, etc. There are complex interrelationships between these data. Therefore, how to effectively integrate, analyze, and utilize this digital information has become a major problem in enterprise information management. Summary of the Invention

[0004] In view of this, the present invention proposes a digital-based enterprise information management method and management system, which can effectively solve the defects of the prior art that it is unable to effectively integrate, analyze, and utilize the digital information of enterprise information.

[0005] The technical solution of the present invention is realized as follows:

[0006] A digital-based enterprise information management method, comprising:

[0007] Converting enterprise information into digital form to form an enterprise information chaos sequence, where the chaos sequence contains various types of data and their interrelationships in enterprise operations;

[0008] Constructing a spatial domain data matrix, mapping the elements in the enterprise information chaos sequence to different positions in the spatial domain data matrix to form a spatial distribution structure of information;

[0009] Extracting and analyzing time-domain features from the spatial domain data matrix to generate a time-domain data sequence, where the time-domain data sequence reflects the variation law of enterprise information over time;

[0010] Calculating a management index according to the time-domain data sequence, where the management index is used to evaluate the efficiency and quality of enterprise information management;

[0011] Monitoring and managing enterprise information using the management index.

[0012] As a further alternative of the digital-based enterprise information management method, the conversion of enterprise information into digital form to form an enterprise information chaos sequence, where the chaos sequence contains various data and their interrelationships in enterprise operations, specifically includes:

[0013] Collect various data in enterprise operations;

[0014] Extract key data features according to the requirements and goals of enterprise operations;

[0015] Convert the extracted key data features into digital form and calculate the chaos degree at each time point;

[0016] Arrange the converted digital form and chaos degree in chronological order to form an enterprise information chaos sequence.

[0017] As a further alternative of the digital-based enterprise information management method, the construction of a spatial domain data matrix, mapping the elements in the enterprise information chaos sequence to different positions in the spatial domain data matrix to form a spatial distribution structure of information, specifically includes:

[0018] Create a spatial domain data matrix;

[0019] Access the elements in the enterprise information chaos sequence one by one, and calculate the position of each element in the spatial domain data matrix based on the mapping rule of chronological order;

[0020] Fill the calculated element values into the corresponding positions in the spatial domain data matrix.

[0021] As a further alternative of the digital-based enterprise information management method, the extraction and analysis of time domain features from the spatial domain data matrix to generate a time domain data sequence, specifically includes:

[0022] Calculate the spatial weighted average, spatial standard deviation, and spatial autocorrelation coefficient at each time point in the spatial domain data matrix;

[0023] Organize the calculated spatial weighted average, spatial standard deviation, and spatial autocorrelation coefficient in chronological order;

[0024] Construct a time domain data sequence according to the organized eigenvalue.

[0025] As a further alternative of the digital-based enterprise information management method, the calculation of the spatial weighted average at each time point in the spatial domain data matrix, and the specific formula is:

[0026]

[0027] Among them, Ti represents the spatial weighted average at the i-th time point, xij represents the element value at the j-th column of the i-th row in the matrix, wij represents the weight of the element at the j-th column of the i-th row, and n is the number of columns or rows of the matrix;

[0028] Calculating the spatial standard deviation at each time point in the spatial domain data matrix, the specific formula is:

[0029]

[0030] Among them, Si represents the spatial standard deviation at the i-th time point;

[0031] Calculating the spatial autocorrelation coefficient at each time point in the spatial domain data matrix, the specific formula is:

[0032]

[0033] Among them, R(k) represents the spatial autocorrelation coefficient at a time delay of k, m is the number of rows of the matrix, is the average value of all elements, xi represents the element value at the i-th row in the matrix, and kj represents the element at the spatial position j after a time delay of k.

[0034] As a further optional solution of the digital-based enterprise information management method, calculating the management index according to the time domain data sequence specifically includes:

[0035] Calculating the average value and standard deviation of the time domain data sequence;

[0036] Calculating the coefficient of variation of the time domain data sequence based on the average value and standard deviation of the time domain data sequence, where the coefficient of variation is used to measure the degree of dispersion of the data;

[0037] Calculating the management index based on the average value, standard deviation and coefficient of variation of the time domain data sequence.

[0038] As a further optional solution of the digital-based enterprise information management method, the specific formula for calculating the average value of the time domain data sequence is:

[0039]

[0040] Among them, represents the average value, n represents the total number of elements in the time domain data sequence, and xi is the observed value at the i-th time point in the time domain data sequence;

[0041] Calculating the standard deviation of the time domain data sequence, the specific formula is:

[0042]

[0043] Among them, σ represents the standard deviation;

[0044] Calculate the coefficient of variation of the time-domain data sequence. The specific formula is:

[0045]

[0046] Among them, CV represents the coefficient of variation;

[0047] The specific formula for calculating the management index is:

[0048]

[0049] Among them, MEI represents the management index.

[0050] A digital-based enterprise information management system, comprising:

[0051] An information digitization module, configured to convert enterprise information into digital form to form a chaotic sequence containing various data and their mutual relationships in enterprise operations;

[0052] An airspace data matrix construction module, configured to construct an airspace data matrix and map elements in the enterprise information chaotic sequence to different positions in the airspace data matrix to form a spatial distribution structure of information;

[0053] A time-domain feature extraction and analysis module, configured to extract and analyze time-domain features from the airspace data matrix to generate a time-domain data sequence reflecting the variation law of enterprise information over time;

[0054] A management index calculation module, configured to calculate a management index for evaluating the efficiency and quality of enterprise information management according to the time-domain data sequence;

[0055] A monitoring and management module, configured to monitor and manage enterprise information by using the management index.

[0056] A computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the above digital-based enterprise information management methods are implemented.

[0057] A computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above digital-based enterprise information management methods are implemented.

[0058] The beneficial effects of the present invention are as follows: By converting enterprise information into digital form, the problems of information fragmentation and unstructuredness are solved, enabling various types of data and their interrelationships to be integrated and managed on a unified platform. Digital information is convenient for storage, retrieval, and analysis, greatly improving the efficiency and accuracy of information processing. An airspace data matrix is constructed, and the elements in the chaotic sequence of enterprise information are mapped to different positions, forming a spatial distribution structure of information. This structured representation helps to reveal the internal connections and potential laws between information. Extract and analyze the time-domain features from the airspace data matrix to generate a time-domain data sequence reflecting the changing law of enterprise information over time. This step enables enterprises to grasp the dynamic change trend of information, timely discover potential problems and opportunities. Calculate the management index based on the time-domain data sequence. This index, as an important indicator for evaluating the efficiency and quality of enterprise information management, provides a quantitative reference basis for enterprise management. The application of the management index enables enterprises to objectively evaluate the effectiveness of information management, timely discover the shortcomings in management and make improvements. Use the management index to monitor and manage enterprise information, realizing the intelligence and automation of information management. This method not only improves management efficiency but also reduces the risk of errors caused by human factors. The intelligent monitoring and management system can track the changes in information in real time and issue early warning signals in a timely manner, providing strong guarantee for the stable operation of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0060] Figure 1 It is a schematic flow chart of a method for enterprise information management based on digitization according to the present invention;

[0061] Figure 2 It is a schematic composition diagram of a system for enterprise information management based on digitization according to the present invention;

[0062] Figure 3 It is a schematic composition diagram of a computing device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0064] Reference Figures 1 to 3 , a digital-based enterprise information management method, comprising:

[0065] Converting enterprise information into digital form to form an enterprise information chaos sequence, where the chaos sequence contains various types of data and their interrelationships in enterprise operations;

[0066] Constructing an airspace data matrix and mapping the elements in the enterprise information chaos sequence to different positions in the airspace data matrix to form a spatial distribution structure of information;

[0067] Extracting and analyzing time-domain features from the airspace data matrix to generate a time-domain data sequence, where the time-domain data sequence reflects the changing law of enterprise information over time;

[0068] Calculating a management index according to the time-domain data sequence, where the management index is used to evaluate the efficiency and quality of enterprise information management;

[0069] Monitoring and managing enterprise information by using the management index.

[0070] In this embodiment, by converting enterprise information into digital form, the problems of information fragmentation and unstructuredness are solved, enabling various types of data and their interrelationships to be integrated and managed on a unified platform. The digital-form information is convenient for storage, retrieval, and analysis, greatly improving the efficiency and accuracy of information processing. An airspace data matrix is constructed, and the elements in the enterprise information chaos sequence are mapped to different positions to form a spatial distribution structure of information. This structured representation method helps to reveal the internal connections and potential laws between information. Time-domain features are extracted and analyzed from the airspace data matrix to generate a time-domain data sequence that reflects the changing law of enterprise information over time. This step enables the enterprise to grasp the dynamic change trend of information, timely discover potential problems and opportunities. Calculating the management index according to the time-domain data sequence, which serves as an important indicator for evaluating the efficiency and quality of enterprise information management, provides a quantitative reference basis for enterprise management. The application of the management index enables the enterprise to objectively evaluate the effectiveness of information management, timely discover the shortcoming in management and make improvements. Monitoring and managing enterprise information by using the management index realizes the intelligence and automation of information management. This method not only improves management efficiency but also reduces the risk of errors caused by human factors. The intelligent monitoring and management system can track the changes of information in real time and issue early warning signals in a timely manner, providing strong guarantee for the stable operation of the enterprise.

[0071] Preferably, the converting enterprise information into digital form to form an enterprise information chaos sequence, where the chaos sequence contains various types of data and their interrelationships in enterprise operations, specifically includes:

[0072] Collect various types of data in enterprise operations;

[0073] Extract key data features according to the requirements and goals of enterprise operations;

[0074] Convert the extracted key data features into digital form and calculate the chaos degree at each time point;

[0075] Arrange the converted digital form and chaos degree in chronological order to form an enterprise information chaos sequence.

[0076] In this embodiment, converting the extracted key data features into digital form is an important step to achieve data standardization. Digital-form data is convenient for subsequent calculations and analyses, and is also convenient for integration and comparison with other data sources. The calculation of the chaos degree is a quantitative evaluation of the dynamic characteristics of data. By calculating the chaos degree at each time point, an enterprise can capture the randomness and unpredictability in the data, which is of great significance for understanding the complexity and uncertainty of enterprise operations; arranging the converted digital form and chaos degree in chronological order to form an enterprise information chaos sequence. This step connects discrete data points into a continuous time series, thereby revealing the law of data change over time. The construction of the enterprise information chaos sequence provides rich time-dimensional information for subsequent data analysis and mining, helps the enterprise grasp market trends, predict future changes, and formulate flexible management strategies accordingly.

[0077] It should be noted that converting the extracted key data features into digital form and calculating the chaos degree at each time point, the specific calculation formula is:

[0078]

[0079] where, wi represents the weight of the i-th feature, and the feature value i is the value of the i-th feature, and n is the total number of features;

[0080]

[0081] where, N is the total number of data points, and the data point j is the actual value of the j-th data point, and the predicted value j is the predicted value of the j-th data point based on historical data.

[0082] Preferably, for constructing the spatial domain data matrix and mapping the elements in the enterprise information chaos sequence to different positions in the spatial domain data matrix to form a spatial distribution structure of information, it specifically includes:

[0083] Create a spatial domain data matrix;

[0084] Access the elements in the enterprise information chaos sequence one by one, and calculate the position of each element in the airspace data matrix based on the mapping rule of time sequence;

[0085] Fill the calculated element values into the corresponding positions of the airspace data matrix.

[0086] In this embodiment, by creating an airspace data matrix and mapping the elements in the enterprise information chaos sequence to different positions in the matrix, the conversion of information from time series to spatial distribution is realized. This spatial display method makes the information more intuitive and easy to understand, which helps enterprise decision-makers quickly grasp the overall distribution and change trend of information; in the airspace data matrix, the position of each element represents a specific time point and information category. This clear mapping relationship enhances the readability and interpretability of information. Enterprise decision-makers can more easily identify key points and outliers in the information by observing the element distribution in the matrix, so as to make more accurate decisions; the construction of the airspace data matrix provides an effective tool for the processing of complex information. By mapping information into the matrix, the parallelism and efficiency of matrix operations can be utilized to quickly screen, analyze and mine information, which not only improves the efficiency of information processing, but also reduces the demand for computing resources; the airspace data matrix not only contains the time dimension of information, but also implicitly contains the spatial dimension of information through the position relationship of elements. This multi-dimensional information structure provides a richer and deeper perspective for enterprise data analysis. Enterprises can discover the internal connections and potential laws between information by performing operations such as clustering and association rule mining on the elements in the matrix, so as to provide a more scientific basis for enterprise strategy formulation. By mapping enterprise information to the airspace data matrix, intelligent elements are introduced into enterprise information management. Based on the information representation and processing method of the matrix, it can support more intelligent information retrieval, recommendation and warning functions, and improve the information response speed and decision-making efficiency of enterprises.

[0087] Preferably, extracting and analyzing the time domain features from the airspace data matrix to generate a time domain data sequence specifically includes:

[0088] Calculate the spatial weighted average, spatial standard deviation and spatial autocorrelation coefficient of each time point in the airspace data matrix;

[0089] Organize the calculated spatial weighted average, spatial standard deviation and spatial autocorrelation coefficient in chronological order;

[0090] Construct a time domain data sequence according to the organized eigenvalues.

[0091] Preferably, the specific formula for calculating the spatial weighted average of each time point in the airspace data matrix is:

[0092]

[0093] Among them, Ti represents the spatial weighted average value at the i-th time point, xij represents the element value at the j-th column of the i-th row in the matrix, wij represents the weight of the element at the j-th column of the i-th row, and n is the number of columns or rows of the matrix;

[0094] The calculation of the spatial standard deviation of each time point in the spatial domain data matrix, the specific formula is:

[0095]

[0096] Among them, Si represents the spatial standard deviation at the i-th time point;

[0097] The calculation of the spatial autocorrelation coefficient of each time point in the spatial domain data matrix, the specific formula is:

[0098]

[0099] Among them, R(k) represents the spatial autocorrelation coefficient at a time delay of k, m is the number of rows of the matrix, is the average value of all elements, xi represents the element value at the i-th row in the matrix, and kj represents the element at the spatial position j after a time delay of k.

[0100] In this embodiment, by calculating the spatial weighted average value, spatial standard deviation, and spatial autocorrelation coefficient of each time point in the spatial domain data matrix, the statistical characteristics and change trends of information in the spatial distribution are accurately captured. These eigenvalue can comprehensively reflect the spatial distribution state of information at different time points. Organize the calculated eigenvalues in chronological order to construct a complete time-domain data sequence. The time-domain data sequence not only contains the time dimension of information, but also implicitly contains the spatial dimension characteristics of information; through matrix operations and statistical analysis methods, large-scale data can be efficiently processed, improving the efficiency of information processing. This enables enterprises to obtain and analyze information faster, make decisions in a timely manner to adapt to the rapidly changing market environment. By calculating eigenvalue such as spatial weighted average value, spatial standard deviation, and spatial autocorrelation coefficient, this solution can filter out noise and outliers, improving the accuracy and reliability of information. This helps enterprises make more informed decisions and avoid misjudgments and losses caused by inaccurate information; the construction of the time-domain data sequence enables enterprises to deeply analyze the change trends of information over time, capture potential market opportunities and risks. By introducing eigenvalue such as spatial weighted average value, spatial standard deviation, and spatial autocorrelation coefficient, the dimension of information analysis is expanded. This enables enterprises to analyze and understand information from multiple angles and levels, and discover the internal connections and potential laws between information.

[0101] Preferably, calculating the management index according to the time-domain data sequence specifically includes:

[0102] Calculate the mean and standard deviation of the time-domain data sequence;

[0103] Calculate the coefficient of variation of the time-domain data sequence based on the mean and standard deviation of the time-domain data sequence, where the coefficient of variation is used to measure the degree of dispersion of the data;

[0104] Calculate the management index based on the mean, standard deviation and coefficient of variation of the time-domain data sequence.

[0105] Preferably, the formula for calculating the mean of the time-domain data sequence is:

[0106]

[0107] where, represents the mean, n represents the total number of elements in the time-domain data sequence, and xi is the observed value at the i-th time point in the time-domain data sequence;

[0108] The formula for calculating the standard deviation of the time-domain data sequence is:

[0109]

[0110] where σ represents the standard deviation;

[0111] The formula for calculating the coefficient of variation of the time-domain data sequence is:

[0112]

[0113] where CV represents the coefficient of variation;

[0114] The formula for calculating the management index is:

[0115]

[0116] where MEI represents the management index.

[0117] In this embodiment, first, the mean and standard deviation of the time-domain data sequence are calculated. These two statistical measures are of great significance in data analysis. The mean reflects the central position of the data, while the standard deviation measures the degree of dispersion of the data. The coefficient of variation is the ratio of the standard deviation to the mean and is used to measure the relative degree of dispersion of the data. Introducing the coefficient of variation when calculating the management index can more comprehensively consider the volatility and stability of the data. Based on the mean, standard deviation, and coefficient of variation of the time-domain data sequence, the management index is comprehensively calculated. This step reflects the comprehensive use of multiple statistical measures, making the management index more comprehensive and accurate. As an important indicator for measuring the management performance of an enterprise, the accuracy of the management index is crucial. By calculating the statistical measures of the time-domain data sequence and comprehensively obtaining the management index, the management performance of the enterprise can be more accurately evaluated. This solution avoids the influence of subjective judgment on management evaluation and obtains the management index through objective data analysis and statistical calculations, making the evaluation results more fair and credible. The management index can be used as an important input parameter for the intelligent decision-making system. By analyzing the change trend and fluctuation of the management index, the enterprise can more scientifically formulate and adjust management strategies. By calculating the management index, the enterprise can more deeply understand the weak links and potential risks in the management process, thus promoting the management to develop in a more refined direction.

[0118] A digital-based enterprise information management system, comprising:

[0119] An information digitization module for converting enterprise information into digital form to form a chaotic sequence containing various data and their interrelationships in enterprise operations;

[0120] An airspace data matrix construction module for constructing an airspace data matrix and mapping the elements in the enterprise information chaotic sequence to different positions in the airspace data matrix to form a spatial distribution structure of the information;

[0121] A time-domain feature extraction and analysis module for extracting and analyzing time-domain features from the airspace data matrix to generate a time-domain data sequence reflecting the law of change of enterprise information over time;

[0122] A management index calculation module for calculating a management index for evaluating the efficiency and quality of enterprise information management according to the time-domain data sequence;

[0123] A monitoring and management module for monitoring and managing enterprise information using the management index.

[0124] A computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the above digital-based enterprise information management methods are implemented.

[0125] 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 any one of the above digital-based enterprise information management methods are implemented.

[0126] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A digital-based enterprise information management method, characterized in that: include: Converting enterprise information into digital form to form a chaotic sequence of enterprise information, wherein the chaotic sequence contains various data in enterprise operation and their interrelationships; Constructing a spatial data matrix, mapping the elements in the enterprise information chaotic sequence to different positions of the spatial data matrix to form a spatial distribution structure of information; Extract and analyze time domain features from the spatial domain data matrix to generate a time domain data sequence, wherein the time domain data sequence reflects the change pattern of enterprise information over time; Calculating a management index based on the time domain data sequence, wherein the management index is used to evaluate the efficiency and quality of enterprise information management; The management index is used to monitor and manage enterprise information.

2. A digital enterprise information management method according to claim 1, characterized in that: The enterprise information is converted into digital form to form a chaotic sequence of enterprise information. The chaotic sequence contains various types of data in the enterprise operation and their interrelationships, specifically including: Collect various data in enterprise operations; Extract key data features based on the needs and goals of business operations; Convert the extracted key data features into digital form and calculate the degree of chaos at each time point; Arrange the converted digital form and chaos degree in chronological order to form an enterprise information chaos sequence.

3. The enterprise information management method based on digitization according to claim 1 is characterized in that: The step of constructing a spatial data matrix and mapping the elements in the enterprise information chaotic sequence to different positions of the spatial data matrix to form a spatial distribution structure of information specifically includes: Create a spatial data matrix; Visit the elements in the enterprise information chaos sequence one by one, and calculate the position of each element in the spatial data matrix based on the mapping rule of time sequence; Fill the calculated element values ​​into the corresponding positions of the spatial domain data matrix.

4. The enterprise information management method based on digitization according to claim 1 is characterized in that: The step of extracting and analyzing time domain features from the spatial domain data matrix to generate a time domain data sequence specifically includes: Calculate the spatial weighted mean, spatial standard deviation and spatial autocorrelation coefficient for each time point in the spatial data matrix; The calculated spatial weighted mean, spatial standard deviation and spatial autocorrelation coefficient are organized in chronological order; Construct a time domain data sequence based on the organized eigenvalues.

5. A digital-based enterprise information management method according to claim 4, characterized in that: The specific formula for calculating the spatial weighted average of each time point in the spatial domain data matrix is: Among them, Ti represents the spatial weighted average value at the i-th time point, xij represents the element value of the i-th row and j-th column in the matrix, wij represents the weight of the element of the i-th row and j-th column, and n is the number of columns or rows in the matrix; The specific formula for calculating the spatial standard deviation of each time point in the spatial domain data matrix is: Where Si represents the spatial standard deviation at the i-th time point; The specific formula for calculating the spatial autocorrelation coefficient of each time point in the spatial domain data matrix is: Where R(k) represents the spatial autocorrelation coefficient when the time delay is k, m is the number of rows in the matrix, is the average value of all elements, xi represents the element value of the i-th row in the matrix, and kj represents the element at spatial position j after time delay k.

6. The enterprise information management method based on digitization according to claim 1 is characterized in that: The calculating the management index according to the time domain data sequence specifically includes: Calculate the mean and standard deviation of a time domain data series; Calculate the coefficient of variation of the time domain data sequence based on the mean value and standard deviation of the time domain data sequence, wherein the coefficient of variation is used to measure the degree of dispersion of the data; The management index is calculated based on the mean, standard deviation and coefficient of variation of the time domain data series.

7. A digital-based enterprise information management method according to claim 6, characterized in that: The specific formula for calculating the average value of the time domain data sequence is: in, represents the mean value, n represents the total number of elements in the time domain data sequence, and xi is the observed value at the i-th time point in the time domain data sequence; Calculate the standard deviation of the time domain data series. The specific formula is: Where σ represents the standard deviation; Calculate the coefficient of variation of the time domain data series. The specific formula is: Where CV represents the coefficient of variation; The specific formula for calculating the management index is: Among them, MEI stands for Management Index.

8. A digital enterprise information management system, characterized in that: include: Information digitization module, used to convert enterprise information into digital form, forming a chaotic sequence containing various types of data in enterprise operations and their interrelationships; A spatial data matrix construction module is used to construct a spatial data matrix and map the elements in the enterprise information chaotic sequence to different positions of the spatial data matrix to form a spatial distribution structure of information; A time domain feature extraction and analysis module, used to extract and analyze time domain features from the spatial domain data matrix to generate a time domain data sequence reflecting the law of changes in enterprise information over time; A management index calculation module, used to calculate a management index for evaluating the efficiency and quality of enterprise information management according to the time domain data sequence; The monitoring and management module uses the management index to monitor and manage enterprise information.

9. A computing device, characterized in that The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the digital-based enterprise information management method described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the digital-based enterprise information management method described in any one of claims 1 to 7.

Images