Enterprise service data analysis method and system based on cloud computing

Abstract

The invention relates to the technical field of data processing, and particularly discloses an enterprise service data analysis method and system based on cloud computing, and the method comprises the steps: carrying out the logic verification of data through a business logic rule, and dividing the data into coincidence logic data and non-coincidence logic data; based on the data conforming to the logic, calculating a timeliness offset coefficient of the data, and evaluating the timeliness of data updating; by comparing data records of the ERP system and the AP I interface, a consistency coefficient is calculated, and the consistency of data sources is evaluated; comprehensively analyzing the timeliness offset coefficient and the consistency coefficient to obtain an accuracy coefficient of the data, and dividing the data into accurate data or inaccurate data according to the accuracy coefficient; accurate data is sent to a cloud computing system for real-time updating, a Hash algorithm is used for carrying out Hash calculation on the data, and the integrity of the data in the transmission process is ensured by comparing Hash values before and after sending.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an enterprise service data analysis method and system based on cloud computing. Background Art

[0002] With the rapid development of information technology and the accelerated advancement of enterprise digital transformation, the amount of data faced by enterprises is growing exponentially; these data cover sales, customer relationship management, financial management, human resources, supply chain management, marketing, technical support and services, website and application usage, and security. However, as the scale of data grows, the quality and management of data has become an urgent problem to be solved. Especially for enterprises that rely on data-driven decision-making, the accuracy, timeliness and consistency of data directly affect the effectiveness of business decisions and the operational efficiency of enterprises.

[0003] In the era of big data, enterprises are faced with the challenge of managing and analyzing massive amounts of data, especially as the diversity and complexity of data sources increase, leading to increasingly prominent data quality issues. Traditional data management methods are unable to effectively deal with data logic errors, timeliness deviations, inconsistent sources, and other issues, which in turn affect the scientificity and effectiveness of enterprise decision-making.

[0004] In the process of analyzing enterprise service data in cloud computing, due to the existence of inaccurate data in the process of real-time acquisition of enterprise service data (referred to as "data", the same below), the data does not conform to the actual situation, which will cause the cloud computing system to be unable to process the data accurately, increasing the risk of data processing, and thus causing decision makers to misjudge data based on the cloud computing system. Summary of the invention

[0005] The purpose of the present invention is to provide an enterprise service data analysis method and system based on cloud computing to solve the above-mentioned problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The enterprise service data analysis method based on cloud computing includes the following steps:

[0008] S1: Obtain enterprise service data, perform logical analysis on the data, determine whether the data is logical, and mark the data based on the analysis results to divide the data into logical data and illogical data;

[0009] S2: Based on the logical data, further analyze the data, calculate the timeliness offset coefficient of the data, and evaluate the timeliness of the data;

[0010] S3: Based on the logical data, analyze the information source of the logical data, calculate the consistency coefficient of the data, and evaluate the consistency of the data information source;

[0011] S4: Comprehensively analyze the timeliness offset coefficient of the data and the consistency coefficient of the data to determine whether the data is accurate, and divide the data into accurate data and inaccurate data based on the analysis results;

[0012] S5: Send the accurate data to the cloud computing system, update the data in real time, and perform integrity check based on the transmitted data to evaluate the integrity of the data to ensure that the hash value of the input accurate data is the same as the hash value of the received accurate data.

[0013] As a further solution of the present invention: the logical analysis of the data to determine whether the data is logically consistent specifically includes:

[0014] Define a series of logical rule features based on service requirements;

[0015] For each data record of the logical rule feature, the normal distribution is used as the probability density function to calculate the logical probability of the data;

[0016] Determine whether the logical probability of the enterprise service data is greater than or equal to the preset data logical probability threshold. If so, record it as logically consistent data; if not, record it as illogically inconsistent data.

[0017] As a further solution of the present invention: the logical probability acquisition process of the enterprise service data is:

[0018] The reasonable interval of the preset logic rule is [μ i -σ i , μ i +σ i ];

[0019] Among them, μ i is the expected value, σ i is the standard deviation, i represents the number of data records characterized by the logical rule;

[0020] For the data record x of the logical rule feature i , use the probability density function of the normal distribution to approximate the logical probability and calculate the data record x of the logical rule characteristics i Logical probability within the reasonable range of logical rules;

[0021] The logical probability calculation expression of each logical rule feature is:

[0022]

[0023] In the formula, μ iis the expected value, σ i is the standard deviation, x i Data records that represent characteristics of logical rules, such as sales volume;

[0024] The mean of the logical probabilities of all logical rule features is calculated to obtain the logical probability of the data, denoted as P a .

[0025] As a further solution of the present invention: based on the logical data, the data is further analyzed, and the timeliness offset coefficient of the data is calculated to evaluate the timeliness of the data;

[0026] The process of obtaining the timeliness offset coefficient is as follows:

[0027] Collect all logical data records, including timestamps j , and the corresponding data value v j ;

[0028] Among them, j represents the number of timestamps, and the timestamps are arranged in chronological order;

[0029] For each pair of adjacent time points t j and t j+1 Perform difference calculation to obtain the time interval Δt j ;

[0030] According to the time interval Δt j , calculate the data update frequency f j ;

[0031] For each data update frequency f j , calculate the rate of change R j ;

[0032] Choose a window size w, for each time point t j , calculate the timeliness offset coefficient within the sliding window.

[0033] As a further solution of the present invention: based on the logical data, information source analysis is performed on the logical data, and the consistency coefficient of the data is calculated to evaluate the consistency of the data information source;

[0034] The process of obtaining the consistency coefficient is as follows:

[0035] Obtain data records from the ERP system;

[0036] Get data records of API interface;

[0037] Convert the data records of the ERP system and the API interface into feature vectors, standardize each feature rule, and set the vector score for each rule;

[0038] The data records of the RP system and the data records of the corresponding API interface are regarded as a set of data for comparison;

[0039] For the feature vector information in each set of data, calculate the similarity of the feature vector;

[0040] The similarity of all feature vectors is summed up to get the consistency coefficient, which is recorded as Z a .

[0041] As a further solution of the present invention: the comprehensive analysis of the timeliness offset coefficient of the data and the consistency coefficient of the data to calculate the accuracy coefficient specifically includes:

[0042] Get the timeliness offset coefficient S of the data a ;

[0043] Obtain the consistency coefficient Z of the data a ;

[0044] The timeliness offset coefficient S of the data a The consistency coefficient Z of the data a Perform normalization to obtain the accuracy coefficient of the data, denoted as M a .

[0045] As a further solution of the present invention: the dividing of data into accurate data and inaccurate data specifically includes:

[0046] The accuracy coefficient M of the judgment data a Is it greater than or equal to the preset accuracy coefficient threshold? If so, it is recorded as accurate data; if not, it is recorded as inaccurate data.

[0047] As a further solution of the present invention: the integrity check is performed based on the transmitted data to evaluate the integrity of the data, specifically including:

[0048] Obtain data records for inputting accurate data;

[0049] Use a hash algorithm to perform hash calculations on the input accurate data and the received accurate data to generate two hash values ​​of fixed length;

[0050] Determine whether the hash value of the input accurate data is the same as the hash value of the received accurate data. If so, it means that the data is complete, if not, it means that the data is incomplete.

[0051] The cloud computing-based enterprise service data analysis system includes:

[0052] A data acquisition module, wherein the data acquisition module is used to acquire enterprise service data;

[0053] A data logic verification module, which is used to perform a logical analysis on the data to determine whether the data is logically consistent, and to mark the data according to the analysis results, dividing the data into logically consistent data and illogically inconsistent data;

[0054] A data timeliness analysis module, which further analyzes the data based on logical data, calculates the timeliness offset coefficient of the data, and evaluates the timeliness of the data;

[0055] A data consistency analysis module, which performs information source analysis on the logically consistent data based on the logically consistent data, calculates the consistency coefficient of the data, and evaluates the consistency of the data information source;

[0056] A data accuracy judgment module, which is used to comprehensively analyze the timeliness offset coefficient of the data and the consistency coefficient of the data to judge whether the data is accurate, and divide the data into accurate data and inaccurate data according to the analysis results;

[0057] The data upload and integrity verification module is used to send accurate data to the cloud computing system, update the data in real time, and perform integrity verification based on the transmitted data to evaluate the integrity of the data.

[0058] The enterprise service data analysis system based on cloud computing, the data accuracy judgment module at least includes:

[0059] A consistency coefficient calculation unit, wherein the consistency coefficient calculation unit is used to calculate the consistency coefficient of the data;

[0060] A timeliness offset coefficient calculation unit is used to calculate the timeliness offset coefficient of data.

[0061] Beneficial effects of the present invention:

[0062] (1) The present invention effectively solves the accuracy problem in enterprise data management through a multi-dimensional data verification mechanism, logical analysis, timeliness evaluation and information source consistency check. First, before the data enters the analysis process, strict logical verification is used to eliminate data that does not conform to business rules to ensure the quality of basic data for subsequent analysis. Second, the timeliness of data updates is evaluated by calculating the timeliness offset coefficient to ensure that the data can reflect the latest business conditions. Finally, by comparing the consistency of data from different sources, the authenticity and credibility of the data are enhanced. Such a design can effectively improve the accuracy of data-driven decision-making and provide solid data support for enterprise management.

[0063] (2) The present invention adopts a variety of means to ensure the security and integrity of data. By standardizing and encrypting accurate data, it not only ensures the confidentiality of data during transmission, but also improves the security level of data. In addition, by using a hash algorithm to hash the data and performing hash value comparison at the receiving end, it can effectively detect whether the data has been tampered with during transmission, thereby ensuring the integrity of the data from the source to the destination. This mechanism helps to establish user trust in the data processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention will be further described below in conjunction with the accompanying drawings.

[0065] Figure 1 It is a flowchart of the specific steps of the enterprise service data analysis method based on cloud computing of the present invention;

[0066] Figure 2 Whether the data in the present invention conforms to the logic flow diagram;

[0067] Figure 3 It is a flowchart of the enterprise service data analysis system based on cloud computing in the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example 1

[0070] See also Figure 1 As shown, the present invention is a cloud computing-based enterprise service data analysis method, comprising the following steps:

[0071] S1: Obtain enterprise service data, perform logical analysis on the enterprise service data, determine whether the data is logical, and mark the data based on the analysis results to divide the data into logical data and illogical data;

[0072] The data includes: sales data, customer data, financial data, human resources data, supply chain data, marketing data, technical support and service data, website and application data, and security data;

[0073] S2: Based on the logical data, further analyze the data, calculate the timeliness offset coefficient of the data, and evaluate the timeliness of the data;

[0074] S3: Based on the logical data, analyze the information source of the logical data, calculate the consistency coefficient of the data, and evaluate the consistency of the data information source;

[0075] S4: Comprehensively analyze the timeliness offset coefficient of the data and the consistency coefficient of the data to determine whether the data is accurate, and divide the data into accurate data and inaccurate data based on the analysis results;

[0076] S5: Send accurate data to the cloud computing system, update the data in real time, and perform integrity verification based on the transmitted data to evaluate the integrity of the data.

[0077] Example 2

[0078] See also Figure 2 As shown, based on Example 1, data is acquired, logical analysis is performed on the data, and it is determined whether the enterprise service data is logical. According to the analysis results, the data is marked and divided into logical data and illogical data, which specifically includes:

[0079] Capture data generated by internal enterprise systems (such as ERP) and external interfaces (such as API) in real time through data collection tools provided by cloud computing platforms (such as Amazon Kinesis);

[0080] Store the collected data in a cloud storage service (such as Amazon S3);

[0081] Obtain data and use the data processing framework provided by the cloud computing platform to perform logical verification on the data to check whether the data conforms to the preset business logic rules, including:

[0082] Perform data cleaning on the acquired data, check whether there are missing values ​​in each field in the data, and if so, delete or fill these records;

[0083] Check each field in the data for outliers, and if so, delete or correct those records;

[0084] A series of logic rule features are defined according to the service requirements, wherein the logic rule features include: the sales quantity cannot be a negative number, the unit price multiplied by the sales quantity equals the total sales amount, and the total sales amount cannot be a negative number;

[0085] For each data record of the logical rule feature, use the normal distribution as the probability density function to calculate its logical probability;

[0086] The logical probability acquisition process is:

[0087] The reasonable interval of the preset logic rule is [μ i -σ i , μi +σ i ];

[0088] Among them, μ i is the expected value, σ i is the standard deviation, i represents the number of data records of the logical rule feature, and i is a positive integer greater than 0;

[0089] For the data record x of the logical rule feature i , use the probability density function of the normal distribution to approximate the logical probability and calculate the data record x of the logical rule characteristics i Logical probability within the reasonable range of logical rules;

[0090] Among them, the logical probability calculation expression is:

[0091]

[0092] In the formula, μ i is the expected value, σ i is the standard deviation, x i Data records that represent characteristics of logical rules, such as sales volume;

[0093] The logical probability of all logical rule features is calculated, and the logical probability calculation expression of all logical rule features is:

[0094]

[0095] Where P a is the logical probability of the data;

[0096] The data logical probability P a Compare with the preset data logic probability threshold;

[0097] If the data logical probability P a If the data is greater than or equal to the preset data logic probability threshold, it means that the corresponding data conforms to the overall logic, which is recorded as logically conforming data;

[0098] If the data logical probability P a If the value is smaller than the preset data logic probability threshold, it means that the corresponding data does not conform to the overall logic, which is recorded as illogical data;

[0099] It should be noted that: by obtaining the data records of each logical rule feature of the data, the data logical probability P is calculated a , data logical probability P a It is used to judge whether the data logic is accurate, and when the data logic probability P a The higher the corresponding data accuracy.

[0100] Example 3

[0101] On the basis of Example 2, based on logical data, the data is further analyzed and the timeliness offset coefficient is calculated to evaluate whether the data is updated in a timely manner, specifically including:

[0102] Collect all logical data records, including timestamps j , and the corresponding data value v j ;

[0103] Where j represents the number of timestamps, j is a positive integer greater than 0, and ensures that the timestamps are arranged in chronological order;

[0104] For each pair of adjacent time points t j and t j+1 Perform difference calculation to obtain the time interval Δt j ;

[0105] According to the time interval Δt j , calculate the data update frequency f j ;

[0106] For each data update frequency f j , calculate the rate of change R j ;

[0107] Choose a window size w, for each time point t j , calculate the timeliness offset coefficient within the sliding window;

[0108] Wherein, the update frequency f j The calculation expression is:

[0109]

[0110] Among them, the change rate calculation expression is:

[0111] R j =f j+1 -f j ;

[0112] The calculation expression of the timeliness offset coefficient is:

[0113]

[0114] It should be noted that the timeliness offset coefficient S a The larger the value, the more dramatic the data changes, that is, the higher the volatility of logical data during the update process, this volatility may reduce the timeliness of the data, that is, the update frequency and change range of the data may become less timely than the overall data target, and when the timeliness offset coefficient S a The larger it is, the greater the corresponding data deviation is.

[0115] Example 4

[0116] On the basis of Example 3, based on the logical data, the information source analysis is performed on the logical data, the data records of the ERP system and the API interface are compared, and the consistency coefficient is calculated to evaluate the consistency of the data information source;

[0117] Obtain data records from the ERP system;

[0118] Get data records of API interface;

[0119] Convert the data records of the ERP system and the API interface into feature vectors, standardize each feature rule, and set the vector score for each rule;

[0120] The data records of the RP system and the data records of the corresponding API interface are regarded as a set of data for comparison;

[0121] For the feature vector information in each set of data, calculate the similarity of the feature vector;

[0122] The similarity calculation formula of the feature vector is:

[0123]

[0124] In the formula, H c is the similarity between the data feature vector of the data record of the ERP system and the data feature vector of the data record of the API interface, z c is the data feature vector of the data record of the ERP system, g c is the data feature vector of the data record of the API interface, c represents the number of data records, and c is a positive integer greater than 0;

[0125] The similarities of all feature vectors are summed up to obtain the consistency coefficient;

[0126] The calculation expression of the consistency coefficient is:

[0127]

[0128] In the formula, Z a It represents the consistency coefficient of the data;

[0129] It should be noted that the larger the consistency coefficient of the data, the higher the similarity of the data, which means the lower the possibility of abnormality in the data source, and the stronger the consistency of the information source of the corresponding data, which indicates that the source of the data is more reliable.

[0130] Example 5

[0131] On the basis of Example 4, the timeliness offset coefficient of the data and the consistency coefficient of the data are comprehensively analyzed to determine whether the data is accurate, and the data is divided into accurate data and inaccurate data according to the analysis results, specifically including:

[0132] Get the timeliness offset coefficient S of the data a ;

[0133] Obtain the consistency coefficient Z of the data a ;

[0134] The timeliness offset coefficient S of the data a The consistency coefficient Z of the data a Perform normalization to obtain the accuracy coefficient M of the data a ;

[0135] The accuracy coefficient M of the data a The calculation expression is:

[0136]

[0137] Where b1 and b2 are preset proportional coefficients, and both b1 and b2 are greater than 0, M a is the accuracy coefficient of the data, S a is the temporal offset coefficient of the data, Z a is the consistency coefficient of the data;

[0138] The accuracy coefficient of the data M a Comparing with a preset accuracy coefficient threshold;

[0139] If the accuracy coefficient of the data is M a If it is greater than or equal to the preset accuracy coefficient threshold, it means that the corresponding data is accurate and is recorded as accurate data;

[0140] If the accuracy coefficient of the data is M a If it is less than the preset accuracy coefficient threshold, it means that the corresponding data is inaccurate and is recorded as inaccurate data;

[0141] It should be noted that the accuracy coefficient M a It is used to evaluate the accuracy of data, ensure the accuracy of data during data analysis, and avoid the risk of inaccurate data to the cloud computing system. a The higher the value, the more accurate the corresponding data is and the better the quality of the collected data is.

[0142] Example 6

[0143] On the basis of Example 5, accurate data is sent to the cloud computing system, the data is updated in real time, and integrity verification is performed based on the transmitted data to evaluate the integrity of the data, specifically including:

[0144] Obtain accurate data, standardize and encrypt it before data transmission to ensure data security during transmission;

[0145] Wherein, the encryption process is implemented by using an encryption algorithm;

[0146] Compress data to reduce the time and bandwidth required for transmission;

[0147] Use secure protocols (such as HTTPSL) to send data to the cloud computing system over the network to achieve real-time streaming of data and reduce latency;

[0148] Select a suitable data storage solution (such as a cloud database) in the cloud computing system and store the encrypted data in the cloud;

[0149] Implement data access control policies to ensure that only authorized users can access data;

[0150] Implement data backup and recovery mechanisms to prevent data loss.

[0151] Obtain data records for inputting accurate data;

[0152] Use the SHA-256 algorithm to perform hash calculation on the input accurate data to generate a hash value of fixed length;

[0153] It should be noted that the characteristic of hash algorithm is that a small change in the input data will result in a huge change in the output hash value.

[0154] The hash value calculation expression of the input accurate data is:

[0155] Hash = SHA-256 (DA);

[0156] In the formula, Hash is the hash value of the input accurate data, SHA-256 is the hash value algorithm, and DA is the input accurate data;

[0157] Obtain the data record of receiving accurate data, calculate the hash value of the received accurate data by using the SHA-256 algorithm, and compare the hash value of the input accurate data with the hash value of the received accurate data;

[0158] If the hash value of the input accurate data is the same as the hash value of the received accurate data, the data is complete;

[0159] If the hash value of the input accurate data is different from the hash value of the received accurate data, the data is incomplete;

[0160] It should be noted that the consistency between the hash value of the input accurate data and the hash value of the received accurate data reflects the integrity of the data during the cloud computing transmission process; and the data is complete only when the hash value of the input accurate data remains the same as the hash value of the received accurate data.

[0161] Example 7

[0162] See also Figure 3 As shown, the enterprise service data analysis system based on cloud computing includes:

[0163] A data acquisition module, wherein the data acquisition module is used to acquire enterprise service data;

[0164] A data logic verification module, which is used to perform a logical analysis on the data to determine whether the data is logically consistent, and to mark the data according to the analysis results, dividing the data into logically consistent data and illogically inconsistent data;

[0165] A data timeliness analysis module, which further analyzes the data based on logical data, calculates the timeliness offset coefficient of the data, and evaluates the timeliness of the data;

[0166] A data consistency analysis module, which performs information source analysis on the logically consistent data based on the logically consistent data, calculates the consistency coefficient of the data, and evaluates the consistency of the data information source;

[0167] A data accuracy judgment module, which is used to comprehensively analyze the timeliness offset coefficient of the data and the consistency coefficient of the data to judge whether the data is accurate, and divide the data into accurate data and inaccurate data according to the analysis results;

[0168] The data upload and integrity verification module is used to send accurate data to the cloud computing system, update the data in real time, and perform integrity verification based on the transmitted data to evaluate the integrity of the data.

[0169] The working principle of the present invention is as follows: by performing logical analysis, timeliness evaluation, information source consistency evaluation and comprehensive analysis on the acquired enterprise service data, the accuracy and integrity of the data are ensured; first, the data is logically checked using preset business logic rules to divide the data into logically compliant data and illogically compliant data; then, based on the logically compliant data, its timeliness offset coefficient is calculated to evaluate the timeliness of data update; then, by comparing the data records of the ERP system and the API interface, the consistency coefficient is calculated to evaluate the consistency of the data source; then, by comprehensively analyzing the timeliness offset coefficient and the consistency coefficient, the accuracy coefficient of the data is obtained, and the data is divided into accurate data or inaccurate data accordingly; finally, the accurate data is sent to the cloud computing system for real-time update, and the data is hashed using a hash algorithm, and the integrity of the data during transmission is ensured by comparing the hash values ​​before and after sending; this series of steps ensures the quality and reliability of enterprise service data, and improves business operation efficiency and decision-making accuracy.

[0170] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0171] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0172] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0173] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0174] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An enterprise service data analysis method based on cloud computing, characterized in that: The following steps are involved: S1: Obtain enterprise service data (hereinafter referred to as "data"), perform logical analysis on the data, determine whether the data is logical, and mark the data according to the analysis results to divide the data into logical data and illogical data; S2: Based on the logical data, further analyze the data, calculate the timeliness offset coefficient of the data, and evaluate the timeliness of the data; S3: Based on the logical data, analyze the information source of the logical data, calculate the consistency coefficient of the data, and evaluate the consistency of the data information source; S4: Comprehensively analyze the timeliness offset coefficient of the data and the consistency coefficient of the data to determine whether the data is accurate, and divide the data into accurate data and inaccurate data based on the analysis results; S5: Send the accurate data to the cloud computing system, update the data in real time, and perform integrity check based on the transmitted data to evaluate the integrity of the data to ensure that the hash value of the input accurate data is the same as the hash value of the received accurate data.

2. The enterprise service data analysis method based on cloud computing according to claim 1, characterized in that: The logical analysis of the data to determine whether the data is logically consistent specifically includes: Define a series of logical rule features based on service requirements; For each data record of the logical rule feature, the normal distribution is used as the probability density function to calculate the logical probability of the data; Determine whether the logical probability of the enterprise service data is greater than or equal to the preset data logical probability threshold. If so, record it as logically consistent data; if not, record it as illogically inconsistent data.

3. The enterprise service data analysis method based on cloud computing according to claim 2 is characterized in that: The logical probability acquisition process of the enterprise service data is as follows: The reasonable interval of the preset logic rule is [μ i -σ i , μ i +σ i ]; Among them, μ i is the expected value, σ i is the standard deviation, i represents the number of data records characterized by the logical rule; For the data record x of the logical rule feature i , use the probability density function of the normal distribution to approximate the logical probability and calculate the data record x of the logical rule characteristics i Logical probability within the reasonable range of logical rules; The logical probability calculation expression of each logical rule feature is: In the formula, μ i is the expected value, σ i is the standard deviation, x i Data records that represent characteristics of logical rules, such as sales volume; The mean of the logical probabilities of all logical rule features is calculated to obtain the logical probability of the data, denoted as P a .

4. The enterprise service data analysis method based on cloud computing according to claim 1, characterized in that: Based on the logical data, the data is further analyzed, and the timeliness offset coefficient of the data is calculated to evaluate the timeliness of the data; The process of obtaining the timeliness offset coefficient is as follows: Collect all logical data records, including timestamps j , and the corresponding data value v j ; Among them, j represents the number of timestamps, and the timestamps are arranged in chronological order; For each pair of adjacent time points t j and t j+1 Perform difference calculation to obtain the time interval Δt j ; According to the time interval Δt j , calculate the data update frequency f j ; For each data update frequency f j , calculate the rate of change R j ; Choose a window size w, for each time point t j , calculate the timeliness offset coefficient within the sliding window.

5. The enterprise service data analysis method based on cloud computing according to claim 1, characterized in that: Based on the logical data, the information source of the logical data is analyzed, and the consistency coefficient of the data is calculated to evaluate the consistency of the data information source; The process of obtaining the consistency coefficient is as follows: Obtain data records from the ERP system; Get data records of API interface; Convert the data records of the ERP system and the API interface into feature vectors, standardize each feature rule, and set the vector score for each rule; The data records of the RP system and the data records of the corresponding API interface are regarded as a set of data for comparison; For the feature vector information in each set of data, calculate the similarity of the feature vector; The similarity of all feature vectors is summed up to get the consistency coefficient, which is recorded as Z a .

6. The enterprise service data analysis method based on cloud computing according to claim 1, characterized in that: The comprehensive analysis of the timeliness offset coefficient of the data and the consistency coefficient of the data to calculate the accuracy coefficient specifically includes: Get the timeliness offset coefficient S of the data a ; Obtain the consistency coefficient Z of the data a ; The timeliness offset coefficient S of the data a The consistency coefficient Z of the data a Perform normalization to obtain the accuracy coefficient of the data, denoted as M a .

7. The enterprise service data analysis method based on cloud computing according to claim 1, characterized in that: The classification of data into accurate data and inaccurate data specifically includes: The accuracy coefficient M of the judgment data a Is it greater than or equal to the preset accuracy coefficient threshold? If so, it is recorded as accurate data; if not, it is recorded as inaccurate data.

8. The enterprise service data analysis method based on cloud computing according to claim 1, characterized in that: The integrity check is performed based on the transmitted data to evaluate the integrity of the data, specifically including: Obtain data records for inputting accurate data; Use a hash algorithm to perform hash calculations on the input accurate data and the received accurate data to generate two hash values ​​of fixed length; Determine whether the hash value of the input accurate data is the same as the hash value of the received accurate data. If so, it means that the data is complete, if not, it means that the data is incomplete.

9. The enterprise service data analysis system based on cloud computing is characterized by: The cloud computing-based enterprise service data analysis method as described in claims 1-8 comprises: A data acquisition module, wherein the data acquisition module is used to acquire enterprise service data; A data logic verification module, which is used to perform a logical analysis on the data to determine whether the data is logically consistent, and to mark the data according to the analysis results to divide the data into logically consistent data and illogically inconsistent data; A data timeliness analysis module, which further analyzes the data based on logical data, calculates the timeliness offset coefficient of the data, and evaluates the timeliness of the data; A data consistency analysis module, which performs information source analysis on the logically consistent data based on the logically consistent data, calculates the consistency coefficient of the data, and evaluates the consistency of the data information source; A data accuracy judgment module, which is used to comprehensively analyze the timeliness offset coefficient of the data and the consistency coefficient of the data to judge whether the data is accurate, and divide the data into accurate data and inaccurate data according to the analysis results; The data upload and integrity verification module is used to send accurate data to the cloud computing system, update the data in real time, and perform integrity verification based on the transmitted data to evaluate the integrity of the data.

10. The cloud computing-based enterprise service data analysis system according to claim 9, characterized in that: The data accuracy judgment module at least includes: A consistency coefficient calculation unit, wherein the consistency coefficient calculation unit is used to calculate the consistency coefficient of the data; A timeliness offset coefficient calculation unit is used to calculate the timeliness offset coefficient of data.

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