A method for optimizing enterprise data processing based on cloud computing data fusion

Abstract

The present invention relates to the technical field of enterprise data optimization in cloud computing, and specifically discloses an enterprise data optimization processing method based on cloud computing data fusion. The method collects structured, semi-structured and unstructured data from internal and external data sources in real time through a cloud platform interface, utilizes the storage resources of the cloud computing platform for hierarchical storage, and adopts encryption processing to ensure the security of the data. Through consistency detection and logical analysis, the system calculates the consistency coefficient and logical coefficient of each data respectively, and then evaluates the consistency and logical level of data storage. Through comprehensive evaluation of the consistency coefficient and logical coefficient, the system can calculate the accuracy score of each data and correct inaccurate data based on the score. The present invention introduces a support vector random algorithm to dynamically adjust the data correction coefficient and optimize the correction process, so that data processing has higher flexibility and adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise data optimization based on cloud computing, and in particular to an enterprise data optimization processing method based on cloud computing data fusion. Background Art

[0002] With the continuous advancement of informatization and digitalization, enterprises generate massive amounts of business data in their daily operations. This data comes from diverse systems and devices across multiple areas, such as sales, finance, inventory, and supply chain. As data volumes increase, effectively collecting, storing, and processing this heterogeneous data becomes a significant challenge for enterprise decision support and business optimization. Cloud computing, as a powerful technology platform, provides flexible computing and storage resources to help enterprises process massive amounts of data and support cross-departmental and cross-regional data sharing and analysis. However, with the diversification of data sources, ensuring data consistency, logic, and accuracy, and avoiding decision-making errors due to data quality issues, remains a pressing issue in enterprise data optimization.

[0003] The existing technology has the following deficiencies:

[0004] Existing cloud computing-based data processing methods mostly focus on data collection, storage, and preliminary analysis, but there are still obvious deficiencies in data quality assurance. First, existing technologies often lack effective consistency checks during the data fusion process of multiple data sources, resulting in conflicts or inconsistencies in data from different sources, affecting the accuracy of data analysis. Second, although data logic analysis is a key link in data processing, many existing solutions ignore in-depth analysis of whether the data conforms to business logic, resulting in logical errors not being discovered in a timely manner. In addition, existing methods often only focus on static analysis of data and lack a dynamic accuracy assessment mechanism. They are unable to provide enterprises with real-time and comprehensive accuracy feedback, further affecting the effectiveness of decision support. Therefore, existing technologies have obvious technical gaps in ensuring data quality and improving data consistency and logic. There is an urgent need for an innovative method that can comprehensively evaluate data consistency and logic and calculate accuracy scores. Summary of the Invention

[0005] The purpose of the present invention is to provide an enterprise data optimization processing method based on cloud computing data fusion to solve the problems in the above background.

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

[0007] A method for optimizing enterprise data processing based on cloud computing data fusion, S1: collecting structured, semi-structured, and unstructured data from internal and external data sources through a cloud platform interface, wherein the data sources include: a sales system, a financial system, a supply chain management system, and a market data source;

[0008] S2: Use the storage resources provided by the cloud computing platform to hierarchically store the collected data and encrypt the stored data to ensure data security;

[0009] S3: Perform consistency checks on stored data by comparing the same data in different data sources to identify differences between the data. Based on the identification results, the consistency coefficient of each data item is calculated, and the consistency score of the entire data set is calculated to evaluate the consistency level of data storage.

[0010] S4: Perform logical analysis on the stored data to check whether the data conforms to logical rules. Based on the logical test results, calculate the logical coefficient of each data item and calculate the logical score of the entire data set to evaluate the logical level of the stored data.

[0011] S5: Comprehensively evaluate the accuracy of daily data based on the consistency coefficient and logic coefficient of each data item, and calculate the accuracy score of each data item through normalization processing to determine whether each data item is accurate;

[0012] S6: Based on the judgment result, extract the inaccurate data, calculate the consistency correction coefficient and the logic correction coefficient of the stored data based on the consistency coefficient and the logic coefficient of the inaccurate stored data, correct the inaccurate stored data, and based on the corrected stored data, comprehensively calculate the comprehensive accuracy score of the entire data set to judge the overall accuracy of the stored data.

[0013] As a further solution of the present invention: the calculation of the consistency coefficient of each piece of data specifically includes:

[0014] Obtain the same data item from different data sources and convert it into a probability distribution, including:

[0015] If there are two data sources and data sources , and there are the same data items in both data sources ;

[0016] in, Represents each piece of data;

[0017] The data item Converted to probability distribution, recorded as and ;

[0018] in, Represents a data source Data item The probability distribution of Represents a data source Data item The probability distribution of

[0019] Calculate the difference in probability distribution between two data sources. The calculation expression is:

[0020] ;

[0021] Where, Represents a data source and data sources The difference between

[0022] According to the data source and data sources The difference between the two, calculate the data items The consistency coefficient is calculated as follows:

[0023] ;

[0024] Where, For data items The consistency coefficient of , which represents the consistency coefficient of each data.

[0025] As a further solution of the present invention: the calculation of the consistency score of the entire data set is used to evaluate the consistency level of data storage, specifically including:

[0026] Obtain the consistency coefficient of each data item, and calculate the average of the consistency coefficients of all data in a data source to obtain the consistency score of the entire data set;

[0027] Determine whether the consistency score of the entire data set is greater than or equal to a preset threshold. If so, the corresponding data storage is consistent; if not, the corresponding data storage is inconsistent.

[0028] As a further solution of the present invention: the calculation of the logic coefficient of each piece of data specifically includes:

[0029] Get each field in the dataset as a node of the Bayesian network;

[0030] Define a conditional probability table for each node in the Bayesian network;

[0031] The conditional probability table represents the probability distribution of a node under a given parent node condition;

[0032] Conduct rule testing on each piece of data to verify whether it meets the set of logical rules predetermined by the enterprise;

[0033] The Bayesian inference formula is used to calculate the logical conditional probability for each piece of data. The calculation expression is:

[0034] ;

[0035] Where, Indicates the pieces of data, Indicates the amount of data, represents the prior probability that the data is logically qualified, Represents the likelihood probability of the data under logically qualified conditions, Indicates the The marginal probability of a data point, Indicates the Logical conditional probability of a piece of data;

[0036] Based on the Bayesian inference results, the logical coefficient of each data is calculated. The calculation expression is:

[0037] ;

[0038] Where, Indicates the The logical coefficient of the data.

[0039] As a further solution of the present invention, the calculation of the logic score for the entire data set to evaluate the logic level of the stored data specifically includes:

[0040] Obtain the logic coefficient of each data item and calculate the logic score of the entire data set using the mean calculation formula;

[0041] Determine whether the logic score of the entire data set is greater than or equal to a preset threshold. If so, the entire data set complies with the logic rule; otherwise, the entire data set does not comply with the logic rule.

[0042] As a further solution of the present invention: the calculation of the accuracy score of each piece of data specifically includes:

[0043] Obtain the consistency coefficient and logic coefficient of each data, normalize and comprehensively process the consistency coefficient and logic coefficient of each data, and calculate the accuracy score of each data based on the processing results through the calculation expression of the accuracy coefficient.

[0044] As a further solution of the present invention: the determination of whether each piece of data is accurate specifically includes:

[0045] Determine whether the accuracy score of each piece of data is greater than or equal to a preset threshold. If so, the corresponding data is accurate; if not, the corresponding data is inaccurate.

[0046] As a further solution of the present invention: the calculation of the consistency correction coefficient and the logic correction coefficient of the stored data specifically includes:

[0047] Obtaining the consistency coefficient of inaccurate data during storage and logical coefficient ;

[0048] in, Indicates the pieces of data, Indicates the amount of data;

[0049] Use the support vector random algorithm to train the inaccurate data in the dataset, generate a consistency correction function, and calculate the consistency correction coefficient. The calculation expression is:

[0050] ;

[0051] Where, represents the consistency correction value calculated by the support vector randomized algorithm, Indicates the The consistency correction coefficient of the data, Indicates the The consistency coefficient of the data;

[0052] Use the support vector random algorithm to generate the logic correction function and calculate the logic correction coefficient. The calculation expression is:

[0053] ;

[0054] Where, represents the logical correction value calculated by the support vector random algorithm, Indicates the Logical index of the data, Indicates the Logical correction coefficient of the data.

[0055] As a further solution of the present invention: the correction of inaccurately stored data specifically includes:

[0056] Based on the consistency correction coefficient and the logic correction coefficient, the consistency coefficient and the logic coefficient of each data are corrected respectively to obtain the corrected data consistency coefficient and the logic coefficient. The calculation expression is:

[0057] ;

[0058] ;

[0059] Where, represents the corrected data consistency coefficient, Indicates the The consistency coefficient of the data, Indicates the The consistency correction coefficient of the data, Indicates the corrected data logic coefficient, Indicates the The logical coefficient of the data, Indicates the Logical correction coefficient of the data.

[0060] As a further solution of the present invention, the comprehensive accuracy score of the entire data set is calculated based on the corrected stored data to determine the overall accuracy of the stored data, specifically including:

[0061] Based on the revised data consistency coefficient and logic coefficient, the accuracy coefficient of each data is recalculated respectively. The mean accuracy coefficient of all data is calculated by the mean calculation formula to obtain a comprehensive accuracy score. Based on the comparison between the comprehensive accuracy score and the preset threshold, the accuracy of the stored data after correction is re-determined.

[0062] Beneficial effects of the present invention:

[0063] (1) The present invention provides an enterprise data optimization processing method based on cloud computing data fusion, which aims to solve the problems of multi-source data inconsistency, data logic conflict and data security faced by enterprises in a big data environment. Through the cloud platform interface, the system can collect structured, semi-structured and unstructured data from various data sources such as internal sales systems, financial systems, supply chain management systems, etc. in real time, and adopt a hierarchical storage strategy according to different data types to ensure efficient access and utilization of data in different business scenarios. At the same time, the data is strictly encrypted during storage to ensure that sensitive data will not be leaked or tampered with during storage and transmission. Further, through consistency detection and logical analysis, the system can accurately calculate the consistency coefficient and logical coefficient of each data, and combine the evaluation results of the two to perform intelligent correction on the data. In particular, when there are inconsistencies or logical errors in the data, the unreasonable or non-standard parts in the data can be effectively repaired by combining the calculation of the consistency correction coefficient and the logical correction coefficient, thereby optimizing the overall accuracy of the data. This technical solution not only improves the quality and credibility of the data, but also enhances the data reliability of the enterprise in decision support, business analysis and prediction models, ensuring that data-driven decisions are more scientific and accurate.

[0064] (2) By combining support vector machines, it is possible to automatically calculate the consistency correction coefficient and logic correction coefficient of each piece of data based on the consistency coefficient and logic coefficient of inaccurately stored data, and intelligently correct the data by combining historical data and business rules. Through continuous training and optimization, the correction coefficient can be continuously adjusted to adapt to different data quality issues and business scenarios, ensuring that the correction process is highly flexible and adaptable. This innovative technology not only significantly improves the overall accuracy of the data, but also can fine-tune the data correction strategy according to the actual needs of the enterprise, thereby effectively improving data processing efficiency, reducing human intervention, and providing more accurate and reliable data support for enterprise decision-making, ultimately promoting the improvement of the enterprise's data-driven decision-making capabilities in a dynamic market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The present invention will be further described below with reference to the accompanying drawings.

[0066] Figure 1 This is a flowchart of the specific steps of an enterprise data optimization processing method based on cloud computing data fusion of the present invention;

[0067] Figure 2 It is a flowchart of the steps of evaluating and correcting data accuracy in the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0069] See also Figure 1 As shown, the present invention is a method for optimizing enterprise data processing based on cloud computing data fusion, comprising the following steps:

[0070] S1: Collect structured, semi-structured, and unstructured data from internal and external data sources through cloud platform interfaces. The data sources include: sales systems, financial systems, supply chain management systems, and market data sources;

[0071] S2: Use the storage resources provided by the cloud computing platform to hierarchically store the collected data and encrypt the stored data to ensure data security;

[0072] S3: Perform consistency checks on stored data by comparing the same data in different data sources to identify differences between the data. Based on the identification results, the consistency coefficient of each data item is calculated, and the consistency score of the entire data set is calculated to evaluate the consistency level of data storage.

[0073] S4: Perform logical analysis on the stored data to check whether the data conforms to logical rules. Based on the logical test results, calculate the logical coefficient of each data item and calculate the logical score of the entire data set to evaluate the logical level of the stored data.

[0074] S5: Based on the consistency coefficient and logic coefficient of each data, the accuracy of each data is comprehensively evaluated, and the accuracy score of each data is calculated through normalization processing to determine whether each data is accurate;

[0075] S6: Based on the judgment result, extract the inaccurate data, calculate the consistency correction coefficient and the logic correction coefficient of the stored data based on the consistency coefficient and the logic coefficient of the inaccurate stored data, correct the inaccurate stored data, and based on the corrected stored data, comprehensively calculate the comprehensive accuracy score of the entire data set to judge the overall accuracy of the stored data.

[0076] In S1: structured, semi-structured, and unstructured data are collected from internal and external data sources through the cloud platform interface. The data sources include: sales systems, financial systems, supply chain management systems, and market data sources, specifically including:

[0077] Through the cloud platform interface, the system collects various data required by the enterprise from multiple internal and external data sources in real time;

[0078] Internal data sources include sales systems, financial systems, and supply chain management systems, which store structured data such as order information, financial statements, and inventory data.

[0079] Meanwhile, external data sources include market data sources, such as market analysis data from third-party platforms, social media data, or industry research reports;

[0080] This data may be in semi-structured or unstructured form, such as API data in JSON format, text files, or image data;

[0081] During the data collection process, the system uses the API interface or data synchronization tools provided by the cloud platform to ensure the real-time and integrity of the data;

[0082] For structured data, batch or real-time extraction is performed by directly connecting to the database;

[0083] For semi-structured and unstructured data, the system uses data parsing and conversion tools and uniformly formats them into a structure suitable for storage and analysis for subsequent processing and analysis;

[0084] The entire process ensures that data is smoothly and efficiently aggregated to the cloud platform, supporting enterprises in conducting large-scale data analysis and decision-making.

[0085] In S2, the collected data is stored hierarchically using the storage resources provided by the cloud computing platform. The stored data is encrypted to ensure data security. Specifically, the following steps are performed:

[0086] After data collection is completed, the system uses the storage resources provided by the cloud computing platform to hierarchically store data of different types and levels;

[0087] Structured data, such as financial statements and order records, is typically stored in high-performance relational databases for fast query and analysis. Semi-structured data, such as JSON files and log data, is stored in object storage or NoSQL databases for convenient and efficient processing and access.

[0088] Unstructured data such as images, videos, and documents are stored using file storage or distributed file systems to support large-scale data storage and backup;

[0089] Based on the access frequency and importance of data, the system automatically selects the appropriate storage method and allocates resources through policies;

[0090] In order to ensure the security of data, the system encrypts the stored data;

[0091] All sensitive data, such as customer information and financial data, will be encrypted using an encryption algorithm before being written to cloud storage to ensure that the data will not be leaked or tampered with during storage;

[0092] Industry-standard encryption algorithms, such as AES-256, ensure the security of encrypted data. Strict access control policies are also set for stored encryption keys, ensuring only authorized personnel and applications can decrypt and access sensitive data.

[0093] The data transmission process will also be encrypted through security protocols such as TLS to further enhance the overall security of the data.

[0094] In S3, the stored data is checked for consistency. By comparing the same data in different data sources, the differences between the data are identified. Based on the identification results, the consistency coefficient of each data item is calculated, and the consistency score of the entire data set is calculated to evaluate the consistency level of data storage.

[0095] Calculating the consistency coefficient of each piece of data specifically includes:

[0096] Obtain the same data item from different data sources and convert it into a probability distribution, including:

[0097] If there are two data sources and data sources , and there are the same data items in both data sources ;

[0098] in, Represents each piece of data;

[0099] The data item Converted to probability distribution, recorded as and ;

[0100] in, Represents a data source Data item The probability distribution of Represents a data source Data item The probability distribution of

[0101] Calculate the difference in probability distribution between two data sources. The calculation expression is:

[0102] ;

[0103] Where, Represents a data source and data sources The difference between

[0104] According to the data source and data sources The difference between the two values ​​is calculated. The consistency coefficient is calculated as follows:

[0105] ;

[0106] Where, For data items The consistency coefficient of , which represents the consistency coefficient of each data;

[0107] The calculation of the consistency score of the entire data set is used to evaluate the consistency level of data storage, and specifically includes:

[0108] Obtain the consistency coefficient of each data item, and calculate the average of the consistency coefficients of all data in a data source to obtain the consistency score of the entire data set;

[0109] Determine whether the consistency score of the entire data set is greater than or equal to a preset threshold. If so, the corresponding data storage is consistent; if not, the corresponding data storage is inconsistent.

[0110] It should be noted that the consistency coefficient measures the degree of matching between data from different data sources or at different time points, that is, whether the data remains consistent.

[0111] In S4, the stored data is logically analyzed to detect whether the data conforms to the logical rules. Based on the logical test results, the logical coefficient of each data item is calculated, and the logical score of the entire data set is calculated to evaluate the logical level of the stored data.

[0112] The calculation of the logic coefficient of each piece of data specifically includes:

[0113] Get each field in the dataset as a node of the Bayesian network;

[0114] Define a conditional probability table for each node in the Bayesian network;

[0115] The conditional probability table represents the probability distribution of a node under a given parent node condition;

[0116] Conduct rule testing on each piece of data to verify whether it meets the set of logical rules predetermined by the enterprise;

[0117] The Bayesian inference formula is used to calculate the logical conditional probability for each piece of data. The calculation expression is:

[0118] ;

[0119] Where, Indicates the pieces of data, Indicates the amount of data, represents the prior probability that the data is logically qualified, Represents the likelihood probability of the data under logically qualified conditions, Indicates the The marginal probability of a data point, Indicates the Logical conditional probability of a piece of data;

[0120] Based on the Bayesian inference results, the logical coefficient of each data is calculated. The calculation expression is:

[0121] ;

[0122] Where, Indicates the Logical coefficient of the data;

[0123] The calculation of the logic score for the entire data set is used to evaluate the logic level of the stored data, specifically including:

[0124] Obtain the logic coefficient of each data item and calculate the logic score of the entire data set using the mean calculation formula;

[0125] Determine whether the logic score of the entire data set is greater than or equal to a preset threshold. If so, the entire data set complies with the logic rule; otherwise, the entire data set does not comply with the logic rule.

[0126] It should be noted that the logic coefficient measures whether the data conforms to the predetermined business logic; it indicates whether the data is logically reasonable, that is, whether the value of the data conforms to business rules, constraints and conditions.

[0127] See also Figure 2 As shown, in S5, the accuracy of each piece of data is comprehensively evaluated based on the consistency coefficient and logic coefficient of each piece of data, and the accuracy score of each piece of data is calculated through normalization processing to determine whether each piece of data is accurate, specifically including:

[0128] Calculating the accuracy score of each piece of data specifically includes:

[0129] Obtain the consistency coefficient and logic coefficient of each piece of data, perform normalized comprehensive processing on the consistency coefficient and logic coefficient of each piece of data, and calculate the accuracy score of each piece of data based on the processing results using the calculation expression of the accuracy coefficient;

[0130] The calculation expression for the accuracy score of each piece of data is:

[0131] ;

[0132] Where, Indicates the pieces of data, Indicates the amount of data, No. The consistency coefficient of the data, Indicates the The logical coefficient of the data, and is the preset scale factor, and and All greater than 0;

[0133] The determination of whether each piece of data is accurate specifically includes:

[0134] Determine whether the accuracy score of each piece of data is greater than or equal to a preset threshold. If so, the corresponding data is accurate; if not, the corresponding data is inaccurate.

[0135] It should be noted that the accuracy coefficient comprehensively considers the consistency and logic of the data and measures the overall accuracy of the data.

[0136] In S6, based on the judgment result, the inaccurate data is extracted, and the consistency correction coefficient and the logic correction coefficient of the stored data are calculated based on the consistency coefficient and the logic coefficient of the inaccurate stored data, and the inaccurate stored data is corrected. Based on the corrected stored data, a comprehensive accuracy score of the entire data set is comprehensively calculated to judge the overall accuracy of the stored data;

[0137] The calculation of the consistency correction coefficient and the logic correction coefficient of the stored data specifically includes:

[0138] Obtaining the consistency coefficient of inaccurate data during storage and logical coefficient ;

[0139] in, Indicates the pieces of data, Indicates the amount of data;

[0140] Use the support vector random algorithm to train the inaccurate data in the dataset, generate a consistency correction function, and calculate the consistency correction coefficient. The calculation expression is:

[0141] ;

[0142] Where, represents the consistency correction value calculated by the support vector randomized algorithm, Indicates the The consistency correction coefficient of the data, Indicates the The consistency coefficient of the data;

[0143] Use the support vector random algorithm to generate the logic correction function and calculate the logic correction coefficient. The calculation expression is:

[0144] ;

[0145] Where, represents the logical correction value calculated by the support vector random algorithm, Indicates the Logical index of the data, Indicates the Logical correction coefficient of the data;

[0146] The correction of inaccurately stored data specifically includes:

[0147] Based on the consistency correction coefficient and the logic correction coefficient, the consistency coefficient and the logic coefficient of each data are corrected respectively to obtain the corrected data consistency coefficient and the logic coefficient. The calculation expression is:

[0148] ;

[0149] ;

[0150] Where, represents the corrected data consistency coefficient, Indicates the The consistency coefficient of the data, Indicates the The consistency correction coefficient of the data, Indicates the corrected data logic coefficient, Indicates the The logical coefficient of the data, Indicates the Logical correction coefficient of the data;

[0151] Based on the consistency and logic coefficients of the corrected data, the correction process is optimized and the parameters of the support vector randomization algorithm are dynamically adjusted to improve the correction accuracy. Through historical data analysis, the weights of consistency and logic correction are automatically optimized to adapt to the correction needs of different data sets.

[0152] The comprehensive accuracy score of the entire data set is calculated based on the corrected stored data to determine the overall accuracy of the stored data, specifically including:

[0153] Based on the revised data consistency coefficient and logic coefficient, the accuracy coefficient of each data is recalculated respectively. The mean accuracy coefficient of all data is calculated by the mean calculation formula to obtain a comprehensive accuracy score. Based on the comparison between the comprehensive accuracy score and the preset threshold, the accuracy of the stored data after correction is re-determined.

[0154] The working principle of the present invention: Through multi-dimensional data consistency and logical analysis, the accuracy evaluation and correction of enterprise storage data are achieved, including: collecting structured, semi-structured and unstructured data from multiple internal and external data sources through the cloud platform interface, and using the storage resources provided by the cloud platform for hierarchical storage and encryption processing to ensure data security; performing consistency detection and logical analysis on the stored data, calculating the consistency coefficient and logical coefficient of each data respectively, and comprehensively evaluating the accuracy of the data based on these two coefficients; calculating the accuracy score of each data through normalization processing, and calculating the consistency correction coefficient and logical correction coefficient of inaccurate data, using the support vector random algorithm to correct it, and finally calculating the accuracy score of the corrected data to judge the overall accuracy of the stored data. The present invention improves the quality of enterprise data through efficient data correction and optimization strategies, ensures the reliability and consistency of data, and provides a solid foundation for enterprise decision support and data analysis.

[0155] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0156] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using 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 program 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, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer 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 drive.

[0157] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0158] 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.

[0159] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope 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. A method for optimizing enterprise data processing based on cloud computing data fusion, characterized in that: The following steps are involved: S1: Collect structured, semi-structured, and unstructured data from internal and external data sources through cloud platform interfaces. The data sources include: sales systems, financial systems, supply chain management systems, and market data sources; S2: Use the storage resources provided by the cloud computing platform to hierarchically store the collected data and encrypt the stored data to ensure data security; S3: Perform consistency checks on stored data by comparing the same data in different data sources to identify differences between the data. Based on the identification results, the consistency coefficient of each data item is calculated, and the consistency score of the entire data set is calculated to evaluate the consistency level of data storage. S4: Perform logical analysis on the stored data to check whether the data conforms to logical rules. Based on the logical test results, calculate the logical coefficient of each data item and calculate the logical score of the entire data set to evaluate the logical level of the stored data. S5: Based on the consistency coefficient and logic coefficient of each data, the accuracy of each data is comprehensively evaluated, and the accuracy score of each data is calculated through normalization processing to determine whether each data is accurate; S6: extracting inaccurate data based on the judgment result, calculating the consistency correction coefficient and the logic correction coefficient of the stored data based on the consistency coefficient and the logic coefficient of the inaccurate stored data, correcting the inaccurate stored data, and comprehensively calculating the comprehensive accuracy score of the entire data set based on the corrected stored data to determine the overall accuracy of the stored data; The calculation of the consistency correction coefficient and the logic correction coefficient of the stored data specifically includes: Obtaining the consistency coefficient of inaccurate data during storage and logical coefficient ; in, Indicates the pieces of data, Indicates the amount of data; Use the support vector random algorithm to train the inaccurate data in the dataset, generate a consistency correction function, and calculate the consistency correction coefficient. The calculation expression is: ； Where, represents the consistency correction value calculated by the support vector randomized algorithm, Indicates the The consistency correction coefficient of the data, Indicates the The consistency coefficient of the data; Use the support vector random algorithm to generate the logic correction function and calculate the logic correction coefficient. The calculation expression is: ； Where, represents the logical correction value calculated by the support vector random algorithm, Indicates the Logical index of the data, Indicates the Logical correction coefficient of the data.

2. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: Calculating the consistency coefficient of each piece of data specifically includes: Obtain the same data item from different data sources and convert it into a probability distribution, including: If there are two data sources and data sources , and there are the same data items in both data sources ; in, Represents each piece of data; The data item Converted to probability distribution, recorded as and ; in, Represents a data source Data item The probability distribution of Represents a data source Data item The probability distribution of Calculate the difference in probability distribution between two data sources. The calculation expression is: ； Where, Represents a data source and data sources The difference between According to the data source and data sources The difference between the two, calculate the data items The consistency coefficient is calculated as follows: ； Where, For data items The consistency coefficient of , which represents the consistency coefficient of each data.

3. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: The calculation of the consistency score of the entire data set is used to evaluate the consistency level of data storage, specifically including: Obtain the consistency coefficient of each data item, and calculate the average of the consistency coefficients of all data in a data source to obtain the consistency score of the entire data set; Determine whether the consistency score of the entire data set is greater than or equal to a preset threshold. If so, the corresponding data storage is consistent; if not, the corresponding data storage is inconsistent.

4. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: The calculation of the logic coefficient of each piece of data specifically includes: Get each field in the dataset as a node of the Bayesian network; Define a conditional probability table for each node in the Bayesian network; The conditional probability table represents the probability distribution of a node under the condition of a given parent node; Conduct rule testing on each piece of data to verify whether it meets the set of logical rules predetermined by the enterprise; The Bayesian inference formula is used to calculate the logical conditional probability for each piece of data. The calculation expression is: ； Where, Indicates the pieces of data, Indicates the amount of data, represents the prior probability that the data is logically qualified, Represents the likelihood probability of the data under logically qualified conditions, Indicates the The marginal probability of a data point, Indicates the Logical conditional probability of a piece of data; Based on the Bayesian inference results, the logical coefficient of each data is calculated. The calculation expression is: ； Where, Indicates the The logical coefficient of the data.

5. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: The calculation of the logic score for the entire data set is used to evaluate the logic level of the stored data, specifically including: Obtain the logic coefficient of each data item and calculate the logic score of the entire data set using the mean calculation formula; Determine whether the logic score of the entire data set is greater than or equal to a preset threshold. If so, the entire data set complies with the logic rule; otherwise, the entire data set does not comply with the logic rule.

6. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: Calculating the accuracy score of each piece of data specifically includes: Obtain the consistency coefficient and logic coefficient of each data, normalize and comprehensively process the consistency coefficient and logic coefficient of each data, and calculate the accuracy score of each data based on the processing results through the calculation expression of the accuracy coefficient.

7. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: The determination of whether each piece of data is accurate specifically includes: Determine whether the accuracy score of each piece of data is greater than or equal to a preset threshold. If so, the corresponding data is accurate; if not, the corresponding data is inaccurate.

8. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: The correction of inaccurately stored data specifically includes: Based on the consistency correction coefficient and the logic correction coefficient, the consistency coefficient and the logic coefficient of each data are corrected respectively to obtain the corrected data consistency coefficient and the logic coefficient. The calculation expression is: ； ； Where, represents the corrected data consistency coefficient, Indicates the The consistency coefficient of the data, Indicates the The consistency correction coefficient of the data, Indicates the corrected data logic coefficient, Indicates the The logical coefficient of the data, Indicates the Logical correction coefficient of the data.

9. The enterprise data optimization processing method based on cloud computing data fusion according to claim 1 is characterized in that: The comprehensive accuracy score of the entire data set is calculated based on the corrected stored data to determine the overall accuracy of the stored data, specifically including: Based on the revised data consistency coefficient and logic coefficient, the accuracy coefficient of each data is recalculated respectively. The mean accuracy coefficient of all data is calculated by the mean calculation formula to obtain a comprehensive accuracy score. Based on the comparison between the comprehensive accuracy score and the preset threshold, the accuracy of the stored data after correction is re-determined.

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