A big data-based enterprise management system and its execution method

Abstract

The present invention relates to the field of information technology and data science and technology, and discloses an enterprise management system based on big data and an execution method thereof, comprising the following steps: step 1, establishing a multi-source data acquisition module, a data analysis module and a data management module; step 2, dividing the multi-source data acquisition module into platforms, the divided platforms including a customer relationship management platform, an Internet of Things device platform, an e-commerce platform, an operation platform and a third-party data platform, and then transmitting the data information obtained by each platform to the data analysis module through a network; step 3, establishing a cloud computing platform, a cloud analysis platform and a cloud cleaning platform in the analysis module, performing calculations on the cloud computing platform, performing multi-source data clarity determination on the cloud analysis platform, and setting a cleaning frequency on the cloud cleaning platform; and step 4, the data management module managing the image processing functions of the multi-source data acquisition module, the data analysis module and the data management module according to the multi-source data clarity determination data.

Description

Technical Field

[0001] The present invention relates to the field of information technology and data science technology, and specifically to a big data-based enterprise management system and an execution method thereof. Background Art

[0002] The background technology behind big data-based enterprise management systems and their implementation methods is closely tied to the rapid advancement of digitalization and information technology. With the continuous advancement of information technology, particularly the innovation of big data technology, enterprise management systems are increasingly adopting big data to improve operational efficiency and decision-making quality. These technologies are capable of processing and analyzing massive amounts of data, helping enterprises extract valuable information from both unstructured and structured data.

[0003] Traditional business management is undergoing a gradual transformation. Big data-based systems are not only changing management models but also providing companies with unprecedented competitive advantages. Through efficient data integration, precise analytical capabilities, and powerful decision support, companies can gain an advantage in the fiercely competitive market.

[0004] However, while enjoying the benefits of big data, businesses also face challenges in data processing and analysis. Existing data processing technologies may not perform optimally when processing massive amounts of unstructured data, such as text, images, and videos, leading to insufficient data analysis and utilization.

[0005] Therefore, while ensuring compliance and data security, companies must continuously explore and innovate to adapt to rapidly changing market environments and technological advancements. This requires not only improving existing technologies but also exploring new techniques and methods to ensure the efficiency and accuracy of data analysis and utilization. Through these efforts, companies can better leverage big data to drive sustainable business development and enhance competitiveness. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides an enterprise management system based on big data and its execution method, which has the advantage of improving data processing capabilities and solves the problem of being unable to process massive unstructured data.

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: an enterprise management system based on big data, comprising a multi-source data acquisition module, a data analysis module and a data management module;

[0008] The multi-source data acquisition module is used to convert the multi-source image information in the enterprise management system into multi-source data information. The multi-source data acquisition module includes a customer relationship management platform, an Internet of Things device platform, an e-commerce platform, an operation platform and a third-party data platform;

[0009] The customer relationship management platform obtains customer data sets and historical exchange data sets through the customer relationship management cloud system, numbers them, and connects them to the data analysis module through the network. The Internet of Things device platform obtains estimated image usage data sets and actual operation data sets through sensors and smart devices, numbers them, and connects them to the data analysis module through the network. The e-commerce platform obtains online transaction data and user behavior data through Internet information, numbers them, and connects them to the data analysis module through the network. The operation platform obtains supply chain data and financial data through the enterprise resource planning system, numbers them, and connects them to the data analysis module through the network. The third-party data platform obtains industry trend data through the enterprise management cloud system, numbers them, and connects them to the data analysis module through the network. The multi-source data acquisition module is connected to the data analysis module through the network.

[0010] The data analysis module is used to calculate the data set information of the multi-source data acquisition module. The data analysis module includes a cloud computing platform, a cloud analysis platform and a cloud cleaning platform. The cloud computing platform and the cloud analysis platform are connected through a network. The cloud computing platform calculates the customer relationship management index based on the customer data set and the historical exchange data set. The cloud computing platform calculates the image transmission clarity of IoT devices based on the estimated image usage dataset and the actual operation dataset. The cloud computing platform calculates the multi-source image conversion rate based on online transaction data, user behavior data, supply chain data set, financial data set and industry trend data , the cloud computing platform is connected to the cloud analysis platform via a network, and the cloud analysis platform is connected to the cloud cleaning platform via a network;

[0011] The multi-source data acquisition module, the data analysis module and the data management module are connected via a network.

[0012] Preferably, the customer relationship management platform numbers the customer data set and the historical exchange data set according to their characteristics, and the customer data set number is 、 、 、… , the historical exchange data set number is 、 、 、… .

[0013] Preferably, the cloud computing platform calculates the customer relationship management index based on the customer data set and the historical exchange data set , and its calculation formula is:

[0014] In the formula, represents the customer relationship management index, represents the sum of the customer dataset and the historical exchange dataset, represents the customer relationship management coefficient, Indicates the type of customer data. Indicates the type of historical exchange data.

[0015] Preferably, the IoT device platform numbers the estimated image usage dataset and the actual operation dataset according to their characteristics, and the estimated image usage dataset is numbered as 、 、 、… , the actual operation data set number is 、 、 、… .

[0016] Preferably, the cloud computing platform calculates the image transmission clarity of IoT devices based on the estimated image usage dataset and the actual operation dataset. , and its calculation formula is:

[0017] In the formula, Indicates the clarity of image transmission of IoT devices. 、 、 、… Indicates the sum of the estimated image datasets, 、 、 、… Represents the sum of the actual operation data set.

[0018] Preferably, the e-commerce platform numbers online transaction data and user behavior data according to their characteristics, and the online transaction data and user behavior data are numbered as and The operation platform numbers the supply chain data and financial data according to their characteristics. The supply chain data and financial data are numbered as and The third-party data platform numbers the industry trend data according to its characteristics. The industry trend data is numbered .

[0019] Preferably, the cloud computing platform calculates the multi-source image conversion rate based on online transaction data, user behavior data, supply chain data set, financial data set and industry trend data , and its calculation formula is:

[0020] In the formula, Indicates the conversion rate of multi-source images. Represents the image information conversion data of the e-commerce platform and represents the e-commerce platform conversion coefficient constant, Indicates the image information conversion data of the operation platform and represents the operating platform conversion coefficient constant, Indicates the image information conversion data of the third-party data platform. Indicates the conversion coefficient constant of the third-party data platform.

[0021] Preferably, the cloud analysis platform determines the clarity of multi-source data based on the information calculated by the cloud computing platform, and the determination steps are as follows:

[0022] When IoT devices transmit images with high clarity ≥96%, when the conversion rate of multiple source images ≥80% while the customer relationship management index ≥0.9, the clarity of multi-source data was judged to be between 90% and 100%;

[0023] 80%≤ When the image transmission clarity of IoT devices ≤96%, 65%≤ when multi-source image conversion rate ≤80% while 0.6≤Customer Relationship Management Index ≤0.9, the clarity of multi-source data was determined to be between 65% and 90%;

[0024] When IoT devices transmit images with high clarity ≤80%, when the conversion rate of multiple source images ≤65% while the customer relationship management index ≤0.6, the clarity of multi-source data is judged to be below 65%.

[0025] Preferably, the cloud cleaning platform sets the cleaning frequency according to the multi-source data clarity determination result of the cloud analysis platform, and the data analysis module is connected to the data management module via a network.

[0026] Preferably, a method for executing enterprise management based on big data comprises the following steps:

[0027] Step 1: Establish a multi-source data acquisition module, a data analysis module, and a data management module;

[0028] Step 2: The multi-source data acquisition module is divided into separate platforms, including the customer relationship management platform, the Internet of Things device platform, the e-commerce platform, the operation platform and the third-party data platform, and the data information obtained from each platform is transmitted to the data analysis module through the network;

[0029] Step 3: Establish a cloud computing platform, a cloud analysis platform, and a cloud cleaning platform in the analysis module. Perform calculations on the cloud computing platform, determine the clarity of multi-source data on the cloud analysis platform, and set the cleaning frequency on the cloud cleaning platform based on the multi-source data clarity determination results of the cloud analysis platform.

[0030] Step 4: The data management module manages the image processing functions of the multi-source data acquisition module, the data analysis module and the data management module according to the multi-source data clarity determination data.

[0031] Compared with the existing technology, the present invention provides an enterprise management system based on big data and its execution method, which has the following beneficial effects:

[0032] 1. The present invention can automatically receive images and other multi-source data from IoT devices through a cloud analysis platform and perform clarity determination in real time. The intelligent automation process reduces manual intervention and improves the speed and efficiency of data processing. By setting specific thresholds (such as transmission clarity, conversion rate, and customer relationship management index), the cloud platform can intelligently evaluate data quality and serve as a central hub for data analysis modules, multi-source data acquisition modules, and data management modules to access data for further data analysis and clarity determination. This promotes information sharing and collaboration among modules, thereby enhancing data processing capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the structure of the present invention;

[0034] Figure 2 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0035] 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 creative efforts are within the scope of protection of the present invention.

[0036] See also Figure 1-2 , an enterprise management system based on big data, including a multi-source data acquisition module, a data analysis module and a data management module;

[0037] The multi-source data acquisition module is used to convert the multi-source image information in the enterprise management system into multi-source data information. The multi-source data acquisition module includes the customer relationship management platform, the Internet of Things device platform, the e-commerce platform, the operation platform and the third-party data platform;

[0038] The customer relationship management platform obtains customer data sets and historical exchange data sets through the customer relationship management cloud system, numbers them, and connects them to the data analysis module through the network. The Internet of Things device platform obtains estimated image usage data sets and actual operation data sets through sensors and smart devices, numbers them, and connects them to the data analysis module through the network. The e-commerce platform obtains online transaction data and user behavior data through Internet information, numbers them, and connects them to the data analysis module through the network. The operation platform obtains supply chain data and financial data through the enterprise resource planning system, numbers them, and connects them to the data analysis module through the network. The third-party data platform obtains industry trend data through the enterprise management cloud system, numbers them, and connects them to the data analysis module through the network. The multi-source data acquisition module and the data analysis module are connected through the network.

[0039] The data analysis module is used to calculate the data set information of the multi-source data acquisition module. The data analysis module includes a cloud computing platform, a cloud analysis platform, and a cloud cleaning platform. The cloud computing platform and the cloud analysis platform are connected through a network. The cloud computing platform calculates the customer relationship management index based on the customer data set and the historical exchange data set. The cloud computing platform calculates the image transmission clarity of IoT devices based on the estimated image usage dataset and the actual operation dataset. The cloud computing platform calculates the conversion rate of multi-source images based on online transaction data, user behavior data, supply chain data sets, financial data sets and industry trend data. ,The cloud computing platform and the cloud analysis platform are connected through a network, and the cloud analysis platform and the cloud cleaning platform are connected through a network;

[0040] The multi-source data acquisition module, the data analysis module and the data management module are connected through a network.

[0041] The customer relationship management platform numbers the customer dataset and the historical exchange dataset according to their characteristics. The customer dataset is numbered as 、 、 、… , the historical exchange data set number is 、 、 、… .

[0042] The cloud computing platform calculates the customer relationship management index based on the customer data set and the historical exchange data set , and its calculation formula is:

[0043] In the formula, represents the customer relationship management index, represents the sum of the customer dataset and the historical exchange dataset, represents the customer relationship management coefficient, Indicates the type of customer data. Indicates the type of historical exchange data.

[0044] The IoT device platform numbers the estimated image usage dataset and the actual operation dataset according to their characteristics. The estimated image usage dataset is numbered as 、 、 、… , the actual operation data set number is 、 、 、… .

[0045] The cloud computing platform calculates the image transmission clarity of IoT devices based on the estimated image usage dataset and the actual operation dataset , and its calculation formula is:

[0046] In the formula, Indicates the clarity of image transmission of IoT devices. 、 、 、… Indicates the sum of the estimated image datasets, 、 、 、… Represents the sum of the actual operation data set.

[0047] The e-commerce platform numbers online transaction data and user behavior data according to their characteristics. The online transaction data and user behavior data are numbered as and The operation platform numbers the supply chain data and financial data according to their characteristics. The supply chain data and financial data are numbered as and , the third-party data platform numbers the industry trend data according to its characteristics. The industry trend data is numbered .

[0048] The cloud computing platform calculates multi-source image conversion rates based on online transaction data, user behavior data, supply chain data sets, financial data sets, and industry trend data. , and its calculation formula is:

[0049] In the formula, Indicates the conversion rate of multi-source images. Represents the image information conversion data of the e-commerce platform and represents the e-commerce platform conversion coefficient constant, Indicates the image information conversion data of the operation platform and represents the operating platform conversion coefficient constant, Indicates the image information conversion data of the third-party data platform. Indicates the conversion coefficient constant of the third-party data platform.

[0050] The cloud analysis platform determines the clarity of multi-source data based on the information calculated by the cloud computing platform. The determination steps are as follows:

[0051] When IoT devices transmit images with high clarity ≥96%, when the conversion rate of multiple source images ≥80% while the customer relationship management index ≥0.9, the clarity of multi-source data was judged to be between 90% and 100%;

[0052] 80%≤ When the image transmission clarity of IoT devices ≤96%, 65%≤ when multi-source image conversion rate ≤80% while 0.6≤Customer Relationship Management Index ≤0.9, the clarity of multi-source data was determined to be between 65% and 90%;

[0053] When IoT devices transmit images with high clarity ≤80%, when the conversion rate of multiple source images ≤65% while the customer relationship management index ≤0.6, the clarity of multi-source data is judged to be below 65%.

[0054] The advantages are: the cloud analysis platform can automatically receive images and other multi-source data from IoT devices and make clarity judgments in real time. The intelligent automation process reduces manual intervention and improves the speed and efficiency of data processing. By setting specific thresholds (such as transmission clarity, conversion rate, customer relationship management index), the cloud platform can intelligently evaluate data quality and provide further data analysis and clarity judgment. The cloud platform can serve as a central hub for data analysis modules, multi-source data acquisition modules, and data management modules to access data, promote information sharing and collaboration between modules, and thus improve data processing capabilities.

[0055] The cloud cleaning platform sets the cleaning frequency based on the multi-source data clarity determination results of the cloud analysis platform. The data analysis module and the data management module are connected through the network.

[0056] The cleaning frequency specifically refers to the image information cleaning process during the conversion of multi-source data from images. The setting steps are as follows:

[0057] (1) When the clarity of multi-source data is judged to be between 90% and 100%, the cleaning frequency is set to 1 time;

[0058] (2) When the clarity of multi-source data is determined to be between 65% and 90%, the cleaning frequency is set to 3 times;

[0059] (3) When the clarity of multi-source data is judged to be below 65% and the cleaning frequency is set to 4 times, if the data is not clear, the equipment fault troubleshooting program will be started immediately.

[0060] The advantages are: by adjusting the cleaning frequency according to the clarity of the data, the cloud cleaning platform can allocate computing and storage resources more efficiently. For data with high clarity, reducing the number of cleaning times can free up resources for other tasks, thereby improving overall resource utilization efficiency. For data with clarity far below the standard, increasing the cleaning frequency and starting a troubleshooting program can help to promptly discover potential equipment or system problems. This preventive measure can reduce long-term costs and losses and ensure the stable operation of the system. Setting different cleaning frequencies for data of different clarity can increase the speed of overall data processing. Clear data can quickly pass through the cleaning process, thereby saving time to deal with the problem of massive unstructured data.

[0061] A method for executing enterprise management based on big data, comprising the following steps:

[0062] Step 1: Establish a multi-source data acquisition module, a data analysis module, and a data management module;

[0063] Step 2: The multi-source data acquisition module is divided into separate platforms, including the customer relationship management platform, the Internet of Things device platform, the e-commerce platform, the operation platform and the third-party data platform, and the data information obtained from each platform is transmitted to the data analysis module through the network;

[0064] Step 3: Establish a cloud computing platform, a cloud analysis platform, and a cloud cleaning platform in the analysis module. Perform calculations on the cloud computing platform, determine the clarity of multi-source data on the cloud analysis platform, and set the cleaning frequency on the cloud cleaning platform based on the multi-source data clarity determination results of the cloud analysis platform.

[0065] Step 4: The data management module manages the image processing functions of the multi-source data acquisition module, the data analysis module and the data management module according to the multi-source data clarity determination data.

[0066] The advantages are: through the collaboration between the multi-source data acquisition module, the data analysis module and the data management module, and the interconnection between the modules through the network, real-time information sharing can be achieved. The purpose of this is to be able to track the conversion process of image information and data information in real time. In addition, by platform classification processing of modules, the resources of the cloud computing platform can be effectively expanded, such as increasing computing instances, improving storage capacity and optimizing data processing framework, thereby enhancing the ability to process massive unstructured data. At the same time, setting a reasonable data cleaning frequency can effectively process and analyze large-scale data sets to ensure the operating efficiency of the entire system and the accuracy of data processing.

[0067] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise management system based on big data, characterized in that: Including multi-source data acquisition module, data analysis module and data management module; The multi-source data acquisition module is used to convert the multi-source image information in the enterprise management system into multi-source data information. The multi-source data acquisition module includes a customer relationship management platform, an Internet of Things device platform, an e-commerce platform, an operation platform and a third-party data platform; The data analysis module is used to calculate the data set information of the multi-source data acquisition module. The data analysis module includes a cloud computing platform, a cloud analysis platform and a cloud cleaning platform. The cloud computing platform and the cloud analysis platform are connected through a network. The cloud computing platform calculates the customer relationship management index based on the customer data set and the historical exchange data set. The cloud computing platform calculates the image transmission clarity of IoT devices based on the estimated image usage dataset and the actual operation dataset. The cloud computing platform calculates the multi-source image conversion rate based on online transaction data, user behavior data, supply chain data set, financial data set and industry trend data , the cloud computing platform is connected to the cloud analysis platform via a network, and the cloud analysis platform is connected to the cloud cleaning platform via a network; The multi-source data acquisition module, the data analysis module and the data management module are connected via a network; The customer relationship management platform numbers the customer data set and the historical exchange data set according to their characteristics. The customer data set number is 、 、 、… , the historical exchange data set number is 、 、 、… ; The cloud computing platform calculates the customer relationship management index based on the customer data set and the historical exchange data set , and its calculation formula is: In the formula, represents the customer relationship management index, represents the sum of the customer dataset and the historical exchange dataset, represents the customer relationship management coefficient, Indicates the type of customer data. Indicates the type of historical exchange data; The e-commerce platform numbers online transaction data and user behavior data according to their characteristics. The online transaction data and user behavior data are numbered as follows: and The operation platform numbers the supply chain data and financial data according to their characteristics. The supply chain data and financial data are numbered as and The third-party data platform numbers the industry trend data according to its characteristics. The industry trend data is numbered ; The cloud computing platform calculates multi-source image conversion rates based on online transaction data, user behavior data, supply chain data sets, financial data sets, and industry trend data. , and its calculation formula is: In the formula, Indicates the conversion rate of multi-source images. Represents the image information conversion data of the e-commerce platform and represents the e-commerce platform conversion coefficient constant, Indicates the image information conversion data of the operation platform and represents the operating platform conversion coefficient constant, Indicates the image information conversion data of the third-party data platform. Indicates the conversion coefficient constant of the third-party data platform.

2. The enterprise management system based on big data according to claim 1, characterized in that: The IoT device platform numbers the estimated image usage dataset and the actual operation dataset according to their features. The estimated image usage dataset is numbered as follows: 、 、 、… , the actual operation data set number is 、 、 、… .

3. The enterprise management system based on big data according to claim 2, characterized in that: The cloud computing platform calculates the image transmission clarity of IoT devices based on the estimated image usage dataset and the actual operation dataset , and its calculation formula is: In the formula, Indicates the clarity of image transmission of IoT devices. 、 、 、… Indicates the sum of the estimated image datasets, 、 、 、… Represents the sum of the actual operation data set.

4. The enterprise management system based on big data according to claim 1, characterized in that: The cloud analysis platform determines the clarity of multi-source data based on the information calculated by the cloud computing platform, and the determination steps are as follows: When IoT devices transmit images with high clarity ≥96%, when the conversion rate of multiple source images ≥80% while the customer relationship management index ≥0.9, the clarity of multi-source data was judged to be between 90% and 100%; 80%≤ When the image transmission clarity of IoT devices ≤96%, 65%≤ when multi-source image conversion rate ≤80% while 0.6≤Customer Relationship Management Index ≤0.9, the clarity of multi-source data was determined to be between 65% and 90%; When IoT devices transmit images with high clarity ≤80%, when the conversion rate of multiple source images ≤65% while the customer relationship management index ≤0.6, the clarity of multi-source data is judged to be below 65%.

5. The enterprise management system based on big data according to claim 4, characterized in that: The cloud cleaning platform sets the cleaning frequency according to the multi-source data clarity determination result of the cloud analysis platform, and the data analysis module is connected to the data management module via a network.

6. A big data-based enterprise management execution method applied to the system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Establish a multi-source data acquisition module, a data analysis module, and a data management module; Step 2: The multi-source data acquisition module is divided into separate platforms, including the customer relationship management platform, the Internet of Things device platform, the e-commerce platform, the operation platform and the third-party data platform, and the data information obtained from each platform is transmitted to the data analysis module through the network; Step 3: Establish a cloud computing platform, a cloud analysis platform, and a cloud cleaning platform in the analysis module. Perform calculations on the cloud computing platform, determine the clarity of multi-source data on the cloud analysis platform, and set the cleaning frequency on the cloud cleaning platform. Step 4: The data management module manages the image processing functions of the multi-source data acquisition module, the data analysis module and the data management module according to the multi-source data clarity determination data.

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