A data integrated management system and method based on the Internet of Things

Abstract

The present invention discloses a data integration management system and method based on the Internet of Things, which relates to the technical field of data integration. The method comprises the following steps: S1: connecting each computer in a local area network to the data integration management system, and collecting statistics on the resource usage of each computer in the local area network; S2: analyzing the historical situation of each computer in the local area network accessing system resources, and generating a usage rule for each computer in the local area network through data mining; S3: setting a corresponding resource usage limit for each computer according to the resource usage rule, and monitoring the real-time resource usage of each computer in the local area network; S4: analyzing the monitoring data, and issuing an early warning for the monitored abnormal data according to the data analysis result; and integrating the resource application of the computers connected to the local area network to avoid possible resource conflicts and effectively protect the integrity of shared resources.

Description

Technical Field

[0001] The present invention relates to the technical field of data integration, and in particular to a data integration management system and method based on the Internet of Things. Background Art

[0002] Integrated data management can integrate data from different application scenarios for shared use. It is the foundation of information construction and digital transformation, and can help enterprises maximize the value of data and improve their operational efficiency and decision-making capabilities. Computer data in a local area network can be shared and managed by establishing shared folders. When multiple computers use the same resources in the local area network at the same time, resource conflicts may occur. For example, if multiple computers share printer resources, when multiple computers send print tasks to a printer at the same time, the printer may be blocked or the print task may be lost; if two computers share cloud file storage resources, when multiple computers open the same file in the shared folder at the same time and modify it, it may cause file corruption or data loss. This leads to data loss or corruption, which affects work efficiency and causes certain losses.

[0003] Therefore, in order to solve the above problems or part of the problems, the present invention provides a data integration management system and method based on the Internet of Things. Summary of the Invention

[0004] The purpose of the present invention is to provide a data integration management system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a data integration management method based on the Internet of Things, comprising the following steps:

[0006] S1: Connect each computer in the LAN to the integrated data management system and collect statistics on the resource usage of each computer in the LAN;

[0007] S2: Analyze the historical access of each computer in the LAN to system resources and generate the usage rules of each computer for the LAN resources through data mining;

[0008] S3: Set resource usage limits for each computer according to resource usage rules, and monitor the real-time resource usage of each computer in the LAN;

[0009] S4: Analyze the monitoring data and issue early warnings for abnormal data detected based on the data analysis results.

[0010] Furthermore, the S1 includes:

[0011] Step S1-1: The user determines the range of computers to be managed, designs interfaces for each computer selected for access to the system, and transmits the data statistics of each computer to the data integration system;

[0012] Step S1-2: Integrate the usage of each computer connected to the system, integrate the data from multiple data sources into one data set, and save the relevant data records; so as to facilitate further analysis of the computer resource usage based on historical usage records.

[0013] Furthermore, the S2 includes:

[0014] Step S2-1: Extract historical data required for data analysis, including time, computer name, address, resource type, access method, and other information; perform data preprocessing, including data cleaning and data conversion, to ensure data usability and convert the data into a format suitable for analysis in preparation for subsequent data mining;

[0015] Step S2-2: Split the historical data of each computer into multiple transactions based on time or type. Each transaction contains one or more operations, resulting in a transaction set containing the historical operations of each computer. Data mining can reveal patterns and trends hidden in the data. These transaction sets can reflect the patterns of computers' access to shared resources.

[0016] Step S2-3: Perform statistical analysis on the transaction sets of each computer through data mining. Use an association rule algorithm to generate a frequent transaction set based on the frequent item set mining algorithm. Generate association rules based on the frequent transaction set to obtain the association between multiple computers' operations on shared resources within the local area network. For example, if computer A accesses resource a, then computer B is very likely to access resource a.

[0017] Step S2-4: Evaluate the generated association rules and calculate their confidence to determine the accuracy and feasibility of the rules. Select the rules with a confidence greater than the minimum confidence and output the rules that meet the conditions. Confidence = number of occurrences of the rule / number of occurrences of the premise item. The number of occurrences indicates the number of times the rule appears in the rule data set, and the premise item indicates the premise part of the association rule.

[0018] Step S2-5: Analyze resource usage rules when multiple computers simultaneously apply for the same resource, and calculate the request priority P of each computer according to the following formula:

[0019]

[0020] Among them, δ1, δ2, and δ3 represent the weights of each influencing factor, C represents the degree of correlation between the current computer and the computers with existing resources in the current resource allocation situation, and T i represents the time when the current computer occupies the resource for the i-th time, n represents the total number of times the current computer has occupied the resource in history, and N represents the total number of times each computer in the local area network has applied for the resource. After calculating the priority of each applicant through the above formula, resources can be allocated in descending order of priority to ensure that resources are used by applicants who need them most, so as to improve resource utilization and computer business processing efficiency.

[0021] When multiple computers apply for the same resource at the same time, the priority of each computer is sorted, and the computer with the highest priority uses the resource first; according to the derived rules, automatic resource allocation can be achieved during automatic resource scheduling.

[0022] Furthermore, the S3 includes:

[0023] Step S3-1: Monitor the real-time resource usage of each computer in the local area network to obtain the resource usage of the current system and each computer;

[0024] Step S3-2: Receive resource requests from each computer, automatically allocate resources, and provide feedback on real-time data throughout the resource usage process; this facilitates data updates and ensures the accuracy of automated allocation.

[0025] Step S3-3: Monitor the real-time feedback data, and predict the next operation of any computer in the feedback data based on the similarity between the current operation of any computer and the operation contained in any transaction in the transaction set. Combined with historical data, the start time of the next operation of the computer can be predicted through time series analysis or regression analysis. When it is monitored that there is a risk of resource conflict in the next operations of multiple computers, priority analysis can be performed in advance to prioritize the multiple computers predicted to perform synchronous operations, so as to improve the work efficiency of the computers.

[0026] Furthermore, in S4, the real-time resource usage of each computer in the local area network is obtained, and the resource application of each monitored computer is analyzed according to the historical data corresponding to each computer, including the applicant, application type, application amount, application time, etc.; an early warning is issued for abnormal resource applications monitored, including initial resource applications, abnormal resource application amounts, etc.

[0027] A data integrated management system based on the Internet of Things, the system includes: a data statistics module, a data analysis module and a resource scheduling and supervision module;

[0028] The data statistics module is used to connect to each computer connected to the data integration management system and to collect statistics on the resources applied for and called by each computer;

[0029] The data analysis module is used to analyze the resource usage of each computer connected to the data integration management system;

[0030] The resource scheduling supervision module is used to monitor the real-time resource scheduling of each computer connected to the data integration management system, and to issue early warnings for abnormal data based on the analysis results of the acquired monitoring data.

[0031] Furthermore, the data statistics module includes a database, an interface design unit, a system resource integration unit, a resource flow monitoring unit and a resource usage recording unit;

[0032] The interface management unit is used to transmit the data statistics results of each computer to the data integration management system;

[0033] The system resource integration unit is used to integrate various resources in the system and to collect statistics on the resource usage of each computer;

[0034] The resource flow monitoring unit is used to monitor the flow of various resources between various computers in the system;

[0035] The resource usage recording unit is used to record the resource usage of each computer in the access system, so as to understand the usage of the computer and to facilitate further data analysis based on the historical usage records;

[0036] Provide data query and reporting functions through the database;

[0037] Furthermore, the data analysis module includes a data processing unit, a data mining unit, and a prediction analysis unit;

[0038] The data processing unit is used to perform conventional pre-processing operations on the received data, including cleaning, denoising and other operations, so as to ensure the availability of the data and reduce the uncertainty in the data;

[0039] The data mining unit is used to mine and analyze the data statistical results in order to discover key information in the data and mine the data change rules or trends;

[0040] The prediction analysis unit is used to predict the resource usage in the system based on historical data and real-time monitoring data, and can perform predictions through methods such as time series analysis and regression analysis.

[0041] Furthermore, the resource scheduling and supervision module includes an automated scheduling unit, a monitoring data acquisition unit, and an abnormality warning unit;

[0042] The automated scheduling unit is used to receive resource call requests from various computers, allocate resources to them, and provide feedback on real-time data throughout the resource scheduling process;

[0043] The monitoring data acquisition unit is used to obtain the resource usage of the current system and each computer, and send the acquired monitoring data to the data analysis module for analysis;

[0044] The abnormal warning unit is used to issue an early warning for monitored abnormal data in order to protect the security of system resources.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention connects to each computer connected to the data integration management system through a data statistics module and collects statistics on various resources applied for and called by each computer; analyzes the resource usage of each computer connected to the data integration management system through a data analysis module; monitors the real-time resource scheduling of each computer connected to the data integration management system through a resource scheduling supervision module, and issues early warnings for abnormal data based on the analysis results of the acquired monitoring data. The data integration management system monitors multiple connected computers, performs integrated management of resource applications of computers connected to a local area network, and issues early warnings for monitored abnormal data; thus, possible resource conflicts are avoided, and the integrity and security of shared resources are effectively protected, thereby improving the efficiency of computers in using shared resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 This is a schematic diagram of the module structure of a data integration management system and method based on the Internet of Things of the present invention;

[0049] Figure 2 The present invention is a method flow diagram of a data integration management system and method based on the Internet of Things. DETAILED DESCRIPTION

[0050] 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.

[0051] See also Figure 1 、 Figure 2 The present invention is further described with reference to the accompanying drawings and specific examples.

[0052] Example 1: Figure 1 As shown, this embodiment provides a data integrated management system based on the Internet of Things, the system includes: a data statistics module, a data analysis module and a resource scheduling and supervision module;

[0053] The data statistics module is used to connect to each computer connected to the data integration management system and to collect statistics on the resources applied for and called by each computer; the data statistics module includes a database, an interface design unit, a system resource integration unit, a resource flow monitoring unit, and a resource usage recording unit;

[0054] The interface management unit is used to transmit the data statistics results of each computer to the data integration management system;

[0055] The system resource integration unit is used to integrate various resources within the system and to collect statistics on the resource usage of each computer;

[0056] The resource flow monitoring unit is used to monitor the flow of various resources between computers in the system;

[0057] The resource usage recording unit is used to record the resource usage of each computer in the access system, so as to understand the computer usage and conduct further data analysis based on the historical usage records;

[0058] Provide data query and reporting functions through the database;

[0059] The data analysis module is used to analyze the resource usage of each computer connected to the data integration management system; the data analysis module includes a data processing unit, a data mining unit, and a prediction analysis unit;

[0060] The data processing unit is used to perform conventional pre-processing operations on the received data, including cleaning, denoising and other operations, so as to ensure the availability of the data and reduce the uncertainty in the data;

[0061] The data mining unit is used to mine and analyze the data statistical results in order to discover key information in the data and mine the data change rules or trends;

[0062] The prediction analysis unit is used to predict the resource usage in the system based on historical data and real-time monitoring data, and can perform predictions through methods such as time series analysis and regression analysis.

[0063] The resource scheduling and supervision module is used to monitor the real-time resource scheduling of each computer connected to the data integration management system and issue early warnings for abnormal data based on the analysis results of the acquired monitoring data. The resource scheduling and supervision module includes an automated scheduling unit, a monitoring data acquisition unit, and an abnormality early warning unit.

[0064] The automated scheduling unit is used to receive resource call requests from each computer, allocate resources to them, and provide feedback on real-time data throughout the resource scheduling process;

[0065] The monitoring data acquisition unit is used to obtain the resource usage of the current system and each computer, and send the acquired monitoring data to the data analysis module for analysis;

[0066] The abnormal warning unit is used to issue an early warning for the monitored abnormal data. For example, it is monitored that the current computer Y's application amount for resource b is x. According to the historical usage of resource b by computer Y recorded in the system, the application amount is found to be abnormal through normal distribution or regression analysis. The abnormal data is then recorded and reported, and the historical record is updated according to the exception handling results.

[0067] Example 2: Figure 2 As shown, this embodiment provides a data integration management method based on the Internet of Things, which is implemented based on an Internet of Things-based data integration management system in the embodiment, and specifically includes the following steps:

[0068] S1: Connect each computer in the LAN to the integrated data management system and collect statistics on the resource usage of each computer in the LAN;

[0069] Step S1-1: The user determines the range of computers to be managed, designs interfaces for each computer selected for access to the system, and transmits the data statistics of each computer to the data integration system;

[0070] Step S1-2: Integrate the usage of each computer connected to the system, integrate the data from multiple data sources into one data set, and save the relevant data records; so as to facilitate further analysis of the computer resource usage based on historical usage records.

[0071] S2: Analyze the historical access of each computer in the LAN to system resources and generate the usage rules of each computer for the LAN resources through data mining;

[0072] Step S2-1: Extract historical data required for data analysis, including time, computer name, address, resource type, access method, and other information; perform data preprocessing, including data cleaning and data conversion, to ensure data usability and convert the data into a format suitable for analysis in preparation for subsequent data mining;

[0073] Step S2-2: Split the historical data of each computer into multiple transactions based on time or type. Each transaction contains one or more operations, resulting in a transaction set containing the historical operations of each computer. Data mining can reveal patterns and trends hidden in the data. These transaction sets can reflect the patterns of computers' access to shared resources.

[0074] Step S2-3: Perform statistical analysis on the transaction sets of each computer through data mining. Use an association rule algorithm to generate a frequent transaction set based on the frequent item set mining algorithm. Generate association rules based on the frequent transaction set to obtain the association between multiple computers' operations on shared resources within the local area network. For example, if computer A accesses resource a, then computer B is very likely to access resource a.

[0075] Step S2-4: Evaluate the generated association rules and calculate their confidence to determine the accuracy and feasibility of the rules. Select the rules with a confidence greater than the minimum confidence and output the rules that meet the conditions. Confidence = number of occurrences of the rule / number of occurrences of the premise item. The number of occurrences indicates the number of times the rule appears in the rule data set, and the premise item indicates the premise part of the association rule.

[0076] Step S2-5: Analyze resource usage rules when multiple computers simultaneously apply for the same resource, and calculate the request priority P of each computer according to the following formula:

[0077]

[0078] Among them, δ1, δ2, and δ3 represent the weights of each influencing factor, C represents the degree of correlation between the current computer and the computers with existing resources in the current resource allocation situation, and T irepresents the time when the current computer occupies the resource for the i-th time, n represents the total number of times the current computer has occupied the resource in history, and N represents the total number of times each computer in the local area network has applied for the resource. After calculating the priority of each applicant through the above formula, resources can be allocated in descending order of priority to ensure that resources are used by applicants who need them most, so as to improve resource utilization and computer business processing efficiency.

[0079] When multiple computers apply for the same resource at the same time, the priority of each computer is sorted, and the computer with the highest priority uses the resource first; according to the derived rules, automatic resource allocation can be achieved during automatic resource scheduling.

[0080] For example, suppose three computers, A, B, and C, simultaneously apply for the same resource, each with a different priority, with A having the highest priority, B second, and C having the lowest priority. Assume that the resource can only be used by one computer at a time. If a non-preemptive approach is used, resource allocation can be performed as follows:

[0081] 1. A applies for a resource. If no other computer is currently using the resource, A directly obtains the right to use the resource.

[0082] 2. Since A is using the resource, analyze the relevance of B, C, and A separately and update their priority data;

[0083] 3. After A releases the resources, the one with a higher priority among B and C will apply for resources first.

[0084] S3: Set resource usage limits for each computer according to resource usage rules, and monitor the real-time resource usage of each computer in the LAN;

[0085] Step S3-1: Monitor the real-time resource usage of each computer in the local area network to obtain the resource usage of the current system and each computer;

[0086] Step S3-2: Receive resource requests from each computer, automatically allocate resources, and provide feedback on real-time data throughout the resource usage process; this facilitates data updates and ensures the accuracy of automated allocation.

[0087] Step S3-3: Monitor the real-time feedback data, and predict the next operation of any computer in the feedback data based on the similarity between the current operation of any computer and the operation contained in any transaction in the transaction set. Combined with historical data, the start time of the next operation of the computer can be predicted through time series analysis or regression analysis. When it is monitored that there is a risk of resource conflict in the next operations of multiple computers, priority analysis can be performed in advance to prioritize the multiple computers predicted to perform synchronous operations, so as to improve the work efficiency of the computers.

[0088] For example, if a conflict is detected when multiple computers access a database at the same time, data loss or logical errors may occur if multiple computers write to the same database at the same time. Based on the priority analysis results of each computer, user operation conflicts can be avoided by controlling read and write permissions.

[0089] S4: Analyze monitoring data and issue warnings for abnormal data based on the results of the data analysis. In S4, the real-time resource usage of each computer in the LAN is obtained. The resource application status of each monitored computer is analyzed. Based on the historical data corresponding to each computer, including the applicant, application type, application amount, application time, etc., warnings are issued for abnormal resource applications detected, including initial resource applications and abnormal resource application amounts.

[0090] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0091] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A data integration management method based on the Internet of Things, characterized by: The following steps are involved: S1: Connect each computer in the LAN to the integrated data management system and collect statistics on the resource usage of each computer in the LAN; S2: Analyze the historical access of each computer in the LAN to system resources and generate the usage rules of each computer for the LAN resources through data mining; S3: Set resource usage limits for each computer according to resource usage rules, and monitor the real-time resource usage of each computer in the LAN; S4: Analyze the monitoring data and issue early warnings for abnormal data detected based on the data analysis results; The S2 includes: Step S2-1: extract historical data required for data analysis and perform data preprocessing on it; Step S2-2: Split the historical data of each computer into multiple transactions based on time or type. Each transaction includes one or more operations, thereby obtaining a transaction set including the historical operations of each computer. Step S2-3: Statistically analyze the transaction set of historical operations of each computer through data mining, and use association rule algorithms to generate association rules to obtain the association between operations of multiple computers on shared resources in the local area network; Step S2-4: Evaluate the generated association rules, calculate their confidence, select the rules with confidence greater than the minimum confidence, and output the rules that meet the conditions; where confidence = number of occurrences of the rule / number of occurrences of the premise item, the number of occurrences indicates the number of times the rule appears in the rule data set, and the premise item indicates the premise part of the association rule; Step S2-5: Analyze resource usage rules when multiple computers simultaneously apply for the same resource, and calculate the request priority P of each computer according to the following formula: ； Among them, δ1, δ2, and δ3 represent the weights of each influencing factor, C represents the degree of correlation between the current computer and the computers with existing resources in the current resource allocation situation, and T i represents the time when the current computer occupied the resource for the i-th time, n represents the total number of times the current computer has occupied the resource in history, and N represents the total number of times each computer in the LAN has applied for the resource; When multiple computers apply for the same resource at the same time, the computer with the highest priority will be given priority to use the resource.

2. The data integrated management method based on the Internet of Things according to claim 1, characterized in that: Said S1 comprises: Step S1-1: The user determines the range of computers to be managed, designs interfaces for each computer selected for access to the system, and transmits the data statistics of each computer to the integrated data management system; Step S1-2: Integrate the usage of each computer connected to the system, integrate the data from multiple data sources into one data set, and save the relevant data records.

3. The data integrated management method based on the Internet of Things according to claim 1, characterized in that: Said S3 includes: Step S3-1: Monitor the real-time resource usage of each computer in the local area network to obtain the resource usage of the current system and each computer; Step S3-2: Receive resource requests from each computer, automatically allocate resources, and provide feedback on real-time data throughout the resource usage process; Step S3-3: Monitor the real-time feedback data, and predict the next operation of any computer based on the similarity between the current operation of any computer in the feedback data and the operation contained in any transaction in the transaction set. Combined with historical data, the start time of the next operation of the computer can be predicted through time series analysis or regression analysis. When it is monitored that there is a risk of resource conflict in the next operations of multiple computers, priority analysis can be performed in advance to prioritize the multiple computers predicted to perform synchronous operations.

4. The data integrated management method based on the Internet of Things according to claim 1, characterized in that: In S4, the real-time resource usage of each computer in the local area network is obtained, the resource application of each monitored computer is analyzed, and based on the historical data corresponding to each computer, an early warning is issued for the monitored abnormal resource application.

5. A data integration management system based on the Internet of Things, the system being used to implement the data integration management method based on the Internet of Things according to any one of claims 1 to 4, characterized in that: The system includes: a data statistics module, a data analysis module and a resource scheduling and supervision module; The data statistics module is used to connect to each computer connected to the data integration management system and to collect statistics on the resources applied for and called by each computer; The data analysis module is used to analyze the resource usage of each computer connected to the data integration management system; The resource scheduling supervision module is used to monitor the real-time resource scheduling of each computer connected to the data integration management system, and to issue early warnings for abnormal data based on the analysis results of the acquired monitoring data.

6. The data integrated management system based on the Internet of Things according to claim 5, characterized in that: The data statistics module includes a database, an interface design unit, a system resource integration unit, a resource flow monitoring unit and a resource usage recording unit; The interface design unit is used by the user to determine the range of computers that need to be managed, design interfaces for each computer selected for access to the system, and transmit the data statistics of each computer to the integrated data management system; The system resource integration unit is used to integrate various resources in the system and to collect statistics on the resource usage of each computer; The resource flow monitoring unit is used to monitor the flow of various resources between various computers in the system; The resource usage recording unit is used to record the resource usage of each computer in the access system.

7. The data integrated management system based on the Internet of Things according to claim 5, characterized in that: The data analysis module includes a data processing unit, a data mining unit and a prediction analysis unit; The data processing unit is used to perform conventional pre-processing operations on the received data; The data mining unit is used to perform mining analysis on the data statistical results; The prediction analysis unit is used to predict the resource usage in the system based on historical data and real-time monitoring data.

8. The data integrated management system based on the Internet of Things according to claim 5, characterized in that: The resource scheduling and supervision module includes an automated scheduling unit, a monitoring data acquisition unit, and an abnormality warning unit; The automated scheduling unit is used to receive resource call requests from various computers, allocate resources to them, and provide feedback on real-time data throughout the resource scheduling process; The monitoring data acquisition unit is used to obtain the resource usage of the current system and each computer, and send the acquired monitoring data to the data analysis module for analysis; The abnormal warning unit is used to issue an early warning for the monitored abnormal data.

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