Tablet computer operation monitoring system and method based on Internet of Things

Abstract

The present invention discloses a tablet computer operation monitoring system and method based on the Internet of Things, comprising a smart device, a communication network transmission, an operation monitoring system, an interface display terminal, and a real-time update module. The present invention relates to the field of data processing technology. The tablet computer operation monitoring system and method based on the Internet of Things, by providing a tablet computer operation monitoring system, collects and aggregates real-time operation data of the smart device for storage, establishes an operation model based on the initial operation data of the smart device, processes the collected real-time operation data, extracts abnormal data content for analysis, and introduces the abnormal situation into the operation model to generate optimized data. In this way, not only can the original conventional anomalies that occurred be quickly matched during the monitoring process, but also the optimized data can be quickly obtained for personnel to confirm and use, thereby effectively improving the efficiency of tablet computer operation monitoring and operation optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a tablet computer operation monitoring system and method based on the Internet of Things. Background Art

[0002] The reference patent name is: Mobile hard disk operation monitoring system based on the Internet of Things (patent publication number: CN117555761A, patent publication date: 2024-02-13), including: a data acquisition module for collecting temperature data, vibration data and voltage data; a data analysis module for obtaining the target starting segment and the ending segment of the voltage data according to the temperature data, vibration data and voltage data, and obtaining the target ending segment; a data decomposition module for obtaining the target starting segment traversal set and the target ending segment traversal set according to the target starting segment and the target ending segment; obtaining the optimal IMF component of the temperature data according to the target starting segment traversal set and the target ending segment traversal set; a data denoising module for denoising the temperature data according to the optimal IMF component of the temperature data. By continuously traversing the endpoints and the value of the envelope point, the influence of the EMD endpoint effect is greatly reduced, thereby achieving the purpose of effectively removing noise data.

[0003] Based on the description in the above-mentioned document, during the operation of existing tablet computers, abnormal data will be generated, which will affect the use of the tablet computer. For example, the trigger command cannot be responded to due to insufficient memory or unstable network. The abnormal problems obtained by monitoring cannot be directly matched and applied using the original processed solutions, so that the efficiency of subsequent data optimization is low. Therefore, the present invention provides a tablet computer operation monitoring system and method based on the Internet of Things. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a tablet computer operation monitoring system and method based on the Internet of Things, which solves the problem that abnormal data is generated during the operation of the existing tablet computer, affecting the use of the tablet computer, and the abnormal problems obtained by monitoring cannot be directly matched and applied using the original processed solutions, resulting in low efficiency of subsequent data optimization.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A tablet computer operation monitoring system based on the Internet of Things, comprising:

[0006] Smart devices, by installing corresponding monitoring software on the tablet computer, upload the operation data;

[0007] Communication network transmission: connecting smart devices with the operation monitoring system through wireless communication technology for data transmission;

[0008] The operation monitoring system receives and processes the operation data of smart devices to implement data analysis, optimization and transmission operations, including:

[0009] The operation data acquisition module collects and aggregates the real-time operation data of smart devices for storage, and also marks the path of the collected data;

[0010] The operation data processing module builds an operation model based on the initial operation data of the smart device, processes the collected real-time operation data, extracts abnormal data content for analysis, and introduces the abnormal situation into the operation model to generate optimized data;

[0011] The optimization data transmission module converts the obtained optimization data into optimization instructions and transmits them to the real-time update module through the communication network to complete the optimization operation of the smart device;

[0012] The interface display terminal displays the operating status and warning information in the form of charts and text, and personnel can also perform remote control;

[0013] The real-time update module receives the optimized data and transmits it to the smart device for update operations.

[0014] Preferably, the operation data acquisition module collects, summarizes and stores the real-time operation data in the following steps:

[0015] A1. Collect and classify real-time running data, and store them according to the type at the time of data collection;

[0016] A2. Mark the collection location, output path, output method, and receiving location of different types of data to form multiple traceability paths for different types of data;

[0017] A3. When an anomaly is found in a certain data, the initial collection location can be found by tracing the path, and the optimized data can be transmitted and updated.

[0018] Preferably, the operation of establishing the operation model based on the initial operation data of the smart device in the operation data processing module is:

[0019] B1. Form an initial data set by extracting and aggregating the initial operating data of smart devices from the repository;

[0020] B2. Based on the initial data set, establish an operation model that operates in the same manner as the actual smart device, and update the operation model data according to the real-time operation data to maintain consistency with the real-time operation data update;

[0021] B3. Introduce historical abnormal data and data that generate corresponding problems and solutions into the operation model to determine and arrange subsequent solutions;

[0022] B4. After running the model and applying the optimized data, it is determined whether the data update of the actual smart device is achieved based on the optimized results.

[0023] Preferably, the operation of processing the collected real-time operation data in the operation data processing module is:

[0024] C1. Extract abnormal data based on real-time operation data and mark and extract abnormal data through instruction response algorithm;

[0025] C2. The extracted abnormal data is introduced into the operation model for abnormal data matching. After matching, the corresponding problem data is obtained. The unmatched abnormal data is processed by combining network search and human judgment;

[0026] C3. Generate corresponding solution data based on the problem data fed back by the corresponding abnormal data. After the solution data is generated, it is transmitted to the personnel control to decide whether to complete the data optimization operation of the smart device.

[0027] Preferably, the instruction response algorithm in C1 performs the marking and extraction operations of abnormal data as follows:

[0028] c11. Command response operation is the subsequent response data generated by triggering a command through a normally running application during the operation of the smart device;

[0029] c12. There are two situations for marking abnormal data:

[0030] Case 1: After the application triggers the command, the command will be transmitted to the corresponding operation interface response, and then the interface data after the command triggering will be displayed after judging whether the response is completed;

[0031] Case 2: After the application triggers the command, the command will be transmitted to the corresponding operation interface for response. Whether the time from the command trigger to the response display meets the normal response threshold;

[0032] c13. Sort out the results according to the corresponding situations to form abnormal data and normal data, extract the abnormal data, and filter and delete the normal data.

[0033] Preferably, the results produced in different situations in c12 are:

[0034] Result 1: After the trigger command is applied, the response interface data is displayed normally. The time from the command trigger to the response display is recorded as T, and the average response threshold based on the normal response is obtained as S. If T≤S, the response interface data is normal data. Conversely, if T>S, the response interface data is abnormal data.

[0035] Result 2: After the application triggers the command, the response interface data cannot be displayed, then the response interface data is abnormal data;

[0036] The resulting abnormal data are collected to form an abnormal data set marked as M.

[0037] Preferably, the abnormal data in C2 is introduced into the running model to perform the abnormal data matching operation as follows:

[0038] c21. Based on any abnormal data subset selected from the abnormal data set M, first trace the application name data generated by the abnormal data subset;

[0039] c22. First, match the application name data with the application name of the historical abnormal data in the running model, and complete the match if the application name data content of the two is the same;

[0040] c23. Then, the abnormal data subset is matched with the abnormal data with the same application name obtained by matching. At this time, the abnormal data that meets the threshold after the similarity calculation is matched, and the matched data is marked as a regular abnormal data set. Conversely, the abnormal data that fails to meet the similarity threshold is marked as a new abnormal data set.

[0041] Preferably, the similarity calculation method in c23 is:

[0042] By comparing the sequential content features in the abnormal data subset with the sequential content features in each abnormal data in the running model, and marking the number of content feature words in the abnormal data subset as m, the number of words that are the same as the sequential content features in each abnormal data in the running model is n, and the set pixel threshold is marked as l. The conditions for satisfying the conventional abnormal data set are: , otherwise it is a new type of abnormal data set.

[0043] Preferably, the operation of generating corresponding solution data from the problem data fed back by the corresponding abnormal data in C3 is:

[0044] c31. After the data identified as a regular abnormal data set is fed back by the running model with the corresponding problem, the corresponding solution data is matched and transmitted;

[0045] c32. If a new abnormal data set is determined, it will be transmitted to the interface display terminal controlled by personnel, and repaired through network search or by professionals;

[0046] c33. The solution data generated based on c31-c32 are all transmitted to the interface display terminal, and personnel control whether to optimize.

[0047] The present invention also discloses a monitoring method for a tablet computer operation monitoring system based on the Internet of Things, which specifically includes the following steps:

[0048] D1. Start the corresponding monitoring software installed on the tablet computer and connect the smart device to the interface display terminal of the running monitoring system through wireless communication technology to complete data transmission;

[0049] D2. Receive and process the operating data of smart devices through the operation monitoring system, extract abnormal data and match them to obtain corresponding solutions. Finally, personnel control whether the solutions are implemented to complete the optimization of smart devices.

[0050] D3. When optimization is determined, the data is transmitted to the real-time update module through the communication network, thereby completing the data transmission to the smart device for optimization operation.

[0051] The present invention provides a tablet computer operation monitoring system and method based on the Internet of Things. Compared with the existing technology, it has the following advantages:

[0052] (1) The tablet computer operation monitoring system and method based on the Internet of Things, by setting up a tablet computer operation monitoring system, collects and aggregates the real-time operation data of the smart device for storage, establishes an operation model based on the initial operation data of the smart device, processes the collected real-time operation data, extracts abnormal data content for analysis, and introduces the abnormal situation into the operation model to generate optimized data. In this way, not only can the original conventional abnormalities that have occurred be quickly matched during the monitoring process, but also the optimized data can be obtained quickly for personnel to confirm and use, thereby effectively improving the efficiency of tablet computer operation monitoring and operation optimization.

[0053] (2) The operation monitoring system and method of the tablet computer based on the Internet of Things introduces the extracted abnormal data into the operation model to match the abnormal data. After matching, the corresponding problem data is obtained, and the unmatched abnormal data is processed by combining network search and human judgment, and the corresponding solution data is generated from the problem data fed back by the corresponding abnormal data. After the solution data is generated, it is transmitted to the human control to decide whether to complete the data optimization operation of the smart device, so as to more accurately match the specific problem of the corresponding application and improve the accuracy of the optimization of the corresponding abnormal data.

[0054] (3) The operation monitoring system and method of the tablet computer based on the Internet of Things uses the instruction response operation to generate subsequent response data after a certain application in normal operation triggers an instruction during the operation of the smart device, and divides the conventional abnormal data set and the new abnormal data set based on the response result and the similarity threshold, so as to quickly process the conventional abnormal data set, while the new abnormal data set is processed by human judgment, so as to make the monitoring results clearer and improve the accuracy of the monitoring system processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a functional block diagram of the operation monitoring system of the present invention;

[0056] Figure 2 This is an operational flow chart of the data processing module of the present invention;

[0057] Figure 3 An operational flow chart of the monitoring method for running the present invention;

[0058] Figure 4 This is a logic judgment diagram of the data processing module of the present invention. DETAILED DESCRIPTION

[0059] 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.

[0060] See also Figures 1-4 , the present invention provides two technical solutions:

[0061] Example 1: A tablet computer operation monitoring system based on the Internet of Things, comprising:

[0062] Smart devices, by installing corresponding monitoring software on the tablet computer, upload the operation data;

[0063] Communication network transmission: connecting smart devices with the operation monitoring system through wireless communication technology for data transmission;

[0064] The operation monitoring system receives and processes the operation data of smart devices to implement data analysis, optimization and transmission operations, including:

[0065] The operation data acquisition module collects and aggregates the real-time operation data of smart devices for storage, and also marks the path of the collected data;

[0066] The operation data processing module builds an operation model based on the initial operation data of the smart device, processes the collected real-time operation data, extracts abnormal data content for analysis, and introduces the abnormal situation into the operation model to generate optimized data;

[0067] The optimization data transmission module converts the obtained optimization data into optimization instructions and transmits them to the real-time update module through the communication network to complete the optimization operation of the smart device;

[0068] The interface display terminal displays the operating status and warning information in the form of charts and text, and personnel can also perform remote control;

[0069] The real-time update module receives the optimized data and transmits it to the smart device for update operations.

[0070] Among them, by setting up a tablet computer operation monitoring system, the real-time operation data of the smart device is collected and summarized for storage, the operation model is established based on the initial operation data of the smart device, the collected real-time operation data is processed, the abnormal data content is extracted for analysis, and the abnormal situation is introduced into the operation model to generate optimized data. In this way, not only can the original regular anomalies that occurred be quickly matched during the monitoring process, but the optimized data can also be obtained more quickly for personnel to confirm and use, thereby effectively improving the efficiency of tablet computer operation monitoring and operation optimization.

[0071] In the embodiment of the present invention, the steps of the operation data acquisition module collecting, summarizing and storing the real-time operation data are as follows:

[0072] A1. Collect and classify real-time running data, and store them according to the type at the time of data collection;

[0073] A2. Mark the collection location, output path, output method, and receiving location of different types of data to form multiple traceability paths for different types of data;

[0074] A3. When an anomaly is found in a certain data, the initial collection location can be found by tracing the path, and the optimized data can be transmitted and updated.

[0075] Among them, the tracing path is to find the application and touch instruction location where the data anomaly starts according to the corresponding tracing path retained after discovering abnormal data, so as to match the same application and touch instruction location to find the corresponding problems and solutions, and solve the abnormal problems more quickly.

[0076] In the embodiment of the present invention, the operation of establishing the operation model based on the initial operation data of the smart device in the operation data processing module is:

[0077] B1. Form an initial data set by extracting and aggregating the initial operating data of smart devices from the repository;

[0078] B2. Based on the initial data set, establish an operation model that operates in the same manner as the actual smart device, and update the operation model data according to the real-time operation data to maintain consistency with the real-time operation data update;

[0079] B3. Introduce historical abnormal data and data that generate corresponding problems and solutions into the operation model to determine and arrange subsequent solutions;

[0080] B4. After running the model and applying the optimized data, it is determined whether the data update of the actual smart device is achieved based on the optimized results.

[0081] In the embodiment of the present invention, the operation of processing the collected real-time operation data in the operation data processing module is as follows:

[0082] C1. Extract abnormal data based on real-time operation data and mark and extract abnormal data through instruction response algorithm;

[0083] C2. The extracted abnormal data is introduced into the operation model for abnormal data matching. After matching, the corresponding problem data is obtained. The unmatched abnormal data is processed by combining network search and human judgment;

[0084] C3. Generate corresponding solution data based on the problem data fed back by the corresponding abnormal data. After the solution data is generated, it is transmitted to the personnel control to decide whether to complete the data optimization operation of the smart device.

[0085] By introducing the extracted abnormal data into the operation model to match the abnormal data, the corresponding problem data is obtained after matching, and the unmatched abnormal data is processed in combination with network search and human judgment, and the corresponding solution data is generated from the problem data fed back by the corresponding abnormal data. After the solution data is generated, it is transmitted to the human control to decide whether to complete the data optimization operation of the smart device, so as to more accurately match the specific problems of the corresponding application and improve the accuracy of the optimization of the corresponding abnormal data.

[0086] In the embodiment of the present invention, the instruction response algorithm in C1 performs the marking and extraction operations of abnormal data as follows:

[0087] c11. Command response operation is the subsequent response data generated by triggering a command through a normally running application during the operation of the smart device;

[0088] c12. There are two situations for marking abnormal data:

[0089] Case 1: After the application triggers the command, the command will be transmitted to the corresponding operation interface response, and then the interface data after the command triggering will be displayed after judging whether the response is completed;

[0090] Case 2: After the application triggers the command, the command will be transmitted to the corresponding operation interface for response. Whether the time from the command trigger to the response display meets the normal response threshold;

[0091] c13. Sort out the results according to the corresponding situations to form abnormal data and normal data, extract the abnormal data, and filter and delete the normal data.

[0092] In the embodiment of the present invention, the results generated in different situations in c12 are:

[0093] Result 1: After the trigger command is applied, the response interface data is displayed normally. The time from the command trigger to the response display is recorded as T, and the average response threshold based on the normal response is obtained as S. If T≤S, the response interface data is normal data. Conversely, if T>S, the response interface data is abnormal data.

[0094] Result 2: After the application triggers the command, the response interface data cannot be displayed, then the response interface data is abnormal data;

[0095] The resulting abnormal data are collected to form an abnormal data set marked as M.

[0096] In the embodiment of the present invention, the abnormal data in C2 is introduced into the running model and the matching operation of the abnormal data is performed as follows:

[0097] c21. Based on any abnormal data subset selected from the abnormal data set M, first trace the application name data generated by the abnormal data subset;

[0098] c22. First, match the application name data with the application name of the historical abnormal data in the running model, and complete the match if the application name data content of the two is the same;

[0099] c23. Then, the abnormal data subset is matched with the abnormal data with the same application name obtained by matching. At this time, the abnormal data that meets the threshold after the similarity calculation is matched, and the matched data is marked as a regular abnormal data set. Conversely, the abnormal data that fails to meet the similarity threshold is marked as a new abnormal data set.

[0100] Among them, the instruction response operation is to use the subsequent response data generated by the smart device after triggering the instruction through a normally running application during operation, and divide the regular abnormal data set and the new abnormal data set based on the response results and similarity threshold, so as to quickly process the regular abnormal data set, and the new abnormal data set is processed by human judgment, so as to make the monitoring results clearer and improve the accuracy of the monitoring system processing.

[0101] In the embodiment of the present invention, the similarity calculation method in c23 is:

[0102] By comparing the sequential content features in the abnormal data subset with the sequential content features in each abnormal data in the running model, and marking the number of content feature words in the abnormal data subset as m, the number of words that are the same as the sequential content features in each abnormal data in the running model is n, and the set pixel threshold is marked as l. The conditions for satisfying the conventional abnormal data set are: , otherwise it is a new type of abnormal data set.

[0103] In the embodiment of the present invention, the operation of generating corresponding solution data from the problem data fed back by the corresponding abnormal data in C3 is as follows:

[0104] c31. After the data identified as a regular abnormal data set is fed back by the running model with the corresponding problem, the corresponding solution data is matched and transmitted;

[0105] c32. If a new abnormal data set is determined, it will be transmitted to the interface display terminal controlled by personnel, and repaired through network search or by professionals;

[0106] c33. The solution data generated based on c31-c32 are all transmitted to the interface display terminal, and personnel control whether to optimize.

[0107] Comparative experiment

[0108] The tablet computer with known abnormal data is installed with corresponding monitoring software of the existing operation monitoring system and the operation monitoring system of the present invention. The operation data of the tablet computer is analyzed and processed according to the existing operation monitoring system and the operation monitoring system of the present invention. The time when all abnormal data are found and the time when the solution data are found are determined, as well as the accuracy of the matching of the corresponding abnormal data. The specific results are shown in Table 1:

[0109] Table 1 Comparison of results

[0110] The time it takes for the interface to display all abnormal data Extract abnormal data and the accuracy of matching known abnormal data Use existing operation monitoring system 97s 91.2% Using the present invention to operate the monitoring system 49s 100.0%

[0111] In summary, when the operation monitoring system of the present invention is used to process the operation data monitoring of a tablet computer, the time required to display the feedback of abnormal data and the corresponding solution data is shorter, and the matching is finally completed with a higher matching accuracy, so it is more suitable for monitoring the operation of a tablet computer based on the Internet of Things.

[0112] The difference between the second embodiment and the first embodiment is that the present invention further discloses a monitoring method for a tablet computer operation monitoring system based on the Internet of Things, which specifically includes the following steps:

[0113] D1. Start the corresponding monitoring software installed on the tablet computer and connect the smart device to the interface display terminal of the running monitoring system through wireless communication technology to complete data transmission;

[0114] D2. Receive and process the operating data of smart devices through the operation monitoring system, extract abnormal data and match them to obtain corresponding solutions. Finally, personnel control whether the solutions are implemented to complete the optimization of smart devices.

[0115] D3. When optimization is determined, the data is transmitted to the real-time update module through the communication network, thereby completing the data transmission to the smart device for optimization operation.

[0116] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0117] 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.

[0118] 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. A tablet computer operation monitoring system based on the Internet of Things, characterized by: include: Smart devices, by installing corresponding monitoring software on the tablet computer, upload the operation data; Communication network transmission: connecting smart devices with the operation monitoring system through wireless communication technology for data transmission; The operation monitoring system receives and processes the operation data of smart devices to implement data analysis, optimization and transmission operations, including: The operation data acquisition module collects and aggregates the real-time operation data of smart devices for storage, and also marks the path of the collected data; The operation data processing module builds an operation model based on the initial operation data of the smart device, processes the collected real-time operation data, extracts abnormal data content for analysis, and introduces the abnormal situation into the operation model to generate optimized data; The optimization data transmission module converts the obtained optimization data into optimization instructions and transmits them to the real-time update module through the communication network to complete the optimization operation of the smart device; The interface display terminal displays the operating status and warning information in the form of charts and text, and personnel can also perform remote control; Real-time update module, which receives optimization data and transmits it to the smart device for update operation; The operation of establishing the operation model based on the initial operation data of the smart device in the operation data processing module is as follows: B1. Form an initial data set by extracting and aggregating the initial operating data of smart devices from the repository; B2. Based on the initial data set, establish an operation model that operates in the same manner as the actual smart device, and update the operation model data according to the real-time operation data to maintain consistency with the real-time operation data update; B3. Introduce historical abnormal data and data that generate corresponding problems and solutions into the operation model to determine and arrange subsequent solutions; B4. After running the model and applying the optimized data, determine whether the data update of the actual smart device is achieved based on the optimized results; The operation of processing the collected real-time operation data in the operation data processing module is as follows: C1. Extract abnormal data based on real-time operation data and mark and extract abnormal data through instruction response algorithm; C2. The extracted abnormal data is introduced into the operation model for abnormal data matching. After matching, the corresponding problem data is obtained. The unmatched abnormal data is processed by combining network search and human judgment; C3. Generate corresponding solution data based on the problem data fed back by the corresponding abnormal data. After the solution data is generated, it is transmitted to the personnel control to decide whether to complete the data optimization operation of the smart device.

2. The tablet computer operation monitoring system based on the Internet of Things according to claim 1, characterized in that: The steps of the operation data acquisition module collecting, summarizing and storing real-time operation data are as follows: A1. Collect and classify real-time running data, and store them according to the type at the time of data collection; A2. Mark the collection, output path, output method, and receipt of different types of data to form multiple traceability paths for different types of data; A3. When an anomaly is found in a certain data, the initial collection location can be found by tracing the path, and the optimized data can be transmitted and updated.

3. The tablet computer operation monitoring system based on the Internet of Things according to claim 1, characterized in that: The instruction response algorithm in C1 performs the marking and extraction operations of abnormal data as follows: c11. Command response operation is the subsequent response data generated by triggering a command through a normally running application during the operation of the smart device; c12. There are two situations for marking abnormal data: Case 1: After the application triggers the command, the command will be transmitted to the corresponding operation interface response, and then the interface data after the command triggering will be displayed after judging whether the response is completed; Case 2: After the application triggers the command, the command will be transmitted to the corresponding operation interface for response. Whether the time from the command trigger to the response display meets the normal response threshold; c13. Sort out the results according to the corresponding situations to form abnormal data and normal data, extract the abnormal data, and filter and delete the normal data.

4. The tablet computer operation monitoring system based on the Internet of Things according to claim 3, characterized in that: The results produced in different situations in c12 are: Result 1: After the trigger command is applied, the response interface data is displayed normally. The time from the command trigger to the response display is recorded as T, and the average response threshold based on the normal response is obtained as S. If T≤S, the response interface data is normal data. Conversely, if T>S, the response interface data is abnormal data. Result 2: After the application triggers the command, the response interface data cannot be displayed, then the response interface data is abnormal data; The resulting abnormal data are collected to form an abnormal data set marked as M.

5. The tablet computer operation monitoring system based on the Internet of Things according to claim 4, characterized in that: The abnormal data in C2 is introduced into the running model and the matching operation of the abnormal data is as follows: c21. Based on any abnormal data subset selected from the abnormal data set M, first trace the application name data generated by the abnormal data subset; c22. First, match the application name data with the application name of the historical abnormal data in the running model, and complete the match if the application name data content of the two is the same; c23. Then, the abnormal data subset is matched with the abnormal data with the same application name obtained by matching. At this time, the abnormal data that meets the threshold after the similarity calculation is matched, and the matched data is marked as a regular abnormal data set. Conversely, the abnormal data that fails to meet the similarity threshold is marked as a new abnormal data set.

6. The tablet computer operation monitoring system based on the Internet of Things according to claim 5, characterized in that: The similarity calculation method in c23 is: By comparing the sequential content features in the abnormal data subset with the sequential content features in each abnormal data in the running model, and marking the number of content feature words in the abnormal data subset as m, the number of words that are identical to the sequential content features in each abnormal data in the running model is n, and the set similarity threshold is marked as l. The conditions for satisfying the conventional abnormal data set are: Otherwise it is a new type of abnormal data set.

7. The tablet computer operation monitoring system based on the Internet of Things according to claim 5, characterized in that: The operation of generating corresponding solution data from the problem data fed back by the corresponding abnormal data in C3 is as follows: c31. After the data identified as a regular abnormal data set is fed back by the running model with the corresponding problem, the corresponding solution data is matched and transmitted; c32. If a new abnormal data set is determined, it will be transmitted to the interface display terminal controlled by personnel, and repaired through network search or by professionals; c33. The solution data generated based on c31-c32 are all transmitted to the interface display terminal, and personnel control whether to optimize.

8. The method for monitoring a tablet computer operation monitoring system based on the Internet of Things according to claim 1, characterized in that: The specific steps include: D1. Start the corresponding monitoring software installed on the tablet computer and connect the smart device to the interface display terminal of the running monitoring system through wireless communication technology to complete data transmission; D2. Receive and process the operating data of smart devices through the operation monitoring system, extract abnormal data and match them to obtain corresponding solutions. Finally, personnel control whether the solutions are implemented to complete the optimization of smart devices. D3. When optimization is determined, the data is transmitted to the real-time update module through the communication network, thereby completing the data transmission to the smart device for optimization operation.

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