Device management method and device using partition data management

Abstract

The present invention relates to a device management method and device using partition data management. By performing dimensionality reduction processing to remove redundant features of the partition data, the partition data is used as a training data set, and the feature markers corresponding to the partition data are used as the training set to train a classification model for marking and classifying the partition data; the partition data is input into the classification model to output a first classification marker to determine a first mapping table; analyze the approximation results of adjacent sorted partition data in the first mapping table; according to the approximation results, perform partition data reshaping and marker sorting adjustment on the first mapping table to obtain a second mapping table; optimize the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management. Based on this, by performing multiple data processing on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, and the accuracy is improved and the data noise is reduced for further large model training.

Description

Technical Field

[0001] The present invention relates to the technical field of laboratory equipment data management, and particularly to a device management method and device using partition data management. Background Art

[0002] Laboratory equipment is an essential tool in research work, and regular inspections, calibrations, and maintenance of the equipment should be ensured. Laboratory equipment status management refers to the process of effectively managing and maintaining various scientific instruments, equipment, and facilities in a laboratory. The purpose is to ensure that the equipment is always in good operating condition, improve the work efficiency of the laboratory, ensure the accuracy and reliability of experimental results, and extend the service life of the equipment.

[0003] In the actual management of laboratory equipment data, due to the large types and space occupied by various laboratory equipment, the workload of laboratory space planning and data management is huge. Therefore, in the laboratory equipment data management platform, laboratory equipment is generally managed by partitions, and partition data is determined by pre - division. However, the partition management of laboratory equipment is greatly interfered by human factors, resulting in low reference and imprecision for data training and data management in large models when applying such data. Summary of the Invention

[0004] Based on this, in view of the problem that the partition management of laboratory equipment is greatly interfered by human factors, resulting in low reference and imprecision for data training and data management in large models when applying such data, it is necessary to provide a device management method and device using partition data management.

[0005] A device management method using partition data management includes the steps of:

[0006] Removing redundant features of the partition data through dimensionality reduction processing, using the partition data as a training data set, and using the feature markers corresponding to the partition data as a training set to train a classification model for marking and classifying the partition data;

[0007] Inputting the partition data into the classification model to output a first classification marker to determine a first mapping table; wherein, the first mapping table includes the mapping relationship and marker sorting between the partition data and the first classification marker;

[0008] Analyzing the approximation results of adjacent - sorted partition data in the first mapping table;

[0009] Reshaping the partition data and adjusting the marker sorting of the first mapping table according to the approximation results to obtain a second mapping table;

[0010] Optimize the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for managing the partition data.

[0011] The above device management method using partition data management removes redundant features of the partition data through dimensionality reduction processing, uses the partition data as a training data set, uses the feature markers corresponding to the partition data as a training set, and trains a classification model for marking and classifying the partition data; inputs the partition data into the classification model, outputs a first classification marker to determine a first mapping table; analyzes the approximation results of adjacent sorted partition data in the first mapping table; reshapes the partition data and adjusts the marker sorting in the first mapping table according to the approximation results to obtain a second mapping table; optimizes the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for managing the partition data. Based on this, by performing multiple data processing on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, and the accuracy is enhanced and the data noise is reduced for further large model training.

[0012] In one embodiment, the partition data includes device partitions, and each device partition has multiple feature markers, as follows:

[0013] ;

[0014] Among them, represents the th data value of the th feature marker of the

[0015] In one embodiment, the process of removing redundant features of the partition data through dimensionality reduction processing includes the steps of:

[0016] Perform standardization processing on the partition data to obtain standardized data;

[0017] Extract the principal components of the standardized data to construct a principal component matrix;

[0018] Perform dimensionality reduction processing on the principal component matrix to obtain a dimensionality reduction matrix to remove redundant data.

[0019] In one embodiment, the process of using the partition data as a training data set, using the feature markers corresponding to the partition data as a training set, and training a classification model for marking and classifying the partition data includes the steps of:

[0020] Construct a training data set with the dimensionality reduction matrix, construct a training set with the partition data, and train the mapping relationship based on the decision tree model to obtain the classification model.

[0021] In one embodiment, the process of analyzing the approximation results of adjacent sorted partition data in the first mapping table includes the steps of:

[0022] Construct an approximate analysis matrix according to the first mapping table, and calculate the approximation of adjacent partition data based on the approximate analysis matrix as the approximate result.

[0023] In one embodiment, the process of analyzing the approximate result of partition data sorted adjacent to each other in the first mapping table includes the steps of:

[0024] Optimize the transition smoothness and data boundaries of the partition data through data reshaping and data optimization.

[0025] In one embodiment, it further includes the steps of:

[0026] Merge some partition data to reduce data fragmentation.

[0027] A device management device using partition data management, comprising:

[0028] A model training module, configured to remove redundant features of partition data through dimensionality reduction processing, use the partition data as a training data set, use the feature labels corresponding to the partition data as a training set, and train a classification model for label classification of the partition data;

[0029] A mapping establishment module, configured to input the partition data into the classification model, output a first classification label, and determine a first mapping table; wherein, the first mapping table includes the mapping relationship and label sorting between the partition data and the first classification label;

[0030] An approximate analysis module, configured to analyze the approximate result of partition data sorted adjacent to each other in the first mapping table;

[0031] A mapping update module, configured to perform partition data reshaping and label sorting adjustment on the first mapping table according to the approximate result to obtain a second mapping table;

[0032] A mapping optimization module, configured to optimize the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management.

[0033] The above-mentioned device management device using partition data management removes redundant features of partition data through dimensionality reduction processing, uses the partition data as a training data set, uses the feature labels corresponding to the partition data as a training set, and trains a classification model for label classification of partition data; inputs the partition data into the classification model, outputs a first classification label to determine a first mapping table; analyzes the approximation results of adjacent sorted partition data in the first mapping table; performs partition data reshaping and label sorting adjustment on the first mapping table according to the approximation results to obtain a second mapping table; optimizes the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management. Based on this, by performing multiple data processes on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, the accuracy is enhanced for further large model training, and the data noise is reduced.

[0034] A computer storage medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the device management method using partition data management according to any one of the above embodiments is implemented.

[0035] The above-mentioned computer storage medium removes redundant features of partition data through dimensionality reduction processing, uses the partition data as a training data set, uses the feature labels corresponding to the partition data as a training set, and trains a classification model for label classification of partition data; inputs the partition data into the classification model, outputs a first classification label to determine a first mapping table; analyzes the approximation results of adjacent sorted partition data in the first mapping table; performs partition data reshaping and label sorting adjustment on the first mapping table according to the approximation results to obtain a second mapping table; optimizes the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management. Based on this, by performing multiple data processes on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, the accuracy is enhanced for further large model training, and the data noise is reduced.

[0036] A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the device management method using partition data management according to any one of the above embodiments is implemented.

[0037] The above computer device removes redundant features of partition data through dimensionality reduction processing, uses the partition data as a training data set, uses the feature markers corresponding to the partition data as a training set, and trains a classification model for marking and classifying the partition data; inputs the partition data into the classification model, outputs a first classification marker to determine a first mapping table; analyzes the approximation results of adjacent sorted partition data in the first mapping table; reshapes the partition data and adjusts the marker sorting in the first mapping table according to the approximation results to obtain a second mapping table; optimizes the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management. Based on this, by performing multiple data processing on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, the accuracy is enhanced for further large model training, and the data noise is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of a device management method using partition data management according to an embodiment of an application;

[0039] Figure 2 is a structural diagram of a device management device module using partition data management according to an embodiment of an application;

[0040] Figure 3 is a schematic diagram of a computer structure according to an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] To better understand the purpose, technical solution, and technical effects of the present invention, the present invention will be further described and explained below with reference to the accompanying drawings and embodiments. At the same time, it is stated that the embodiments described below are only used to explain the present invention and are not used to limit the present invention.

[0042] An embodiment of the present invention provides a device management method using partition data management.

[0043] Figure 1 is a flowchart of a device management method using partition data management according to an embodiment of an application, as Figure 1 shown, a device management method using partition data management according to an embodiment of an application includes the steps of:

[0044] S100, removing redundant features of partition data through dimensionality reduction processing, using the partition data as a training data set, using the feature markers corresponding to the partition data as a training set, and training a classification model for marking and classifying the partition data;

[0045] S101, inputting the partition data into the classification model, outputting a first classification marker to determine a first mapping table; wherein, the first mapping table includes the mapping relationship and marker sorting between the partition data and the first classification marker;

[0046] S102. Analyze the approximation results of adjacent sorted partition data in the first mapping table;

[0047] S103. Reshape the partition data and adjust the label sorting of the first mapping table according to the approximation results to obtain a second mapping table;

[0048] S104. Optimize the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management.

[0049] In the embodiment of the present application, the data source of the partition data is the management system of laboratory equipment. The management system determines the partition data and feature marks by partitioning the management of laboratory equipment. Among them, the feature mark is the label data pre-marked according to the partition data, which is used to characterize the management features of the partition data. The execution subject of the method in this embodiment, as an intermediary to connect the management system and the AI large model, performs secondary processing on the partition data of the management system as the training data of the AI large model. Further enhance the accuracy of laboratory equipment partition management through the AI large model and improve the generalization of partition data application.

[0050] Among them, the partition data includes the equipment partitions recorded by the management system. Each equipment partition has a unified partition standard and projection method, and is correspondingly marked with a feature mark, and the feature mark is used as the data record of the equipment partition.

[0051] Preferably, the partition data is as follows: ; Among them, represents the th data value of the

[0052] Based on this, the process of removing redundant features of the partition data through dimensionality reduction in step S100 includes the steps of: performing standardization processing on the partition data to obtain standardized data;

[0053] Extract the principal components of the standardized data and construct a principal component matrix;

[0054] Perform dimensionality reduction processing on the principal component matrix to obtain a dimensionality reduction matrix to remove redundant data.

[0055] Referring to the above steps, the partition data is standardized in the form of matrix to obtain standardized data in matrix form .

[0056] Extract the principal components of the standardized data to construct a principal component matrix as , perform dimensionality reduction processing on the principal component matrix to obtain a dimensionality reduction matrix , as follows: .

[0057] Among them, the column vectors of the principal component matrix are the first principal component directions, and the retained principal components after dimensionality reduction are principal components.

[0058] Preferably, in step S100, the process of using the partition data as the training data set, using the feature labels corresponding to the partition data as the training set, and training the classification model for labeling and classifying the partition data includes the steps of:

[0059] Constructing a training data set with the dimensionality reduction matrix, constructing a training set with the partition data, and training the mapping relationship based on the decision tree model to obtain the classification model.

[0060] As in the above steps, using the dimensionality reduction matrix to construct the training data set . is the dimensionality reduction feature vector of the device partition ; , is the st feature label, is the feature label set.

[0061] The mapping relationship is as follows, and its model function is characterized as: . Based on the model function, the classification model is trained. The classification model outputs the first mapping table based on the model function according to the re-input of the device partition in the partition data.

[0062] Preferably, the process of analyzing the approximation results of the partition data sorted adjacently in the first mapping table in step S102 includes the steps of:

[0063] Constructing an approximation analysis matrix according to the first mapping table, and calculating the approximation of adjacent partition data according to the approximation analysis matrix as the approximation result.

[0064] For the adjacent sorting of the first mapping table, that is, the adjacent sorting of the device partitions, construct the approximation analysis matrix , where and both represent device partitions.

[0065] ; where, represents whether the device partitions and are sorted adjacently, and based on this, construct the feature similarity matrix , as follows:

[0066] Where: represents the device partition According to the dimensionality reduction matrix The calculated eigenvector Represents the device partition According to the dimensionality reduction matrix The eigenvector space calculated is single; For the vector The Euclidean norm of For the vector The Euclidean norm of As the approximate result

[0067] Preferably, in step S102, the process of analyzing the approximate results of the partition data sorted adjacently in the first mapping table includes the steps of:

[0068] Optimize the transition smoothness and data boundaries of the partition data through data reshaping and data optimization

[0069] Based on the approximate result , identify adjacent device partitions with approximate results greater than the set approximate threshold, perform partition adjustment and unified feature marking of the device partitions, improve the spatial transition smoothness of the device partitions with approximate feature markings, optimize the data boundaries of the device partitions reflected in the second mapping table, facilitate improving the data smoothness of the third mapping table, and is beneficial to enhancing the training efficiency of the subsequent AI large model

[0070] Preferably, it further includes the steps of:

[0071] Merge some partition data to reduce data fragmentation

[0072] Among them, the partition data can be merged through a clustering analysis algorithm

[0073] In one embodiment, by setting the core parameters of the clustering analysis algorithm, while screening and isolating some device partitions, merge the device partitions forming clusters to reduce the data fragmentation of the subsequent input to the AI large model

[0074] In the third mapping table after completing the above steps, the device partitions and feature marking mappings with the above data reprocessing are used as the training data of the AI large model

[0075] The device management method using partition data management in any of the above embodiments removes redundant features of the partition data through dimensionality reduction processing, uses the partition data as a training data set, and uses the feature markers corresponding to the partition data as a training set to train a classification model for label classification of the partition data; inputs the partition data into the classification model, outputs a first classification label to determine a first mapping table; analyzes the approximation results of the partition data sorted adjacent to each other in the first mapping table; reshapes the partition data and adjusts the label sorting in the first mapping table according to the approximation results to obtain a second mapping table; optimizes the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management. Based on this, by performing multiple data processes on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, the accuracy is enhanced for further large model training, and the data noise is reduced.

[0076] An embodiment of the present invention further provides a device management device using partition data management.

[0077] Figure 2 The module structure diagram of the device management device using partition data management in one embodiment is as Figure 2 shown. The device management device using partition data management in one embodiment includes:

[0078] A model training module 100, configured to remove redundant features of the partition data through dimensionality reduction processing, use the partition data as a training data set, and use the feature markers corresponding to the partition data as a training set to train a classification model for label classification of the partition data;

[0079] A mapping establishment module 101, configured to input the partition data into the classification model, output a first classification label to determine a first mapping table; wherein, the first mapping table includes the mapping relationship and label sorting between the partition data and the first classification label;

[0080] An approximation analysis module 102, configured to analyze the approximation results of the partition data sorted adjacent to each other in the first mapping table;

[0081] A mapping update module 103, configured to reshape the partition data and adjust the label sorting in the first mapping table according to the approximation results to obtain a second mapping table;

[0082] A mapping optimization module 104, configured to optimize the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management.

[0083] The above device management device using partition data management removes redundant features of partition data through dimensionality reduction processing, uses the partition data as a training data set, uses the feature tags corresponding to the partition data as a training set, and trains a classification model for marking and classifying partition data; inputs the partition data into the classification model, outputs a first classification tag to determine a first mapping table; analyzes the approximation results of adjacent sorted partition data in the first mapping table; reshapes the partition data and adjusts the tag sorting in the first mapping table according to the approximation results to obtain a second mapping table; optimizes the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management. Based on this, by performing multiple data processes on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, and the accuracy is enhanced and the data noise is reduced for further large model training.

[0084] An embodiment of the present invention also provides a computer storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the device management method using partition data management in any of the above embodiments is implemented.

[0085] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the related art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: various media such as removable storage devices, RAM, ROM, magnetic disks, or optical discs that can store program codes.

[0087] Corresponding to the above computer storage medium, in one embodiment, a computer device is further provided. The computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. Among them, when the processor executes the program, it implements a device management method using partition data management as described in any of the above embodiments.

[0088] The computer device can be a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a device management method using partition data management. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0089] The above computer device removes redundant features of partition data through dimensionality reduction processing, uses the partition data as a training data set, and uses the feature labels corresponding to the partition data as a training set to train a classification model for label classification of the partition data; inputs the partition data into the classification model, outputs a first classification label to determine a first mapping table; analyzes the approximation results of adjacent sorted partition data in the first mapping table; reshapes the partition data and adjusts the label sorting in the first mapping table according to the approximation results to obtain a second mapping table; optimizes the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management. Based on this, by performing multiple data processes on the partition data of the laboratory equipment originally recorded in the system, the data reference value of the partition data is improved, and the accuracy is enhanced and the data noise is reduced for further large model training.

[0090] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0091] The above embodiments only represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be understood as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A device management method using partition data management, characterized in that, Including steps: Removing redundant features of the partition data through dimensionality reduction processing, using the partition data as a training data set, and using the feature labels corresponding to the partition data as a training set to train a classification model for label classification of the partition data; the partition data includes device partitions recorded by a management system, each device partition has a unified partitioning standard and projection method, and is correspondingly labeled with feature labels, and the feature labels are used as data records of the device partitions; Inputting the partition data into the classification model, outputting a first classification label to determine a first mapping table; wherein, the first mapping table includes the mapping relationship and label sorting between the partition data and the first classification label; Analyzing the approximation results of adjacent sorted partition data in the first mapping table, including steps: Construct an approximate analysis matrix according to the first mapping table, and calculate the approximation of adjacent partition data based on the approximate analysis matrix as the approximate result; construct the approximate analysis matrix according to the adjacent sorting of the first mapping table, that is, the adjacent sorting of device partitions. , where and both represent device partitions; ; where represents a device partition and whether they are adjacent in sorting, and construct a feature similarity matrix based on this as follows: where: represents a device partition is the eigenvector calculated according to the dimensionality reduction matrix , represents a device partition is the eigenvector calculated according to the dimensionality reduction matrix ; is the Euclidean norm of the vector , is the Euclidean norm of the vector ; is used as the approximate result; reshape the partition data of the first mapping table and adjust the label sorting according to the approximate result to obtain a second mapping table; optimize the spatial distribution of the partition data in the second mapping table to obtain a third mapping table for partition data management.

2. The device management method using partition data management according to claim 1, wherein The partitioned data includes device partitions, and each device partition has multiple feature markers as follows: ; among them, represents the th device partition's th feature marker's data value.

3. The device management method using partition data management according to claim 1, characterized in that, The process of removing redundant features of the partition data through dimensionality reduction processing includes steps: Performing standardization processing on the partition data to obtain standardized data; Extracting the principal components of the standardized data to construct a principal component matrix; Performing dimensionality reduction processing on the principal component matrix to obtain a dimensionality reduction matrix to remove redundant data.

4. The device management method using partition data management according to claim 1, wherein The process of using the partition data as a training data set and using the feature labels corresponding to the partition data as a training set to train a classification model for label classification of the partition data includes steps: Constructing a training data set based on the dimensionality reduction matrix, constructing a training set with the partition data, and training the mapping relationship based on a decision tree model to obtain the classification model.

5. The device management method using partition data management according to claim 1, characterized in that, Including steps: Optimizing the transition smoothness and data boundaries of the partition data through data reshaping and data optimization.

6. The device management method using partition data management according to claim 1, characterized in that, Also including steps: Merging some partition data to reduce data fragmentation.

7. An equipment management device using partition data management, characterized in that, Including: A model training module for removing redundant features of partition data through dimensionality reduction processing, using the partition data as a training data set, and using the feature labels corresponding to the partition data as a training set to train a classification model for label classification of the partition data; the partition data includes device partitions recorded by a management system, each device partition has a unified partitioning standard and projection method, and is correspondingly labeled with feature labels, and the feature labels are used as data records of the device partitions; Inputting the partition data into the classification model, outputting a first classification label to determine a first mapping table; wherein, the first mapping table includes the mapping relationship and label sorting between the partition data and the first classification label; Analyzing the approximation results of adjacent sorted partition data in the first mapping table, including steps: Constructing an approximation analysis matrix according to the first mapping table, and calculating the approximation of adjacent partition data according to the approximation analysis matrix as the approximation result; Adjacent sorting of the first mapping table, that is, adjacent sorting of device partitions, to construct an approximate analysis matrix , and both represent device partitions; ; among them, represents a device partition and whether they are adjacent sorted, and based on this, a feature similarity matrix is constructed as follows: where: represents the eigenvector calculated according to the dimensionality reduction matrix for the device partition , represents the single eigenvector space calculated according to the dimensionality reduction matrix for the device partition ; is the Euclidean norm of the vector , is the Euclidean norm of the vector ; is used as the approximate result; according to the approximate result, partition data reshaping and label sorting adjustment are performed on the first mapping table to obtain a second mapping table; Performing spatial distribution optimization on the partition data in the second mapping table to obtain a third mapping table for partition data management.

8. A computer storage medium having computer instructions stored thereon, characterized in that, When the computer instructions are executed by a processor, the device management method using partition data management as described in any one of claims 1 to 6 is implemented.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the program, the device management method using partition data management as described in any one of claims 1 to 6 is implemented.

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