Laboratory region division method and device using decision tree model

Abstract

The invention relates to a laboratory region division method and device using a decision tree model. The decision tree model is used for calibrating corresponding laboratory region units for laboratory equipment data and laboratory personnel data. And training a region classification model. And acquiring latest laboratory equipment data and laboratory personnel data, inputting the data into the region classification model, and outputting laboratory region unit data. According to the similar matrix, analyzing the similarity of the laboratory area unit data, and generating a laboratory area map; and performing space optimization based on the laboratory region map to obtain a laboratory region division map for representing laboratory region division. On the basis, a data model is merged into a computer system, so that laboratory region division can be carried out on laboratory equipment data and laboratory personnel data of a cloud platform, the data comprehensiveness of a laboratory management data model is improved, and a data processing technical means for laboratory region division is provided.

Description

Technical Field

[0001] The present application relates to the technical field of laboratory data processing, and in particular to a method and device for dividing laboratory areas using a decision tree model. Background Art

[0002] At present, with the increase in the number of scientific researchers and the enrichment of scientific research methods, scientific research laboratories are gradually becoming popular and shared. In laboratory management, including laboratory personnel management, drug management, equipment management and safety management, all can be completed through big data. The only difference is that the division of laboratory areas is related to various instruments, equipment and personnel, and it is difficult to divide and manage laboratory areas through a single data.

[0003] Therefore, the current laboratory area divisions of each laboratory are mainly determined by artificial divisions in each laboratory. In the current laboratory management data model, there is a lack of data models for laboratory area divisions, resulting in a lack of visual presentation of laboratory management data model data management. Summary of the invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a method and device for laboratory area division using a decision tree model, so as to solve the problem that the existing technology lacks a data model for laboratory area division, resulting in insufficient means of managing laboratory management data models.

[0005] The present application embodiment provides a method for dividing laboratory areas using a decision tree model, comprising the steps of: Acquire the laboratory equipment data and laboratory personnel data historically stored in the cloud platform, and use a decision tree model to calibrate corresponding laboratory area units for the laboratory equipment data and laboratory personnel data; Using the laboratory equipment data and laboratory personnel data as training samples and the laboratory area units as result samples to train a regional classification model; Acquire the latest laboratory equipment data and laboratory personnel data and input them into the regional classification model, and output laboratory regional unit data; Generate a laboratory area map according to a similarity matrix and by analyzing the similarity of the laboratory area unit data; Spatial optimization is performed based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division.

[0006] The laboratory area division method using the decision tree model of the embodiment of the present application obtains the laboratory equipment data and laboratory personnel data stored historically on the cloud platform, and uses the decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data. The laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area unit is used as a result sample to train the regional classification model. The latest laboratory equipment data and laboratory personnel data are obtained and input into the regional classification model, and the laboratory area unit data is output. According to the similarity matrix and the similarity of the laboratory area unit data are analyzed, a laboratory area map is generated; spatial optimization is performed based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division. Based on this, the laboratory equipment data and laboratory personnel data that are beneficial to the cloud platform are used to divide the laboratory area, improve the data comprehensiveness of the laboratory management data model, and provide data processing technology means for the laboratory area division.

[0007] As one of the optional embodiments, before the process of using the laboratory equipment data and laboratory personnel data as training samples and the laboratory area unit as result samples to train the area classification model, the process further includes the following steps: Performing feature selection on the laboratory equipment data and the laboratory personnel data through a feature processing model to obtain data features; Performing dimensionality reduction processing on the data features, removing redundant features in the data features, and obtaining effective data features; The valid data features are input into the feature processing model to infer laboratory equipment data and laboratory personnel data used as training samples.

[0008] As one of the optional embodiments, the process of obtaining the laboratory equipment data and laboratory personnel data historically stored in the cloud platform and calibrating the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data using a decision tree model includes the steps of: Determine laboratory area classification information of the laboratory equipment data and laboratory personnel data according to the decision tree model; The laboratory area classification information is marked on the partition unit to obtain the laboratory area unit.

[0009] As one of the optional embodiments, the region classification model is a deep learning framework; The process of using the laboratory equipment data and laboratory personnel data as training samples and the laboratory area unit as result samples to train the area classification model comprises the steps of: Strengthening the training samples and the result samples using gradient weighted class activation mapping; The visualization level of laboratory area unit data output by the area classification model is improved according to the result sample and the training sample.

[0010] As one of the optional embodiments, the process of generating a laboratory area map based on a similarity matrix and analyzing the similarity of the laboratory area unit data includes the steps of: The adjacent regions are reshaped according to the similarities to obtain a preliminary map.

[0011] As one of the optional embodiments, the process of performing space optimization based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division includes the steps of: The boundary of the preliminary map is optimized by a boundary optimization algorithm to reduce boundary fragmentation.

[0012] As one of the optional embodiments, the process of performing space optimization based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division further includes the steps of: Normalizing the laboratory area unit data, and dividing the data into groups according to the normalization results; The areas of laboratory area unit data of the same group are merged to obtain the laboratory area division map.

[0013] The embodiment of the present application also provides a laboratory area division device using a decision tree model, including: A data calibration module is used to obtain the laboratory equipment data and laboratory personnel data historically stored in the cloud platform, and use a decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data; A module training module, used to train a regional classification model by taking the laboratory equipment data and laboratory personnel data as training samples and the laboratory area unit as result samples; A data reconstruction module, used to obtain the latest laboratory equipment data and laboratory personnel data and input them into the regional classification model, and output laboratory regional unit data; A map generation module, used for generating a laboratory area map according to a similarity matrix and analyzing the similarity of the laboratory area unit data; The map optimization module is used to perform space optimization based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division.

[0014] The laboratory area division device using the decision tree model of the embodiment of the present application obtains the laboratory equipment data and laboratory personnel data stored historically on the cloud platform, and uses the decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data. The laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area unit is used as a result sample to train the regional classification model. The latest laboratory equipment data and laboratory personnel data are obtained and input into the regional classification model, and the laboratory area unit data is output. According to the similarity matrix and the similarity of the laboratory area unit data are analyzed, a laboratory area map is generated; spatial optimization is performed based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division. Based on this, the laboratory equipment data and laboratory personnel data that are beneficial to the cloud platform are used to divide the laboratory area, improve the data comprehensiveness of the laboratory management data model, and provide data processing technology means for the laboratory area division.

[0015] At least one embodiment of the present application further provides a data control device, including: one or more memories non-transitorily storing computer-executable instructions; One or more processors are configured to execute computer executable instructions, wherein the computer executable instructions, when executed by the one or more processors, implement the laboratory area division method using a decision tree model according to any embodiment of the present application.

[0016] The above-mentioned data control device obtains the laboratory equipment data and laboratory personnel data stored historically in the cloud platform, and uses the decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data. The laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area units are used as result samples to train the regional classification model. The latest laboratory equipment data and laboratory personnel data are obtained and input into the regional classification model, and the laboratory area unit data is output. According to the similarity matrix and the similarity of the laboratory area unit data are analyzed, a laboratory area map is generated; based on the laboratory area map, spatial optimization is performed to obtain a laboratory area division map for characterizing the laboratory area division. Based on this, the data model is incorporated into the computer system to realize the laboratory area division of the laboratory equipment data and laboratory personnel data that are beneficial to the cloud platform, improve the data comprehensiveness of the laboratory management data model, and provide data processing technology means for the laboratory area division.

[0017] At least one embodiment of the present application further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, a laboratory area division method using a decision tree model according to any embodiment of the present application is implemented.

[0018] The above-mentioned non-transient computer-readable storage medium obtains the laboratory equipment data and laboratory personnel data stored historically on the cloud platform, and uses a decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data. The laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area units are used as result samples to train the regional classification model. The latest laboratory equipment data and laboratory personnel data are obtained and input into the regional classification model, and the laboratory area unit data is output. According to the similarity matrix and the similarity of the laboratory area unit data are analyzed, a laboratory area map is generated; based on the laboratory area map, spatial optimization is performed to obtain a laboratory area division map for characterizing the laboratory area division. Based on this, by incorporating the data model into the computer system, the laboratory equipment data and laboratory personnel data of the cloud platform are used to perform laboratory area division, the data comprehensiveness of the laboratory management data model is improved, and data processing technology means for laboratory area division are provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a method for dividing laboratory areas using a decision tree model according to an embodiment of an application; Figure 2 A flow chart of a method for dividing laboratory areas using a decision tree model according to a preferred embodiment; Figure 3 This is a schematic diagram of laboratory area unit data classification based on convolutional neural network; Figure 4 A module structure diagram of a laboratory area division device using a decision tree model according to an embodiment of an application; Figure 5 A schematic block diagram of a data control device provided by the present invention; Figure 6 A schematic diagram of a non-transitory computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be clearly and completely described below in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] Unless otherwise defined, the technical terms or scientific terms used in this application should be understood by people with ordinary skills in the field to which this application belongs. "First", "second" and similar words used in this application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] In order to keep the following description of the embodiments of the present application clear and concise, the present application omits detailed descriptions of some known functions and known components.

[0023] An embodiment of the present application provides a method for dividing laboratory areas using a decision tree model.

[0024] Figure 1 FIG. 1 is a flow chart of a method for dividing laboratory areas using a decision tree model according to an embodiment of an application. Figure 1 As shown, a laboratory area division method using a decision tree model in an embodiment of the application includes steps S100 to S104: S100, obtaining laboratory equipment data and laboratory personnel data historically stored in the cloud platform, and using a decision tree model to calibrate corresponding laboratory area units for the laboratory equipment data and laboratory personnel data; S101, using the laboratory equipment data and laboratory personnel data as training samples, and using the laboratory area units as result samples, to train an area classification model; S102, obtaining the latest laboratory equipment data and laboratory personnel data and inputting them into the regional classification model, and outputting laboratory regional unit data; S103, generating a laboratory area map by analyzing the similarity of the laboratory area unit data according to the similarity matrix; S104, performing space optimization based on the laboratory area map to obtain a laboratory area division map for characterizing laboratory area division.

[0025] Both laboratory equipment data and laboratory personnel data are recorded data of the laboratory data management platform. Among them, laboratory equipment data includes equipment location information, and laboratory personnel information includes personnel activity range information. Laboratory area unit calibration is performed according to the laboratory area corresponding to the equipment location information, and laboratory area unit calibration is performed according to the laboratory area corresponding to the personnel activity range information. According to the pre-divided multiple categories of area units, laboratory equipment data and laboratory personnel data are calibrated to the corresponding area units, and the area units of the same category are merged as laboratory area units.

[0026] Figure 2 FIG. 1 is a flow chart of a method for dividing laboratory areas using a decision tree model according to an embodiment of an application. Figure 2 As shown, the process of obtaining the laboratory equipment data and laboratory personnel data historically stored in the cloud platform in step S100, and calibrating the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data using a decision tree model, includes steps S200 and S201: S200, determining laboratory area classification information of the laboratory equipment data and laboratory personnel data according to the decision tree model; S201, marking laboratory area classification information on the partition unit to obtain the laboratory area unit.

[0027] The decision tree model is pre-trained based on the data of the cloud platform and the pre-divided regional units. According to the pre-trained model, the laboratory equipment data and laboratory personnel data are classified to obtain the laboratory regional classification information corresponding to the regional unit, and the laboratory regional classification information is marked on the partition unit to obtain the laboratory regional unit, which is convenient for the subsequent training of the deep learning framework.

[0028] Preferably, if Figure 2 As shown, in step S101, the laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area unit is used as a result sample. Before the process of training the area classification model, steps S202 to S204 are also included: S202, performing feature selection on the laboratory equipment data and the laboratory personnel data through a feature processing model to obtain data features; S203, performing dimensionality reduction processing on the data features, removing redundant features in the data features, and obtaining effective data features; S204, inputting the valid data features into the feature processing model, and inferring laboratory equipment data and laboratory personnel data used as training samples.

[0029] In order to meet the data training requirements of the deep learning framework, the feature processing model performs feature selection on the laboratory equipment data and the laboratory personnel data to obtain data features. Among them, the feature processing module can use the underlying logic of the decision tree model and effectively capture data features through continuous iteration of the data mind.

[0030] At the same time, additional feature processing models can be deployed to capture data features in order to better meet the data requirements of the subsequent deep learning framework. Preferably, the feature processing model uses a high-performance feature selection framework based on XGBoost, which, in conjunction with the decision tree model, can eliminate the data overfitting problem in the laboratory area unit calibration process and improve the accuracy of data feature capture.

[0031] Through dimensionality reduction processing, data noise is eliminated and data features are made more suitable for deep learning frameworks. Preferably, local linear embedding is used to reduce the dimensionality of data features. Local linear embedding can retain the topological characteristics of effective data features and better cooperate with the merging of partition units in laboratory area unit calibration.

[0032] Among them, the feature processing model can adopt a computer model based on the inverse algorithm, and the inverse algorithm can adopt an algorithm that can be deployed to a computer model, such as the Tagger inverse algorithm, to infer the processed valid data features into laboratory equipment data and laboratory personnel data suitable for deep learning framework training.

[0033] Preferably, if Figure 2 As shown, in step S101, the laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area unit is used as a result sample to train the area classification model, including step S205 and step S206: S205, strengthening the training sample and the result sample by using gradient weighted class activation mapping; S206: improving the visualization of laboratory area unit data output by the area classification model according to the result sample and the training sample.

[0034] Before step S205, the result samples are visualized and the laboratory area units are processed into visualization areas. Based on the visualization selection of the result samples, the training samples are synchronously processed and converted into visualization data. The mapping relationship of the data is strengthened by gradient weighted class activation mapping, and the visualization of the laboratory area unit data output by the regional classification model is improved, so as to facilitate the subsequent visualization space processing and improve the intuitiveness of the regional division.

[0035] Preferably, if Figure 2 As shown, the process of generating a laboratory area map according to the similarity matrix and analyzing the similarity of the laboratory area unit data in step S103 includes step S300: S300, reshaping the adjacent regions according to the similarity to obtain a preliminary map.

[0036] like Figure 3 As shown in the figure, the laboratory area unit data includes regional visualization image classification (image classification) and regional natural description (image captioning). The regional natural description is used as the basis for similarity analysis of the similarity matrix. Correspondingly, the regional classification model uses a deep learning framework based on convolutional neural networks. The laboratory area unit data is image merged according to similarity, and the categories of the merged laboratory area units are reduced to form a preliminary map.

[0037] Preferably, if Figure 2 As shown, the process of performing space optimization based on the laboratory area map in step S104 to obtain a laboratory area division map for characterizing the laboratory area division includes step S400: S400, optimizing the boundary of the preliminary map by using a boundary optimization algorithm to reduce boundary fragmentation.

[0038] Preferably, the boundary optimization algorithm may use a K-means clustering algorithm to optimize boundary division and merge similar areas in the preliminary map to reduce the fragmentation of the partitions in the preliminary map.

[0039] Preferably, the boundary after the boundary optimization algorithm is processed as follows:

[0040] in, Represents the boundary demarcation point; Represents the position of the i-th boundary calibration point; is the total number of boundary calibration points; is the bandwidth, which determines the smoothness of the boundary function; It is a boundary smoothing function. Through smoothing filtering, sequence smoothing and other boundary smoothing functions, the boundary of the preliminary map is optimized, the boundary fragmentation is reduced, and the partition effect of the preliminary map is improved, which serves as a laboratory area division map representing the laboratory area division.

[0041] Preferably, if Figure 2As shown, the process of performing space optimization based on the laboratory area map in step S104 to obtain a laboratory area division map for characterizing the laboratory area division also includes steps S500 and S501: S500, normalizing the laboratory area unit data, and dividing the data into groups according to the normalization results; S501, merging areas of laboratory area unit data of the same group to obtain the laboratory area division map.

[0042] Through normalization processing, groups of normalized data are set, and areas with similar data and laboratory area unit data belonging to the same group are merged to further reduce the fragmentation of visual partitions in the laboratory area division map and ensure the division effect.

[0043] Based on this, the laboratory area division map serves as a data supplement to the laboratory data management system in the cloud platform. It provides image visualization of laboratory area division based on recording laboratory equipment data, laboratory personnel data and other data, enriching the visualization presentation method of the laboratory data management system.

[0044] The laboratory area division method using the decision tree model of the embodiment of the present application obtains the laboratory equipment data and laboratory personnel data stored historically on the cloud platform, and uses the decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data. The laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area unit is used as a result sample to train the regional classification model. The latest laboratory equipment data and laboratory personnel data are obtained and input into the regional classification model, and the laboratory area unit data is output. According to the similarity matrix and the similarity of the laboratory area unit data are analyzed, a laboratory area map is generated; spatial optimization is performed based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division. Based on this, the laboratory equipment data and laboratory personnel data that are beneficial to the cloud platform are used to divide the laboratory area, improve the data comprehensiveness of the laboratory management data model, and provide data processing technology means for the laboratory area division.

[0045] The embodiment of the present application also provides a laboratory area division device using a decision tree model.

[0046] Figure 4 FIG. 1 is a module structure diagram of a laboratory area division device using a decision tree model according to an embodiment of an application. Figure 4 As shown, a laboratory area division device using a decision tree model according to an embodiment of an application includes: The data calibration module 100 is used to obtain the laboratory equipment data and laboratory personnel data historically stored in the cloud platform, and calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data using a decision tree model; The module training module 101 is used to train a regional classification model by taking the laboratory equipment data and laboratory personnel data as training samples and the laboratory area unit as result samples; The data reconstruction module 102 is used to obtain the latest laboratory equipment data and laboratory personnel data and input them into the regional classification model, and output laboratory regional unit data; A map generation module 103, for generating a laboratory area map according to a similarity matrix and analyzing the similarity of the laboratory area unit data; The map optimization module 104 is used to perform space optimization based on the laboratory area map to obtain a laboratory area division map for representing the laboratory area division.

[0047] The laboratory area division device using the decision tree model of the embodiment of the present application obtains the laboratory equipment data and laboratory personnel data stored historically on the cloud platform, and uses the decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data. The laboratory equipment data and laboratory personnel data are used as training samples, and the laboratory area unit is used as a result sample to train the regional classification model. The latest laboratory equipment data and laboratory personnel data are obtained and input into the regional classification model, and the laboratory area unit data is output. According to the similarity matrix and the similarity of the laboratory area unit data are analyzed, a laboratory area map is generated; spatial optimization is performed based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division. Based on this, the laboratory equipment data and laboratory personnel data that are beneficial to the cloud platform are used to divide the laboratory area, improve the data comprehensiveness of the laboratory management data model, and provide data processing technology means for the laboratory area division.

[0048] At least one embodiment of the present application also provides a data control device. Figure 5 A schematic block diagram of a data control device provided in at least one embodiment of the present application. Figure 5 As shown, the data control device 20 may include one or more memories 200 and one or more processors 201. The memory 200 is used to store computer executable instructions non-transiently; the processor 201 is used to run the computer executable instructions, and when the computer executable instructions are run by the processor 201, the processor 201 may execute one or more steps in the laboratory area division method using a decision tree model according to any embodiment of the present application.

[0049] The specific implementation and related explanation of each step of the method for dividing laboratory areas using a decision tree model can be found in the above-mentioned embodiment of the method for dividing laboratory areas using a decision tree model, and will not be repeated here. Figure 5 The components of the data control device 20 shown are merely exemplary and non-limiting. The data control device 20 may also have other components according to actual application requirements.

[0050] In one embodiment, the processor 201 and the memory 200 can communicate with each other directly or indirectly. For example, the processor 201 and the memory 200 can communicate via a network connection. The network may include a wireless network, a wired network, and / or any combination of a wireless network and a wired network, and the present application does not limit the type and function of the network. For another example, the processor 201 and the memory 200 can also communicate via a bus connection. The bus may be a peripheral component interconnect standard (PCI) bus or an extended industrial standard architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 may be arranged at a remote data server end (cloud) or a distributed energy system end (local end), or may be arranged at a client end (for example, a mobile device such as a mobile phone). For example, the processor 201 may be a device having data processing capability and / or instruction execution capability such as a central processing unit (CPU), a tensor processor (TPU) or a graphics processor GPU, and may control other components in the data prediction device 20 to perform the desired functions. The central processing unit (CPU) may be an X86 or ARM architecture, etc.

[0051] In one embodiment, the memory 200 may include any combination of one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disk read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer executable instructions may be stored on the computer-readable storage medium, and the processor 201 may execute the computer executable instructions to implement various functions of the data prediction device 20. Various applications and various data, as well as various data used and / or generated by the application, etc. may also be stored in the memory 200.

[0052] It should be noted that the data control device 20 can achieve technical effects similar to the aforementioned laboratory area division method using the decision tree model, and the repeated parts will not be repeated.

[0053] At least one embodiment of the present application also provides a non-transitory computer-readable storage medium. Figure 6 A schematic diagram of a non-transitory computer-readable storage medium provided for at least one embodiment of the present application. Figure 6 As shown, one or more computer executable instructions 301 may be non-transitory stored on a non-transitory computer readable storage medium 30. For example, when the computer executable instructions 301 are executed by a computer, the computer may execute one or more steps in the laboratory area division method using a decision tree model according to any embodiment of the present application.

[0054] In one embodiment, the non-transitory computer-readable storage medium 30 may be applied to the above-mentioned data control device 20 , for example, it may be the memory 200 in the data control device 20 .

[0055] In one embodiment, the description of the non-transitory computer-readable storage medium 30 may refer to the description of the memory 200 in the embodiment of the data control device 20, and the repeated parts will not be repeated.

[0056] It should be noted that the memory 200 stores different non-transiently stored computer executable instructions, and the data control device 20 corresponds to a firmware upgrade device. When the computer executable instructions are executed by the processor 201, the processor 201 can execute one or more steps in the laboratory area division method using a decision tree model according to any embodiment of the present application.

[0057] There are a few points to note about this application:

[0058] (1) The drawings of the embodiments of the present application only relate to the structures related to the embodiments of the present application, and other structures may refer to the general design.

[0059] (2) For the sake of clarity, the thickness and size of layers or structures are exaggerated in the drawings used to describe the embodiments of the present invention. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element, or there may be intervening elements.

[0060] (3) In the absence of conflict, the embodiments of the present application and the features in the embodiments may be combined with each other to obtain new embodiments. The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. The protection scope of the present application shall be based on the protection scope of the claims.

[0061] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0062] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A laboratory area division method using a decision tree model, characterized in that: Includes steps: Acquire the laboratory equipment data and laboratory personnel data historically stored in the cloud platform, and use a decision tree model to calibrate corresponding laboratory area units for the laboratory equipment data and laboratory personnel data; Using the laboratory equipment data and laboratory personnel data as training samples and the laboratory area units as result samples to train a regional classification model; Acquire the latest laboratory equipment data and laboratory personnel data and input them into the regional classification model, and output laboratory regional unit data; Generate a laboratory area map according to a similarity matrix and by analyzing the similarity of the laboratory area unit data; Spatial optimization is performed based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division.

2. The method for dividing laboratory areas using a decision tree model according to claim 1, characterized in that: Before the process of using the laboratory equipment data and laboratory personnel data as training samples and the laboratory area unit as result samples to train the area classification model, the process further includes the following steps: Performing feature selection on the laboratory equipment data and the laboratory personnel data through a feature processing model to obtain data features; Performing dimensionality reduction processing on the data features, removing redundant features in the data features, and obtaining effective data features; The valid data features are input into the feature processing model to infer laboratory equipment data and laboratory personnel data used as training samples.

3. The method for dividing laboratory areas using a decision tree model according to claim 1, characterized in that: The process of obtaining the laboratory equipment data and laboratory personnel data historically stored in the cloud platform and calibrating the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data using a decision tree model includes the steps of: Determine laboratory area classification information of the laboratory equipment data and laboratory personnel data according to the decision tree model; The laboratory area classification information is marked on the partition unit to obtain the laboratory area unit.

4. The method for dividing laboratory areas using a decision tree model according to claim 1, characterized in that: The region classification model is a deep learning framework; The process of using the laboratory equipment data and laboratory personnel data as training samples and the laboratory area unit as result samples to train the area classification model comprises the steps of: Strengthening the training samples and the result samples using gradient weighted class activation mapping; The visualization level of laboratory area unit data output by the area classification model is improved according to the result sample and the training sample.

5. The method for dividing laboratory areas using a decision tree model according to claim 1, characterized in that: The process of generating a laboratory area map according to a similarity matrix and analyzing the similarity of the laboratory area unit data comprises the steps of: The adjacent regions are reshaped according to the similarities to obtain a preliminary map.

6. The method for dividing laboratory areas using a decision tree model according to claim 5, characterized in that: The process of performing spatial optimization based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division comprises the steps of: The boundary of the preliminary map is optimized by a boundary optimization algorithm to reduce boundary fragmentation.

7. The method for dividing laboratory areas using a decision tree model according to claim 5, characterized in that: The process of performing spatial optimization based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division also includes the steps of: Normalizing the laboratory area unit data, and dividing the data into groups according to the normalization results; The areas of laboratory area unit data of the same group are merged to obtain the laboratory area division map.

8. A laboratory area division device using a decision tree model, characterized in that: include: A data calibration module is used to obtain the laboratory equipment data and laboratory personnel data historically stored in the cloud platform, and use a decision tree model to calibrate the corresponding laboratory area units for the laboratory equipment data and laboratory personnel data; A module training module, used to train a regional classification model by taking the laboratory equipment data and laboratory personnel data as training samples and the laboratory area unit as result samples; A data reconstruction module, used to obtain the latest laboratory equipment data and laboratory personnel data and input them into the regional classification model, and output laboratory regional unit data; A map generation module, used for generating a laboratory area map according to a similarity matrix and analyzing the similarity of the laboratory area unit data; The map optimization module is used to perform space optimization based on the laboratory area map to obtain a laboratory area division map for characterizing the laboratory area division.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the laboratory area division method using a decision tree model as described in any one of claims 1 to 7 is implemented.

10. A data control device, characterized in that: include: one or more memories non-transitorily storing computer-executable instructions; One or more processors are configured to run computer executable instructions, wherein the computer executable instructions, when executed by the one or more processors, implement the laboratory area division method using a decision tree model as described in any one of claims 1 to 7.

Images