Asset management method based on distributed experimental equipment and related equipment

Abstract

This application belongs to the field of data processing, and particularly relates to an asset management method and related equipment based on distributed experimental equipment. The method includes: monitoring the equipment deployment status in a distributed experimental equipment management system; inputting the equipment deployment status into an intelligent editing model to perform automatic topology aggregation and task pattern recognition processing to obtain equipment topology editing tasks matching the distributed experimental equipment management system; constructing or updating the equipment topology network of the distributed experimental equipment management system based on the equipment topology editing tasks; performing asset comparison checks between the equipment topology network and historical equipment topology networks; and updating the corresponding asset management information of the distributed experimental equipment management system based on the asset comparison check results. This method can improve the efficiency of asset management for distributed experimental equipment, the accuracy of asset discovery, reduce system operational risks, and enhance system stability and reliability.

Description

Technical Field

[0001] This application belongs to the field of data processing, and in particular relates to an asset management method and related equipment based on distributed experimental equipment. Background Technology

[0002] To promote the widespread adoption of intelligent applications across various industries and sectors, distributed experimental equipment generates a large amount of data.

[0003] In related technologies, laboratories deploy various types of experimental equipment, each with different usage and maintenance requirements. This diversity and complexity makes asset management difficult, especially when multiple devices need to be updated and monitored simultaneously. Furthermore, experimental equipment often requires costly maintenance and regular inspections; failure to promptly detect or address equipment problems can lead to expensive repair or replacement costs, as well as the loss of experimental data.

[0004] Therefore, there is an urgent need to design an asset management solution based on distributed experimental equipment to solve at least one of the above-mentioned technical problems. Summary of the Invention

[0005] This application provides an asset management method and related equipment based on distributed experimental equipment, which can improve the asset management efficiency of distributed experimental equipment, the accuracy of asset discovery, reduce system operation risks, and enhance system stability and reliability.

[0006] Firstly, this application provides an asset management method based on distributed experimental equipment, applied to a distributed experimental equipment management and control system. The distributed experimental equipment management and control system is used to operate and manage experimental equipment, which includes different types of equipment. The asset management method based on distributed experimental equipment includes:

[0007] Monitor the equipment deployment status in the distributed experimental equipment management system;

[0008] The equipment deployment information is input into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, so as to obtain the equipment topology editing task matched by the distributed experimental equipment management system.

[0009] Based on the device topology editing task, construct or update the device topology network of the distributed experimental equipment management and control system;

[0010] Perform an asset comparison check between the device topology network and the historical device topology network;

[0011] The asset management information corresponding to the distributed experimental equipment management system is updated based on the asset comparison check results.

[0012] Secondly, embodiments of this application provide a distributed experimental equipment management and control system. This system is used to operate and manage experimental equipment, which includes different types of equipment. The distributed experimental equipment management and control system includes:

[0013] The monitoring unit is configured to monitor the equipment deployment status in the distributed experimental equipment management and control system;

[0014] The generation unit is configured to input the device deployment information into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, so as to obtain the device topology editing task matched by the distributed experimental equipment management system.

[0015] The editing unit is configured to construct or update the device topology network of the distributed experimental equipment management and control system based on the device topology editing task.

[0016] The comparison unit is configured to perform an asset comparison check between the device topology network and the historical device topology network;

[0017] The management unit is configured to update the asset management information corresponding to the distributed experimental equipment control system based on the asset comparison check results.

[0018] Thirdly, embodiments of this application provide an intelligent computing platform, the intelligent computing platform comprising:

[0019] At least one processor, memory, and input / output unit;

[0020] The memory is used to store computer programs, the processor is used to call the computer programs stored in the memory to execute the asset management method based on distributed experimental equipment in the first aspect, and the input / output unit is used to receive user input and display the output information of the computer programs stored in the memory.

[0021] Fourthly, a computer-readable storage medium is provided, comprising instructions that, when executed on a computer, cause the computer to perform the asset management method of the first aspect based on a distributed experimental device.

[0022] The technical solution provided in this application embodiment can be applied to a distributed experimental equipment management and control system. This system is used to operate and manage experimental equipment, including different types of devices. In this solution, firstly, the deployment status of the devices in the distributed experimental equipment management and control system is monitored. Then, the device deployment status is input into an intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, thereby obtaining a device topology editing task matching the distributed experimental equipment management and control system. Thus, by monitoring the device deployment status and inputting it into the intelligent editing model, the system can automatically perform automatic topology aggregation and task pattern recognition processing. This intelligent management makes equipment management more efficient and accurate, reduces the need for manual intervention, and improves management efficiency. Then, based on the device topology editing task, the device topology network of the distributed experimental equipment management and control system is constructed or updated. Here, based on the device topology editing task, the system can construct or update the device topology network of the distributed experimental equipment management and control system in real time. This real-time update ensures that the system always reflects the actual device layout and connection relationships, which is beneficial for timely detection and resolution of problems in the topology network. Furthermore, an asset comparison check is performed between the device topology network and the historical device topology network. Here, by comparing the device topology network with the historical device topology network, the system can promptly detect any changes or anomalies. This comparison helps identify potential configuration errors, device malfunctions, or security vulnerabilities, allowing for timely remediation or prevention. Finally, the asset management information corresponding to the distributed experimental equipment management system is updated based on the asset comparison results. Thus, the system can update the asset management information corresponding to the distributed experimental equipment management system based on the asset comparison results. This timely information update ensures the accuracy and completeness of asset management information, providing reliable data support for subsequent decision-making. The technical solution of this application, through intelligent device management, real-time topology network updates, asset comparison checks, and asset management information updates, improves the efficiency of distributed experimental equipment asset management, the accuracy of asset discovery, reduces system operational risks, and enhances system stability and reliability. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a flowchart illustrating an asset management method based on distributed experimental equipment according to an embodiment of this application.

[0025] Figure 2 This is a schematic diagram of the structure of a distributed experimental equipment management and control system according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0029] To promote the widespread adoption of intelligent applications across various industries and sectors, distributed experimental equipment generates a large amount of data.

[0030] In related technologies, laboratories deploy various types of experimental equipment, each with different usage and maintenance requirements. This diversity and complexity makes asset management difficult, especially when multiple devices need to be updated and monitored simultaneously. Furthermore, experimental equipment often requires costly maintenance and regular inspections; failure to promptly detect or address equipment problems can lead to expensive repair or replacement costs, as well as the loss of experimental data.

[0031] Therefore, there is an urgent need to design an asset management solution based on distributed experimental equipment to solve at least one of the above-mentioned technical problems.

[0032] This application provides an asset management method and related equipment based on distributed experimental equipment.

[0033] Specifically, the asset management scheme based on distributed experimental equipment can be applied to a distributed experimental equipment control system. This system is used to operate and manage experimental equipment, which includes different types of devices. In this scheme, firstly, the deployment status of the equipment in the distributed experimental equipment control system is monitored. Then, the equipment deployment status is input into an intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, thereby obtaining a matching equipment topology editing task for the distributed experimental equipment control system. Thus, by monitoring the equipment deployment status and inputting it into the intelligent editing model, the system can automatically perform automatic topology aggregation and task pattern recognition processing. This intelligent management makes equipment management more efficient and accurate, reduces the need for manual intervention, and improves management efficiency. Then, based on the equipment topology editing task, the equipment topology network of the distributed experimental equipment control system is constructed or updated. Here, based on the equipment topology editing task, the system can construct or update the equipment topology network of the distributed experimental equipment control system in real time. This real-time update ensures that the system always reflects the actual equipment layout and connection relationships, which is beneficial for timely detection and resolution of problems in the topology network. Finally, an asset comparison check is performed between the equipment topology network and the historical equipment topology network. Here, by comparing the device topology network with historical device topologies, the system can promptly detect any changes or anomalies. This comparison helps identify potential configuration errors, device malfunctions, or security vulnerabilities, allowing for timely remediation or prevention. Finally, the asset management information corresponding to the distributed experimental equipment management system is updated based on the asset comparison results. This timely information update ensures the accuracy and completeness of asset management information, providing reliable data support for subsequent decision-making.

[0034] In the asset management solution based on distributed experimental equipment, intelligent equipment management, real-time topology network updates, asset comparison and inspection, and asset management information updates are used to improve the efficiency of asset management, the accuracy of asset discovery, reduce system operation risks, and enhance system stability and reliability.

[0035] The asset management solution based on distributed experimental equipment provided in this application can also be executed by an electronic device, such as a server, server cluster, or cloud server. This electronic device can also be a terminal device such as a mobile phone, computer, tablet computer, wearable device, or dedicated device (such as a dedicated terminal device with an asset management system based on distributed experimental equipment). These electronic devices can also carry the chips described in the above embodiments. Alternatively, these electronic devices can also install a service program for executing the asset management solution based on distributed experimental equipment.

[0036] Figure 1 A schematic diagram illustrating an asset management method based on distributed experimental equipment provided in this application embodiment, as shown below. Figure 1 The method includes the following steps:

[0037] 101. Monitor the equipment deployment status in the distributed experimental equipment management system.

[0038] In this embodiment, the distributed experimental equipment management system is used to operate and manage experimental equipment. This experimental equipment includes different types of devices.

[0039] In this embodiment, the asset management method based on the distributed test equipment safety management system involves a relatively complex system architecture, requiring full consideration of communication, data transmission, and security between the server and client. The following is an introduction to the system's technical background and related equipment:

[0040] Alternatively, the distributed testing equipment safety management system is designed based on a client / server architecture. A client / server architecture separates the user interface from the application logic; the user communicates with the server through the client, and the server processes requests and returns results. This architecture is typically used for applications requiring complex logic processing and large amounts of data storage.

[0041] Microservice architecture is an architectural pattern that breaks down an application into a series of small, independently deployed services. Each microservice can be developed, deployed, and scaled independently, communicating through lightweight communication protocols, thereby achieving a highly cohesive and loosely coupled system architecture.

[0042] Qt is a cross-platform C++ application development framework for developing graphical user interface (GUI) applications. It provides a rich set of GUI components and tools, simplifying the development process of cross-platform applications while offering good performance and scalability.

[0043] Distributed testing equipment refers to testing devices deployed at different locations or nodes. These devices can collaborate to complete testing tasks and communicate and exchange data via a network. Distributed testing equipment typically features high flexibility and scalability, allowing for dynamic configuration and deployment as needed.

[0044] The server is the core of the entire system, responsible for implementing various functional modules and processing data. In this system, the server adopts a microservice architecture, containing multiple independent functional modules, such as runtime status monitoring, audit log collection and analysis, ledger management, and resource management. Each functional module can be deployed and extended independently, interacting through a lightweight communication mechanism.

[0045] The client is the interface through which users interact with the system, responsible for receiving user input, displaying information, and sending requests to the server. In this system, the client is developed based on QT and includes functional modules such as user authentication, device management, information management, test parameter management, file management, software management, and remote control. Through communication with the server, the client enables user interaction with the system and asset management.

[0046] In summary, the asset management method based on the distributed test equipment safety management system combines C / S architecture, microservice architecture, and QT development technology to achieve real-time management and intelligent control of distributed test equipment, providing strong support for the safety, stability, and reliability of test equipment.

[0047] As an optional embodiment, in step 101, the device deployment status of the distributed experimental equipment management system is monitored. Monitoring the device deployment status of the distributed experimental equipment management system typically involves the following specific aspects:

[0048] Online device status monitoring: The system monitors the online status of each device in real time, including whether the device is in operation, standby, or faulty state. By monitoring the online status, the system can promptly detect the operational status of the devices, ensuring normal connection and operation.

[0049] Device connectivity monitoring: The system monitors the connectivity between different devices, including network connection status and whether communication protocols are functioning correctly. This helps to understand the communication status between devices and promptly identify connectivity problems or communication failures.

[0050] Equipment resource utilization monitoring: This monitors the resource utilization of equipment, including the utilization rates of resources such as CPU, memory, and storage. By monitoring resource utilization, equipment load can be adjusted in a timely manner to avoid over-utilization of resources that could lead to system performance degradation or equipment failure.

[0051] Device topology monitoring: This involves monitoring the topology of devices, including the logical connections and physical connection paths between them. By monitoring the topology, the relationships between devices can be understood, topology anomalies or connection problems can be detected, and normal communication between devices can be ensured.

[0052] Equipment operation log monitoring: Monitors the equipment's operation logs, recording equipment operation and status. By monitoring the operation logs, abnormal equipment behavior or operational errors can be detected in a timely manner, allowing for prompt handling and ensuring the normal operation of the equipment.

[0053] In summary, monitoring the equipment deployment in a distributed experimental equipment management system includes real-time monitoring of equipment online status, connectivity, resource utilization, topology, and operation logs. This monitoring helps the system promptly identify potential problems and ensures the normal operation and management of the equipment.

[0054] 102. Input the equipment deployment information into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, so as to obtain the equipment topology editing task matched by the distributed experimental equipment management system.

[0055] As an optional embodiment, the intelligent editing model includes at least the following structure: a feature extraction layer, a topology aggregation layer, a task pattern recognition layer, and a task generation layer. Specifically, the feature extraction layer extracts key feature information from the input device deployment data for subsequent processing and analysis. These features may include device type, device status, connection relationships, resource utilization, etc. Through feature extraction, raw data can be transformed into feature representations that the model can process. The topology aggregation layer is responsible for organizing the extracted feature information into a device topology structure and performing an automatic topology aggregation process. This includes determining the connection relationships between devices, the network topology structure, and device hierarchical relationships. Through topology aggregation, topological relationships between devices can be established, providing a foundation for subsequent task pattern recognition. The task pattern recognition layer analyzes and identifies the aggregated device topology structure to determine the device topology editing tasks that the system needs to perform. This includes identifying anomalies or problems in the topology and determining the optimal editing strategy or optimization scheme. Through task pattern recognition, corresponding editing tasks can be generated based on the system status and requirements. The task generation layer generates actual device topology editing tasks based on the results of task pattern recognition, including operations such as adding new devices, deleting old devices, and adjusting connection relationships. This layer is responsible for translating task patterns into specific operational steps to update and optimize the equipment topology. The generated tasks will be used to update the equipment topology network in the distributed experimental equipment management system, thereby maintaining the normal operation and management of the system. In summary, the structure of the intelligent editing model includes a feature extraction layer, a topology aggregation layer, a task pattern recognition layer, and a task generation layer. These layers work together to process and analyze the equipment deployment status, thereby achieving intelligent editing and management of the equipment topology.

[0056] Based on the above model structure, in step 102, the device deployment information is input into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing to obtain the device topology editing task matched by the distributed experimental equipment management system. This can be achieved through the following steps:

[0057] 201. Through the feature extraction layer, the equipment deployment features of the distributed experimental equipment management and control system are extracted from the equipment deployment status;

[0058] 202. Through the topology aggregation layer, the distributed experimental equipment management system is automatically aggregated based on the device deployment characteristics to obtain the target network structure to be updated or constructed.

[0059] 203. Through the task pattern recognition layer, the target network structure to be updated or built is identified to obtain the type of topology editing task to be executed.

[0060] 204. Through the task generation layer, based on the type of topology editing task to be executed, create a device topology editing task that matches the target network structure to be updated or built.

[0061] In this embodiment, the device deployment features include at least: device access status, device connection features, device subnet characteristics, and device attribute features. Specifically, the device deployment features are key information extracted from the device deployment status, used to perform automatic topology aggregation and task pattern recognition processing in the intelligent editing model.

[0062] Device Access Status: This feature describes the current access status of the device, including online, offline, standby, or fault status. This feature can indicate whether the device is available and its current operating status.

[0063] Device connectivity characteristics: This characteristic describes the connectivity between devices, including the connection method in the network topology, connection type (such as wired or wireless connection), connection speed, and other information. This characteristic is crucial for determining the communication paths and connection nature between devices.

[0064] Device Subnet Characteristics: This characteristic describes the subnet or network area to which the device belongs, used to distinguish device deployment within different network areas. This helps in the partitioning and optimization of large-scale networks.

[0065] Device attribute characteristics: This characteristic describes the device's attribute information, including device type, manufacturer, model, hardware configuration, software version, etc. This attribute information is crucial for device identification, classification, and management, and also helps in performing tasks specific to certain types of devices.

[0066] By extracting these device deployment characteristics, the intelligent editing model can more accurately understand the device deployment situation, thereby performing effective automatic topology aggregation and task pattern recognition processing, and ultimately generating topology editing tasks suitable for updating or building the target network structure. The extraction and utilization of these characteristics enable the system to dynamically adjust the device topology according to the actual situation, ensuring system stability and performance optimization.

[0067] For example, suppose there is a distributed experimental equipment management system, which includes several devices, each connected to a central server. In step 201, the following features are extracted from the device deployment: Assuming device access status: Device 1 is online, Device 2 is online, Device 3 is offline, and Device 4 is online. Assuming device connection characteristics: Device 1 is connected via wired connection, Device 2 via wireless connection, Device 3 is not connected, and Device 4 is connected via wired connection. Assuming subnet characteristics: Devices 1 and 2 belong to subnet A, Device 3 belongs to subnet B, and Device 4 belongs to subnet A. Assuming device attribute characteristics: Device 1 is a server, Device 2 is a sensor, Device 3 is a storage device, and Device 4 is a server. In step 202, based on the extracted features, the devices are grouped into the corresponding subnets, and the connection relationships between the devices are established to form a topology. For example, subnet A includes Device 1 and Device 4, which are connected via wired connection. Subnet B only has Device 3 and is not connected to any other devices. In subnet A, Device 1 is a server, and Device 4 is also a server. In step 203, the aggregated topology is analyzed to identify the types of tasks that need to be performed. For example, if there are many server devices in subnet A, server resource allocation may need to be optimized. If devices in subnet B are offline, reconnection or replacement may be necessary. In step 204, corresponding topology editing tasks are generated based on the identified task types. For example, resources may be reallocated to server devices in subnet A to achieve a more balanced load. Fault diagnosis may be performed on devices in subnet B to repair connectivity issues or replace devices. This example demonstrates how the intelligent editing model extracts features from device deployment, aggregates topology, identifies task types, and generates corresponding editing tasks at each level to optimize the topology of the distributed experimental equipment management system.

[0068] In an optional embodiment, step 202 above, where the distributed experimental equipment management system is automatically topologically aggregated based on the device deployment characteristics through a topology aggregation layer to obtain the target network structure to be updated or constructed, can be implemented as follows:

[0069] 301. Select a starting device as the starting point for the depth-first search; the starting device is any device in the distributed experimental equipment management system.

[0070] 302. Starting from the initial device, perform a depth-first search on the distributed experimental equipment management system to obtain the connection relationships between the devices in the distributed experimental equipment management system;

[0071] 303. Based on the connection relationships between various devices, construct candidate topology network structures between the various devices;

[0072] 304. Based on the candidate topology network structure among the various devices, determine the target network structure that needs to be updated or constructed.

[0073] Specifically, in 302, during the depth-first search process, a local optimal solution search is performed on the branch to which each device belongs, from the top-level device in the branch to the bottom-level device in the branch, so that the branch reaches the local search exhaustion.

[0074] Then, after reaching the lowest-level device, it jumps to the branch to which the adjacent other devices belong and performs the same local search exhaustive process until the connection relationship between the various devices is established.

[0075] In the above optional embodiments, step 202 involves automatically performing topology aggregation on the devices in the distributed experimental equipment management system using a topology aggregation layer. This process aims to construct or update the target network structure. In step 301, the system first selects a starting device as the starting point for depth-first search (DFS). This starting device can be any device in the management system, and the selection criteria can be based on the importance, location, functionality, or any other appropriate criteria of the devices in the network. In step 302, starting from the selected starting device, the system uses the depth-first search method to explore and record the connections between devices. Depth-first search is an algorithm used to traverse or search a tree or graph. It expands along the depth of the tree, searching the branches of the tree as deeply as possible. During this process, the system searches for local optima in the branches to which each device belongs, meaning that the search starts from the top-level device of each branch and continues until the bottom-level device of the branch, ensuring that the connections within the branches are fully explored and recorded. In step 303, based on the device connections obtained through depth-first search, the system constructs one or more candidate topology network structures. This step is based on the actual device connection situation and may result in several different network topologies for subsequent selection and optimization. In step 304, based on the constructed candidate topology network structures, the target network structure that needs to be updated or constructed is finally determined. This may involve selecting an optimal structure, or optimizing one of multiple structures based on specific application requirements and parameters.

[0076] The key to this process lies in the effective execution of depth-first search and the precise construction of connections between devices. This approach systematically explores all possible network connections, ensuring that the constructed network structure reflects both the actual device deployment and the system's operational requirements. Furthermore, local optimum search ensures that the configuration of each branch or connection point achieves local optima during the construction process, thereby optimizing overall network performance and resource allocation.

[0077] As an optional embodiment, in step 302, for the current device, the system accesses the current device and performs a hierarchical-first search on the branch to which the current device belongs to obtain a local optimum. The search proceeds from the top-level device in the branch to the bottom-level device, ensuring that the branch reaches a local exhaustive search. After obtaining the local optimum search result for the current device, or after reaching the bottom-level device to complete the hierarchical-first search on the branch to which the current device belongs, the system marks the current device and its branch as visited. Then, the system jumps to another device adjacent to the current device, accesses the other device, and determines whether the other device is already visited.

[0078] If the adjacent device is not already visited, a hierarchical search is performed on that adjacent device to find a local optimum. If the adjacent device is already visited, the search continues to the next adjacent device. This process continues until all adjacent devices have been searched. Then, the search backtracks to the non-adjacent device and performs a hierarchical search on its branch to find a local optimum. Finally, this search process is applied to all known devices in the distributed experimental equipment management system to obtain the connection relationships between the devices.

[0079] Implementing the method combining depth-first search and hierarchy-first search in the above-mentioned optional embodiments of a distributed experimental equipment management system can bring several beneficial effects. Specifically, obtaining local optima through hierarchy-first search optimizes resources and connections within each branch, improving not only the efficiency of each branch but also overall system-level performance. Closer and more efficient connections between devices reduce latency and increase data processing speed. Depth-first search comprehensively explores the connections between all devices, ensuring the completeness and accuracy of the network topology. This accurate topology mapping is crucial, especially in experimental environments requiring high network reliability, ensuring correct data transmission and inter-device collaboration. This search mechanism allows the system to quickly locate network faults or performance bottlenecks, facilitating timely fault diagnosis and repair. Marking visited devices and branches helps avoid redundant checks and resource waste, effectively improving maintenance efficiency.

[0080] For example, suppose in a large-scale laboratory environment containing multiple sensors, servers, and storage devices, a critical data analysis server suddenly fails to receive data from other sensor devices. Using the search method described above, the system can start from this server and delve into all connected branches to quickly identify that the data failure is due to a disconnected sensor. By performing a hierarchical search on the disconnected sensor branches, it can further determine whether the problem is a hardware failure or a configuration error, allowing for rapid action to fix the problem and restore normal operation of the entire experimental setup.

[0081] This example demonstrates that this search technology not only helps to quickly locate and resolve problems, but also improves the overall network efficiency and stability by optimizing device connectivity and resource allocation.

[0082] Optionally, in the above steps, after sorting the devices in the branch to which the current device belongs according to their relevance weights and hierarchical levels, and constructing a search queue for the branch to which the current device belongs based on the sorting results, a real-time learning model can be used to predict the search queue of the branch to which the current device belongs, based on historical search behavior data and the real-time operating data of each device in the branch to which the current device belongs, to obtain a local search probability distribution. The local search probability distribution indicates the probability that a locally optimal search result will appear at each device node in the search queue. Furthermore, based on the local search probability distribution, redundant paths are invalidated for each device in the search queue, and / or a pruning strategy is executed to filter and delete device nodes whose probability of appearing a locally optimal search result is lower than a set probability threshold, to obtain an optimized search queue for the branch to which the current device belongs.

[0083] Thus, in the above optional embodiments, the system not only utilizes depth-first and hierarchy-first search techniques to explore connections between devices, but also introduces device ranking based on relevance weights and hierarchical levels. It combines historical search data and real-time operational data to predict the local search probability distribution of the search queue through a real-time learning model. This method improves search efficiency and optimizes network resource allocation. Examples illustrating the beneficial effects of these measures are as follows:

[0084] Imagine a laboratory containing hundreds of servers and switches that requires routine network architecture checks and optimizations. By ranking devices using correlation weights and hierarchy, the system can prioritize searching critical and high-level devices, which often have a significant impact on network performance. Combined with local search probability distributions predicted by a real-time learning model, the system can focus on devices more likely to experience problems or require adjustments, thus significantly improving search efficiency and effectiveness.

[0085] In complex network structures, especially in environments with multiple paths and redundant connections, excessive path exploration consumes additional computational resources and prolongs search time. By using local search probability distributions to indicate which device nodes are unlikely to have problems or have reached an optimized state, the system can effectively prune these paths, avoiding invalid and redundant searches, thus making the search more efficient and goal-oriented.

[0086] Consider a widely deployed IoT environment with numerous devices and complex interactions between them. When a device malfunctions, quickly locating the source of the problem is a top priority. By analyzing device operational data in real time and utilizing historical search behavior data, the system can predict potential fault points and prioritize checking these points. This not only accelerates fault location but also ensures stable system operation and service continuity.

[0087] Taking a laboratory control system as an example, the system contains thousands of sensors and signal controllers. By predicting which equipment nodes may malfunction through real-time learning models, the system can adjust the operating status of the experimental signal controllers in real time to respond to real-time experimental needs or unexpected situations, such as accidents or experimental congestion, ensuring smooth and safe experiments.

[0088] This approach, which combines real-time learning with optimized search queues, not only improves the efficiency and response speed of the entire control system, but also reduces maintenance costs and overall system complexity, and enhances the reliability of equipment and networks.

[0089] In practical applications, the real-time learning model can be further optionally represented by the following formula:

[0090]

[0091] Where (P(Y=1|X)) represents the local search probability distribution predicted by the real-time learning model based on device X in the branch Y to which the current device belongs, x1, x2, ..., x n These represent the real-time operational data characteristics and historical search behavior data characteristics of each device in branch Y to which the current device belongs, β0, β1, ..., β n This represents the correlation weights corresponding to each feature item.

[0092] In another optional embodiment, in step 202 above, the distributed experimental equipment management system is automatically topologically aggregated based on the device deployment characteristics through a topology aggregation layer to obtain the target network structure to be updated or constructed. This can be achieved as follows:

[0093] 401. Select a starting device as the starting point for the breadth-first search; the starting device is any device in the distributed experimental equipment management system.

[0094] 402. Starting from the starting device, a global priority search queue is used to traverse all devices connected to the starting device layer by layer until the connection status between the starting device and other connected devices is obtained, then jump to the adjacent device connected to the starting device.

[0095] 403, continue traversing all devices connected to the adjacent device until the connection status between the adjacent device and other connected devices is obtained, then jump to the next adjacent device connected to the adjacent device;

[0096] 404. Execute the above traversal process until the search reaches the outermost device to obtain the global network structure of the distributed experimental equipment management system.

[0097] In this embodiment, implementing a breadth-first search (BFS) strategy through a topology aggregation layer to automatically aggregate and update the network structure of the distributed experimental equipment management system brings many beneficial effects. This strategy effectively constructs the global network structure of the entire system by selecting a starting device and traversing all connected devices layer by layer. The following are some specific benefits and examples of this method:

[0098] By starting from a selected initial device and traversing all devices using a breadth-first search, the network structure diagram of the entire distributed system can be constructed efficiently. This global view is crucial for understanding the overall network layout, the relationships between devices, and the communication paths.

[0099] Having a complete global network structure allows system administrators and network planners to better optimize networks and allocate resources. For example, when it is necessary to adjust or increase network capacity, the optimal locations for adding devices or adjusting their paths can be planned based on the current network structure.

[0100] When a network failure occurs, having a complete global network view allows for quick and accurate location of the problem. Furthermore, it enables more strategic fault recovery, allowing for the selection of optimal routes and alternative equipment to restore normal service as quickly as possible.

[0101] For example, in a large-scale smart laboratory, hundreds or even thousands of sensors, cameras, and control units are deployed to monitor the laboratory's production line status, environmental conditions, and safety status in real time. By implementing this breadth-first search strategy, starting with a core monitoring device located in the central control room, the system can traverse all devices connected to this device throughout the laboratory, layer by layer, to obtain a detailed device connection diagram. This network diagram not only helps laboratory managers monitor equipment status in real time but also allows for rapid location of faulty equipment in case of malfunctions, reducing downtime and ensuring production efficiency and employee safety. Through this global search and automatic network aggregation method, faster deployment, more precise control, and more efficient fault handling can be achieved when developing and maintaining large-scale distributed monitoring systems. This is of great significance for improving the overall system's operational efficiency and reliability.

[0102] 103. Based on the device topology editing task, construct or update the device topology network of the distributed experimental equipment management and control system.

[0103] In distributed experimental equipment management systems, constructing or updating the equipment topology network based on equipment topology editing tasks can bring several beneficial effects. This operation typically aims to optimize the structure and performance of the entire management system, improving operational efficiency and system responsiveness. The following are some specific benefits that can be achieved using this method, along with corresponding examples:

[0104] Updating the network topology optimizes communication paths between devices, reduces data transmission latency, and improves data processing speed. This is especially important for systems that rely on real-time data analytics. By regularly building or updating the network topology, system administrators can adjust the network structure based on actual needs and device usage. This not only increases the system's flexibility and scalability but also allows for network optimization according to new security policies or technical standards. An accurate device topology helps to quickly identify and isolate system fault points, accelerates the fault recovery process, and reduces system downtime. Furthermore, it can prevent chain reactions of failures and protect the overall operational status of the system.

[0105] For example, suppose an intelligent experimental management system is deployed in a large city. This system encompasses various experimental monitoring devices, traffic lights, and emergency response units. As the city's experimental needs change and new technologies are introduced, the system network requires continuous adjustment and optimization. Through device topology editing tasks, the system can update its network structure in real time. For instance, it can reconfigure the data aggregation points of experimental monitoring points to cope with increased data traffic during peak experimental periods. This flexible network adjustment not only ensures the real-time performance and accuracy of experimental data processing but also makes experimental control more efficient, thereby effectively reducing experimental congestion and improving the overall operational efficiency of the city's experimental system. Furthermore, when new equipment is introduced, updating the device topology network ensures the smooth integration of these new technologies, guaranteeing the stable operation of the entire system.

[0106] In summary, system topology updates based on device topology editing tasks can not only improve system performance and reliability, but also enhance the system's adaptability to new technologies and changes, thereby achieving more efficient and intelligent management.

[0107] 104. Update the asset management information corresponding to the distributed experimental equipment management system based on the asset comparison inspection results.

[0108] In a laboratory environment, asset management is crucial to ensure the efficient and accurate use and maintenance of valuable instruments and equipment. Updating asset management information in a distributed laboratory equipment control system based on asset comparison checks yields a range of beneficial results, further improving laboratory operational efficiency and asset utilization. Regular asset comparison checks and updates to asset management information help laboratory managers accurately track the usage status and location of each piece of equipment. This precise data support prevents equipment loss and misuse, ensuring all assets are properly managed and maintained. Updated asset management information allows laboratory administrators to better understand equipment usage frequency and maintenance cycles, thereby optimizing equipment scheduling and usage strategies. This not only improves equipment utilization efficiency but also extends equipment lifespan. Timely updated asset management information helps laboratories accurately assess equipment maintenance needs and budget plans. By preventing overuse and ensuring timely repairs, the costs of emergency repairs and equipment replacements due to malfunctions can be significantly reduced.

[0109] For example, a university biotechnology laboratory is equipped with various sophisticated instruments, such as high-speed centrifuges, microscopes, and spectrometers. The laboratory manages these devices through a distributed experimental equipment control system. At the end of each semester, the laboratory administrator conducts a comprehensive asset review, assessing the status of all equipment and corresponding asset records. Based on the review results, the administrator updates the asset management information in the system, including the current location, status (in use, under repair, obsolescence, etc.), and maintenance history of the equipment. This information update helps the laboratory effectively manage its assets, such as choosing appropriate times for equipment maintenance, scheduling equipment usage to prevent overuse of certain equipment, and planning ahead for future equipment upgrades or replacements.

[0110] By regularly updating the asset ratio check results, the laboratory not only ensured the smooth progress of research work, but also improved the utilization efficiency of equipment, reduced unexpected equipment failures and related costs, thereby ensuring the optimal use of funds and the efficient operation of research activities.

[0111] In this embodiment, the device deployment status of the distributed experimental equipment management system is monitored. Then, the device deployment status is input into an intelligent editing model to perform automatic topology aggregation and task pattern recognition processing to obtain a device topology editing task matching the distributed experimental equipment management system. Thus, by monitoring the device deployment status and inputting it into the intelligent editing model, the system can automatically perform automatic topology aggregation and task pattern recognition processing. This intelligent management makes equipment management more efficient and accurate, reduces the need for manual intervention, and improves management efficiency. Then, based on the device topology editing task, the device topology network of the distributed experimental equipment management system is constructed or updated. Here, based on the device topology editing task, the system can construct or update the device topology network of the distributed experimental equipment management system in real time. This real-time update ensures that the system always reflects the actual device layout and connection relationships, which is beneficial for timely detection and resolution of problems in the topology network. Furthermore, an asset comparison check is performed between the device topology network and the historical device topology network. Here, by performing an asset comparison check between the device topology network and the historical device topology network, the system can promptly detect any changes or anomalies. This comparison check helps to discover potential configuration errors, equipment failures, or security vulnerabilities, thereby enabling timely measures to repair or prevent them. Finally, the asset management information corresponding to the distributed experimental equipment control system is updated based on the asset comparison and inspection results. In this way, the system can update the asset management information corresponding to the distributed experimental equipment control system according to the asset comparison and inspection results. This timely information update ensures the accuracy and completeness of asset management information, providing reliable data support for subsequent decision-making.

[0112] In another embodiment of this application, a distributed experimental equipment management and control system is also provided. This system is used to operate and manage experimental equipment, which includes different types of equipment. (See also...) Figure 2 The distributed experimental equipment management and control system includes the following units:

[0113] The monitoring unit is configured to monitor the equipment deployment status in the distributed experimental equipment management and control system;

[0114] The generation unit is configured to input the device deployment information into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, so as to obtain the device topology editing task matched by the distributed experimental equipment management system.

[0115] The editing unit is configured to construct or update the device topology network of the distributed experimental equipment management and control system based on the device topology editing task.

[0116] The comparison unit is configured to perform an asset comparison check between the device topology network and the historical device topology network;

[0117] The management unit is configured to update the asset management information corresponding to the distributed experimental equipment control system based on the asset comparison check results.

[0118] Further optionally, the intelligent editing model includes at least the following structure: a feature extraction layer, a topology aggregation layer, a task pattern recognition layer, and a task generation layer;

[0119] The generation unit inputs the device deployment information into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, in order to obtain the device topology editing task matched by the distributed experimental equipment management system, and is configured as follows:

[0120] Through a feature extraction layer, the device deployment features of the distributed experimental equipment management and control system are extracted from the device deployment status; the device deployment features include at least: device access status, device connection features, device subnet features, and device attribute features.

[0121] Through the topology aggregation layer, the distributed experimental equipment management and control system automatically aggregates the topology based on the device deployment characteristics to obtain the target network structure to be updated or constructed.

[0122] The task pattern recognition layer identifies the target network structure to be updated or built to obtain the type of topology editing task to be executed.

[0123] Through the task generation layer, device topology editing tasks that match the target network structure to be updated or built are created based on the type of topology editing task to be executed.

[0124] Further optionally, the generation unit, through a topology aggregation layer, automatically aggregates the topology of the distributed experimental equipment management system based on the device deployment characteristics to obtain the target network structure to be updated or constructed, and is configured as follows:

[0125] Select a starting device as the starting point for the depth-first search; the starting device can be any device in the distributed experimental equipment management system.

[0126] Starting from the initial device, a depth-first search is performed on the distributed experimental equipment management system to obtain the connection relationships between the devices in the distributed experimental equipment management system. During the depth-first search, a local optimum search is performed on the branch to which each device belongs, from the top-level device in the branch to the bottom-level device in the branch, so that the branch reaches the local exhaustion of the search. After reaching the bottom-level device, the search jumps to the branch to which other adjacent devices belong and performs the same local exhaustion process until the connection relationship between the devices is established.

[0127] Based on the connection relationships between various devices, construct candidate topology network structures among the devices;

[0128] Based on the candidate topology network structure among various devices, determine the target network structure that needs to be updated or built.

[0129] Further optionally, the generation unit, starting from the starting device, performs a depth-first search on the distributed experimental equipment management system to obtain the connection relationships between the devices in the distributed experimental equipment management system, and is configured as follows:

[0130] For the current device, access the current device and perform a hierarchical priority search on the branch to which the current device belongs to obtain a local optimum search. Search from the top-level device in the branch to the bottom-level device in the branch to exhaust the local search of the branch.

[0131] After obtaining the search results for the local optimum of the current device, or after searching to the lowest-level device to complete the hierarchical priority search of the branch to which the current device belongs, mark the current device and the branch to which the current device belongs as visited; and,

[0132] Jump to another device adjacent to the current device, access the other device, and determine whether the other device is already accessed;

[0133] If another device adjacent to the current device is not in an visited state, then a hierarchical priority search is performed on the other device adjacent to the current device to obtain a local optimum search.

[0134] If another device adjacent to the current device is already visited, then proceed to the next device adjacent to the current device;

[0135] Until all devices adjacent to the current device have been searched, backtrack to another device that is not adjacent to the current device, and perform a hierarchical priority search on the branch to which the other device that is not adjacent to the current device belongs to obtain a local optimum search.

[0136] The above search process is performed on all known devices in the distributed experimental equipment management system to obtain the connection relationships between the devices in the distributed experimental equipment management system.

[0137] Further optionally, the generation unit, before performing a depth-first search on the distributed experimental equipment management system starting from the starting device to obtain the connection relationships between the devices in the distributed experimental equipment management system, is further configured to:

[0138] Obtain the correlation weights between each device; these correlation weights are used to measure the degree of task correlation between devices.

[0139] For the current device, sort the devices in the branch to which the current device belongs according to their relevance weight and level, and build a search queue for the branch to which the current device belongs based on the sorting results;

[0140] The search queue of the branch to which the current device belongs is used to indicate the search order of each device in the branch to which the current device belongs.

[0141] Further optionally, the generation unit, for the current device, after sorting each device in the branch to which the current device belongs according to its relevance weight and its level, and constructing a search queue for the branch to which the current device belongs based on the sorting results, is further configured to:

[0142] Based on historical search behavior data and real-time operation data of each device in the current device's branch, a real-time learning model is used to predict the search queue of the current device's branch in order to obtain the local search probability distribution.

[0143] The local search probability distribution is used to indicate the probability of finding a locally optimal solution search result at each device node in the search queue.

[0144] Based on the local search probability distribution, redundant paths are invalidated for each device in the search queue, and / or a pruning strategy is executed to filter and delete device nodes whose probability of finding a local optimal solution search result is lower than a set probability threshold, so as to obtain the optimized search queue of the branch to which the current device belongs.

[0145] Further, optionally, the real-time learning model is represented by the following formula:

[0146]

[0147] Where (P(Y=1|X)) represents the local search probability distribution predicted by the real-time learning model based on device X in the branch Y to which the current device belongs, x1, x2, ..., x n These represent the real-time operational data characteristics and historical search behavior data characteristics of each device in branch Y to which the current device belongs, β0, β1, ..., β n This represents the correlation weights corresponding to each feature item.

[0148] Further optionally, the generation unit, through a topology aggregation layer, automatically aggregates the topology of the distributed experimental equipment management system based on the device deployment characteristics to obtain the target network structure to be updated or constructed, and is configured as follows:

[0149] Select a starting device as the starting point for the breadth-first search; the starting device is any device in the distributed experimental equipment management system.

[0150] Starting from the starting device, a global priority search queue is used to traverse all devices connected to the starting device layer by layer until the connection status between the starting device and other connected devices is obtained, then jump to the adjacent device connected to the starting device.

[0151] Continue traversing all devices connected to the adjacent device until the connection status between the adjacent device and other connected devices is obtained, then jump to the next adjacent device connected to the adjacent device;

[0152] The above traversal process is executed until the outermost device is searched to obtain the global network structure of the distributed experimental equipment management system.

[0153] In this embodiment, intelligent device management, real-time topology network updates, asset comparison checks, and asset management information updates improve the efficiency of asset management for distributed experimental equipment, increase the accuracy of asset discovery, reduce system operation risks, and enhance system stability and reliability.

[0154] In another embodiment of this application, an intelligent computing platform is also provided, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0155] Memory, used to store computer programs;

[0156] When the processor executes a program stored in memory, it implements the asset management method based on distributed experimental equipment as described in the method embodiment.

[0157] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc.

[0158] This application provides an asset management method for building a low-power computing unit based on distributed experimental equipment.

[0159] For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0160] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0161] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0162] The aforementioned processor 1110 can be a general-purpose processor, including artificial intelligence processors, graphics processing units (GPUs), machine learning units (MLUs), central processing units (CPUs), network processors (NPs), etc.; it can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0163] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed, can implement the steps that can be performed by an electronic device in the above method embodiments.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A method for asset management based on distributed experimental equipment, characterized in that, A method is applied to a distributed experimental equipment management and control system, which is used to operate and manage experimental equipment, including different types of equipment; the method includes: Monitor the equipment deployment status in the distributed experimental equipment management system; The equipment deployment information is input into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, so as to obtain the equipment topology editing task matched by the distributed experimental equipment management system. Based on the device topology editing task, construct or update the device topology network of the distributed experimental equipment management and control system; Perform an asset comparison check between the device topology network and the historical device topology network; The asset management information corresponding to the distributed experimental equipment control system is updated based on the asset comparison inspection results. Before performing a depth-first search on the distributed experimental equipment management system, starting from the initial device, to obtain the connection relationships between the devices in the distributed experimental equipment management system, the process also includes: Obtain the correlation weights between each device; these correlation weights are used to measure the degree of task correlation between devices. For the current device, sort the devices in the branch to which the current device belongs according to their relevance weight and level, and build a search queue for the branch to which the current device belongs based on the sorting results; The search queue of the branch to which the current device belongs is used to indicate the search order of each device in the branch to which the current device belongs; For the current device, after sorting the devices in the branch to which the current device belongs according to their relevance weight and level, and constructing a search queue for the branch to which the current device belongs based on the sorting results, the process also includes: Based on historical search behavior data and real-time operation data of each device in the current device's branch, a real-time learning model is used to predict the search queue of the current device's branch in order to obtain the local search probability distribution. The local search probability distribution is used to indicate the probability of finding a locally optimal solution search result at each device node in the search queue. Based on the local search probability distribution, redundant paths are invalidated for each device in the search queue, and / or a pruning strategy is executed to filter and delete device nodes whose probability of finding a local optimal solution search result is lower than a set probability threshold, so as to obtain the optimized search queue of the branch to which the current device belongs.

2. The asset management method based on distributed experimental equipment according to claim 1, characterized in that, The intelligent editing model includes at least the following structure: feature extraction layer, topology aggregation layer, task pattern recognition layer, and task generation layer; The step of inputting the equipment deployment information into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, in order to obtain the equipment topology editing task matched by the distributed experimental equipment management system, includes: Through a feature extraction layer, the device deployment features of the distributed experimental equipment management and control system are extracted from the device deployment status; the device deployment features include at least: device access status, device connection features, device subnet features, and device attribute features. Through the topology aggregation layer, the distributed experimental equipment management and control system automatically aggregates the topology based on the device deployment characteristics to obtain the target network structure to be updated or constructed. The task pattern recognition layer identifies the target network structure to be updated or built to obtain the type of topology editing task to be executed. Through the task generation layer, device topology editing tasks that match the target network structure to be updated or built are created based on the type of topology editing task to be executed.

3. The distributed lab equipment based asset management method of claim 2, wherein, The step of automatically aggregating the topology of the distributed experimental equipment management system based on the device deployment characteristics through the topology aggregation layer to obtain the target network structure to be updated or constructed includes: Select a starting device as the starting point for the depth-first search; the starting device can be any device in the distributed experimental equipment management system. Starting from the initial device, a depth-first search is performed on the distributed experimental equipment management system to obtain the connection relationships between the devices in the distributed experimental equipment management system. During the depth-first search, a local optimum search is performed on the branch to which each device belongs, from the top-level device in the branch to the bottom-level device in the branch, so that the branch reaches the local exhaustion. After reaching the bottom-level device, the search jumps to the branch to which other adjacent devices belong and performs the same local exhaustion process until the connection relationship between the devices is established. Based on the connection relationships between various devices, construct candidate topology network structures among the devices; Based on the candidate topology network structure among various devices, determine the target network structure that needs to be updated or built.

4. The distributed lab equipment based asset management method of claim 3, wherein, Starting from the initial device, a depth-first search is performed on the distributed experimental equipment management system to obtain the connection relationships between the devices in the distributed experimental equipment management system, including: For the current device, access the current device and perform a hierarchical priority search on the branch to which the current device belongs to obtain a local optimum search. Search from the top-level device in the branch to the bottom-level device in the branch to exhaust the local search of the branch. After obtaining the search results for the local optimum of the current device, or after searching to the lowest-level device to complete the hierarchical priority search of the branch to which the current device belongs, mark the current device and the branch to which the current device belongs as visited; and, Jump to another device adjacent to the current device, access the other device, and determine whether the other device is already accessed; If another device adjacent to the current device is not in an visited state, then a hierarchical priority search is performed on the other device adjacent to the current device to obtain a local optimum solution. If another device adjacent to the current device is already visited, then proceed to the next device adjacent to the current device; Until all devices adjacent to the current device have been searched, backtrack to another device that is not adjacent to the current device, and perform a hierarchical priority search on the branch to which the other device that is not adjacent to the current device belongs to obtain a local optimum search. The above search process is performed on all known devices in the distributed experimental equipment management system to obtain the connection relationships between the devices in the distributed experimental equipment management system.

5. The asset management method based on distributed experimental equipment according to claim 4, characterized in that, The real-time learning model is expressed by the following formula: ； in, This indicates that the real-time learning model predicts the local search probability distribution based on device X within the branch Y to which the current device belongs. These are the real-time operational data characteristics and historical search behavior data characteristics of each device in branch Y to which the current device belongs. This represents the correlation weights corresponding to each feature item.

6. The asset management method based on distributed experimental equipment according to claim 2, characterized in that, The step of automatically aggregating the topology of the distributed experimental equipment management system based on the device deployment characteristics through the topology aggregation layer to obtain the target network structure to be updated or constructed includes: Select a starting device as the starting point for the breadth-first search; the starting device is any device in the distributed experimental equipment management system. Starting from the starting device, a global priority search queue is used to traverse all devices connected to the starting device layer by layer until the connection status between the starting device and other connected devices is obtained, then jump to the adjacent device connected to the starting device. Continue traversing all devices connected to the adjacent device until the connection status between the adjacent device and other connected devices is obtained, then jump to the next adjacent device connected to the adjacent device; The above traversal process is executed until the outermost device is searched to obtain the global network structure of the distributed experimental equipment management system.

7. A distributed experimental equipment management and control system, characterized in that, The distributed experimental equipment management and control system is used to implement the asset management method according to any one of claims 1-6 to operate and manage experimental equipment, wherein the experimental equipment includes different types of equipment, and the distributed experimental equipment management and control system includes: The monitoring unit is configured to monitor the equipment deployment status in the distributed experimental equipment management and control system; The generation unit is configured to input the device deployment information into the intelligent editing model to perform automatic topology aggregation and task pattern recognition processing, so as to obtain the device topology editing task matched by the distributed experimental equipment management system. The editing unit is configured to construct or update the device topology network of the distributed experimental equipment management and control system based on the device topology editing task. The comparison unit is configured to perform an asset comparison check between the device topology network and the historical device topology network; The management unit is configured to update the asset management information corresponding to the distributed experimental equipment control system based on the asset comparison check results.

8. An intelligent computing electronic device, characterized in that, The intelligent computing electronic device includes: At least one processor, memory, and input / output unit; The memory is used to store computer programs, the processor is used to call the computer programs stored in the memory to execute the asset management method based on distributed experimental equipment as described in any one of claims 1 to 6, and the input / output unit is used to receive user input and display the output information of the computer programs stored in the memory.

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