Intelligent campus operation and maintenance management system based on digital twinning

Abstract

The application discloses a kind of based on digital twin wisdom campus operation management system, it is related to wisdom campus operation management technical field, including digital twin monitoring module, intelligent task scheduling and resource optimization module, space optimization and path planning module.The system described in the application improves the visual and intelligent level of equipment management by monitoring equipment state in real time, constructs accurate equipment virtual model, improves task execution efficiency by optimizing task scheduling and resource allocation, while reducing equipment energy consumption and prolonging equipment life, improves equipment collaborative work capability by solving the space conflict between equipment, optimizes path planning and task execution sequence, improves the safety and stability of wisdom campus operation management system.

Description

Technical Field

[0001] This invention relates to the field of smart campus operation and maintenance management technology, specifically a smart campus operation and maintenance management system based on digital twins. Background Technology

[0002] In recent years, with the rapid development of IoT, AI, and cloud computing technologies, the construction of smart campuses has gradually entered a stage of in-depth application. Smart campus operation and maintenance management, as a key component of smart campus construction, involves multiple aspects such as equipment monitoring, resource scheduling, and space optimization. Traditional campus operation and maintenance management methods mainly rely on fixed rules and manual intervention, and the monitoring and management of equipment operating status mostly adopts discrete data collection methods, making it difficult to achieve systematic and intelligent operation and maintenance optimization. To improve the management efficiency of equipment on campus, researchers have proposed IoT-based remote monitoring systems, machine learning-based equipment status prediction methods, and big data analysis-based operation and maintenance optimization schemes. However, these methods often suffer from problems such as data silos, untimely responses, and insufficient intelligence in practical applications, making it difficult to meet the modern smart campus's demand for efficient, intelligent, and dynamically optimized management. Therefore, smart campus operation and maintenance management methods based on digital twin technology have gradually become a research hotspot, aiming to use digital twin models to achieve real-time mapping of physical equipment, combined with intelligent scheduling algorithms and space optimization strategies, to improve the intelligence level of campus operation and maintenance.

[0003] However, existing smart campus operation and maintenance technologies still have many limitations, making it difficult to achieve efficient, collaborative, and precise equipment management. Firstly, most existing equipment monitoring and data acquisition methods are based on single sensors or decentralized data acquisition systems, resulting in insufficient real-time performance and completeness of equipment data, making it difficult to construct accurate virtual models of campus equipment. Secondly, existing resource scheduling methods are mainly based on preset rules or static scheduling algorithms, making it difficult to dynamically optimize equipment resource allocation in complex and ever-changing campus environments. This can lead to resource shortages for high-load equipment and significant resource waste for low-load equipment. Furthermore, existing equipment management systems typically lack spatial conflict analysis capabilities, making it difficult to effectively resolve conflicts between mobile and static equipment in physical space. For example, path conflicts between cleaning robots and security equipment, and viewpoint obstruction issues between drone delivery and monitoring systems, result in poor coordination of equipment operation and maintenance, further reducing the overall efficiency of the system. Therefore, there is an urgent need for a smart campus operation and maintenance management method based on digital twins. This method would construct high-precision digital twin models of equipment, combine them with intelligent scheduling algorithms to achieve dynamic optimization of resource allocation, and optimize collaborative equipment management through spatial conflict analysis, thereby improving the efficiency and intelligence level of smart campus equipment operation and maintenance. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing smart campus equipment management methods suffer from incomplete equipment data collection, difficulty in constructing high-precision digital twin models, rigid resource scheduling methods that cannot adapt to dynamically changing campus operation and maintenance needs, and a lack of spatial conflict analysis mechanisms, resulting in low efficiency in the collaborative management of mobile and static equipment. The invention addresses how to construct virtual equipment models through digital twin technology to achieve real-time equipment monitoring, intelligent resource scheduling, and spatial conflict optimization, thereby improving the level of intelligence in smart campus equipment operation and maintenance.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a smart campus operation and maintenance management system based on digital twins, comprising a digital twin monitoring module, an intelligent task scheduling and resource optimization module, and a space optimization and path planning module; the digital twin monitoring module is used to collect data information of static and mobile devices within the campus, construct a virtual model of campus equipment based on digital twins, and monitor and synchronize the working status of equipment within the campus in real time; the intelligent task scheduling and resource optimization module includes an intelligent scheduling module and a resource allocation module, the intelligent scheduling module is used to construct an intelligent scheduling algorithm, and the resource allocation module is used to perform dynamic optimal allocation analysis of resources for static and mobile devices within the campus; the space optimization and path planning module includes a spatial conflict detection module and a conflict optimization module, the spatial conflict detection module is used to analyze spatial conflicts between devices, and the conflict optimization module is used to optimize the collaborative operation and maintenance management between devices based on the spatial conflict analysis results.

[0007] As a preferred embodiment of the smart campus operation and maintenance management system based on digital twins described in this invention, the construction of a virtual model of campus equipment based on digital twins includes collecting real-time operating data of all equipment in the campus, transmitting it to the digital twin platform, mapping each equipment to a virtual model, and updating the operating status, location, and working time of each equipment in real time.

[0008] As a preferred embodiment of the digital twin-based smart campus operation and maintenance management system described in this invention, the intelligent scheduling algorithm includes: collecting computational load information of tasks to be executed within the campus, obtaining computational capability parameters of various static and mobile devices within the campus, evaluating the optimal execution device for the task based on the ratio of task computational load to device computational capability, and optimizing task allocation; during task scheduling, obtaining energy consumption characteristic parameters of each device, calculating the execution energy consumption of the task on different devices, and determining the optimal task allocation scheme based on the comprehensive optimization objective of task execution time and device energy consumption; constructing a resource matching degree scoring model for tasks, calculating the matching degree score between tasks and devices based on task computational load, device adaptability, and task execution requirements; calculating the device health index based on device runtime, computational load, and historical fault data, and evaluating the health status of each device; constructing an intelligent scheduling optimization objective function, combining task time weight, computational load, energy consumption optimization coefficient, resource matching degree score, and device health index to comprehensively calculate the optimal task scheduling scheme, so that task scheduling meets the optimization objectives of minimizing task execution time, optimizing system energy consumption, maximizing task matching degree, and optimizing device health status.

[0009] As a preferred embodiment of the smart campus operation and maintenance management system based on digital twins described in this invention, the intelligent scheduling algorithm further includes: acquiring information on the task computing load, required number of devices, and task complexity of all devices within the campus; constructing a task resource matching degree scoring calculation model based on the information; calculating the resource matching degree score for each task; performing data normalization processing on the task computing load, device usage time, current device computing load, and device fault prediction score; constructing a device health index calculation model; and calculating the health index for each device.

[0010] As a preferred embodiment of the smart campus operation and maintenance management system based on digital twins described in this invention, the analysis of spatial conflicts between devices includes: predicting possible conflicts between devices by real-time monitoring of the running path of mobile devices and the relative position of mobile devices and static devices; spatially mapping the virtual model of the devices; calculating the optimal path using path planning in combination with the current geographical location and task objectives of the devices; updating the path plan in real time; and constructing a path optimization objective function based on path cost, spatial conflict cost, and average conflict index.

[0011] As a preferred embodiment of the digital twin-based smart campus operation and maintenance management system of the present invention, the analysis of spatial conflicts between devices further includes: constructing a three-dimensional spatial model based on the location information, operating status information, and task allocation of all devices within the campus, and determining the spatial distribution relationship and movement trajectory of each device in the campus environment; calculating the path cost of mobile devices, including path length, energy consumption required for device movement, and travel time required for task execution; determining the optimal travel path of the device based on the path cost calculation results, and updating the task scheduling information of the device in real time after path changes; calculating the spatial conflict cost based on the three-dimensional spatial model of the device, the spatial conflict cost is determined based on the spatial distance between devices, the task execution priority of the device, and the path adjustment cost; if the spatial conflict cost exceeds a preset threshold, the travel path of the mobile device is adjusted or the task scheduling order between devices is optimized; calculating the average conflict index, which is calculated based on the spatial conflict cost of all devices and used to assess the overall conflict risk level in the campus environment; if the average conflict index exceeds a set threshold, path optimization and task rescheduling are performed.

[0012] As a preferred embodiment of the smart campus operation and maintenance management system based on digital twins described in this invention, the optimized collaborative operation and maintenance management between devices includes constructing a collaborative operation and maintenance management strategy based on spatial conflict cost and average conflict index. When the average conflict index is less than a first conflict threshold, the risk of conflict between devices is low, and the operation and maintenance team focuses on task execution efficiency, following the shortest path optimization principle and the optimal task allocation strategy, without needing to make additional adjustments to the priority of device tasks, maintaining the existing scheduling plan. When the average conflict index is greater than or equal to the first conflict threshold and less than the second conflict threshold, the minimum safe distance between devices may be violated, posing a moderate risk of conflict. The operation and maintenance team needs to respond immediately and adjust the device paths to avoid conflict. Open potential conflict zones and re-plan the travel order based on the priority of urgent tasks to ensure that high-priority task devices have priority passage. At the same time, assess the spatial conflict cost. If it exceeds the first cost threshold, take measures to increase the travel altitude or change the travel direction to reduce the conflict risk. When the average conflict index is greater than or equal to the second conflict threshold, the distance between devices is extremely close, and there is a serious risk of conflict. The operation and maintenance team must immediately activate the emergency plan, re-plan the path for all devices, find alternative optimal paths, and high-priority devices can directly adopt the new path, while low-priority devices need to choose to delay execution or detour depending on the situation. At the same time, implement a multi-level task scheduling strategy to reasonably postpone non-urgent tasks to low-traffic periods to alleviate conflict pressure.

[0013] Another objective of this invention is to provide a smart campus operation and maintenance management method based on digital twins, which can perform dynamic optimal allocation analysis of resources for static and mobile devices within the campus by constructing an intelligent scheduling algorithm, thereby solving the problem of poor coordination in current smart campus operation and maintenance management technologies.

[0014] As a preferred embodiment of the smart campus operation and maintenance management method based on digital twins described in this invention, the method includes: collecting data information of static and mobile devices within the campus; constructing a virtual model of campus equipment based on digital twins; monitoring and synchronizing the working status of equipment within the campus in real time; constructing an intelligent scheduling algorithm to perform dynamic optimal allocation analysis of resources for static and mobile devices within the campus; analyzing spatial conflicts between devices; and optimizing collaborative operation and maintenance management between devices based on the spatial conflict analysis results.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program as steps to implement a smart campus operation and maintenance management method based on digital twins.

[0016] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a smart campus operation and maintenance management method based on digital twins.

[0017] The beneficial effects of this invention are as follows: The smart campus operation and maintenance management system based on digital twins provided by this invention improves the visualization and intelligence level of equipment management by real-time monitoring of equipment status and constructing accurate virtual models of equipment. By optimizing task scheduling and resource allocation, it improves task execution efficiency, reduces equipment energy consumption, and extends equipment life. By resolving spatial conflicts between equipment, it improves the collaborative working ability of equipment. By optimizing path planning and task execution order, it improves the security and stability of the smart campus operation and maintenance system. This invention achieves better results in terms of intelligent management level, resource utilization efficiency, and maintenance costs. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 The first embodiment of the present invention provides an overall flowchart of a smart campus operation and maintenance management system based on digital twins.

[0020] Figure 2The following is an overall flowchart of a smart campus operation and maintenance management method based on digital twins, provided as a second embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a smart campus operation and maintenance management system based on digital twins is provided, comprising:

[0023] Digital twin monitoring module 100, intelligent task scheduling and resource optimization module 200, and spatial optimization and path planning module 300.

[0024] The digital twin monitoring module 100 is used to collect data information of static and mobile equipment on campus, construct a virtual model of campus equipment based on digital twin, and monitor and synchronize the working status of equipment on campus in real time. The intelligent task scheduling and resource optimization module 200 includes an intelligent scheduling module 201 and a resource allocation module 202. The intelligent scheduling module 201 is used to construct an intelligent scheduling algorithm, and the resource allocation module 202 is used to perform dynamic optimal allocation analysis of resources for static and mobile equipment on campus. The spatial optimization and path planning module 300 includes a spatial conflict detection module 301 and a conflict optimization module 302. The spatial conflict detection module 301 is used to analyze spatial conflicts between devices, and the conflict optimization module 302 is used to optimize the collaborative operation and maintenance management between devices based on the spatial conflict analysis results.

[0025] Furthermore, building a virtual model of campus equipment based on digital twins involves collecting real-time operating data of all equipment on campus, transmitting it to the digital twin platform, mapping each device to a virtual model, and updating the operating status, location, and working time of each device in real time.

[0026] It should be noted that by collecting operational data from all static and mobile devices within the smart campus, including device operating status, location, working hours, power consumption, and task execution, and transmitting this data in real time to the digital twin platform, global virtual modeling of the devices can be achieved. Within this platform, the status of each physical device can be accurately mapped to its corresponding digital twin model, and the system can update device status in real time, making device management more visual and precise. This transforms campus device management from a static recording mode to a dynamic real-time monitoring mode. Compared to traditional periodic inspections, this method significantly improves the ability to control device status. Through holographic modeling of device data, maintenance personnel can accurately determine the current operating status of the equipment and predict potential faults or performance degradation trends based on the digital twin system, thereby taking preventative maintenance measures to avoid sudden failures affecting the normal operation of the campus.

[0027] It should be noted that the construction of the intelligent scheduling algorithm includes collecting computational load information of tasks to be executed on campus, obtaining computational capability parameters of various static and mobile devices on campus, evaluating the optimal execution device for the task based on the ratio of task computational load to device computational capability, and optimizing task allocation; during task scheduling, obtaining energy consumption characteristic parameters of each device, calculating the execution energy consumption of the task on different devices, and determining the optimal task allocation scheme based on the comprehensive optimization objective of task execution time and device energy consumption; constructing a resource matching degree scoring model for tasks, calculating the matching degree score between tasks and devices based on task computational load, device adaptability, and task execution requirements; calculating the device health index based on device runtime, computational load, and historical fault data, and evaluating the health status of each device; constructing an intelligent scheduling optimization objective function, combining task time weight, computational load, energy consumption optimization coefficient, resource matching degree score, and device health index to comprehensively calculate the optimal task scheduling scheme, so that task scheduling meets the optimization objectives of minimizing task execution time, optimizing system energy consumption, maximizing task matching degree, and optimizing device health status.

[0028] It should also be noted that the construction of the intelligent scheduling algorithm also includes obtaining information on the task computing load, required number of devices, and task complexity of all devices on campus, constructing a task resource matching degree scoring calculation model based on the information, and calculating the resource matching degree score for each task; performing data normalization processing on task computing load, device usage time, current device computing load, and device fault prediction score, constructing a device health index calculation model, and calculating the health index for each device.

[0029] It should also be noted that a specific preferred scheme for constructing an intelligent scheduling algorithm includes dynamically and optimally allocating resources for static and mobile devices within a smart campus environment, and constructing a task scheduling optimization objective function, expressed as:

[0030] ;

[0031] in, This indicates the number of campus maintenance tasks. Number the campus maintenance tasks. For the task Time weighting For the task The computational load, For equipment The computational speed For the task Is it assigned to a device? The indicator variable, 1 indicates allocation, 0 indicates no allocation. For energy consumption optimization coefficient, For equipment Execute the task Total energy consumption Optimize the task-resource matching coefficient. For the first Resource matching score for each task. Optimize the coefficients for the equipment health index. For the total number of devices, For equipment Health index; Calculate the first The resource matching score for each task is expressed as:

[0032] ;

[0033] in, Load-weighted parameters for calculating the match score. The number of devices used to weight the matching score. A complexity-weighted parameter for matching score. For the task Number of devices required For the task Complexity score; computing device The health index is expressed as:

[0034] ;

[0035] in, To control the usage time weighting parameters, To calculate the load-weighted parameters, Weighting parameters for fault scoring, For equipment The current computing load, For equipment Usage duration, For equipment Fault prediction score.

[0036] Furthermore, based on the optimization objective function, and comprehensively considering the task's computational load, device computing speed, device energy consumption, task-resource matching degree, and device health index, a mathematical optimization model is constructed to calculate the optimal task scheduling scheme. During task scheduling, the algorithm selects appropriate devices to execute tasks based on factors such as task time weight, resource requirements, computational load, and urgency, while also considering the device health index to avoid high-load devices frequently executing tasks and accelerating wear and tear. The intelligent scheduling algorithm can adaptively adjust the task allocation method according to task requirements and device status, ensuring that tasks can be completed in the shortest possible time while reducing device energy consumption and improving overall resource utilization efficiency. In addition, it can also evaluate the health status of devices in real time, avoiding overuse of high-load devices and thus extending device lifespan. Compared to fixed scheduling schemes, intelligent scheduling algorithms can optimize resource allocation based on device status and task requirements, improving overall operating efficiency. The introduction of an energy consumption optimization coefficient ensures that device energy consumption is minimized, reducing unnecessary energy consumption while meeting task requirements, which contributes to the construction of green campuses. By calculating the device health index, the intelligent scheduling system can avoid overuse of high-load devices, thereby extending device lifespan and improving device stability.

[0037] It should be noted that analyzing spatial conflicts between devices includes predicting potential conflicts between devices by real-time monitoring of the running paths of mobile devices and the relative positions of mobile devices and static devices, spatial mapping of the virtual models of devices, calculating the optimal path using path planning in conjunction with the current geographical location and task objectives of the devices, updating the path plan in real time, and constructing a path optimization objective function based on path cost, spatial conflict cost, and average conflict index.

[0038] It should also be noted that the analysis of spatial conflicts between devices also includes: based on the location information, operating status information, and task allocation of all devices on campus, a three-dimensional spatial model is constructed based on the collected data to determine the spatial distribution relationship and movement trajectory of each device in the campus environment; the path cost of mobile devices is calculated, including path length, energy consumption required for device movement, and travel time required for task execution; based on the path cost calculation results, the optimal travel path of the device is determined, and the task scheduling information of the device is updated in real time after the path changes; based on the three-dimensional spatial model of the device, the spatial conflict cost is calculated, which is determined based on the spatial distance between devices, the task execution priority of the device, and the path adjustment cost; if the spatial conflict cost exceeds a preset threshold, the travel path of the mobile device is adjusted or the task scheduling order between devices is optimized; the average conflict index is calculated, which is based on the spatial conflict cost of all devices and is used to assess the overall conflict risk level in the campus environment; if the average conflict index exceeds a set threshold, path optimization and task rescheduling are performed.

[0039] It should also be noted that a specific preferred approach to analyzing spatial conflicts between devices includes predicting potential conflicts by real-time monitoring of the mobile device's operating path and the relative position of the mobile device to the static device, spatially mapping the virtual model of the device, calculating the optimal path using path planning based on the device's current geographical location and task objectives, updating the path plan in real time, and constructing a path optimization objective function, expressed as:

[0040] ;

[0041] in, Indicates campus equipment Run to device Path cost, For a set of paths, The weight parameters for the conflict cost term, For equipment Spatial conflict costs, The weighting parameters for the space conflict early warning index are... The average conflict index is used to calculate the path cost, which is expressed as:

[0042] ；

[0043] ；

[0044] ；

[0045] ;

[0046] in, For equipment Move to device Euclidean distance For equipment Move to device The energy consumed in the process For equipment Move to device Required travel time For the task Is it assigned to a device? The indicator variable, 1 indicates allocation, 0 indicates no allocation. This is the path length weighting coefficient. Energy consumption weighting coefficient For moving time weighting coefficients, , , Corresponding devices Location in the three-dimensional coordinate system of the campus , , Corresponding devices Location in the three-dimensional coordinate system of the campus For equipment Mobile power consumption, equipment movement speed, For equipment The energy conversion efficiency; the spatial conflict cost is calculated and expressed as:

[0047] ;

[0048] in, This represents the exponential decay coefficient of spatial conflict. The minimum safe distance between devices; calculate the average conflict index, expressed as:

[0049] ;

[0050] When the path of a mobile device overlaps with that of a static device or a collision is possible, the device's path is automatically adjusted to avoid the collision.

[0051] Furthermore, optimizing collaborative operation and maintenance management between devices includes constructing collaborative operation and maintenance management strategies based on spatial conflict costs and average conflict index. When the average conflict index is less than the first conflict threshold (0.3), the risk of conflict between devices is low, and the operation and maintenance team focuses on task execution efficiency, following the shortest path optimization principle and optimal task allocation strategy. No additional adjustments to device task priorities are needed, and the existing scheduling plan is maintained. When the average conflict index is greater than or equal to the first conflict threshold but less than the second conflict threshold (0.7), the minimum safe distance between devices may be breached, posing a medium risk of conflict. The operation and maintenance team needs to respond immediately, adjust device paths to avoid potential conflict areas, and take emergency measures. Task priorities are re-planned to ensure that high-priority tasks have priority. At the same time, the cost of spatial conflicts is assessed. If it exceeds the first cost threshold (0.5), measures such as increasing the travel height or changing the travel direction are taken to reduce the risk of conflict. When the average conflict index is greater than or equal to the second conflict threshold, the distance between devices is extremely close, and there is a serious risk of conflict. The operation and maintenance team must immediately activate the emergency plan, re-plan the paths of all devices, and find alternative optimal paths. High-priority devices can directly adopt the new paths, while low-priority devices need to choose to delay execution or detour depending on the situation. At the same time, a multi-level task scheduling strategy is implemented to reasonably postpone non-urgent tasks to low-traffic periods to alleviate conflict pressure.

[0052] It should be noted that in a smart campus environment, mobile devices (such as cleaning robots and patrol drones) and static devices (such as surveillance cameras and elevators) may experience spatial conflicts, leading to decreased equipment operating efficiency and even safety hazards. By monitoring the equipment's operating paths and relative positions in real time, potential spatial conflicts can be predicted, and equipment path planning and collaborative management can be optimized based on conflict analysis results. A path optimization objective function is used to calculate the optimal movement path between devices, and a spatial conflict early warning index is introduced to quantify the potential conflict risk between devices. Furthermore, when a conflict occurs, the system can automatically adjust the path, optimize the task execution order, and adjust task priorities according to urgency. This ensures efficient collaboration between mobile and static devices, reducing path conflicts and interference between devices. For example, when cleaning robots and patrol drones are in use, the system can automatically adjust the path, optimize the task execution order, and adjust task priorities according to urgency. This ensures efficient collaboration between mobile and static devices, reducing path conflicts and interference between devices. When a sweeping robot and a patrol drone meet in a corridor, the system can automatically adjust their paths to allow them to pass smoothly without interfering with each other's work. Furthermore, in emergency situations (such as when a security alarm triggers and a patrol robot needs priority access to a certain area), the system can dynamically adjust task priorities to ensure critical tasks are executed first. By monitoring equipment status in real time and calculating optimal paths, the system avoids spatial interference between devices, improving operational efficiency. Based on spatial conflict analysis, the system can adaptively adjust task priorities to ensure critical tasks are handled first, improving response speed to emergency tasks. With the support of intelligent scheduling strategies, the equipment can operate safely in the campus environment, avoiding collisions or task failures caused by spatial conflicts, thus improving the overall security and reliability of campus operation and maintenance management.

[0053] Example 2, refer to Figure 2 As an embodiment of the present invention, a smart campus operation and maintenance management method based on digital twins is provided, including S1: collecting data information of static and mobile devices in the campus, constructing a virtual model of campus equipment based on digital twins, and monitoring and synchronizing the working status of equipment in the campus in real time; S2: constructing an intelligent scheduling algorithm to perform dynamic optimal allocation analysis of resources for static and mobile devices in the campus; S3: analyzing spatial conflicts between devices, and optimizing collaborative operation and maintenance management between devices based on the spatial conflict analysis results.

[0054] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0056] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0057] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart campus operation and maintenance management system based on digital twinning, characterized in that, The application relates to a digital twin monitoring module (100), an intelligent task scheduling and resource optimization module (200) and a space optimization and path planning module (300). The digital twin monitoring module (100) is used for collecting static device and mobile device data information in a campus, constructing a virtual model of campus devices based on digital twins, and monitoring and synchronizing the working states of the devices in the campus in real time. The intelligent task scheduling and resource optimization module (200) comprises an intelligent scheduling module (201) and a resource allocation module (202), the intelligent scheduling module (201) is used for constructing an intelligent scheduling algorithm, and the resource allocation module (202) is used for dynamically and optimally allocating resources to the static devices and the mobile devices in the campus. The space optimization and path planning module (300) comprises a space conflict detection module (301) and a conflict optimization module (302), the space conflict detection module (301) is used for analyzing the space conflicts among the devices, and the conflict optimization module (302) is used for optimizing the collaborative operation and management among the devices based on the analysis results of the space conflicts. The construction of the intelligent scheduling algorithm comprises collecting the computing load information of tasks to be executed in the campus, acquiring the computing capacity parameters of the static devices and the mobile devices in the campus, evaluating the optimal execution device of the task based on the ratio of the task computing load to the device computing capacity, and optimizing the task allocation. In the task scheduling process, the energy consumption characteristic parameters of the devices are acquired, the execution energy consumption of the task on different devices is calculated, and the optimal task allocation scheme is determined based on the comprehensive optimization target of the task execution time and the device energy consumption. A resource matching degree score model of the task is constructed, and the matching degree score of the task and the device is calculated based on the task computing load, the device adaptive capacity and the task execution demand. The health index of the device is calculated based on the running length of the device, the computing load condition and the historical fault data, and the health state of each device is evaluated. An intelligent scheduling optimization objective function is constructed, the optimal task scheduling scheme is calculated by combining the task time weight, the computing load, the energy consumption optimization coefficient, the resource matching degree score and the health index of the device, and the optimization objective of minimizing the task execution time, optimizing the system energy consumption, maximizing the task matching degree and optimizing the health state of the device is met. The construction of the intelligent scheduling algorithm further comprises acquiring the task computing load, the required device quantity and the task complexity information of all the devices in the campus, constructing a task resource matching degree score calculation model, and calculating the resource matching degree score of each task. The data normalization processing is performed on the task computing load, the device use length, the current computing load of the device and the device fault prediction score, a device health index calculation model is constructed, and the health index of each device is calculated. The analysis of the space conflicts among the devices further comprises constructing a three-dimensional space model based on the position information, the running state information and the task allocation condition of all the devices in the campus, and determining the spatial distribution relationship and the motion trajectory of the devices in the campus environment. ​ The path cost of the mobile device is calculated, the path cost including path length, energy consumption required by device movement, and travel time required by task execution, the optimal travel path of the device is determined based on the calculation result of the path cost, and the task scheduling information of the device is updated in real time after the path is changed; The spatial conflict cost is calculated based on the three-dimensional space model of the device, the spatial conflict cost being determined based on the spatial distance between devices, the task execution priority of the device, and the path adjustment cost, and if the spatial conflict cost exceeds a preset threshold, the travel path of the mobile device is adjusted or the task scheduling sequence between devices is optimized; The average conflict index is calculated based on the spatial conflict cost of all devices, and is used to evaluate the overall conflict risk level in the campus environment, and if the average conflict index exceeds a set threshold, path optimization and task rescheduling are performed; The spatial conflict between devices is analyzed, including constructing a path optimization objective function, which is represented as: ； in, Indicates campus equipment Run to device Path cost, For a set of paths, The weight parameters for the conflict cost term, For equipment Spatial conflict costs, The weighting parameters for the space conflict early warning index are... The average conflict index is used to calculate the path cost, which is expressed as: ； ； ； ； wherein, is the device moving to the device Euclidean space distance, is the device moving to the device energy consumed in the process, is the device moving to the device travel time required, is the task whether assigned to the device the indication variable, 1 indicates assignment, 0 indicates unassignment, is the path length weight coefficient, is the energy consumption weight coefficient, is the moving time weight coefficient, , , respectively corresponding to the position of the device in the three-dimensional coordinates of the campus, , , respectively corresponding to the position of the device in the three-dimensional coordinates of the campus, is the moving power consumption of the device , is the moving speed of the device , is the energy conversion efficiency of the device ; the spatial conflict cost is calculated and expressed as: ； wherein, is the exponential decay coefficient for spatial collisions, is the minimum safety distance between devices; the average collision index is calculated and expressed as: ； When the path of the mobile device overlaps or collides with the static device, the path of the device is automatically adjusted to avoid collision.

2. The digital-twin-based intelligent campus operation and maintenance management system of claim 1, wherein: The construction of the virtual model of the campus device based on digital twinning includes collecting real-time running data of all devices in the campus and transmitting the data to a digital twinning platform, each device is mapped as a virtual model, and the running state, position, and working time of each device are updated in real time.

3. The digital-twin-based intelligent campus operation and maintenance management system of claim 1, wherein: The analysis of the spatial conflict between devices includes predicting possible conflicts between devices by monitoring the running path of the mobile device and the relative position of the mobile device and the static device in real time, spatial mapping the virtual model of the device, combining the current geographic position and task target of the device, calculating the best path using path planning, and updating the path plan in real time, and constructing a path optimization objective function based on path cost, spatial conflict cost, and average conflict index.

4. The digital-twin-based intelligent campus operation and maintenance management system of claim 1, wherein: The collaborative operation and maintenance management between devices includes constructing a collaborative operation and maintenance management strategy based on the spatial conflict cost and the average conflict index. When the average conflict index is less than a first conflict threshold, the situation is that the conflict risk between devices is low, the operation and maintenance team focuses on task execution efficiency, follows the shortest path optimization principle and the optimal task allocation strategy, and does not need to adjust the task priority of the device additionally, and the existing scheduling plan is maintained; When the average conflict index is greater than or equal to the first conflict threshold and less than a second conflict threshold, the minimum safe distance between devices may be touched, there is a moderate conflict risk, the operation and maintenance team needs to respond immediately, adjusts the device path to avoid potential conflict areas, and re-plans the travel order based on the emergency task priority to ensure that high-priority task devices pass first, while evaluating the spatial conflict cost, if the cost exceeds a first cost threshold, measures such as increasing the travel height or changing the travel direction are taken to reduce the conflict risk; When the average conflict index is greater than or equal to the second conflict threshold, the distance between devices is extremely close, there is a serious conflict risk, and the operation and maintenance team needs to immediately start an emergency plan, re-plan the path of all devices, find an alternative optimal path, high-priority devices can directly adopt a new path, while low-priority devices need to select delayed execution or detour according to the situation, and implement a multi-level task scheduling strategy to reasonably postpone non-urgent tasks to a low-traffic period to relieve the conflict pressure.

5. A method for using the digital-twin-based intelligent campus operation and maintenance management system according to any one of claims 1-4, characterized in that: The method comprises collecting data information of static devices and mobile devices in the campus, constructing a virtual model of the campus devices based on digital twinning, and monitoring and synchronizing the working state of the devices in the campus in real time. An intelligent scheduling algorithm is constructed to dynamically and optimally allocate resources to the static devices and mobile devices in the campus. The spatial conflict between the devices is analyzed, and based on the analysis result of the spatial conflict, the collaborative operation and maintenance management between the devices is optimized. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the smart campus operation and maintenance management method based on digital twinning in claim 5.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the smart campus operation and maintenance management method based on digital twinning in claim 5.

Images