Intelligent campus operation and maintenance management system based on digital twinning

Abstract

The invention discloses an intelligent campus operation and maintenance management system based on digital twinning, which relates to the technical field of intelligent campus operation and maintenance management and comprises a digital twinning monitoring module, an intelligent task scheduling and resource optimization module and a space optimization and path planning module. According to the system, the visual and intelligent level of equipment management is improved by monitoring the equipment state in real time and constructing an accurate equipment virtual model, the task execution efficiency is improved by optimizing task scheduling and resource allocation, meanwhile, the energy consumption of the equipment is reduced, the service life of the equipment is prolonged, the space conflict between the equipment is solved, and the equipment management efficiency is improved. The cooperative work capability of the equipment is improved, the path planning and task execution sequence is optimized, and the safety and stability of the intelligent campus operation and maintenance system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart campus operation and maintenance management, and specifically to a smart campus operation and maintenance management system based on digital twins. Background Art

[0002] In recent years, with the rapid development of the Internet of Things, artificial intelligence and cloud computing technologies, the construction of smart campuses has gradually entered the stage of in-depth application; as a key component of smart campus construction, smart campus operation and maintenance management involves equipment monitoring, resource scheduling, space optimization and other aspects; traditional campus operation and maintenance management methods mainly rely on fixed rules and manual intervention, and most of the equipment operation status monitoring and management adopt discrete data collection methods, which makes it difficult to achieve systematic and intelligent operation and maintenance optimization; in order to improve the management efficiency of equipment on campus, researchers have proposed a remote monitoring system based on the Internet of Things, a device status prediction method based on machine learning, and an operation and maintenance optimization solution based on big data analysis; however, these methods often have problems such as data islands, untimely response, and insufficient intelligence in actual applications, which makes it difficult to meet the management needs of modern smart campuses for efficient, intelligent, and dynamic optimization; therefore, the operation and maintenance management method of smart campuses based on digital twin technology has 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, the existing smart campus operation and maintenance technology still has many limitations, making it difficult to achieve efficient, coordinated and accurate equipment management. First, the existing equipment monitoring and data collection methods are mostly based on a single sensor or a decentralized data collection system, resulting in insufficient real-time and integrity of equipment data, making it difficult to build an accurate campus equipment virtual model. Second, the existing resource scheduling methods are mainly based on preset rules or static scheduling algorithms, which make it difficult to dynamically optimize the resource allocation of equipment in a complex and changeable campus environment, resulting in resource shortages for high-load equipment and serious resource waste for low-load equipment. In addition, the existing equipment management system usually lacks spatial conflict analysis capabilities, making it difficult to effectively solve the conflict problems between mobile devices and static devices in physical space, such as the path conflict between cleaning robots and security equipment, and the visual occlusion problem between drone delivery and monitoring systems, resulting in poor coordination of equipment operation and maintenance, further reducing the overall operation and maintenance efficiency of the system. Therefore, there is an urgent need for a smart campus operation and maintenance management method based on digital twins, which can achieve dynamic optimization allocation of resources by building a high-precision digital twin model of equipment and combining it with an intelligent scheduling algorithm, and optimize equipment collaborative management through spatial conflict analysis to improve the operation and maintenance efficiency and intelligence level of smart campus equipment. Summary of the invention

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

[0005] Therefore, the technical problems solved by the present invention are: the existing smart campus equipment management methods have the problems of incomplete equipment data collection, difficulty in building a high-precision digital twin model, rigid resource scheduling methods, and inability to adapt to the dynamically changing campus operation and maintenance needs, lack of spatial conflict analysis mechanism, resulting in low efficiency in the collaborative management of mobile devices and static devices, and how to build a virtual model of equipment through digital twin technology to achieve real-time monitoring of equipment, intelligent scheduling of resources and optimization of spatial conflicts, and improve the intelligence level of smart campus equipment operation and maintenance.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: 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 equipment and mobile equipment on campus, build a virtual model of campus equipment based on digital twins, and monitor and synchronize the working status of equipment on 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 build an intelligent scheduling algorithm, and the resource allocation module is used to perform dynamic optimal allocation analysis of resources for static equipment and mobile equipment on campus; the space optimization and path planning module includes a space conflict detection module and a conflict optimization module, the space 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 solution of the digital twin-based smart campus operation and maintenance management system described in the present invention, the construction of a digital twin-based campus equipment virtual model includes collecting real-time operating data of all equipment on campus and transmitting it to the digital twin platform. Each device is mapped into a virtual model, and the operating status, location, and working hours of each device are updated in real time.

[0008] As a preferred solution of the digital twin-based smart campus operation and maintenance management system described in the present invention, wherein: the construction of an intelligent scheduling algorithm includes collecting the computing load information of tasks to be executed on campus, and obtaining the computing power parameters of each static device and mobile device on campus, based on the ratio of task computing load to device computing power, evaluating the optimal execution device of the task, and optimizing task allocation; in the task scheduling process, obtaining the energy consumption characteristic parameters of each device, calculating the execution energy consumption of the task on different devices, and determining the optimal task allocation plan based on the comprehensive optimization goal of task execution time and device energy consumption; constructing a task resource matching scoring model, and calculating the matching score between the task and the device based on the task computing load, device adaptation capability and task execution requirements; calculating the device health index based on the device running time, computing load and historical fault data, and evaluating the health status of each device; constructing an intelligent scheduling optimization objective function, combining task time weight, computing load, energy consumption optimization coefficient, resource matching score and device health index, and comprehensively calculating the optimal task scheduling plan, so that task scheduling meets the optimization goals of minimizing task execution time, optimizing system energy consumption, maximizing task matching and optimizing device health status.

[0009] As an optimal solution for the digital twin-based smart campus operation and maintenance management system described in the present invention, the construction of an intelligent scheduling algorithm also includes obtaining the task computing load, the required number of equipment and the task complexity information of all equipment on campus, constructing a task resource matching score calculation model based on the information, and calculating the resource matching score of each task; performing data normalization processing on the task computing load, equipment usage time, current equipment computing load and equipment failure prediction score, constructing an equipment health index calculation model, and calculating the health index of each device.

[0010] As a preferred solution of the digital twin-based smart campus operation and maintenance management system described in the present invention, the analysis of spatial conflicts between devices includes real-time monitoring of the running path of mobile devices and the relative position of mobile devices and static devices, predicting possible conflicts between devices, spatially mapping the virtual models of the devices, and using path planning to calculate the optimal path in combination with the current geographical location and mission objectives of the devices, 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.

[0011] As a preferred solution of the digital twin-based smart campus operation and maintenance management system described in the present invention, the analysis of spatial conflicts between devices also includes building a three-dimensional spatial model based on the location information, operation status information and task allocation of all devices in the campus, and determining the spatial distribution relationship and motion trajectory of each device in the campus environment; calculating the path cost of the mobile device, the path cost includes the path length, the energy consumption required for device movement and the travel time required for task execution, and determining the optimal travel path of the device based on the path cost calculation result, and updating the task scheduling information of the device in real time after the path is changed; 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 device task execution priority and the path adjustment cost, if the spatial conflict cost exceeds the 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, 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, if the average conflict index exceeds the set threshold, path optimization and task rescheduling are performed.

[0012] As a preferred solution of the digital twin-based smart campus operation and maintenance management system described in the present invention, the collaborative operation and maintenance management between the optimized 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 the first conflict threshold, the risk of conflict between devices is low, and 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 make additional adjustments to the device task priority to maintain 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 safety distance between devices may be touched, and there is a medium conflict risk. The operation and maintenance team needs to respond immediately and adjust the device path to avoid Open potential conflict areas and re-plan the travel order based on the priority of emergency tasks to ensure that high-priority task equipment has priority. At the same time, evaluate the spatial conflict cost. If it exceeds the first cost threshold, take measures such as increasing the travel height or changing the travel direction 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 needs to immediately initiate the emergency plan, re-plan the path for all equipment, and find alternative optimal paths. High-priority equipment can directly adopt the new path, while low-priority equipment needs to choose to delay execution or detour according to 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 object of the present invention is to provide a smart campus operation and maintenance management method based on digital twins, which can construct an intelligent scheduling algorithm to perform dynamic optimal allocation analysis of resources for static and mobile devices on campus, thereby solving the problem of poor coordination in current smart campus operation and maintenance management technologies.

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

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a smart campus operation and maintenance management method based on digital twins.

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

[0017] Beneficial effects of the present invention: The digital twin-based smart campus operation and maintenance management system provided by the present invention monitors the equipment status in real time, builds an accurate equipment virtual model, improves the visualization and intelligence level of equipment management, improves the task execution efficiency by optimizing task scheduling and resource allocation, and reduces equipment energy consumption and extends equipment life. By resolving spatial conflicts between devices, the ability of equipment to work together is improved, path planning and task execution sequence are optimized, and the security and stability of the smart campus operation and maintenance system are improved. The present invention achieves better results in terms of intelligent management level, resource utilization efficiency and maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 An overall flow chart of a smart campus operation and maintenance management system based on digital twins provided for the first embodiment of the present invention.

[0020] Figure 2An overall flow chart of a smart campus operation and maintenance management method based on digital twins provided for the second embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a smart campus operation and maintenance management system based on digital twins, including: Digital twin monitoring module 100, intelligent task scheduling and resource optimization module 200, space optimization and path planning module 300.

[0023] The digital twin monitoring module 100 is used to collect data information of static equipment and mobile devices on campus, build a virtual model of campus equipment based on digital twins, 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 build an intelligent scheduling algorithm, and the resource allocation module 202 is used to perform dynamic optimal allocation analysis of resources for static equipment and mobile devices on campus; the space optimization and path planning module 300 includes a space conflict detection module 301 and a conflict optimization module 302. The space 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.

[0024] Furthermore, building a virtual model of campus equipment based on digital twins includes collecting real-time operating data of all equipment on campus and transmitting it to the digital twin platform. Each device is mapped as a virtual model, and the operating status, location, and working hours of each device are updated in real time.

[0025] It should be noted that by collecting the operating data of all static and mobile devices in the smart campus, including equipment operating status, location, working hours, power consumption, task execution status, etc., and transmitting these data to the digital twin platform in real time, global virtual modeling of the equipment can be achieved; in this platform, the status of each physical device can be accurately mapped to its corresponding digital twin model, and the system can update the equipment status in real time, making the equipment management more visual and precise; the management of campus equipment is transformed from a static recording mode to a dynamic real-time monitoring mode. Compared with the traditional regular inspection method, this method can significantly improve the ability to control the equipment status; through holographic modeling of equipment data, operation and maintenance personnel can accurately know the current operating status of the equipment, and predict possible equipment failures or performance degradation trends based on the digital twin system, so as to take maintenance measures in advance to avoid sudden failures affecting the normal operation of the campus.

[0026] It should be noted that the construction of an intelligent scheduling algorithm includes collecting the computing load information of tasks to be executed on campus, and obtaining the computing power parameters of various static devices and mobile devices on campus. Based on the ratio of task computing load to device computing power, the optimal execution device of the task is evaluated to optimize task allocation; in the task scheduling process, the energy consumption characteristic parameters of each device are obtained, the execution energy consumption of the task on different devices is calculated, and the optimal task allocation plan is determined based on the comprehensive optimization goal of task execution time and device energy consumption; a task resource matching scoring model is constructed, and the matching score between the task and the device is calculated based on the task computing load, device adaptation capability and task execution requirements; the device health index is calculated based on the device running time, computing load and historical fault data, and the health status of each device is evaluated; an intelligent scheduling optimization objective function is constructed, and the optimal task scheduling plan is comprehensively calculated by combining task time weight, computing load, energy consumption optimization coefficient, resource matching score and device health index, so that task scheduling meets the optimization goals of minimizing task execution time, optimizing system energy consumption, maximizing task matching and optimizing device health status.

[0027] It should also be noted that building an intelligent scheduling algorithm also includes obtaining the task computing load, required number of equipment and task complexity information of all equipment on campus, building a task resource matching score calculation model based on the information, and calculating the resource matching score of each task; performing data normalization on the task computing load, equipment usage time, current equipment computing load and equipment failure prediction score, building an equipment health index calculation model, and calculating the health index of each device.

[0028] It should also be noted that a specific preferred solution for constructing an intelligent scheduling algorithm includes dynamically allocating resources for static devices and mobile devices on campus in a smart campus environment, and constructing a task scheduling optimization objective function, which is expressed as: ; in, Indicates the number of campus operation and maintenance tasks, Number the campus operation and maintenance task. For the task The time weight, For the task The computational load, For equipment The calculation rate, For the task Is it assigned to a device? An indicator variable, 1 means allocated, 0 means unallocated, is the energy consumption optimization coefficient, For equipment Execute the task The total energy consumption, is the task-resource matching optimization coefficient, For the The resource matching score of each task, is the equipment health index optimization coefficient, is the number of all devices, For equipment Calculate the health index of The resource matching score of a task is expressed as: ; in, The load weighting parameter for the calculation of the matching score, The weighting parameter for the number of devices scoring the match, is the complexity weighting parameter for the matching score, For the task The number of devices required, For the task Complexity score of; computing equipment The health index is expressed as: ; in, To control the usage time weighting parameter, To calculate the load weighting parameters, is the fault scoring weighting parameter, For equipment The current computational load, For equipment Duration of use, For equipment Failure prediction score.

[0029] Furthermore, based on the optimization objective function, the task's computing load, equipment computing rate, equipment energy consumption, task-resource matching and equipment health index are comprehensively considered, and the optimal task scheduling scheme is calculated by constructing a mathematical optimization model. In the task scheduling process, the algorithm selects appropriate equipment to perform tasks based on factors such as the task's time weight, resource requirements, computing load, and urgency, while considering the equipment health index to avoid high-load equipment from frequently performing tasks and accelerating loss. The intelligent scheduling algorithm can adaptively adjust the task allocation method according to task requirements and equipment status to ensure that the task can be completed in the shortest time, while reducing equipment energy consumption and improving overall resource utilization efficiency. In addition, it can also evaluate the health status of the equipment in real time to avoid excessive use of high-load equipment, thereby extending the service life of the equipment. Compared with fixed scheduling schemes, the intelligent scheduling algorithm can optimize resource allocation and improve overall operating efficiency according to the equipment status and task requirements. The introduction of the energy consumption optimization coefficient ensures that the equipment energy consumption is minimized, while meeting the task requirements and reducing unnecessary energy consumption, which is conducive to the construction of a green campus. By calculating the equipment health index, the intelligent scheduling system can avoid excessive use of high-load equipment, thereby extending the equipment life and improving the stability of the equipment.

[0030] It should be noted that the analysis of spatial conflicts between devices includes real-time monitoring of the running path of mobile devices and the relative position of mobile devices and static devices, predicting possible conflicts between devices, spatially mapping the virtual models of the devices, and using path planning to calculate the best path based on the current geographical location and mission objectives of the devices. The path plan is updated in real time, and the path optimization objective function is constructed based on the path cost, spatial conflict cost, and average conflict index.

[0031] It should also be noted that the analysis of spatial conflicts between devices also includes building a three-dimensional spatial model based on the location information, operating status information and task allocation of all devices on campus, and based on the collected data to determine the spatial distribution relationship and movement trajectory of each device in the campus environment; calculating the path cost of the mobile device, the path cost includes the path length, the energy consumption required for device movement and the travel time required for task execution, based on the path cost calculation result, determine the optimal travel path of the device, and update the task scheduling information of the device in real time after the path changes; based on the three-dimensional spatial model of the device, calculate the spatial conflict cost, the spatial conflict cost is determined based on the spatial distance between devices, the device task execution priority and the path adjustment cost, if the spatial conflict cost exceeds the preset threshold, adjust the travel path of the mobile device or optimize the task scheduling order between devices; calculate the average conflict index, 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, if the average conflict index exceeds the set threshold, perform path optimization and task rescheduling.

[0032] It should also be noted that a specific preferred solution for analyzing spatial conflicts between devices includes real-time monitoring of the running path of the mobile device and the relative position of the mobile device and the static device, predicting possible conflicts between devices, spatially mapping the virtual model of the device, and using path planning to calculate the best path in combination with the current geographical location and mission objectives of the device, and updating the path plan in real time to construct a path optimization objective function, which is expressed as: ; in, Indicates campus equipment Run to device The path cost, is a set of paths, is the weight parameter of the conflict cost term, For equipment The spatial conflict cost, is the weight parameter of the spatial conflict warning index, is the average conflict index; calculate the path cost, expressed as: ； ； ； ; in, For equipment Move to Device The Euclidean space distance, For equipment Move to Device The energy consumed in the process For equipment Move to Device The required travel time, For the task Is it assigned to a device? An indicator variable, 1 means allocated, 0 means unallocated, is the path length weight coefficient, is the energy consumption weight coefficient, is the moving time weight coefficient, , , Corresponding devices The location in the three-dimensional coordinates of the campus, , , Corresponding devices The location in the three-dimensional coordinates of the campus, For equipment The mobile power consumption, equipment The moving speed, For equipment The energy conversion efficiency of the system is: ; in, is the exponential decay coefficient of spatial conflict, is the minimum safety distance between devices; calculate the average conflict index, expressed as: ; When the path of a mobile device overlaps or may collide with a static device, the path of the device is automatically adjusted to avoid collision.

[0033] Furthermore, optimizing the collaborative operation and maintenance management between devices includes building a collaborative operation and maintenance management strategy based on spatial conflict cost 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, follows the shortest path optimization principle and the optimal task allocation strategy, and does not need to make additional adjustments to the device task priority to maintain 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 (0.7), the minimum safe distance between devices may be reached, and there is a medium conflict risk. The operation and maintenance team needs to respond immediately, adjust the device path to avoid potential conflict areas, and respond based on emergency The task priority re-plans the travel order to ensure that high-priority task equipment has priority, and at the same time evaluates the spatial conflict cost. 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 needs to immediately initiate the emergency plan, re-plan the paths for all devices, and find alternative optimal paths. High-priority devices can directly adopt the new path, while low-priority devices need to choose to delay execution or detour according to 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.

[0034] It should be noted that in a smart campus environment, mobile devices (such as cleaning robots, patrol drones) and static devices (such as surveillance cameras, elevators, etc.) may have spatial conflicts, resulting in reduced equipment operating efficiency and even safety hazards; by real-time monitoring of equipment operation paths and their relative positions, possible spatial conflicts are predicted, and equipment path planning and collaborative management are optimized based on conflict analysis results; the path optimization objective function is used to calculate the optimal movement path between devices, and a spatial conflict warning index is introduced to quantify the potential conflict risks between devices; in addition, when a conflict occurs, the system can automatically adjust the path, optimize the task execution order, and adjust the task priority according to the degree of urgency; ensuring efficient collaboration between mobile devices and static devices to reduce path conflicts and interference between devices; for example, when cleaning When the sweeping robot and the patrol drone meet in the corridor, the system can automatically adjust their paths so that they can pass smoothly without affecting each other's work; in addition, when an emergency task occurs (such as after the security alarm is triggered, the patrol robot needs to enter a certain area first), the system can dynamically adjust the task priority to ensure that key tasks are executed first; by real-time monitoring of equipment status and calculating the optimal path, the equipment can avoid interference in space and improve equipment operation efficiency; based on the results of spatial conflict analysis, the system can adaptively adjust the task priority to ensure that key tasks are given priority and improve the response speed of emergency tasks; with the support of intelligent scheduling strategies, the equipment can operate safely in the campus environment, avoid collisions or task failures caused by spatial conflicts, and improve the safety and reliability of the overall campus operation and maintenance management.

[0035] Example 2, reference Figure 2 , is an embodiment of the present invention, and provides a smart campus operation and maintenance management method based on digital twins, including S1: collecting data information of static equipment and mobile equipment on campus, building a virtual model of campus equipment based on digital twins, and real-time monitoring and synchronization of the working status of equipment on campus; S2: building an intelligent scheduling algorithm to perform dynamic optimal allocation analysis of resources for static equipment and mobile equipment on campus; S3: analyzing spatial conflicts between devices, and optimizing collaborative operation and maintenance management between devices based on the results of spatial conflict analysis.

[0036] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0037] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0038] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0039] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logical function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A smart campus operation and maintenance management system based on digital twins, characterized in that: include: Digital twin monitoring module (100), intelligent task scheduling and resource optimization module (200), space optimization and path planning module (300); The digital twin monitoring module (100) is used to collect data information of static devices and mobile devices on campus, build a virtual model of campus devices based on digital twins, and monitor and synchronize the working status of devices on 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), wherein 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 devices and mobile devices on 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 to analyze space conflicts between devices; and the conflict optimization module (302) is used to optimize the collaborative operation and maintenance management between devices based on the space conflict analysis results.

2. The digital twin-based smart campus operation and maintenance management system according to claim 1, characterized in that: The construction of a campus equipment virtual model based on digital twins includes collecting real-time operating data of all equipment on campus and transmitting it to the digital twin platform. Each device is mapped into a virtual model, and the operating status, location, and working hours of each device are updated in real time.

3. The digital twin-based smart campus operation and maintenance management system according to claim 2, characterized in that: The construction of the intelligent scheduling algorithm includes collecting the computing load information of the tasks to be executed on campus, and obtaining the computing power parameters of each static device and mobile device on campus, and evaluating the optimal execution device of the task based on the ratio of the task computing load to the device computing power, and optimizing the task allocation; During the task scheduling process, the energy consumption characteristic parameters of each device are obtained, the execution energy consumption of tasks on different devices is calculated, and the optimal task allocation plan is determined based on the comprehensive optimization goal of task execution time and device energy consumption; Build a resource matching scoring model for tasks, and calculate the matching score between tasks and devices based on task computing load, device adaptability, and task execution requirements; Calculate the equipment health index based on equipment operation time, load conditions and historical fault data to evaluate the health status of each device; Construct an intelligent scheduling optimization objective function, combine task time weight, computing load, energy consumption optimization coefficient, resource matching score and equipment health index, and comprehensively calculate the optimal task scheduling plan so that task scheduling can meet the optimization goals of minimizing task execution time, optimizing system energy consumption, maximizing task matching and optimizing equipment health status.

4. The digital twin-based smart campus operation and maintenance management system according to claim 3, characterized in that: The construction of the intelligent scheduling algorithm also includes obtaining the task computing load, the number of required devices and the task complexity information of all devices on campus, constructing a task resource matching score calculation model based on the information, and calculating the resource matching score of each task; The task computing load, equipment usage time, equipment current computing load and equipment failure prediction score are normalized, and an equipment health index calculation model is built to calculate the health index of each device.

5. The digital twin-based smart campus operation and maintenance management system according to claim 4, characterized in that: The analysis of spatial conflicts between devices includes real-time monitoring of the running path of the mobile device and the relative position of the mobile device and the static device, predicting possible conflicts between devices, spatially mapping the virtual model of the device, and using path planning to calculate the best path in combination with the current geographical location and mission objectives of the device, 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.

6. The digital twin-based smart campus operation and maintenance management system according to claim 5, characterized in that: The analysis of spatial conflicts between devices also includes building a three-dimensional spatial model based on the location information, operation status information and task allocation of all devices on campus and based on the collected data to determine the spatial distribution relationship and movement trajectory of each device in the campus environment; Calculate the path cost of the mobile device, which includes the path length, the energy consumption required for the device to move, and the travel time required for task execution. Based on the path cost calculation results, determine the optimal travel path for the device, and update the device's task scheduling information in real time after the path changes; Based on the three-dimensional spatial model of the device, the spatial conflict cost is calculated. The spatial conflict cost is determined based on the spatial distance between devices, the device task execution priority and the path adjustment cost. If the spatial conflict cost exceeds the preset threshold, the mobile device's travel path is adjusted or the task scheduling order 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. If the average conflict index exceeds the set threshold, path optimization and task rescheduling are performed.

7. The digital twin-based smart campus operation and maintenance management system according to claim 6, characterized in that: The collaborative operation and maintenance management between the optimized 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 the first conflict threshold, the risk of conflict 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 make additional adjustments to the device task priority, 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 reached, and there is a medium conflict risk. The operation and maintenance team needs to respond immediately, adjust the device path to avoid the potential conflict area, and re-plan the travel order based on the priority of emergency tasks to ensure that high-priority task equipment has priority. At the same time, the spatial conflict cost is evaluated. If it exceeds the 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 and there is a serious risk of conflict. The operation and maintenance team needs to immediately initiate an emergency plan, re-plan the paths of all devices, and find alternative optimal paths. High-priority devices can directly adopt the new path, while low-priority devices need to choose to delay execution or detour according to 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.

8. A method for a smart campus operation and maintenance management system based on digital twins as claimed in any one of claims 1 to 7, characterized in that: This includes collecting data information from static and mobile devices on campus, building a virtual model of campus equipment based on digital twins, and monitoring and synchronizing the working status of campus equipment in real time; Build intelligent scheduling algorithms to analyze the dynamic optimal allocation of resources for static and mobile devices on campus; Analyze the spatial conflicts between devices and optimize the collaborative operation and maintenance management between devices based on the results of the spatial conflict analysis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the smart campus operation and maintenance management method based on digital twins described in claim 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the smart campus operation and maintenance management method based on digital twins described in claim 8 are implemented.

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