Ecological environment inspection, detection, operation and maintenance management method for industrial internet identifier analysis

Abstract

The invention relates to the technical field of industrial internet and ecological environment detection, in particular to an ecological environment inspection and detection operation and maintenance management method based on an industrial internet identification analysis technology, which realizes intelligent distribution and dynamic scheduling of inspection and detection tasks by combining a particle swarm optimization algorithm in an operation and maintenance management process, and improves the operation and maintenance efficiency. The operation and maintenance management efficiency and the intelligent level are effectively improved, and the problems of low task distribution efficiency, insufficient dynamic scheduling capability, unbalanced load distribution and the like in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of industrial Internet and ecological environment detection technology, and in particular to an ecological environment inspection, testing, operation and maintenance management method based on industrial Internet identification resolution technology. Background Art

[0002] With the rapid development of the industrial Internet, identification resolution technology has gradually become an important means of equipment management and information interconnection. The field of ecological environment inspection and testing involves a large number of distributed equipment, such as pollution source monitoring equipment, water quality testing equipment, air quality monitoring equipment, etc. These equipment require regular or irregular inspection and maintenance to ensure their normal operation and the accuracy of monitoring data. However, the existing operation and maintenance management methods mainly face the following problems:

[0003] Inefficient task allocation

[0004] The existing operation and maintenance task allocation usually relies on manual or simple rule-based allocation methods. It is difficult to dynamically adjust the task priority according to the real-time changes in the equipment operation status, resulting in high-priority tasks cannot be responded to in a timely manner, affecting the normal operation of the equipment.

[0005] Lack of intelligent dynamic scheduling capabilities

[0006] When new tasks are added or the operating status of equipment changes, existing systems often lack intelligent dynamic scheduling capabilities and are unable to quickly generate optimized task allocation plans in complex environments, which can easily lead to waste of resources or task delays.

[0007] Unbalanced load distribution

[0008] The task load distribution of operation and maintenance personnel is unbalanced, resulting in some personnel being overloaded with tasks while other personnel resources are idle, reducing the overall operation and maintenance efficiency.

[0009] Insufficient technical adaptability

[0010] The existing operation and maintenance management system is not adaptable enough to distributed environments, and it is difficult to effectively handle scenarios where equipment is widely distributed and has complex and changeable status.

[0011] In order to solve the above problems, an intelligent operation and maintenance management method based on industrial Internet identity resolution technology is needed, which uses efficient optimization algorithms to reasonably allocate and dynamically adjust tasks, thereby improving the efficiency, intelligence and adaptability of operation and maintenance management. Summary of the invention

[0012] The present invention provides an ecological environment inspection and testing operation and maintenance management method based on industrial Internet identity resolution technology. By combining the particle swarm optimization algorithm, it realizes the intelligent allocation and dynamic scheduling of inspection and testing tasks, effectively improves the operation and maintenance management efficiency and intelligence level, and solves the problems of low task allocation efficiency, insufficient dynamic scheduling capability, and unbalanced load distribution in the prior art.

[0013] The present invention provides an ecological environment inspection, detection, operation and maintenance management method for industrial Internet identity resolution, characterized by comprising the following steps:

[0014] A. Equipment identification registration: assign a unique identification to the ecological environment inspection and testing equipment, and upload the equipment information to the Industrial Internet identification resolution platform;

[0015] B. Equipment operation information collection: The equipment operation status, including temperature and humidity, pollutant concentration and power information, is collected through sensors, and the equipment operation status data is uploaded to the cloud platform and stored in the identification resolution platform;

[0016] C. Task generation: Generate operation and maintenance tasks based on equipment operation status and historical data analysis, including task priority, equipment location and maintenance requirements, and assign a unique ID to the task;

[0017] D. Dynamic priority adjustment: adjust task priorities in real time according to equipment operating status and operation and maintenance resources, and feed the priorities back to the task allocation process;

[0018] E. Task allocation: The particle swarm optimization algorithm is used to allocate tasks and generate the optimal matching solution between tasks and operation and maintenance personnel;

[0019] F. Dynamic scheduling: During the task execution process, the equipment and task status are monitored in real time, and the task allocation is dynamically adjusted through the particle swarm optimization algorithm for new tasks or changes in equipment operation status;

[0020] G. Task execution and feedback: Operation and maintenance personnel perform maintenance operations according to task assignments and upload maintenance records to the identity resolution platform;

[0021] H. Result optimization: Optimize the operation and maintenance management process based on historical maintenance records and analysis results, and adjust the maintenance cycle and task allocation strategy.

[0022] Preferably, the dynamic priority adjustment is calculated by the following formula:

[0023] Priority(i)=α·Urgency(i)+β·Impact(i)

[0024] in:

[0025] Priority(i) indicates the priority of task i;

[0026] Urgency(i) indicates the urgency of task i;

[0027] Impact(i) represents the impact scope of task i;

[0028] α, β are adjustment coefficients.

[0029] Preferably, the fitness function of the particle swarm optimization algorithm is defined by the following formula:

[0030]

[0031] in:

[0032] F is the fitness value;

[0033] Priority(i) is the priority of task i;

[0034] T(i) is the response time of task i;

[0035] L is the load balancing index of the operation and maintenance personnel, indicating the fairness of task allocation. The smaller the better.

[0036] w 1 and w 2 It is the weight coefficient used to balance the weight between priority response and load balancing.

[0037] Preferably, the calculation formula of the load balancing index L in the fitness function F is:

[0038]

[0039] Where N j represents the number of tasks for operation and maintenance personnel j;

[0040] Indicates the average number of tasks assigned;

[0041] N represents the total number of operation and maintenance personnel.

[0042] Preferably, the speed and position of the particle swarm optimization algorithm are updated by the following formula:

[0043] Speed ​​Update:

[0044] v i =w·v i +c 1 ·r 1 ·(pBest-x i )+c 2 ·r 2 ·(gBest-x i )

[0045] Location Updates:

[0046] x i =x i +v i

[0047] in:

[0048] x i : Particle position, which indicates the specific value of the current solution in the search space. In this technical solution, the position corresponds to the allocation scheme of a certain task, and the position of each particle corresponds to a matching scheme between a task and an operation and maintenance personnel;

[0049] v i : Particle speed, which determines the movement amplitude and direction of particles in the search space in each iteration. In the technical solution, speed update is used to adjust the weight and priority of task allocation;

[0050] pBest is the individual optimal position of the particle. The optimal solution found by each particle in the iteration represents the optimal task allocation scheme in the particle's history. pBest records the optimal matching state in each task allocation scheme and is used to guide the particle to move to a better solution in the search space.

[0051] gBest is the global optimal position of the particle. The global optimal solution found among all particles represents the current optimal task allocation scheme. In this scheme, gBest corresponds to the overall optimal task and operation and maintenance personnel allocation scheme, including that all high-priority tasks are responded to quickly;

[0052] w is the inertia weight; it controls the influence of the current velocity of the particle on the next velocity, and balances the global search and local search capabilities. In the task allocation process, a larger www value can increase the diversity of the allocation scheme and prevent falling into the local optimum;

[0053] c 1 ,c 2 is the learning factor; c 1 Guide particles to approach their individual optimal solutions and encourage particles to search autonomously; c 2 Guide particles to approach the global optimal solution and encourage particles to search collaboratively. In task allocation, learning factors help the system achieve a balance between individual optimization and overall optimization.

[0054] r 1 ,r 2 A random number in the range of [0, 1] is used to introduce randomness into the search and avoid falling into the local optimum. In dynamic scheduling, random numbers can increase the diversity of task allocation schemes and improve the system's ability to adapt to sudden tasks.

[0055] The maximum number of iterations of the particle swarm optimization algorithm is set to 100 times, and the calculation is stopped in advance when the fitness value change is less than the set threshold.

[0056] Preferably, the dynamic scheduling includes:

[0057] 1. For newly added tasks, a new task allocation plan is dynamically generated by updating the particle position and velocity in the particle swarm optimization algorithm;

[0058] 2. When the operating status of the equipment changes, the task priority is adjusted in real time, the fitness function is optimized, and resources are reallocated according to the optimization results.

[0059] Preferably, the unique identifier registered for the equipment identifier includes the equipment number, geographical location, operating status, task type and historical maintenance record.

[0060] Preferably, the result optimization includes:

[0061] 1. Analyze historical task allocation records and optimize the parameters of the particle swarm optimization algorithm through a real-time feedback mechanism, including the inertia weight w and the learning factor c 1 ,c 2 ;

[0062] 2. Adjust the weight parameters α and β in the task priority calculation according to the equipment failure frequency and task completion efficiency.

[0063] Technical Effects

[0064] The ecological environment inspection and detection operation and maintenance management method based on industrial Internet identity resolution proposed in the present invention realizes the high efficiency of task allocation, the real-time scheduling and the optimization of resource utilization by combining the particle swarm optimization algorithm and the dynamic priority adjustment mechanism. The specific technical effects are as follows:

[0065] 1. Improve task allocation efficiency and ensure timely response to high-priority tasks

[0066] Intelligent adjustment of task priority:

[0067] Through the dynamic priority adjustment mechanism, the urgency and impact scope of the task are adjusted in real time according to the equipment operating status (such as fault level, operating status, etc.) and the availability of operation and maintenance resources, so that high-priority tasks can be allocated resources first.

[0068] Effect:

[0069] Tasks with high fault levels or wide impacts can be responded to quickly, avoiding equipment interruption or inaccurate monitoring data due to processing delays. This improves the accuracy of resource allocation and reduces the resource usage of low-priority tasks.

[0070] 2. Dynamically adapt to new tasks or changes in equipment operating status to enhance the real-time and flexibility of the scheduling system

[0071] Dynamic scheduling mechanism:

[0072] During the task execution process, by real-time monitoring of the equipment operating status and task progress, it is possible to quickly respond to new tasks or changes in equipment operating status and re-optimize the task allocation plan. The particle swarm optimization algorithm generates a new optimal allocation plan by adjusting the fitness function in real time in a dynamic environment.

[0073] Effect:

[0074] Improve the system's ability to respond to sudden tasks and ensure the continuity and reliability of system operation. Implement adaptive adjustment of task allocation in complex scenarios to avoid rigidity or failure of resource allocation.

[0075] 3. Optimize the load distribution of operation and maintenance personnel and improve overall resource utilization

[0076] Load balancing optimization:

[0077] The load balancing indicator of operation and maintenance personnel is introduced into the fitness function of the particle swarm optimization algorithm to avoid too many tasks being concentrated on a small number of operation and maintenance personnel and maximize the use of idle resources.

[0078] Effect:

[0079] The reasonable allocation of operation and maintenance tasks improves the work efficiency of personnel and reduces the impact of overload on operation and maintenance quality. It also improves the work coordination efficiency of the entire operation and maintenance team and makes resource utilization more efficient.

[0080] 4. Improve the automation and intelligence level of ecological environment monitoring and operation

[0081] Introduction of Industrial Internet Identity Resolution Technology:

[0082] By registering the unique identification of the equipment and managing its real-time status online, efficient linkage between equipment operating status, operation and maintenance tasks, and resources is achieved.

[0083] Effect:

[0084] Automatically generate task lists, reduce manual intervention, and improve data accuracy and real-time performance. Realize intelligent management of the entire life cycle of equipment, a closed-loop operation from status monitoring to task execution to result optimization.

[0085] 5. Improve the robustness of task allocation schemes and adapt to a variety of complex scenarios

[0086] Global optimization capability of particle swarm optimization algorithm:

[0087] The particle swarm optimization algorithm can quickly find the global optimal or suboptimal solution of task allocation under multi-objective constraints through the combination of global search and local search, and can maintain a high optimization efficiency even in complex environments with a large number of tasks and limited resources.

[0088] Effect:

[0089] Ensure the stability of the task allocation plan and reduce resource waste or task failure caused by allocation errors. Adapt to the complex scenarios of large-scale distributed detection equipment and support the coordinated optimization of multiple devices, multiple tasks and multiple resources.

[0090] 6. Strengthen task result feedback and system optimization capabilities

[0091] Result feedback and dynamic parameter optimization:

[0092] Based on operation and maintenance records and task completion data, the parameters of the particle swarm algorithm (such as inertia weight w and learning factor c) are dynamically optimized. 1 ,c 2 ), and adjust the weight parameters α, β of the priority calculation at the same time.

[0093] Effect:

[0094] Continuously optimize system performance to make task allocation and scheduling more in line with actual scenario requirements. Predict future task allocation requirements based on historical data to improve the intelligence of the system.

[0095] 7. Reduce operation and maintenance costs and improve overall system efficiency

[0096] Efficient operation and maintenance resource management:

[0097] The present invention reduces resource waste and ineffective scheduling through intelligent optimization of task allocation and scheduling.

[0098] Effect:

[0099] Reduce redundant operations of maintenance personnel and equipment, reduce labor costs, improve task completion efficiency, make equipment maintenance cycles more scientific and reasonable, and extend equipment life.

[0100] This technical solution realizes efficient and intelligent task allocation and dynamic scheduling by combining the particle swarm optimization algorithm with the industrial Internet identity resolution technology. In the process of technical implementation, the particle swarm optimization algorithm uses the fitness function to comprehensively consider the priority, response time and load balancing indicators of the task, dynamically adjusts the allocation plan of tasks and resources, and optimizes operation and maintenance management from both global and local levels. Through the dynamic priority adjustment mechanism, the urgency and impact range of the task are accurately quantified, and the weight parameters are optimized by combining real-time monitoring and historical data, which further improves the response efficiency of high-priority tasks and the flexibility of resource scheduling. In complex scenarios where new tasks are added or the status of equipment changes, the system quickly generates optimization plans based on the global optimal and individual optimal feedback mechanisms, and uses inertia weights and random searches to avoid falling into local optimality. Compared with traditional methods, this solution replaces manual intervention with automated scheduling, significantly improves resource utilization and task processing efficiency, and achieves an optimization effect of both real-time and precision in the full process management of ecological environment monitoring tasks.

[0101] The present invention effectively solves the problems of low task allocation efficiency, poor scheduling flexibility and low resource utilization in the prior art by introducing the combination of industrial Internet identity resolution technology and particle swarm optimization algorithm, significantly improving the intelligence and automation level of ecological environment inspection, testing and operation and maintenance, and is suitable for complex, multi-task and multi-resource dynamic management scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 It is a block diagram of the management steps of this device. DETAILED DESCRIPTION

[0103] Embodiment 1:

[0104] An ecological environment inspection, testing, operation and maintenance management method for industrial Internet identity resolution includes the following steps:

[0105] A. Equipment identification registration: assign a unique identification to the ecological environment inspection and testing equipment, and upload the equipment information to the Industrial Internet identification resolution platform;

[0106] B. Equipment operation information collection: The equipment operation status, including temperature and humidity, pollutant concentration and power information, is collected through sensors, and the equipment operation status data is uploaded to the cloud platform and stored in the identification resolution platform;

[0107] C. Task generation: Generate operation and maintenance tasks based on equipment operation status and historical data analysis, including task priority, equipment location and maintenance requirements, and assign a unique ID to the task;

[0108] D. Dynamic priority adjustment: adjust task priorities in real time according to equipment operating status and operation and maintenance resources, and feed the priorities back to the task allocation process;

[0109] E. Task allocation: The particle swarm optimization algorithm is used to allocate tasks and generate the optimal matching solution between tasks and operation and maintenance personnel;

[0110] F. Dynamic scheduling: During the task execution process, the equipment and task status are monitored in real time, and the task allocation is dynamically adjusted through the particle swarm optimization algorithm for new tasks or changes in equipment operation status;

[0111] G. Task execution and feedback: Operation and maintenance personnel perform maintenance operations according to task assignments and upload maintenance records to the identity resolution platform;

[0112] H. Result optimization: Optimize the operation and maintenance management process based on historical maintenance records and analysis results, and adjust the maintenance cycle and task allocation strategy.

[0113] Embodiment 2:

[0114] The difference between this embodiment and embodiment 1 is that this embodiment also includes the following features:

[0115] The dynamic priority adjustment is calculated by the following formula:

[0116] Priority(i)=α·Urgency(i)+β·Impact(i)

[0117] in:

[0118] Priority(i) indicates the priority of task i;

[0119] Urgency(i) indicates the urgency of task i;

[0120] Impact(i) represents the impact scope of task i;

[0121] α, β are adjustment coefficients.

[0122] Preferably, the fitness function of the particle swarm optimization algorithm is defined by the following formula:

[0123]

[0124] in:

[0125] F is the fitness value;

[0126] Priority(i) is the priority of task i;

[0127] T(i) is the response time of task i;

[0128] L is the load balancing index of the operation and maintenance personnel, indicating the fairness of task allocation. The smaller the better.

[0129] w 1 and w 2 It is the weight coefficient used to balance the weight between priority response and load balancing.

[0130] Preferably, the calculation formula of the load balancing index L in the fitness function F is:

[0131]

[0132] Where N j represents the number of tasks for operation and maintenance personnel j;

[0133] Indicates the average number of tasks assigned;

[0134] N represents the total number of operation and maintenance personnel

[0135] Preferably, the speed and position of the particle swarm optimization algorithm are updated by the following formula:

[0136] Speed ​​Update:

[0137] v i =w·v i +c 1 ·r 1 ·(pBest-x i )+c 2 ·r 2 ·(gBest-x i )

[0138] Location Updates:

[0139] x i =x i +v i

[0140] in:

[0141] x i : Particle position, which indicates the specific value of the current solution in the search space. In this technical solution, the position corresponds to the allocation scheme of a certain task, and the position of each particle corresponds to a matching scheme between a task and an operation and maintenance personnel;

[0142] v i : Particle speed, which determines the movement amplitude and direction of particles in the search space in each iteration. In the technical solution, speed update is used to adjust the weight and priority of task allocation;

[0143] pBest is the individual optimal position of the particle. The optimal solution found by each particle in the iteration represents the optimal task allocation scheme in the particle's history. pBest records the optimal matching state in each task allocation scheme and is used to guide the particle to move to a better solution in the search space.

[0144] gBest is the global optimal position of the particle. The global optimal solution found among all particles represents the current optimal task allocation scheme. In this scheme, gBest corresponds to the overall optimal task and operation and maintenance personnel allocation scheme, including that all high-priority tasks are responded to quickly;

[0145] w is the inertia weight; it controls the influence of the current velocity of the particle on the next velocity, and balances the global search and local search capabilities. In the task allocation process, a larger www value can increase the diversity of the allocation scheme and prevent falling into the local optimum;

[0146] c 1 ,c 2 is the learning factor; c 1 Guide particles to approach their individual optimal solutions and encourage particles to search autonomously; c 2 Guide particles to approach the global optimal solution and encourage particles to search collaboratively. In task allocation, learning factors help the system achieve a balance between individual optimization and overall optimization.

[0147] r 1 ,r 2 A random number in the range of [0, 1] is used to introduce randomness into the search and avoid falling into the local optimum. In dynamic scheduling, random numbers can increase the diversity of task allocation schemes and improve the system's ability to adapt to sudden tasks.

[0148] The maximum number of iterations of the particle swarm optimization algorithm is set to 100 times, and the calculation is stopped in advance when the fitness value change is less than the set threshold.

[0149] Preferably, the dynamic scheduling includes:

[0150] 1. For newly added tasks, a new task allocation plan is dynamically generated by updating the particle position and velocity in the particle swarm optimization algorithm;

[0151] 2. When the operating status of the equipment changes, the task priority is adjusted in real time, the fitness function is optimized, and resources are reallocated according to the optimization results.

[0152] Preferably, the unique identifier registered for the equipment identifier includes the equipment number, geographical location, operating status, task type and historical maintenance record.

[0153] Preferably, the result optimization includes:

[0154] 1. Analyze historical task allocation records and optimize the parameters of the particle swarm optimization algorithm through a real-time feedback mechanism, including the inertia weight w and the learning factor c 1 ,c 2 ;

[0155] 2. Adjust the weight parameters α and β in the task priority calculation according to the equipment failure frequency and task completion efficiency.

[0156] Embodiment 3:

[0157] This embodiment is an application example, which is as follows:

[0158] Implementation scenario background:

[0159] There are four monitoring devices in a certain ecological environment monitoring system, which are located in different locations and are used to detect air quality. The equipment operation status is uploaded to the Industrial Internet ID resolution platform in real time, and four maintenance tasks are generated based on the collected data. There are three operation and maintenance personnel in the system, each responsible for task execution in different areas.

[0160] Equipment and task information

[0161]

[0162] Operation and maintenance personnel information

[0163] Personnel Number Current location Current load Movable distance P1 City Center Low No restrictions P2 Eastern part of the city high 15km P3 City West middle No restrictions

[0164] Task priority calculation

[0165] According to the formula:

[0166] Priority(i)=α·Urgency(i)+β·Impact(i)

[0167] Assuming α=0.7,β=0.3,calculate the priority of each task:

[0168]

[0169] Particle swarm optimization task allocation

[0170] 1. Initialization

[0171] Particle position: Each particle represents a task allocation scheme, for example:

[0172] Particle 1: T1→P1, T2→P2, T3→P3, T4→P1

[0173] Particle 2: T1→P2, T2→P3, T3→P1, T4→P3

[0174] Fitness function:

[0175] F=w 1 ·∑ i Priority(i)·T(i)+w 2 ·L

[0176] Assume that w 1 =0.8,w 2 =0.2, where:

[0177] T(i): Task response time (calculated by the distance from the device to the person).

[0178] L: Operation and maintenance load balancing indicator (the smaller the difference in the number of tasks for each person, the higher the load balancing).

[0179] 2. Iterative Optimization

[0180] Through iterative updates of the particle swarm optimization algorithm, the optimal task allocation solution is found:

[0181] Particle update speed and position:

[0182] v i =w·v i +c 1 ·r 1 ·(pBest-x i )+c 2 ·r 2 ·(gBest-x i )

[0183] The best solution after update:

[0184] T1→P1 (high priority tasks are handled by nearby P1 with the shortest response time).

[0185] T2→P2 (the current position of P2 is closest to T2).

[0186] T3→P3 (P3 is close to the west and is suitable for processing T3).

[0187] T4→P1 (low priority tasks can be assigned to P1 with low load).

[0188] Final task allocation plan

[0189] Task Number Task Priority Assign personnel Response time T1 35.5 P1 5 minutes T2 8.6 P2 10 minutes T3 16.1 P3 8 minutes T4 4.3 P1 15 minutes

[0190] System optimization effect

[0191] 1. Efficient allocation: high-priority task T1 is responded to quickly, and low-priority task T4 is processed later, which meets actual needs.

[0192] 2. Load balancing: P1 processes 2 tasks, P2 and P3 process 1 task each, and the task distribution is balanced and reasonable.

[0193] 3. Dynamic response: If a new task T5 is added, the system can quickly re-optimize the allocation to ensure real-time performance.

[0194] Example summary:

[0195] Through the particle swarm optimization algorithm and dynamic priority adjustment mechanism, this technical solution achieves efficient and intelligent allocation of complex tasks, and shows obvious efficiency improvement and resource optimization effects in actual scenarios. This method can be widely used in the operation and maintenance management of ecological environment monitoring equipment.

Claims

1. An ecological environment inspection, testing, operation and maintenance management method for industrial Internet identity resolution, characterized in that: The following steps are involved: A. Equipment identification registration: assign a unique identification to the ecological environment inspection and testing equipment, and upload the equipment information to the Industrial Internet identification resolution platform; B. Equipment operation information collection: The equipment operation status, including temperature and humidity, pollutant concentration and power information, is collected through sensors, and the equipment operation status data is uploaded to the cloud platform and stored in the identification resolution platform; C. Task generation: Generate operation and maintenance tasks based on equipment operation status and historical data analysis, including task priority, equipment location and maintenance requirements, and assign a unique ID to the task; D. Dynamic priority adjustment: adjust task priorities in real time according to equipment operating status and operation and maintenance resources, and feed the priorities back to the task allocation process; E. Task allocation: The particle swarm optimization algorithm is used to allocate tasks and generate the optimal matching solution between tasks and operation and maintenance personnel; F. Dynamic scheduling: During the task execution process, the equipment and task status are monitored in real time, and the task allocation is dynamically adjusted through the particle swarm optimization algorithm for new tasks or changes in equipment operation status; G. Task execution and feedback: Operation and maintenance personnel perform maintenance operations according to task assignments and upload maintenance records to the identity resolution platform; H. Result optimization: Optimize the operation and maintenance management process based on historical maintenance records and analysis results, and adjust the maintenance cycle and task allocation strategy.

2. According to claim 1, an ecological environment inspection, detection, operation and maintenance management method for industrial Internet identity resolution is characterized in that: The dynamic priority adjustment is calculated by the following formula: Priority(i)=α·Urgency(i)+β·Impact(i) in: Priority(i) indicates the priority of task i; Urgency(i) indicates the urgency of task i; Impact(i) represents the impact scope of task i; α and β are adjustment coefficients, which are dynamically tuned through historical task data.

3. According to claim 1, an ecological environment inspection, detection, operation and maintenance management method for industrial Internet identity resolution is characterized in that: The fitness function of the particle swarm optimization algorithm is defined by the following formula: in: F is the fitness value, which evaluates the quality of the particle task allocation scheme. The fitness function directly determines the effect of task allocation in this scheme, combining task priority, response time and load balancing indicators; Priority(i) is the priority of task i; T(i) is the response time of task i; L is the load balancing index of the operation and maintenance personnel, indicating the fairness of task allocation. The smaller the better. w1 and w2 are weight coefficients used to balance the weight between priority response and load balancing. The default value is set to 0.5, and the specific adjustment depends on the scenario requirements.

4. According to claim 3, an ecological environment inspection, detection, operation and maintenance management method for industrial Internet identity resolution is characterized in that: The calculation formula of the load balancing index L in the fitness function F is: Where N j represents the number of tasks for operation and maintenance personnel j; Indicates the average number of tasks assigned; N represents the total number of operation and maintenance personnel.

5. According to the method of ecological environment inspection, detection, operation and maintenance management for industrial Internet identity resolution according to claim 1 or 3, it is characterized in that: The velocity and position of the particle swarm optimization algorithm are updated by the following formula: Speed ​​Update: v i =w·v i +c1·r1·(pBest-x i )+c2·r2·(gBest-x i ) Location Updates: x i =x i +v i in: x i : Particle position, which indicates the specific value of the current solution in the search space. In this technical solution, the position corresponds to the allocation scheme of a certain task, and the position of each particle corresponds to a matching scheme between a task and an operation and maintenance personnel; v i : Particle speed, which determines the movement amplitude and direction of particles in the search space in each iteration. In the technical solution, speed update is used to adjust the weight and priority of task allocation; pBest is the individual optimal position of the particle. The optimal solution found by each particle in the iteration represents the optimal task allocation scheme in the particle's history. pBest records the optimal matching state in each task allocation scheme and is used to guide the particle to move to a better solution in the search space. gBest is the global optimal position of the particle. The global optimal solution found among all particles represents the current optimal task allocation scheme. In this scheme, gBest corresponds to the overall optimal task and operation and maintenance personnel allocation scheme, including that all high-priority tasks are responded to quickly; w is the inertia weight; it controls the influence of the current velocity of the particle on the next velocity, and balances the global search and local search capabilities. In the task allocation process, a larger www value can increase the diversity of the allocation scheme and prevent falling into the local optimum; c1 and c2 are learning factors. c1 guides particles to approach their individual optimal solutions and encourages particles to search autonomously. c2 guides particles to approach the global optimal solution and encourages particles to search collaboratively. In task allocation, learning factors help the system achieve a balance between individual optimization and overall optimization. r1 and r2 are random numbers in the range of [0, 1], which are used to introduce randomness into the search and avoid falling into the local optimum. In dynamic scheduling, random numbers can increase the diversity of task allocation schemes and improve the system's ability to adapt to sudden tasks.

6. According to claim 1, an ecological environment inspection, detection, operation and maintenance management method for industrial Internet identity resolution is characterized in that: The dynamic scheduling includes:

1. For newly added tasks, a new task allocation plan is dynamically generated by updating the particle position and velocity in the particle swarm optimization algorithm; 2. When the operating status of the equipment changes, the task priority is adjusted in real time, the fitness function is optimized, and resources are reallocated according to the optimization results.

7. The method for ecological environment inspection, detection, operation and maintenance management for industrial Internet identity resolution according to claim 1 is characterized in that: The maximum number of iterations of the particle swarm optimization algorithm is set to 100 times, and the calculation is stopped in advance when the fitness value change is less than the set threshold.

8. The method for ecological environment inspection, detection, operation and maintenance management for industrial Internet identity resolution according to claim 1 is characterized in that: The unique identifier registered for the equipment identification includes but is not limited to the equipment number, geographic location, operating status, task type and historical maintenance records.

9. The method for ecological environment inspection, detection, operation and maintenance management for industrial Internet identity resolution according to claim 1 is characterized in that: The result optimization includes:

1. Analyze historical task allocation records and optimize the parameters of the particle swarm optimization algorithm through a real-time feedback mechanism, including the inertia weight w and learning factors c1, c2; 2. Adjust the weight parameters α and β in the task priority calculation according to the equipment failure frequency and task completion efficiency.

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