Cloud-edge Collaborative Communication Scheduling Method for Panoramic Monitoring of UHV Converter Stations

Abstract

The present invention discloses a cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations, including: optimizing and converting the evaluation attributes of communication resources in the UHV converter station into an optimization problem of the comprehensive task attributes by an artificial bee colony algorithm. After each leading bee completes the search, it shares the solution information with the follower bees. After the follower bees complete the search process, if a solution has not been updated after a preset number of cycles, the solution is discarded, and the leading bee corresponding to the solution is converted into a scout bee. The scout bee generates a new solution to replace the current solution of the leading bee, and then returns to the search process of the leading bee, records the optimal value obtained for the current task attributes, and determines whether the maximum iteration period number is reached. If so, the optimal comprehensive task attributes are output. The advantages of the present invention are: solving the problems of transmission delay, uneven load, and resource waste, selecting the optimal task allocation method according to different communication network methods in the UHV converter station, and having a faster processing speed for the collected data when the scale of the edge nodes is large.

Description

Technical Field

[0001] The present invention relates to the field of communication in ultra-high voltage converter stations, and more particularly to a cloud-edge collaborative communication scheduling method for panoramic monitoring of ultra-high voltage converter stations. Background Art

[0002] As a core component of the ultra-high voltage power system, the reliable operation of the ultra-high voltage direct current converter station plays a crucial role in the sustainable development of the power industry. In order to ensure the normal operation of the ultra-high voltage converter station, the measurement data of various remote control terminals and sensors in the converter station shows exponential growth, and a large amount of data needs to be sampled and processed in real time. Traditional manual data collection can no longer meet the safety guarantee requirements.

[0003] At present, State Grid Corporation has built a large number of cloud platform master stations to transmit the collected data to the cloud master station for processing. Some companies have established a new centralized-distributed joint control cyber power physical system (CPPS) model using the power Internet of Things architecture of the cloud master station, improving data fusion in the power system. However, in UHV converter stations, a large number of images are required for fault warning and intelligent decision-making, and high requirements are placed on the resolution of the images. Traditional cloud master station deployments are usually far from the acquisition side, and data transmission delays result in real-time performance not meeting the requirements. In response to the dilemmas faced by cloud master stations, edge computing has been proposed as a new type of computing method. By deploying lightweight computing devices with limited computing resources near the data acquisition end, the delay during data transmission is effectively reduced. The literature "Shi Weisong, Sun Hui, Cao Jie, Zhang Quan, Liu Wei. Edge Computing: A New Computing Model in the Era of Internet of Everything [J]. Journal of Computer Research and Development, 2017, 54(05): 907-924" proposed the definition and basic principles of edge computing. The edge computing model still supports the traditional cloud computing model and can also connect to remote computing resources to achieve data sharing and collaboration. The challenges posed by edge computing include programmability, naming rules, data abstraction, service management, data privacy protection and security, theoretical basis, and business models. The edge devices in the cloud-edge collaborative computing model have the ability to calculate and analyze. By performing calculations at the edge of the network, while expanding the overall computing power, it can effectively reduce the occupancy of network bandwidth and the computing and storage resources of the cloud master station. Existing technologies have also achieved the goal of "multi-sensing, multi-state access, unified model, and unified Internet of Things" for the cloud-edge coordinated Internet of Things system through the design of modules such as cloud-edge interaction protocols and rule engines. The literature "Cui Lihua, Yan Boyuan, Zhao Yongli. Implementation Mechanism of SOON for Edge Computing and Cloud Computing Collaboration (Invited) [J]. Optical Communication Research, 2018(06): 38-41+65" combined with artificial intelligence technology to propose the concept of a self-optimizing optical network, and based on the idea of edge computing and cloud computing collaboration, proposed an implementation mechanism for a self-optimizing optical network for edge computing and cloud computing collaboration. Through the collaborative mechanism of optical transmission nodes and control nodes, it dynamically provides the computing resources required for artificial intelligence for the optical network. However, in UHV converter stations, the communication network method is complex, and random channels can lead to random data transmission delays. In addition, the data traffic loads are different in different working states in UHV converter stations, and the time-varying traffic loads also result in uncertainty in delays. In summary, in existing technologies, there are problems in UHV DC converter stations such as backlogs at some edge hotspots, untimely data processing due to uneven task distribution and complex communication network methods, and waste of resources by scheduling data to the cloud computing center when there are sufficient computing resources for data processing at the edge end.

[0004] Chinese Patent Publication No. CN112261146A, an edge-cloud collaborative communication system based on message communication and file transfer, includes a message communication service module, a file transfer service module, and an application client; the message communication service module is used to forward messages sent between application clients on the basis of the edge-cloud collaborative infrastructure; the file transfer module is used to forward files sent between application clients on the basis of the edge-cloud collaborative infrastructure; the application client is used in the edge-cloud collaborative intelligent application to implement the interface for communication between edge-cloud collaborative intelligent application components. Although this patent application is an edge-cloud collaborative communication system, this system only involves the basic concept issues of edge-cloud synchronization and cannot solve the problems of uneven load in UHV DC converter stations and waste of resources in scheduling data to the cloud computing center. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that there are problems of transmission delay, uneven load in the existing UHV DC converter station, and waste of resources in scheduling data to the cloud computing center, and when the current traditional algorithm faces the complex communication network in the converter station, it can often only optimize a specific communication method, and when the scale of edge nodes is large, the processing speed of the collected data is slow.

[0006] The present invention realizes the solution of the above technical problems through the following technical means: a cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations, the method comprising: optimizing and converting the communication resource evaluation attributes in the UHV converter station into an optimization problem of the artificial bee colony algorithm for the comprehensive task attribute P, P = P ws +P c +P t , P ws is the ratio of the current load value to the set standard load value, P c is the ratio of the current resource cost to the set standard resource cost, P t is the ratio of the current time consumption to the set standard time cost. Each leading bee conducts a search. After the search process is completed, the information of the solution is shared with the follower bees. The follower bees calculate the selection probability of each solution. If the selection probability is greater than the randomly generated random number, the follower bees generate a new solution. If the new solution is better than the old solution, the new solution is retained and the old solution is discarded. After the follower bees complete the search process, if a solution has not been updated after a preset number of cycles, the solution is discarded, and the leading bee corresponding to this solution is converted into a scout bee. The scout bee generates a new solution to replace the current solution of the leading bee, and then returns to the search process of the leading bee, records the optimal value obtained for the current task attribute, and determines whether the maximum iteration period number is reached. If so, the optimal comprehensive task attribute is output.

[0007] From the three aspects of load balancing, resource consumption, and time consumption, the present invention optimally converts the communication resource evaluation attributes in the UHV converter station into an optimization problem of the comprehensive task attribute P by the artificial bee colony algorithm. According to the optimization results, tasks are reasonably allocated to solve the problem of uneven load, the resource consumption and scheduling time are reasonably allocated, the resource scheduling time is reduced, the cost expenditure is reduced, the resource utilization rate is improved, and resource waste is reduced. When the current traditional algorithm faces the complex communication network in the converter station, it can often only optimize a specific communication method, while the present invention uses a heuristic algorithm to select the optimal task allocation method according to different communication network methods in the UHV converter station. When the scale of the edge node is large, the processing speed of the collected data is faster than the prior art.

[0008] Further, the specific process of the artificial bee colony algorithm includes:

[0009] Step 1: Randomly select a cloud master station of the UHV converter station, randomly generate an initial ant colony, and randomly generate an initial solution;

[0010] Step 2: Each leading bee generates a new solution;

[0011] Step 3: The leading bee calculates the fitness value of the new solution. If the fitness value of the new solution is better than that of the old solution, the leading bee remembers the new solution and discards the old solution; otherwise, the old solution is retained;

[0012] Step 4: The leading bee shares the information of the solution with the follower bees, and the follower bees calculate the selection probability of each solution;

[0013] Step 5: Generate a random number in the interval [-1, 1]. If the probability value of the solution calculated by the follower bee is greater than this random number, the follower bee generates a new solution, calculates the fitness value of the new solution. If the fitness value of the new solution is better than that of the old solution, the follower bee remembers the new solution and discards the old solution; otherwise, the old solution is retained;

[0014] Step 6: After all follower bees complete the search process, if a solution has not been further updated after a preset number of cycles, this solution falls into a local optimum, this solution is discarded, and the leading bee corresponding to this solution turns into a scout bee. The scout bee generates a new solution to replace the current solution of the leading bee, and then returns to Step 2 to start repeating the cycle;

[0015] Step 7: Record the optimal value obtained for the current task attribute, determine whether the maximum iteration period number is reached. If not, return to Step 5; if so, output the optimal load P ws , resource consumption P c and time consumption P t .

[0016] Even further, the said Step 2 includes:

[0017] Each leading bee generates a new solution through the formula

[0018] v i,j = x i,j + 2a(φ - 0.5)(x i,j - x k,j ) + bφ(x best,j - x i,j )

[0019] where x i,j is the current solution, v i,j is the new solution, both being solutions of the task attribute P m in the resource scheduling strategy. P m takes P ws , P c or P t ; a is the first weight factor and k is the k-th iteration cycle, maxk is the maximum number of iteration cycles, φ is a constant and φ ∈ [0, 1], x k,j is the solution of the k-th iteration cycle, b is the second weight factor and x best,j is the current optimal solution.

[0020] Furthermore, step three includes:

[0021] Calculate the fitness value of the new solution through the formula where m = ws,c,t, P mi is the i-th new solution of the task attribute P m , and P mmin is the minimum value of the solutions of the task attribute P m .

[0022] Furthermore, step four includes:

[0023] The onlooker bees calculate the selection probability of each solution through the formula where NP represents the number of initial nectar sources.

[0024] Furthermore, the formula for the onlooker bees to generate a new solution is the same as the principle of the formula for the leading bees to generate a new solution, and the formula for the onlooker bees to calculate the fitness value of the new solution is the same as the principle of the formula for the leading bees to calculate the fitness value of the new solution.

[0025] Furthermore, step six includes:

[0026] The scout bees generate a new solution to replace the current solution of the leading bees through the formula x i,j = x min,j + rand[0, 1](x max,j - x min,j ) where x min,jRepresents the minimum value among all current solutions, x max,j Represents the maximum value among all current solutions, and rand[0, 1] represents a random number between 0 and 1.

[0027] Furthermore, the cloud master station of the UHV converter station has 600 edge clusters, and each edge cluster contains 1 to 50 tasks.

[0028] Furthermore, the scale of the randomly generated initial ant colony is 1000.

[0029] Furthermore, the maximum number of iteration cycles is 200.

[0030] The advantages of the present invention are as follows:

[0031] (1) Considering from three aspects of load balancing, resource consumption, and time consumption, the present invention optimally converts the communication resource evaluation attributes in the UHV converter station into the problem of optimizing the comprehensive task attribute P by the artificial bee colony algorithm. According to the optimization results, tasks are reasonably allocated to solve the problem of uneven load, reasonably allocate resource consumption and scheduling time, reduce the resource scheduling time, reduce the cost expenditure, improve the resource utilization rate, and reduce resource waste. When the current traditional algorithms face the complex communication network in the converter station, they often can only optimize a specific communication method. However, the present invention uses a heuristic algorithm to select the optimal task allocation method according to different communication network methods in the UHV converter station. When the scale of edge nodes is large, the processing speed of the collected data is faster than that of the prior art.

[0032] (2) When the traditional bee colony algorithm searches for feasible solutions, it randomly selects two nectar sources, that is, two solutions, to make a difference for updating. In the UHV converter station, there are many edge nodes. Completely randomly selecting nectar sources will lead to a slow convergence speed and consume a large amount of computing resources in the process of searching for the optimal solution. The solution update of the algorithm of the present invention uses the known optimal solution for updating, which speeds up the convergence speed of the algorithm.

[0033] (3) The traditional method searches by a fixed step size. The step size selection is random and has no direction, resulting in more time and computing resource consumption. The algorithm of the present invention always retains the solution with the optimal fitness value for subsequent calculations, which speeds up the search speed and improves the convergence speed. However, in the later stage of the algorithm search, it may be affected by the optimal solution of the fitness value and fall into a local optimal solution. Therefore, two variables a and b are introduced as the coefficients of the update term of the classical bee colony algorithm and the update term of the optimal solution of the fitness value respectively. a is the reciprocal of the difference between the maximum number of iterations and the current number of iterations, and b is 1 minus a. As the number of iterations changes continuously, a increases continuously and b decreases continuously, reducing the influence of the update term of the optimal solution of the fitness value on the entire search and reducing the possibility of falling into a local optimal solution in the later stage of the algorithm.

[0034] (4) In the case of a small number of tasks, there is not much difference between the traditional bee colony algorithm and the algorithm of the present invention. However, when faced with a large amount of image data in the UHV converter station, the convergence speed of the traditional bee colony algorithm becomes slower, and the resource consumption of the traditional bee colony algorithm will be significantly higher than that of this algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is the algorithm flowchart of the cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations disclosed in the embodiments of the present invention;

[0036] Figure 2 is a schematic diagram of the cost comparison between the cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations disclosed in the embodiments of the present invention, the particle swarm algorithm, and the artificial bee colony algorithm;

[0037] Figure 3 is a schematic diagram of the completion time comparison between the cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations disclosed in the embodiments of the present invention, the particle swarm algorithm, and the artificial bee colony algorithm;

[0038] Figure 4 is a schematic diagram of the load balancing degree comparison between the cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations disclosed in the embodiments of the present invention, the particle swarm algorithm, and the artificial bee colony algorithm. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] As Figure 1 shown, the cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations is applied to a network environment composed of a cloud end, an edge end, and various sensors. The method includes: optimizing and converting the communication resource evaluation attributes in the UHV converter station into an optimization problem of the load P ws , resource consumption P c , and consumption time P t of the artificial bee colony algorithm. Input the data collected by various sensors into the artificial bee colony algorithm model, and respectively optimize P ws , P c , P t . P ws , P c , P tThey are the ratios of the current load value to the set standard load value, the current resource cost to the set standard resource cost, and the current time consumption to the set standard time consumption respectively. The above three attributes are evaluated through the comprehensive task attribute P, and the calculation formula of P is P = P ws + P c + P t , and the process executed by the artificial ant colony algorithm model is as follows: Each leading bee conducts a search. After completing the search process, it shares the solution information with the follower bees. The follower bees calculate the selection probability of each solution. If the selection probability is greater than the randomly generated random number, the follower bees generate a new solution. If the new solution is better than the old solution, the new solution is retained and the old solution is discarded. After the follower bees complete the search process, if a solution has not been updated after a preset number of cycles, the solution is discarded, and the leading bee corresponding to this solution turns into a scout bee. The scout bee generates a new solution to replace the current solution of the leading bee, and then returns to the search process of the leading bee, records the optimal value obtained by the current task attribute, and judges whether the maximum iteration period number is reached. If so, the optimal load P ws , resource cost P c , and consumed time P t are output.

[0041] The specific process of the artificial bee colony algorithm includes:

[0042] S1: Randomly select a UHV converter station cloud master station, randomly generate an initial ant colony, and randomly generate an initial solution; in this embodiment, the UHV converter station cloud master station has 600 edge clusters, and each edge cluster contains 1 - 50 tasks. The scale of the randomly generated initial ant colony is 1000.

[0043] S2: Each leading bee generates a new solution; the specific process is as follows:

[0044] Each leading bee generates a new solution through the formula

[0045] v i,j = x i,j + 2a(φ - 0.5)(x i,j - x k,j ) + bφ(x best,j - x i,j )

[0046] where x i,j is the current solution, v i,j is the new solution, both are solutions of the task attribute P m in the resource scheduling strategy, P m takes P ws , P c or P t ; a is the first weight factor and k is the k - th iteration period, maxk is the maximum iteration period number, φ is a constant and φ ∈ [0, 1], xk,j is the solution for the k-th iteration period, b is the second weight factor and x best,j is the current optimal solution.

[0047] S3: Calculate the fitness value of the new solution by the scout bee. If the fitness value of the new solution is better than that of the old solution, the scout bee remembers the new solution and discards the old solution; otherwise, the old solution is retained. The specific process is as follows:

[0048] Calculate the fitness value of the new solution through the formula where m = ws,c,t, P mi is the i-th new solution of the task attribute P m and P mmin is the minimum value of the solutions of the task attribute P m of the solutions.

[0049] S4: The scout bee shares the solution information with the follower bees, and the follower bees calculate the selection probability of each solution. The specific process is as follows:

[0050] The follower bees calculate the selection probability of each solution through the formula where NP represents the number of initial nectar sources, and calculate the sum of the fitness values of all nectar sources.

[0051] S5: Generate a random number in the interval [-1, 1]. If the probability value of the solution calculated by the follower bee is greater than this random number, the follower bee generates a new solution, calculates the fitness value of the new solution. If the fitness value of the new solution is better than that of the old solution, the follower bee remembers the new solution and discards the old solution; otherwise, the old solution is retained. The formula for the follower bee to generate a new solution is the same as the principle of the formula for the scout bee to generate a new solution, and the formula for the follower bee to calculate the fitness value of the new solution is the same as the principle of the formula for the scout bee to calculate the fitness value of the new solution.

[0052] S6: After all follower bees complete the search process, if a solution has not been further updated after a preset number of cycles, this solution falls into a local optimum, this solution is discarded, and the scout bee corresponding to this solution turns into a scout bee. The scout bee generates a new solution to replace the current solution of the scout bee, and then returns to S2 to start repeating the loop. The specific process is as follows:

[0053] The scout bee generates a new solution to replace the current solution of the scout bee through the formula x i,j = x min,j + rand[0,1](x max,j - x min,j ), where x min,j represents the minimum value among all current solutions, x max,j represents the maximum value among all current solutions, and rand[0,1] represents a random number between 0 and 1.

[0054] S7: Record the optimal value obtained for the current comprehensive task attribute P, and determine whether the maximum iteration period has been reached. If not, return to S5; if so, output the load, resource cost, and consumption time corresponding to the optimal comprehensive task attribute P. In this embodiment, the maximum iteration period is 200.

[0055] The present invention combines multiple virtual machines and conducts experiments on the EdgeCloudSim emulator. One cloud data center and 600 edge clusters are selected. Each edge cluster randomly contains 1 - 50 hotspots, and the number of tasks increases from 100 to 500. In order to evaluate the cloud-edge collaborative scheduling model and improve the algorithm, the present invention modifies the economic cost weight, load balancing weight, and completion time weight, and believes that the algorithm is effective. The present invention compares the particle swarm optimization algorithm (PSO), the artificial bee colony algorithm (ABC), and the improved artificial bee colony algorithm (DABC) provided by the present invention, and measures their impacts on economic cost, completion time, and load balancing through changes in the number of tasks. As Figures 2 - 4 shown in the comparison charts of economic cost, completion time, and load balancing degree, the improved algorithm can effectively reduce costs, and as the number of tasks increases, the growth rate of the improved artificial bee colony algorithm of the present invention gradually slows down. This indicates that more tasks are deployed at the edge, nearby local servers are selected to schedule data, unnecessary resource waste is reduced, and the scheduling cost is lowered. Overall, the improved algorithm can effectively reduce costs, and the more tasks there are, the more obvious the effect.

[0056] Through the above technical solutions, the present invention considers three aspects: load balancing, resource cost, and consumption time, and optimally converts the communication resource evaluation attributes in the UHV converter station into the problem of optimizing the comprehensive task attribute P by the artificial bee colony algorithm. According to the optimization results, tasks are reasonably allocated to solve the problem of uneven load, the resource cost and scheduling time are reasonably allocated, the resource scheduling time is reduced, the cost expenditure is lowered, the resource utilization rate is increased, and resource waste is reduced.

[0057] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations, Characterized in that, It includes: Optimize and transform the communication resource evaluation attributes in the UHV converter station into the optimization problem of the comprehensive task attribute P by the artificial bee colony algorithm. , is the ratio of the current load value to the set standard load value, is the ratio of the current resource cost to the set standard resource cost, is the ratio of the current time consumption to the set standard time cost. Each leading bee conducts a search. After the search process is completed, the solution information is shared with the follower bees. The follower bees calculate the selection probability of each solution. If the selection probability is greater than the randomly generated random number, the follower bees generate new solutions. If the new solutions are better than the old solutions, the new solutions are retained and the old solutions are discarded. After the follower bees complete the search process, if there is a solution that has not been updated after a preset number of cycles, the solution is discarded. The leading bee corresponding to this solution turns into a scout bee. The scout bee generates a new solution to replace the current solution of the leading bee, and then returns to the search process of the leading bee. Record the optimal value obtained for the current task attribute and determine whether the maximum iteration period number is reached. If so, output the optimal comprehensive task attribute. The specific process of the artificial bee colony algorithm includes: Step 1: Randomly select a main UHV converter station in the cloud, randomly generate an initial bee colony, and randomly generate an initial solution; Step 2: Each leading bee generates a new solution; Each leading bee uses the formula Generate a new solution, where is the current solution, is the new solution, and both are task attributes in the resource scheduling strategy solution, Take 、 or ; is the first weight factor and , is the iteration period, is the maximum number of iteration periods, is a constant and , is the solution of the iteration period, is the second weight factor and , is the current optimal solution; Step 3: The leading bee calculates the fitness value of the new solution. If the fitness value of the new solution is better than that of the old solution, the leading bee remembers the new solution and discards the old solution; otherwise, the old solution is retained; Step 4: The leading bee shares the solution information with the follower bees, and the follower bees calculate the selection probability of each solution; Step 5: Generate a random number in the interval [-1, 1]. If the probability value of the solution calculated by the follower bee is greater than this random number, the follower bee generates a new solution, calculates the fitness value of the new solution. If the fitness value of the new solution is better than that of the old solution, the follower bee remembers the new solution and discards the old solution; otherwise, the old solution is retained; Step 6: After all follower bees complete the search process, if a solution has not been further updated after a preset number of cycles, this solution falls into a local optimum, this solution is discarded, and the leading bee corresponding to this solution turns into a scout bee. The scout bee generates a new solution to replace the current solution of the leading bee, and then returns to Step 2 to start repeating the cycle; Step 7: Record the optimal value obtained for the current task attributes, and determine whether the maximum number of iteration cycles has been reached. If not, return to Step 5; if so, output the optimal load , resource consumption and elapsed time .

2. The cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations according to claim 1, Characterized in that, The said Step 3 includes: Calculate the fitness value of the new solution through the formula , where , is the i-th new solution of the task attribute , and is the minimum value of the solutions of the task attribute .

3. The cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations according to claim 2, Characterized in that, The said Step 4 includes: The follower bees calculate the selection probability of each solution through the formula , where represents the number of initial nectar sources.

4. The cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations according to claim 3, Characterized in that, The formula for the follower bee to generate a new solution in Step 5 is the same as the principle of the formula for the leading bee to generate a new solution, and the formula for the follower bee to calculate the fitness value of the new solution is the same as the principle of the formula for the leading bee to calculate the fitness value of the new solution.

5. The cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations according to claim 4, Characterized in that, The said Step 6 includes: The scout bee generates a new solution to replace the current solution of the leading bee through the formula where represents the minimum value among all current solutions, represents the maximum value among all current solutions, and represents a random number between 0 and 1.

6. The cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations according to claim 1, Characterized in that, The UHV converter station cloud main station has 600 edge clusters, and each edge cluster contains 1 to 50 tasks.

7. The cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations according to claim 1, Characterized in that, The scale of the randomly generated initial bee colony is 1000.

8. The cloud-edge collaborative communication scheduling method for panoramic monitoring of UHV converter stations according to claim 1, Characterized in that, The maximum number of iteration cycles is 200.

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