Algorithm dynamic deployment and control method and system for intelligent inspection of wind power plant

Abstract

The invention provides an algorithm dynamic deployment and control method and system for intelligent inspection of a wind power plant. The algorithm dynamic deployment and control method comprises the following steps: evaluating available computing resources of a server; based on the evaluation result, performing batch division on the to-be-deployed and controlled algorithm, and determining the total batch frequency and the execution sequence; and performing polling execution on each batch of algorithms according to the execution sequence and a preset trigger rule to complete dynamic deployment and control. According to the invention, the intelligent routing inspection function of the dynamic configuration of the wind power plant can be realized, and the resource waste of the server, unnecessary cost control such as full-amount upgrade and even replacement of the server and the conditions of full-amount deployment migration of an information system and the like are effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of wind farm inspection, and in particular to an algorithmic dynamic deployment method and system for intelligent wind farm inspection. Background Technology

[0002] With the development of wind power generation technology and the comprehensive development of information technology and intelligence in wind farms, some wind farms that were built earlier have relatively outdated information equipment and low configuration. However, it is relatively difficult to carry out dynamic identification and intelligent inspection through image and video dynamic AI recognition technology. Summary of the Invention

[0003] This application provides an algorithmic dynamic deployment method and system for intelligent inspection of wind farms, in order to solve the problems in the background art.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of the embodiments of this application, an algorithmic dynamic deployment method for intelligent inspection of wind farms is provided, comprising: Assess the available computing resources of the server; Based on the evaluation results, the algorithms to be deployed are divided into batches, and the total number of batches and execution order are determined. Based on preset trigger rules, the algorithms of each batch are executed in turn according to the execution order to complete dynamic deployment.

[0006] According to one embodiment of this application, the step of dividing the algorithms to be deployed into batches based on the evaluation results includes: Based on the assessment of available computing resources on the server, a preset upper limit for running algorithms in a single batch is determined, and all algorithms to be deployed are divided according to the following rules: (1) The device channels operated by the same batch-type algorithm do not overlap; (2) The number of algorithms included in each batch shall not exceed the preset upper limit of the single batch of running algorithms; (3) Algorithm that prioritizes the allocation of high weights.

[0007] According to one embodiment of this application, determining the total number of batches and the execution order specifically includes: After the algorithm's batch partitioning is completed, all batches are sorted sequentially by number to form an algorithm batch queue; And execute sequentially starting with the batch with the smallest number.

[0008] According to one embodiment of this application, the preset triggering rule includes timed triggering, with only one batch executed at a time, and a preset time interval between batches.

[0009] According to one embodiment of this application, when a certain batch of algorithms is executed, the recording function of the relevant device is automatically turned on; after the batch of algorithms is completed, the recording is automatically stopped, and the recorded video is compressed, encrypted and stored to a designated disk, while generating a recording record.

[0010] According to a second aspect of the embodiments of this application, an algorithmic dynamic deployment system for intelligent inspection of wind farms is provided, comprising: The configuration module is used to configure the preset trigger rules for polling; The grouping module determines the preset upper limit of the algorithm to run in a single batch based on the evaluation results of the available computing resources of the server, and divides all algorithms to be deployed into batches and determines the total number of batches and execution order. The polling module schedules the algorithms for each batch based on the batch division results and preset triggering rules.

[0011] According to one embodiment of this application, the step of batching all algorithms to be deployed specifically includes: All algorithms to be deployed are categorized according to the following rules: (1) The device channels operated by the same batch-type algorithm do not overlap; (2) The number of algorithms included in each batch shall not exceed the preset upper limit of the number of algorithms to be run in a single batch; (3) Algorithm that prioritizes the allocation of high weights.

[0012] According to one embodiment of this application, determining the total number of batches and the execution order specifically includes: After completing the batch division, the grouping module sorts all batches by number and stores them persistently, while marking the first batch that needs to be executed immediately.

[0013] According to one embodiment of this application, the specific working process of the polling module includes: During polling, it is determined whether the current time is within the allowed execution time range. If it is not within the range, the status is marked and the process waits. If it is within the range, the first batch of algorithms is executed. After execution, the process waits for a preset time interval before executing the next batch of algorithms. The preset time interval and the allowed execution time range are both set by preset triggering rules.

[0014] According to one embodiment of this application, when the polling module executes a certain batch of algorithms, it automatically starts the recording function of the relevant device; after the batch of algorithms is completed, it automatically stops recording, compresses and encrypts the recorded video and stores it to the designated disk, and generates a recording record.

[0015] Compared with the existing technology, the beneficial effects of adopting the above technical solution are as follows: the present invention can realize the intelligent inspection function of dynamic configuration of wind farm, effectively avoid the waste of server resources and unnecessary cost control such as full server upgrade or even replacement, as well as the full deployment and migration of information systems. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] Figure 1 This is a flowchart of the algorithmic dynamic deployment method for intelligent inspection of wind farms proposed in this application.

[0018] Figure 2 This is a schematic diagram of the algorithmic dynamic deployment system for intelligent inspection of wind farms proposed in an embodiment of this application.

[0019] Figure 3 This is a flowchart of the algorithm dynamic deployment system for intelligent inspection of wind farms proposed in this application.

[0020] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar modules or modules having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. Rather, the embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0022] For wind farms built in the early stages with low server configurations, effectively deploying AI image recognition technology for intelligent inspection without large-scale hardware upgrades is crucial. Based on this, this application proposes a dynamic algorithm deployment method for intelligent wind farm inspection. By deploying only a limited number of algorithms at specific times and polling them within a specified time range each day, the continuous pressure on server resources is reduced. Simultaneously, an algorithm-triggered dynamic recording strategy is adopted, rather than traditional 24 / 7 recording, significantly saving disk storage space. Please refer to [reference needed]. Figure 1 The specific plan is as follows: S101. Evaluate the available computing resources of the server.

[0023] In order for the subsequent algorithm deployment to meet the current hardware resource constraints, it is necessary to first evaluate the server's computing resources and generate an evaluation result, which will be used for the dynamic grouping of subsequent algorithms.

[0024] In one embodiment, the available resources of CPU, memory and bandwidth are mainly considered. Generally, the CPU specification is no less than a 12th generation i5, the memory specification is no less than 16GB, and the theoretical minimum configuration can run 4 algorithms at the same time.

[0025] S102. Based on the evaluation results, divide the algorithms to be deployed into batches and determine the total number of batches and the execution order.

[0026] In this embodiment, a preset upper limit for running algorithms in a single batch is determined based on the assessment results of the server's available computing resources. Then, all algorithms to be deployed are divided into batches and executed in batches to reduce the continuous pressure on server resources. Specifically, the following rules are used for batch division: (1) The device channels operated by the same batch-type algorithm do not overlap; (2) The number of algorithms included in each batch shall not exceed the preset upper limit of the number of algorithms to be run in a single batch; (3) Prioritize the allocation of algorithms with high weights. Specifically, the priority can be determined based on the algorithm type or by manual modification by the administrator.

[0027] After batch division, in this embodiment, all batches are sorted sequentially by number to form an algorithm batch queue. When executing algorithms subsequently, execution starts from the batch with the smallest number. In this embodiment, higher-priority algorithms are assigned to the batches with earlier numbers in the algorithm batch queue.

[0028] S103. Using preset trigger rules, the algorithms of each batch are executed in turn according to the execution order to complete dynamic deployment.

[0029] To further reduce the continuous pressure on server resources, this embodiment executes the algorithm only within a specific time range. Specifically, the preset triggering rules include timed triggering (i.e., setting a allowed execution time range), with only one batch executed at a time, and a preset time interval between batches. During polling, it is determined whether the current time is within the allowed execution time range. If it is not within the range, the status is marked and a wait is initiated; if it is within the range, the algorithm for the corresponding batch is executed. After execution, the preset time interval is waited before the next batch is evaluated. Both the preset time interval and the allowed execution time range are set by the preset triggering rules.

[0030] It should be noted that when executing a batch of algorithms, the recording function of the relevant devices is automatically turned on; after the batch of algorithms is completed, the recording is automatically stopped, and the recorded video is compressed, encrypted and stored to the specified disk, while generating a recording record.

[0031] In some embodiments, the algorithm dynamic deployment method for intelligent inspection of wind farms proposed in this application is mainly implemented through software programs. Part of it is implemented by deploying the software to the server, and the other part is implemented by platform access through the monitoring equipment on the wind turbine. In practical applications, intelligent inspection software can be added to video surveillance or existing information system servers. This software is used to implement the algorithm dynamic deployment method for intelligent inspection of wind farms in this embodiment. Its main functions are as follows: (1) Installation part: Install the database, start the caching software and streaming media service platform, and execute the intelligent inspection platform program installation package and the algorithm platform program installation package. (2) Verification part: Open the browser and enter the login address to log in normally; access the platform on the device; receive and display the device information on the intelligent inspection platform; create a new algorithm and deploy it. The inspection platform receives alarm data and the verification is completed.

[0032] The algorithm dynamic deployment method for intelligent wind farm inspection presented in this application addresses the current situation of low-configuration server equipment in some wind farms, meeting the computing power requirements of intelligent inspection without altering or only requiring minimal hardware upgrades. Specifically, based on the actual server configuration, the algorithms to be deployed are dynamically scheduled and executed in a round-robin fashion; and hardware resources are dynamically allocated and released according to the round-robin scheduling. That is, all algorithms that need to be executed are executed only in specific batches. When other batches are executing, the remaining batches immediately release resources to wait for their own batch to execute, which can effectively solve the problem of not executing all the tasks at the same time and reduce the pressure of resource occupation.

[0033] For further details, please refer to... Figure 2 This embodiment also provides an algorithmic dynamic deployment system for intelligent inspection of wind farms, mainly including a configuration module, a grouping module, and a polling module. The configuration module is mainly used to configure preset trigger rules. These preset trigger rules include the execution time interval and the allowed execution time range.

[0034] The grouping module determines the preset upper limit of the algorithm to run in a single batch based on the evaluation results of the available computing resources of the server, and divides all algorithms to be deployed into batches and determines the total number of batches and execution order.

[0035] The polling module schedules the algorithms for each batch based on the batch division results and preset triggering rules.

[0036] Furthermore, in the grouping module, algorithms are grouped primarily based on conditions such as channel conflict avoidance, a preset upper limit for single-batch algorithm execution, and algorithm weight priority. The preset upper limit for single-batch algorithm execution is mainly determined by an assessment of the server's available computing resources. When setting this limit, it is necessary to ensure that the server's computing resources still have a certain redundancy when running each batch of algorithms, guaranteeing the server's normal operation. Specifically, this embodiment uses the following rules to divide all algorithms to be deployed: (1) The device channels operated by the same batch-type algorithm do not overlap; (2) The number of algorithms included in each batch shall not exceed the preset upper limit of the number of algorithms to be run in a single batch; (3) Algorithm that prioritizes the allocation of high weights.

[0037] Meanwhile, after completing the batch division, the grouping module sorts all batches by number and stores them persistently, while marking the first batch that needs to be executed immediately.

[0038] In this embodiment, the polling module mainly determines whether the current time is within the allowed execution time range when polling. If it is not within the range, it marks the status and waits; if it is within the range, it executes the first batch of algorithms. After execution, it waits for a preset time interval before executing the next batch of algorithms. The preset time interval and the allowed execution time range are both set by preset triggering rules.

[0039] Furthermore, when the polling module executes a batch of algorithms, it automatically starts the recording function of the relevant devices; after the batch of algorithms is completed, it automatically stops recording, compresses and encrypts the recorded video, stores it to the designated disk, and generates a recording record.

[0040] To more clearly illustrate the working process of the algorithm-driven dynamic control system for intelligent wind farm inspection in this embodiment, the following will combine... Figure 3 The complete working process of this system is described below: Step A: Determine whether the configured batch division parameters and preset trigger rules are valid. If valid, proceed with subsequent operations; otherwise, terminate the process directly.

[0041] Step B: Is this the first time creating a task? If yes, proceed to step C. If no, clear the existing task, stop the currently executing algorithm, and proceed to step C.

[0042] Step C: Create batches. Divide all algorithms into batches based on conditions such as channel conflict avoidance, maximum number of algorithms per batch, and algorithm weight priority.

[0043] Step D: Number the divided batches and initialize the current batch number to 1.

[0044] Step E: Determine whether the current time is within the specified allowed execution time range and whether the current batch number is less than or equal to the total batch number. If yes, proceed to step F; otherwise, proceed to step I.

[0045] Step F: Stop executing the algorithm of the previous batch. If the current batch number is 1, skip this operation.

[0046] Step G: Call the device preset points corresponding to all algorithms in the current batch, execute the algorithms of this batch, and change the status to running and synchronize screen recording.

[0047] Step H: Wait for the preset time interval, the current batch number is incremented by 1, and then proceed to step E.

[0048] Step 1: Stop all algorithms, mark subsequent batches as pending execution, and assign a pause position to the stopped batch, then proceed to Step E. With limited resources, all batch tasks may not be completed within a day. To prevent tasks from restarting after stopping and having to start from the first batch again, which could mean later tasks are never executed, this embodiment marks the stopped batch position. When execution resumes, the batch number at the time of stopping is the current batch number, effectively avoiding the problem of subsequent tasks being unable to execute.

[0049] This application reads the server configuration, combines it with the configuration parameters given by the administrator, groups the algorithms to be executed according to specific rules, and polls and controls them at given time intervals. At any given time, only the specified algorithm is controlled. During the algorithm control period, the CPU or GPU is selected based on the configuration. Screen recording of the algorithm control is carried out simultaneously, thereby achieving the goal of making full use of server resources.

[0050] It should be further explained that in this embodiment, the order of algorithm deployment and the removal and addition of new algorithms to existing batches can be achieved through dynamic intervention via alarm data configuration and manual intervention. Simultaneously, during the operation of the inspection system, algorithms can be stopped and woken up according to configuration or manual intervention, and the period and size of recorded videos can be retained according to the configuration. Specifically, alarm configuration intervention includes assigning execution levels to alarms; higher levels are executed earlier, and those with the same level are randomly assigned. Manual intervention involves adjusting the batches after batching, moving algorithms requiring limited execution to earlier batches, while the following rules must be met during adjustment: 1. If a batch task is being executed, no tasks can be added or removed from the current batch or the batch to be executed; 2. During the stopping process, tasks can be arbitrarily assigned as long as the maximum number of batches allocated is not exceeded.

[0051] Based on the same technical concept, this application also provides an electronic device that can implement the algorithmic dynamic deployment method for intelligent wind farm inspection provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic equipment. Figure 4 As shown, the electronic device may include: At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 4 The example used is the connection between the processor and memory via a bus. The bus... Figure 4 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 4 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0052] In this embodiment of the invention, the memory stores instructions executable by at least one processor. By executing the instructions stored in the memory, the at least one processor can execute the aforementioned algorithmic dynamic control method for intelligent wind farm inspection. The processor can implement... Figure 4 The functions of each module in the device shown.

[0053] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0054] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0055] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable array, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the algorithmic dynamic deployment method for intelligent wind farm inspection disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0056] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.

[0057] By designing and programming the processor, the code corresponding to the algorithmic dynamic deployment method for intelligent wind farm inspection described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during operation. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0058] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions, which, when executed on a computer, cause the computer to execute the algorithmic dynamic control method for intelligent inspection of wind farms described above.

[0059] In some alternative embodiments, the present invention also provides a method for the dynamic deployment of an algorithm for intelligent inspection of wind farms, which can also be implemented as a program product including program code. When the program product is run on a device, the program code is used to cause the control device to perform the steps in the method for the dynamic deployment of an algorithm for intelligent inspection of wind farms according to various exemplary embodiments of the present invention as described in this specification.

[0060] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0064] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0065] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An algorithmic dynamic deployment method for intelligent inspection of wind farms, characterized in that, include: Assess the available computing resources of the server; Based on the evaluation results, the algorithms to be deployed are divided into batches, and the total number of batches and execution order are determined. Based on preset trigger rules, the algorithms of each batch are executed in turn according to the execution order to complete dynamic deployment.

2. The algorithmic dynamic deployment method for intelligent inspection of wind farms according to claim 1, characterized in that, The process of dividing the algorithms to be deployed into batches based on the evaluation results includes: Based on the assessment of available computing resources on the server, a preset upper limit for running algorithms in a single batch is determined, and all algorithms to be deployed are divided according to the following rules: (1) The device channels operated by the same batch-type algorithm do not overlap; (2) The number of algorithms included in each batch shall not exceed the preset upper limit of the single batch of running algorithms; (3) Algorithm that prioritizes the allocation of high weights.

3. The algorithmic dynamic deployment method for intelligent inspection of wind farms according to claim 1, characterized in that, The determination of the total number of batches and the execution order specifically includes: After the algorithm's batch partitioning is completed, all batches are sorted sequentially by number to form an algorithm batch queue; And execute sequentially starting with the batch with the smallest number.

4. The algorithmic dynamic deployment method for intelligent inspection of wind farms according to claim 1, characterized in that, The preset triggering rules include timed triggering, with only one batch executed at a time, and a preset time interval between batches.

5. The algorithmic dynamic deployment method for intelligent inspection of wind farms according to claim 1, characterized in that, When executing a batch of algorithms, the recording function of the relevant devices is automatically turned on; after the batch of algorithms is completed, the recording is automatically stopped, and the recorded video is compressed, encrypted and stored to the specified disk, while generating a recording record.

6. An algorithm-based dynamic deployment and control system for intelligent inspection of wind farms, characterized in that, include: The configuration module is used to configure the preset trigger rules for polling; The grouping module determines the preset upper limit of the algorithm to run in a single batch based on the evaluation results of the available computing resources of the server, and divides all algorithms to be deployed into batches and determines the total number of batches and execution order. The polling module schedules the algorithms for each batch based on the batch division results and preset triggering rules.

7. The algorithmic dynamic deployment and control system for intelligent inspection of wind farms according to claim 6, characterized in that, The process of batching all algorithms to be deployed specifically includes: All algorithms to be deployed are categorized according to the following rules: (1) The device channels operated by the same batch-type algorithm do not overlap; (2) The number of algorithms included in each batch shall not exceed the preset upper limit of the number of algorithms to be run in a single batch; (3) Algorithm that prioritizes the allocation of high weights.

8. The algorithmic dynamic deployment and control system for intelligent inspection of wind farms according to claim 6 or 7, characterized in that, The determination of the total number of batches and the execution order specifically includes: After completing the batch division, the grouping module sorts all batches by number and stores them persistently, while marking the first batch that needs to be executed immediately.

9. The algorithmic dynamic deployment and control system for intelligent inspection of wind farms according to claim 6, characterized in that, The specific working process of the polling module includes: During polling, it is determined whether the current time is within the allowed execution time range. If it is not within the range, it is marked as pending execution and waits for the execution time to arrive. If it is within the range, the first batch of algorithms is executed. After execution, the next batch of algorithms is executed after a preset time interval. The preset time interval and the allowed execution time range are both set by preset trigger rules.

10. The algorithmic dynamic deployment and control system for intelligent inspection of wind farms according to claim 6, characterized in that, When the polling module executes a batch of algorithms, it automatically starts the recording function of the relevant devices; after the batch of algorithms is completed, it automatically stops recording, compresses and encrypts the recorded video and stores it to the designated disk, and generates a recording record.

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