Power generation equipment detection method and system based on large model

Abstract

The invention relates to a power generation equipment detection method and system based on a large model, and the method comprises the steps: generating a power generation equipment detection task and an equipment detection time period based on a preset power generation detection large model, and selecting a power generation equipment detection worker; setting a test detection task, obtaining real-time environment sensing data, generating a pre-estimated environment interference value of the target detection area in the equipment detection time period according to the real-time environment sensing data, judging whether the pre-estimated environment interference value is greater than or equal to a preset environment interference safety threshold value or not, and if yes, judging that the pre-estimated environment interference value is greater than or equal to the preset environment interference safety threshold value; and if yes, generating a detection task correction instruction, and prompting the power generation equipment detection personnel to adjust the detection time according to the detection task correction instruction. According to the method, detection interference judgment based on the actual environment is realized, so that the safety and feasibility of detection task execution are improved, and efficient and high-safety power generation equipment maintenance is realized.

Description

Technical Field

[0001] The present application relates to the technical field of power generation equipment detection, and in particular to a power generation equipment detection method and system based on a large model. Background Art

[0002] Power generation equipment refers to mechanical and electrical equipment used to convert various forms of energy into electrical energy. These equipment can be based on a variety of energy types, such as fossil fuels, nuclear energy, hydropower, wind energy, solar energy and other renewable energy sources. Equipment that uses wind energy to generate electricity is wind power generation equipment.

[0003] The inspection and maintenance of wind power generation equipment during use is extremely important. Regarding the inspection and maintenance of wind power equipment, an invention patent with a publication number of CN118428931A was disclosed in the prior art on August 2, 2024, which specifically discloses a wind power equipment inspection method, system, equipment and medium, and relates to the field of wind power technology. The system includes: an unplanned maintenance module, which is used to collect defective tasks and centrally manage them; a planned maintenance module, which is used to formulate different maintenance projects and perform regular maintenance on equipment according to preset time periods; an equipment change management module, which is used to initiate equipment change applications and determine different equipment change degrees for corresponding approval processes; a maintenance knowledge base module, which is used to establish a maintenance knowledge base and train a question-and-answer model based on the data in the maintenance knowledge base to generate a question-and-answer robot to provide corresponding fault handling measures; a scheduling module, which is used to calculate the tasks in the task pool using preset influencing factors to determine the priority order of task execution, and then set the corresponding task execution time for the task.

[0004] The technical solution in the above patent document is mainly to detect multiple data and realize intelligent maintenance of wind power based on multi-dimensional data. However, it still has some disadvantages. Specifically, it does not take into account that most wind power equipment is installed outdoors and needs to be climbed for maintenance. If the interference caused by the environment is ignored, it will at least affect the maintenance and detection process and efficiency of the equipment, and at worst cause safety problems. Summary of the invention

[0005] Based on this, it is necessary to provide a method and system for detecting interference based on the actual environment to address the above technical problems, thereby improving the safety and feasibility of executing detection tasks and realizing an efficient and highly safe large-model-based power generation equipment detection method and system.

[0006] The technical solution of the present invention is as follows:

[0007] A large model-based power generation equipment detection method, the method comprising:

[0008] Based on the preset power generation detection model, the power generation equipment detection task and equipment detection time period are generated according to the historical power generation operation data of the power generation equipment to be detected, and the power generation equipment detection personnel are selected according to the power generation equipment detection task;

[0009] A test detection task is set according to the power generation equipment detection task and the power generation equipment to be detected, and real-time environmental sensing data collected by the power generation equipment detection personnel when performing the test detection task in the target detection area is obtained, wherein the target detection area is the area where the power generation equipment to be detected is located;

[0010] Generate an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the maintenance personnel of the power generation equipment during the equipment detection time period;

[0011] It is determined whether the estimated environmental interference value is greater than or equal to a preset environmental interference safety threshold. If so, a detection task correction instruction is generated, and the power generation equipment inspection personnel are prompted to adjust the inspection time according to the detection task correction instruction.

[0012] Optionally, setting a test task according to the power generation equipment detection task and the power generation equipment to be detected includes:

[0013] Acquire the power generation components to be inspected and the estimated inspection duration according to the power generation equipment inspection task;

[0014] Generate a detection task execution path according to the power generation component to be detected and the basic device structure of the device to be detected, wherein the detection task execution path includes a plurality of device detection path points;

[0015] Generating a device test detection area according to the device detection path point, wherein one device detection path point corresponds to one device test detection area;

[0016] The data collection category corresponding to each of the equipment detection route points is obtained, and a test detection branch task is set according to the data collection category and the equipment test detection area, wherein one equipment test detection area corresponds to one test detection branch task.

[0017] Optionally, the equipment test detection area includes a cabin exit area, a cabin top stay area and a blade body area; the real-time environmental sensing data includes cabin exit sensing data, cabin top sensing data and blade body sensing data;

[0018] Generating an estimated environmental interference value of the target detection area within the device detection time period according to the real-time environmental sensing data, including:

[0019] Extracting wind speed sensing information, temperature sensing information and visibility sensing score from the cabin exit sensing data, wherein the visibility sensing score is a score set by the power generation equipment inspector according to the visibility outside the cabin exit;

[0020] Extracting the average wind speed inside the cabin and the average wind speed outside the cabin according to the wind speed sensing information, and extracting the real-time sensed temperature of the cabin outlet at each data collection time point, as well as the maximum sensed temperature and the minimum sensed temperature among the real-time sensed temperatures according to the temperature sensing information, wherein the number of the real-time sensed temperatures is n;

[0021] Acquire the real-time protective equipment of the power generation equipment inspector, and generate a real-time suitable temperature according to the real-time protective equipment, wherein the real-time suitable temperature is set based on the thickness of the real-time protective equipment of the power generation equipment inspector;

[0022] Generate a cabin exit interference value according to the average wind speed, the average wind speed outside the cabin, the real-time sensed temperature, the maximum sensed temperature, the minimum sensed temperature and the real-time suitable temperature;

[0023] top dust accumulation information, top slope information and top wind speed information from the cabin top sensing data, and generate a cabin top interference value according to the top dust accumulation information, top slope information and top wind speed information;

[0024] Extracting blade wind speed information from the blade body sensing data, and generating a blade environmental interference value according to the blade wind speed information;

[0025] Obtaining the estimated weather data for the equipment detection time period, and generating an environmental difference coefficient according to the difference between the estimated weather data and the test detection weather data, wherein the test detection weather data is the weather data when the test detection task is performed;

[0026] According to the cabin outlet interference value Dr, the cabin top interference value Dc, the blade environment interference value Db and the environmental difference coefficient Ce, the estimated environmental interference value EDs is generated based on the following formula:

[0027] EDs = Ce (Dr + Dc + Db);

[0028] Among them, EDs is the estimated environmental interference value, Ce is the environmental difference coefficient, Dr is the cabin outlet interference value, Dc is the cabin top interference value, and Db is the blade environmental interference value.

[0029] Optionally, the cabin exit interference value is generated based on the following formula:

[0030]

[0031] Among them, Dr is the cabin exit interference value, β1 is the hatch interference coefficient, Vout is the average wind speed outside the cabin, Vin is the average wind speed inside the cabin, Vs is the standard wind speed difference, β2 is the temperature interference coefficient, α1 is the first temperature interference coefficient, Ti is the i-th real-time sensed temperature, Ts is the real-time suitable temperature, α2 is the second temperature interference coefficient, Tmax is the maximum sensed temperature, Tmin is the minimum sensed temperature, and Vb is the visibility sensing score.

[0032] Optionally, generating a cabin top interference value according to the top dust accumulation information, the top slope information and the top wind speed information includes:

[0033] Extracting the estimated dust accumulation thickness and the top surface humidity according to the top dust accumulation information, and generating the estimated walking slope according to the top slope information;

[0034] Extract the top real-time wind speed at each data collection time point according to the top wind speed information, generate the top average wind speed according to each top real-time wind speed, and generate the wind disturbance coefficient according to the top average wind speed;

[0035] According to the estimated dust thickness, top surface humidity, estimated walking slope and wind disturbance coefficient, the cabin top disturbance value is generated based on the following formula:

[0036]

[0037] Among them, Dc is the interference value on the top of the cabin, δ is the wind disturbance coefficient, Th is the estimated dust accumulation thickness, Ths is the preset safe dust accumulation thickness, Hu is the top surface humidity, Hus is the preset safe surface humidity, and Sp is the estimated walking slope.

[0038] Optionally, a large model-based power generation equipment detection system comprises:

[0039] A maintenance task setting module is used to generate power generation equipment inspection tasks and equipment inspection time periods based on a preset power generation inspection large model and according to the historical power generation operation data of the power generation equipment to be inspected, and select power generation equipment inspection personnel according to the power generation equipment inspection tasks;

[0040] A real-time data sensing module, used to set a test detection task according to the power generation equipment detection task and the power generation equipment to be detected, and obtain real-time environmental sensing data collected when the power generation equipment detection personnel perform the test detection task in the target detection area, wherein the target detection area is the area where the power generation equipment to be detected is located;

[0041] An interference data generating module, used to generate an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the maintenance personnel of the power generation equipment during the equipment detection time period;

[0042] The detection task execution module is used to determine whether the estimated environmental interference value is greater than or equal to a preset environmental interference safety threshold. If it is determined to be yes, a detection task correction instruction is generated, and the power generation equipment inspection personnel are prompted to adjust the inspection time according to the detection task correction instruction.

[0043] Optionally, the real-time data sensing module is further used for:

[0044] According to the power generation equipment detection task, the power generation component to be detected and the estimated detection duration are obtained; according to the power generation component to be detected and the basic equipment structure of the equipment to be detected, the detection task execution path is generated, wherein the detection task execution path includes multiple equipment detection path points; according to the equipment detection path points, an equipment test and detection area is generated, wherein one equipment detection path point corresponds to one equipment test and detection area; the data collection category corresponding to each of the equipment detection path points is obtained, and the test and detection branch task is set according to the data collection category and the equipment test and detection area, wherein one equipment test and detection area corresponds to one test and detection branch task.

[0045] Optionally, the equipment test detection area includes a cabin exit area, a cabin top stay area and a blade body area; the real-time environment sensing data includes cabin exit sensing data, cabin top sensing data and blade body sensing data; and the interference data generating module is further used for:

[0046] Extract wind speed sensing information, temperature sensing information and visibility sensing score from the cabin exit sensing data, wherein the visibility sensing score is a score set by the power generation equipment inspector according to the visibility outside the cabin exit; extract the average wind speed inside the cabin and the average wind speed outside the cabin according to the wind speed sensing information, and extract the real-time sensing temperature of the cabin exit at each data collection time point according to the temperature sensing information, as well as the maximum sensing temperature and the minimum sensing temperature among the real-time sensing temperatures, wherein the number of the real-time sensing temperatures is n; obtain the real-time protective equipment of the power generation equipment inspector, and generate a real-time suitable temperature according to the real-time protective equipment, wherein the real-time suitable temperature is based on the real-time protective equipment of the power generation equipment inspector. The thickness of the equipment is set; the cabin outlet interference value is generated according to the average wind speed, the average wind speed outside the cabin, the real-time sensed temperature, the maximum sensed temperature, the minimum sensed temperature and the real-time suitable temperature; the top dust accumulation information, the top slope information and the top wind speed information in the cabin top sensing data, and the cabin top interference value is generated according to the top dust accumulation information, the top slope information and the top wind speed information; the blade wind speed information is extracted from the blade body sensing data, and the blade environment interference value is generated according to the blade wind speed information; the estimated weather data of the equipment detection time period is obtained, and the environmental difference coefficient is generated according to the difference between the estimated weather data and the test detection weather data, wherein the test detection weather data is the weather data when the test detection task is performed;

[0047] According to the cabin outlet interference value Dr, the cabin top interference value Dc, the blade environment interference value Db and the environmental difference coefficient Ce, the estimated environmental interference value EDs is generated based on the following formula:

[0048] EDs = Ce (Dr + Dc + Db);

[0049] Among them, EDs is the estimated environmental interference value, Ce is the environmental difference coefficient, Dr is the cabin outlet interference value, Dc is the cabin top interference value, and Db is the blade environmental interference value.

[0050] Optionally, a computer device is also provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned large-model-based power generation equipment detection method when executing the computer program.

[0051] Optionally, a computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned large-model-based power generation equipment detection method are implemented.

[0052] The technical effects achieved by the present invention are as follows:

[0053] The above-mentioned large-model-based power generation equipment detection method and system, in turn, generates power generation equipment detection tasks and equipment detection time periods based on the historical power generation operation data of the power generation equipment to be detected based on the preset power generation detection large model, and selects power generation equipment detection personnel according to the power generation equipment detection tasks; sets test detection tasks according to the power generation equipment detection tasks and the power generation equipment to be detected, and obtains real-time environmental sensing data collected by the power generation equipment detection personnel when performing the test detection tasks in the target detection area, wherein the target detection area is the area where the power generation equipment to be detected is located; generates an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the power generation equipment maintenance personnel during the equipment detection time period; determines whether the estimated environmental interference value is greater than or equal to the preset environmental interference safety threshold, and if the determination is yes, generates a detection task correction instruction, and prompts the power generation equipment detection personnel to adjust the detection time according to the detection task correction instruction. When it is necessary to inspect the power generation equipment to be inspected, the present invention no longer adopts the previous maintenance mode of fixed time period, but adopts targeted maintenance after analyzing the operation data. Specifically, the historical power generation operation data of the power generation equipment to be inspected is first obtained, and then the power generation equipment inspection task and equipment inspection time period are generated according to the historical power generation operation data through the preset power generation inspection large model, so as to realize the operation data analysis based on the large model and realize the efficient generation of power generation equipment inspection tasks and equipment inspection time periods. After the equipment inspection time period is set, the power generation equipment inspection task needs to be executed within the equipment inspection time period.Next, in order to ensure that suitable inspection personnel can be found for the power generation equipment inspection task, the power generation equipment inspection personnel are selected through the power generation equipment inspection task. Then, in order to set the test inspection task according to the power generation equipment inspection task and the power generation equipment to be inspected, and obtain the real-time environmental sensing data collected by the power generation equipment inspection personnel when performing the test inspection task in the target inspection area. The acquisition of the real-time detection data is the data collected by the power generation equipment inspection personnel when performing the detection test. The data can effectively reflect the environmental data faced by the current power generation equipment to be inspected when inspecting it at the current stage. Based on the data, the subsequent execution of the complete Whether the power generation equipment is safe during the inspection task, then, in order to more comprehensively analyze the environment that needs to be faced during the time period of executing the task, the estimated environmental interference value of the target detection area during the equipment inspection time period is set according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the power generation equipment maintenance personnel during the equipment inspection time period, and then judge whether the estimated environmental interference value is greater than or equal to the preset environmental interference safety threshold. If it is judged as no, it means that the interference will not affect the execution of the inspection task during the next task execution. At this time, the maintenance task execution instruction is generated, and the maintenance engineer is instructed to perform equipment maintenance during the equipment maintenance time period according to the maintenance task execution instruction. If it is judged as yes, it means that the environmental interference received at this time according to the original plan is too strong, and there is a possibility of safety risks. At this time, the inspection task correction instruction is generated, and the power generation equipment inspection personnel are prompted to adjust the inspection time according to the inspection task correction instruction. Therefore, the present invention first sets the detection task through the power generation detection large model to improve the detection efficiency and detection accuracy, and then performs detection tests before the formal detection and obtains the test data during the detection test process. Then, according to the test data and the equipment detection time period, it estimates the interference encountered when the power generation equipment detection task is formally executed, and then realizes the judgment of detection interference based on the actual environment, thereby improving the safety and feasibility of the execution of the detection task and realizing efficient and high-safety power generation equipment maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a flow chart of a method for detecting power generation equipment based on a large model in one embodiment;

[0055] Figure 2 It is a structural block diagram of a power generation equipment detection system based on a large model in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] In one embodiment, a terminal is provided, which is used to: generate a power generation equipment detection task and an equipment detection time period based on a preset power generation detection large model and according to historical power generation operation data of the power generation equipment to be detected, and select a power generation equipment detection personnel according to the power generation equipment detection task; set a test detection task according to the power generation equipment detection task and the power generation equipment to be detected, and obtain real-time environmental sensing data collected by the power generation equipment detection personnel when performing the test detection task in a target detection area, wherein the target detection area is an area where the power generation equipment to be detected is located; generate an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the power generation equipment maintenance personnel during the equipment detection time period; determine whether the estimated environmental interference value is greater than or equal to a preset environmental interference safety threshold, and if so, generate a detection task correction instruction, and prompt the power generation equipment detection personnel to adjust the detection time according to the detection task correction instruction.

[0058] The terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices.

[0059] In one embodiment, Figure 1 As shown, a large model-based power generation equipment detection method is provided, the method comprising:

[0060] Step S100: Based on a preset power generation detection model, a power generation equipment detection task and an equipment detection time period are generated according to the historical power generation operation data of the power generation equipment to be detected, and a power generation equipment detection personnel are selected according to the power generation equipment detection task;

[0061] Step S200: setting a test task according to the power generation equipment detection task and the power generation equipment to be detected, and acquiring real-time environmental sensing data collected by the power generation equipment detection personnel when performing the test task in the target detection area, wherein the target detection area is the area where the power generation equipment to be detected is located;

[0062] Step S300: generating an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the maintenance personnel of the power generation equipment during the equipment detection time period;

[0063] Step S400: determine whether the estimated environmental interference value is greater than or equal to a preset environmental interference safety threshold. If so, generate a detection task correction instruction, and prompt the power generation equipment inspection personnel to adjust the inspection time according to the detection task correction instruction.

[0064] In this embodiment, when it is necessary to inspect the power generation equipment to be inspected, the previous fixed time period maintenance mode is no longer adopted. Instead, targeted maintenance is adopted after analyzing the operation data. Specifically, the historical power generation operation data of the power generation equipment to be inspected is first obtained, and then the power generation equipment inspection task and equipment inspection time period are generated according to the historical power generation operation data through the preset power generation inspection large model, so as to realize the operation data analysis based on the large model and realize the efficient generation of power generation equipment inspection tasks and equipment inspection time periods. After setting the equipment inspection time period, it is necessary to perform the power generation equipment inspection task within the equipment inspection time period. Next, in order to ensure that suitable inspection personnel can be found for the power generation equipment inspection task, the power generation equipment inspection personnel are selected through the power generation equipment inspection task. Then, in order to set the test inspection task according to the power generation equipment inspection task and the power generation equipment to be inspected, and obtain the real-time environmental sensing data collected by the power generation equipment inspection personnel when performing the test inspection task in the target inspection area. The acquisition of the real-time detection data is the data collected by the power generation equipment inspection personnel when performing the detection test. The data can effectively reflect the environmental data faced by the current power generation equipment to be inspected when inspecting it at the current stage. Based on the data, the subsequent execution of the complete Whether the power generation equipment is safe during the inspection task, then, in order to more comprehensively analyze the environment that needs to be faced during the time period of executing the task, the estimated environmental interference value of the target detection area during the equipment inspection time period is set according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the power generation equipment maintenance personnel during the equipment inspection time period, and then judge whether the estimated environmental interference value is greater than or equal to the preset environmental interference safety threshold. If it is judged as no, it means that the interference will not affect the execution of the inspection task during the next task execution. At this time, the maintenance task execution instruction is generated, and the maintenance engineer is instructed to perform equipment maintenance during the equipment maintenance time period according to the maintenance task execution instruction. If it is judged as yes, it means that the environmental interference received at this time according to the original plan is too strong, and there is a possibility of safety risks. At this time, the inspection task correction instruction is generated, and the power generation equipment inspection personnel are prompted to adjust the inspection time according to the inspection task correction instruction. Therefore, the present invention first sets the detection task through the power generation detection large model to improve the detection efficiency and detection accuracy, and then performs detection tests before the formal detection and obtains the test data during the detection test process. Then, according to the test data and the equipment detection time period, it estimates the interference encountered when the power generation equipment detection task is formally executed, and then realizes the judgment of detection interference based on the actual environment, thereby improving the safety and feasibility of the execution of the detection task and realizing efficient and high-safety power generation equipment maintenance.

[0065] In one embodiment, step S200: setting a test task according to the power generation equipment detection task and the power generation equipment to be detected, includes:

[0066] Step S211: acquiring the power generation components to be tested and the estimated test duration according to the power generation equipment test task;

[0067] Step S212: generating a detection task execution path according to the power generation component to be detected and the basic device structure of the device to be detected, wherein the detection task execution path includes a plurality of device detection path points;

[0068] Step S213: generating a device test detection area according to the device detection path point, wherein one device detection path point corresponds to one device test detection area;

[0069] Step S214: obtaining the data collection category corresponding to each of the equipment detection route points, and setting the test detection branch task according to the data collection category and the equipment test detection area, wherein one equipment test detection area corresponds to one test detection branch task.

[0070] In this embodiment, the power generation component to be detected is a component that needs to be detected. After obtaining the power generation component to be detected, the detection time of the power generation component to be detected, that is, the estimated monitoring duration, is obtained by comparing the power generation component to be detected with a preset standard detection component time database. In this step, different detection times are set in advance according to different components, so after obtaining the power generation component to be detected, the estimated detection duration can be efficiently obtained, which is conducive to the subsequent test task to be carried out according to the estimated detection duration, and this time is used as a detection unit to estimate the detection market for subsequent actual execution tasks to improve safety. Then, the equipment test detection area is generated according to the equipment detection path point, wherein one equipment detection path point corresponds to one equipment test detection area. In this step, the equipment detection path point is taken as three examples, namely, the cabin exit, cabin top and blade of the wind power equipment. The corresponding equipment test detection areas are the cabin exit area, the cabin top stay area and the blade body area. Among them, the area along the cabin exit outward and located at the top of the wind power equipment for personnel to stay for a short time is the cabin top stay area. When setting the equipment test detection area, the area is formed by expanding the preset distance outward with the equipment detection path point as the core. Specifically, the cabin exit area is formed after the first distance is uniformly expanded outward with the airport exit as the core. The cabin top stay area is formed after the second distance is uniformly expanded outward with the cabin top as the core. The blade main body area is formed after the third distance is uniformly expanded outward with the airport exit as the core. The first distance is 0.5 meters. The second distance is 1 meter. The third distance is 2 meters. It should be noted that the equipment test detection area is not limited to a plane area, it can also be a three-dimensional area. For example, when detecting blades, the area is formed after the blade is uniformly expanded outward by 2 meters as a whole, and the area is three-dimensional. Because of the randomness during detection, the power generation equipment detection personnel may not necessarily perform detection in a plane area, and the high probability of surround detection is complete, so a three-dimensional detection method is set to improve the accuracy of detection. Then, the data collection category of each of the equipment detection path points is obtained, and the test detection branch task is set according to the data collection category and the equipment test detection area, wherein one equipment test detection area corresponds to one test detection branch task. The correspondence between the data collection category and the equipment detection path point is pre-set and data is stored, so when the equipment detection path point is obtained, the corresponding data collection category can be obtained. The setting of the test and detection branch task is achieved by integrating the data collection category and the equipment test and detection area.

[0071] Furthermore, by setting the test and detection branch task, when obtaining the real-time environmental sensing data collected by the power generation equipment inspector when performing the test and detection task in the target detection area, the power generation equipment inspector is instructed through the test and detection branch task, and the time for the power generation equipment inspector to collect each set of data is at least the estimated detection duration. Generally speaking, by adopting multiple sets of data, that is, collecting multiple sets of data with the estimated detection duration as the collection time unit as the real-time environmental sensing data.

[0072] In one embodiment, the equipment test detection area includes a cabin exit area, a cabin top stay area, and a blade body area; the real-time environmental sensing data includes cabin exit sensing data, cabin top sensing data, and blade body sensing data;

[0073] Step S300: generating an estimated environmental interference value of the target detection area within the device detection time period according to the real-time environmental sensing data, including:

[0074] Step S310: extracting wind speed sensing information, temperature sensing information and visibility sensing score from the cabin exit sensing data, wherein the visibility sensing score is a score set by the power generation equipment inspector according to the visibility outside the cabin exit;

[0075] In this step, the wind speed sensing information is the pressure detected outside and inside the cabin outlet, that is, the information can represent the pressure inside and outside the cabin. The temperature sensing information is the temperature data outside the cabin outlet.

[0076] Step S320: extracting the average wind speed inside the cabin and the average wind speed outside the cabin according to the wind speed sensing information, and extracting the real-time sensed temperature of the cabin outlet at each data collection time point, and the maximum sensed temperature and the minimum sensed temperature among the real-time sensed temperatures according to the temperature sensing information, wherein the number of the real-time sensed temperatures is n;

[0077] In this step, the wind speed sensing information is obtained after multiple tests, that is, the wind speed sensing information includes multiple groups of wind speeds inside the cabin and wind speeds outside the cabin. The average value of each wind speed inside the cabin is calculated to generate the average wind speed inside the cabin Vin, and the average value of each pressure outside the cabin is calculated to generate the average wind speed outside the cabin Vout.

[0078] Step S330: obtaining the real-time protective equipment of the power generation equipment inspector, and generating a real-time suitable temperature according to the real-time protective equipment, wherein the real-time suitable temperature is set based on the thickness of the real-time protective equipment of the power generation equipment inspector;

[0079] In this step, the real-time protective equipment is the protective clothing worn by the power generation equipment inspector during the inspection. Protective clothing of different thicknesses gives different body sensations to the power generation equipment inspector. If the protective clothing is too thick, it is easy to cause discomfort after working for a long time. Therefore, a survey and record of multiple subjects wearing different clothes to perform inspection work within a unit working time are conducted in advance, so as to establish a relationship between protective equipment of different thicknesses and the corresponding suitable temperature, wherein the unit working time is preset, generally set to 1 hour. For example, the protective equipment of the first thickness is suitable for working in an environment of 26°. The protective equipment of the second thickness is suitable for working in an environment of 16°. The protective equipment of the third thickness is suitable for working in an environment of 12°. Generally speaking, the unit working time is set to 1 hour, which is a long time. In this application, the inspection time for the components is generally less than 1 hour, that is, the estimated inspection duration is less than 1 hour. At this time, the appropriate working temperature corresponding to 1 hour is pre-calculated, so within 1 hour, the temperature is likely to be acceptable, so the unit working time is set to be greater than the estimated inspection duration.

[0080] Step S340: generating a cabin outlet interference value according to the average wind speed, the average wind speed outside the cabin, the real-time sensed temperature, the maximum sensed temperature, the minimum sensed temperature and the real-time suitable temperature;

[0081] Step S350: obtaining top dust accumulation information, top slope information and top wind speed information from the cabin top sensing data, and generating a cabin top interference value according to the top dust accumulation information, top slope information and top wind speed information;

[0082] Step S360: extracting blade wind speed information from the blade body sensing data, and generating a blade environmental interference value according to the blade wind speed information;

[0083] Step S370: obtaining the estimated weather data for the equipment detection time period, and generating an environmental difference coefficient according to the difference between the estimated weather data and the test detection weather data, wherein the test detection weather data is the weather data when the test detection task is performed;

[0084] In this step, in order to determine whether the external environment during the equipment detection time period is suitable for detection work, it is necessary to compare the weather data during the equipment detection time period with the data obtained during the test to obtain the difference between the two, so as to use the actual data obtained during the test as a reference to make an estimate of the actual detection in the future, and because the detection time is close to the time example of the equipment detection time period, the accuracy of the predicted data is higher. Specifically, the estimated weather data of the equipment detection time period is first obtained, and then the two are compared. After comparison, the difference between the estimated weather data and the test detection weather data is obtained. According to the percentage of the difference, a coefficient can be generated to represent the difference between the two environmental data. Specifically, the environmental difference coefficient is generated according to the difference between the estimated weather data and the test detection weather data. For example, if the wind speed during the test is level 3, if the estimated weather data shows that the wind speed suddenly increases to level 6 during the equipment detection time period, the corresponding environmental difference coefficient has a first value. If the estimated weather data shows that the wind speed suddenly increases to level 9 during the equipment detection time period, the corresponding environmental difference coefficient has a second value. The second value is greater than the first value. Generally speaking, the second value is at least 2 times the first value. Of course, the generation of the environmental difference coefficient does not rely solely on one environmental parameter. The above is only an example and is not a specific limitation. When actually generating the environmental difference coefficient, multiple environmental parameters can be weighted, and each environmental parameter can be compared with the test weather data to generate a comparison coefficient, and then the comparison coefficients are added together to finally generate the environmental difference coefficient.

[0085] Step S380: Generate an estimated environmental interference value EDs based on the following formula according to the cabin outlet interference value Dr, the cabin top interference value Dc, the blade environmental interference value Db and the environmental difference coefficient Ce:

[0086] EDs = Ce (Dr + Dc + Db);

[0087] Among them, EDs is the estimated environmental interference value, Ce is the environmental difference coefficient, Dr is the cabin outlet interference value, Dc is the cabin top interference value, and Db is the blade environmental interference value.

[0088] In this embodiment, in order to improve the accuracy and reference value of the estimated environmental interference value, wind speed sensing information, temperature sensing information and visibility sensing score are first extracted from the cabin outlet sensing data, and then the average wind speed inside the cabin and the average wind speed outside the cabin are extracted according to the wind speed sensing information, and the real-time sensing temperature of the cabin outlet at each data collection time point, as well as the maximum sensing temperature and the minimum sensing temperature of each real-time sensing temperature are extracted according to the temperature sensing information, and then the real-time protective equipment of the power generation equipment inspection personnel is obtained, and the real-time suitable temperature is generated as a reference according to the real-time protective equipment, and the cabin outlet interference value is generated according to the average wind speed, the average wind speed outside the cabin, the real-time sensing temperature, the maximum sensing temperature, the minimum sensing temperature and the real-time suitable temperature, and then the cabin top interference value, the blade environmental interference value and the environmental difference coefficient are generated respectively, and finally the estimated environmental interference value is generated by combining the above data, so that the estimated environmental interference value includes as much environmental interference that may be encountered in the entire maintenance path as possible to improve the reference value.

[0089] In one embodiment, the cabin exit interference value is generated based on the following formula:

[0090]

[0091] Among them, Dr is the cabin exit interference value, β1 is the hatch interference coefficient, Vout is the average wind speed outside the cabin, Vin is the average wind speed inside the cabin, Vs is the standard wind speed difference, β2 is the temperature interference coefficient, α1 is the first temperature interference coefficient, Ti is the i-th real-time sensed temperature, Ts is the real-time suitable temperature, α2 is the second temperature interference coefficient, Tmax is the maximum sensed temperature, Tmin is the minimum sensed temperature, and Vb is the visibility sensing score.

[0092] In one embodiment, in step S350, generating a cabin top interference value according to the top dust accumulation information, the top slope information and the top wind speed information includes:

[0093] Step S351: extracting the estimated dust accumulation thickness and the top surface humidity according to the top dust accumulation information, and generating the estimated walking slope according to the top slope information;

[0094] Step S352: extracting the top real-time wind speed at each data collection time point according to the top wind speed information, generating the top average wind speed according to each of the top real-time wind speeds, and generating the wind disturbance coefficient according to the top average wind speed;

[0095] In this step, the top average wind speed is positively correlated with the wind disturbance coefficient, that is, the greater the top average wind speed, the greater the wind disturbance coefficient. The detection personnel measure the interference under different wind speeds in advance and generate the interference coefficient, so the corresponding relationship between wind speed and interference effect can be set. Therefore, after obtaining the top average wind speed, the wind disturbance coefficient can be generated.

[0096] Step S353: Generate the cabin top interference value based on the following formula according to the estimated dust accumulation thickness, top surface humidity, estimated walking slope and wind disturbance coefficient:

[0097]

[0098] Among them, Dc is the interference value on the top of the cabin, δ is the wind disturbance coefficient, Th is the estimated dust accumulation thickness, Ths is the preset safe dust accumulation thickness, Hu is the top surface humidity, Hus is the preset safe surface humidity, and Sp is the estimated walking slope.

[0099] In this step, the safety dust thickness and the safety surface humidity are both pre-determined and set based on the protective shoes that the power generation equipment inspectors are uniformly equipped with. By setting the safety dust thickness and the safety surface humidity, a reference is provided to better measure the current slippery degree of the cabin top. The estimated walking slope is measured by the power generation equipment inspector using a mini level. Of course, the ratio can also be set based on the experience of the power generation equipment inspector, but the accuracy is lower. Generally speaking, measurements have been taken when assembling wind power equipment, so only data extraction is required at this time.

[0100] In this embodiment, the estimated dust accumulation thickness and the top surface humidity are first extracted according to the top dust accumulation information, and the estimated walking slope is generated according to the top slope information, and the real-time wind speed at the top at each data collection time point is extracted, and the top average wind speed is generated according to each of the real-time wind speeds, and the wind disturbance coefficient is generated according to the top average wind speed, thereby achieving the generation of the cabin top interference value after integrating the estimated dust accumulation thickness, top surface humidity, estimated walking slope and wind disturbance coefficient, and then realizing the digitized measurement of the interference received when operating on the cabin top.

[0101] In one embodiment, in step S360, generating a blade environmental interference value according to the blade wind speed information includes:

[0102] In this embodiment, the real-time wind speed of the blade area contained in the blade wind speed information is calculated to average the blade area wind speed, and the blade area wind speed average value Vp is obtained. Then, the actual weight M, estimated windward area A and normal detection posture of the power generation equipment inspector are obtained, and the drag coefficient Cd is generated according to the normal detection posture.

[0103] Next, the blade environmental interference value is generated based on the following formula:

[0104] FA=M*Cd*A*ρ*Vp 2 ;

[0105] Among them, FA is the blade environmental interference value, M is the actual weight of the power generation equipment inspector, Cd is the drag coefficient, A is the estimated windward area, Vp is the average wind speed in the blade area, and ρ is the air density.

[0106] In this embodiment, the blade environmental interference value is generated by integrating the blade environmental interference value, the actual weight of the power generation equipment inspector, the drag coefficient, the estimated windward area and the average wind speed in the blade area.

[0107] In one embodiment, in step S100, selecting a power generation equipment inspection personnel according to the power generation equipment inspection task includes:

[0108] In this embodiment, the inspection difficulty of the power generation equipment inspection task is first obtained, and inspection personnel who have handled the inspection difficulty are selected based on the inspection difficulty. Then, inspection personnel who have no work arrangements within the equipment inspection time period are selected, and the personnel finally determined are the power generation equipment inspection personnel.

[0109] In one embodiment, the steps of setting up the power generation detection model include:

[0110] First, collect the conventional operation data of the conventional time period of the wind power equipment that has been disclosed in various regions. Among them, the conventional operation data includes: wind speed and wind direction, blade speed, main shaft, vibration data of the generator, temperature, humidity, pressure, historical faults and maintenance records of power generation output power. Then, extract features, including vibration amplitude features, frequency features, wind speed fluctuations and frequency domain features. Then based on LSTM (Long Short-Term Memory Network) or Convolutional Neural Network (CNN). Then, divide the data into training set, verification set and test set. Then, train the model with the training set, and adjust and verify the parameters with the verification set. According to the evaluation results of the model, continuously optimize, such as through feature importance evaluation, eliminate irrelevant or redundant features. Alternatively, adjust hyperparameters through grid search or Bayesian optimization to improve the accuracy of the model. Finally, set the trained model in the monitoring system of the wind power equipment, analyze the operation data in real time, predict the components that need to be detected or repaired, and use it to generate power generation equipment detection tasks and equipment detection time periods according to the historical power generation operation data of the power generation equipment to be detected.

[0111] like Figure 2 As shown, a power generation equipment detection system based on a large model is also provided, the system comprising:

[0112] A maintenance task setting module is used to generate power generation equipment inspection tasks and equipment inspection time periods based on a preset power generation inspection large model and according to the historical power generation operation data of the power generation equipment to be inspected, and select power generation equipment inspection personnel according to the power generation equipment inspection tasks;

[0113] A real-time data sensing module, used to set a test detection task according to the power generation equipment detection task and the power generation equipment to be detected, and obtain real-time environmental sensing data collected when the power generation equipment detection personnel perform the test detection task in the target detection area, wherein the target detection area is the area where the power generation equipment to be detected is located;

[0114] An interference data generating module, used to generate an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the maintenance personnel of the power generation equipment during the equipment detection time period;

[0115] The detection task execution module is used to determine whether the estimated environmental interference value is greater than or equal to a preset environmental interference safety threshold. If it is determined to be yes, a detection task correction instruction is generated, and the power generation equipment inspection personnel are prompted to adjust the inspection time according to the detection task correction instruction.

[0116] In another embodiment, the real-time data sensing module is further used for:

[0117] According to the power generation equipment detection task, the power generation component to be detected and the estimated detection duration are obtained; according to the power generation component to be detected and the basic equipment structure of the equipment to be detected, the detection task execution path is generated, wherein the detection task execution path includes multiple equipment detection path points; according to the equipment detection path points, an equipment test and detection area is generated, wherein one equipment detection path point corresponds to one equipment test and detection area; the data collection category corresponding to each of the equipment detection path points is obtained, and the test and detection branch task is set according to the data collection category and the equipment test and detection area, wherein one equipment test and detection area corresponds to one test and detection branch task.

[0118] In another embodiment, the equipment test detection area includes a cabin exit area, a cabin top stay area, and a blade body area; the real-time environment sensing data includes cabin exit sensing data, cabin top sensing data, and blade body sensing data; and the interference data generation module is further used to:

[0119] Extract wind speed sensing information, temperature sensing information and visibility sensing score from the cabin exit sensing data, wherein the visibility sensing score is a score set by the power generation equipment inspector according to the visibility outside the cabin exit; extract the average wind speed inside the cabin and the average wind speed outside the cabin according to the wind speed sensing information, and extract the real-time sensing temperature of the cabin exit at each data collection time point according to the temperature sensing information, as well as the maximum sensing temperature and the minimum sensing temperature among the real-time sensing temperatures, wherein the number of the real-time sensing temperatures is n; obtain the real-time protective equipment of the power generation equipment inspector, and generate a real-time suitable temperature according to the real-time protective equipment, wherein the real-time suitable temperature is based on the real-time protective equipment of the power generation equipment inspector. The thickness of the equipment is set; the cabin outlet interference value is generated according to the average wind speed, the average wind speed outside the cabin, the real-time sensed temperature, the maximum sensed temperature, the minimum sensed temperature and the real-time suitable temperature; the top dust accumulation information, the top slope information and the top wind speed information in the cabin top sensing data, and the cabin top interference value is generated according to the top dust accumulation information, the top slope information and the top wind speed information; the blade wind speed information is extracted from the blade body sensing data, and the blade environment interference value is generated according to the blade wind speed information; the estimated weather data of the equipment detection time period is obtained, and the environmental difference coefficient is generated according to the difference between the estimated weather data and the test detection weather data, wherein the test detection weather data is the weather data when the test detection task is performed;

[0120] According to the cabin outlet interference value Dr, the cabin top interference value Dc, the blade environment interference value Db and the environmental difference coefficient Ce, the estimated environmental interference value EDs is generated based on the following formula:

[0121] EDs = Ce (Dr + Dc + Db);

[0122] Among them, EDs is the estimated environmental interference value, Ce is the environmental difference coefficient, Dr is the cabin outlet interference value, Dc is the cabin top interference value, and Db is the blade environmental interference value.

[0123] The interference data generation module is further used to generate a cabin exit interference value based on the following formula:

[0124]

[0125] Among them, Dr is the cabin exit interference value, β1 is the hatch interference coefficient, Vout is the average wind speed outside the cabin, Vin is the average wind speed inside the cabin, Vs is the standard wind speed difference, β2 is the temperature interference coefficient, α1 is the first temperature interference coefficient, Ti is the i-th real-time sensed temperature, Ts is the real-time suitable temperature, α2 is the second temperature interference coefficient, Tmax is the maximum sensed temperature, Tmin is the minimum sensed temperature, and Vb is the visibility sensing score.

[0126] In another embodiment, the interference data generation module is further used to: extract the estimated dust accumulation thickness and the top surface humidity according to the top dust accumulation information, and generate the estimated walking slope according to the top slope information; extract the top real-time wind speed at each data collection time point according to the top wind speed information, generate the top average wind speed according to each top real-time wind speed, and generate the wind disturbance coefficient according to the top average wind speed; generate the cabin top interference value based on the following formula according to the estimated dust accumulation thickness, top surface humidity, estimated walking slope and wind disturbance coefficient:

[0127]

[0128] Among them, Dc is the interference value on the top of the cabin, δ is the wind disturbance coefficient, Th is the estimated dust accumulation thickness, Ths is the preset safe dust accumulation thickness, Hu is the top surface humidity, Hus is the preset safe surface humidity, and Sp is the estimated walking slope.

[0129] In another embodiment, the interference data generation module is also used to: calculate the average value of the real-time wind speed of the blade area contained in the blade wind speed information, and obtain the average wind speed value Vp of the blade area, and then obtain the actual weight M, estimated windward area A and normal detection posture of the power generation equipment inspector, and generate the drag coefficient Cd according to the normal detection posture.

[0130] Next, the blade environmental interference value is generated based on the following formula:

[0131] FA=M*Cd*A*ρ*Vp 2 ;

[0132] Among them, FA is the blade environmental interference value, M is the actual weight of the power generation equipment inspector, Cd is the drag coefficient, A is the estimated windward area, Vp is the average wind speed in the blade area, and ρ is the air density.

[0133] In another embodiment, the maintenance task setting module is also used to: obtain the inspection difficulty of the power generation equipment inspection task, and select inspection personnel who have handled the inspection difficulty according to the inspection difficulty, and then select inspection personnel who have no work arrangements during the equipment inspection time period, and the finally determined personnel are the power generation equipment inspection personnel.

[0134] In another embodiment, the maintenance task setting module is also used to collect conventional operation data of wind power equipment disclosed in various regions during conventional time periods. Wherein, the conventional operation data includes: wind speed and wind direction, blade speed, main shaft, vibration data of generator, temperature, humidity, pressure, historical faults and maintenance records of power generation output power. Then, extract features, including vibration amplitude features, frequency features, wind speed fluctuations and frequency domain features. Then based on LSTM (Long Short-Term Memory Network) or Convolutional Neural Network (CNN). Then, divide the data into training set, verification set and test set. Then, train the model using the training set, and adjust and verify the parameters using the verification set. According to the evaluation results of the model, continuously optimize, such as through feature importance evaluation, eliminate irrelevant or redundant features. Alternatively, adjust hyperparameters through grid search or Bayesian optimization to improve the accuracy of the model. Finally, set the trained model in the monitoring system of the wind power equipment, analyze the operation data in real time, predict the components that need to be detected or repaired, and use it to generate power generation equipment detection tasks and equipment detection time periods according to the historical power generation operation data of the power generation equipment to be detected.

[0135] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned large-model-based power generation equipment detection method when executing the computer program.

[0136] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned large-model-based power generation equipment detection method are implemented.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A large model-based power generation equipment detection method, characterized in that: The method comprises: Based on the preset power generation detection model, the power generation equipment detection task and equipment detection time period are generated according to the historical power generation operation data of the power generation equipment to be detected, and the power generation equipment detection personnel are selected according to the power generation equipment detection task; A test detection task is set according to the power generation equipment detection task and the power generation equipment to be detected, and real-time environmental sensing data collected by the power generation equipment detection personnel when performing the test detection task in the target detection area is obtained, wherein the target detection area is the area where the power generation equipment to be detected is located; Generate an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the maintenance personnel of the power generation equipment during the equipment detection time period; It is determined whether the estimated environmental interference value is greater than or equal to a preset environmental interference safety threshold. If so, a detection task correction instruction is generated, and the power generation equipment inspection personnel are prompted to adjust the inspection time according to the detection task correction instruction.

2. The large model-based power generation equipment detection method according to claim 1, characterized in that: The test detection task is set according to the power generation equipment detection task and the power generation equipment to be detected, including: Acquire the power generation components to be inspected and the estimated inspection duration according to the power generation equipment inspection task; Generate a detection task execution path according to the power generation component to be detected and the basic device structure of the device to be detected, wherein the detection task execution path includes a plurality of device detection path points; Generating a device test detection area according to the device detection path point, wherein one device detection path point corresponds to one device test detection area; The data collection category corresponding to each of the equipment detection route points is obtained, and a test detection branch task is set according to the data collection category and the equipment test detection area, wherein one equipment test detection area corresponds to one test detection branch task.

3. The large model-based power generation equipment detection method according to claim 2, characterized in that: The equipment test detection area includes the cabin exit area, the cabin top stay area and the blade body area; the real-time environmental sensing data includes cabin exit sensing data, cabin top sensing data and blade body sensing data; Generating an estimated environmental interference value of the target detection area within the device detection time period according to the real-time environmental sensing data, including: Extracting wind speed sensing information, temperature sensing information and visibility sensing score from the cabin exit sensing data, wherein the visibility sensing score is a score set by the power generation equipment inspector according to the visibility outside the cabin exit; Extracting the average wind speed inside the cabin and the average wind speed outside the cabin according to the wind speed sensing information, and extracting the real-time sensed temperature of the cabin outlet at each data collection time point, as well as the maximum sensed temperature and the minimum sensed temperature among the real-time sensed temperatures according to the temperature sensing information, wherein the number of the real-time sensed temperatures is n; Acquire the real-time protective equipment of the power generation equipment inspector, and generate a real-time suitable temperature according to the real-time protective equipment, wherein the real-time suitable temperature is set based on the thickness of the real-time protective equipment of the power generation equipment inspector; Generate a cabin exit interference value according to the average wind speed, the average wind speed outside the cabin, the real-time sensed temperature, the maximum sensed temperature, the minimum sensed temperature and the real-time suitable temperature; top dust accumulation information, top slope information and top wind speed information from the cabin top sensing data, and generate a cabin top interference value according to the top dust accumulation information, top slope information and top wind speed information; Extracting blade wind speed information from the blade body sensing data, and generating a blade environmental interference value according to the blade wind speed information; Obtaining the estimated weather data for the equipment detection time period, and generating an environmental difference coefficient according to the difference between the estimated weather data and the test detection weather data, wherein the test detection weather data is the weather data when the test detection task is performed; According to the cabin outlet interference value Dr, the cabin top interference value Dc, the blade environment interference value Db and the environmental difference coefficient Ce, the estimated environmental interference value EDs is generated based on the following formula: EDs = Ce (Dr + Dc + Db); Among them, EDs is the estimated environmental interference value, Ce is the environmental difference coefficient, Dr is the cabin outlet interference value, Dc is the cabin top interference value, and Db is the blade environmental interference value.

4. The large model-based power generation equipment detection method according to claim 3, characterized in that: The cabin exit interference value is generated based on the following formula: Among them, Dr is the cabin exit interference value, β1 is the hatch interference coefficient, Vout is the average wind speed outside the cabin, Vin is the average wind speed inside the cabin, Vs is the standard wind speed difference, β2 is the temperature interference coefficient, α1 is the first temperature interference coefficient, Ti is the i-th real-time sensed temperature, Ts is the real-time suitable temperature, α2 is the second temperature interference coefficient, Tmax is the maximum sensed temperature, Tmin is the minimum sensed temperature, and Vb is the visibility sensing score.

5. The large model-based power generation equipment detection method according to claim 3, characterized in that: Generating a cabin top interference value according to the top dust accumulation information, the top slope information and the top wind speed information includes: Extracting the estimated dust accumulation thickness and the top surface humidity according to the top dust accumulation information, and generating the estimated walking slope according to the top slope information; Extract the top real-time wind speed at each data collection time point according to the top wind speed information, generate the top average wind speed according to each top real-time wind speed, and generate the wind disturbance coefficient according to the top average wind speed; The disturbance value on the top of the cabin is generated based on the estimated thickness of dust accumulation, top surface humidity, estimated walking slope and wind disturbance coefficient.

6. A power generation equipment detection system based on a large model, characterized in that: The system comprises: A maintenance task setting module is used to generate power generation equipment inspection tasks and equipment inspection time periods based on a preset power generation inspection large model and according to the historical power generation operation data of the power generation equipment to be inspected, and select power generation equipment inspection personnel according to the power generation equipment inspection tasks; A real-time data sensing module, used to set a test detection task according to the power generation equipment detection task and the power generation equipment to be detected, and obtain real-time environmental sensing data collected when the power generation equipment detection personnel perform the test detection task in the target detection area, wherein the target detection area is the area where the power generation equipment to be detected is located; An interference data generating module, used to generate an estimated environmental interference value of the target detection area within the equipment detection time period according to the real-time environmental sensing data, wherein the estimated environmental interference value is used to indicate the degree of interference of the external environment to the maintenance personnel of the power generation equipment during the equipment detection time period; The detection task execution module is used to determine whether the estimated environmental interference value is greater than or equal to a preset environmental interference safety threshold. If it is determined to be yes, a detection task correction instruction is generated, and the power generation equipment inspection personnel are prompted to adjust the inspection time according to the detection task correction instruction.

7. The large model-based power generation equipment detection system according to claim 6, characterized in that: The real-time data sensing module is also used for: According to the power generation equipment detection task, the power generation component to be detected and the estimated detection duration are obtained; according to the power generation component to be detected and the basic equipment structure of the equipment to be detected, the detection task execution path is generated, wherein the detection task execution path includes multiple equipment detection path points; according to the equipment detection path points, an equipment test and detection area is generated, wherein one equipment detection path point corresponds to one equipment test and detection area; the data collection category corresponding to each of the equipment detection path points is obtained, and the test and detection branch task is set according to the data collection category and the equipment test and detection area, wherein one equipment test and detection area corresponds to one test and detection branch task.

8. The large model-based power generation equipment detection system according to claim 7, characterized in that: The equipment test detection area includes a cabin exit area, a cabin top stay area and a blade body area; the real-time environment sensing data includes cabin exit sensing data, cabin top sensing data and blade body sensing data; the interference data generation module is also used to: Extract wind speed sensing information, temperature sensing information and visibility sensing score from the cabin exit sensing data, wherein the visibility sensing score is a score set by the power generation equipment inspector according to the visibility outside the cabin exit; extract the average wind speed inside the cabin and the average wind speed outside the cabin according to the wind speed sensing information, and extract the real-time sensing temperature of the cabin exit at each data collection time point according to the temperature sensing information, as well as the maximum sensing temperature and the minimum sensing temperature among the real-time sensing temperatures, wherein the number of the real-time sensing temperatures is n; obtain the real-time protective equipment of the power generation equipment inspector, and generate a real-time suitable temperature according to the real-time protective equipment, wherein the real-time suitable temperature is based on the real-time protective equipment of the power generation equipment inspector. The thickness of the equipment is set; the cabin outlet interference value is generated according to the average wind speed, the average wind speed outside the cabin, the real-time sensed temperature, the maximum sensed temperature, the minimum sensed temperature and the real-time suitable temperature; the top dust accumulation information, the top slope information and the top wind speed information in the cabin top sensing data, and the cabin top interference value is generated according to the top dust accumulation information, the top slope information and the top wind speed information; the blade wind speed information is extracted from the blade body sensing data, and the blade environment interference value is generated according to the blade wind speed information; the estimated weather data of the equipment detection time period is obtained, and the environmental difference coefficient is generated according to the difference between the estimated weather data and the test detection weather data, wherein the test detection weather data is the weather data when the test detection task is performed; According to the cabin outlet interference value Dr, the cabin top interference value Dc, the blade environment interference value Db and the environmental difference coefficient Ce, the estimated environmental interference value EDs is generated based on the following formula: EDs = Ce (Dr + Dc + Db); Among them, EDs is the estimated environmental interference value, Ce is the environmental difference coefficient, Dr is the cabin outlet interference value, Dc is the cabin top interference value, and Db is the blade environmental interference value.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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