A new energy power station management system based on user feedback

Abstract

The present invention relates to the field of patrol technology, and in particular to a new energy power station management system based on user feedback. The system includes a user feedback module, a patrol monitoring module, a data processing module, and a data analysis module. The present invention establishes a three-dimensional environmental model to assist patrol equipment in quickly finding detection points, avoiding missed detection and false detection, and automatically collects patrol data through intelligent patrol equipment to collect patrol environment information, thereby reducing the time and labor costs of manual patrol and improving patrol efficiency. By analyzing, understanding, and processing the acquired patrol environment images, obstacles in the patrol route are accurately identified, and the current patrol environment is judged based on the real-time obstacle ratio, thereby improving the accuracy of anomaly detection. The patrol route is dynamically adjusted according to real-time patrol data and user feedback, ensuring that the patrol equipment completes the patrol task safely and efficiently in a complex environment, generating work orders and assigning them to the corresponding processing team, and improving management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of patrol inspection technology, and in particular to a new energy power station management system based on user feedback. Background Art

[0002] Currently, new energy power stations primarily utilize photovoltaic power generation and wind power generation. For photovoltaic power stations, multiple string-type photovoltaic power generation systems are typically deployed on the ground. While this type of station has a simple structure and low maintenance costs, it can be prone to overheating, undervoltage, and unstable output power in some components when sunlight is poor. To ensure the safe and stable operation of new energy power stations, regular inspections are necessary. A common inspection method involves manual inspections by maintenance personnel, often equipped with specialized inspection tools, at each site. However, the lack of intelligent methods and the subjectivity of manual inspections can easily lead to missed inspections or missed inspections. Therefore, inspections at new energy power stations are increasingly being integrated with automated and intelligent technologies, creating safer inspection techniques.

[0003] A Chinese patent document with publication number CN115018434A discloses a remote operation and maintenance management system for new energy power stations, which includes a data layer, a service layer, a business layer, and a display layer. It is connected to the monitoring system through an API interface to obtain data related to operation and maintenance work, and all operation and maintenance management work is managed online. The platform solves the problem of difficult information transmission, and automatically distributes and pushes operation and maintenance work in the form of task management, and displays the status of the task execution process and automatically issues alarm reminders. It can be seen that the existing digital construction of new energy power stations lacks automation technology to simplify daily management work, and lacks optimization of inspection processes based on user feedback, which makes the management efficiency of new energy power stations low. Summary of the Invention

[0004] To this end, the present invention provides a new energy power station management system based on user feedback to overcome the lack of real-time analysis of the inspection environment of each monitoring point in the existing technology, and optimizes the inspection route in combination with user feedback, making it easy for inspections to cause false detections and missed detections, resulting in low inspection efficiency.

[0005] To achieve the above objectives, the present invention provides a new energy power station management system based on user feedback, comprising:

[0006] A user feedback module, which is connected to the user terminal and is used to receive feedback data submitted by the user, wherein the feedback data includes device operation data;

[0007] A patrol monitoring module, which includes several intelligent patrol devices and is used to automatically collect patrol data, including patrol route images;

[0008] a data processing module connected to the inspection monitoring module, configured to identify obstacles in the inspection route based on the inspection route image, determine the current inspection environment based on the real-time obstacle ratio, and determine abnormal conditions of the inspection route based on the current inspection environment;

[0009] A data analysis module is connected to the user feedback module and the inspection and monitoring module respectively, and is used to classify and prioritize the feedback data, generate work orders, and determine whether to update the work orders based on abnormal conditions of the inspection routes.

[0010] Furthermore, the data processing module includes an identification unit, an environment perception unit and an abnormality monitoring unit, wherein:

[0011] The recognition unit is used to extract features from the inspection route image and match the environment model with the features to identify obstacles in the inspection route;

[0012] The environment perception unit is used to determine the real-time obstacle ratio in the inspection route image, and determine the current inspection environment based on the real-time obstacle ratio;

[0013] The abnormality monitoring unit is used to determine whether to mark a current detection point as an abnormal detection point based on the current inspection environment, and to determine the abnormality of the current inspection route according to the abnormal detection point.

[0014] Furthermore, the recognition unit includes a modeling subunit, a feature extraction subunit, a feature matching subunit and a comparison subunit, wherein:

[0015] The modeling subunit is used to generate a 3D environment model using a laser radar;

[0016] The feature extraction subunit is used to extract features from the inspection route image to obtain a feature descriptor;

[0017] The feature matching subunit is used to calculate the distance between feature descriptors, calculate the distance between each pair of feature descriptors, and select the feature descriptor with the smallest distance as the matching feature;

[0018] The comparison subunit is used to compare the matched features with the geometric shapes of obstacles in the environment model to identify obstacles.

[0019] Furthermore, the environment perception unit includes an acquisition subunit and a determination subunit, wherein:

[0020] The acquisition subunit is used to calculate the percentage of the number of obstacle pixels in any of the inspection route images to the total number of pixels to obtain a real-time obstacle ratio;

[0021] The determination subunit determines the real-time obstacle ratio according to the standard obstacle ratio range, determines the current inspection environment as the first inspection environment according to the determination result, and determines the current inspection environment as the first inspection environment or the second inspection environment in combination with the image stability analysis result;

[0022] The current inspection environment includes a first inspection environment and a second inspection environment.

[0023] Furthermore, the determination subunit determines that the current inspection environment is the first inspection environment when the real-time obstacle ratio is less than or equal to the minimum value of the standard obstacle ratio range;

[0024] When the real-time obstacle ratio is within the standard obstacle ratio range, the current inspection environment is determined in combination with the image stability analysis result; when the real-time obstacle ratio is greater than or equal to the maximum value of the standard obstacle ratio range, the current inspection environment is determined to be the second inspection environment;

[0025] When the first analysis result is obtained, determining that the current inspection environment is the second inspection environment;

[0026] When the second analysis result is obtained, it is determined that the current inspection environment is the first inspection environment.

[0027] Furthermore, the data processing module also includes a stability analysis unit,

[0028] The stability analysis unit is used to perform stability analysis on the inspection route image to obtain a stability analysis result, which includes a first analysis result and a second analysis result.

[0029] Furthermore, the stability analysis unit includes an acquisition subunit, a calculation subunit and a first analysis subunit, wherein:

[0030] The acquisition subunit is used to obtain the current inspection route image and the previous inspection route image, and use the current inspection route image as the current frame and the previous inspection route image as the reference frame;

[0031] The calculation subunit is used to track the pixel motion between two frames, obtain the motion rate vector, the feature point and the pixel displacement change corresponding to the feature point;

[0032] The first analysis subunit is used to analyze the pixel displacement variation to determine whether jitter exists and obtain a first analysis result.

[0033] Furthermore, the stability analysis unit further includes a second analysis subunit and a third analysis subunit, wherein,

[0034] The second analysis subunit is used to analyze the background complexity of the current inspection image;

[0035] The third analysis subunit is configured to determine a stability analysis result in combination with background complexity when determining that no jitter exists.

[0036] Furthermore, the abnormality monitoring unit includes a marking subunit and a determining subunit, wherein:

[0037] The marking subunit is used to mark the current detection point as an abnormal detection point when the current inspection environment is the second inspection environment;

[0038] The detection points correspond one-to-one to the components of the new energy power station;

[0039] The determination subunit is used to calculate the percentage of abnormal detection points to the total detection points to obtain a real-time abnormality ratio, compare the allowed abnormality ratio with the real-time abnormality ratio, and determine the abnormality of the inspection route based on the comparison result;

[0040] Among them, when the real-time abnormality ratio is less than or equal to the allowed abnormality ratio, the inspection route is judged to be normal; when the real-time abnormality ratio is greater than the allowed abnormality ratio, the inspection route is judged to be abnormal.

[0041] Furthermore, the data analysis module includes a work order generation unit and a work order update unit, wherein:

[0042] The work order generating unit is used to classify user feedback according to feedback type, and prioritize feedback data according to the urgency of the feedback, and generate a number of work orders;

[0043] The work order updating unit is used to update the inspection route corresponding to the work order.

[0044] Compared with the existing technology, the beneficial effect of the present invention lies in that, by establishing a three-dimensional environmental model, it assists patrol equipment in quickly finding detection points, avoiding missed detections and false detections, and automatically collects patrol data through intelligent patrol equipment to collect patrol environment information and equipment status information, thereby reducing the time and labor costs of manual patrols and improving patrol efficiency. By analyzing, understanding and processing the acquired patrol environment images, obstacles in the patrol route can be accurately identified, and the current patrol environment can be judged based on the real-time obstacle ratio, thereby improving the accuracy of anomaly detection. The patrol route can be dynamically adjusted according to real-time patrol data and user feedback, ensuring that patrol equipment can complete patrol tasks safely and efficiently in complex environments, generating work orders and assigning them to corresponding processing teams, and improving management efficiency.

[0045] Furthermore, when it is determined that the real-time obstacle ratio is less than or equal to the minimum value of the standard obstacle ratio range, it means that there are no obstacles in the current inspection route image. Since there are no obstacles, the background complexity is low, and it is a simple ground or single background feature. In this environment, it is easy to perceive and identify, which is suitable for drone inspection. When the real-time obstacle ratio is greater than or equal to the maximum value of the standard obstacle ratio range, it means that most areas are occupied by obstacles, indicating that there are a lot of objects in the environment, which will significantly affect the inspection path and perception ability of the drone. When the real-time obstacle ratio is within the standard obstacle ratio range, it means that there are some objects in the environment, which may affect the path planning and perception ability of the drone. The current inspection environment is determined by combining the background complexity in the inspection route image to improve the accuracy of understanding the inspection environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of the structure of a new energy power station management system based on user feedback according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the structure of a data processing module according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic structural diagram of an identification unit according to an embodiment of the present invention;

[0049] Figure 4 Schematic diagram of the structure of the environment perception unit according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0053] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0054] See also Figure 1 As shown, it is a structural diagram of a new energy power station management system based on user feedback according to an embodiment of the present invention. The present invention provides a new energy power station management system based on user feedback, including:

[0055] A user feedback module is connected to the user terminal and is used to receive feedback data submitted by the user, wherein the feedback data includes device operation data, and the device operation data includes image data and video data;

[0056] A patrol monitoring module, which includes several intelligent patrol devices and is used to automatically collect patrol data, including patrol route images;

[0057] a data processing module connected to the inspection monitoring module, configured to identify obstacles in the inspection route based on the inspection route image, determine the current inspection environment based on the real-time obstacle ratio, and determine abnormal conditions of the inspection route based on the current inspection environment;

[0058] A data analysis module is connected to the user feedback module and the inspection and monitoring module respectively, and is used to classify and prioritize the feedback data, generate work orders, and determine whether to update the work orders based on abnormal conditions of the inspection routes.

[0059] In this embodiment, the user feedback module is a mobile application that can record inspection data, upload pictures and videos, and submit feedback. The intelligent inspection equipment includes drones, ground robots, and fixed monitoring equipment. The drones are equipped with high-definition cameras, thermal imagers, and environmental sensors for automatic ground inspections; robots are used for ground inspections and can carry various detection equipment, such as infrared cameras and vibration sensors; fixed monitoring equipment is installed at key locations in the power station to continuously monitor environmental parameters and equipment status; a three-dimensional model is established for the inspection scene, and then the model is updated. Each component in the photovoltaic power station is regarded as an independent module. By classifying the modules, the new energy power station can quickly find the required components during the inspection. During the inspection process, the relevant information of each new energy power station component needs to be obtained from the database and input into the corresponding module; each component has its own unique parameter information and related model; for example, for circular components, it can be modeled using the quaternion method; for square and rectangular components, the quaternion method can be used to establish their models; for triangular components, the Euler method or the quadrilateral method can be used to establish their models. When establishing the environmental model, some feature data and corresponding environmental parameters also need to be determined. During inspections, the connection relationships between different components can be identified to determine whether the components are operating normally. Simulation analysis of new energy power station components can also be performed to determine the working status of each component.

[0060] By establishing a three-dimensional environmental model, auxiliary inspection equipment can quickly find the inspection point to avoid missed inspections and false inspections. Intelligent inspection equipment can automatically collect inspection data to collect inspection environment information and equipment status information, reduce the time and manpower costs of manual inspections, and improve inspection efficiency. By analyzing, understanding and processing the obtained inspection environment images, obstacles in the inspection route can be accurately identified, and the current inspection environment can be judged based on the real-time obstacle ratio to improve the accuracy of anomaly detection. The inspection route can be dynamically adjusted according to real-time inspection data and user feedback to ensure that the inspection equipment can complete the inspection task safely and efficiently in a complex environment. Work orders can be generated and assigned to the corresponding processing team to improve management efficiency.

[0061] See Figure 2 , which is a structural diagram of a data processing module according to an embodiment of the present invention;

[0062] Specifically, the data processing module includes an identification unit, an environment perception unit and an abnormality monitoring unit, wherein:

[0063] The recognition unit is used to extract features from the inspection route image and match the environment model with the features to identify obstacles in the inspection route;

[0064] The environment perception unit is used to determine the real-time obstacle ratio in the inspection route image, and determine the current inspection environment based on the real-time obstacle ratio;

[0065] The abnormality monitoring unit is used to determine whether to mark a current detection point as an abnormal detection point based on the current inspection environment, and to determine the abnormality of the current inspection route according to the abnormal detection point.

[0066] See Figure 3 , which is a schematic structural diagram of an identification unit according to an embodiment of the present invention;

[0067] Specifically, the recognition unit includes a modeling subunit, a feature extraction subunit, a feature matching subunit and a comparison subunit, wherein:

[0068] The modeling subunit is used to generate a 3D environment model using a laser radar;

[0069] The feature extraction subunit is used to extract features from the inspection route image to obtain a feature descriptor;

[0070] The feature matching subunit is used to calculate the distance between feature descriptors, calculate the distance between each pair of feature descriptors, and select the feature descriptor with the smallest distance as the matching feature;

[0071] The comparison subunit is used to compare the matched features with the geometric shapes of obstacles in the environment model to identify obstacles.

[0072] See Figure 4 , which is a schematic diagram of the structure of the environment perception unit according to an embodiment of the present invention;

[0073] Specifically, the environment perception unit includes an acquisition subunit and a determination subunit, wherein:

[0074] The acquisition subunit is used to calculate the percentage of the number of obstacle pixels in any of the inspection route images to the total number of pixels to obtain a real-time obstacle ratio;

[0075] The determination subunit determines the real-time obstacle ratio according to the standard obstacle ratio range, determines the current inspection environment as the first inspection environment according to the determination result, and determines the current inspection environment as the first inspection environment or the second inspection environment in combination with the image stability analysis result;

[0076] The current inspection environment includes a first inspection environment and a second inspection environment.

[0077] In this embodiment, the first inspection environment indicates that there are no obstacles, or even if there are some obstacles, the complexity of the background is low. Therefore, from the overall point of view, the environmental information perception performance is strong, which can ensure the accurate positioning of the drone and reliable inspection path planning; the second inspection environment indicates that due to the large proportion of obstacles or the high complexity of the background, the scene perception matching degree is low and the perception ability is poor.

[0078] Specifically, the determination subunit determines that the current inspection environment is the first inspection environment when the real-time obstacle ratio is less than or equal to the minimum value of the standard obstacle ratio range;

[0079] When the real-time obstacle ratio is within the standard obstacle ratio range, the current inspection environment is determined in combination with the image stability analysis result; when the real-time obstacle ratio is greater than or equal to the maximum value of the standard obstacle ratio range, the current inspection environment is determined to be the second inspection environment;

[0080] When the first analysis result is obtained, determining that the current inspection environment is the second inspection environment;

[0081] When the second analysis result is obtained, it is determined that the current inspection environment is the first inspection environment.

[0082] In this embodiment, the standard obstacle ratio represents the threshold for determining that there are no obstacles in the inspection route image, that is, all visible areas are open and there are no objects blocking the line of sight. The standard obstacle ratio range is set to 0% to 90%.

[0083] When it is determined that the real-time obstacle ratio is less than or equal to the minimum value of the standard obstacle ratio range, it means that there are no obstacles in the current inspection route image. Since there are no obstacles, the background complexity is low, and it is a simple ground or single background feature. In this environment, it is easy to perceive and identify, which is suitable for drone inspection. When the real-time obstacle ratio is greater than or equal to the maximum value of the standard obstacle ratio range, it means that most areas are occupied by obstacles, indicating that there are a lot of objects in the environment, which will significantly affect the inspection path and perception ability of the drone. When the real-time obstacle ratio is within the standard obstacle ratio range, it means that there are some objects in the environment, which may affect the path planning and perception ability of the drone. The current inspection environment is determined by combining the background complexity in the inspection route image to improve the accuracy of understanding the inspection environment.

[0084] Specifically, the data processing module also includes a stability analysis unit,

[0085] The stability analysis unit is used to perform stability analysis on the inspection route image to obtain a stability analysis result, which includes a first analysis result and a second analysis result.

[0086] Specifically, the stability analysis unit includes an acquisition subunit, a calculation subunit and a first analysis subunit, wherein:

[0087] The acquisition subunit is used to obtain the current inspection route image and the previous inspection route image, and use the current inspection route image as the current frame and the previous inspection route image as the reference frame;

[0088] The calculation subunit is used to track the pixel motion between two frames, obtain the motion rate vector, the feature point and the pixel displacement change corresponding to the feature point;

[0089] The first analysis subunit is used to analyze the pixel displacement variation to determine whether jitter exists and obtain a first analysis result.

[0090] Specifically, the stability analysis unit further includes a second analysis subunit and a third analysis subunit, wherein:

[0091] The second analysis subunit is used to analyze the background complexity of the current inspection image;

[0092] The third analysis subunit is configured to determine a stability analysis result in combination with background complexity when determining that there is no jitter;

[0093] The first analysis subunit compares the standard displacement difference with the pixel displacement variation:

[0094] If the pixel displacement change is less than or equal to the standard displacement difference, it is determined that there is no jitter;

[0095] If the pixel displacement change is greater than the standard displacement difference, it is determined that jitter exists and a first analysis result is obtained;

[0096] The second analysis subunit determines that the background complexity of the current inspection image is the first background complexity or the second background complexity;

[0097] The third analysis subunit obtains a first analysis result when it is determined that there is no jitter and the background complexity of the current inspection image is the first background complexity, and obtains a second analysis result when it is determined that there is no jitter and the background complexity of the current inspection image is the second background complexity.

[0098] In this embodiment, a stability analysis is performed on the inspection route image to analyze the quality of the image and obtain a stability analysis result. The first analysis result indicates that the quality of the current inspection image is low, and the second analysis result indicates that the quality of the current inspection image is high. The first background complexity indicates high background complexity, and the second background complexity indicates low background complexity. The background complexity is judged by analyzing the features, textures, color changes and object diversity in the current inspection route image. The edge detection algorithm is used to evaluate the number and distribution of edges in the image, that is, Canny edge detection is used to detect the edges in the image. The background complexity is reflected according to the density, inner area and distribution of the edge. The density of the edge is compared with the set density threshold. If the density of the edge is greater than or equal to the density threshold, the current background complexity is judged to be high background complexity. If the density of the edge is less than the density threshold, the area of ​​the inner area of ​​the edge formed after edge detection is calculated. If the area is greater than or equal to the set area threshold, the current background complexity is judged to be high background complexity. If the area is less than The area threshold is set to analyze whether the edge distribution is concentrated or uniform. If it is concentrated, the current background complexity is determined to be high; if it is uniform, the current background complexity is determined to be low. The ratio of edge pixels to the entire image is calculated to obtain the edge density. The density threshold is in the range of 0.05-0.1 and is adjusted according to the characteristics of the actual image. For example, in a complex urban environment, the density threshold is set to 0.1 or higher, while in a simple natural scene, 0.05 is sufficient. The area threshold is set to 5000 pixels and should be adjusted according to the resolution and characteristics of the image. It decreases as the resolution of the image decreases. For example, in a high-resolution image, the area threshold is set to 5000 pixels. By analyzing the connectivity and distribution pattern of the edge, it is determined whether the edge is concentrated in certain areas or uniformly distributed. The standard displacement difference represents the threshold for determining the displacement change between adjacent frames. If the displacement change exceeds this threshold, it is considered that jitter exists. The standard displacement difference is set to 10 pixels.

[0099] Specifically, the abnormality monitoring unit includes a marking subunit and a determining subunit, wherein:

[0100] The marking subunit is used to mark the current detection point as an abnormal detection point when the current inspection environment is the second inspection environment;

[0101] The detection points correspond one-to-one to the components of the new energy power station;

[0102] The determination subunit is used to calculate the percentage of abnormal detection points to the total detection points to obtain a real-time abnormality ratio, compare the allowed abnormality ratio with the real-time abnormality ratio, and determine the abnormality of the inspection route based on the comparison result;

[0103] Among them, when the real-time abnormality ratio is less than or equal to the allowed abnormality ratio, the inspection route is judged to be normal; when the real-time abnormality ratio is greater than the allowed abnormality ratio, the inspection route is judged to be abnormal.

[0104] The allowed abnormality ratio in this embodiment indicates the ratio of detection point abnormalities allowed to occur in the inspection route. The detection point abnormality means that the corresponding scene perception matching ability is poor. The higher the calculated real-time abnormality ratio is, when it is determined that the real-time abnormality ratio is greater than the allowed abnormality ratio, it means that the path planning is insufficient, and the work order needs to be updated.

[0105] Specifically, the data analysis module includes a work order generation unit and a work order update unit, wherein:

[0106] The work order generating unit is used to classify user feedback according to feedback type, and prioritize feedback data according to the urgency of the feedback, and generate a number of work orders;

[0107] The work order updating unit is used to update the inspection route corresponding to the work order.

[0108] In this embodiment, when it is determined that the real-time abnormality ratio is greater than the allowed abnormality ratio, a dynamic programming algorithm (such as RRT*) is used to recalculate the inspection path to update the inspection route corresponding to the work order.

[0109] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0110] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A new energy power station management system based on user feedback, characterized in that: include, A user feedback module, which is connected to the user terminal and is used to receive feedback data submitted by the user, wherein the feedback data includes device operation data; A patrol monitoring module, which includes several intelligent patrol devices and is used to automatically collect patrol data, including patrol route images; a data processing module connected to the inspection monitoring module, configured to identify obstacles in the inspection route based on the inspection route image, determine the current inspection environment based on the real-time obstacle ratio, and determine abnormal conditions of the inspection route based on the current inspection environment; a data analysis module, connected to the user feedback module and the inspection monitoring module, respectively, for classifying and prioritizing the feedback data, generating work orders, and determining whether to update the work orders based on abnormalities in the inspection routes; The data processing module includes an identification unit, an environment perception unit, an abnormality monitoring unit and a stability analysis unit, wherein: The recognition unit is used to extract features from the inspection route image and match the environment model with the features to identify obstacles in the inspection route; The environment perception unit is used to determine the real-time obstacle ratio in the inspection route image, and determine the current inspection environment based on the real-time obstacle ratio; The abnormality monitoring unit is used to determine whether to mark the current detection point as an abnormal detection point based on the current inspection environment, and to determine the abnormality of the current inspection route according to the abnormal detection point; The stability analysis unit is used to perform stability analysis on the inspection route image to obtain a stability analysis result, wherein the stability analysis result includes a first analysis result and a second analysis result; The stability analysis result indicates the quality of the current inspection image, and the quality of the current inspection image corresponding to the first analysis result is lower than the quality of the current inspection image corresponding to the second analysis result.

2. The new energy power station management system based on user feedback according to claim 1 is characterized in that: The recognition unit includes a modeling subunit, a feature extraction subunit, a feature matching subunit and a comparison subunit, wherein: The modeling subunit is used to generate a 3D environment model using a laser radar; The feature extraction subunit is used to extract features from the inspection route image to obtain a feature descriptor; The feature matching subunit is used to calculate the distance between feature descriptors, calculate the distance between each pair of feature descriptors, and select the feature descriptor with the smallest distance as the matching feature; The comparison subunit is used to compare the matched features with the geometric shapes of obstacles in the environment model to identify obstacles.

3. The new energy power station management system based on user feedback according to claim 1 is characterized in that: The environment perception unit includes an acquisition subunit and a determination subunit, wherein: The acquisition subunit is used to calculate the percentage of the number of obstacle pixels in any of the inspection route images to the total number of pixels to obtain a real-time obstacle ratio; The determination subunit determines the real-time obstacle ratio according to the standard obstacle ratio range, determines the current inspection environment as the first inspection environment according to the determination result, and determines the current inspection environment as the first inspection environment or the second inspection environment in combination with the image stability analysis result; The current inspection environment includes a first inspection environment and a second inspection environment.

4. The new energy power station management system based on user feedback according to claim 3 is characterized in that: The determination subunit determines that the current inspection environment is the first inspection environment when the real-time obstacle ratio is less than or equal to the minimum value of the standard obstacle ratio range; When the real-time obstacle ratio is within the standard obstacle ratio range, the current inspection environment is determined based on the image stability analysis results; When the first analysis result is obtained, determining that the current inspection environment is the second inspection environment; When the second analysis result is obtained, determining that the current inspection environment is the first inspection environment; When the real-time obstacle ratio is greater than or equal to the maximum value of the standard obstacle ratio range, the current inspection environment is determined to be the second inspection environment.

5. The new energy power station management system based on user feedback according to claim 1 is characterized in that: The stability analysis unit includes an acquisition subunit, a calculation subunit and a first analysis subunit, wherein: The acquisition subunit is used to obtain the current inspection route image and the previous inspection route image, and use the current inspection route image as the current frame and the previous inspection route image as the reference frame; The calculation subunit is used to track the pixel motion between two frames, obtain the motion rate vector, the feature point and the pixel displacement change corresponding to the feature point; The first analysis subunit is used to analyze the pixel displacement variation to determine whether there is jitter, and obtain a first analysis result when it is determined that there is jitter.

6. The new energy power station management system based on user feedback according to claim 5 is characterized in that: The stability analysis unit further includes a second analysis subunit and a third analysis subunit, wherein, The second analysis subunit is used to analyze the background complexity of the current inspection image; The third analysis subunit is configured to determine a stability analysis result in combination with background complexity when determining that there is no jitter; Among them, the third analysis subunit obtains a first analysis result when it is determined that there is no jitter and the background complexity of the current inspection image is the first background complexity, and obtains a second analysis result when it is determined that there is no jitter and the background complexity of the current inspection image is the second background complexity.

7. The new energy power station management system based on user feedback according to claim 4 is characterized in that: The abnormality monitoring unit includes a marking subunit and a determining subunit, wherein: The marking subunit is used to mark the current detection point as an abnormal detection point when the current inspection environment is the second inspection environment; The detection points correspond one-to-one to the components of the new energy power station; The determination subunit is used to calculate the percentage of abnormal detection points to the total detection points to obtain a real-time abnormality ratio, compare the allowed abnormality ratio with the real-time abnormality ratio, and determine the abnormality of the inspection route based on the comparison result; Among them, when the real-time abnormality ratio is less than or equal to the allowed abnormality ratio, the inspection route is judged to be normal; when the real-time abnormality ratio is greater than the allowed abnormality ratio, the inspection route is judged to be abnormal.

8. The new energy power station management system based on user feedback according to claim 7 is characterized in that: The data analysis module includes a work order generation unit and a work order update unit, wherein: The work order generating unit is used to classify user feedback according to feedback type, and prioritize feedback data according to the urgency of the feedback, and generate a number of work orders; The work order updating unit is used to update the inspection route corresponding to the work order.

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