Intelligent multi-dimensional power transmission state inspection system based on sensitive sensing technology

Abstract

The invention discloses an intelligent multi-dimensional power transmission state inspection system based on a sensitive sensing technology, and the system is characterized in that the system comprises an image collection module, an image processing module, and a cloud platform data processing module; the image collection module utilizes the sensitive sensing technology to shoot the condition of the power transmission system through the unmanned aerial vehicle to obtain an image, the obtained image is transmitted in a signal mode, and the image processing module processes the image by means of the image recognition technology to reflect the image into digital information. The cloud platform data processing module processes the digital information to verify data, intelligently detects whether the power transmission state of the power transmission system is normal or not from multiple dimensions, and investigates abnormal conditions. By combining the sensitive sensing technology, the unmanned aerial vehicle technology and the cloud platform data processing technology, the efficient and intelligent multi-dimensional power transmission state inspection device is realized, the power transmission state inspection efficiency can be remarkably improved, the labor cost is reduced, and powerful support is provided for intelligent and automatic development of a power grid system.

Description

Technical Field

[0001] The present invention relates to the field of panoramic intelligent monitoring systems for power transmission lines, and in particular to an intelligent multi-dimensional power transmission status inspection system with sensitive and reliable sensitive sensing technology. Background Art

[0002] Sensitive sensing technology refers to the technology that uses hardware devices such as cameras, combined with cutting-edge technologies such as voice recognition and image recognition, to transmit signals from the physical world to an information processing system, and then draw corresponding conclusions. The intelligent multi-dimensional transmission status inspection device with sensitive sensing technology is a high-tech product that has emerged in the context of the intelligent transformation of the power industry. With the rapid development of the power industry, the requirements for the inspection, monitoring and maintenance of power equipment are becoming increasingly higher. Traditional manual inspection methods have problems such as low efficiency, insufficient accuracy, and slow response, and cannot meet the needs of modern power systems. Therefore, the power industry urgently needs a device that can sensitively perceive the transmission status and realize intelligent inspection to improve inspection efficiency and accuracy and ensure the stable operation of the power system. Summary of the Invention

[0003] Purpose of the invention: The purpose of the present invention is to provide an intelligent multi-dimensional power transmission status inspection system with sensitive and reliable sensitive sensing technology.

[0004] Technical solution: The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology described in the present invention is characterized by including an image collection module, an image processing module and a cloud platform data processing module; the image collection module uses sensitive sensing technology to capture the power transmission system status through a drone camera to obtain an image, and transmits the obtained image in the form of a signal; the image processing module uses image recognition technology to process the image and reflect it into digital information; the cloud platform data processing module processes the digital information to verify the data, and intelligently detects whether the power transmission status of the power transmission system is operating normally from multiple dimensions, and at the same time investigates abnormal situations.

[0005] Furthermore, in the image collection module, the drone takes impact pictures from multiple angles. The images uploaded by the drone are intelligently processed, and the intelligent system filters out images that are questionable due to repetition and blur and cannot be used as a basis for inspection. The images are manually processed by deletion and replacement methods, and the images after intelligent processing are uploaded to the image processing module.

[0006] Furthermore, the image processing module utilizes intelligent processing to set power transmission device specifications and automatically screen power transmission device images for uploading.

[0007] Furthermore, the image processing module processes the obtained image, pre-sets a database, compares the image with the standard image in the database, and refers to the typical image in the database to draw data conclusions, which are uploaded to the cloud platform for algorithm comparison. The data conclusions are compared with the standard data of the standard image to determine whether the power transmission status has changed and the extent of the change.

[0008] Furthermore, the image processing module uses the NRBO algorithm to provide an optimization strategy. The specific verification process is as follows:

[0009] (1) Population initialization: At the beginning of the algorithm, a set of populations is generated. During the search process, each population has a corresponding responsibility result.

[0010] (2) The system uses OpenCV as an image processing library and applies the Newton-Raphson optimization algorithm to perform target recognition on the obtained images;

[0011] (3) Setting the objective function and processing the images obtained in the image collection module through the image processing module;

[0012] (4) Define Pest: Using the above population, bring the population into the objective function for evaluation, sort the population influence degree, and take the image deviation coefficient that is closest to the database influence coefficient as Pest;

[0013] (5) rank the degree of population impact;

[0014] (6) Shrinking the range: When using the algorithm to calculate, it is necessary to ensure that the calculated value is within the range;

[0015] (7) Using the algorithm NRSR, determine the value Ns and use the current solution to assist in updating its position to enhance the current solution;

[0016] (8) Enhanced algorithm accuracy: The algorithm accuracy is enhanced by applying an adaptive coefficient called δ;

[0017] (9) Refined parameters: Improve the formula by considering factors that affect the target parameters

[0018] (10) Using Newton-Raphson search rule;

[0019] (11) Trap avoidance operations to improve the effectiveness of NRBO in dealing with practical problems and avoid local optimality;

[0020] (12) Verify the value and output the final value.

[0021] Furthermore, the objective function established in step (3) is:

[0022]

[0023] Where P is the deviation coefficient of the transmission device, w is the degree of damage, γ is the weather environment factor, c is the influencing factor of the service life of the transmission line, d is the influence change of the rust of the transmission line, sinθ is the sine value of the inclination angle of the transmission device, MOA is the standard value of the transmission line in the intact state, and n is the standard value deviation proportional coefficient.

[0024] Furthermore, the step (5) uses the following formula to randomly select a population and calculate its impact:

[0025] x n =A+rand×(BA), n=1, 2,...

[0026] Where A is the standard value of the population in the database influence coefficient; B is the current value of the population;

[0027] Substitute the obtained data into the following formula, rank the populations according to their impact, and count the populations with the highest and lowest impacts;

[0028]

[0029] The population with the highest impact is set as x w ; The population with the lowest impact is set as x b .

[0030] Furthermore, the step (7) uses the current solution to assist in updating its position to enhance the current solution, and the formula is as follows:

[0031]

[0032] Among them, IT represents the current iteration, and Max IT represents the maximum number of iterations, maintaining a balance between the exploration and development stages. The parameter δa adjusts itself during the iteration process and varies between 1 and -1.

[0033] Furthermore, the search rule in step (10) is:

[0034] y w =r1×(Mean(Z n+1 +x n )+r1×Δx)

[0035] y h =r1×(Mean(Z n+1 +x n )-r1×Δx)

[0036]

[0037] Introducing vector, y w and y b Is to use Z n+1 and x n The positions of the two generated vectors, r represents a random number between (0, 1).

[0038] Furthermore, the step (12) combines each comparison value to output the optimal individual of the intelligent multi-dimensional power transmission status inspection device using sensitive perception technology, that is, the optimal solution compared with the typical image in the database:

[0039]

[0040] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: It utilizes sensitive sensing technology to improve the efficiency of power transmission status inspections and reduce labor costs. By combining sensitive sensing technology with drone technology and cloud platform data processing technology, it realizes a highly efficient, intelligent, and multi-dimensional power transmission status inspection device. This device can significantly improve the efficiency of power transmission status inspections, reduce labor costs, and provide strong support for the intelligent and automated development of power grid systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the system module of the present invention;

[0042] Figure 2 Schematic diagram of the NRBO algorithm of the present invention;

[0043] Figure 3 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0045] like Figure 1 As shown, the present invention is an intelligent multi-dimensional power transmission status inspection device based on sensitive perception technology, which includes image collection, image processing, and cloud platform data processing blocks.

[0046] Using sensitive perception technology, the transmission device system status is captured by drone cameras to obtain images, which are transmitted in the form of signals. With the help of image recognition technology, the images are processed to reflect them into digital information, which is further processed to verify the data.

[0047] The aforementioned image collection block utilizes cutting-edge drone technology. Drones regularly patrol their coverage areas, using cameras to collect images of power transmission status. This unique feature allows drones to capture images from multiple angles, ensuring the proper functioning of power transmission systems through multi-dimensional verification. Furthermore, the images uploaded by drones undergo intelligent processing. This intelligent system filters out images that are questionable, such as duplication and blur, and cannot be used as verification data. These images are then manually processed through methods such as deletion and replacement. These processed images are then uploaded to the image processing block.

[0048] The image collection area mentioned above requires attention to the angle of the transmission device, the degree of line rust, wire wear, line deformation, tilt, and the duplication of transmission devices. Using intelligent processing, transmission device specifications are set and transmission device images are automatically screened for upload.

[0049] The image processing block, combined with the image collection block, processes the images. A pre-set database is used to compare the images with standard images and typical images in the database. Data conclusions are then uploaded to the cloud platform for algorithm comparison. By comparing these data conclusions with the standard data from the standard images, it is determined whether the power transmission status has changed and the extent of the change, thereby achieving effective transmission status inspections.

[0050] The image collection, image processing, and cloud platform data processing blocks mentioned above use sensitive perception technology to intelligently detect whether the transmission status of the transmission system is operating normally from multiple dimensions, and at the same time promptly investigate abnormal situations to ensure the normal operation of the transmission system.

[0051] Intelligent multi-dimensional transmission status inspection based on sensitive perception technology, after analysis and comparison, if there is no abnormality in the transmission device and transmission line, it will contact the transmission system terminal to detect the system transmission parameters (such as voltage and current) to detect whether the transmission efficiency fluctuates and whether the system can operate stably.

[0052] like Figure 2 As shown, the image processing module uses the NRBO algorithm to provide an optimization strategy. The specific verification process is as follows:

[0053] Step 1: Population initialization: At the beginning of the algorithm, a set of populations is generated. During the search process, each population has a corresponding responsibility result.

[0054] The system uses OpenCV as the image processing library and applies the Newton-Raphson optimization algorithm (NRBO) to perform target recognition on the obtained images.

[0055] Set the objective function, process the images obtained in the image collection module through the image processing module, obtain relevant parameters for processing, and set the objective function as follows:

[0056]

[0057] Where P is the deviation coefficient of the transmission device, w is the degree of damage, γ is the weather environment factor, c is the influencing factor of the service life of the transmission line, d is the influence change of the rust of the transmission line, sinθ is the sine value of the inclination angle of the transmission device, MOA is the standard value of the transmission line in the intact state, and n is the standard value deviation proportional coefficient.

[0058] Step 2: Define Pest: Using the above population, bring the population into the objective function for evaluation, sort the population influence degree, and take the image deviation coefficient that is closest to the database influence coefficient as Pest.

[0059] Rank the degree of impact on populations:

[0060] The following formula is used to randomly select a population and calculate its impact:

[0061] x n =A+rand×(BA), n=1, 2,... (2)

[0062] Where A is the standard value of the population in the database influence coefficient; B is the current value of the population.

[0063] Step 3: Substitute the obtained data into the following formula, arrange the populations according to their impact, and count the populations with the highest and lowest impact.

[0064] The population with the highest impact is set as x w ; The population with the lowest impact is set as x b , the formula is as follows:

[0065]

[0066] Step 4: Narrow the range: When using the algorithm, you need to ensure that the calculated values ​​are within the range. Take the parameter range randa, which represents normally distributed random numbers with mean 0 and variance 1.

[0067] Step 5: Use the NRSR algorithm to determine the value Ns and use the current solution to assist in updating its position to enhance the current solution. The formula is as follows:

[0068]

[0069] The above formula has random parameters, so the algorithm has stronger search ability and better balances development and exploration capabilities.

[0070] Step 6: Enhance algorithm accuracy: Based on experience, the proposed algorithm must be able to strike a balance between diversity and aggregation in order to find the optimal solution in the search space and eventually converge to the global solution. The algorithm accuracy can be enhanced by applying an adaptive coefficient called δ. The expression of δ is as follows:

[0071]

[0072] Where IT represents the current iteration, and Max IT represents the maximum number of iterations. To maintain a balance between the exploration and development phases, the parameter δa adjusts itself during the iteration process. From the above formula, we can see that the value of δa varies between 1 and -1.

[0073] Refined parameters: Considering the adaptive parameter δa, the proposed Ns is improved by taking into account factors that affect the target parameters (including speed, weather, angle, drone problems, etc.), while significantly reducing the number of iterations. The Δx in formula (7) is improved as follows:

[0074]

[0075] In the above formula, Xb represents the best solution obtained so far, and rand(1, dim) is a random number with dim-dimensional decision variables. At this point, the above formula (5) can be rewritten as:

[0076] x n+1 =x n -Ns (7)

[0077] Step 7: Newton-Raphson search rule:

[0078] Introducing vector, y w and y b Is to use Z n+1 and x n The positions of the two generated vectors, r represents a random number between (0, 1).

[0079] y w =r1×(Mean(Z n+1 +x n )+r1×Δx) (8)

[0080] y h =r1×(Mean(Z n+1 +x n )-r1×Δx) (9)

[0081]

[0082] The enhanced version of NRSR is shown below:

[0083]

[0084] Step 8: Trap Avoidance Operation (TAO):

[0085] Improve the effectiveness of NRBO in dealing with practical problems and avoid local optimality.

[0086]

[0087] Step 9: Verify the value and output the final value:

[0088] Combined with the above comparison values, the optimal individual of the intelligent multi-dimensional transmission status inspection device using sensitive perception technology is output, that is, the optimal solution compared with the typical image in the database.

[0089] like Figure 3 As shown in the figure, the line images taken by the drone are compared with the typical images pre-input in the database using sensitive perception technology to avoid the local optimal process. The optimal image in the output group is the optimal solution for the abnormal power transmission status after the above-mentioned system is intelligently optimized by the NRSR algorithm.

[0090] The status of the transmission line is monitored in the above manner. If there is no abnormality in the transmission device or transmission line, the transmission system terminal is contacted to detect the system transmission parameters (such as voltage and current) to detect whether the transmission efficiency fluctuates and whether the system can operate stably.

Claims

1. An intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology, characterized in that: It includes an image collection module, an image processing module and a cloud platform data processing module; the image collection module uses sensitive perception technology to capture the transmission system status through drone cameras to obtain images, and transmits the obtained images in the form of signals. The image processing module uses image recognition technology to process the images and reflect them into digital information. The cloud platform data processing module processes these digital information to verify the data, and intelligently detects whether the transmission status of the transmission system is operating normally from multiple dimensions, and at the same time investigates abnormal situations.

2. The intelligent multi-dimensional power transmission status inspection system based on sensitive sensing technology according to claim 1 is characterized in that: In the image collection module, the drone takes impact pictures from multiple angles. The images uploaded by the drone are intelligently processed, and the intelligent system filters out images that are questionable such as duplication and blur and cannot be used as a basis for inspection. The images are manually processed by deletion and replacement methods, and the images after intelligent processing are uploaded to the image processing module.

3. The intelligent multi-dimensional power transmission status inspection system based on sensitive sensing technology according to claim 1 is characterized in that: The image processing module utilizes intelligent processing to set power transmission device specifications and automatically screen power transmission device images for uploading.

4. The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology according to claim 1 is characterized in that: The image processing module processes the obtained image, pre-sets a database, compares the image with the standard image in the database, and refers to the typical image in the database to draw data conclusions, which are uploaded to the cloud platform for algorithm comparison. The data conclusions are compared with the standard data of the standard image to determine whether the power transmission status has changed and the extent of the change.

5. The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology according to claim 1 is characterized in that: The image processing module uses the NRBO algorithm to provide an optimization strategy. The specific verification process is as follows: (1) Population initialization: At the beginning of the algorithm, a set of populations is generated. During the search process, each population has a corresponding responsibility result. (2) The system uses OpenCV as an image processing library and applies the Newton-Raphson optimization algorithm to perform target recognition on the obtained images; (3) Setting the objective function and processing the images obtained in the image collection module through the image processing module; (4) Define Pest: Using the above population, bring the population into the objective function for evaluation, sort the population influence degree, and take the image deviation coefficient that is closest to the database influence coefficient as Pest; (5) rank the degree of population impact; (6) Shrinking the range: When using the algorithm to calculate, it is necessary to ensure that the calculated value is within the range; (7) Using the algorithm NRSR, determine the value Ns and use the current solution to assist in updating its position to enhance the current solution; (8) Enhanced algorithm accuracy: The algorithm accuracy is enhanced by applying an adaptive coefficient called δ; (9) Refined parameters: Improve the formula by considering factors that affect the target parameters (10) Using Newton-Raphson search rule; (11) Trap avoidance operations to improve the effectiveness of NRBO in dealing with practical problems and avoid local optimality; (12) Verify the value and output the final value.

6. The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology according to claim 5 is characterized in that: The objective function set up in step (3) is: Where P is the deviation coefficient of the transmission device, w is the degree of damage, γ is the weather environment factor, c is the influencing factor of the service life of the transmission line, d is the influence change of the rust of the transmission line, sinθ is the sine value of the inclination angle of the transmission device, MOA is the standard value of the transmission line in the intact state, and n is the standard value deviation proportional coefficient.

7. The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology according to claim 5 is characterized in that: The step (5) uses the following formula to randomly select a population and calculate its impact: x n =A+rand×(B-A),n=1,2,... Where A is the standard value of the population in the database influence coefficient; B is the current value of the population; Substitute the obtained data into the following formula, rank the populations according to their impact, and count the populations with the highest and lowest impacts; The population with the highest impact is set as x w ; The population with the lowest impact is set as x b .

8. The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology according to claim 5 is characterized in that: The step (7) uses the current solution to assist in updating its position to enhance the current solution, and the formula is as follows: Among them, IT represents the current iteration, and Max IT represents the maximum number of iterations, maintaining a balance between the exploration and development stages. The parameter δa adjusts itself during the iteration process and varies between 1 and -1.

9. The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology according to claim 5 is characterized in that: The step (10) searches for rules: y w =r1×(Mean(Z n+1 +x n )+r1×Δx) y h =r1×(Mean(Z n+1 +x n )-r1×Δx) Introducing vector, y w and y b Is to use Z n+1 and x n The positions of the two generated vectors, r represents a random number between (0, 1).

10. The intelligent multi-dimensional power transmission status inspection system with sensitive sensing technology according to claim 5 is characterized in that: The step (12) combines the comparison values ​​to output the optimal individual of the intelligent multi-dimensional power transmission status inspection device using sensitive perception technology, that is, the optimal solution compared with the typical image in the database:

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