High-efficiency data transmission system for power inspection robots integrating 5G communication

Abstract

This invention relates to the field of smart grid operation and maintenance, and addresses the problem of insufficient information transmission efficiency from inspection robots, leading to delays in high-risk inspection information. Specifically, it is a high-efficiency data transmission system for power inspection robots that integrates 5G communication. This invention determines the risk level of inspection results, records routine and low-risk information, and sends it sequentially or migrates it back to the management platform when network conditions are good. For high-risk emergency information, data is sent immediately. Simultaneously, communication quality is monitored during inspection. If communication quality is substandard, only high-risk data is transmitted back, thus maximizing the communication channel for high-risk data transmission. Furthermore, when communication quality is poor, the inspection robot can be selectively moved to an area with better communication quality during communication testing for temporary information transmission, thereby ensuring the timeliness of high-risk information transmission to the greatest extent possible.

Description

Technical Field

[0001] This invention relates to the field of smart grid operation and maintenance, specifically to a high-efficiency data transmission system for power inspection robots that integrates 5G communication. Background Technology

[0002] The safe and stable operation of power supply and transmission systems is crucial to the normal operation of national production and daily life. Power equipment inspection is a fundamental task to effectively ensure the safe operation of the power system and improve power supply reliability. It is mainly divided into routine inspection and special inspection. Special inspection is generally carried out in high temperature weather, under heavy load, before the operation of newly put equipment, and after strong winds, fog, snow, hail, and thunderstorms. The existing inspection method is mainly manual inspection, with manual or handheld computer recording. Manual inspection has obvious shortcomings such as high labor intensity, low work efficiency, scattered detection quality, and high management costs. Human negligence can easily lead to missed or false detections, which may create hidden dangers for major power accidents. With the rapid development of robot technology, it is possible to combine robot technology with power applications and carry detection equipment based on outdoor robot mobile platforms to replace manual equipment inspection.

[0003] Power inspection robots use mobile robots as carriers, visible light cameras, infrared thermal imagers, and other detection instruments as payload systems, and multi-field information fusion of machine vision, electromagnetic field, GPS, and GIS as navigation systems for autonomous movement and inspection. Embedded computers serve as the software and hardware development platform for the control system, which can greatly improve the current situation of manual inspection of power systems.

[0004] Existing power inspection robots, due to their high mobility and large operating range, generally perform large-scale operations. Therefore, the timely transmission of inspection information is particularly important, especially for high-risk faults detected during inspections. The time it takes for information to be transmitted directly affects the timeliness of power maintenance and repair operations. However, existing power inspection robots are generally located in outdoor environments or areas with dense power lines that are prone to electromagnetic interference, which makes it impossible to guarantee the stability of communication quality. Therefore, a more efficient communication transmission system is needed to ensure the timeliness of high-risk inspection information.

[0005] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0006] This invention, during power line inspections, performs preliminary analysis of the inspection results using edge computing and local model deployment. Based on the preliminary analysis, it determines the data type and thus the risk level of the inspection results. Routine and low-risk information is recorded and sequentially transmitted or migrated back to the management platform when network conditions are good. High-risk and urgent information is transmitted immediately, maximizing the timeliness of high-risk information transmission and ensuring timely response to subsequent maintenance and repairs. This addresses the problem of insufficient information transmission efficiency from inspection robots, which leads to delays in high-risk inspection information. Therefore, this invention proposes a high-efficiency data transmission system for power line inspection robots that integrates 5G communication.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A high-efficiency data transmission system for power inspection robots integrating 5G communication includes a data processing module, a fault inspection module, an inspection planning module, a communication control module, and a communication detection module. The inspection planning module is used to acquire the travel path of the power inspection robot and divide the travel path to obtain communication shielded segments and communication normal segments.

[0009] The fault inspection module is used for the inspection work of the power inspection robot when it travels on the driving path, and performs edge computing on the inspection results to obtain real-time detection results, and sends the real-time detection results to the data processing module.

[0010] The data processing module is used to perform local model matching on the real-time detection results, match the data risk type according to the ontology model, obtain two data types: high-risk data and regular risk data, and send the data risk type to the communication control module.

[0011] The communication detection module obtains the current route segment through the inspection planning module, and performs communication monitoring when it is in a normal communication segment to obtain the communication health level, and sends the communication health level and the corresponding route segment to the communication control module;

[0012] The communication control module acquires the data risk type and the communication health status of the current road segment. It determines the timing of data transmission based on the data risk type, determines the data transmission location based on the communication health status, and finally transmits the data.

[0013] In a preferred embodiment of the present invention, the inspection planning module divides the driving path by line density. If the line density is greater than a set threshold, it is determined to be a communication blockage segment; if the line density is not greater than the set threshold, it is determined to be a normal communication segment.

[0014] The method by which the inspection planning module obtains the network density on the current path is as follows: the total length of the line within a fixed area is obtained through the fault inspection module. At the same time, the line spacing in the current area is statistically analyzed, and the average spacing is calculated by arithmetic mean. The inspection planning module calculates the standard deviation of the line spacing to obtain the spacing discrete value. The inspection planning module calculates the total line length, the average line spacing, and the spacing discrete value using a formula to obtain the network density.

[0015] In a preferred embodiment of the present invention, the fault inspection module runs through a preset inspection program during inspection and obtains real-time detection results through edge computing. The inspection results include visible light images and infrared light images. The fault inspection module identifies and analyzes the visible light images through algorithms to obtain abnormal appearance information in the line. The abnormal appearance information includes equipment damage, equipment detachment, foreign objects on the equipment, and abnormal equipment wrapping.

[0016] The fault inspection module acquires temperature anomaly information in the line through infrared light images. The temperature anomaly information is divided into low-risk high temperature signals and high-risk high temperature signals according to the proportion of temperature exceeding the threshold. The appearance anomaly information and temperature anomaly information are real-time detection results.

[0017] In a preferred embodiment of the present invention, after the data processing module obtains the appearance abnormality information, it compares the appearance abnormality information with a big data model and obtains the data risk type with the highest confidence level based on the comparison overlap.

[0018] The data processing module acquires temperature anomaly information. If it is a high-risk high temperature signal, it is determined to be high-risk data; if it is a low-risk high temperature signal, it is determined to be normal risk data.

[0019] In a preferred embodiment of the present invention, when the communication detection module acquires the current road segment, it assigns a number to the current road segment and simultaneously acquires the driving path through the inspection planning module, and assigns a number to all the normally communicating segments in the driving path in sequence. The communication detection module then matches the current road segment with the normally communicating segments in the driving path and assigns them the same number.

[0020] In a preferred embodiment of the present invention, the communication detection module performs the following steps for communication detection:

[0021] Step 1: The communication detection module acquires the current communication mode and collects network signal strength, signal-to-noise ratio, and bit error rate data multiple times for the communication mode;

[0022] Step 2: The communication detection module statistically analyzes the network signal strength, signal-to-noise ratio, and bit error rate acquired multiple times, and generates a distribution curve;

[0023] Step 3: The communication detection module obtains the distribution weight through the distribution curve, compares the distribution weight with the set weight threshold sequence, and generates a good communication signal or a communication interference signal based on the comparison result.

[0024] In a preferred embodiment of the present invention, the method for the communication detection module to generate the distribution curve is as follows:

[0025] The communication detection module plots signal strength curves, signal-to-noise ratio curves, and bit error rate curves with network signal strength, signal-to-noise ratio, and bit error rate on the horizontal axis and the number of occurrences on the vertical axis, respectively.

[0026] When the communication detection module obtains the distribution weight, it selects the highest point on the distribution curve and multiplies the highest point by a set coefficient to obtain the threshold value. It then selects the horizontal coordinates corresponding to all vertical axes that are greater than the threshold value to obtain the signal strength range, signal-to-noise ratio range, and bit error rate range, respectively.

[0027] The weighted threshold sequence obtained by the communication detection module includes a signal strength sequence, a signal-to-noise ratio (SNR) range sequence, and a bit error rate (BER) sequence. These sequences are compared one-to-one with the signal strength range, SNR range, and BER range, respectively. If the signal strength range, SNR range, and BER range are all within the set signal strength sequence, the communication is considered good; otherwise, communication is considered to be interfering.

[0028] In a preferred embodiment of the present invention, when the communication control module acquires data with a risk type of conventional risk, it records the data; when it determines that the data risk type is high-risk data, it directly records the data and immediately sends it to the cloud.

[0029] When the communication control module determines that the communication health of the current road segment is good, it sends both regular risk data and high-risk data. When it determines that the communication health of the current road segment is interference, it sends high-risk data.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. In this invention, when inspecting power lines, the inspection results are initially analyzed using edge computing and local model deployment. Based on the results of the initial analysis, the data type is determined to assess the risk level of the inspection results. Routine and low-risk information is recorded and sent sequentially or returned to the management platform for data migration when network conditions are good. For high-risk and emergency information, data is sent immediately to maximize the timeliness of high-risk information transmission and ensure subsequent maintenance and repair response time.

[0032] 2. In this invention, when inspecting a power system, communication quality is monitored at different locations. When the communication quality meets the standard, the previously recorded inspection results and data can be transmitted back. When the communication quality does not meet the standard, only high-risk data is transmitted back, thereby maximizing the communication channel for the transmission of high-risk data, ensuring transmission stability and speed. At the same time, when the communication quality is too poor, the communication detection results can be traced back, thereby selectively moving the inspection robot to an area with better communication quality during the communication detection for temporary information transmission. Attached Figure Description

[0033] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0034] Figure 1 This is a system block diagram of the present invention;

[0035] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0036] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1:

[0038] Please see Figure 1 - Figure 2 As shown, the high-efficiency data transmission system for power inspection robots integrating 5G communication includes a data processing module, a fault inspection module, an inspection planning module, a communication control module, and a communication detection module. The inspection planning module is used to acquire the travel path of the power inspection robot and divide the travel path. The method for dividing the travel path by the inspection planning module is line density. If the line density is greater than the set threshold, it is determined to be a communication blockage segment. If the line density is not greater than the set threshold, it is determined to be a normal communication segment.

[0039] The inspection planning module obtains the network density along the current path as follows: It obtains the total line length L within a fixed area through the fault inspection module. Simultaneously, it statistically analyzes the line spacing within the current area and calculates the average interval X using an arithmetic mean. The inspection planning module then calculates the standard deviation of the line spacing to obtain the interval dispersion value S. Finally, the inspection planning module uses a formula to calculate the network density ρ based on the total line length, average line interval, and interval dispersion value. Where k is a preset coefficient, the value of k is related to the threshold set for line density, and the formula is a dimensionless calculation.

[0040] The fault inspection module is used for inspection operations when the power inspection robot travels along the path. During the inspection operation, the fault inspection module runs according to the preset inspection program and performs edge computing on the inspection results. The inspection results include visible light images and infrared light images. The fault inspection module uses algorithms to identify and analyze the visible light images to obtain appearance abnormalities in the line. The fault inspection module uses infrared light images to obtain temperature abnormalities in the line. Appearance abnormalities and temperature abnormalities are real-time detection results. Appearance abnormalities include equipment damage, equipment detachment, foreign objects on equipment, and abnormal equipment wrapping. Temperature abnormalities are divided into low-risk high-temperature signals and high-risk high-temperature signals according to the proportion of temperature exceeding the threshold. The real-time detection results are sent to the data processing module.

[0041] After the data processing module obtains the data, it performs local model matching on the appearance anomaly information in the real-time detection results, compares the appearance anomaly information with the big data model, and obtains the data risk type with the highest confidence based on the comparison overlap. The data processing module obtains temperature anomaly information. If it is a high-risk high temperature signal, it is judged as high-risk data. If it is a low-risk high temperature signal, it is judged as regular risk data.

[0042] If any of the abnormal appearance information and abnormal temperature information contain high-risk data, the entire data will be classified as high-risk data. If both the abnormal appearance information and abnormal temperature information are normal-risk data, the entire data will be classified as normal-risk data, and the data risk type will be sent to the communication control module.

[0043] The communication detection module obtains the current road segment through the inspection planning module, and at the same time obtains the driving path through the inspection planning module. The communication normal segments in the driving path are numbered in sequence. When the communication detection module obtains the current road segment, it matches the current road segment with the communication normal segments in the driving path, assigns the same number to the current road segment, and performs communication monitoring when it is in a communication normal segment to obtain the communication health level. The communication health level and the corresponding road segment are then sent to the communication control module.

[0044] The communication detection module performs the following steps for communication detection:

[0045] Step 1: The communication detection module acquires the current communication mode and collects network signal strength, signal-to-noise ratio, and bit error rate data multiple times for the communication mode;

[0046] Step 2: The communication detection module statistically analyzes the network signal strength, signal-to-noise ratio, and bit error rate acquired multiple times, and generates a distribution curve. The method for generating the distribution curve by the communication detection module is as follows:

[0047] The communication detection module plots signal strength curves, signal-to-noise ratio curves, and bit error rate curves with network signal strength, signal-to-noise ratio, and bit error rate on the horizontal axis and the number of occurrences on the vertical axis, respectively.

[0048] Step 3: The communication detection module obtains the distribution weights through the distribution curve, compares the distribution weights with the set weight threshold series, and generates a good communication signal or a communication interference signal based on the comparison results;

[0049] When the communication detection module obtains the distribution weight, it selects the highest point on the distribution curve and multiplies the highest point by a set coefficient to obtain the threshold value. Then, it selects the horizontal coordinates corresponding to all vertical axes that are greater than the threshold value to obtain the signal strength range, signal-to-noise ratio range, and bit error rate range, respectively.

[0050] The weighted threshold sequence obtained by the communication detection module includes a signal strength sequence, a signal-to-noise ratio range sequence, and a bit error rate sequence. It is compared one-to-one with the signal strength range, signal-to-noise ratio range, and bit error rate range, respectively. If the signal strength range is within the set signal strength sequence, the signal-to-noise ratio range is within the set signal-to-noise ratio sequence, and the bit error rate range is within the set bit error rate sequence, then the communication is considered to be good; otherwise, the communication is considered to be interference.

[0051] The communication control module obtains the data risk type. When the data risk type is normal risk data, the communication control module records the data. When the data risk type is determined to be high-risk data, the data is recorded directly and sent to the cloud immediately.

[0052] Simultaneously, the communication health status of the current road segment is obtained. When the communication control module determines that the communication health status of the current road segment is good, it sends regular risk data and high-risk data. When it determines that the communication health status of the current road segment is communication interference, it sends high-risk data.

[0053] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A high-efficiency data transmission system for power inspection robots integrating 5G communication, characterized in that, It includes a data processing module, a fault inspection module, an inspection planning module, a communication control module, and a communication detection module. The inspection planning module is used to acquire the travel path of the power inspection robot and divide the travel path to obtain communication shielded segments and communication normal segments. The fault inspection module is used for the inspection work of the power inspection robot when it travels on the driving path, and performs edge computing on the inspection results to obtain real-time detection results, and sends the real-time detection results to the data processing module. The data processing module is used to perform local model matching on the real-time detection results, match the data risk type according to the ontology model, obtain two data types: high-risk data and regular risk data, and send the data risk type to the communication control module. The communication detection module obtains the current route segment through the inspection planning module, and performs communication monitoring when it is in a normal communication segment to obtain the communication health level, and sends the communication health level and the corresponding route segment to the communication control module; The communication control module acquires the data risk type and the communication health status of the current road segment. It determines the timing of data transmission based on the data risk type, determines the data transmission location based on the communication health status, and finally transmits the data. The inspection planning module divides the driving path by line density. If the line density is greater than the set threshold, it is determined to be a communication blockage segment. If the line density is not greater than the set threshold, it is determined to be a normal communication segment. The method by which the inspection planning module obtains the network density on the current path is as follows: the total length of the line within a fixed area is obtained through the fault inspection module. At the same time, the line spacing in the current area is statistically analyzed, and the average spacing is calculated by arithmetic mean. The inspection planning module calculates the standard deviation of the line spacing to obtain the spacing discrete value. The inspection planning module calculates the total line length, the average line spacing, and the spacing discrete value using a formula to obtain the network density. When performing inspection operations, the fault inspection module runs through a preset inspection program and obtains real-time detection results through edge computing. The inspection results include visible light images and infrared light images. The fault inspection module uses algorithms to identify and analyze the visible light images to obtain abnormal appearance information in the line. The abnormal appearance information includes equipment damage, equipment detachment, foreign objects on the equipment, and abnormal equipment wrapping. The fault inspection module acquires temperature anomaly information in the line through infrared light images. The temperature anomaly information is divided into low-risk high temperature signals and high-risk high temperature signals according to the proportion of temperature exceeding the threshold. The appearance anomaly information and temperature anomaly information are real-time detection results. After obtaining the appearance anomaly information, the data processing module compares the appearance anomaly information with the big data model and obtains the data risk type with the highest confidence level based on the comparison overlap. The data processing module acquires temperature anomaly information. If it is a high-risk high temperature signal, it is determined to be high-risk data; if it is a low-risk high temperature signal, it is determined to be normal risk data. When the communication control module obtains data with a risk type of "normal risk", it records the data. When it determines that the data risk type is "high-risk data", it directly records the data and immediately sends it to the cloud. When the communication control module determines that the communication health of the current road segment is good, it sends both regular risk data and high-risk data. When it determines that the communication health of the current road segment is interference, it sends high-risk data.

2. The high-efficiency data transmission system for power inspection robots integrating 5G communication as described in claim 1, characterized in that, When the communication detection module acquires the current road segment, it assigns a number to the current road segment and simultaneously acquires the driving path through the inspection planning module. It then assigns a number to each segment with normal communication in the driving path in sequence. The communication detection module matches the current road segment with the segments with normal communication in the driving path and assigns them the same number.

3. The high-efficiency data transmission system for power inspection robots integrating 5G communication as described in claim 1, characterized in that, The communication detection module performs the following steps for communication detection: Step 1: The communication detection module acquires the current communication mode and collects network signal strength, signal-to-noise ratio, and bit error rate data multiple times for the communication mode; Step 2: The communication detection module statistically analyzes the network signal strength, signal-to-noise ratio, and bit error rate acquired multiple times, and generates a distribution curve; Step 3: The communication detection module obtains the distribution weight through the distribution curve, compares the distribution weight with the set weight threshold sequence, and generates a good communication signal or a communication interference signal based on the comparison result.

4. The high-efficiency data transmission system for power inspection robots integrating 5G communication as described in claim 1, characterized in that, The method by which the communication detection module generates the distribution curve is as follows: The communication detection module plots signal strength curves, signal-to-noise ratio curves, and bit error rate curves with network signal strength, signal-to-noise ratio, and bit error rate on the horizontal axis and the number of occurrences on the vertical axis, respectively. When the communication detection module obtains the distribution weight, it selects the highest point on the distribution curve and multiplies the highest point by a set coefficient to obtain the threshold value. It then selects the horizontal coordinates corresponding to all vertical axes that are greater than the threshold value to obtain the signal strength range, signal-to-noise ratio range, and bit error rate range, respectively. The weighted threshold sequence obtained by the communication detection module includes a signal strength sequence, a signal-to-noise ratio (SNR) range sequence, and a bit error rate (BER) sequence. These sequences are compared one-to-one with the signal strength range, SNR range, and BER range, respectively. If the signal strength range, SNR range, and BER range are all within the set signal strength sequence, the communication is considered good; otherwise, communication is considered to be interfering.

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