A communication method for power equipment inspection data

Abstract

This invention discloses a communication method for power equipment inspection data, relating to the field of inspection data communication transmission technology. It includes selecting different data communication methods through a calculation and analysis module. This invention can assess the data size and communication method, and the two can work together to dynamically select the communication method based on the real-time size of the inspection data. This significantly improves the flexibility and adaptability of the communication method. Furthermore, when transmitting large amounts of data, this invention can automatically select the most suitable communication method, significantly improving transmission speed, reducing network congestion and latency, ensuring data real-time performance and integrity. Through real-time evaluation of data transmission efficiency, transmission strategies can be further optimized to improve transmission efficiency. The dynamic adjustment of communication methods in this invention avoids resource waste and maximizes the utilization of the advantages of different communication methods, not only improving the overall efficiency of data transmission but also reducing communication costs.

Description

Technical Field

[0001] This invention relates to the field of inspection data communication and transmission technology, specifically a communication method for power equipment inspection data. Background Technology

[0002] Electrical equipment often suffers from leakage or short circuits due to aging, insulation damage, and component failure, which can cause the equipment to overheat and, in severe cases, fire. Therefore, it is necessary to inspect electrical equipment to identify problems and prevent them from happening in the first place. During the inspection, various types of data are collected and then transmitted through communication methods.

[0003] However, current methods for transmitting inspection data typically use only one communication method, making it difficult to use different communication methods depending on the size of the data. This can lead to slow data transmission, network congestion, and increased latency when transmitting large amounts of data, such as high-definition video or large amounts of sensor data, which can seriously affect the real-time performance and availability of the data. Summary of the Invention

[0004] The purpose of this invention is to provide a communication method for power equipment inspection data, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a communication method for power equipment inspection data, comprising the following steps:

[0006] Step 1: Collect temperature data, humidity data, vibration data, infrared image data, and sound data at the target detection location of the power equipment using the data acquisition module;

[0007] Step II: Input temperature data, humidity data, vibration data, infrared image data, and sound data into the processing module. The processing module cleans and denoises the input data to output the original data size. Then, it compresses and encodes the processed data. During this process, the original data size, data compression algorithm, and data encoding method are obtained. Based on the data compression algorithm and data encoding method, the compression ratio and the data expansion rate after encoding are analyzed.

[0008] Step 3: Input the original data size into the calculation and analysis module. The calculation module combines the data compression algorithm, data encoding method, compression ratio, and data expansion rate after encoding to output the preprocessed data size DDP. Based on the processed data size DDP, limit threshold one and limit threshold two are set to select the corresponding communication method. Then, data is transmitted using the selected communication method. During the data transmission process, the transmission time, channel bandwidth, and noise power are recorded. Finally, the transmission efficiency NOC is output based on the transmission time NOCA and the preprocessed data size DDP.

[0009] Step IV: Input the communication method and transmission efficiency NOC corresponding to the preprocessed data size DDP into the adjustment module. The adjustment module adjusts the communication method and optimizes the transmission process according to the transmission efficiency NOC to improve data transmission efficiency.

[0010] Optionally, the calculation and analysis module includes: a data size evaluation submodule, a data communication analysis submodule, and a data transmission efficiency submodule.

[0011] Optionally, the data size evaluation submodule is based on the original data size DDPA and combines data compression algorithm A to output compression ratio DA, and combines data encoding method B to output encoded data expansion rate DB. By combining the original data size DDPA, compression ratio DA and data expansion rate DB, the preprocessed data size DDP is output. Based on the preprocessed data size DDP, the volume of each data type after preprocessing can be accurately calculated, thereby accurately evaluating its transmission requirements.

[0012] The data size assessment submodule provides a quantitative way to measure the volume of data after preprocessing. In power equipment inspection, the data includes various types such as images, videos, and sensor readings, and the data volume may vary greatly. The data size assessment submodule accurately calculates the volume of each data type after preprocessing, thereby accurately assessing its transmission requirements and providing data support for selecting the optimal communication method.

[0013] The data communication analysis submodule further organizes the preprocessed data size DDP, based on...

[0014] The data communication transmission method is selected based on two thresholds: STA (limited by a threshold 1) and STB (limited by a threshold 2). These thresholds represent communication methods for low-bandwidth, medium-bandwidth, and high-bandwidth requirements, respectively. The data communication analysis submodule dynamically selects the appropriate communication method based on the data size, thereby ensuring data transmission quality while reducing transmission costs and time. The choice of communication method directly affects the speed, quality, and cost of data transmission.

[0015] The data communication analysis submodule dynamically selects the most suitable communication method based on the size of the data, thereby ensuring transmission quality while minimizing transmission costs and time.

[0016] Optionally, the data transmission efficiency submodule selects the communication method based on the preprocessed data size DDP, and then performs data transmission based on this communication method to obtain the transmission time NOCA, channel bandwidth W, and noise power NOCN. By combining the transmission time NOCA, channel bandwidth W, noise power NOCN, and preprocessed data size DDP, the transmission efficiency NOC is output. The transmission efficiency NOC is used to measure the data transmission efficiency and performance, thereby evaluating the data transmission performance under different communication methods and selecting the communication method with the best performance.

[0017] Transmission efficiency is an important indicator for measuring data transmission efficiency and performance. By calculating transmission efficiency, we can evaluate the data transmission performance under different communication methods and select the communication method with the best performance. By calculating transmission efficiency and selecting the most efficient communication method, we can improve the efficiency and accuracy of data transmission.

[0018] Optionally, the data communication analysis submodule specifically comprises:

[0019] Communication methods with low bandwidth requirements correspond to slower but more stable connections, including Bluetooth and Zigbee in wireless network environments;

[0020] Communication methods with medium broadband requirements correspond to faster wireless connections, including Wi-Fi, 4G, and 5G mobile networks;

[0021] Communication methods requiring high broadband capabilities correspond to dedicated high-speed wireless links and wired connections.

[0022] Optionally, the data communication analysis submodule specifically comprises:

[0023] Communication methods with low bandwidth requirements correspond to slower but more stable connections, including Bluetooth and Zigbee in wireless network environments;

[0024] Communication methods with medium broadband requirements correspond to faster wireless connections, including Wi-Fi, 4G, and 5G mobile networks;

[0025] Communication methods requiring high broadband capabilities correspond to dedicated high-speed wireless links and wired connections.

[0026] Optionally, in the data communication analysis submodule:

[0027] Communication methods with low bandwidth requirements are characterized by low power consumption and low data rate. These methods are suitable for transmitting small amounts of data and control signals, and have low cost and power consumption.

[0028] Communication methods with medium broadband requirements have medium data rates and better coverage, making them suitable for transmitting medium-sized data and for real-time communication.

[0029] Communication methods with high broadband requirements feature high data rates and low latency, making them suitable for transmitting large amounts of data and high-performance communication.

[0030] Optionally, the data acquisition module uses a power equipment inspection terminal, which includes handheld inspection devices and drone inspections.

[0031] Optionally, the power equipment inspection terminal is equipped with infrared sensors, temperature sensors, and vibration sensors to collect status data of the power equipment. The power equipment inspection terminal is used to collect data on transformers, circuit breakers, and transmission lines of the power equipment. The drone inspection conducts aerial inspection of the power equipment through a preset route and uses onboard sensors to collect data.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] I. This invention outputs the preprocessed data size through a data size evaluation submodule. This submodule provides a quantitative method to measure the volume of data after preprocessing, which is crucial for selecting an appropriate communication method. In power equipment inspection, data includes various types such as images, videos, and sensor readings, and the data volume may vary greatly. This submodule can accurately calculate the volume of each data type after preprocessing, thereby accurately assessing its transmission requirements and providing data support for selecting the optimal communication method. By accurately calculating the preprocessed data size, we can more accurately assess the data transmission requirements, thus avoiding the selection of overly expensive or inefficient communication methods. By accurately calculating the preprocessed data size and selecting an appropriate communication method, we can effectively solve these problems and improve the efficiency and accuracy of data transmission.

[0034] Second, this invention outputs communication methods for low, medium, and high bandwidth requirements through a data communication analysis submodule. This submodule can dynamically select the most suitable communication method based on the data size, thereby minimizing transmission costs and time while ensuring transmission quality. The choice of communication method directly affects the speed, quality, and cost of data transmission. By dynamically selecting the communication method based on the size of the pre-processed data, the most suitable communication method can be selected in each case, thereby maximizing the efficiency and accuracy of data transmission. Dynamically selecting the communication method allows for flexible adjustment of the communication method according to different data sizes and requirements, thereby maximizing data transmission efficiency and reducing costs.

[0035] Third, this invention outputs transmission efficiency through a data transmission efficiency submodule. This submodule can accurately calculate the data transmission efficiency under each communication method, and then select the most efficient communication method to ensure the timeliness and accuracy of inspection data. Transmission efficiency is an important indicator for measuring data transmission efficiency and performance. By calculating the transmission efficiency, the data transmission performance under different communication methods can be evaluated and the optimal communication method can be selected. By calculating the transmission efficiency and selecting the most efficient communication method, the efficiency and accuracy of data transmission can be improved. By continuously optimizing the communication method and transmission process, the transmission efficiency can be further improved and the cost reduced.

[0036] IV. This invention iteratively adjusts the compression ratio based on transmission efficiency to continuously optimize and improve the processed data size, the analysis results of the data communication analysis submodule, and the transmission efficiency. By iteratively adjusting the compression ratio, the data volume can be reduced while ensuring data integrity, thereby accelerating transmission speed and reducing latency. Dynamic adjustment allows for the selection of the most suitable communication method and compression ratio based on current network conditions, device performance, and other factors, improving communication flexibility and reliability. Iterative adjustment of the compression ratio allows for real-time updates of the transmission efficiency value, bringing it closer to a predetermined threshold, thereby improving overall transmission efficiency. This method can select multiple communication methods according to different situations, improving communication flexibility and adaptability. By precisely controlling the compression ratio and selecting appropriate communication methods, this method can significantly improve data transmission efficiency and reduce latency and transmission costs. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the steps of the communication method for power equipment inspection data.

[0038] Figure 2 This is a schematic diagram of the overall structure of the communication method for power equipment inspection data.

[0039] Figure 3 This is a schematic diagram of the calculation and analysis module in the communication method for power equipment inspection data. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] The communication method for power equipment inspection data differs from existing methods. Existing methods typically only support one communication mode and cannot adapt to different communication modes based on data size. This leads to slow data transmission and delays when transmitting large amounts of data. Furthermore, existing technologies may lack a mechanism for real-time evaluation and dynamic adjustment of the communication mode based on data size, resulting in insufficient resource utilization and low communication efficiency.

[0042] The communication module for power equipment inspection data, through the coordinated operation of data size assessment and communication mode selection, dynamically selects the communication mode based on the real-time size of the inspection data. This significantly improves the flexibility and adaptability of the communication method. When transmitting large amounts of data, this invention can automatically select the most suitable communication mode, significantly improving transmission speed, reducing network congestion and latency, and ensuring data real-time performance and integrity. Furthermore, through real-time evaluation of the data transmission efficiency formula, the transmission strategy can be further optimized to improve transmission efficiency. Moreover, by dynamically adjusting the communication mode, this invention avoids resource waste and maximizes the utilization of the advantages of different communication methods. This not only improves the overall efficiency of data transmission but also reduces communication costs.

[0043] Example 1: Please refer to Figures 1 to 3 This embodiment provides a communication method for power equipment inspection data, including the following steps:

[0044] Step 1: Collect temperature data, humidity data, vibration data, infrared image data, and sound data at the target detection location of the power equipment using the data acquisition module;

[0045] Step II: Input temperature data, humidity data, vibration data, infrared image data, and sound data into the processing module. The processing module cleans and denoises the input data to output the original data size, and then compresses and encodes the processed data.

[0046] Step 3: Input the original data size into the calculation and analysis module. The calculation module first outputs the preprocessed data size DDP, and selects the corresponding communication method according to the preprocessed data size DDP. Then, it uses the selected communication method to transmit data and records the transmission time NOCA. Finally, it outputs the transmission efficiency NOC based on the transmission time NOCA and the preprocessed data size DDP.

[0047] Step IV: Input the preprocessed data size DDP, the corresponding communication method, and the transmission efficiency NOC into the adjustment module. The adjustment module adjusts the communication method and optimizes the transmission process according to the transmission efficiency NOC to improve data transmission efficiency.

[0048] The calculation and analysis module includes: a data size assessment submodule, a data communication analysis submodule, and a data transmission efficiency submodule.

[0049] In this embodiment, the three sub-modules in the computational analysis module provide the outstanding substantive features and significant progress of this method. This method can dynamically select the most suitable communication method based on data of different sizes, thereby fully utilizing the potential of the communication network and improving the efficiency and accuracy of data transmission. By accurately calculating the pre-processed data size (DDP) and transmission efficiency (NOC), this method can quantitatively evaluate the data transmission requirements and performance, providing data support for selecting the optimal communication method. This method is not only applicable to the communication of power equipment inspection data but can also be widely applied to other scenarios requiring dynamic selection of communication methods. By dynamically selecting communication methods and optimizing the transmission process, this method can significantly improve data transmission efficiency and reduce costs. By accurately calculating and processing data size and selecting the communication method with optimal performance, this method can ensure the accuracy and reliability of data transmission. This method provides a new approach and solution for the communication of power equipment inspection data and is expected to promote technological development and innovation in related fields.

[0050] Please see Figures 1 to 3 The data size evaluation submodule processes data as follows:

[0051] ;

[0052] in:

[0053] DDP refers to the size of the preprocessed data, DDPA refers to the size of the original data, A refers to the data compression algorithm, DA refers to the compression ratio 0<DA≤1, B refers to the data encoding method, and DB refers to the data expansion rate after encoding DB≥1.

[0054] The data size assessment submodule processes data as follows: the original data size DDPA is input into the data size assessment submodule, and the preprocessed data size DDP is output based on the compression ratio DA and the data expansion rate DB after encoding.

[0055] In this embodiment: First, data compression algorithm A in this submodule represents one or more algorithms for reducing data size. These algorithms reduce data size by identifying redundant information in the data, such as repeated data blocks and predictable patterns, and deleting or replacing them with a more compact representation. Specifically, the LZ77 algorithm can be used. This algorithm is a dictionary-based compression algorithm that achieves compression by finding and replacing repeated phrases. This algorithm is widely used in the compression of text and binary data. Huffman coding is used, which is a lossless data compression algorithm that assigns different length codes to characters based on their frequency of occurrence. Huffman coding performs well in the compression of text, image, and audio data.

[0056] Compression ratio DA refers to the percentage by which data compression algorithm A can reduce the size of the original data. Compression ratio DA is usually expressed as the ratio of the original data size to the compressed data size. Compression ratio provides a quantitative metric for evaluating the performance of compression algorithms.

[0057] Data encoding method B refers to the process of converting data into a specific format or representation. Encoding methods may involve bit-level representation of data, character set selection, error detection and correction, etc. The encoding method determines the representation of data in the computer system, thus affecting the storage and transmission of data. Specifically, it can be ASCII encoding, which is used to represent English characters and numbers, or Unicode encoding, which has a wide range of applications. It can also use binary encoding, which directly converts data into binary form for storage and transmission. This encoding method is simple and efficient, but may not be suitable for directly representing human-readable characters.

[0058] Data inflation rate (DB) after encoding refers to the percentage increase in data size after encoding. It is usually expressed as the ratio of the size of the encoded data to the size of the original data.

[0059] This submodule calculates the final size of the data after compression and encoding. Therefore, the purpose of this submodule is:

[0060] In many applications, data acquisition and processing need to be performed in real time. If data is compressed and encoded immediately after each acquisition before its size is measured, this significantly increases processing latency and may impact overall system performance. This submodule allows for rapid estimation of the pre-processed data size without actual compression and encoding, which is crucial for system design and resource planning. Different data compression algorithms and encoding methods may have varying performance and effects. Even for the same algorithm and encoding method, compression and expansion rates can differ depending on the data content. Therefore, it is difficult to accurately predict the compressed and encoded data size before actual data acquisition. This submodule provides a statistically and empirically based estimate, aiding in better planning and management. When managing data resources, directly collecting compressed and encoded data may involve modifying or transforming the original data, which could affect data integrity and consistency. This submodule allows estimation without changing the original data, thus ensuring data integrity and consistency. In practical applications, the system may need to process different types and formats of data and may need to adjust the compression algorithm and encoding method according to requirements. This submodule can easily adapt to these changes without performing actual compression and encoding operations on each dataset. In some cases, direct compression and encoding operations may involve additional hardware and software resources, as well as related maintenance and management costs. Using this submodule can be a more cost-effective alternative, especially when resources are limited.

[0061] This submodule is used to evaluate the preprocessed data size (DDP). It is calculated based on the original data size (DDPA), compression ratio (DA), and encoded data expansion rate (DB). The importance of this formula lies in providing a quantitative method to measure the volume of data after preprocessing. This is crucial for selecting an appropriate communication method. In power equipment inspection, data may include various types such as images, videos, and sensor readings, and the data volume may vary greatly. This submodule allows us to accurately calculate the volume of each data type after preprocessing, thereby more accurately assessing its transmission requirements and providing data support for selecting the optimal communication method. DDP, the preprocessed data size, directly determines the selection of subsequent communication methods and transmission costs. By accurately calculating DDP, we can more accurately assess data transmission requirements, thus avoiding the selection of overly expensive or inefficient communication methods. In existing technologies, the inability to dynamically select communication methods based on data size easily leads to slow transmission and latency issues when transmitting large amounts of data. By accurately calculating DDP and selecting an appropriate communication method, we can effectively solve these problems and improve the efficiency and accuracy of data transmission.

[0062] Please see Figures 1 to 3The data communication analysis submodule processes data as follows:

[0063] The preprocessed data size DDP is input into the data communication analysis submodule;

[0064] If DDP≤STA, choose the communication method with low bandwidth requirements;

[0065] Communication methods with low bandwidth requirements correspond to slower but more stable connections, including Bluetooth and Zigbee in wireless network environments;

[0066] Communication methods with low bandwidth requirements are characterized by low power consumption and low data rate. These methods are suitable for transmitting small amounts of data and control signals, and have low cost and power consumption.

[0067] If STA < DDP ≤ STB, choose the communication method with medium bandwidth requirements;

[0068] Communication methods with medium broadband requirements correspond to faster wireless connections, including Wi-Fi, 4G, and 5G mobile networks;

[0069] Communication methods with medium broadband requirements have medium data rates and better coverage, making them suitable for transmitting medium-sized data and for real-time communication.

[0070] When DDP > STB, select the communication method that meets the high bandwidth requirements;

[0071] Communication methods requiring high broadband capabilities correspond to dedicated high-speed wireless links and wired connections.

[0072] Communication methods with high broadband requirements have high data rates and low latency, making them suitable for transmitting large amounts of data and high-performance communication.

[0073] in:

[0074] STA refers to threshold threshold one, where STA is 5MB; STB refers to threshold threshold two, where STB is 100MB.

[0075] The data communication analysis submodule processes as follows: The preprocessed data size DDP is input into the data communication analysis submodule, and communication methods with low, medium and high bandwidth requirements are output based on the limited threshold STA and limited threshold STB.

[0076] In this embodiment, this submodule compares the preprocessed data size DDP with set thresholds STA and STB to select an appropriate communication method. This provides a flexible and efficient way to handle data transmission needs of varying sizes. In power equipment inspection, the amount of data to be transmitted can sometimes be very large, such as high-definition video, while at other times it can be small, such as simple sensor readings. This submodule can dynamically select the most suitable communication method based on the data size, thereby ensuring transmission quality while minimizing transmission costs and time. The choice of communication method directly affects the speed, quality, and cost of data transmission. By dynamically selecting the communication method based on DDP, we can ensure that the most suitable communication method is selected in every situation, thereby maximizing the efficiency and accuracy of data transmission. In existing technologies, because the communication method is fixed, the potential of the communication network cannot be fully utilized when transmitting data of different sizes. However, by dynamically selecting the communication method, we can flexibly adjust the communication method according to different data sizes and needs, thereby maximizing the efficiency of data transmission and reducing costs.

[0077] Please see Figures 1 to 3 The data transmission efficiency submodule processes the data as follows:

[0078] ;

[0079] in:

[0080] NOC refers to transmission efficiency, NOCA refers to transmission time, W refers to channel bandwidth, NOCS refers to signal power, NOCN refers to noise power, NOCS / NOCN refers to signal-to-noise ratio, DDP / NOCA refers to data transmission rate, and QA refers to time efficiency factor. QA takes into account various overheads during transmission, such as protocol overhead and error retransmission. QA is usually less than 1.

[0081] The data transmission efficiency submodule processes data as follows: the preprocessed data size DDP is input into the data transmission efficiency submodule, and the transmission efficiency NOC is output based on the transmission time NOCA and the channel bandwidth W.

[0082] In this embodiment, this submodule is used to calculate data transmission efficiency, considering multiple factors such as transmission time, channel bandwidth, and signal-to-noise ratio. The importance of this submodule lies in its provision of a quantitative indicator to measure data transmission efficiency and performance. In power equipment inspection, the level of data transmission efficiency directly affects the real-time performance and accuracy of the inspection. This submodule can accurately calculate the data transmission efficiency under each communication method, thereby selecting the most efficient communication method to ensure the timeliness and accuracy of inspection data. Transmission efficiency (NOC) is an important indicator for measuring data transmission efficiency and performance. By calculating NOC, we can evaluate the data transmission performance under different communication methods and select the optimal communication method. In existing technologies, the inability to accurately evaluate the data transmission efficiency under different communication methods often leads to blind selection of communication methods. Calculating NOC and selecting the most efficient communication method can effectively solve this problem, improving the efficiency and accuracy of data transmission. Furthermore, by continuously optimizing communication methods and transmission processes, we can further improve transmission efficiency and reduce costs.

[0083] It is worth noting that, based on the comparative analysis of transmission efficiency NOC and transmission efficiency threshold TS, the compression ratio DA in the data size assessment submodule is iteratively evaluated to continuously optimize and improve the processed data size DDP, the analysis results of the data communication analysis submodule, and the transmission efficiency NOC.

[0084] The specific work process is as follows:

[0085] If the transmission efficiency NOC ≤ TS, then proceed with the iteration.

[0086] The formula for calculating the iteration is: DA new =DA old ×(1+∆DAP); The maximum number of iterations is 100;

[0087] If the transmission efficiency NOC > TS, then stop the iteration and output the final compression ratio DA and transmission efficiency NOC.

[0088] in:

[0089] TS refers to the transmission efficiency threshold, with TS set to 0.75 and DA... new Refers to the compression ratio and DA after iteration. old The compression ratio before iteration is referred to as ΔDAP, which refers to the adjustment amount of the compression ratio.

[0090] In this embodiment: Inspection data typically relies on a fixed communication method, unable to be flexibly adjusted based on data size. This iterative method, however, dynamically adjusts the compression ratio and communication method according to the size and importance of different data, thereby optimizing transmission efficiency. For large amounts of memory, iteratively adjusting the compression ratio can reduce data volume while ensuring data integrity, thus accelerating transmission and reducing latency. Dynamic adjustment allows for the selection of the most suitable communication method and compression ratio based on current network conditions, device performance, and other factors, improving communication flexibility and reliability. Iteratively adjusting the compression ratio allows for real-time updates to the transmission efficiency (NOC) value, bringing it closer to a predetermined threshold, thereby improving overall transmission efficiency. The iterative process enables the compression ratio (DA) to dynamically adjust the adjustment amount (∆DAP) based on the difference between the current transmission efficiency and the predetermined threshold, achieving precise control over the compression ratio. During the iteration process, the compression ratio can be adjusted according to the current transmission efficiency, data size, and network conditions. This method dynamically selects the most suitable communication method, such as satellite communication, mobile communication networks, or wired networks, based on factors such as network conditions. Existing technologies typically only allow for fixed communication methods and compression rates. However, this method achieves dynamic optimization of communication methods and compression rates through iterative adjustments. Unlike existing technologies that can only satisfy one communication method, this method can select multiple communication methods according to different situations, improving the flexibility and adaptability of communication. By precisely controlling the compression rate and selecting appropriate communication methods, this method can significantly improve data transmission efficiency, reduce latency and transmission costs. Under this iterative application, there is a faster data transmission speed and lower latency, enabling inspection personnel to obtain equipment status information more promptly and improving inspection efficiency. By optimizing the communication method and compression rate, the bandwidth and storage resources required for data transmission can be reduced, thereby reducing operation and maintenance costs. While ensuring data transmission efficiency, data security can be enhanced through encryption and other technologies to prevent data leakage and tampering.

[0091] In the specific implementation process, the various sub-modules in this method are used to form the overall system architecture. By inputting the original data size DDPA into the data size evaluation sub-module, the preprocessed data size DDP is output. This sub-module provides a quantitative method to measure the volume of data after preprocessing, which is crucial for subsequent selection of appropriate communication methods. In power equipment inspection, the data includes various types such as images, videos, and sensor readings, and the data volume may vary greatly. This sub-module can accurately calculate the volume of each data type after preprocessing, thereby more accurately assessing its transmission requirements and providing data support for selecting the optimal communication method. By accurately calculating DDP, the data transmission requirements can be more accurately assessed, thereby avoiding the selection of overly expensive or inefficient communication methods. By accurately calculating DDP and selecting appropriate communication methods, we can effectively solve these problems and improve the efficiency and accuracy of data transmission.

[0092] By inputting the preprocessed data size (DDP) into the data communication analysis submodule, the module outputs communication methods for low, medium, and high bandwidth requirements. This submodule can dynamically select the most suitable communication method based on the data size, thereby minimizing transmission costs and time while ensuring transmission quality. The choice of communication method directly affects the speed, quality, and cost of data transmission. By dynamically selecting the communication method based on the DDP, the most suitable communication method can be selected in each case, thereby maximizing the efficiency and accuracy of data transmission. Dynamically selecting the communication method allows for flexible adjustment of the communication method according to different data sizes and requirements, thereby maximizing data transmission efficiency and reducing costs.

[0093] By inputting the preprocessed data size DDP into the data transmission efficiency submodule, the transmission efficiency NOC can accurately calculate the data transmission efficiency under each communication method. This allows the selection of the most efficient communication method to ensure the timeliness and accuracy of inspection data. The transmission efficiency NOC is an important indicator for measuring data transmission efficiency and performance. By calculating the transmission efficiency NOC, the data transmission performance under different communication methods can be evaluated and the optimal communication method can be selected. Calculating the transmission efficiency NOC and selecting the most efficient communication method can improve the efficiency and accuracy of data transmission. Furthermore, by continuously optimizing the communication method and transmission process, we can further improve transmission efficiency and reduce costs.

[0094] Based on a comparative analysis of transmission efficiency (NOC) and transmission efficiency threshold (TS), the compression ratio (DA) in the data size assessment submodule is iteratively adjusted to continuously optimize and improve the processed data size (DDP), the analysis results of the data communication analysis submodule, and the transmission efficiency (NOC). By iteratively adjusting the compression ratio, the data volume can be reduced while ensuring data integrity, thereby accelerating transmission speed and reducing latency. Through dynamic adjustment, the most suitable communication method and compression ratio can be selected based on current network conditions, equipment performance, and other factors, improving the flexibility and reliability of communication. By iteratively adjusting the compression ratio, the value of transmission efficiency (NOC) can be updated in real time to bring it closer to the predetermined threshold, thereby improving overall transmission efficiency. This method can select multiple communication methods according to different situations, improving the flexibility and adaptability of communication. By precisely controlling the compression ratio and selecting appropriate communication methods, this method can significantly improve data transmission efficiency and reduce latency and transmission costs.

[0095] This allows the various sub-modules to cooperate and calculate in pairs, and also enables overall looping and iteration, giving the overall system an automated optimization and update effect, thus improving its adaptability.

[0096] Example 2: Please refer to Figure 1 , Figure 2 and Figure 3 The data acquisition module utilizes a power equipment inspection terminal, which includes a handheld inspection device and a drone inspection system. The power equipment inspection terminal is equipped with infrared sensors, temperature sensors, and vibration sensors to collect status data of the power equipment. It is used to collect data from transformers, circuit breakers, and transmission lines. The drone inspection system conducts aerial inspections of the power equipment along a pre-set flight path, utilizing onboard sensors to collect data.

[0097] In this embodiment: the data acquisition module can perform excellent inspections of power equipment. With the cooperation of drones and handheld inspection devices, it can cover most existing power equipment and inspect key locations on the power equipment, such as transformers, circuit breakers and transmission lines, to fully obtain the operating status data of the power equipment. Accurate data collection helps with subsequent data analysis and organization, and facilitates observation by management personnel.

[0098] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A communication method for power equipment inspection data, characterized in that: Includes the following steps: Step 1: Collect temperature data, humidity data, vibration data, infrared image data, and sound data at the target detection location of the power equipment using the data acquisition module; Step II: Input temperature data, humidity data, vibration data, infrared image data, and sound data into the processing module. The processing module cleans and denoises the input data to output the original data size. Then, it compresses and encodes the processed data. During this process, the original data size, data compression algorithm, and data encoding method are obtained. Based on the data compression algorithm and data encoding method, the compression ratio and the data expansion rate after encoding are analyzed. Step 3: Input the original data size into the calculation and analysis module. The calculation module combines the data compression algorithm, data encoding method, compression ratio, and data expansion rate after encoding to output the preprocessed data size DDP. Based on the processed data size DDP, limit threshold one and limit threshold two are set to select the corresponding communication method. Then, data is transmitted using the selected communication method. During the data transmission process, the transmission time, channel bandwidth, and noise power are recorded. Finally, the transmission efficiency NOC is output based on the transmission time NOCA and the preprocessed data size DDP. Step IV: Input the communication method and transmission efficiency NOC corresponding to the preprocessed data size DDP into the adjustment module. The adjustment module adjusts the communication method and optimizes the transmission process according to the transmission efficiency NOC to improve data transmission efficiency. The calculation and analysis module includes a data size evaluation submodule, a data communication analysis submodule, and a data transmission efficiency submodule; The data size assessment submodule is based on the original data size DDPA and combines data compression algorithm A to output compression ratio DA, and combines data encoding method B to output encoded data expansion rate DB. By combining the original data size DDPA, compression ratio DA and data expansion rate DB, the preprocessed data size DDP is output. Based on the preprocessed data size DDP, the volume of each data type after preprocessing can be accurately calculated, thereby accurately assessing its transmission requirements. The data communication analysis submodule further organizes the preprocessed data size DDP and selects the data communication transmission mode based on the limited threshold STA and the limited threshold STB. These modes are for low-bandwidth, medium-bandwidth, and high-bandwidth requirements, respectively. The data communication analysis submodule dynamically selects the appropriate communication mode based on the data size, thereby ensuring data transmission quality while reducing transmission costs and time. The choice of communication mode directly affects the speed, quality, and cost of data transmission. The data transmission efficiency submodule processes data as follows: The ; Wherein: DDP refers to the size of the preprocessed data, NOC refers to the transmission efficiency, NOCA refers to the transmission time, W refers to the channel bandwidth, NOCS refers to the signal power, NOCN refers to the noise power, NOCS / NOCN refers to the signal-to-noise ratio, DDP / NOCA refers to the data transmission rate, and QA refers to the time efficiency factor. The data transmission efficiency submodule selects the communication method based on the preprocessed data size DDP, and then performs data transmission based on this communication method to obtain the transmission time NOCA, channel bandwidth W, and noise power NOCN. By combining the transmission time NOCA, channel bandwidth W, noise power NOCN, and preprocessed data size DDP, the transmission efficiency NOC is output. The transmission efficiency NOC is used to measure the data transmission efficiency and performance, thereby evaluating the data transmission performance under different communication methods and selecting the communication method with the best performance.

2. The communication method for power equipment inspection data according to claim 1, characterized in that: The data communication analysis submodule is specifically as follows: Communication methods with low bandwidth requirements correspond to slower but more stable connections, including Bluetooth and Zigbee in wireless network environments; Communication methods with medium broadband requirements correspond to faster wireless connections, including Wi-Fi, 4G, and 5G mobile networks; Communication methods requiring high broadband capabilities correspond to dedicated high-speed wireless links and wired connections.

3. The communication method for power equipment inspection data according to claim 1, characterized in that: In the data communication analysis submodule: Communication methods with low bandwidth requirements are characterized by low power consumption and low data rate. These methods are suitable for transmitting small amounts of data and control signals, and have low cost and power consumption. Communication methods with medium broadband requirements have medium data rates and better coverage, making them suitable for transmitting medium-sized data and for real-time communication. Communication methods with high broadband requirements feature high data rates and low latency, making them suitable for transmitting large amounts of data and high-performance communication.

4. The communication method for power equipment inspection data according to claim 2, characterized in that: The data acquisition module uses a power equipment inspection terminal, which includes handheld inspection devices and drone inspections.

5. The communication method for power equipment inspection data according to claim 4, characterized in that: The power equipment inspection terminal is equipped with infrared sensors, temperature sensors, and vibration sensors to collect status data of power equipment. The terminal is used to collect data on transformers, circuit breakers, and transmission lines of power equipment. The drone inspection conducts aerial inspections of power equipment along a preset route and collects data using onboard sensors.

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