Power distribution automation non-inductive operation and maintenance method and system

Abstract

According to the power distribution automation non-inductive operation and maintenance method and system, an intelligent acquisition box is installed on a power distribution tower feeder terminal device, and an intelligent data node with a signal acquisition and transmission function is constructed; performing dynamic key generation and protocol adaptation according to hardware features of the intelligent data node, and establishing a secure communication link with a unique identifier; deploying the operation and maintenance software to the terminal equipment through the secure communication link to form an operation and maintenance platform with a multi-protocol analysis capability; sending an instruction to the data node through the operation and maintenance platform, collecting, compressing and processing equipment operation data, and obtaining a standardized data stream; inputting the data flow into a fault feature model for analysis, and generating a diagnosis report and a processing strategy; and issuing an instruction to the data node according to the strategy to complete parameter optimization and verification. Safe, efficient and low-power-consumption remote operation and maintenance of power distribution automation equipment are realized, and meanwhile, the safety of data transmission and the general adaptability of the system are ensured.

Description

Technical Field

[0001] The present application belongs to the field of sensorless operation and maintenance technology, and in particular, relates to a sensorless operation and maintenance method and system for distribution automation. Background Art

[0002] At present, the distribution automation system plays an important role in the operation and maintenance of power grids. The existing distribution automation operation and maintenance technology mainly adopts manual climbing of poles or entering stations for maintenance and troubleshooting. This traditional operation and maintenance mode requires operation and maintenance personnel to bring special equipment to the site, establish a communication connection with the feeder terminal equipment through physical connection, and then perform operations such as data collection, fault diagnosis and parameter adjustment. In addition, some remote operation and maintenance systems have also appeared in the prior art, which realize remote data collection and control functions by installing communication modules on the distribution equipment, but these systems often require complex wiring projects and are prone to communication failures in harsh environments.

[0003] However, the existing technology has the following shortcomings: First, traditional manual pole climbing operations have great safety hazards and low efficiency, especially in severe weather conditions or when the equipment is installed at a high position, the operation is more difficult; second, the existing remote operation and maintenance systems generally have problems such as complex installation, high power consumption, and poor communication stability, which are difficult to meet the requirements of long-term field operation; finally, due to the large number of brands and models of distribution equipment and the different communication protocols of different equipment, the operation and maintenance system has poor versatility, which increases the difficulty of system deployment and maintenance. Summary of the invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a distribution automation non-sensing operation and maintenance method and system, which is used to realize safe, efficient and low-power consumption remote operation and maintenance of distribution automation equipment without pole climbing operations, while ensuring the security of data transmission and the universal adaptability of the system.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a distribution automation non-sensing operation and maintenance method, comprising:

[0007] Install the intelligent data acquisition box on the feeder terminal equipment of the distribution tower according to the modular installation rules to build an intelligent data node with signal acquisition and transmission functions;

[0008] Dynamic encryption key generation and protocol adaptation are performed based on the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier;

[0009] Deploy distributed operation and maintenance software to terminal devices based on secure communication links to form an intelligent operation and maintenance platform with multi-protocol parsing capabilities;

[0010] Using the intelligent operation and maintenance platform to send a collection instruction to the intelligent data node, the collected raw data of the equipment operation is processed with low power consumption compression to generate a standardized data stream;

[0011] Input standardized data streams into the preset fault feature model, generate real-time diagnostic reports through multi-dimensional correlation analysis, and form a processing strategy including fault levels;

[0012] According to the processing strategy, control instructions are sent to the intelligent data node via the secure communication link to complete remote parameter optimization and obtain verification data packets.

[0013] Preferably, the intelligent data acquisition box is installed on the feeder terminal device of the distribution tower according to the modular installation rules to construct an intelligent data node with signal acquisition and transmission functions, including:

[0014] The carbon fiber shell of the intelligent acquisition box is waterproofed and sealed to obtain a basic shell with an IP67 protection grade, and the internal space of the basic shell is divided to obtain a signal processing cabin and a power supply cabin;

[0015] The power supply module in the power supply compartment is configured with low power consumption through an adaptive power management algorithm to obtain a stable operating voltage, and a communication chip is installed in the signal processing compartment to obtain a data processing unit;

[0016] The data processing unit is fixedly installed based on the mortise and tenon structure to form a welding-free connection structure, and the data processing unit is connected to the serial port of the feeder terminal device using a waterproof interface to obtain an initial data path;

[0017] Directional installation of signal enhancement antennas is performed to establish wireless signal coverage areas, and the optimal installation position of antennas is determined through signal strength detection to obtain signal transmission channels;

[0018] The data path is used to collect the operating parameters of the feeder terminal equipment, perform data preprocessing and caching, obtain the equipment status information, and send the equipment status information to the intelligent data node through the signal transmission channel;

[0019] The device status information is verified through the data integrity verification algorithm to confirm the available status of the intelligent data node, and a node online confirmation signal is generated to obtain an intelligent data node with signal collection and transmission functions.

[0020] Preferably, dynamic encryption key generation and protocol adaptation are performed according to the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier, including:

[0021] Extracting hardware features of the intelligent data node to obtain a feature information table including a device identification code and hardware parameters, and dividing the feature information table into encryption levels according to a hierarchical security strategy to obtain a hierarchical encryption scheme;

[0022] Extract the device identification code from the hierarchical encryption scheme, generate a dynamic session key in combination with the timestamp information, obtain the device authentication credential, and write the device authentication credential into the encryption chip to obtain the encrypted communication basis;

[0023] Identify the communication protocol type of the feeder terminal device based on the encrypted communication foundation, generate a protocol mapping table, obtain a protocol conversion rule, and establish a data exchange channel through the protocol conversion rule to obtain a protocol adaptation interface;

[0024] The communication data is encrypted and packaged using the protocol adapter interface to form a secure data packet, and the secure data packet is authenticated using the device authentication credentials to obtain a trusted data stream;

[0025] Perform communication quality detection on trusted data streams, establish transmission quality evaluation indicators, obtain link status information, and select the optimal transmission path based on the link status information to obtain a secure communication link with a unique identifier.

[0026] Preferably, the distributed operation and maintenance software is deployed to the terminal device based on a secure communication link to form an intelligent operation and maintenance platform with multi-protocol parsing capabilities, including:

[0027] Perform link quality detection on the secure communication link to obtain a communication bandwidth parameter table, and divide the software deployment task queue based on the communication bandwidth parameter table to obtain a distributed deployment solution;

[0028] Send the software infrastructure package to the terminal device, obtain the operating environment configuration data, and generate the operation and maintenance software deployment list according to the operating environment configuration data to obtain the software module distribution strategy;

[0029] The communication protocol library of the feeder terminal device is loaded into the terminal device according to the software module distribution strategy to obtain a multi-protocol parsing engine, and the protocol compatibility check is performed on the multi-protocol parsing engine to obtain a protocol parsing result;

[0030] Build the equipment management database according to the protocol analysis results to obtain equipment archive information, and establish the equipment operation parameter table through the equipment archive information to obtain the operation and maintenance monitoring benchmark;

[0031] Associating the human-computer interaction component with the operation and maintenance monitoring benchmark to obtain a visual operation interface, and configuring a data display template according to the visual operation interface to obtain an operation and maintenance management interface;

[0032] By integrating the multi-protocol parsing engine and device archive information through the operation and maintenance management interface, the software function verification is completed, and an intelligent operation and maintenance platform with multi-protocol parsing capabilities is obtained.

[0033] Preferably, the intelligent operation and maintenance platform is used to send a collection instruction to the intelligent data node, and the collected raw data of the equipment operation is processed with low power consumption compression to generate a standardized data stream, including:

[0034] Extract the collection parameter list from the intelligent operation and maintenance platform to form a data collection task list, and classify the data collection task list according to the device type to obtain the classified collection instructions;

[0035] Encrypt and package the classified collection instructions to obtain a secure collection instruction set, and send the secure collection instruction set to the feeder terminal device through the intelligent data node to obtain a device response signal;

[0036] Collecting the original data of device operation based on the device response signal to obtain an operation status data packet, and performing low-power compression on the operation status data packet through a data compression algorithm to obtain a compressed data set;

[0037] Reorganize the compressed data set according to the communication protocol format to obtain a standard data structure, and perform data verification on the standard data structure to obtain a valid data packet;

[0038] Convert valid data packets into a standard data format to obtain data in a unified format, and timestamp the data in the unified format to obtain a data stream with timing information;

[0039] The data stream with time series information is subjected to noise reduction and filtering to remove outliers and obtain a standardized data stream.

[0040] Preferably, the standardized data stream is input into a preset fault feature model, a real-time diagnostic report is generated through multi-dimensional correlation analysis, and a processing strategy including fault levels is formed, including:

[0041] Extract features from the standardized data stream to obtain the equipment operation feature set, and perform trend analysis on the equipment operation feature set through a time series association algorithm to obtain a feature association graph;

[0042] Based on the characteristic association map, the abnormal degree of each parameter is calculated to obtain an abnormal indicator set, and the abnormal indicator set is graded by threshold to obtain a graded fault indicator;

[0043] Match the graded fault indicators with the historical fault database to obtain the fault type determination result, and perform risk assessment based on the fault type determination result to obtain the fault risk level;

[0044] Construct real-time diagnostic information according to the fault risk level, obtain a diagnostic result report, and extract key information from the diagnostic result report to obtain a real-time diagnostic report;

[0045] Extract fault handling suggestions from real-time diagnostic reports, generate a list of treatment plans, and prioritize the list of treatment plans to obtain an emergency response plan;

[0046] The emergency response plan is combined with the equipment operation constraints to obtain parameter adjustment suggestions, and control limits are set according to the parameter adjustment suggestions to obtain a processing strategy that includes the fault level.

[0047] Preferably, issuing control instructions to the intelligent data node via a secure communication link according to the processing strategy to complete remote parameter optimization and obtain a verification data packet includes:

[0048] Convert the parameter adjustment suggestions in the processing strategy into a device control instruction set to obtain a parameter adjustment sequence, and set the control operation steps according to the parameter adjustment sequence to obtain a control execution plan;

[0049] Generate an instruction control message according to the control execution plan to obtain a security control instruction, and encrypt and transmit the security control instruction through a secure communication link to obtain a trusted control instruction;

[0050] Send the trusted control instructions to the intelligent data node, obtain the instruction response status, and monitor the instruction response status in real time to obtain control execution feedback;

[0051] Based on the feedback from the control execution, the operating parameter change data is collected to obtain the parameter adjustment effect data, and the parameter adjustment effect data is compared and analyzed to obtain the control response result;

[0052] The compliance of the control response results is checked through the parameter verification program to obtain the parameter validity report, and the equipment status is evaluated based on the parameter validity report to obtain the control verification result;

[0053] The control verification result is packaged into a data message to obtain a control process record, and the control process record is formatted according to the communication specification to obtain a verification data packet.

[0054] The present invention also proposes a distribution automation sensorless operation and maintenance system, comprising:

[0055] The acquisition module is used to install the intelligent acquisition box on the feeder terminal equipment of the distribution tower according to the modular installation rules to build an intelligent data node with signal acquisition and transmission functions;

[0056] An adaptation module, used to perform dynamic encryption key generation and protocol adaptation according to the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier;

[0057] Deployment module, used to deploy distributed operation and maintenance software to terminal devices based on secure communication links, forming an intelligent operation and maintenance platform with multi-protocol parsing capabilities;

[0058] The sending module is used to use the intelligent operation and maintenance platform to send collection instructions to the intelligent data node, perform low-power compression processing on the collected raw data of equipment operation, and generate a standardized data stream;

[0059] The input module is used to input the standardized data stream into the preset fault feature model, generate a real-time diagnosis report through multi-dimensional correlation analysis, and form a processing strategy including the fault level;

[0060] The sending module is used to send control instructions to the intelligent data node via the secure communication link according to the processing strategy, complete remote parameter optimization and obtain verification data packets.

[0061] The present invention is also a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0062] The present invention is also a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0063] The beneficial effects of the present invention are that, compared with the prior art, at least the equipment installation without high-altitude operation is realized through the modular installation of the intelligent acquisition box, which reduces the safety risks of the operation and maintenance personnel, and the environmental adaptability and continuous working ability of the equipment are improved by adopting waterproof sealing and low-power design. A secure communication link with a unique identifier is established through dynamic encryption key generation and protocol adaptation mechanism, which effectively prevents data leakage and illegal access and improves communication security. The intelligent operation and maintenance platform formed based on the distributed software deployment solution has multi-protocol parsing capabilities, solves the problem of incompatibility of communication protocols of equipment of different brands, and improves the versatility of the system. The collected raw data of equipment operation is processed by low-power compression processing technology to generate standardized data streams, which significantly reduces the data transmission volume and improves communication efficiency. The standardized data stream is analyzed by multi-dimensional correlation analysis, the abnormal situation of the equipment is quickly and accurately identified, and a processing strategy containing the fault level is automatically generated, which improves the accuracy and timeliness of fault diagnosis. Finally, remote parameter optimization is carried out according to the processing strategy, and the precise regulation of equipment parameters is realized, which avoids the subjective errors in traditional manual debugging and improves the operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flow chart of a distribution automation sensorless operation and maintenance method proposed by the present invention;

[0065] Figure 2 It is a structural diagram of a distribution automation sensorless operation and maintenance system proposed by the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0067] The present invention proposes a distribution automation non-sensing operation and maintenance method, such as Figure 1 As shown, including:

[0068] Step 1: Install the intelligent data acquisition box on the feeder terminal equipment of the distribution tower according to the modular installation rules to build an intelligent data node with signal acquisition and transmission functions.

[0069] Specifically, the smart acquisition box uses a carbon fiber shell with an IP67 protection level. The interior of the smart acquisition box is divided into two independent spaces: a signal processing compartment and a power supply compartment. The signal processing compartment is equipped with a high-performance communication chip and a data processing unit, and the power supply compartment is equipped with a power supply module. The power supply module in the power supply compartment is configured for low power consumption through an adaptive power management algorithm to obtain a stable operating voltage.

[0070] In the embodiment, a mortise and tenon structure is used for fixed installation, without welding, and a waterproof interface is used to directly connect to the serial port of the feeder terminal device to establish a data path. A signal enhancement antenna is installed on the top of the device, and the best installation position is found through signal strength detection to ensure signal coverage. For example, in actual applications, the power consumption of the smart acquisition box is only 3W, and the standby power consumption can be reduced to 0.5W through adaptive power management, and the IP67 protection level ensures that the device works normally in the temperature range of -40℃ to 85℃.

[0071] Step 2: Dynamic encryption key generation and protocol adaptation are performed according to the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier.

[0072] Specifically, step 2 includes:

[0073] Step 2.1, after completing the installation of the smart acquisition box, extract the hardware features of the smart data node and generate a feature information table. The hardware features include: device identification code and hardware parameters; according to the security level requirements of different data, divide the feature information table into encryption levels to obtain a hierarchical encryption scheme.

[0074] Step 2.2, generate a dynamic session key using the device identification code and timestamp information in each hierarchical encryption scheme, obtain the device authentication credential, and write the device authentication credential into the encryption chip to form the basis for encrypted communication.

[0075] Step 2.3, based on the encrypted communication foundation, identify the communication protocol type of the feeder terminal equipment, establish a protocol mapping table and protocol conversion rules, establish a data exchange channel through the protocol conversion rules, and obtain a protocol adaptation interface.

[0076] Step 2.4: Encrypt and package the communication data through the protocol adapter interface to form a secure data packet, and use the device authentication credentials to authenticate the secure data packet to obtain a trusted data stream to ensure data transmission security.

[0077] Step 2.5, perform communication quality detection on the trusted data stream, establish transmission quality evaluation indicators, obtain link status information, and select the optimal transmission path based on the link status information to obtain a secure communication link with a unique identifier.

[0078] Step 3: Deploy the distributed operation and maintenance software to the operation and maintenance terminal based on the secure communication link to form an intelligent operation and maintenance platform with multi-protocol parsing capabilities.

[0079] Specifically, step 3 includes:

[0080] Step 3.1, perform link quality detection on the secure communication link to obtain a communication bandwidth parameter table, and divide the task queue for distributed operation and maintenance software deployment based on the communication bandwidth parameter table to obtain a deployment plan for the distributed operation and maintenance software;

[0081] Step 3.2, sending the software infrastructure package to the operation and maintenance terminal, obtaining the operating environment configuration data, and generating a deployment list of the distributed operation and maintenance software according to the operating environment configuration data, and obtaining the distribution strategy of the distributed operation and maintenance software module;

[0082] Step 3.3, loading the communication protocol library of the feeder terminal equipment into the operation and maintenance terminal according to the distribution strategy of the distributed operation and maintenance software module, obtaining a multi-protocol parsing engine, and performing a protocol compatibility check on the multi-protocol parsing engine to obtain a protocol parsing result;

[0083] Step 3.4, construct the equipment management database according to the protocol analysis results, obtain the equipment archive information, and establish the equipment operation parameter table through the equipment archive information to obtain the operation and maintenance monitoring benchmark;

[0084] Step 3.5, associate the human-computer interaction component with the operation and maintenance monitoring benchmark to obtain a visual operation interface, and configure a data display template according to the visual operation interface to obtain an operation and maintenance management interface;

[0085] Step 3.6, integrate the multi-protocol parsing engine and device archive information through the operation and maintenance management interface, complete the software function verification, and obtain an intelligent operation and maintenance platform with multi-protocol parsing capabilities.

[0086] Step 4: Use the intelligent operation and maintenance platform to send collection instructions to the intelligent data node, perform low-power compression processing on the collected raw data of device operation, and generate a standardized data stream.

[0087] Specifically, step 4 includes:

[0088] Step 4.1, extracting the collection parameter list from the intelligent operation and maintenance platform to form a data collection task list, and classifying the data collection task list according to the device type to obtain the classified collection instructions;

[0089] Step 4.2, encrypt and package the classified collection instructions to obtain a secure collection instruction set, and send the secure collection instruction set to the feeder terminal device through the intelligent data node to obtain a device response signal;

[0090] Step 4.3, collecting the original data of the device operation based on the device response signal to obtain an operation status data packet, and performing low-power compression on the operation status data packet through a data compression algorithm to obtain a compressed data set;

[0091] Step 4.4, reorganize the compressed data set according to the communication protocol format to obtain a standard data structure, and perform data verification on the standard data structure to obtain a valid data packet;

[0092] Step 4.5, converting the valid data packet into a standard data format to obtain unified format data, and time stamping the unified format data to obtain a data stream with timing information;

[0093] Step 4.6, perform noise reduction and filtering on the data stream with time series information, remove outliers, and obtain a standardized data stream.

[0094] Step 5: Input the standardized data stream into the preset fault feature model, generate a real-time diagnosis report through multi-dimensional correlation analysis, and form a processing strategy including the fault level.

[0095] Specifically, step 5 includes:

[0096] Step 5.1, extract features from the standardized data stream to obtain a device operation feature set, and perform trend analysis on the device operation feature set using a time series association algorithm to obtain a feature association graph;

[0097] Step 5.2, based on the feature association map, the abnormal degree of each parameter is calculated to obtain an abnormal indicator set, and the abnormal indicator set is threshold graded to obtain a graded fault indicator;

[0098] Step 5.3, matching the graded fault index with the historical fault database to obtain the fault type determination result, and performing risk assessment based on the fault type determination result to obtain the fault risk level;

[0099] Step 5.4, constructing real-time diagnostic information according to the fault risk level, obtaining a diagnostic result report, and extracting key information from the diagnostic result report to obtain a real-time diagnostic report;

[0100] Step 5.5, extracting fault handling suggestions from the real-time diagnosis report, generating a list of handling solutions, and prioritizing the list of handling solutions to obtain an emergency response plan;

[0101] Step 5.6, combining the emergency response plan with the equipment operation constraints to obtain parameter adjustment suggestions, and setting control limits according to the parameter adjustment suggestions to obtain the processing strategy including the fault level.

[0102] Step 6: Send control instructions to the intelligent data node via the secure communication link according to the processing strategy to complete remote parameter optimization and obtain a verification data packet.

[0103] Specifically, step 6 includes:

[0104] Step 6.1, converting the parameter adjustment suggestions in the processing strategy into a device control instruction set to obtain a parameter adjustment sequence, and setting the control operation steps according to the parameter adjustment sequence to obtain a control execution plan;

[0105] Step 6.2, generating an instruction control message according to the control execution plan to obtain a security control instruction, and encrypting and transmitting the security control instruction through a secure communication link to obtain a trusted control instruction;

[0106] Step 6.3, sending the trusted control command to the intelligent data node, obtaining the command response status, and monitoring the command response status in real time to obtain control execution feedback;

[0107] Step 6.4, based on the control execution feedback, collect the operation parameter change data, obtain the parameter adjustment effect data, and compare and analyze the parameter adjustment effect data to obtain the control response result;

[0108] Step 6.5, perform compliance check on the control response result through the parameter verification program to obtain a parameter validity report, and perform equipment status evaluation based on the parameter validity report to obtain the control verification result;

[0109] Step 6.6, packing the control verification result into a data message to obtain a control process record, and converting the format of the control process record according to the communication specification to obtain a verification data packet.

[0110] The feeder terminal equipment on a distribution tower has communication anomalies. The system first collects the original data packet size of 2MB through the intelligent data node, including 50 operating parameters such as voltage, current, and power factor. After low-power compression processing, the data packet is reduced to 500KB. Feature extraction analysis found that the voltage fluctuated frequently, with a fluctuation range of ±7% of the nominal value, exceeding the normal threshold of ±5%. The system determines this fault type as voltage anomaly with a medium risk level. Based on historical data matching, processing suggestions are generated: adjust the parameters of the reactive compensation device. The system then issued a control instruction to adjust the power factor setting value from 0.95 to 0.98, and collected the adjusted voltage fluctuation within 15 minutes to ±4%, verifying that the control effect achieved the expected goal. The entire process does not require personnel to climb the pole to operate, which fully reflects the advantages of non-sensing operation and maintenance.

[0111] The present invention also proposes a distribution automation sensorless operation and maintenance system, such as Figure 2 As shown, including:

[0112] The acquisition module is used to install the intelligent acquisition box on the feeder terminal equipment of the distribution tower according to the modular installation rules to build an intelligent data node with signal acquisition and transmission functions;

[0113] An adaptation module, used to perform dynamic encryption key generation and protocol adaptation according to the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier;

[0114] Deployment module, used to deploy distributed operation and maintenance software to terminal devices based on secure communication links, forming an intelligent operation and maintenance platform with multi-protocol parsing capabilities;

[0115] The sending module is used to use the intelligent operation and maintenance platform to send collection instructions to the intelligent data node, perform low-power compression processing on the collected raw data of equipment operation, and generate a standardized data stream;

[0116] The input module is used to input the standardized data stream into the preset fault feature model, generate a real-time diagnosis report through multi-dimensional correlation analysis, and form a processing strategy including the fault level;

[0117] The sending module is used to send control instructions to the intelligent data node via the secure communication link according to the processing strategy, complete remote parameter optimization and obtain verification data packets.

[0118] In the embodiment, the process of step 1 is specifically as follows:

[0119] (1) The carbon fiber shell of the intelligent acquisition box is waterproofed and sealed to obtain a basic shell with an IP67 protection grade, and the internal space of the basic shell is divided to obtain a signal processing compartment and a power supply compartment;

[0120] (2) The power supply module in the power supply compartment is configured to have low power consumption through an adaptive power management algorithm to obtain a stable operating voltage, and a high-performance communication chip is installed in the signal processing compartment to obtain a data processing unit;

[0121] (3) The data processing unit is fixedly installed based on the mortise and tenon structure to form a welding-free connection structure, and the data processing unit is connected to the serial port of the feeder terminal device using a waterproof interface to obtain an initial data path;

[0122] (4) Directional installation of the signal enhancement antenna to establish a wireless signal coverage area, and determine the optimal installation position of the antenna through signal strength detection to obtain a signal transmission channel;

[0123] (5) Using the data path to collect the operating parameters of the feeder terminal equipment, perform data preprocessing and caching, obtain equipment status information, and send the equipment status information to the intelligent data node through the signal transmission channel;

[0124] (6) The device status information is verified through the data integrity verification algorithm to confirm the available status of the intelligent data node, and a node online confirmation signal is generated to obtain an intelligent data node with signal collection and transmission functions.

[0125] Specifically, it starts with the basic hardware construction of the smart acquisition box. The smart acquisition box uses carbon fiber as the shell, which has the characteristics of light weight, high strength and corrosion resistance. The shell is processed by a multi-layer sealing structure, including a primary rubber sealing ring, a secondary silicone sealing layer and a tertiary waterproof coating, reaching the IP67 protection level standard, that is, it is completely dustproof and can be immersed in 1 meter deep water for 30 minutes without water ingress. The inside of the shell is divided into a signal processing compartment and a power compartment by an isolation board, of which the signal processing compartment occupies 70% of the space and the power compartment occupies 30%. The two compartments are isolated by an anti-electromagnetic interference shielding layer. The power compartment of the smart acquisition box is equipped with an adaptive power management unit, which adopts the Buck-Boost circuit architecture, the input voltage range is 9V-36V, and dynamic power regulation is achieved through an adaptive power management algorithm. The algorithm dynamically adjusts the working mode according to the data acquisition and transmission load, provides sufficient power support when the acquisition frequency is high, and reduces the output power in the standby state. In actual operation, the working voltage is stable at 12V, the ripple factor is less than 1%, the standby power consumption is as low as 0.5W, and the peak power consumption does not exceed 5W. The signal processing module is equipped with a high-performance communication chip with a main frequency of 1.2GHz, built-in 4MB Flash storage and 512KB RAM, which constitute the core of the data processing unit.

[0126] The data processing unit adopts an innovative mortise and tenon structure for fixed installation. Tenons and grooves are designed on the inner wall of the collection box and the outer shell of the processing unit, and the corresponding buckles are used to achieve firm positioning. This welding-free connection structure makes installation and maintenance more convenient and has strong seismic resistance. The data processing unit is connected to the serial port of the feeder terminal device through a waterproof interface with a waterproof rating of IP68. The interface adopts a 7-pin design and includes signal lines such as TX, RX, and GND to establish an initial data path. The signal enhancement antenna adopts an omnidirectional antenna design, with an operating frequency band covering 470MHz-690MHz and a gain of 5dBi. When the antenna is installed, the direction is adjusted through a three-dimensional adjustable bracket, supporting 360-degree horizontal rotation and 90-degree pitch adjustment. The optimal position is determined by signal strength detection. The detection algorithm collects 100 signal strength sampling values, calculates the average signal strength and standard deviation, and selects the position with the highest signal strength and the smallest fluctuation as the installation point. The signal coverage area radius can reach 300 meters, and the transmission rate can reach up to 2Mbps without obstruction.

[0127] After the data path is established, the operating parameters of the feeder terminal equipment are collected. The collected parameters include key indicators such as voltage, current, active power, reactive power, power factor, etc., and the sampling frequency is 1 time / second. The collected raw data is preprocessed, including steps such as removing outliers, digital filtering, and data standardization. The data cache adopts a circular queue structure with a cache depth of 1000 records. When the cache is close to full, it automatically triggers data upload. The processed device status information is sent to the intelligent data node through the established signal transmission channel. For the transmitted device status information, the CRC32 check algorithm is used to verify the data integrity. The verification process includes three steps: data segmentation, generating check codes, and comparison verification. The size of each data packet is 1KB, and the check codes are calculated separately and attached to the end of the data packet. The receiving end recalculates the check code and compares it with the received check code. Only when it matches completely can the data be confirmed to be valid. When three consecutive data packet checks pass, the node online confirmation signal is generated, indicating that the intelligent data node has stable signal collection and transmission functions.

[0128] For example, an intelligent acquisition box is installed on a distribution tower. The power supply compartment inside the acquisition box provides a 12V stable power supply, and the communication chip in the signal processing compartment collects feeder terminal equipment data once a second. Through on-site signal strength testing, the average signal strength of -65dBm was measured at an antenna height of 2.5 meters, a horizontal angle of 45 degrees, and a pitch angle of 15 degrees, and the standard deviation was less than 2dBm, which was determined to be the optimal installation position. The collected raw data contains 50 parameters, each data packet size is 1KB, and the integrity reaches 99.9% after CRC32 verification. During the 24-hour test, a total of 86,400 data points were collected, and the data transmission success rate reached 99.8%, which fully verified the reliability of the system. The entire installation process does not require pole climbing, which fully reflects the advantages of non-sensing operation and maintenance.

[0129] In the embodiment, the process of step 2 is specifically as follows:

[0130] (1) Extracting hardware features of intelligent data nodes to obtain a feature information table containing device identification codes and hardware parameters, and classifying the feature information table into encryption levels according to a hierarchical security strategy to obtain a hierarchical encryption scheme;

[0131] (2) Extract the device identification code from the hierarchical encryption scheme, generate a dynamic session key in combination with the timestamp information, obtain the device authentication credential, and write the device authentication credential into the encryption chip to obtain the encrypted communication basis;

[0132] (3) Identify the communication protocol type of the feeder terminal device based on the encrypted communication foundation, generate a protocol mapping table, obtain a protocol conversion rule, and establish a data exchange channel through the protocol conversion rule to obtain a protocol adaptation interface;

[0133] (4) Encrypt and package the communication data using the protocol adapter interface to form a secure data packet, and authenticate the secure data packet using the device authentication credential to obtain a trusted data stream;

[0134] (5) Perform communication quality detection on the trusted data stream, establish transmission quality evaluation indicators, obtain link status information, and select the optimal transmission path based on the link status information to obtain a secure communication link with a unique identifier.

[0135] Specifically, hardware feature extraction includes two aspects: device identification code generation and hardware parameter collection. The device identification code consists of a 32-bit string, in which the first 8 bits represent the device type, the middle 16 bits are timestamps, and the last 8 bits are random serial numbers. Hardware parameters include basic information such as processor model, storage capacity, communication interface type, operating voltage, and operating temperature. This information is combined to form a feature information table, which is stored in JSON format. The feature information table is divided into three levels according to the data security level: basic information level, operating parameter level, and control instruction level. The basic information level contains public information such as device model and installation location, and is encrypted using basic DES; the operating parameter level contains operating data such as voltage and current, and is encrypted using AES-128; the control instruction level contains sensitive operations such as parameter settings and switch control, and is encrypted using AES-256. Different levels use different key lengths and encryption algorithms to form a hierarchical encryption scheme.

[0136] After extracting the device identification code from the hierarchical encryption scheme, it is combined with the current UTC timestamp to generate a dynamic session key. The session key generation process uses the HMAC-SHA256 algorithm, with the device identification code as the key and the timestamp as the message input to generate a 32-byte session key. This dynamic session key, together with the device certificate, constitutes the device authentication credential, which is written into the secure storage area of ​​the encryption chip to establish the encrypted communication foundation. The encryption chip adopts the TPM2.0 standard and has hardware-level key protection capabilities. Based on the established encrypted communication foundation, the communication protocol of the feeder terminal equipment is identified. By sending a standard query command, the protocol type is determined according to the format characteristics of the device response message. Currently supported protocol types include mainstream industrial protocols such as Modbus-RTU, IEC 60870-5-101, and DNP3. A protocol mapping table is generated based on the identification results. The mapping table contains the corresponding relationship between key information such as command words, data formats, and verification methods. Protocol conversion rules are formulated based on the mapping table to achieve seamless conversion between different protocols.

[0137] The protocol adapter interface processes the communication data based on the conversion rules. First, the original data is parsed according to the source protocol format, the payload data is extracted, and then re-encapsulated according to the target protocol requirements, adding necessary fields such as the protocol header and checksum. The encapsulated data packet is encrypted using the session key to form a secure data packet. Each data packet contains verification information of the device authentication credentials. The receiving end ensures the authenticity of the data source through verification and generates a trusted data stream.

[0138] Communication quality detection revolves around trusted data flows, and the main monitoring indicators include transmission delay, packet loss rate, and signal strength. Transmission delay is calculated by round-trip time, taking the average value of 100 tests; packet loss rate counts the number of lost packets in 1,000 data packets; signal strength directly reads the RSSI value of the communication module. Based on these indicators, a transmission quality scoring system is established, and the link quality score is calculated using a weighted average method. When there are multiple available transmission paths, the path with the highest score is selected as the primary channel, and the other paths are used as backup. In this way, a secure communication link with a unique identifier is finally established.

[0139] Take the communication example of the feeder terminal equipment in a distribution station area as an example: the equipment identification code is generated as "FTU20240305123456", and the characteristic information table is formed in combination with the hardware parameters (CPU: ARM Cortex-M4, RAM: 256KB, Flash: 2MB, communication interface: RS485). The data is encrypted hierarchically, and the operation data is encrypted using AES-128 with a key length of 16 bytes. The dynamic session key is generated in combination with the current timestamp 1709654400. The device is determined to use the Modbus-RTU protocol through protocol identification, and a mapping relationship with the IEC104 protocol is established. The data packet is encrypted and transmitted, and the communication quality test results show that the average delay is 50ms, the packet loss rate is 0.1%, the signal strength is -65dBm, and the comprehensive score is 92 points, which meets the stable communication standard. The whole process is completed automatically without manual intervention, which reflects the characteristics of senseless operation and maintenance.

[0140] In the embodiment, the process of step 3 is specifically as follows:

[0141] (1) Perform link quality detection on the secure communication link to obtain a communication bandwidth parameter table, and divide the software deployment task queue based on the communication bandwidth parameter table to obtain a distributed deployment solution;

[0142] (2) Sending a software infrastructure package to the terminal device to obtain operating environment configuration data, and generating an operation and maintenance software deployment list based on the operating environment configuration data to obtain a software module distribution strategy;

[0143] (3) Loading the communication protocol library of the feeder terminal device into the terminal device according to the software module distribution strategy to obtain a multi-protocol parsing engine, and performing a protocol compatibility check on the multi-protocol parsing engine to obtain a protocol parsing result;

[0144] (4) Build an equipment management database according to the protocol analysis results to obtain equipment archive information, and establish an equipment operation parameter table based on the equipment archive information to obtain the operation and maintenance monitoring benchmark;

[0145] (5) Associating the human-computer interaction component with the operation and maintenance monitoring benchmark to obtain a visual operation interface, and configuring a data display template according to the visual operation interface to obtain an operation and maintenance management interface;

[0146] (6) The multi-protocol parsing engine and device file information are integrated through the operation and maintenance management interface to complete the software function verification and obtain an intelligent operation and maintenance platform with multi-protocol parsing capabilities.

[0147] Specifically, the quality of the secure communication link is tested. The test content includes three aspects: bandwidth test, delay test and stability test. The bandwidth test adopts the incremental data packet method, starting from 1KB and increasing to 1MB at 2 times the speed, and recording the transmission time of each data packet. The delay test sends 100 32-byte probe packets to count the round-trip time. The stability test lasts for 30 minutes, sending a 512-byte data packet per second and recording the packet loss. These test data are sorted into a communication bandwidth parameter table, including indicators such as effective bandwidth, average delay, and packet loss rate. Based on the obtained communication bandwidth parameter table, the software deployment tasks are prioritized. Core functional modules such as the communication protocol parsing engine have the highest priority, followed by the human-computer interaction interface, and the data analysis tool is the last. A transmission time window is allocated to each task according to the bandwidth status to avoid communication congestion caused by large-scale data transmission. The task queue is sorted according to dependencies and priorities to form a distributed deployment plan.

[0148] The software infrastructure package includes the runtime environment, basic class library and configuration framework, with a total size of about 20MB. When sending the infrastructure package to the terminal device, a block transmission strategy is adopted, with each block size of 1MB. After the transmission is completed, an MD5 check is performed. After receiving the infrastructure package, the terminal device returns the operating environment information, including hardware parameters such as processor architecture, memory size, storage space, and software environment data such as operating system version and installed components. Based on these configuration data, a software deployment list adapted to the current environment is generated, the list of components and configuration parameters to be installed are clarified, and a software module distribution strategy is formulated. According to the distribution strategy, the feeder terminal equipment communication protocol library is loaded into the terminal device. The protocol library contains parsing modules for common industrial protocols such as Modbus, DNP3, and IEC101 / 104. The size of each protocol module is between 100KB and 500KB. The loading process adopts a dynamic loading mechanism to load the corresponding protocol on demand according to the device type. After the multi-protocol parsing engine is established, a protocol compatibility check is performed, including protocol parsing test and data conversion test. The parsing test verifies the recognition and parsing accuracy of various messages, and the data conversion test ensures the correct data mapping between different protocols.

[0149] The protocol parsing results are used to build the equipment management database. The database adopts a relational structure and mainly includes the equipment basic information table, communication parameter table and operation status table. The equipment basic information table records static information such as equipment model, installation location, and commissioning time; the communication parameter table stores configuration parameters such as communication address, baud rate, and verification method; and the operation status table saves real-time data and historical records. Based on these tables, the equipment operation parameter table is established, and the data type, range, unit and other attributes of various monitoring quantities are defined to form an operation and maintenance monitoring benchmark. The human-computer interaction component is developed using Web technology and includes three main parts: navigation bar, data display area, and control panel. After being associated with the operation and maintenance monitoring benchmark, display controls for various data are automatically generated, such as numerical displays, trend charts, status indicators, etc. The visual interface supports custom layouts, and operators can adjust the control position and display mode as needed. The data display template presets common pages such as real-time monitoring, historical query, and alarm management to facilitate rapid deployment of applications.

[0150] Finally, the multi-protocol parsing engine is connected to the operation and maintenance management interface to realize the complete process of data collection, display, and storage. Software function verification includes multiple links such as communication stability test, data parsing accuracy test, interface response performance evaluation, etc. After all of them pass, the construction of the intelligent operation and maintenance platform is completed.

[0151] For example, when a distribution substation deploys a senseless operation and maintenance platform, the link quality test is first performed. The test results show: effective bandwidth 2Mbps, average delay 35ms, packet loss rate 0.1%. Software deployment adopts block transmission, and the infrastructure package 20MB is divided into 20 blocks for transmission, and each block is verified to be correct. The terminal device is configured with a quad-core ARM processor, 4GB memory, 32GB storage space, and runs the Linux system. Three protocol parsing modules are loaded: Modbus-RTU (150KB), IEC104 (300KB), and DNP3 (200KB). The information of 50 feeder terminal devices is recorded in the database, and each device monitors 30 operating parameters. The operation and maintenance interface sets 4 display areas: device list, real-time data, trend curve, and alarm information. During the test, the correctness of protocol parsing reached 99.9%, the interface refresh delay was less than 100ms, and the data storage rate reached 1000 points per second, fully meeting the performance requirements of senseless operation and maintenance.

[0152] In the embodiment, the process of step 4 is specifically as follows:

[0153] (1) Extracting the collection parameter list from the intelligent operation and maintenance platform to form a data collection task list, and classifying the data collection task list according to the device type to obtain the classified collection instructions;

[0154] (2) Encrypt and package the classified collection instructions to obtain a secure collection instruction set, and send the secure collection instruction set to the feeder terminal device through the intelligent data node to obtain a device response signal;

[0155] (3) collecting the original data of device operation based on the device response signal to obtain an operation status data packet, and performing low-power compression on the operation status data packet through a data compression algorithm to obtain a compressed data set;

[0156] (4) reorganizing the compressed data set according to the communication protocol format to obtain a standard data structure, and performing data verification on the standard data structure to obtain a valid data packet;

[0157] (5) Convert the valid data packets into a standard data format to obtain unified format data, and timestamp the unified format data to obtain a data stream with timing information;

[0158] (6) Perform noise reduction filtering on the data stream with time series information to remove outliers and obtain a standardized data stream.

[0159] Specifically, the collection parameter list is extracted from the intelligent operation and maintenance platform. The collection parameter list contains various data items required for equipment operation status monitoring, mainly including basic electrical quantities such as voltage, current, active power, reactive power, power factor, and auxiliary monitoring quantities such as switch status, temperature, and humidity. Each parameter item contains attribute information such as parameter ID, data type, collection cycle, and data address. Based on this information, a data collection task list is formed, and the parameters required to be collected for different types of equipment are grouped and sorted to generate targeted classified collection instructions. The classified collection instructions are encrypted using the AES-128 encryption algorithm, and the key is generated by the device ID and timestamp. The encrypted instructions are packaged into an instruction set in a standard format, and each instruction contains fields such as device address, function code, data address, and data length. The instruction set is sent to the corresponding feeder terminal device through the secure communication link established by the intelligent data node. After receiving the instruction, the feeder terminal device returns a response signal containing an execution status code to confirm the reception and processing of the instruction.

[0160] Based on the response signal returned by the device, the actual data collection work begins. The collected raw data forms an operating status data packet, which contains information such as sampling time, value, unit, and status flag. For these raw data, a compression scheme combining differential encoding and run-length encoding is used for processing. Differential encoding records the difference between adjacent data points and has a good compression effect for slowly changing parameters; run-length encoding compresses and stores continuously repeated values. The compressed data set significantly reduces the amount of data transmission. The compressed data set needs to be reorganized according to the communication protocol format used by the feeder terminal equipment. The reorganization process includes steps such as adding a protocol header, splitting data blocks, and calculating check codes. The protocol header contains information such as the source address, destination address, and message type. The data block size is dynamically adjusted according to the communication bandwidth, and the check code is calculated using the CRC32 algorithm. The reorganized data structure undergoes integrity verification to ensure that the data has not been tampered with or lost, forming a valid data packet.

[0161] When valid data packets are converted into standard data formats, a unified data structure template is used. The template defines the storage format of various types of data, including attributes such as value type (integer, floating point), value range, and accuracy requirements. Timestamps accurate to milliseconds are added to the converted unified format data to record the specific time when the data is generated, forming a data stream with timing information. The timestamp uses the UTC format to ensure the consistency of data in different time zones. The median filtering method is used to reduce the noise of the data stream with timing information, and the median of multiple consecutive data points is taken as the effective value to effectively remove sudden interference. The 3σ principle is used to judge the outliers, calculate the mean and standard deviation of the data sequence, and mark the values ​​that exceed the mean ±3 times the standard deviation as outliers and remove them. The processed data stream meets the standardization requirements and is convenient for subsequent analysis.

[0162] For example, in the data collection of feeder terminal equipment in a distribution station area, a collection list containing 30 monitoring parameters was first extracted. These parameters were classified according to voltage (A / B / C phase voltage), current (A / B / C phase current), power (active power, reactive power), etc., to form 6 groups of classified collection instructions. After each group of instructions is encrypted by AES-128, the data length increases from 64 bytes to 80 bytes. After receiving the instruction, the feeder terminal equipment returns the status code 0x00, indicating normal reception. The size of the collected original data packet is 2KB, containing the sampling values ​​of each parameter within 1 minute (sampling frequency 1Hz). The voltage data is processed by differential encoding (for example, A phase voltage: 10.5kV, 10.51kV, 10.49kV is converted to the reference value 10.5kV plus the difference sequence 0, +0.01, -0.01), combined with run-length encoding to process the switch state, the data volume is reduced to 800 bytes. The data is reorganized according to the Modbus-RTU protocol format, and fields such as device address (0x01), function code (0x03), and CRC checksum are added. Three abnormal values ​​are recorded during the standardization process (the instantaneous value of phase A current exceeds twice the rated value), and a complete standardized data stream is obtained after noise reduction filtering.

[0163] In the embodiment, the process of step 5 is specifically as follows:

[0164] (1) Extract features from the standardized data stream to obtain a device operation feature set, and perform trend analysis on the device operation feature set using a time series association algorithm to obtain a feature association graph;

[0165] (2) Calculate the abnormal degree of each parameter based on the feature association map to obtain an abnormal indicator set, and perform threshold classification on the abnormal indicator set to obtain a graded fault indicator;

[0166] (3) Matching the graded fault indicators with the historical fault database to obtain the fault type determination result, and performing risk assessment based on the fault type determination result to obtain the fault risk level;

[0167] (4) Construct real-time diagnostic information according to the fault risk level, obtain a diagnostic result report, and extract key information from the diagnostic result report to obtain a real-time diagnostic report;

[0168] (5) Extract fault handling suggestions from the real-time diagnostic report, generate a list of treatment plans, and prioritize the list of treatment plans to obtain an emergency response plan;

[0169] (6) The emergency response plan is combined with the equipment operation constraints to obtain parameter adjustment suggestions, and control limits are set according to the parameter adjustment suggestions to obtain a processing strategy that includes the fault level.

[0170] Specifically, the fault diagnosis process in the distribution automation non-sensing operation and maintenance method first extracts features from the standardized data stream. Feature extraction is carried out around the three dimensions of statistical characteristics, fluctuation characteristics, and correlation characteristics of electrical parameters. Statistical characteristics include indicators such as mean, standard deviation, and peak factor; fluctuation characteristics include characteristics such as rate of change, periodicity, and trend; and correlation characteristics analyze the correlation between parameters. The time series association algorithm is used to analyze the change patterns of these features over time. The algorithm uses a sliding window method with a window length of 10 minutes and a step length of 1 minute. The change trend of each feature is calculated, and finally a feature association map reflecting the law of parameter change is formed.

[0171] Based on the obtained characteristic association map, the degree of abnormality of each parameter is calculated. The degree of abnormality calculation adopts a multi-dimensional scoring method to conduct a comprehensive evaluation from the aspects of parameter value deviation, change rate, and fluctuation range. Different weights are set for each dimension, with a deviation weight of 0.4, a change rate weight of 0.3, and a fluctuation range weight of 0.3. The scoring results form an abnormal indicator set, and three levels are set according to the score: mild abnormality (score 60-75 points), moderate abnormality (score 75-90 points), and severe abnormality (score above 90 points) to form a graded fault indicator. The graded fault indicator is matched and analyzed with the historical fault database, which contains the characteristic patterns and processing methods of typical fault cases. The matching adopts a pattern recognition algorithm to calculate the similarity between the current fault indicator and the historical case. Cases with a similarity of more than 85% are considered to be of the same type of fault. The fault type is determined based on the matching results, and the risk level is evaluated based on factors such as the frequency of fault occurrence, the scope of impact, and the difficulty of handling. The risk level is divided into four levels: prompt, warning, serious, and critical, and each level corresponds to a different processing response time limit.

[0172] Real-time diagnostic information is constructed according to the determined fault risk level. The information content includes faulty equipment identification, fault type description, abnormal parameter list, risk level assessment, etc. The diagnostic information is organized into a diagnostic result report in a standard format, and key information is extracted from the report, including important indicators such as the time of fault occurrence, impact range, abnormal parameter values, and change trends, to generate a concise real-time diagnostic report. The nature and impact of the fault are analyzed from the real-time diagnostic report, and fault handling suggestions are generated based on historical handling experience. The handling suggestions include specific operational guidance such as emergency disposal measures, parameter adjustment plans, and operation mode switching. These suggestions are organized into a list of handling plans, and they are prioritized according to factors such as fault risk level, handling urgency, and implementation difficulty to form an emergency response plan.

[0173] The emergency response plan needs to be combined with the equipment operation constraints, which include equipment rated parameters, operating limits, protection settings and other technical requirements. By screening the constraints, safe and feasible parameter adjustment suggestions are determined, and specific control limits are set accordingly, ultimately forming a handling strategy that includes the fault level.

[0174] For example, the feeder terminal equipment in a distribution station area detected that the voltage of phase A continued to drop from the nominal value of 10kV. The data within 10 minutes showed that the voltage value dropped from 10kV to 9.4kV, with a change rate of 0.1kV / minute, and the power factor dropped from 0.95 to 0.85. The feature extraction results showed that the voltage deviation was 16% (exceeding the standard limit of ±5%), with a score of 95 points; the power factor deviation was 10.5%, with a score of 80 points, forming a classification index of severe abnormality and moderate abnormality. Similar cases (similarity 89%) were found by matching with the historical database, and it was determined to be a reactive compensation device failure. Combined with the historical record that the failure had caused a decline in regional power supply quality, the risk level was determined to be "serious" and required to be handled within 30 minutes. Based on the diagnosis results, the processing suggestions were generated: temporarily cut off the faulty compensation device, adjust the parameters of the adjacent compensation device, and set the power factor adjustment target value to 0.92. Taking into account the equipment operation constraints (voltage not less than 9kV, power factor not less than 0.85), the control limits of the monitoring parameters were set: voltage lower limit 9.2kV, power factor lower limit 0.9, and the final processing strategy was formed. Through this non-sensing operation and maintenance method, equipment failures were discovered and resolved in a timely manner, avoiding the expansion of failures.

[0175] In the embodiment, the process of step 6 is specifically as follows:

[0176] (1) Convert the parameter adjustment suggestions in the processing strategy into a device control instruction set to obtain a parameter adjustment sequence, and set the control operation steps according to the parameter adjustment sequence to obtain a control execution plan;

[0177] (2) Generate a command control message according to the control execution plan to obtain a security control instruction, and encrypt and transmit the security control instruction through a secure communication link to obtain a trusted control instruction;

[0178] (3) Send the trusted control command to the intelligent data node, obtain the command response status, and monitor the command response status in real time to obtain control execution feedback;

[0179] (4) Based on the feedback from the control execution, the operating parameter change data is collected to obtain the parameter adjustment effect data, and the parameter adjustment effect data is compared and analyzed to obtain the control response result;

[0180] (5) Perform compliance check on the control response results through the parameter verification program to obtain a parameter validity report, and perform equipment status assessment based on the parameter validity report to obtain the control verification result;

[0181] (6) Packing the control verification result into a data message to obtain a control process record, and converting the format of the control process record according to the communication specification to obtain a verification data packet.

[0182] Specifically, the parameter adjustment suggestion includes the adjustment target value and the adjustment step, which need to be converted into an instruction format that can be recognized by the device. The control instruction set adopts a step-by-step adjustment method, and divides the difference between the target value and the current value into multiple adjustment steps according to the security step. Each step contains information such as parameter address, write value, and execution time to form a complete parameter adjustment sequence. According to the parameter type and the size of the adjustment amount, the waiting time and observation cycle of each step of adjustment are set, and finally a control execution plan including the execution order and time arrangement is formed. The control execution plan needs to be converted into a standard instruction control message, and the message format follows the device communication protocol specification. The control message contains fields such as function code, data address, write value, and check code. Each field is encoded according to the protocol requirements to generate a secure control instruction. Through the established secure communication link, the control instruction is encrypted using the AES-256 encryption algorithm. The encryption process includes three steps: key generation, data block segmentation, and encryption operation. The encrypted instruction has the characteristics of anti-tampering and non-forgeability, forming a trusted control instruction.

[0183] Trusted control instructions are sent to the feeder terminal device through the intelligent data node. After receiving the instruction, the device immediately returns the response status code. The response status includes information such as the instruction reception flag, execution status flag, and error code. These response states are monitored in real time, and the execution status of each instruction is recorded, including execution time, completion status, abnormal information, etc., to form a complete control execution feedback. Based on the execution feedback information, the operating parameter change data during the control process is collected. The collection frequency is dynamically adjusted according to the parameter change speed. The sampling rate is increased when the change is drastic, and the sampling rate is reduced when the change is gentle. The collected data includes the parameter values ​​before, during, and after the control, and the parameter changes caused by each adjustment step are recorded. The collected data is compared and analyzed with the control target, and indicators such as parameter adjustment deviation, adjustment speed, and stability are calculated to generate the control response result.

[0184] The parameter verification program performs compliance checks on the control response results, including whether the parameters are within the allowable range, whether the equipment operation constraints are met, and whether the control objectives are achieved. A parameter validity report is generated based on the inspection results. The report contains evaluation indicators such as parameter comparison data, constraint compliance, and target achievement. Based on the validity report, the equipment status is evaluated to determine whether the equipment is operating normally and whether the control effect meets the standards, thereby obtaining the control verification results. The control verification results are packaged into data messages in a standard format. The message content includes information such as the time series of the control process, parameter change records, and verification evaluation results. According to the requirements of the communication specification, the message is format converted and encoded to ensure the standardization and compatibility of the data, and finally a complete verification data packet is formed.

[0185] For example: the power factor of a feeder terminal device in a distribution station area needs to be adjusted, the current value is 0.85, and the target value is 0.92. The adjustment sequence is divided into 7 steps according to the step size of 0.01. Each step of adjustment generates a control message, and the message format is: function code 0x06 (single parameter write), data address 0x1000 (power factor register), write value (incremented by 0.01), CRC check code. The instruction is sent through the secure communication link, and the device returns the response code 0x00 (execution successful). During the period, 60 points of power factor data were collected, and the recorded values ​​gradually increased from 0.85: 0.86 (first step), 0.87 (second step)... until 0.92 (seventh step). Parameter verification shows: the final value of 0.92 meets the target requirements, the voltage remains within the range of 10kV±0.2kV, and the operation is stable. The verification result is packaged into a 500-byte data message, which contains a complete record of the adjustment process. The whole process took 3.5 minutes and achieved precise adjustment of the power factor, demonstrating the efficiency and accuracy of sensorless operation and maintenance.

[0186] The modular installation of the intelligent acquisition box enables the installation of equipment without high-altitude operations, reducing the safety risks of operation and maintenance personnel. At the same time, the waterproof sealing and low-power design improve the environmental adaptability and continuous working ability of the equipment. A secure communication link with a unique identifier is established through dynamic encryption key generation and protocol adaptation mechanism, which effectively prevents data leakage and illegal access and improves communication security. The intelligent operation and maintenance platform formed based on the distributed software deployment solution has multi-protocol parsing capabilities, solves the problem of incompatibility of communication protocols of equipment of different brands, and improves the versatility of the system. Low-power compression processing technology is used to process the collected raw data of equipment operation to generate standardized data streams, which significantly reduces the amount of data transmission and improves communication efficiency. Through multi-dimensional correlation analysis, the standardized data stream is analyzed to quickly and accurately identify equipment abnormalities, and automatically generate processing strategies including fault levels, which improves the accuracy and timeliness of fault diagnosis. Finally, remote parameter optimization is carried out according to the processing strategy to achieve precise control of equipment parameters, avoid subjective errors in traditional manual debugging, and improve operation and maintenance efficiency.

[0187] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0188] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0189] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0190] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distribution automation non-sensing operation and maintenance method, characterized in that: include: Install the intelligent data acquisition box on the feeder terminal equipment of the distribution tower according to the modular installation rules to build an intelligent data node with signal acquisition and transmission functions; Dynamic encryption key generation and protocol adaptation are performed based on the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier; Deploy distributed operation and maintenance software to terminal devices based on secure communication links to form an intelligent operation and maintenance platform with multi-protocol parsing capabilities; Using the intelligent operation and maintenance platform to send a collection instruction to the intelligent data node, the collected raw data of the equipment operation is processed with low power consumption compression to generate a standardized data stream; Input standardized data streams into the preset fault feature model, generate real-time diagnostic reports through multi-dimensional correlation analysis, and form a processing strategy including fault levels; According to the processing strategy, control instructions are sent to the intelligent data node via the secure communication link to complete remote parameter optimization and obtain verification data packets.

2. The distribution automation non-sensing operation and maintenance method according to claim 1 is characterized in that: According to the modular installation rules, the intelligent data acquisition box is installed on the feeder terminal equipment of the distribution tower to build an intelligent data node with signal acquisition and transmission functions, including: The carbon fiber shell of the intelligent acquisition box is waterproofed and sealed to obtain a basic shell with an IP67 protection grade, and the internal space of the basic shell is divided to obtain a signal processing cabin and a power supply cabin; The power supply module in the power supply compartment is configured with low power consumption through an adaptive power management algorithm to obtain a stable operating voltage, and a communication chip is installed in the signal processing compartment to obtain a data processing unit; The data processing unit is fixedly installed based on the mortise and tenon structure to form a welding-free connection structure, and the data processing unit is connected to the serial port of the feeder terminal device using a waterproof interface to obtain an initial data path; Directional installation of signal enhancement antennas is performed to establish wireless signal coverage areas, and the optimal installation position of antennas is determined through signal strength detection to obtain signal transmission channels; The data path is used to collect the operating parameters of the feeder terminal equipment, perform data preprocessing and caching, obtain the equipment status information, and send the equipment status information to the intelligent data node through the signal transmission channel; The device status information is verified through the data integrity verification algorithm to confirm the available status of the intelligent data node, and a node online confirmation signal is generated to obtain an intelligent data node with signal collection and transmission functions.

3. The distribution automation non-sensing operation and maintenance method according to claim 1 is characterized in that: Dynamic encryption key generation and protocol adaptation are performed based on the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier, including: Extracting hardware features of the intelligent data node to obtain a feature information table including a device identification code and hardware parameters, and dividing the feature information table into encryption levels according to a hierarchical security strategy to obtain a hierarchical encryption scheme; Extract the device identification code from the hierarchical encryption scheme, generate a dynamic session key in combination with the timestamp information, obtain the device authentication credential, and write the device authentication credential into the encryption chip to obtain the encrypted communication basis; Identify the communication protocol type of the feeder terminal device based on the encrypted communication foundation, generate a protocol mapping table, obtain a protocol conversion rule, and establish a data exchange channel through the protocol conversion rule to obtain a protocol adaptation interface; The communication data is encrypted and packaged using the protocol adapter interface to form a secure data packet, and the secure data packet is authenticated using the device authentication credentials to obtain a trusted data stream; Perform communication quality detection on trusted data streams, establish transmission quality evaluation indicators, obtain link status information, and select the optimal transmission path based on the link status information to obtain a secure communication link with a unique identifier.

4. The distribution automation non-sensing operation and maintenance method according to claim 1 is characterized in that: Distributed operation and maintenance software is deployed to terminal devices based on secure communication links to form an intelligent operation and maintenance platform with multi-protocol parsing capabilities, including: Perform link quality detection on the secure communication link to obtain a communication bandwidth parameter table, and divide the software deployment task queue based on the communication bandwidth parameter table to obtain a distributed deployment solution; Send the software infrastructure package to the terminal device, obtain the operating environment configuration data, and generate the operation and maintenance software deployment list according to the operating environment configuration data to obtain the software module distribution strategy; The communication protocol library of the feeder terminal device is loaded into the terminal device according to the software module distribution strategy to obtain a multi-protocol parsing engine, and the protocol compatibility check is performed on the multi-protocol parsing engine to obtain a protocol parsing result; Build the equipment management database according to the protocol analysis results to obtain equipment archive information, and establish the equipment operation parameter table through the equipment archive information to obtain the operation and maintenance monitoring benchmark; Associating the human-computer interaction component with the operation and maintenance monitoring benchmark to obtain a visual operation interface, and configuring a data display template according to the visual operation interface to obtain an operation and maintenance management interface; By integrating the multi-protocol parsing engine and device archive information through the operation and maintenance management interface, the software function verification is completed, and an intelligent operation and maintenance platform with multi-protocol parsing capabilities is obtained.

5. The distribution automation non-sensing operation and maintenance method according to claim 1 is characterized in that: The intelligent operation and maintenance platform sends collection instructions to the intelligent data nodes, and the collected raw data of the equipment operation is processed with low power consumption compression to generate standardized data streams, including: Extract the collection parameter list from the intelligent operation and maintenance platform to form a data collection task list, and classify the data collection task list according to the device type to obtain the classified collection instructions; Encrypt and package the classified collection instructions to obtain a secure collection instruction set, and send the secure collection instruction set to the feeder terminal device through the intelligent data node to obtain a device response signal; Collecting the original data of device operation based on the device response signal to obtain an operation status data packet, and performing low-power compression on the operation status data packet through a data compression algorithm to obtain a compressed data set; Reorganize the compressed data set according to the communication protocol format to obtain a standard data structure, and perform data verification on the standard data structure to obtain a valid data packet; Convert valid data packets into a standard data format to obtain data in a unified format, and timestamp the data in the unified format to obtain a data stream with timing information; The data stream with time series information is subjected to noise reduction and filtering to remove outliers and obtain a standardized data stream.

6. The distribution automation non-sensing operation and maintenance method according to claim 1 is characterized in that: The standardized data stream is input into the preset fault feature model, and a real-time diagnostic report is generated through multi-dimensional correlation analysis, and a processing strategy including fault levels is formed, including: Extract features from the standardized data stream to obtain the equipment operation feature set, and perform trend analysis on the equipment operation feature set through a time series association algorithm to obtain a feature association graph; Based on the characteristic association map, the abnormal degree of each parameter is calculated to obtain an abnormal indicator set, and the abnormal indicator set is graded by threshold to obtain a graded fault indicator; Match the graded fault indicators with the historical fault database to obtain the fault type determination result, and perform risk assessment based on the fault type determination result to obtain the fault risk level; Construct real-time diagnostic information according to the fault risk level, obtain a diagnostic result report, and extract key information from the diagnostic result report to obtain a real-time diagnostic report; Extract fault handling suggestions from real-time diagnostic reports, generate a list of treatment plans, and prioritize the list of treatment plans to obtain an emergency response plan; The emergency response plan is combined with the equipment operation constraints to obtain parameter adjustment suggestions, and control limits are set according to the parameter adjustment suggestions to obtain a processing strategy that includes the fault level.

7. The distribution automation non-sensing operation and maintenance method according to claim 1 is characterized in that: According to the processing strategy, control instructions are issued to the intelligent data node via a secure communication link to complete remote parameter optimization and obtain verification data packets, including: Convert the parameter adjustment suggestions in the processing strategy into a device control instruction set to obtain a parameter adjustment sequence, and set the control operation steps according to the parameter adjustment sequence to obtain a control execution plan; Generate an instruction control message according to the control execution plan to obtain a security control instruction, and encrypt and transmit the security control instruction through a secure communication link to obtain a trusted control instruction; Send the trusted control instructions to the intelligent data node, obtain the instruction response status, and monitor the instruction response status in real time to obtain control execution feedback; Based on the feedback from the control execution, the operating parameter change data is collected to obtain the parameter adjustment effect data, and the parameter adjustment effect data is compared and analyzed to obtain the control response result; The compliance of the control response results is checked through the parameter verification program to obtain the parameter validity report, and the equipment status is evaluated based on the parameter validity report to obtain the control verification result; The control verification result is packaged into a data message to obtain a control process record, and the control process record is formatted according to the communication specification to obtain a verification data packet.

8. A distribution automation non-sensing operation and maintenance system, used to implement the distribution automation non-sensing operation and maintenance method according to any one of claims 1 to 7, characterized in that: The distribution automation sensorless operation and maintenance system comprises: The acquisition module is used to install the intelligent acquisition box on the feeder terminal equipment of the distribution tower according to the modular installation rules to build an intelligent data node with signal acquisition and transmission functions; An adaptation module, used to perform dynamic encryption key generation and protocol adaptation according to the hardware characteristics of the intelligent data node to establish a secure communication link with a unique identifier; Deployment module, used to deploy distributed operation and maintenance software to terminal devices based on secure communication links, forming an intelligent operation and maintenance platform with multi-protocol parsing capabilities; The sending module is used to use the intelligent operation and maintenance platform to send collection instructions to the intelligent data node, perform low-power compression processing on the collected raw data of equipment operation, and generate a standardized data stream; The input module is used to input the standardized data stream into the preset fault feature model, generate a real-time diagnosis report through multi-dimensional correlation analysis, and form a processing strategy including the fault level; The sending module is used to send control instructions to the intelligent data node via the secure communication link according to the processing strategy, complete remote parameter optimization and obtain verification data packets.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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