Digital monitoring operation and maintenance method for drop-out fuse in alpine region

Abstract

The invention relates to the technical field of digital monitoring, in particular to an alpine region drop-out fuse digital monitoring operation and maintenance method, which comprises the steps of data acquisition, data analysis, remote monitoring, operation and maintenance management, unmanned aerial vehicle inspection and maintenance decision support. According to the digital monitoring operation and maintenance method for the drop-out fuse in the alpine region, the digital monitoring operation and maintenance method has remarkable beneficial effects in the aspects of improving equipment reliability, reducing operation and maintenance cost, optimizing resource allocation, promoting technical innovation and the like, and efficient and safe operation of a power system in the alpine region is assisted; by implementing the digital monitoring operation and maintenance method, the drop-out fuse in the alpine region can realize higher operation reliability and maintenance efficiency, so that the operation and maintenance cost is reduced, the safety and stability of a power system are improved, and the digital monitoring operation and maintenance method has important significance for guaranteeing power supply.

Description

Technical Field

[0001] The present invention relates to the field of digital monitoring technology, and in particular to a digital monitoring and operation method for drop-out fuses in high-cold areas. Background Art

[0002] In high-altitude and cold regions, the operation and maintenance of power systems face unique challenges. As a vital protective device in the power system, drop-out fuses are responsible for ensuring the safe and stable operation of the system. However, complex environmental conditions, such as low temperature, high humidity, wind and snow, not only affect the performance of the fuse, but may also cause early damage and failure of the equipment, thereby affecting the stable supply of electricity. Therefore, establishing an efficient operation and maintenance management system is crucial to improving equipment reliability and operation and maintenance efficiency.

[0003] Traditional operation and maintenance management mainly relies on manual inspection and regular maintenance, which is not only inefficient, but also difficult to ensure timely and effective inspection and maintenance under complex weather conditions. At the same time, empirical judgments based on historical data often fail to effectively identify potential failure risks. Therefore, a new, digital monitoring and operation method is urgently needed to accurately and efficiently manage drop-out fuses in high-altitude cold regions.

[0004] The core of digital monitoring and operation and maintenance methods is to use sensors, wireless communication technology and big data analysis to monitor the operating status of equipment in real time and perform intelligent processing. The arrangement of sensors can collect multi-dimensional data such as temperature, humidity, vibration and current in real time, and transmit it to the cloud or local data center to form complete equipment operation information. Big data analysis and machine learning technology can help quickly identify potential failure modes and predict the remaining service life of equipment, thereby improving the foresight of operation and maintenance and reducing the risks of sudden failures.

[0005] In addition, with the help of drone inspection technology, fuses can be regularly inspected in bad weather that is not suitable for manual on-site inspection, and the equipment status can be automatically analyzed with image recognition technology to further improve the safety and efficiency of monitoring. By establishing an intelligent operation and maintenance management system and integrating monitoring data with maintenance records, scientific operation and maintenance scheduling can be achieved, and the configuration and use of maintenance resources can be optimized with the support of digital archives, thereby enhancing decision-making support capabilities.

[0006] In summary, the digital monitoring and operation and maintenance method provides a comprehensive and systematic solution for the management of drop-out fuses in high-altitude and cold areas. It can effectively cope with maintenance challenges in complex environments and achieve continuous and healthy operation of equipment. Summary of the invention

[0007] The object of the present invention is to provide a digital monitoring and operation method for drop-out fuses in high-cold areas to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a digital monitoring and operation method for drop-out fuses in high-cold areas, comprising the following steps:

[0009] S1: Data Collection

[0010] Sensor layout: Various sensors are installed at key locations of the drop-out fuse, such as temperature sensors, humidity sensors, vibration sensors, and current sensors, to collect equipment operating status data in real time;

[0011] Wireless communication: Transmit the collected data to the cloud or local data center via wireless networks or low-power wide area networks (LPWAN);

[0012] During the data transmission process, an encryption processor is used to encrypt the data transmitted to the cloud or local data center using one of the Rabin key and the ElGamal key;

[0013] S2: Data Analysis

[0014] Big data analysis: Use big data technology to analyze the collected equipment data to identify potential failure modes and risk points;

[0015] Machine learning: Apply machine learning algorithms to train historical operation and maintenance data to predict the failure rate and remaining service life of equipment, thereby improving the foresight of maintenance;

[0016] S3: Remote Monitoring

[0017] Real-time monitoring: By establishing a monitoring platform, the status of the fuse can be monitored in real time to detect abnormal situations in time;

[0018] Early warning mechanism: Set the safety threshold of equipment operation. Once the monitoring data exceeds the set range, the system automatically sends an alarm to notify the relevant operation and maintenance personnel;

[0019] S4: Operation and Maintenance Management

[0020] Intelligent operation and maintenance: Through the operation and maintenance management system, monitoring data and maintenance records are integrated to achieve intelligent operation and maintenance scheduling and optimize maintenance plans;

[0021] Digital archives: Create digital archives for each fuse to record its operating data, work history and maintenance records to facilitate subsequent analysis and decision-making;

[0022] S5: Drone inspection

[0023] Inspection application: In high-altitude and cold regions, the use of drones for regular inspections can overcome the limitations of bad weather and improve inspection efficiency and safety;

[0024] Image recognition: Combine image recognition technology to analyze images taken by drones and automatically detect appearance defects and environmental impacts of fuses;

[0025] S6: Maintenance Decision Support

[0026] Decision-making model: By building a maintenance decision support model, scientific maintenance suggestions are provided to operation and maintenance personnel, and the configuration and use of maintenance resources are optimized based on data-driven;

[0027] Training and feedback: Regularly train operation and maintenance personnel on digital monitoring technology, and continuously optimize the monitoring system and operation and maintenance strategy based on operation and maintenance feedback.

[0028] Preferably, during maintenance decision support in S6, digital monitoring technology enables operation and maintenance personnel to formulate maintenance plans based on real data.

[0029] Preferably, S5 utilizes drones and intelligent monitoring systems for remote inspection and evaluation, allowing operation and maintenance personnel to quickly obtain equipment status information under extreme weather conditions, thereby improving response speed.

[0030] Preferably, during maintenance decision support in S6, a maintenance decision support model is established to provide scientific and effective maintenance suggestions to operation and maintenance personnel based on data-driven analysis results.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The digital monitoring and operation and maintenance method of drop-out fuses in high-cold areas improves equipment reliability: through real-time monitoring and data analysis, it can promptly discover and solve potential performance problems of fuses, thereby significantly improving equipment reliability, reducing failure rates, and ensuring the stable operation of the power system; optimizing maintenance resource allocation, digital monitoring technology enables operation and maintenance personnel to formulate maintenance plans based on real data, thereby effectively reducing unnecessary costs for manual inspection and maintenance and improving the efficiency of resource allocation.

[0033] 2. The digital monitoring and operation and maintenance method of drop-out fuses in high-altitude cold regions reduces operation and maintenance costs: real-time monitoring and intelligent analysis can minimize power outages caused by sudden failures, reduce the cost of emergency repairs and replacement equipment, and reduce overall operation and maintenance costs; improve response speed: using drones and intelligent monitoring systems for remote inspection and evaluation, operation and maintenance personnel can quickly obtain equipment status information under extreme weather conditions, thereby improving response speed and taking maintenance measures in a timely manner; enhance decision-making support capabilities, establish a maintenance decision support model, and provide scientific and effective maintenance recommendations to operation and maintenance personnel based on data-driven analysis results, making the decision-making process more scientific and precise.

[0034] 3. The digital monitoring and operation and maintenance method of drop-out fuses in this high-altitude and cold region promotes technological progress and innovation: Regular digital monitoring technology training not only improves the skill level of operation and maintenance personnel, but also promotes the application of new technologies in actual operation and maintenance, and promotes technological innovation and progress; by reducing the sudden failure rate and reducing resource waste, digital operation and maintenance methods can reduce environmental impact to a certain extent, promote the power industry to move towards a greener and more sustainable development direction, and enhance public trust: Systematic and transparent operation and maintenance management methods can enhance the public's trust in power companies and their operation and maintenance work, enhance corporate image, and help establish a good sense of social responsibility.

[0035] 4. The digital monitoring and operation and maintenance method of drop-type fuses in high-altitude cold areas scientifically applies data encryption technology to the security of digital monitoring and operation and maintenance by performing data encryption based on quadratic congruence and data encryption based on digital signatures on data information, thereby improving the security of digital monitoring and operation and maintenance data and enhancing the data security experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0037] Figure 1 It is a schematic diagram of the digital monitoring operation and maintenance process of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] See also Figure 1 , the present invention provides a technical solution:

[0041] A digital monitoring and operation method for drop-out fuses in high-cold areas, comprising the following steps:

[0042] S1: Data Collection

[0043] Sensor layout: Various sensors are installed at key locations of the drop-out fuse, such as temperature sensors, humidity sensors, vibration sensors, and current sensors, to collect equipment operating status data in real time.

[0044] Wireless communication: Transmit the collected data to the cloud or local data center via wireless networks or low-power wide area networks (LPWAN);

[0045] During the data transmission process, an encryption processor is used to encrypt the data transmitted to the cloud or local data center using one of the Rabin keys and the ElGamal key.

[0046] Sensors transmit data information to the cloud or local data center through data transmission channels. Several groups of sensors transmit data information to encrypted servers through 4G / 5G networks, and the data information is transmitted in text form.

[0047] The encryption processor performs public key encryption on important parts of the data information. The encryption process includes performing data encryption based on quadratic congruence, namely Rabin key encryption, on the data information.

[0048] An improvement of the RSA cryptosystem of the Rabin cryptosystem, the Rabin cryptosystem can be considered as an RSA cryptosystem with constant values ​​of e and d, e = 2, d = 1 / 2. That is, encryption is c = m ^ 2modn, decryption is m=c ^ (1 / 2)modn.

[0049] Rabin key generation: select two large prime numbers p and q, which are required to be multiples of p and q4 plus 3, calculate n = pq, the public key is n, which is published to the public, and the private key is (p, q), which is kept privately by the web page. The user sends a plaintext m to the web page, where 0 < m < n, and uses the public key n to calculate: c = m 2 (modn), c is the ciphertext.

[0050] The encrypted data information is published in the cloud or local data center. When the machine or operation and maintenance personnel browse the data, the server configured in the cloud or local data center will load and decrypt the data and judge the correctness of the decryption.

[0051] After receiving the ciphertext c, the cloud or local data center uses the private key (p, q) hidden in time to calculate:

[0052] Calculation: m1, m2, m3, m4, satisfying

[0053] m1(mod p)=m p ; m1(mod q)=m q ;

[0054] m2(mod p)=m p ; m2(mod q)=qm q ;

[0055] m3(mod p)=pm p ; m3(mod q)=m q ;

[0056] m4(mod p)=pm p ; m4(mod q)=qm q ;

[0057] Therefore, the real plaintext m must be one of the four numbers: m1, m2, m3, m4. Observe the four numbers and eliminate those meaningless "garbled codes". The remaining one is the real plaintext m, and the decryption is completed.

[0058] If the decryption result is correct, the data information will be displayed in plain text in the cloud or local data center for browsing. If the decryption result is incorrect, the data information will still be displayed in cipher text in the cloud or local data center.

[0059] An improvement of the RSA cryptosystem of the Rabin cryptosystem, the Rabin cryptosystem can be considered as an RSA cryptosystem with constant values ​​of e and d, e = 2, d = 1 / 2. That is, encryption is c = m ^ 2modn, decryption is m=c^ (1 / 2)modn.

[0060] Rabin key generation:

[0061] Given a prime number p, z* p A member g on the matrix, 1<g<p, and an element y, find an integer x (0≤x≤p-2) such that g x =y mod p.

[0062] The public key is (p,g,y), which is published to the public; the private key is x, which is kept privately by the web page.

[0063] The encrypted data information is published in the cloud or local data center. When other users open the web page to browse the data, the configuration server in the cloud or local data center will load and decrypt the data and judge the correctness of the decryption.

[0064] Send plaintext m to the cloud or local data center, where 0<m<p. Select a random number k, (k,p-1)=1, and calculate: y1=g k mod p, and then use the public key y to calculate: y2 = my k mod p.

[0065] The ciphertext is composed of y1 and y2 in series, that is, the ciphertext c = y1 ‖ y2.

[0066] If the decryption result is correct, the data information will be displayed in plain text in the cloud or local data center for browsing. If the decryption result is incorrect, the data information will still be displayed in cipher text in the cloud or local data center.

[0067] After receiving the ciphertext c, the cloud or local data center uses its own private key x to calculate:

[0068] m=y2 / y1 x

[0069] =my k / (g k ) x

[0070] =mg xk / g xk mod p

[0071] When generating a digital signature, select a large prime number p, select g as the primitive element of the field GF(p), select a positive integer x, and calculate y=g x(mod p). The public key is (p, g, y), the private key is x, send data information m to the web page, use H to process the data information m, get h = H (m), select a random number k, satisfying 0 < k < p-1, and (k, p-1) = 1, calculate: The webpage sends the signature (m, r, s) to the browsing user. After receiving the message m and the signature (r, s), the browsing user calculates: h = H (m) and uses the public key (p, g, y) to verify whether y r r s =g H(m) (mod p), if so, then (m, r, s) is the signature information of the data transmission user.

[0072] S2: Data Analysis

[0073] Big data analysis: Use big data technology to analyze the collected equipment data to identify potential failure modes and risk points;

[0074] Machine learning: Apply machine learning algorithms to train historical operation and maintenance data to predict the failure rate and remaining service life of equipment, thereby improving the foresight of maintenance;

[0075] S3: Remote Monitoring

[0076] Real-time monitoring: By establishing a monitoring platform, the status of the fuse can be monitored in real time to detect abnormal situations in time;

[0077] Early warning mechanism: Set the safety threshold of equipment operation. Once the monitoring data exceeds the set range, the system automatically sends an alarm to notify the relevant operation and maintenance personnel;

[0078] S4: Operation and Maintenance Management

[0079] Intelligent operation and maintenance: Through the operation and maintenance management system, monitoring data and maintenance records are integrated to achieve intelligent operation and maintenance scheduling and optimize maintenance plans;

[0080] Digital archives: Create digital archives for each fuse to record its operating data, work history and maintenance records to facilitate subsequent analysis and decision-making;

[0081] For the historical data of all parameters, the AR time series model and SOM adaptive neural network algorithm are used to calculate the transfer probability matrix of each parameter; on this basis, the multivariate time series composed of various parameters are clustered by the DBSCAN density clustering algorithm to obtain m clusters;

[0082] Substitute the real-time data of online monitoring into the transfer probability matrix to obtain the transfer probability sequence, and judge whether the multivariate parameters at each time point belong to m classes; when the transfer probability sequence of each parameter simultaneously appears a value greater than the set number of 0s and the data in the set time period thereafter does not belong to m classes, it is considered to be abnormal, and the data in the corresponding time period is regarded as abnormal data;

[0083] Clear all abnormal data and mark their locations. When a marked missing mask is detected on the original data, a missing mask sequence with the same size as the original data is generated. Each element in the missing mask sequence is a binary variable, 1 represents missing data, and 0 represents normal data.

[0084] The data loss caused by the failure of the sampling communication equipment is mainly manifested as discrete missing points, continuous missing segments or a combination of the two. When the sampling terminal contacts are loose or other interference occurs, discrete missing points may appear in the data waveform. In each transmission link, the loss of data packets caused by temporary communication failures will lead to continuous missing segments. It can be seen that the situation of continuous missing segments is much more serious than that of discrete missing points. In order to describe the actual situation when data is lost, it is necessary to model the process of data loss and design a generation model of binary loss mask. The expression of related variables is: normal data is X(x1, x2, ..., x n-1 , x n ), the missing binary mask is M(m1, m2, ..., m n-1 , m n ), the data under the missing mask is X'(x'1, x'2, ... x' n-1 , x' n ), the Gaussian noise of missing data is The missing rate is γ∈(0,1), the number of missing persons is NMD, and the number of production operators is ⊙.

[0085] NMD=nγ.

[0086] The missing data are treated as normal data minus the element-wise product of the binary missing mask and the normal data, and the mask is then mixed with Gaussian noise. In the product, the data at each position in the binary missing mask is a binary code, where 1 indicates that the data at that position is missing and 0 indicates normal. In order to unify the two types of missing, the discrete missing points are regarded as special continuous missing segments with a length of 1.

[0087] Under a given missing rate, if NMD missing points are randomly selected directly from the n bits of M, the probability of discrete missing is much greater than the probability of continuous missing; if the NMD missing points are first divided into several continuous fragments and then these fragments are randomly placed in M, collisions may occur, thereby reducing the actual missing rate γ.

[0088] The generation of missing masks can be adjusted very flexibly. In fact, the overall missing rate of power system measurement data is low, of which continuous missing accounts for the majority. Therefore, continuous missing is also a missing type that needs to be paid attention to.

[0089] After detecting the missing mask on the original data, all data needs to be normalized to improve the recovery effect. In this scheme, the value is considered to be normalized to (0,1).

[0090] When repairing this type of data, it is not enough to refer only to the continuity between the directly adjacent normal data and the missing data (such as the data half an hour ago and half an hour later), which leads to poor performance. Instead, periodicity should be considered, which also means that the data in the adjacent period must be referenced.

[0091] Based on the real-time acquired non-sound data, the missing mask sequence is truncated and reorganized into a matrix;

[0092] According to the time continuity of the left and right edge data of the matrix, the left and right edge data of the matrix are filled with each other, and the filled matrix is ​​divided into smaller square matrixes in the column direction. The division windows of adjacent square matrixes are set with a certain overlapping area, and the partially overlapping area is regarded as the filling of the upper and lower edges of the square matrix;

[0093] Since in the known missing mask, the data at both ends of the missing fragment have data directly adjacent to them horizontally, and the repair difficulty is lower than that of the data inside the missing fragment, each missing fragment is divided into smaller sub-fragment pairs from both ends to the middle in proportion;

[0094] Adopting step-by-step repair, each time only repairs the outermost sub-segment pairs of the missing segment, and then performs the next repair on this basis until the repair is complete, so as to obtain a more refined repair effect;

[0095] The results of the hierarchical processing are input into the convolutional autoencoder to improve the data.

[0096] S5: Drone inspection

[0097] Inspection application: In high-altitude and cold regions, the use of drones for regular inspections can overcome the limitations of bad weather and improve inspection efficiency and safety;

[0098] Image recognition: Combine image recognition technology to analyze images taken by drones and automatically detect appearance defects and environmental impacts of fuses;

[0099] S6: Maintenance Decision Support

[0100] Decision-making model: By building a maintenance decision support model, scientific maintenance suggestions are provided to operation and maintenance personnel, and the configuration and use of maintenance resources are optimized based on data-driven;

[0101] Training and feedback: Regularly train operation and maintenance personnel on digital monitoring technology, and continuously optimize the monitoring system and operation and maintenance strategy based on operation and maintenance feedback.

[0102] Improve equipment reliability: Through real-time monitoring and data analysis, potential performance problems of fuses can be discovered and resolved in a timely manner, thereby significantly improving equipment reliability, reducing failure rates, and ensuring stable operation of the power system.

[0103] Optimize maintenance resource allocation: Digital monitoring technology allows operation and maintenance personnel to formulate maintenance plans based on real data, thereby effectively reducing unnecessary costs of manual inspection and maintenance and improving the efficiency of resource allocation.

[0104] Reduce operation and maintenance costs: Real-time monitoring and intelligent analysis can minimize power outages caused by sudden failures, reduce the cost of emergency repairs and equipment replacement, and reduce overall operation and maintenance costs.

[0105] Improve response speed: Using drones and intelligent monitoring systems for remote inspection and assessment, maintenance personnel can quickly obtain equipment status information under extreme weather conditions, thereby improving response speed and taking maintenance measures in a timely manner.

[0106] Enhance decision-making support capabilities: Establish a maintenance decision support model to provide scientific and effective maintenance suggestions to operation and maintenance personnel based on data-driven analysis results, making the decision-making process more scientific and accurate.

[0107] Promote technological progress and innovation: Regular digital monitoring technology training not only improves the skills of operation and maintenance personnel, but also promotes the application of new technologies in actual operation and maintenance, and promotes technological innovation and progress.

[0108] Environmentally friendly effect: By reducing the sudden failure rate and reducing resource waste, digital operation and maintenance methods can reduce environmental impact to a certain extent and promote the power industry to move towards a greener and more sustainable development direction.

[0109] Improve public trust: Systematic and transparent operation and maintenance management methods can enhance the public's trust in power companies and their operation and maintenance work, improve corporate image, and help establish a good sense of social responsibility.

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0111] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital monitoring and operation method for drop-out fuses in high-cold areas, characterized in that: The following steps are involved: S1: Data Collection Sensor layout: Various sensors are installed at key locations of the drop-out fuse, such as temperature sensors, humidity sensors, vibration sensors, and current sensors, to collect equipment operating status data in real time; Wireless communication: Transmit the collected data to the cloud or local data center via wireless networks or low-power wide area networks (LPWAN); During the data transmission process, an encryption processor is used to encrypt the data transmitted to the cloud or local data center using one of the Rabin key and the ElGamal key; S2: Data Analysis Big data analysis: Use big data technology to analyze the collected equipment data to identify potential failure modes and risk points; Machine learning: Apply machine learning algorithms to train historical operation and maintenance data to predict the failure rate and remaining service life of equipment, thereby improving the foresight of maintenance; S3: Remote Monitoring Real-time monitoring: By establishing a monitoring platform, the status of the fuse can be monitored in real time to detect abnormal situations in time; Early warning mechanism: Set the safety threshold of equipment operation. Once the monitoring data exceeds the set range, the system automatically sends an alarm to notify the relevant operation and maintenance personnel; S4: Operation and Maintenance Management Intelligent operation and maintenance: Through the operation and maintenance management system, monitoring data and maintenance records are integrated to achieve intelligent operation and maintenance scheduling and optimize maintenance plans; Digital archives: Create digital archives for each fuse to record its operating data, work history and maintenance records to facilitate subsequent analysis and decision-making; S5: Drone inspection Inspection application: In high-altitude and cold regions, the use of drones for regular inspections can overcome the limitations of bad weather and improve inspection efficiency and safety; Image recognition: Combine image recognition technology to analyze images taken by drones and automatically detect appearance defects and environmental impacts of fuses; S6: Maintenance Decision Support Decision-making model: By building a maintenance decision support model, scientific maintenance suggestions are provided to operation and maintenance personnel, and the configuration and use of maintenance resources are optimized based on data-driven; Training and feedback: Regularly train operation and maintenance personnel on digital monitoring technology, and continuously optimize the monitoring system and operation and maintenance strategy based on operation and maintenance feedback.

2. According to claim 1, a digital monitoring and operation method for drop-out fuses in high-cold areas is characterized by: When providing maintenance decision support in S6, digital monitoring technology enables operation and maintenance personnel to formulate maintenance plans based on real data.

3. According to claim 1, a digital monitoring and operation method for drop-out fuses in cold regions is characterized by: The S5 uses drones and intelligent monitoring systems to perform remote inspection and evaluation, and operation and maintenance personnel can quickly obtain equipment status information under extreme weather conditions, thereby improving response speed.

4. According to claim 1, a digital monitoring and operation method for drop-out fuses in cold regions is characterized by: During the maintenance decision support in S6, a maintenance decision support model is established to provide scientific and effective maintenance suggestions to the operation and maintenance personnel based on data-driven analysis results.

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