Intelligent maintenance power cabinet one-key emergency power-off method and system

Abstract

The invention relates to the technical field of power system maintenance and safety, and discloses an intelligent maintenance power cabinet one-key emergency power-off method and system, and the method comprises the steps: installing a sensor at a key part of a maintenance power cabinet, and collecting the data of the power cabinet in real time; a wireless transmission technology is utilized to transmit data acquired by the sensor to a monitoring center in real time; the monitoring center processes the received data by adopting big data analysis and an artificial intelligence algorithm, and analyzes whether an emergency power-off demand exists or not; when the monitoring center judges that emergency power-off is needed, an electromechanical element connected through a hard wire executes emergency power-off operation, and a power supply of the power cabinet is cut off. According to the method, the operation state data and the environment parameters of the power cabinet are collected in real time by installing the sensors at key parts of the power cabinet, comprehensive monitoring of the power cabinet is ensured, and the analysis accuracy and the decision-making efficiency of the state of the power cabinet are improved through multi-modal data fusion and an attention mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of power system maintenance and safety, and particularly to a one-key emergency power-off method and system for an intelligent maintenance power cabinet. Background Art

[0002] In the field of power system maintenance and safety, the one-key emergency power-off technology of intelligent maintenance power cabinets has become one of the key technologies to ensure the stable operation of the power grid. With the continuous improvement of industrial automation and intelligence levels, the safety performance and emergency response capabilities of power cabinets have received increasing attention. Traditional emergency power-off methods mostly rely on electromechanical components connected by hard wires to cut off the power supply through physical contact. However, these methods have certain limitations in terms of response speed, safety, and reliability.

[0003] In recent years, although some solutions based on sensor technology and wireless transmission technology have been proposed to improve the monitoring and control systems of power cabinets, these solutions generally have problems such as insufficient data processing capabilities and imperfect safety measures. In terms of data analysis, existing technologies mostly use single-sensor data or simple data analysis methods, making it difficult to accurately judge the complex operating states of power cabinets. In addition, the security and privacy protection measures for data transmission in existing technologies are not sufficient, and it is easy to be interfered with or attacked externally. In our invention, by introducing big data analysis and artificial intelligence algorithms, it is possible to more accurately identify the abnormal states of power cabinets and make power-off decisions in advance, thus effectively avoiding the occurrence of power accidents. At the same time, encryption and compression algorithms are used to ensure the security and efficiency of data transmission. Our invention shows significant advantages compared with existing technologies in terms of improving the response speed, safety, and reliability of emergency power-off operations, providing a more solid guarantee for the safe operation of the power system. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a one-key emergency power-off method for an intelligent maintenance power cabinet. Compared with traditional emergency power-off methods that rely on physical contact, the present invention can respond more quickly to emergencies and cut off the power supply in a timely manner, thereby reducing the risk of accidents. By introducing big data analysis and artificial intelligence algorithms, the present invention can more accurately identify the abnormal states of power cabinets, make power-off decisions in advance, avoid power accidents caused by misjudgment or delay, and improve the reliability of the system.

[0006] To solve the above technical problems, the present invention provides the following technical solution, an intelligent maintenance power cabinet one-key emergency power-off method, including: installing sensors at key parts of the maintenance power cabinet to collect power cabinet data in real time; using wireless transmission technology to transmit the data collected by the sensors to the monitoring center in real time; the monitoring center processes the received data using big data analysis and artificial intelligence algorithms to analyze whether there is a need for emergency power-off; when the monitoring center determines that emergency power-off is required, an electromechanical component connected by a hard wire is used to perform an emergency power-off operation to cut off the power supply of the power cabinet.

[0007] As a preferred solution of the intelligent maintenance power cabinet one-key emergency power-off method described in the present invention, wherein: the collection of power cabinet data includes the operating state data and environmental parameters of the power cabinet;

[0008] By installing different sensors at key parts of the power cabinet, the internal circuit data of the power cabinet and the external environment change data are monitored in real time.

[0009] As a preferred solution of the intelligent maintenance power cabinet one-key emergency power-off method described in the present invention, wherein: the wireless transmission technology includes that the sensor collects data, attaches a timestamp, generates a one-time key, encrypts the data and the timestamp using the one-time key, and the encrypted data packet and HMAC are sent through the wireless link together.

[0010] As a preferred solution of the intelligent maintenance power cabinet one-key emergency power-off method described in the present invention, wherein: the real-time transmission to the monitoring center includes that the monitoring center receives the data packet, generates a one-time key using the same time synchronization algorithm, decrypts the data using the one-time key, and verifies the HMAC using the one-time key;

[0011] Receive the data collected by the sensor, perform feature recognition on the data using the trained model, and perform preliminary screening according to the recognition result to exclude redundant and low-value data. The screened data is compressed by a compression algorithm, and data enhancement is performed during the compression process.

[0012] As a preferred solution of the intelligent maintenance power cabinet one-key emergency power-off method described in the present invention, wherein: the monitoring center further includes combining multi-modal data of sound, vibration, and temperature, using a new deep learning method to extract the features of each modal data, and dynamically allocating weights for fusion through an attention mechanism. The fused data is input into the decision-making system, and the decision result is compared with the actual consequence to optimize the model:

[0013] Q(s,a)←Q(s,a)+α(r+γmax a’ Q(s’,a’)-Q(s,a))

[0014] Among them, Q(s,a) is the Q-value of taking action a in state s, r is the reward, γ is the discount factor, α is the learning rate, s’ is the next state, and a’ is the next action corresponding to s’.

[0015] As a preferred solution of the one-key emergency power-off method for the intelligent maintenance power cabinet described in the present invention, among them: the data received by using big data analysis and artificial intelligence algorithms includes extracting characteristics that can represent the state of the power cabinet by combining time series feature extraction and pattern recognition;

[0016] Let the original feature set be X, and define an improved feature selection function:

[0017] F select (X) = W·Φ(X)

[0018] Among them, F select is the output of the feature selection model, W is the weight matrix, and Φ(X) represents the feature mapping of the feature matrix

[0019] Combining time series analysis and machine learning techniques for fault prediction:

[0020]

[0021] Among them, P predict (T) represents the predicted fault probability, T represents the data points changing with time, g(T i ) is the feature extraction function, T i is the time series data, and f is a non-linear function.

[0022] As a preferred solution of the one-key emergency power-off method for the intelligent maintenance power cabinet described in the present invention, among them: the execution of the emergency power-off operation includes that when the monitoring center detects an abnormality in the power system or receives a one-key emergency power-off instruction, the system immediately sends a wireless energy transmission signal, the control system activates the electromagnetic isolation device, and the movable power contact point quickly moves away under the action of the magnetic field to cut off the power connection;

[0023] After the control system detects that the power contact point has moved away, it confirms that the power-off operation is completed and sends a confirmation message of successful power-off to the monitoring center through a wireless signal.

[0024] Another object of the present invention is to provide an intelligent maintenance power cabinet one-key emergency power-off system, which can process much more complex multi-sensor data, realize the comprehensive monitoring and accurate judgment of the operation state of the power cabinet, and ensure the security of data transmission and privacy protection by adopting encryption and compression algorithms, reducing the risk of external interference or attack.

[0025] As a preferred solution of an intelligent maintenance power cabinet one - key emergency power - off system according to the present invention, it includes: a data acquisition module, a transmission module, a processing module, and an execution module;

[0026] The data acquisition module includes sensors and an installation structure, and is used for real - time monitoring of the power cabinet status;

[0027] The transmission module includes wireless transmission equipment and is responsible for transmitting data to the monitoring center;

[0028] The processing module includes a data processing server and an artificial intelligence algorithm, and is used for analyzing data and making power - off decisions;

[0029] The execution module includes electromechanical components connected by hard wires and is used for executing emergency power - off instructions.

[0030] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of an intelligent maintenance power cabinet one - key emergency power - off method are realized.

[0031] A computer - readable storage medium stores a computer program thereon. It is characterized in that when the computer program is executed by a processor, the steps of an intelligent maintenance power cabinet one - key emergency power - off method are realized.

[0032] The beneficial effects of the present invention: Through intelligent control means, one - key emergency power - off is realized, which greatly shortens the response time of the power - off operation, can cut off the power supply in the first time, and prevent the expansion of accidents. By using big - data analysis and artificial intelligence algorithms, the recognition accuracy of abnormal states of the power cabinet is improved, ensuring timely power - off before potential dangers occur, and significantly enhancing the safety performance of the power system. The physical contact and wear of traditional electromechanical components are reduced, the risk of power - off failure caused by component failures is lowered, and the overall reliability of the system is improved. By adopting advanced data - processing technologies, a large amount of complex data can be processed and analyzed, better monitoring the operation status of the power cabinet, and providing support for maintenance decisions. The intelligent monitoring and control system reduces the need for manual inspection and maintenance, and lowers the long - term operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following - described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0034] Figure 1Schematic flow diagram of a one - key emergency power - off method for an intelligent maintenance power cabinet provided by an embodiment of the present invention.

[0035] Figure 2 Schematic diagram of the working modules of a one - key emergency power - off system for an intelligent maintenance power cabinet provided by an embodiment of the present invention. Detailed implementation manners

[0036] To make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0038] Secondly, the so - called "one embodiment" or "embodiment" herein refers to specific features, structures, or characteristics that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively mutually exclusive with other embodiments.

[0039] The present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for the convenience of explanation, the cross - sectional views showing the device structure will be enlarged locally not in accordance with the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention here. In addition, in actual production, three - dimensional spatial dimensions including length, width, and depth should be included.

[0040] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner, and outer" are based on the orientation or positional relationships shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0041] Unless otherwise clearly specified and defined in the present invention, the terms "installed, connected, connected" shall be understood in a broad sense. For example, it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0042] Example 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for one-key emergency power-off of an intelligent maintenance power cabinet, including:

[0043] S1: Install sensors at key parts of the maintenance power cabinet to collect power cabinet data in real time.

[0044] Furthermore, install temperature sensors at key parts of the power cabinet to monitor temperature changes in real time; install different sensors at key parts of the power cabinet to monitor the internal circuit data of the power cabinet and the data of external environment changes in real time.

[0045] Use a current sensor in series in the circuit of the power cabinet to measure the real-time current value of the circuit, connect a voltage sensor to the circuit to monitor the real-time situation of each phase voltage of the power cabinet, and calculate the real-time power consumption by combining the current and voltage data;

[0046] Use a frequency sensor to monitor the frequency of the power supply output by the power cabinet to detect whether it is within the normal working range;

[0047] Install a vibration sensor on the mechanical components of the power cabinet to monitor the vibration of the equipment and analyze whether there is a mechanical fault;

[0048] Install a humidity sensor inside the power cabinet to monitor the environmental humidity;

[0049] Use a leakage current sensor to monitor the leakage situation of the power cabinet.

[0050] S2: Use wireless transmission technology to transmit the data collected by the sensors to the monitoring center in real time.

[0051] Furthermore, the sensor and the monitoring center pre-share an initial key. Before each data transmission, the two generate a one-time key based on an accurate time synchronization mechanism. This key is calculated by a hash function based on the current time and the initial key, ensuring the uniqueness and timeliness of the key. The data is encrypted with this one-time key before transmission, and the receiving end also uses an algorithm based on time synchronization to decrypt the data.

[0052] After the data is encapsulated, link encryption technology is used to protect it. Link encryption ensures the security of data during transmission. At the same time, a hash-based message authentication code (HMAC) is appended to each data packet before encryption. This HMAC is generated based on the data content and a one-time key. After receiving the data packet, the monitoring center uses the same key and hash algorithm to verify the HMAC to ensure the integrity and authenticity of the data.

[0053] In an optional embodiment, data encryption can be implemented based on a time-synchronized one-time key. Specifically, the sensor and the monitoring center pre-generate a set of random key sequences and store them securely. Each time data is transmitted, the Nth key in the key sequence is used in order. After each 24-hour transmission cycle, the next set of key sequences is updated through a secure channel. The key sequences are encrypted and stored using AES-256. Each key is decrypted individually when in use. The receiving end maintains the same key index and automatically matches the currently used key.

[0054] In another optional embodiment, data encryption can also be achieved through a geographically dynamic key. Specifically, GPS modules are built into the sensor and the monitoring center to obtain the precise geographical location. The longitude and latitude coordinates are converted into digital feature values (such as the concatenation of the last four digits after the decimal point). A dynamic key is generated by combining the device's unique ID through the SHA-3 algorithm. The location is updated and the key is regenerated every 5 minutes. A location offset tolerance of ±0.5 km is set to prevent out-of-step caused by minor positioning errors.

[0055] It should be noted that the encrypted data packet and the HMAC are sent through a wireless link. The monitoring center receives the data packet, generates a one-time key using the same time synchronization algorithm, decrypts the data using the one-time key, and verifies the HMAC using the one-time key to ensure that the data has not been tampered with.

[0056] S3: The monitoring center uses big data analysis and artificial intelligence algorithms to process the received data and analyze whether there is a need for an emergency power outage.

[0057] Furthermore, the monitoring center receives the data packet, generates a one-time key using the same time synchronization algorithm, decrypts the data using the one-time key, and verifies the HMAC using the one-time key;

[0058] Receives the data collected by the sensor, uses the trained model to perform feature recognition on the data, and makes a preliminary screening according to the recognition result to exclude redundant and low-value data. The screened data is compressed through a compression algorithm, and data augmentation is performed during the compression process.

[0059] Combining sound, vibration, and temperature multimodal data, three sensors are used to collect sound (s), vibration (v), and temperature (T) respectively e) Data, using timestamps to synchronize data:

[0060] D(t) = {s(t), v(t), T e (t)}

[0061] Among them, D(t) is the data set collected at time t, including sound, vibration, and temperature data.

[0062] Using a convolutional neural network (CNN) in deep learning to extract features:

[0063] F i (x) = CNN i (x)

[0064] Among them, F i (x) is the feature extraction result of the i-th sensor data x; through the attention mechanism, weights are dynamically assigned for fusion, and the fused data is input into the decision-making system. The decision result is compared with the actual consequence for optimizing the model:

[0065] Q(s,a) ← Q(s,a) + α(r + γmax a’ Q(s’,a’) - Q(s,a))

[0066] Among them, Q(s,a) is the Q value of taking action a in state s, r is the reward, γ is the discount factor, α is the learning rate, s’ is the next state, and a’ is the next action corresponding to s’.

[0067] Furthermore, using big data analysis and artificial intelligence algorithms to process the received data includes combining time series feature extraction and pattern recognition to extract the characteristics that can represent the state of the power cabinet;

[0068] Let the original feature set be X, and define an improved feature selection function:

[0069] F select (X) = W·Φ(X)

[0070] Among them, F select is the output of the feature selection model, W is the weight matrix, and Φ(X) represents the feature mapping of the feature matrix; combining time series analysis and machine learning techniques for fault prediction:

[0071]

[0072] Among them, P predict (T) represents the predicted fault probability, T represents the data points changing with time, g(T i ) is the feature extraction function, T i is the time series data, and f is the non-linear function.

[0073] S4: When the monitoring center determines that an emergency power-off is required, the electromechanical components connected by hard wires perform an emergency power-off operation to cut off the power supply of the power cabinet.

[0074] Furthermore, when the monitoring center detects an abnormality in the power system or receives an emergency power-off command, the system immediately sends a wireless energy transmission signal. The control system activates the electromagnetic isolation device, and the movable power contact point quickly moves away under the action of the magnetic field to cut off the power connection.

[0075] After the control system detects that the power contact point has moved away, it confirms that the power-off operation is completed and sends a confirmation message of successful power-off to the monitoring center via a wireless signal.

[0076] In an alternative embodiment, the emergency power-off operation can be achieved by rapid breaking of a mechanical spring. Specifically, an electromagnetic locking device is installed at a critical circuit node. When an abnormal signal is triggered, the electromagnetic coil instantaneously cuts off the power to release the spring energy storage. The spring mechanical force pushes the conductive arm to complete physical separation at a speed of 0.1 second, and a mechanical self-locking structure is used to maintain the open state. An optical sensor verifies the contact separation distance (≥10 mm).

[0077] In another alternative embodiment, the emergency power-off operation can also be achieved by arc extinction with high-pressure gas. Specifically, a sulfur hexafluoride (SF6) gas arc extinction chamber is installed at the contact. When a power-off command is received, two operations are triggered simultaneously: the electromagnetic drive mechanism separates the contacts and releases high-pressure gas to suppress arc generation. The high-speed air flow blowing technology is used to accelerate the arc extinction, and an ultraviolet light sensor is used to detect whether the arc has completely disappeared, and a pressure sensor verifies the airtightness of the arc extinction chamber.

[0078] Embodiment 2, the second embodiment of the present invention, which is different from the previous embodiment in that:

[0079] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0080] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a defined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0081] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0082] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0083] Embodiment 3, referring to Figure 2 , is an embodiment of the present invention, which provides an intelligent maintenance power cabinet one-key emergency power-off system, characterized in that it includes a data acquisition module 1, a transmission module 2, a processing module 3, and an execution module 4;

[0084] The data acquisition module 1 includes sensors and an installation structure, and is used to monitor the status of the power cabinet in real time;

[0085] The transmission module 2 includes wireless transmission devices and is responsible for transmitting data to the monitoring center;

[0086] The processing module 3, including a data processing server and an artificial intelligence algorithm, is used to analyze data and make a power-off decision;

[0087] The execution module 4, including electromechanical components connected by hard wires, is used to execute the emergency power-off instruction.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A one-key emergency power-off method for an intelligent maintenance power cabinet, characterized in that: including, install sensors at key parts of the maintenance power cabinet to collect power cabinet data in real time; use wireless transmission technology to transmit the data collected by the sensors to the monitoring center in real time; the monitoring center uses big data analysis and artificial intelligence algorithms to process the received data and analyze whether there is a need for emergency power-off; when the monitoring center determines that an emergency power-off is required, an electromechanical component connected by a hard wire is used to perform an emergency power-off operation to cut off the power supply of the power cabinet.

2. The one-key emergency power-off method for an intelligent maintenance power cabinet according to claim 1, wherein: The collection of power cabinet data includes the operating status data and environmental parameters of the power cabinet; different sensors are installed at key parts of the power cabinet to monitor the internal circuit data and external environment change data of the power cabinet in real time.

3. The one-key emergency power-off method for an intelligent maintenance power cabinet according to claim 2, wherein: The wireless transmission technology includes that the sensor collects data, attaches a timestamp, generates a one-time key, encrypts the data and the timestamp with the one-time key, and the encrypted data packet and HMAC are sent through the wireless link together.

4. The one-key emergency power-off method for an intelligent maintenance power cabinet according to claim 3, wherein: The real-time transmission to the monitoring center includes that the monitoring center receives the data packet, generates a one-time key using the same time synchronization algorithm, decrypts the data with the one-time key, and verifies the HMAC with the one-time key; receive the data already collected by the sensor, use the trained model to perform feature recognition on the data, and perform preliminary screening according to the recognition result to exclude redundant and low-value data. The screened data is compressed by a compression algorithm, and data enhancement is performed during the compression process.

5. The one-key emergency power-off method for an intelligent maintenance power cabinet according to claim 4, characterized in that: The monitoring center further includes combining sound, vibration, and temperature multi-modal data, using a new deep learning method to extract the features of each modal data, and dynamically allocating weights through an attention mechanism for fusion. The fused data is input into the decision-making system, and the decision result is compared with the actual consequence for optimizing the model: Q(s,a) ← Q(s,a) + α(r + γ max a′ Q(s′,a′) - Q(s,a)) Among them, Q(s,a) is the Q-value of taking action a in state s, r is the reward, γ is the discount factor, α is the learning rate, s′ is the next state, and a′ is the next action corresponding to s ′ ​ 6. The one-key emergency power-off method for an intelligent maintenance power cabinet according to claim 5, characterized in that: The processing of the received data using big data analysis and artificial intelligence algorithms includes combining time series feature extraction and pattern recognition to extract the characteristics that can represent the state of the power cabinet; Let the original feature set be X, and define an improved feature selection function: F select (X) = W·Φ(X) Among them, F select is the output of the feature selection model, W is the weight matrix, and Φ(X) represents the feature mapping of the feature matrix combine time series analysis and machine learning techniques for fault prediction: Among them, P predict (T) represents the predicted failure probability, T represents the data points changing with time, and g(T i ) is the feature extraction function, T i is the time series data, and f is the non-linear function.

7. The one-key emergency power-off method for an intelligent maintenance cabinet according to claim 6, characterized in that: The execution of the emergency power-off operation includes that when the monitoring center detects an abnormality in the power system or receives a one-key emergency power-off instruction, the system immediately sends a wireless energy transmission signal, the control system activates the electromagnetic isolation device, and the movable power contact point quickly moves away under the action of the magnetic field to cut off the power connection; after the control system detects that the power contact point has moved away, it confirms that the power-off operation is completed and sends a confirmation message of successful power-off to the monitoring center through a wireless signal.

8. A system adopting an intelligent maintenance power cabinet one-key emergency power-off method as described in any one of claims 1 to 7, characterized in that: including a data acquisition module, a transmission module, a processing module, and an execution module; the data acquisition module includes sensors and installation structures for monitoring the state of the power cabinet in real time; the transmission module includes wireless transmission devices responsible for transmitting data to the monitoring center; the processing module includes a data processing server and artificial intelligence algorithms for analyzing data and making power-off decisions; the execution module includes electromechanical components connected by hard wires for executing emergency power-off instructions.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements 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 computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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