Mine power monitoring system and data processing method based on cloud computing

Abstract

The present invention relates to the technical field of underground power supply in mines, and specifically discloses a mine power monitoring system based on cloud computing and a data processing method thereof, a data acquisition module, a data management module, a real-time monitoring module, a fault detection module, a deep analysis module, and a remote control module; the data acquisition module is used to acquire real-time power data from various power devices in the underground power system, and send the power data to the data management module; the data management module is used to receive the power data and store it in a cloud database, and provide data query, analysis, and historical trend viewing functions; the present invention acquires the power data of various underground power devices in real time, and uploads the data to the cloud database, displays the real-time operating status of the underground power devices to operators through a graphical interface, dynamically updates monitoring data, discovers potential problems and responds to them, locates the fault location, and generates alarm information and processing suggestions, thereby achieving rapid response and fault repair.

Description

Technical Field

[0001] The present invention belongs to the technical field of underground mine power supply, and in particular relates to a mine power monitoring system based on cloud computing and a data processing method thereof. Background Art

[0002] Cloud computing is the current mainstream distributed data computing technology. It has the advantages of security, reliability, flexible expansion, strong data processing capabilities, and high utilization efficiency of data processing equipment. It can organically combine a variety of scattered computing resources and achieve efficient integration of data computing resources with the help of virtual technology.

[0003] With the continuous improvement of the mechanization and automation level of coal mines, the dependence on power supply safety is getting stronger and stronger. The failure of underground power supply and distribution equipment often brings serious safety hazards to underground production. Unlike general substations, underground mine substations are mainly composed of central substations and several mining area substations. The equipment of the central substation is scattered and the environment is harsh. The inspection and operation of the power equipment are all completed by the staff. Usually, the traditional three-shift staff is on duty, and the information communication between the staff and the dispatchers on the ground is not timely, which is easy to cause accidents. Because the capacity of underground cables in mines is often too large, and the underground power supply distance is short, the cables are mostly copper cables with very low resistance, and the current setting cannot meet the selectivity of protection. At the same time, due to the complex geological conditions in the mine, the narrow tunnels and humid air, the power equipment is prone to overcurrent heating, and the power supply lines are dragged during construction or movement, which is easy to cause line short circuits.

[0004] Therefore, it is necessary to propose a cloud computing-based mine power monitoring system and its data processing method to solve the problem that the mine underground power monitoring system in the prior art cannot quickly understand and control the operation of the substation.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide a mine power monitoring system based on cloud computing and a data processing method thereof to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The mine power monitoring system based on cloud computing includes: data acquisition module, data management module, real-time monitoring module, fault detection module, in-depth analysis module, and remote control module;

[0009] The data acquisition module is used to acquire real-time power data from various power devices in the underground power system and send the power data to the data management module;

[0010] The data management module is used to receive the power data and store it in a cloud database, and provide data query, analysis and historical trend viewing functions;

[0011] The real-time monitoring module is used to display the operating status of the underground power system in real time through a graphical interface based on the power data, dynamically update the monitoring data and discover potential problems;

[0012] The fault detection module is used to perform abnormality detection according to the potential problem, locate the fault location and generate processing suggestions and alarm information;

[0013] The deep analysis module is used to perform trend analysis and fault prediction on the power data through data mining and machine learning, so as to optimize the configuration of power resources of the underground power system;

[0014] The remote control module is used to remotely control each of the power devices through a PC or a mobile terminal based on a remote operation interface, and remotely adjust power parameters.

[0015] Preferably, the data acquisition module is further used to establish a connection with each of the power devices through a hardware interface or a communication protocol, and collect the power data of each of the power devices in real time; perform preliminary processing after data collection based on edge computing technology, exclude outliers or unreasonable data, and remove high-frequency noise;

[0016] Use NTP or synchronize with the clock of the underground power system to achieve time synchronization between the data acquisition module and the power equipment; encapsulate the power data containing the timestamp according to a preset data format, and transmit the encapsulated power data to the data management module through the MQTT protocol.

[0017] Preferably, the data management module is further used to use a distributed database to store the received power data, and to back up the power data using an incremental backup method; to perform data verification on the received power data, and when an error occurs, the system triggers a corresponding exception handling mechanism;

[0018] The stored power data is queried according to the content input by the user, and the user is allowed to select different conditions for combined query; the integrated analysis tool performs real-time analysis on the stored power data to identify abnormal fluctuations in the power data;

[0019] The stored power data is visualized through visualization tools to display the power usage trend, power fluctuation trend, and failure trend of each power device; a cache mechanism is used to reduce the query pressure of the cloud database, and the historical power data is compressed and stored.

[0020] Preferably, the real-time monitoring module is further used to obtain the importance of each state quantity of the underground power system, wherein the evaluation standard of the importance of each state quantity is the degree of influence of the state on the entire underground power system, and the state quantity with lower importance is regarded as a safe state and processed;

[0021] The state quantity of each of the power devices is sampled according to the operating state, and the sampled state quantity is analyzed to see whether it is an acceptable state. If so, the sampling of each of the power devices continues; otherwise, the state quantity is considered to be a risky state. Real-time push of the state quantity is achieved through WebSocket, data is dynamically updated, and delayed updates are reduced.

[0022] Preferably, the fault detection module is further used to analyze the fluctuation of the power data by using anomaly detection algorithm and threshold detection to identify abnormal data fluctuations; and to perform multivariate data association analysis on the abnormal data fluctuations in the power data in combination with the risk status, so as to locate the source of the fault;

[0023] Based on the located fault source, topological analysis is used in combination with the underground power system to predict the propagation path of the fault; the specific type of fault and the order of occurrence are inferred through a graph neural network in combination with the real-time power data to generate a fault inference result;

[0024] The urgency of the fault is determined according to the fault inference result and the risk status, and emergency response strategies and processing suggestions of different levels are generated; the emergency response strategies and processing suggestions are optimized and generated by the restricted area search method according to the urgency.

[0025] Preferably, the deep analysis module is further used to use Kafka to perform data integration and standardization processing on the real-time power data to obtain the processed power data; use Fourier transform, spatial interpolation and sliding window to extract the periodic characteristics, spatial characteristics and trend characteristics of load fluctuations in the historical window from the processed power data respectively;

[0026] According to the periodic characteristics, spatial characteristics and trend characteristics in the historical window, a machine learning model trained based on historical fault data is used to perform fault prediction and generate a fault prediction result; according to the periodic characteristics, spatial characteristics and trend characteristics in the historical window, a LSTM is used to predict the change trend of the load fluctuation within a preset time period and generate a trend prediction result;

[0027] Based on the fault prediction result and the trend prediction result, the configuration of power resources of the underground power system is optimized.

[0028] Preferably, the remote control module is further used to connect and exchange data with the power device using the MQTT protocol based on the remote operation interface; obtain the status and real-time operation data of the power device, and send the control instructions input by the user to the power device;

[0029] After executing the control instruction, the execution result and current status of the power equipment are returned in real time, and a real-time warning is issued for abnormal situations.

[0030] The data processing method of the mine power monitoring system based on cloud computing includes:

[0031] Acquire real-time power data of each power device in the underground power system, and clean and format the collected power data;

[0032] The processed power data is stored in a cloud database, and the operating status of the underground power system is displayed in real time through a graphical interface;

[0033] Monitor the status of the power equipment in real time, dynamically update monitoring data and discover potential problems, detect abnormalities in time and issue alarms;

[0034] Performing trend analysis and fault prediction on the power data through data mining and machine learning, generating analysis results, and optimizing the configuration of power resources of the underground power system according to the analysis results;

[0035] Based on the remote operation interface, remotely control the power equipment according to the analysis results to perform scheduling, start and stop, and parameter adjustment operations;

[0036] The execution result and current status of the electric power equipment are returned in real time, and abnormal situations are warned in real time.

[0037] Preferably, the method also includes logging all user operations, recording in detail the time, executor, and content of each operation, and regularly auditing the logs; regularly updating the system version, fixing known vulnerabilities and defects, and upgrading the control module of the power equipment.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention acquires the power data of each power equipment in the mine in real time, and uploads the data to the cloud database, displays the real-time operation status of the power equipment in the mine to the operator through a graphical interface, dynamically updates the monitoring data, discovers potential problems and responds to them, locates the fault location and generates alarm information and processing suggestions, thereby achieving rapid response and fault repair, combining data mining and machine learning technologies to perform trend analysis and fault prediction, further optimizing the configuration and scheduling of power resources, and reducing the operation risk of the power system. The system also supports remote operation functions. Through the PC or mobile terminal, the operator can remotely adjust the parameters of the power equipment in the mine to ensure the smooth operation of key power equipment.

[0040] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a framework diagram of a mine power monitoring system based on cloud computing of the present invention;

[0042] Figure 2 The data processing method of the present invention is a flow chart. DETAILED DESCRIPTION

[0043] 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.

[0044] Embodiment 1:

[0045] See also Figure 1 As shown, the mine power monitoring system based on cloud computing includes: data acquisition module, data management module, real-time monitoring module, fault detection module, in-depth analysis module, and remote control module;

[0046] The data acquisition module is used to obtain real-time power data from various power devices in the underground power system and send the power data to the data management module;

[0047] The data management module is used to receive power data and store it in a cloud database, and provides data query, analysis and historical trend viewing functions;

[0048] The real-time monitoring module is used to display the operating status of the underground power system in real time through a graphical interface based on power data, dynamically update monitoring data and discover potential problems;

[0049] The fault detection module is used to detect anomalies based on potential problems, locate fault locations, and generate processing suggestions and alarm information;

[0050] The deep analysis module is used to perform trend analysis and fault prediction on power data through data mining and machine learning, and optimize the configuration of power resources in the underground power system;

[0051] The remote control module is used to remotely control various power equipment and remotely adjust power parameters through a PC or mobile terminal based on a remote operation interface.

[0052] The data acquisition module is also used to establish a connection with each power device through a hardware interface or a communication protocol to collect power data of each power device in real time;

[0053] Based on edge computing technology, preliminary processing is performed after data collection to exclude outliers or unreasonable data and remove high-frequency noise;

[0054] Use NTP or synchronize the clock with the underground power system to achieve time synchronization between the data acquisition module and the power equipment;

[0055] NTP (Network Time Protocol) is a protocol used to synchronize computer clocks in a computer network. It ensures that the time of each device in the network is consistent, so as to coordinate and synchronize the time of each system, which is especially important in distributed systems, financial transactions, communications and other scenarios.

[0056] The power data containing the timestamp is encapsulated according to a preset data format, and the encapsulated power data is transmitted to the data management module through the MQTT protocol.

[0057] MQTT (Message Queuing Telemetry Transport) is a lightweight, publish / subscribe-based messaging protocol that is particularly suitable for low-bandwidth, high-latency, and unreliable network environments. MQTT is commonly used for communication between Internet of Things (IoT) devices, but can also be used in other scenarios that require low power consumption and efficient communication.

[0058] The data management module is also used to store the received power data using a distributed database, and to back up the power data using an incremental backup method;

[0059] Perform data verification on the received power data. When an error occurs, the system will trigger the corresponding exception handling mechanism;

[0060] Query the stored power data according to the content input by the user, and allow the user to select different conditions for combined query;

[0061] Integrated analytical tools analyze stored power data in real time to identify abnormal fluctuations in power data;

[0062] Analysis tools include: machine learning and anomaly detection tools (such as TensorFlow, Scikit-learn, Isolation Forest); statistical analysis tools (such as R, MATLAB); analysis content includes: load analysis of underground power systems (such as peak load, average load), power factor analysis, voltage and current fluctuation trends, anomaly detection and alarm analysis, and automatic identification of abnormal fluctuations or faults in power data.

[0063] Visualize the stored power data through visualization tools to show the power usage trend, power fluctuation trend, and failure trend of each power equipment;

[0064] Use the cache mechanism to reduce the query pressure on the cloud database and compress and store historical power data.

[0065] The real-time monitoring module is also used to obtain the importance of each state quantity of the underground power system, where the evaluation standard of the importance of each state quantity is the degree of influence of the state on the entire underground power system, and the state quantity with lower importance is regarded as a safe state and processed;

[0066] According to the operating status, the state quantity of each power equipment is sampled, and the sampled state quantity is analyzed to see whether it is an acceptable state. If so, the sampling of each power equipment is continued. Otherwise, the state quantity is considered to be a risky state.

[0067] Real-time push of status data is achieved through WebSocket, which dynamically updates data and reduces delayed updates.

[0068] Land power grids have been developing for many years in risk level research and have certain reference value. According to the operation guarantee requirements and risk situation of underground power systems, and referring to the classification standards of land power grids, the risk level of underground power systems is divided into three levels: level 1 risk, level 2 risk and level 3 risk.

[0069] In order to increase the degree of differentiation, the risk situation under extreme circumstances is used as the grading threshold, and the reference standards are: ① It can be regarded as a safe state and level 1: the underground power system operates normally within the safety range, and only a few or a small number of power equipment parameters fluctuate abnormally, which belongs to the low risk level; ② Level 1 and level 2: the underground power system can operate, and a few or a small number of power equipment fail, which belongs to the medium risk level; ③ Level 2 and level 3: there are obvious fault points in the underground power system or risk problems that seriously threaten underground safety, which belongs to the high risk level.

[0070] The fault detection module is also used to analyze the fluctuation of power data using anomaly detection algorithms and threshold detection to identify abnormal data fluctuations;

[0071] Combined with the risk status, multivariate data correlation analysis is performed on abnormal data fluctuations in power data to locate the source of the fault;

[0072] Based on the located fault source, the fault propagation path is predicted by using topological analysis combined with the underground power system;

[0073] Combined with real-time power data, the graph neural network is used to infer the specific type of fault and the order in which it occurs, generating fault inference results;

[0074] Determine the urgency of the fault based on the fault speculation results and risk status, and generate emergency response strategies and handling suggestions of different levels;

[0075] Emergency response strategies and handling suggestions are optimized and generated through the restricted area search method according to the degree of urgency.

[0076] The basic idea of ​​the forbidden zone search method: (1) Define the forbidden zone: The forbidden zone refers to those solutions that cannot meet the problem constraints or perform poorly under certain constraints. For example, areas where abnormal conditions currently exist or specific power equipment. (2) Limit the search scope: By clarifying which areas are forbidden zones, the search process is limited to those areas where the optimal solution may be found, avoiding unnecessary searches. (3) Recursive or iterative search: The forbidden zone search method usually combines backtracking, branch and bound, heuristic search and other techniques to gradually explore the possible solution space and find the best solution while satisfying the forbidden zone constraints.

[0077] The deep analysis module is also used to use Kafka to perform data integration and standardization on real-time power data to obtain processed power data;

[0078] Fourier transform, spatial interpolation and sliding window are used to extract the periodic characteristics, spatial characteristics and trend characteristics of load fluctuation in the historical window from the processed power data respectively;

[0079] According to the periodic characteristics, spatial characteristics and trend characteristics within the historical window, a machine learning model trained based on historical fault data is used to predict faults and generate fault prediction results;

[0080] Based on the periodic characteristics, spatial characteristics and trend characteristics in the historical window, LSTM is used to predict the changing trend of load fluctuations within a preset time period and generate trend prediction results;

[0081] Based on the fault prediction results and trend prediction results, the configuration of power resources in the underground power system is optimized.

[0082] The remote control module is also used to connect and exchange data with the power equipment using the MQTT protocol based on the remote operation interface;

[0083] Obtain the status and real-time operation data of the power equipment, and send the control instructions input by the user to the power equipment;

[0084] After executing the control instructions, the execution results and current status of the power equipment are returned in real time, and real-time warnings are issued for abnormal situations.

[0085] Since remote control involves sensitive operations, all transmitted data should be encrypted. SSL / TLS encryption protocols are usually used to ensure the confidentiality and integrity of the communication content. A two-way authentication mechanism is also applied, that is, both the device and the operating platform must verify their identities to ensure that the data transmission process will not be attacked or disguised by external attacks. At the same time, user operations should be strictly authorized according to permissions to avoid risks caused by improper operations.

[0086] Embodiment 2:

[0087] See also Figure 2 As shown, the data processing method of the mine power monitoring system based on cloud computing includes:

[0088] Obtain real-time power data of each power device in the underground power system, and clean and format the collected power data;

[0089] Filter the collected data to remove noise and invalid data. For example, remove outliers or erroneous data to ensure data accuracy. Convert data from different power equipment or collection sensors into a standard format for subsequent processing and analysis.

[0090] Ensure that data from different sources have a unified timestamp to avoid errors caused by time differences.

[0091] The processed power data is stored in the cloud database, and the operating status of the underground power system is displayed in real time through a graphical interface;

[0092] Perform necessary filtering and aggregation on the real-time inflow data, and quickly analyze and process the real-time data. At the same time, perform statistical analysis on historical data, such as calculating the average load, operating time, and failure frequency of the equipment.

[0093] Configure backup strategies (such as incremental backup, full backup, etc.) and perform data backup regularly to ensure data security and integrity.

[0094] Monitor the status of power equipment in real time, dynamically update monitoring data and discover potential problems, detect abnormalities in time and issue alarms;

[0095] Set alarm levels according to the severity of different abnormalities, such as warning (yellow), major alarm (red), etc. The system can send alarm notifications to operation and maintenance personnel in a variety of ways, such as SMS, email, program push, etc., and all alarm events need to be recorded and stored in the cloud database for later analysis and tracing.

[0096] Through data mining and machine learning, trend analysis and fault prediction of power data are carried out to generate analysis results, and the configuration of power resources of the underground power system is optimized according to the analysis results;

[0097] Based on the remote operation interface, remotely control power equipment according to the analysis results to perform scheduling, start and stop, and parameter adjustment operations;

[0098] Issue control commands through the user interface (PC or mobile) to operate power equipment. Common commands include equipment start and stop, voltage regulation, load switching, etc. Support one-click operation and batch control, and can uniformly dispatch multiple devices.

[0099] The execution results and current status of the power equipment are returned in real time, and real-time warnings are issued for abnormal situations.

[0100] All user operations are logged, with detailed records of the time, executor, and content of each operation, and log audits are performed regularly; system versions are updated regularly, known vulnerabilities and defects are fixed, and control modules of power equipment are upgraded.

[0101] From the above, it can be seen that the present invention obtains the power data of each power equipment in the mine in real time, and uploads the data to the cloud database, displays the real-time operation status of the power equipment in the mine to the operator through a graphical interface, dynamically updates the monitoring data, discovers potential problems and responds, locates the fault location and generates alarm information and processing suggestions, thereby achieving rapid response and fault repair, combining data mining and machine learning technology to perform trend analysis and fault prediction, further optimize the configuration and scheduling of power resources, and reduce the operation risk of the power system. The system also supports remote operation functions. Through the PC or mobile terminal, the operator can remotely adjust the parameters of the mine's underground power equipment to ensure the smooth operation of key power equipment.

[0102] Embodiment 3:

[0103] The embodiment of the present invention also provides a computer-readable storage medium, on which a program of a mine power monitoring system based on cloud computing as described above is stored, and when the program is executed by a processor, each process of the mine power monitoring system embodiment described above is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0104] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0105] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0106] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0107] 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. The mine power monitoring system based on cloud computing is characterized by: include: Data acquisition module, data management module, real-time monitoring module, fault detection module, in-depth analysis module, remote control module; The data acquisition module is used to acquire real-time power data from various power devices in the underground power system and send the power data to the data management module; The data management module is used to receive the power data and store it in a cloud database, and provide data query, analysis and historical trend viewing functions; The real-time monitoring module is used to display the operating status of the underground power system in real time through a graphical interface based on the power data, dynamically update the monitoring data and discover potential problems; The fault detection module is used to perform abnormality detection according to the potential problem, locate the fault location and generate processing suggestions and alarm information; The deep analysis module is used to perform trend analysis and fault prediction on the power data through data mining and machine learning, so as to optimize the configuration of power resources of the underground power system; The remote control module is used to remotely control each of the power devices through a PC or a mobile terminal based on a remote operation interface, and remotely adjust power parameters.

2. The cloud computing-based mine power monitoring system according to claim 1, characterized in that: The data acquisition module is also used for: Establishing a connection with each of the power devices through a hardware interface or a communication protocol to collect the power data of each of the power devices in real time; Based on edge computing technology, preliminary processing is performed after data collection to exclude outliers or unreasonable data and remove high-frequency noise; Using NTP or by synchronizing with the clock of the underground power system, the data acquisition module is synchronized with the power equipment; The power data including the timestamp is encapsulated according to a preset data format, and the encapsulated power data is transmitted to the data management module through the MQTT protocol.

3. The cloud computing-based mine power monitoring system according to claim 2, characterized in that: The data management module is also used for: Using a distributed database to store the received power data, and using an incremental backup method to back up the power data; Performing data verification on the received power data, when an error occurs, the system will trigger a corresponding exception handling mechanism; Query the stored power data according to the content input by the user, and allow the user to select different conditions for combined query; An integrated analysis tool performs real-time analysis on the stored power data to identify abnormal fluctuations in the power data; Visualizing the stored power data through a visualization tool to display the power usage trend, power fluctuation trend, and failure trend of each power device; A cache mechanism is used to reduce the query pressure on the cloud database, and the historical power data is compressed and stored.

4. The cloud computing-based mine power monitoring system according to claim 3, characterized in that: The real-time monitoring module is also used for: Obtaining the importance of each state quantity of the underground power system, wherein the evaluation standard of the importance of each state quantity is the degree of influence of the state on the entire underground power system, and treating the state quantity with lower importance as a safe state; Sampling the state quantity of each of the electric power equipment according to the operating state, analyzing whether the sampled state quantity is in an acceptable state, if so, continuing to sample each of the electric power equipment, otherwise, considering that the state quantity is in a risky state; Real-time push of the status quantity is achieved through WebSocket, data is dynamically updated and delayed updates are reduced.

5. The cloud computing-based mine power monitoring system according to claim 4, characterized in that: The fault detection module is also used for: Analyzing the fluctuation of the power data by using anomaly detection algorithm and threshold detection to identify abnormal data fluctuations; Performing multivariate data association analysis on abnormal data fluctuations in the power data in combination with the risk status, thereby locating the source of the fault; Predicting the propagation path of the fault by using topological analysis in combination with the underground power system according to the located fault source; In combination with the real-time power data, the specific type of fault and the order of occurrence are inferred through the graph neural network to generate a fault inference result; Determine the urgency of the fault according to the fault inference result and the risk status, and generate emergency response strategies and processing suggestions of different levels; The emergency response strategy and the processing suggestion are generated by optimizing the restricted area search method according to the urgency.

6. The cloud computing-based mine power monitoring system according to claim 5, characterized in that: The in-depth analysis module is also used for: Use Kafka to perform data integration and standardization processing on the real-time power data to obtain the processed power data; Using Fourier transform, spatial interpolation and sliding window to extract the periodic characteristics, spatial characteristics and trend characteristics of load fluctuation in the historical window from the processed power data; According to the periodic characteristics, spatial characteristics and trend characteristics in the historical window, a machine learning model trained based on historical fault data is used to perform fault prediction and generate a fault prediction result; According to the periodic characteristics, spatial characteristics and trend characteristics in the historical window, LSTM is used to predict the change trend of the load fluctuation within a preset time period to generate a trend prediction result; Based on the fault prediction result and the trend prediction result, the configuration of power resources of the underground power system is optimized.

7. The cloud computing-based mine power monitoring system according to claim 6, characterized in that: The remote control module is also used for: Connecting and exchanging data with the power equipment using the MQTT protocol based on the remote operation interface; Acquire the status and real-time operation data of the electric device, and send the control instructions input by the user to the electric device; After executing the control instruction, the execution result and current status of the power equipment are returned in real time, and a real-time warning is issued for abnormal situations.

8. A data processing method for a mine power monitoring system based on cloud computing, characterized in that: include: Acquire real-time power data of each power device in the underground power system, and clean and format the collected power data; The processed power data is stored in a cloud database, and the operating status of the underground power system is displayed in real time through a graphical interface; Monitor the status of the power equipment in real time, dynamically update monitoring data and discover potential problems, detect abnormalities in time and issue alarms; Performing trend analysis and fault prediction on the power data through data mining and machine learning, generating analysis results, and optimizing the configuration of power resources of the underground power system according to the analysis results; Based on the remote operation interface, remotely control the power equipment according to the analysis results to perform scheduling, start and stop, and parameter adjustment operations; The execution result and current status of the electric power equipment are returned in real time, and abnormal situations are warned in real time.

9. The data processing method according to claim 8, characterized in that: The method further comprises: Log all user operations, record the time, executor, and content of each operation in detail, and conduct regular log audits; Regularly update the system version, fix known vulnerabilities and defects, and upgrade the control module of the power equipment.

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