An intelligent joint control method applied to the drainage system of a hydropower station

Abstract

The present invention discloses an intelligent joint control method applied to the drainage system of a hydropower station, comprising calculating the drainage demand of the hydropower station and dividing the drainage demand of the hydropower station into low, medium and high drainage demands; outputting the membership and comprehensive drainage demand of the low, medium and high drainage demands according to fuzzy rules; constructing the start-stop rotation logic of the pump based on the membership of the low, medium and high drainage demands; and performing fault analysis on the operating status of the pump. The method can automatically adjust the start-stop and operating parameters of the above three drainage system equipment according to the actual operating conditions of the hydropower station, so as to ensure the normal operation and safety of the unit and facilitate safe maintenance of the unit, while ensuring that there is no water accumulation and moisture inside the plant, thereby improving the safety and economy of the hydropower station.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydropower station drainage, and in particular relates to an intelligent joint control method applied to a hydropower station drainage system. Background Art

[0002] Currently, the drainage systems of hydropower stations mainly include unit maintenance drainage, leakage drainage systems, and corridor pumping systems. The unit maintenance drainage system discharges water from the penstock downstream when the unit is shut down for maintenance, allowing for safe maintenance of the unit. The system consists of a maintenance valve, drainage pipes, and a drainage gate. The maintenance valve is installed at the upstream end of the penstock, and the drainage pipe connects the maintenance valve and the drainage gate. The drainage gate is installed at the downstream end of the penstock. The maintenance valve and the drainage gate are opened and closed to control the water level in the penstock. The leakage drainage system discharges water leaking from components such as the penstock, volute, and guide vanes downstream when the unit is operating to ensure the normal operation and safety of the unit. The system consists of a leakage valve, leakage pipes, and leakage gates. The leakage valve is installed at the leakage outlet of each component, and the leakage pipes connect the leakage valve and the leakage gate. The leakage gate is installed on the exterior wall of the powerhouse. The leakage of each component is controlled by opening and closing the leakage valve and the leakage gate. The corridor drainage system is used to pump out water on the ground and water leaking from equipment in the factory building when the unit is running or shut down, so as to keep the factory building clean and dry. The system consists of a drainage pump, drainage pipes, drainage gates, etc. The drainage pump is installed in the basement of the factory building or below the ground. The drainage pipes connect the drainage pump and the drainage gates. The drainage gates are installed on the outer wall of the factory building. The amount of water accumulated in the factory building is controlled by opening or closing the drainage pump and the drainage gates.

[0003] The current operating logic of a hydropower station's drainage system is to pre-set the start and stop water levels for each drainage system. Each drainage pump starts when the water level reaches the start level and stops when the water level drops below the set stop level. Multiple drainage pumps rotate based on the number of starts and stops. This conventional drainage system's operating logic is relatively simple. When the water level in the collection well rises rapidly, it cannot accurately determine the number of drainage pumps to start based on the water level fluctuations. Furthermore, the operating characteristics of each drainage pump motor vary, making it impossible for the current drainage pumps to adapt their operating logic in real time based on the motor's current state.

[0004] Therefore, it is necessary to design an intelligent joint control method for the drainage system of a hydropower station to solve the above problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an intelligent joint control method applied to the drainage system of a hydropower station. The method can automatically adjust the start and stop and operating parameters of the above three drainage system equipment according to the actual operation of the hydropower station, so as to ensure the normal operation and safety of the unit and facilitate safe maintenance of the unit, while ensuring that there is no water accumulation and moisture inside the plant, thereby improving the safety and economy of the hydropower station.

[0006] In order to achieve the above technical effects, the technical solution adopted by the present invention is:

[0007] An intelligent joint control method for a drainage system of a hydropower station comprises the following steps:

[0008] S1, calculates the drainage demand of the hydropower station and divides the drainage demand of the hydropower station into low, medium and high drainage demand; outputs the membership degree and comprehensive drainage demand of low, medium and high drainage demand according to fuzzy rules;

[0009] S2, builds the pump start-stop rotation logic based on the membership of low, medium and high drainage demands;

[0010] S3, perform fault analysis on the operating status of the pump.

[0011] Furthermore, when calculating drainage demand, sensors installed at various drainage points are used to monitor the hydropower station's leakage, maintenance drainage, production water drainage and water accumulation in the plant area in real time; the relevant data includes drainage flow data and accumulated water level data, and the data is transmitted to the computer system in the central control room.

[0012] Preferably, in step S1, the specific method for calculating the drainage demand of the hydropower station is:

[0013] S101, assuming that the water level and flow rate ranges from [0, P] meters and [0, N] cubic meters per second, respectively, and the drainage demand ranges from [0, 1], where 0 indicates no drainage is required and 1 indicates the maximum drainage is required;

[0014] Set low, medium and high thresholds for water level and flow respectively; divide water level and flow into several fuzzy subsets, including low, medium and high categories;

[0015] S102, set a membership function for each fuzzy subset to describe its membership degree:

[0016] The membership functions of water levels include:

[0017] The membership function of the low water level is A_L(x)=(Px) / P,x∈[0,P];

[0018] The membership function of the water level is A_M(x)=min(x / (P / 2),(Px) / (P / 2)),x∈[0,P];

[0019] The membership function of the high water level is A_H(x)=x / P,x∈[0,P];

[0020] The membership functions of traffic include:

[0021] The membership function of low flow is A_S(y)=(Ny) / N,y∈[0,N];

[0022] The membership function of the medium flow is A_M(y)=min(y / (N / 2),(Ny) / (N / 2)),y∈[0,N];

[0023] The membership function of high flow is A_L(y)=y / N,y∈[0,N];

[0024] S103, establishing fuzzy rules for water level, flow and drainage demand based on binary comparison ranking method;

[0025] S104, obtaining output data through fuzzy reasoning and defuzzification; fuzzy reasoning calculates the membership of the output data based on the membership of the input data and fuzzy rules; according to the fuzzy rules, the output data belongs to the membership of low, medium and high drainage demands.

[0026] Preferably, in step S104, according to the fuzzy rules, the specific method for outputting the membership of the data to low, medium and high drainage demands is as follows:

[0027] Low drainage demand membership:

[0028] B_L=min(A_L(x),A_S(y));

[0029] Degree of subordination of drainage demand:

[0030] B_M=max(min(A_L(x),A_M(y)),min(A_M(x),A_S(y)),min(A_M(x),A_M(y)));

[0031] High drainage demand membership:

[0032] B_H=max(min(A_L(x),A_L(y)),min(A_M(x),A_L(y)),min(A_H(x),A_S(y)),min(A_H(x),A_M(y)),min(A_H(x),A_L(y)));

[0033] The comprehensive drainage demand is calculated by the centroid method:

[0034] Y=(B_L*0+B_M*0.5+B_H*1) / (B_L+B_M+B_H);

[0035] Where x represents water level data and y represents flow data.

[0036] Preferably, in step S2, the specific method for constructing the pump start-stop rotation logic based on the membership of low, medium and high drainage demands is:

[0037] S201, collect historical drainage data of the drainage system and set the priority of the drainage system:

[0038] The drainage system includes the maintenance drainage system, the leakage drainage system, and the corridor drainage system. The priorities of the drainage systems are set to F1, F2, and F3 according to the average size of the historical drainage volume; the drainage system with the smallest drainage volume is set to the highest priority;

[0039] S202, setting the maximum drainage threshold of the drainage system with F1 priority to Q1, the maximum drainage threshold of the drainage system with F2 priority to Q2, and the maximum drainage threshold of the drainage system with F3 priority to Q3;

[0040] S203, setting a drainage allocation strategy based on the comprehensive drainage demand Y:

[0041] The drainage system with F1 priority is allocated for drainage first. The drainage demand exceeding Q1 is allocated to the drainage system with F2 priority. The drainage demand exceeding Q2 is allocated to the drainage system with F3 priority.

[0042] S204, collecting the operating range and performance curve data of each pump in the drainage system for maintenance, and determining the optimal drainage volume Q' of the pump from the performance curve;

[0043] S205, numbering each pump, the numbered numbers forming an array Z; randomly sorting the numbers in the array Z using a random algorithm to form a sorted pump sequence;

[0044] In step S206, after receiving the allocated drainage demand, the drainage system starts the pumps in sequence according to the pump sequence in step S205 to drain the water. The power is controlled by the pump inverter. When the pump reaches the optimal drainage volume Q', the power is stopped and the next pump in the next sequence is started. This process is repeated until the drainage demand allocated by the drainage system is met.

[0045] S207, when the drainage demand Y exceeds Q3, the controller synchronously increases the power of all activated pumps in the three drainage systems, evenly increasing the power of all pumps in use so that the total drainage volume matches the comprehensive drainage demand Y; the drainage volume of each pump does not exceed its working range.

[0046] Furthermore, the random algorithm is implemented using the Knuth shuffle algorithm to randomly arrange multiple numbered numbers; each time the drainage demand is redistributed, the start and stop order of the pumps is rearranged through the random algorithm.

[0047] Preferably, the maximum drainage thresholds Q1, Q2 and Q3 do not exceed the sum of the optimal drainage capacities Q' of all pumps in the corresponding drainage system.

[0048] Preferably, performing fault analysis on the operating status of the pump includes:

[0049] The computer system receives feedback signals from each drainage system, including real-time displacement data and power data of each pump; fault diagnosis is performed on the collected data as follows:

[0050] Data preprocessing: Perform preprocessing operations such as cleaning, standardization, and noise reduction on the collected data to improve the quality and usability of the data;

[0051] Feature extraction: Use machine learning algorithms to extract features from pre-processed data and extract feature vectors that reflect the operating status of the equipment;

[0052] Fault diagnosis: Use artificial intelligence algorithms such as classification, clustering, or regression to diagnose faults on feature vectors to determine whether the equipment has a fault and the type and extent of the fault.

[0053] Fault alarm: Based on the results of fault diagnosis, an alarm will be issued for the faulty equipment, including information on the fault location, time and cause, and corresponding maintenance suggestions will be provided.

[0054] The beneficial effects of the present invention are as follows:

[0055] 1. The present invention proposes an intelligent joint control method for the drainage system of a hydropower station. This method can automatically adjust the start and stop and operating parameters of the three drainage system devices mentioned above according to the actual operating conditions of the hydropower station, so as to ensure the normal operation and safety of the units and facilitate safe maintenance of the units. At the same time, it ensures that there is no water accumulation and moisture inside the plant, thereby improving the safety and economy of the hydropower station.

[0056] 2. The present invention can reduce the problem of frequent start-stop and long-term no-load or overload operation of the drainage pump, which may cause the equipment to be easily damaged. By using this method, the drainage pump can be put into operation for a long time. Compared with the existing scheme, the trend of water outflow changes can be analyzed in advance. When the water inflow increases, the drainage pump can be started in advance to drain the accumulated water as soon as possible, thereby ensuring the safe and stable operation of the hydropower station. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the process of the present invention;

[0058] Figure 2This is a schematic diagram of the start-stop rotation logic block diagram of the present invention;

[0059] Figure 3 This is a schematic diagram of fault diagnosis in the present invention;

[0060] Figure 4 This is a schematic diagram of a specific case of fault diagnosis in the second embodiment of the present invention. DETAILED DESCRIPTION

[0061] Example 1:

[0062] like Figure 1 As shown, an intelligent joint control method applied to the drainage system of a hydropower station includes the following steps:

[0063] S1, calculates the drainage demand of the hydropower station and divides the drainage demand of the hydropower station into low, medium and high drainage demand; outputs the membership degree and comprehensive drainage demand of low, medium and high drainage demand according to fuzzy rules;

[0064] S2, builds the pump start-stop rotation logic based on the membership of low, medium and high drainage demands;

[0065] S3, perform fault analysis on the operating status of the pump.

[0066] Furthermore, when calculating drainage demand, sensors installed at various drainage points are used to monitor the hydropower station's leakage, maintenance drainage, production water drainage and water accumulation in the plant area in real time; the relevant data includes drainage flow data and accumulated water level data, and the data is transmitted to the computer system in the central control room.

[0067] Preferably, in step S1, the specific method for calculating the drainage demand of the hydropower station is:

[0068] S101, assuming that the water level and flow rate ranges from [0, P] meters and [0, N] cubic meters per second, respectively, and the drainage demand ranges from [0, 1], where 0 indicates no drainage is required and 1 indicates the maximum drainage is required;

[0069] Set low, medium and high thresholds for water level and flow respectively; divide water level and flow into several fuzzy subsets, including low, medium and high categories;

[0070] S102, set a membership function for each fuzzy subset to describe its membership degree:

[0071] The membership functions of water levels include:

[0072] The membership function of the low water level is A_L(x)=(Px) / P,x∈[0,P];

[0073] The membership function of the water level is A_M(x)=min(x / (P / 2),(Px) / (P / 2)),x∈[0,P];

[0074] The membership function of the high water level is A_H(x)=x / P,x∈[0,P];

[0075] The membership functions of traffic include:

[0076] The membership function of low flow is A_S(y)=(Ny) / N,y∈[0,N];

[0077] The membership function of the medium flow is A_M(y)=min(y / (N / 2),(Ny) / (N / 2)),y∈[0,N];

[0078] The membership function of high flow is A_L(y)=y / N,y∈[0,N];

[0079] S103: Establish fuzzy rules for water level, flow rate, and drainage demand based on the binary comparison ranking method; specifically:

[0080] If the water level is low and the flow is small, the drainage demand is low;

[0081] If the water level is low and the flow is medium, the drainage demand is medium;

[0082] If the water level is low and the flow is high, the drainage demand is high;

[0083] If the water level is medium and the flow is small, the drainage demand is medium;

[0084] If the water level is medium and the flow is medium, the drainage demand is medium;

[0085] If the water level is medium and the flow is high, the drainage demand is high;

[0086] If the water level is high and the flow is low, the drainage demand is high;

[0087] If the water level is medium and the flow is medium, the drainage demand is high;

[0088] If the water level is medium and the flow is high, the drainage demand is high.

[0089] S104, obtaining output data through fuzzy reasoning and defuzzification; fuzzy reasoning calculates the membership of the output data based on the membership of the input data and fuzzy rules; according to the fuzzy rules, the output data belongs to the membership of low, medium and high drainage demands.

[0090] Preferably, in step S104, according to the fuzzy rules, the specific method for outputting the membership of the data to low, medium and high drainage demands is as follows:

[0091] Low drainage demand membership:

[0092] B_L=min(A_L(x),A_S(y));

[0093] Degree of subordination of drainage demand:

[0094] B_M=max(min(A_L(x),A_M(y)),min(A_M(x),A_S(y)),min(A_M(x),A_M(y)));

[0095] High drainage demand membership:

[0096] B_H=max(min(A_L(x),A_L(y)),min(A_M(x),A_L(y)),min(A_H(x),A_S(y)),min(A_H(x),A_M(y)),min(A_H(x),A_L(y)));

[0097] The comprehensive drainage demand is calculated by the centroid method:

[0098] Y=(B_L*0+B_M*0.5+B_H*1) / (B_L+B_M+B_H);

[0099] Where x represents water level data and y represents flow data.

[0100] like Figure 2 As shown, preferably, in step S2, the specific method of constructing the pump start-stop rotation logic based on the membership of low, medium and high drainage demands is:

[0101] S201, collect historical drainage data of the drainage system and set the priority of the drainage system:

[0102] The drainage system includes the maintenance drainage system, the leakage drainage system, and the corridor drainage system. The priorities of the drainage systems are set to F1, F2, and F3 according to the average size of the historical drainage volume; the drainage system with the smallest drainage volume is set to the highest priority;

[0103] S202, setting the maximum drainage threshold of the drainage system with F1 priority to Q1, the maximum drainage threshold of the drainage system with F2 priority to Q2, and the maximum drainage threshold of the drainage system with F3 priority to Q3;

[0104] S203, setting a drainage allocation strategy based on the comprehensive drainage demand Y:

[0105] The drainage system with F1 priority is allocated for drainage first. The drainage demand exceeding Q1 is allocated to the drainage system with F2 priority. The drainage demand exceeding Q2 is allocated to the drainage system with F3 priority.

[0106] S204, collecting the operating range and performance curve data of each pump in the drainage system for maintenance, and determining the optimal drainage volume Q' of the pump from the performance curve;

[0107] S205, numbering each pump, the numbered numbers forming an array Z; randomly sorting the numbers in the array Z using a random algorithm to form a sorted pump sequence;

[0108] In step S206, after receiving the allocated drainage demand, the drainage system starts the pumps in sequence according to the pump sequence in step S205 to drain the water. The power is controlled by the pump inverter. When the pump reaches the optimal drainage volume Q', the power is stopped and the next pump in the next sequence is started. This process is repeated until the drainage demand allocated by the drainage system is met.

[0109] S207, when the drainage demand Y exceeds Q3, the controller synchronously increases the power of all activated pumps in the three drainage systems, evenly increasing the power of all pumps in use so that the total drainage volume matches the comprehensive drainage demand Y; the drainage volume of each pump does not exceed its working range.

[0110] Furthermore, the random algorithm is implemented using the Knuth shuffle algorithm to randomly arrange multiple numbered numbers; each time the drainage demand is redistributed, the start and stop order of the pumps is rearranged through the random algorithm.

[0111] Preferably, the maximum drainage thresholds Q1, Q2 and Q3 do not exceed the sum of the optimal drainage capacities Q' of all pumps in the corresponding drainage system.

[0112] like Figure 3 As shown, preferably, performing fault analysis on the operating status of the pump includes:

[0113] The computer system receives feedback signals from each drainage system, including real-time displacement data and power data of each pump; fault diagnosis is performed on the collected data as follows:

[0114] Data preprocessing: Perform preprocessing operations such as cleaning, standardization, and noise reduction on the collected data to improve the quality and usability of the data;

[0115] Feature extraction: Use machine learning algorithms to extract features from pre-processed data and extract feature vectors that reflect the operating status of the equipment;

[0116] Fault diagnosis: Use artificial intelligence algorithms such as classification, clustering, or regression to diagnose faults on feature vectors to determine whether the equipment has a fault and the type and extent of the fault.

[0117] Fault alarm: Based on the results of fault diagnosis, an alarm will be issued for the faulty equipment, including information on the fault location, time and cause, and corresponding maintenance suggestions will be provided.

[0118] Example 2:

[0119] This example provides a specific fault diagnosis case:

[0120] like Figure 4 As shown in the figure, a hydropower station's drainage system consists of a pump, a valve, and two pressure sensors. Pressure sensor 1 measures pressure P1 at the pump outlet, and pressure sensor 2 measures pressure P2 at the valve outlet. Under normal circumstances, P1 should be greater than P2, and the difference between the two should be within a certain range. If P1 is less than P2 or the difference between the two is out of range, a fault has occurred.

[0121] Assume that the normal data model is P1-P2=10±2 kPa. If it is detected that P1-P2 is not within this range, the data is judged to be abnormal and proceed to the next step.

[0122] By analyzing the characteristics of abnormal data, the type and location of the faulty equipment can be determined.

[0123] For example, if P1-P2 is less than 8 kPa, the pressure at the pump outlet is too low, possibly indicating a pump failure. If P1-P2 is greater than 12 kPa, the pressure at the valve outlet is too high, possibly indicating a valve failure. If P1 or P2 deviates significantly from historical data, the corresponding pressure sensor is faulty. The system outputs corresponding fault diagnosis results and alarm signals based on the specific situation.

[0124] Based on the fault diagnosis results, take appropriate measures to correct the fault. For example, if the pump is faulty, adjust the pump speed or replace it; if the valve is faulty, adjust the valve opening or replace it; if the pressure sensor is faulty, clean or replace it. Check whether the corrected pressure data returns to normal. If so, terminate the diagnostic process. If the error persists, repeat the above steps.

Claims

1. An intelligent joint control method applied to the drainage system of a hydropower station, characterized in that: The following steps are involved: S1, calculate the drainage demand of the hydropower station and classify the drainage demand of the hydropower station into low, medium and high drainage demand; According to fuzzy rules, the membership degree of low, medium and high drainage demands and comprehensive drainage demands are output; S2, constructing the pump start-stop rotation logic based on the membership of low, medium, and high drainage demands; In step S2, the specific method of constructing the pump start-stop rotation logic based on the membership of low, medium, and high drainage demands is: S201, collect historical drainage data of the drainage system and set the priority of the drainage system: The drainage system includes the maintenance drainage system, the leakage drainage system, and the corridor drainage system. The priorities of the drainage systems are set to F1, F2, and F3 according to the average size of the historical drainage volume; the drainage system with the smallest drainage volume is set to the highest priority; S202, setting the maximum drainage threshold of the drainage system with F1 priority to Q1, the maximum drainage threshold of the drainage system with F2 priority to Q2, and the maximum drainage threshold of the drainage system with F3 priority to Q3; S203, setting a drainage allocation strategy based on the comprehensive drainage demand Y: The drainage system with F1 priority is allocated for drainage first. The drainage demand exceeding Q1 is allocated to the drainage system with F2 priority. The drainage demand exceeding Q2 is allocated to the drainage system with F3 priority. S204, collecting the operating range and performance curve data of each pump in the drainage system for maintenance, and determining the optimal drainage volume Q' of the pump from the performance curve; S205, numbering each pump, the numbered numbers forming an array Z; randomly sorting the numbers in the array Z using a random algorithm to form a sorted pump sequence; In step S206, after receiving the allocated drainage demand, the drainage system starts the pumps in sequence according to the pump sequence in step S205 to drain the water. The power is controlled by the pump inverter. When the pump reaches the optimal drainage volume Q', the power is stopped and the next pump in the next sequence is started. This process is repeated until the drainage demand allocated by the drainage system is met. S207, when the drainage demand Y exceeds Q3, the controller synchronously increases the power of all activated pumps in the three drainage systems, evenly increasing the power of all pumps in use so that the total drainage volume matches the comprehensive drainage demand Y; the drainage volume of each pump does not exceed its operating range; S3, perform fault analysis on the operating status of the pump.

2. The intelligent joint control method for a drainage system of a hydropower station according to claim 1, characterized in that: In step S1, the specific method for calculating the drainage demand of the hydropower station is: S101, assuming that the water level and flow rate ranges from [0, P] meters and [0, N] cubic meters per second, respectively, and the drainage demand ranges from [0, 1], where 0 indicates no drainage is required and 1 indicates the maximum drainage is required; Set low, medium and high thresholds for water level and flow respectively; divide water level and flow into several fuzzy subsets, including low, medium and high categories; S102, set a membership function for each fuzzy subset to describe its membership degree: The membership functions of water levels include: The membership function of the low water level is A_L(x)=(Px) / P,x∈[0,P]; The membership function of the water level is A_M(x)=min(x / (P / 2),(Px) / (P / 2)),x∈[0,P]; The membership function of the high water level is A_H(x)=x / P,x∈[0,P]; The membership functions of traffic include: The membership function of low flow is A_S(y)=(Ny) / N,y∈[0,N]; The membership function of the medium flow is A_M(y)=min(y / (N / 2),(Ny) / (N / 2)),y∈[0,N]; The membership function of high flow is A_L(y)=y / N,y∈[0,N]; S103, establishing fuzzy rules for water level, flow and drainage demand based on binary comparison ranking method; S104, obtaining output data through fuzzy reasoning and defuzzification; fuzzy reasoning calculates the membership of the output data based on the membership of the input data and fuzzy rules; according to the fuzzy rules, the output data belongs to the membership of low, medium and high drainage demands.

3. The intelligent joint control method for a drainage system of a hydropower station according to claim 2, characterized in that: In step S104, according to the fuzzy rules, the specific method for outputting the membership degree of the data to low, medium and high drainage demands is as follows: Low drainage demand membership: B_L=min(A_L(x),A_S(y)); Degree of subordination of drainage demand: B_M=max(min(A_L(x),A_M(y)),min(A_M(x),A_S(y)),min(A_M(x),A_M(y))); High drainage demand membership: B_H=max(min(A_L(x),A_L(y)),min(A_M(x),A_L(y)),min(A_H(x),A_S(y)),min(A_H(x),A_M(y)),min(A_H(x),A_L(y))); The comprehensive drainage demand is calculated by the centroid method: Y=(B_L*0+B_M*0.5+B_H*1) / (B_L+B_M+B_H); Where x represents water level data and y represents flow data.

4. The intelligent joint control method for a hydropower station drainage system according to claim 1, characterized in that: The maximum drainage thresholds Q1, Q2 and Q3 do not exceed the sum of the optimal drainage capacities Q' of all pumps in the corresponding drainage system.

5. The intelligent joint control method for a drainage system of a hydropower station according to claim 4, characterized in that: Fault analysis of the pump's operating status includes: The computer system receives feedback signals from each drainage system, including real-time displacement data and power data of each pump; fault diagnosis is performed on the collected data as follows: Data preprocessing: Perform preprocessing operations such as cleaning, standardization, and noise reduction on the collected data to improve the quality and usability of the data; Feature extraction: Use machine learning algorithms to extract features from pre-processed data and extract feature vectors that reflect the operating status of the equipment; Fault diagnosis: Use artificial intelligence algorithms such as classification, clustering, or regression to diagnose faults on feature vectors to determine whether a device is faulty and what the fault type and severity are. Fault alarm: Based on the results of fault diagnosis, an alarm will be issued for the faulty equipment, including information on the fault location, time and cause, and corresponding maintenance suggestions will be provided.

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