Decentralized pump station control system and method based on coordinated scheduling

Abstract

This application relates to the field of control system technology, and in particular to a distributed pump station control system and method based on collaborative scheduling. The solution of this application jointly evaluates particulate matter impact, turbulence fluctuations and equipment aging status, quantifies the dynamic impact of the external water environment on the internal wear of the pump station, thereby achieving early prediction and differentiated scheduling of pump station operating performance without increasing additional hardware costs. This significantly reduces the sudden failure rate of the pump station caused by particulate matter wear or operating condition fluctuations, and improves the operational reliability and economy of the entire drainage system.

Description

Technical Field

[0001] This application relates to the field of control system technology, and in particular to a distributed pumping station control system and method based on collaborative scheduling. Background Technology

[0002] With the acceleration of urbanization, drainage systems (especially decentralized pumping station clusters) play a crucial role in flood control, sewage transportation, and environmental management. However, in actual operation, pumping stations generally face severe wear, blockage, and performance degradation caused by particulate matter in the water (such as silt, fibers, and solid waste). Simultaneously, drastic fluctuations in hydrodynamic parameters such as flow rate and water level can cause pump sets to deviate from their high-efficiency range, leading to abnormal operating conditions such as cavitation and vibration, significantly shortening equipment lifespan and increasing the risk of sudden failures.

[0003] Currently, the scheduling and management of decentralized pumping stations mainly relies on two types of technologies: **Round-robin scheduling based on fixed thresholds:** This method allocates operation to each pumping station sequentially or evenly based on a preset water level or time period. While logically simple, it ignores the actual operating status, wear level, and dynamic differences in the external water environment of each pumping station. This can easily lead to the continuous operation of "sick" pumping stations, accelerating performance deterioration and even causing systemic failure. **Scheduling based on a single performance indicator (such as efficiency or energy consumption):** This method focuses on monitoring parameters such as motor power, flow rate, or head, selecting the optimal pumping station through real-time efficiency calculations. However, this approach fails to effectively correlate the microscopic impact and cumulative wear process of particulate matter characteristics (such as concentration, particle size distribution, and hardness) on critical pump components. When encountering high concentrations of particulate matter or fluids with special characteristics, the scheduling system cannot predict the potential damage risk in advance, leading to inaccurate performance predictions.

[0004] Therefore, there is an urgent need for a technical solution that can integrate multi-dimensional real-time data, start from damage mechanism analysis, and realize performance prediction and intelligent differentiated scheduling in order to effectively avoid high wear risks and improve the overall operational reliability and economy of decentralized pump station groups. Summary of the Invention

[0005] To overcome the defects and shortcomings of existing technologies, this application proposes a distributed pump station control method based on collaborative scheduling.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a distributed pumping station control method based on collaborative scheduling, including the following specific steps: Step 1: Obtain the operating data of each decentralized pumping station, the particulate matter situation of the corresponding water body, and the water flow situation; Step 2: Analyze the damage caused by particulate matter to the pump station by obtaining the water flow information and the particulate matter situation of the corresponding water body. Step 3: Analyze the vulnerability of the pumping stations by combining the operation data of the decentralized pumping stations, and predict the operation performance of the pumping stations by combining the results of the particulate matter damage analysis and the vulnerability analysis results. Step 4: Select the operating pump station based on the predicted pump station performance.

[0007] In one implementation of this application, obtaining the operating data of each decentralized pumping station, the particulate matter situation of the corresponding water body, and the water flow situation includes the following specific steps: S101. Pump station operation data is acquired by sensors installed at various locations in the pump station, including operating current, outlet flow rate, inlet pressure, motor speed, bearing temperature, and vibration spectrum; and stored in the first storage module. S102. The particulate matter in the water body is acquired through sensors, including the concentration of particles, particle size distribution, and particle hardness; and stored in the second storage module. S103. Obtain the water flow velocity and turbulence intensity, and store them in the third storage module.

[0008] In one implementation of this application, the analysis of particulate matter damage to the pump station interior includes the following specific aspects: S201. Obtain the pump station inlet pressure, motor speed, and vibration spectrum. Obtain the fluctuation values ​​of the pump station inlet pressure and motor speed within the corresponding time period. The fluctuation value is calculated as follows: the absolute value of the parameter difference between the value of the corresponding parameter at the previous time and the value of the corresponding parameter at the next time is divided by the safe range value of the corresponding parameter, and integrated over the time period and divided by the time standard quantity. By obtaining the vibration amplitude and vibration frequency during the operation of the pump station, the vibration impact anomaly is obtained by multiplying the standardized vibration amplitude and vibration frequency. The fluctuation values ​​of each parameter are weighted and summed to obtain the fluctuation impact anomaly. The fluctuation impact anomaly and the vibration impact anomaly are weighted and summed to obtain the impact value of the pump station on the water flow. S202. Obtain the flow velocity of the water, the concentration of particles in the water, the particle size distribution, and the particle hardness. Compare the flow velocity, particle concentration, particle size distribution, and particle hardness with the corresponding safety values ​​to obtain the anomalies of the corresponding parameters. Multiply the square of the abnormal flow velocity by the product of the abnormal particle concentration, abnormal particle size, and abnormal particle hardness in the water to obtain the particulate impact coefficient. S203. Obtain the turbulence intensity of the water flow and the impact value of the pump station on the water flow. The result of dividing the turbulence intensity by the safe turbulence intensity, multiplying it by the value of 1, and summing it with the impact value of the pump station on the water flow, yields the turbulence impact value. S204. The damage analysis results of particulate matter to the pump station are obtained by multiplying the sum of the standardized turbulence influence value and the value of 1 by the particulate matter impact coefficient.

[0009] In one implementation of this application, the vulnerability analysis of the pumping station includes the following specific aspects: S301. Obtain pump station operating data, including operating current, outlet flow rate, inlet pressure, motor speed, bearing temperature, and vibration spectrum; S302. Calculate the standard deviation of each operating data of the pump station from the set range of the corresponding data to obtain the operating deviation of each operating data. Then, sum the operating deviations of each operating data by weight to obtain the vulnerability of the pump station.

[0010] In one implementation of this application, the prediction of the pump station's operating performance includes the following specific steps: S303. Obtain the vulnerability of the pump station at each location and the results of the analysis of particulate matter damage to the pump station's interior; S304. Obtain the product of the particulate matter damage analysis results to the pump station and the particulate matter influence coefficient. Summate the product with the value 1 and then multiply it by the pump station vulnerability to obtain the abnormal pump station operation performance.

[0011] In one implementation of this application, the selection of the operating pump station includes the following specific steps: The system obtains the calculated operational performance anomalies of each pumping station, sorts the anomalies in ascending order, designates pumping stations with operational performance anomalies less than or equal to the operational performance anomaly threshold as safe pumping stations, and designates pumping stations with operational performance anomalies less than or equal to the operational performance anomaly threshold as dangerous pumping stations, and selects safe pumping stations for drainage operations.

[0012] Secondly, this application also provides a distributed pumping station control system based on collaborative scheduling, including the following specific modules: The module includes an acquisition module, a pump station operation performance prediction module, and an operating pump station selection module. The acquisition module acquires the operating data of each decentralized pumping station, the particulate matter situation of the water body in the corresponding area, and the water flow situation. The pump station operation performance prediction module analyzes the damage caused by particulate matter to the pump station by acquiring the water flow and particulate matter situation in the corresponding area, analyzes the vulnerability of the pump station by combining the operation data of the decentralized pump station, and predicts the operation performance of the pump station by combining the analysis results of the damage caused by particulate matter to the pump station and the analysis results of the pump station vulnerability. The pump station selection module selects pump stations based on the predicted pump station performance.

[0013] Thirdly, this application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a distributed pump station control method based on cooperative scheduling by calling the computer program stored in the memory.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a distributed pump station control method based on cooperative scheduling.

[0015] Compared with the prior art, this application has the following advantages: By jointly assessing particulate matter impact, turbulent fluctuations, and equipment aging status, the dynamic impact of the external water environment on the internal wear of the pumping station is quantified. This enables early prediction and differentiated scheduling of pumping station operating performance without increasing additional hardware costs, significantly reducing the rate of sudden failures caused by particulate matter wear or operating condition fluctuations, and improving the reliability and economy of the entire drainage system. Attached Figure Description

[0016] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process structure in the method embodiments of this application; Figure 2 This is a schematic diagram of the overall process of step one in the method embodiment of this application; Figure 3 This is a schematic diagram of the process structure for step two in the method embodiment of this application; Figure 4 This is a schematic diagram of the structure in the system embodiment of this application. Detailed Implementation

[0017] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0018] Example Please see Figures 1 to 3 , Figure 1 This is a schematic diagram of the overall process of the distributed pumping station control method based on collaborative scheduling provided in the embodiments of this application, which specifically includes the following steps: Step 1: Obtain the operating data of each decentralized pumping station, the particulate matter situation of the corresponding water body, and the water flow situation; In this embodiment, obtaining the operating data of each decentralized pumping station, the particulate matter situation of the corresponding water body, and the water flow situation includes the following specific steps: S101. Pump station operation data is acquired by sensors installed at various locations in the pump station, including operating current, outlet flow rate, inlet pressure, motor speed, bearing temperature, and vibration spectrum; and stored in the first storage module. In one specific embodiment, the pump station operation data is acquired in real time by industrial sensors installed on various key equipment. The operating current is detected by a current transformer to detect the motor inlet current, the outlet flow rate is measured by an electromagnetic flow meter or an ultrasonic flow meter to measure the flow velocity at the pipe cross section, the inlet pressure is obtained by a pressure transmitter installed at the pump inlet flange, the motor speed is read by the feedback signal from an encoder or frequency converter, the bearing temperature is monitored by a built-in thermocouple or resistance temperature sensor, and the vibration spectrum is obtained by using an accelerometer to collect the time-domain vibration signal of the pump body and the motor bearing housing and then converting it by Fourier transform. All data are uniformly collected and stored in the first storage module. S102. The particulate matter in the water body is acquired through sensors, including the concentration of particles, particle size distribution, and particle hardness; and stored in the second storage module. In one specific embodiment, the particulate matter situation in the water body is obtained by an online particulate matter analyzer installed on the pump station intake pool or intake pipeline. The particle concentration is measured using a laser scattering turbidimeter or an ultrasonic attenuation concentration meter. The particle size distribution is continuously sampled and detected using a laser diffraction particle size analyzer or a dynamic image particle analyzer. The particle hardness data is measured in the laboratory using a microhardness tester after offline sampling. A hardness feature library is established according to the material type to achieve online correlation. All data are processed and stored in the second storage module. S103. Obtain the water flow velocity and turbulence intensity, and store them in the third storage module; In one specific embodiment, the water flow is obtained by multi-point velocity probes arranged upstream and downstream of the pump station inlet and inside the pump chamber. The water flow velocity is directly measured by an electromagnetic velocimeter or a Doppler ultrasonic velocimeter to measure the average flow velocity in the pipe or open channel. The turbulence intensity is obtained by acquiring the velocity pulsation component through a high-frequency response laser Doppler velocimeter or a particle image velocimeter system. After statistical variance calculation, the turbulence intensity index is obtained. All data are processed and stored in the third storage module. Step 2: Analyze the damage caused by particulate matter to the pump station by obtaining the water flow information and the particulate matter situation of the corresponding water body. In this embodiment, the analysis of particulate matter damage to the pump station's interior includes the following specific details: S201. Obtain the pump station inlet pressure, motor speed, and vibration spectrum. Obtain the fluctuation values ​​of the pump station inlet pressure and motor speed within the corresponding time period. The fluctuation value is calculated as follows: the absolute value of the parameter difference between the value of the corresponding parameter at the previous time and the value of the corresponding parameter at the next time is divided by the safe range value of the corresponding parameter, and integrated over the time period and divided by the time standard quantity. By obtaining the vibration amplitude and vibration frequency during the operation of the pump station, the vibration impact anomaly is obtained by multiplying the standardized vibration amplitude and vibration frequency. The fluctuation values ​​of each parameter are weighted and summed to obtain the fluctuation impact anomaly. The fluctuation impact anomaly and the vibration impact anomaly are weighted and summed to obtain the impact value of the pump station on the water flow. By weightedly integrating the fluctuation amplitude of the inlet pressure and speed (defined as the integral of the difference between the previous and next time times relative to the safe range) with the anomalies of vibration amplitude and frequency, the degree of interference of the pump station's own operating status on the water flow can be comprehensively quantified, avoiding misjudgment based on a single parameter. This indicator provides a quantitative basis for subsequent analysis of the interaction between internal damage and water flow in pumping stations. Unstable pumping station operation (such as pressure fluctuations and speed fluctuations) exacerbates water flow turbulence, while abnormal vibration directly reflects the excitation of water flow by the mechanical structure. Standardized difference integration and weighted summation are used to eliminate dimensional differences, allowing different parameters to be uniformly measured for their contribution to flow. S202. Obtain the flow velocity, particle concentration, particle size distribution, and particle hardness of the water. Compare the flow velocity, particle concentration, particle size distribution, and particle hardness with their corresponding safety values ​​to identify anomalies in these parameters. The particle impact coefficient is obtained by multiplying the square of the flow velocity anomaly by the product of the anomalies in particle concentration, particle size, and particle hardness. Combining the anomalies in flow velocity, particle concentration, particle size distribution, and hardness as a square product highlights the synergistic amplification effect of particulate matter on wear and impact within the pumping station. The square term of the velocity anomaly emphasizes the quadratic characteristics of particle kinetic energy destruction under high-speed flow. The particle impact force is positively correlated with the square of the velocity, particle mass, hardness, and quantity. This formula reasonably simulates the physical laws of wear under multi-factor coupling: the larger the velocity anomaly and the more numerous and harder the particles, the non-linearly increasing the impact coefficient avoids underestimating the risks under extreme conditions using linear weighting. S203. Obtain the turbulence intensity of the water flow and the impact value of the pump station on the water flow. The result of dividing the turbulence intensity by the safe turbulence intensity, multiplying by 1, and summing this with the impact value of the pump station on the water flow yields the turbulence impact value. Combining the turbulence intensity with the impact value of the pump station on the water flow can characterize the degree of turbulence intensification under the disturbance of the pump station's own operation. When the pump station itself causes flow disturbance, the destructive effect of turbulence is amplified. This formula reflects this coupling effect through multiplicative superposition. Turbulence intensity itself is a measure of water flow instability, while the flow influence caused by the pump station (such as eddies and pressure pulsations) further enhances turbulence and accelerates the erosion of the impeller and volute by particles. The formula "1 + pump station impact value" ensures that when there is no pump station interference (impact value is 0), the result equals the basic turbulence anomaly, which conforms to physical logic. S204. The damage analysis results of particulate matter to the pump station are obtained by multiplying the sum of the standardized turbulence influence value and the value of 1 by the particulate impact coefficient. Turbulence will change the direction and speed of particle movement, causing it to hit the wall more frequently. The stronger the turbulence, the greater the kinetic energy of the particles and the more complex the impact angle. The turbulence influence is used as the multiplier of the particulate impact coefficient. Step 3: Analyze the vulnerability of the pumping stations by combining the operation data of the decentralized pumping stations, and predict the operation performance of the pumping stations by combining the results of the particulate matter damage analysis and the vulnerability analysis results. In this embodiment, the vulnerability analysis of the pumping station includes the following specific contents: S301. Obtain pump station operating data, including operating current, outlet flow rate, inlet pressure, motor speed, bearing temperature, and vibration spectrum; S302. Calculate the standard deviation of each operating data of the pump station from the set range of the corresponding data to obtain the operating deviation of each operating data. The operating deviation of each operating data is weighted and summed to obtain the vulnerability of the pump station. By weighting and summing the standard deviations of each operating data from the set range, the comprehensive risk of the pump station deviating from the health status of multiple key dimensions (current, flow rate, pressure, speed, temperature, vibration) can be quantified. The weighting method can be flexibly adjusted according to the importance of the components to avoid missing early hidden dangers by a single parameter. The standard deviation reflects the degree of data fluctuation. A large deviation means that the equipment is unstable or close to the failure boundary. The weighted fusion of multi-dimensional deviations is in line with the engineering practice of multi-parameter trend analysis in equipment health management and can comprehensively reflect the vulnerability level of the overall structure, electrical and mechanical systems of the pump station.

[0019] In this embodiment, the prediction of pump station operating performance includes the following specific steps: S303. Obtain the vulnerability of the pump station at each location and the results of the analysis of particulate matter damage to the pump station's interior; S304. The product of the particulate matter damage analysis results on the pump station's internal structure and the particulate matter influence coefficient is obtained. This product is then summed with a value of 1 and multiplied by the pump station's vulnerability level to obtain the pump station's abnormal operating performance. Multiplying the particulate matter damage analysis results on the pump station's internal structure and the pump station's vulnerability level, while simultaneously introducing the particulate matter influence coefficient as an adjustment factor, allows for the simultaneous consideration of the combined risks of "external particulate impact" and "internal equipment weakening." This multiplication method emphasizes the dramatic amplification of risk when both factors deteriorate simultaneously, aligning with the "combined internal and external" safety warning logic. Pump stations with high internal vulnerability have reduced resistance to particulate wear (e.g., increased bearing clearance, failure of surface protective layers), at which point the damage caused by particles will increase exponentially. The particulate matter influence coefficient allows the model to be adjusted for different water qualities (e.g., high sediment content), enhancing its practicality and flexibility. Step 4: Select the operating pump station based on the predicted pump station performance; In this embodiment, the selection of the operating pumping station includes the following specific steps: The system obtains the calculated operational performance anomalies of each pumping station, sorts the anomalies in ascending order, designates pumping stations with operational performance anomalies less than or equal to the operational performance anomaly threshold as safe pumping stations, and designates pumping stations with operational performance anomalies less than or equal to the operational performance anomaly threshold as dangerous pumping stations, and selects safe pumping stations for drainage operations.

[0020] It should be noted that the advantages of this embodiment are as follows: by jointly evaluating particulate matter impact, turbulence fluctuations and equipment aging status, the dynamic impact of the external water environment on the internal wear of the pumping station is quantified. Thus, without increasing additional hardware costs, the performance of the pumping station can be predicted in advance and differentiated scheduling can be achieved. This significantly reduces the rate of sudden failures caused by particulate matter wear or operating condition fluctuations, and improves the reliability and economy of the entire drainage system.

[0021] like Figure 4 As shown in the embodiments of this application, a distributed pumping station control system based on collaborative scheduling is also provided, including: The module includes an acquisition module, a pump station operation performance prediction module, and an operating pump station selection module. The acquisition module acquires the operating data of each decentralized pumping station, the particulate matter situation of the corresponding water body, and the water flow situation. The pump station operation performance prediction module analyzes the damage caused by particulate matter to the pump station by acquiring the water flow and particulate matter situation in the corresponding area. It also analyzes the vulnerability of the pump station by combining the operation data of the decentralized pump station and the results of the particulate matter damage analysis and the vulnerability analysis of the pump station. The pump station selection module selects pump stations based on the predicted pump station performance.

[0022] The parameters and steps for each unit module to achieve their respective functions in the distributed pumping station control system based on collaborative scheduling described above can be referred to the parameters and steps in the embodiments of the distributed pumping station control method based on collaborative scheduling described above, and will not be repeated here.

[0023] Embodiments of this application also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a distributed pump station control method based on cooperative scheduling, which can be loaded and executed by the processor as provided in the above embodiments.

[0024] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the distributed pump station control method based on cooperative scheduling provided in the above embodiments, etc. The data storage area may store data involved in the distributed pump station control method based on cooperative scheduling provided in the above embodiments, etc.

[0025] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of a specific application-specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field-programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit this.

[0026] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI bus or an EISA bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0027] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a distributed pump station control method based on cooperative scheduling.

[0028] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0029] The term includes, or any other variation thereof, is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0030] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A decentralized pump station control method based on coordinated scheduling, characterized in that, Includes the following steps: Step 1: Obtain the operating data of each decentralized pumping station, the particulate matter situation of the corresponding water body, and the water flow situation; Step 2: Analyze the damage caused by particulate matter to the pump station by obtaining the water flow information and the particulate matter situation of the corresponding water body. Step 3: Analyze the vulnerability of the pumping stations by combining the operation data of the decentralized pumping stations, and predict the operation performance of the pumping stations by combining the results of the particulate matter damage analysis and the vulnerability analysis results. Step 4: Select the operating pump station based on the predicted pump station performance.

2. The coordinated-scheduling based decentralized pump station control method of claim 1, wherein, The acquisition of operational data from each decentralized pumping station, particulate matter levels in the corresponding water body, and water flow conditions includes the following specific steps: S101. Pump station operation data is acquired by sensors installed at various locations in the pump station, including operating current, outlet flow rate, inlet pressure, motor speed, bearing temperature, and vibration spectrum; and stored in the first storage module. S102. The particulate matter in the water body is acquired through sensors, including the concentration of particles, particle size distribution, and particle hardness; and stored in the second storage module. S103. Obtain the water flow velocity and turbulence intensity, and store them in the third storage module.

3. The coordinated-scheduling based decentralized pump station control method of claim 1, wherein, The analysis of particulate matter damage to the pump station's interior includes the following specific details: S201. Obtain the pump station inlet pressure, motor speed and vibration spectrum. Obtain the fluctuation values ​​of the pump station inlet pressure and motor speed in the corresponding time period. Obtain the vibration amplitude and vibration frequency during the operation of the pump station. Multiply the standardized vibration amplitude and vibration frequency to obtain the vibration impact anomaly. Sum the calculated fluctuation values ​​of each parameter to obtain the fluctuation impact anomaly. Sum the fluctuation impact anomaly and the vibration impact anomaly to obtain the impact value of the pump station on the water flow. S202. Obtain the flow velocity of the water, the concentration of particles in the water, the particle size distribution, and the particle hardness. Compare the flow velocity, particle concentration, particle size distribution, and particle hardness with the corresponding safety values ​​to obtain the anomalies of the corresponding parameters. Multiply the square of the abnormal flow velocity by the product of the abnormal particle concentration, abnormal particle size, and abnormal particle hardness in the water to obtain the particulate impact coefficient. S203. Obtain the turbulence intensity of the water flow and the impact value of the pump station on the water flow. The result of dividing the turbulence intensity by the safe turbulence intensity, multiplying it by the value of 1, and summing it with the impact value of the pump station on the water flow, yields the turbulence impact value. S204. The damage analysis results of particulate matter to the pump station are obtained by multiplying the sum of the standardized turbulence influence value and the value of 1 by the particulate matter impact coefficient.

4. The distributed pumping station control method based on collaborative scheduling according to claim 1, characterized in that, The vulnerability analysis of the pumping station includes the following specific contents: S301. Obtain pump station operating data, including operating current, outlet flow rate, inlet pressure, motor speed, bearing temperature, and vibration spectrum; S302. Calculate the standard deviation of each operating data of the pump station from the set range of the corresponding data to obtain the operating deviation of each operating data. Then, sum the operating deviations of each operating data by weight to obtain the vulnerability of the pump station.

5. The distributed pumping station control method based on collaborative scheduling according to claim 1, characterized in that, The prediction of pump station operating performance includes the following specific steps: S303. Obtain the vulnerability of the pump station at each location and the results of the analysis of particulate matter damage to the pump station's interior; S304. Obtain the product of the particulate matter damage analysis results to the pump station and the particulate matter influence coefficient. Summate the product with the value 1 and then multiply it by the pump station vulnerability to obtain the abnormal pump station operation performance.

6. The distributed pumping station control method based on collaborative scheduling according to claim 1, characterized in that, The selection of the operating pump station includes the following specific steps: The system obtains the calculated operational performance anomalies of each pumping station, sorts the anomalies in ascending order, designates pumping stations with operational performance anomalies less than or equal to the operational performance anomaly threshold as safe pumping stations, and designates pumping stations with operational performance anomalies less than or equal to the operational performance anomaly threshold as dangerous pumping stations, and selects safe pumping stations for drainage operations.

7. The distributed pumping station control method based on collaborative scheduling according to claim 3, characterized in that, The fluctuation value is calculated as follows: the absolute value of the parameter difference between the value of the corresponding parameter at the previous time and the value of the corresponding parameter at the next time is divided by the safe range value of the corresponding parameter, and then integrated over the time period and divided by the time standard quantity.

8. A distributed pumping station control system based on collaborative scheduling, used to implement the distributed pumping station control method based on collaborative scheduling as described in any one of claims 1-7, characterized in that, Includes the following specific modules: The module includes an acquisition module, a pump station operation performance prediction module, and an operating pump station selection module. The acquisition module acquires the operating data of each decentralized pumping station, the particulate matter situation of the water body in the corresponding area, and the water flow situation. The pump station operation performance prediction module analyzes the damage caused by particulate matter to the pump station by acquiring the water flow and particulate matter situation in the corresponding area, analyzes the vulnerability of the pump station by combining the operation data of the decentralized pump station, and predicts the operation performance of the pump station by combining the analysis results of the damage caused by particulate matter to the pump station and the analysis results of the pump station vulnerability. The pump station selection module selects pump stations based on the predicted pump station performance.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the distributed pump station control method based on cooperative scheduling as described in any one of claims 1-7 by calling the computer program stored in the memory.

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