Pressure fluctuation suppression method and device for emulsion pump station

Abstract

The invention discloses a pressure fluctuation suppression method and device for an emulsion pump station, and relates to the technical field of hydraulic transmission, and the method comprises the steps: monitoring the pressure data of a preset position in real time by deploying a pressure sensor of the emulsion pump station, and obtaining a pressure sequence set of a pump outlet, an equipment inlet and a main supply pipeline; inputting the sequence set into a BP neural network model, predicting and outputting the pressure distribution of the main supply pipeline of the next monitoring node, and calculating the pressure deviation; if the pressure deviation is smaller than the preset fluctuation scale, the deviation is eliminated, valve opening and flow proportion optimization analysis is conducted according to pressure distribution, and an adjusting scheme is output; and according to the adjustment scheme, executing optimization control of the emulsion pump station of the next monitoring node. The technical problems of unstable working of the pump station and equipment damage caused by pressure fluctuation in the emulsion pump station are solved, and the technical effects of improving the operation stability of the pump station, reducing equipment abrasion and controlling hysteresis quality through real-time monitoring and optimal adjustment are achieved.

Description

Technical Field

[0001] This application relates to the technical field of hydraulic transmission, and particularly relates to a method and device for suppressing pressure fluctuations of an emulsion pump station. Background Art

[0002] Emulsion pump stations are widely used in fields such as mines, metallurgy, and machining, mainly for providing high-pressure emulsion to meet the lubrication and cooling requirements of hydraulic equipment. However, in actual operation, the problem of pressure fluctuations in emulsion pump stations has always been an important factor affecting the system stability and operation efficiency. Due to the complex working environment, emulsion pump stations usually face multiple sources of pressure fluctuations. For example, transient pressure changes caused by the startup or shutdown of pump groups, the instability of fluid flow in pipelines, and the dynamic changes of the inlet and outlet pressures of equipment. These pressure fluctuations may cause the hydraulic equipment to operate unstably, and even lead to equipment damage and pipeline leakage due to excessive or too low pressure. Summary of the Invention

[0003] This application solves the technical problems of unstable operation of the pump station and equipment damage caused by pressure fluctuations in the emulsion pump station by providing a method and device for suppressing pressure fluctuations of the emulsion pump station.

[0004] This application provides a method for suppressing pressure fluctuations of an emulsion pump station. The method includes: during the operation of the emulsion pump station, real-time monitoring of the pressure data at a predetermined position through a pressure sensor deployed in the emulsion pump station to obtain a pump outlet pressure monitoring sequence, an equipment inlet pressure monitoring sequence, and a main supply pipeline pressure monitoring sequence set; inputting the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set into a pre-trained BP neural network model to predict and output the main supply pipeline predicted pressure distribution at the next monitoring node, and calculating the predicted pressure deviation; if the predicted pressure deviation is less than a predetermined fluctuation scale, for the purpose of eliminating the predicted pressure deviation, performing an optimization analysis of the valve opening and flow distribution ratio according to the main supply pipeline predicted pressure distribution, and outputting an optimization adjustment plan; according to the optimization adjustment plan, performing the optimized control of the emulsion pump station at the next monitoring node.

[0005] The present application also provides a pressure fluctuation suppression device for an emulsion pump station, including: a real-time monitoring module: during the operation of the emulsion pump station, the pressure data at a predetermined position is real-time monitored through a pressure sensor deployed in the emulsion pump station to obtain a pump outlet pressure monitoring sequence, an equipment inlet pressure monitoring sequence, and a main supply pipeline pressure monitoring sequence set; a model prediction module: the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set are input into a pre-trained BP neural network model to predict and output the predicted pressure distribution of the main supply pipeline at the next monitoring node, and the predicted pressure deviation is calculated; an optimization analysis module: if the predicted pressure deviation is less than a predetermined fluctuation scale, for the purpose of eliminating the predicted pressure deviation, the valve opening and flow distribution ratio are optimized and analyzed according to the predicted pressure distribution of the main supply pipeline, and an optimized adjustment plan is output; an optimization control module: according to the optimized adjustment plan, the optimized control of the emulsion pump station at the next monitoring node is executed.

[0006] It is intended to propose a pressure fluctuation suppression method and device for an emulsion pump station through the present application. First, during the operation of the emulsion pump station, the pressure data at a predetermined position is real-time monitored through a pressure sensor deployed in the emulsion pump station to obtain a pump outlet pressure monitoring sequence, an equipment inlet pressure monitoring sequence, and a main supply pipeline pressure monitoring sequence set; subsequently, the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set are input into a pre-trained BP neural network model to predict and output the predicted pressure distribution of the main supply pipeline at the next monitoring node, and the predicted pressure deviation is calculated; further, if the predicted pressure deviation is less than a predetermined fluctuation scale, for the purpose of eliminating the predicted pressure deviation, the valve opening and flow distribution ratio are optimized and analyzed according to the predicted pressure distribution of the main supply pipeline, and an optimized adjustment plan is output; finally, according to the optimized adjustment plan, the optimized control of the emulsion pump station at the next monitoring node is executed. The technical problems of unstable operation of the pump station and equipment damage caused by pressure fluctuations in the emulsion pump station are solved, and the technical effects of improving the operation stability of the pump station, reducing equipment wear, and controlling hysteresis are achieved through real-time monitoring and optimized adjustment. Description of the Drawings

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in the present application to illustrate the operations performed by the devices according to the embodiments of the present application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0008] Figure 1Schematic flow diagram of a method for suppressing pressure fluctuations in an emulsion pump station provided by an embodiment of the present application.

[0009] Figure 2 Schematic structural diagram of a device for suppressing pressure fluctuations in an emulsion pump station provided by an embodiment of the present application.

[0010] Explanation of reference numerals: real-time monitoring module 11, model prediction module 12, optimization analysis module 13, optimization control module 14. Detailed implementation manners

[0011] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0012] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0013] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0014] An embodiment of the present application provides a method for suppressing pressure fluctuations in an emulsion pump station. As Figure 1 shown, the method includes: During the operation of the emulsion pump station, the pressure data at a predetermined position is real-time monitored through a pressure sensor deployed in the emulsion pump station, and a pump outlet pressure monitoring sequence, an equipment inlet pressure monitoring sequence, and a main supply pipeline pressure monitoring sequence set are obtained.

[0015] In the embodiments of the present application, when the emulsion pump station is operating, pressure sensors are installed at key positions of the pump station to collect and monitor the pressure data at each preset position in real time. These data are from multiple important monitoring points such as the outlet of the pump, the inlet of the equipment, and the main supply pipeline. By continuously collecting these pressure data, corresponding monitoring sequences are generated, including the pressure monitoring sequence set at the pump outlet, the pressure monitoring sequence set at the equipment inlet, and the pressure monitoring sequence sets at various positions of the main supply pipeline. The real-time monitoring of these data provides the necessary basic information for subsequent pressure fluctuation analysis and optimization control.

[0016] Furthermore, the present application provides a method for obtaining the pressure monitoring sequence at the pump outlet, the pressure monitoring sequence at the equipment inlet, and the pressure monitoring sequence set of the main supply pipeline, including: Real-time monitoring of the pressure data at a predetermined position through pressure sensors deployed in the emulsion pump station, where the predetermined position includes the pump outlet position, the equipment inlet position, and several pipeline positions in the main supply pipeline; collecting the historical pressure data of the K consecutive monitoring nodes before the next monitoring node, and constructing the pressure monitoring sequence at the pump outlet, the pressure monitoring sequence at the equipment inlet, and the pressure monitoring sequence set of the main supply pipeline, where the pressure monitoring sequence set of the main supply pipeline includes the pressure distribution of the main supply pipeline under K consecutive monitoring nodes.

[0017] Preferably, during the operation of the emulsion pump station, pressure sensors are deployed at specific positions to monitor the pressure data in the emulsion pump station in real time. These predetermined positions include the pump outlet, the equipment inlet, and multiple key pipeline positions inside the main supply pipeline. The selection of these positions aims to comprehensively capture the key information of pressure changes during the operation of the pump station. Among them, the pressure sensor at the pump outlet position is mainly used to monitor the output pressure of the pump station to ensure that the supply capacity of the emulsion meets the requirements. The pressure sensor at the equipment inlet position is used to monitor the emulsion pressure received by each device (such as hydraulic supports, hydraulic cylinders, cutting equipment, etc.) to ensure that the transportation of the emulsion meets the usage requirements of each device. The pressure sensors at multiple pipeline positions inside the main supply pipeline are distributed to record the pressure changes inside the pipeline and reflect the distribution ratio of the emulsion between different devices, so as to dynamically master the overall operation of the emulsion pump station. On the basis of real-time monitoring, the historical pressure data of the first K consecutive monitoring nodes are further collected. These data record the dynamic changes in the recent operation of the pump station and provide time-series information for subsequent analysis. By sorting out the historical data and real-time data, a pump outlet pressure monitoring sequence set, an equipment inlet pressure monitoring sequence set, and a main supply pipeline pressure monitoring sequence set are constructed. Among them, the pressure monitoring sequence set of the main supply pipeline contains the pressure distribution of the main supply pipeline under K consecutive monitoring nodes. This distribution reflects the pressure change trend of the emulsion in the pipeline network and whether the distribution ratio of the emulsion between each device meets the expectation. Through the above process, the key pressure information of the pump station is completely recorded and serialized, laying a foundation for subsequent pressure prediction, distribution optimization, and the implementation of the overall control strategy.

[0018] Input the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set into the pre-trained BP neural network model to predict and output the predicted pressure distribution of the main supply pipeline at the next monitoring node, and calculate the predicted pressure deviation.

[0019] In one embodiment, the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set are input into the pre-trained BP neural network model. Using its learning and prediction ability for complex pressure changes, the predicted pressure distribution of the main supply pipeline at the next monitoring node is generated. This next monitoring node refers to the upcoming time point predicted during the pressure monitoring and control in the operation of the emulsion pump station. By comparing the predicted pressure distribution of the main supply pipeline at the next monitoring node with the standard pressure distribution, the difference value between the two, that is, the predicted pressure deviation, is calculated to evaluate the degree of pressure fluctuation of the current emulsion pump station. This process provides reliable data support for further optimizing the pressure control.

[0020] Further, this application provides an input of the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set into a pre-trained BP neural network model to predict and output the predicted pressure distribution of the main supply pipeline at the next monitoring node, including: Collect a sample pump outlet pressure monitoring sequence, a sample equipment inlet pressure monitoring sequence, a sample main supply pipeline pressure monitoring sequence set according to the emulsion pump station work log in the historical time zone, and collect the main supply pipeline pressure distribution at the historical next monitoring node to obtain the sample main supply pipeline pressure distribution; use the sample pump outlet pressure monitoring sequence, the sample equipment inlet pressure monitoring sequence, and the sample main supply pipeline pressure monitoring sequence set as the input data during training, use the sample main supply pipeline pressure distribution as the supervised data, and combine the gradient descent algorithm to perform iterative training on the BP neural network to obtain a main supply pipeline pressure prediction model that meets the convergence conditions; use the main supply pipeline pressure prediction model to predict the predicted pressure distribution of the main supply pipeline according to the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set.

[0021] Preferably, based on the work logs within the historical time zone of the emulsion pump station (such as half a month, one month, etc.), historical pressure monitoring data is extracted to form a sample pump outlet pressure monitoring sequence set, a sample equipment inlet pressure monitoring sequence set, and a sample main supply pipeline pressure monitoring sequence set. Among them, the sample pump outlet pressure monitoring sequence set records the pressure changes at the outlet of the pump station, reflecting the output state of the emulsion; the sample equipment inlet pressure monitoring sequence set records the pressure changes before the emulsion enters each equipment (such as hydraulic supports, hydraulic cylinders, cutting equipment, etc.), reflecting the actual pressure level of the equipment when receiving the emulsion; the sample main supply pipeline pressure monitoring sequence set covers the pressure data of multiple monitoring points in the main supply pipeline at different times, showing the dynamic changes in the pressure distribution within the pipeline and indicating whether the emulsion distribution is balanced. Then, based on these sample sequences, the pressure distribution of the main supply pipeline at the next monitoring node corresponding to each sequence is collected as a reference value to form a sample main supply pipeline pressure distribution, reflecting the pressure changes of the emulsion pump station during actual operation; subsequently, the collected sample pump outlet, equipment inlet, and main supply pipeline pressure sequence sets are used as input data, representing the pressure changes at different positions of the pump station, and the sample main supply pipeline pressure distribution under the next monitoring node is used as supervised data for the target output of training the BP neural network model. Specifically, a BP neural network is used to construct an initial model structure for predicting the pressure distribution of the emulsion pump station, including an input layer, a hidden layer, and an output layer. Then, the weights and bias values of the BP neural network are initialized using random numbers or a uniform distribution, and the input data is input into the initialized BP neural network for forward propagation. The input data includes the sample pump outlet pressure monitoring sequence, the sample equipment inlet pressure monitoring sequence, and the sample main supply pipeline pressure monitoring sequence set, and is passed layer by layer through the input layer and the hidden layer, generating a prediction result of the pressure distribution of the main supply pipeline at the next monitoring node at the output layer.After that, the mean squared error (MSE) is used as the loss function to calculate the loss value between the prediction result and the supervised data (the pressure distribution of the main supply pipeline of the sample), and the gradients of the loss with respect to the weights and biases of each layer are calculated layer by layer through the backpropagation algorithm. Then, the gradient descent algorithm is used to optimize the model parameters, gradually adjusting the weights and bias values to minimize the value of the loss function. In each iteration, the processes of forward propagation, loss calculation, backpropagation, and parameter update are continuously repeated until the loss value converges or the maximum number of iterations is reached. After the training is completed, the data not used for training is used to test the model performance, and the accuracy of the BP neural network in predicting the pressure distribution of the main supply pipeline is evaluated. If the prediction error on the validation set meets the expected range, the current BP neural network model is saved as the final main supply pipeline pressure prediction model. If the performance does not meet the standard, the hyperparameters (such as the learning rate, the number of neurons in the hidden layer, the training batch size, etc.) are adjusted and retrained to optimize the prediction effect of the model. Then, the real-time pressure monitoring data of the emulsion pump station (the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set) are input into the trained main supply pipeline pressure prediction model, and the model will automatically predict the pressure distribution of the main supply pipeline at the next monitoring node, providing an accurate basis for generating the optimization adjustment plan. This model can be widely applied to the dynamic pressure control of the emulsion pump station, effectively improving the operation stability and efficiency of the pump station.

[0022] Furthermore, the present application provides for calculating the predicted pressure deviation, including: Taking the standard main supply pipeline pressure distribution as a reference, mapping deviation calculation is performed on the predicted pressure distribution of the main supply pipeline to determine the main supply pipeline pressure deviation distribution; deviation mean calculation and deviation distribution dispersion analysis are performed on the main supply pipeline pressure deviation distribution to obtain the pressure deviation mean and the deviation distribution dispersion; weights are set according to the deviation distribution dispersion, and the predicted pressure deviation is obtained by multiplying the pressure deviation mean, where the weights are negatively correlated with the deviation distribution dispersion.

[0023] Optionally, after obtaining the predicted pressure distribution of the main supply pipeline, the predicted pressure distribution of the main supply pipeline is mapped with the standard main supply pipeline pressure distribution, and the difference of each point is calculated by point-by-point comparison to determine the difference between the predicted value and the standard value, obtaining the deviation of all monitoring points in the main supply pipeline from the standard pressure. Then, these deviations are stored point by point to form the pressure deviation distribution of the main supply pipeline, indicating the pressure change situation in the whole pipeline. Subsequently, the deviation mean calculation is performed on the deviation values of all monitoring points in the main supply pipeline pressure deviation distribution, that is, by accumulating the deviation values and then dividing by the number of deviation values, the pressure deviation mean is obtained, and then through the discrete calculation formula Deviation distribution dispersion analysis is performed to calculate the deviation distribution dispersion, where D is the deviation distribution dispersion and N is the total number of monitoring points. is the pressure deviation of the i-th monitoring point, is the average value of the pressure deviation; then, according to the deviation distribution dispersion D, the weight W is set, and this weight W is negatively correlated with the deviation distribution dispersion D, that is, , where, is the adjustment coefficient, which is used to adjust the sensitivity of the weight and is determined according to business requirements and expert decisions. By multiplying the average pressure deviation by the set weight W, the final predicted pressure deviation is calculated. Through the above process, the pressure deviation of the main supply pipeline can be comprehensively evaluated from both the overall and local perspectives. The deviation mean reflects the degree of overall pressure deviation, and the dispersion reflects the uniformity of the pressure deviation. Combining weight adjustment, the predicted pressure deviation can be calculated more accurately, providing a reliable basis for the subsequent formulation of pressure control and optimization schemes.

[0024] If the predicted pressure deviation is less than the predetermined fluctuation scale, for the purpose of eliminating the predicted pressure deviation, optimize the analysis of the valve opening and flow distribution ratio according to the predicted pressure distribution of the main supply pipeline, and output the optimization adjustment scheme.

[0025] In one embodiment, when the calculated predicted pressure deviation is less than the predetermined fluctuation range, it means that the current pressure change is within an acceptable range. However, in order to further improve the stability of the emulsion pump station, optimization adjustment is still required. At this time, aiming at eliminating the predicted pressure deviation, further optimization analysis is carried out. Specifically, according to the predicted pressure distribution of the main supply pipeline, combined with the initial adjustment parameters, simulation adjustment is carried out, the fitness of each initial adjustment parameter is analyzed, and then, taking these fitnesses and initial adjustment parameters as optimization data, the adjustment parameters are optimized through the genetic algorithm to optimize the valve opening and the flow distribution ratio of the emulsion, so as to obtain the optimization adjustment scheme; by adjusting these parameters, fine control of the pressure can be achieved, ensuring that the pressure distribution of each part of the pump station is more uniform and further reducing the fluctuation. Finally, the generated optimization adjustment scheme will be used to guide the operation of the pump station to ensure that the pressure fluctuation during the operation process is effectively suppressed, reduce unnecessary wear, and improve the stability and efficiency of the pump station.

[0026] Further, the present application provides that if the predicted pressure deviation is greater than or equal to a predetermined fluctuation scale, a pump speed sequence, a mine dust density sequence, an emulsion temperature sequence, an ambient temperature sequence, and a continuous working duration are collected; an emulsion viscosity sequence is predicted based on the mine dust density sequence, the emulsion temperature sequence, and the continuous working duration, wherein a BP neural network model is used for emulsion viscosity prediction; an auxiliary main supply pipeline predicted pressure distribution of the next monitoring node is predicted based on the pump speed sequence, the ambient temperature sequence, the emulsion viscosity sequence, and the continuous working duration; a comprehensive main supply pipeline predicted pressure distribution is calculated by mapping the main supply pipeline predicted pressure distribution and the auxiliary main supply pipeline predicted pressure distribution according to a predetermined weight ratio, wherein the weight of the predicted data is 0.8 and the weight of the auxiliary predicted data is 0.2; and an updated predicted pressure deviation is calculated based on the comprehensive main supply pipeline predicted pressure distribution with reference to the standard main supply pipeline pressure distribution.

[0027] Optionally, when the predicted pressure deviation is greater than or equal to a predetermined fluctuation scale, it means that the current pressure change has exceeded the acceptable range. At this time, multiple data sources related to pressure fluctuations will be collected, including a pump speed sequence, a mine dust density sequence, an emulsion temperature sequence, an ambient temperature sequence, and a continuous working duration. Among them, the pump speed sequence reflects the operating state and output capacity of the pump, the mine dust density sequence indicates the concentration of particulate matter in the emulsion operating environment, which may affect pressure and fluidity, the emulsion temperature sequence is used to evaluate the temperature state of the emulsion and its impact on viscosity, the ambient temperature sequence reflects the indirect impact of the external ambient temperature on the emulsion and the operation of the pump station, and the continuous working duration represents the length of time the pump station operates continuously, and its cumulative effect may affect the performance of the emulsion. Subsequently, the collected mine dust density sequence, emulsion temperature sequence, and continuous working duration are used as inputs, and a viscosity prediction model trained by a BP neural network is used to predict the viscosity of the emulsion. The viscosity prediction model calculates the predicted viscosity values of the emulsion at different time points through forward propagation of the input data, and forms an emulsion viscosity sequence with these predicted values for output. This emulsion viscosity sequence reflects the fluidity of the fluid and has a direct impact on pressure fluctuations. Among them, the construction process of the viscosity prediction model is the same as that of the aforementioned main supply pipeline pressure prediction model. After that, according to the pump speed sequence, ambient temperature sequence, emulsion viscosity sequence, and continuous working duration, combined with an auxiliary pressure prediction model, auxiliary pressure prediction is carried out to obtain the auxiliary main supply pipeline pressure distribution at the next monitoring node. The auxiliary distribution is used to supplement and correct the accuracy of the main supply pipeline predicted pressure distribution. Among them, the construction method of the auxiliary pressure prediction model is the same as that of the aforementioned main supply pipeline pressure prediction model. Then, according to the set weight ratio, the main supply pipeline predicted pressure distribution and the auxiliary main supply pipeline predicted pressure distribution are weighted and calculated. The weight of the main supply pipeline predicted pressure distribution is 0.8, which is determined according to the business accuracy requirements and expert decisions. The weight of the auxiliary main supply pipeline predicted pressure distribution is 0.2, which is also determined according to the business accuracy requirements and expert decisions. Through weighted calculation, a comprehensive main supply pipeline predicted pressure distribution can be obtained. Finally, based on the standard main supply pipeline pressure distribution, the comprehensive main supply pipeline pressure distribution is compared with the standard value, and the updated predicted pressure deviation is calculated in the same way as above. This deviation reflects the change in the main supply pipeline pressure under the combined action of multiple data sources. Through the above process, the emulsion pump station is comprehensively evaluated by combining various operating parameters and environmental conditions, improving the accuracy of pressure prediction. Through the calculation of the comprehensive main supply pipeline pressure distribution, the actual situation of pressure fluctuations can be more accurately reflected, and a more refined optimization and adjustment plan can be formulated to ensure the stable operation of the pump station in a complex environment.

[0028] Further, the present application provides that if the updated predicted pressure deviation is greater than or equal to a predetermined fluctuation scale, a deviation overflow ratio is calculated based on the updated predicted pressure deviation and the predetermined fluctuation scale; a predetermined optimization iteration number is obtained by matching based on the deviation overflow ratio; for the purpose of eliminating the updated predicted pressure deviation and with the predetermined optimization iteration number as a constraint, an optimization analysis of the valve opening and flow distribution ratio is performed according to the predicted pressure distribution of the main supply pipeline, and an optimized adjustment scheme is output.

[0029] Optionally, if the updated predicted pressure deviation is greater than or equal to the predetermined fluctuation scale, it means that there are significant anomalies or deviations in the current pressure distribution of the main supply pipeline. At this time, the updated predicted pressure deviation is divided by the predetermined fluctuation scale to obtain the deviation overflow ratio. This deviation overflow ratio represents the relative ratio of the updated pressure deviation exceeding the predetermined range and is used to guide the number of iterations required in the optimization process. Subsequently, a reasonable optimization iteration number is matched according to the deviation overflow ratio to ensure the optimization accuracy and efficiency. For example, if the deviation overflow ratio belongs to [1, 2), a smaller number of iterations (such as 10 times) is selected for rapid convergence. If the deviation overflow ratio belongs to [2, 3), a medium number of iterations (such as 20 times) is selected. If the deviation overflow ratio belongs to [2, +∞), a larger number of iterations (such as 50 times) is selected to improve the optimization accuracy. In actual operation, these iteration numbers can be dynamically adjusted to balance the optimization accuracy and calculation efficiency. After that, with the goal of eliminating the updated predicted pressure deviation, an optimization analysis of the valve opening and flow distribution ratio is performed in combination with the predicted pressure distribution of the main supply pipeline, that is, the initial values of the valve opening and flow distribution ratio are randomly set, and then these initial values are simulated, and the simulation results are combined with the genetic algorithm for iterative optimization. In each iteration, the valve opening and flow ratio are adjusted until the predetermined optimization iteration number is reached. Finally, according to the optimization analysis results, the initial parameters with the highest fitness generated during the iteration process are output as the optimized adjustment scheme. This optimized adjustment scheme includes the valve opening setting values of each key pipeline, the flow distribution ratio between different devices, and the predicted pressure distribution after adjustment, which will be used to guide the actual operation of the pumping station to eliminate or significantly reduce the updated predicted pressure deviation. By matching the optimization iteration number according to the deviation overflow ratio, the computational complexity can be dynamically adjusted during the optimization process, ensuring both the accuracy of the optimization and the analysis efficiency. The finally output optimized adjustment scheme can accurately adjust the operating parameters of the emulsion pumping station, significantly suppress pressure fluctuations, reduce unnecessary wear, and improve the stability and operating efficiency of the pumping station.

[0030] Further, the present application provides an optimization analysis of the valve opening and flow distribution ratio according to the predicted pressure distribution of the main supply pipeline, including: Randomly generate multiple valve opening parameters and multiple flow distribution ratios based on the valve opening space and the flow distribution ratio space, and integrate them to obtain multiple initial adjustment parameters; render the comprehensive predicted pressure distribution of the main supply pipeline into the simulation space of the emulsion pump station, and in the simulation space, perform simulation adjustments according to the multiple initial adjustment parameters to obtain multiple simulated pressure distributions of the main supply pipeline; based on the standard main supply pipeline pressure distribution, calculate multiple adjustment fitness values according to the multiple simulated pressure distributions of the main supply pipeline, where the adjustment fitness is negatively correlated with the mean deviation; according to the multiple adjustment fitness values and the multiple initial adjustment parameters, with the valve opening space and the flow distribution ratio space as constraints, use the genetic algorithm to optimize the adjustment parameters until the predetermined optimization iteration times are reached, and output the output optimized adjustment plan.

[0031] Optionally, within the preset valve opening space and flow distribution ratio space, randomly generate multiple sets of initial parameters, including multiple valve opening parameters and multiple flow distribution ratio parameters. By integrating these valve opening and flow ratio parameters into several initial adjustment parameter sets, each parameter set will represent a possible adjustment plan; subsequently, load the comprehensive predicted pressure distribution of the main supply pipeline into the simulation space of the emulsion pump station. This simulation space is a virtual environment used to simulate the internal liquid flow, pressure changes, and the interaction of various control parameters in the pump station. It is constructed through fluid dynamics simulation tools and 3D modeling software, combined with the structural data and actual operation data of components such as pumps, pipelines, and valves, and can accurately simulate the actual situation of emulsion flow in the pipeline. By inputting each set of initial adjustment parameters into the simulation space for simulation respectively, it is possible to understand the impact of valve opening and flow ratio adjustments on the pressure distribution. The simulation space will record the results of each simulation, thereby outputting multiple sets of simulated pressure distributions of the main supply pipeline, representing the pressure changes under different adjustment plans; then, based on the standard main supply pipeline pressure distribution as a benchmark, compare each set of simulated pressure distributions, calculate the mean deviation in the same way as described above, and then map out multiple adjustment fitness values according to the calculated mean deviation. This adjustment fitness F is negatively correlated with the mean deviation, that is, , the greater the adjustment fitness, the closer the adjustment plan is to the standard pressure distribution; then, the genetic algorithm is used to optimize the initial adjustment parameters to find the best valve opening and flow distribution ratio. Specifically, a set of initial parameters with better performance is selected as the parent generation according to the adjustment fitness, and then the selected parent parameter set is cross - combined to generate a new set of offspring parameters. Next, some of the offspring parameter sets are randomly mutated to increase the diversity of the parameter space, and then the new parameter set is substituted into the simulation space for re - simulation to calculate the new adjustment fitness. The above process is repeated until the predetermined optimization iteration times are reached; finally, the adjustment parameter set with the highest final fitness is selected as the optimized adjustment plan for the emulsion pump station. This optimized adjustment plan includes the valve opening setting values of each key pipeline, the flow distribution ratio between different devices, and the predicted pressure distribution after adjustment. By randomly generating parameters, simulating adjustments, and optimizing with the genetic algorithm, the best combination of valve opening and flow distribution ratio can be efficiently explored. The final optimized adjustment plan can effectively reduce pressure deviation and equipment wear, ensure the stable operation of the emulsion pump station, and improve the operation efficiency and reliability of the pump station.

[0032] According to the optimized adjustment plan, perform the optimized control of the emulsion pump station at the next monitoring node.

[0033] In one embodiment, according to the optimized adjustment plan, the emulsion pump station will be adjusted in real - time and the optimized control of the next monitoring node will be performed. Specifically, according to the valve opening and flow distribution ratio set by the optimized plan, the operating parameters of each part of the pump station are automatically adjusted to ensure that the pressure of the main supply pipeline reaches the expected target and reduce any possible pressure fluctuations. After this adjustment is completed, the optimized control of the next monitoring node is carried out, that is, the above - mentioned process is performed again. In this way, through continuous real - time adjustment, the pump station can operate stably continuously, avoid unnecessary wear, and ensure that the transportation and distribution of the emulsion meet the actual requirements.

[0034] In the above, reference is made to Figure 1 A method for suppressing pressure fluctuations in an emulsion pump station according to an embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe a device for suppressing pressure fluctuations in an emulsion pump station according to an embodiment of the present invention.

[0035] A device for suppressing pressure fluctuations in an emulsion pump station according to an embodiment of the present invention is used to solve the technical problems of unstable operation of the pump station and equipment damage caused by pressure fluctuations in the emulsion pump station, and achieves the technical effects of improving the operation stability of the pump station, reducing equipment wear, and controlling hysteresis through real - time monitoring and optimized adjustment. A device for suppressing pressure fluctuations in an emulsion pump station includes: a real - time monitoring module 11, a model prediction module 12, an optimization analysis module 13, and an optimization control module 14.

[0036] Real-time monitoring module 11: During the operation of the emulsion pump station, the pressure data at a predetermined position is monitored in real time through the pressure sensors deployed in the emulsion pump station, and a pump outlet pressure monitoring sequence, an equipment inlet pressure monitoring sequence, and a main supply pipeline pressure monitoring sequence set are obtained; Model prediction module 12: The pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set are input into a pre-trained BP neural network model to predict and output the predicted pressure distribution of the main supply pipeline at the next monitoring node, and the predicted pressure deviation is calculated; Optimization analysis module 13: If the predicted pressure deviation is less than a predetermined fluctuation scale, for the purpose of eliminating the predicted pressure deviation, optimize and analyze the valve opening and flow distribution ratio according to the predicted pressure distribution of the main supply pipeline, and output an optimization adjustment plan; Optimization control module 14: According to the optimization adjustment plan, perform the optimization control of the emulsion pump station at the next monitoring node.

[0037] Further, the real-time monitoring module 11 further includes: The pressure data at a predetermined position is monitored in real time through the pressure sensors deployed in the emulsion pump station, where the predetermined position includes the pump outlet position, the equipment inlet position, and several pipeline positions in the main supply pipeline; The historical pressure data of the previous K consecutive monitoring nodes before the next monitoring node is collected to construct a pump outlet pressure monitoring sequence, an equipment inlet pressure monitoring sequence, and a main supply pipeline pressure monitoring sequence set, where the main supply pipeline pressure monitoring sequence set includes the main supply pipeline pressure distribution under K consecutive monitoring nodes.

[0038] Further, the model prediction module 12 further includes: According to the work log of the emulsion pump station in the historical time zone, collect the sample pump outlet pressure monitoring sequence, the sample equipment inlet pressure monitoring sequence, the sample main supply pipeline pressure monitoring sequence set, and collect the main supply pipeline pressure distribution under the historical next monitoring node to obtain the sample main supply pipeline pressure distribution; Using the sample pump outlet pressure monitoring sequence, the sample equipment inlet pressure monitoring sequence, and the sample main supply pipeline pressure monitoring sequence set as the input data during training, and the sample main supply pipeline pressure distribution as the supervised data, iteratively train the BP neural network in combination with the gradient descent algorithm to obtain a main supply pipeline pressure prediction model that meets the convergence conditions; Using the main supply pipeline pressure prediction model, predict the predicted pressure distribution of the main supply pipeline according to the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence, and the main supply pipeline pressure monitoring sequence set.

[0039] Further, the model prediction module 12 further includes: Based on the standard main supply pipeline pressure distribution, calculate the mapping deviation of the predicted pressure distribution of the main supply pipeline to determine the main supply pipeline pressure deviation distribution; perform deviation mean calculation and deviation distribution dispersion analysis on the main supply pipeline pressure deviation distribution to obtain the pressure deviation mean and the deviation distribution dispersion degree; set the weight according to the deviation distribution dispersion degree, and multiply the pressure deviation mean by the weight to obtain the predicted pressure deviation, where the weight is negatively correlated with the deviation distribution dispersion degree.

[0040] Furthermore, the optimization analysis module 13 further includes: If the predicted pressure deviation is greater than or equal to a predetermined fluctuation scale, collect the pump speed sequence, mine dust density sequence, emulsion temperature sequence, ambient temperature sequence, and continuous working duration; predict the emulsion viscosity sequence based on the mine dust density sequence, emulsion temperature sequence, and continuous working duration, where a BP neural network model is used for emulsion viscosity prediction; predict the predicted pressure distribution of the auxiliary main supply pipeline at the next monitoring node based on the pump speed sequence, ambient temperature sequence, emulsion viscosity sequence, and continuous working duration; perform mapping calculation on the predicted pressure distribution of the main supply pipeline and the predicted pressure distribution of the auxiliary main supply pipeline according to a predetermined weight ratio to obtain the comprehensive predicted pressure distribution of the main supply pipeline, where the weight of the predicted data is 0.8 and the weight of the auxiliary predicted data is 0.2; based on the standard main supply pipeline pressure distribution, calculate the updated predicted pressure deviation according to the comprehensive predicted pressure distribution of the main supply pipeline.

[0041] Furthermore, the optimization analysis module 13 further includes: If the updated predicted pressure deviation is greater than or equal to a predetermined fluctuation scale, calculate the deviation overflow ratio according to the updated predicted pressure deviation and the predetermined fluctuation scale; match and obtain a predetermined number of optimization iteration times based on the deviation overflow ratio; with the aim of eliminating the updated predicted pressure deviation and constrained by the predetermined number of optimization iteration times, perform optimization analysis on the valve opening and flow distribution ratio according to the predicted pressure distribution of the main supply pipeline, and output an optimization adjustment plan.

[0042] Furthermore, the optimization analysis module 13 further includes: Randomly generate a plurality of valve opening parameters and a plurality of flow distribution ratios based on the valve opening space and the flow distribution ratio space, and integrate them to obtain a plurality of initial adjustment parameters; render the comprehensive predicted pressure distribution of the main supply pipeline into the simulation space of the emulsion pump station, and in the simulation space, perform simulation adjustment according to the plurality of initial adjustment parameters to obtain a plurality of simulated pressure distributions of the main supply pipeline; based on the standard pressure distribution of the main supply pipeline, calculate a plurality of adjustment fitness values according to the plurality of simulated pressure distributions of the main supply pipeline, wherein the adjustment fitness value is negatively correlated with the mean deviation; according to the plurality of adjustment fitness values and the plurality of initial adjustment parameters, with the valve opening space and the flow distribution ratio space as constraints, use the genetic algorithm to optimize the adjustment parameters until the predetermined optimization iteration number is reached, and output the output optimized adjustment scheme.

[0043] The pressure fluctuation suppression device of an emulsion pump station provided by an embodiment of the present invention can execute the pressure fluctuation suppression method of an emulsion pump station provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0044] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included individual units and modules are only divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0045] The above specific embodiments do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to the design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for suppressing pressure fluctuations in an emulsion pump station, characterized in that: Methods include: During the operation of the emulsion pump station, the pressure data of the predetermined position is monitored in real time by the pressure sensor deployed in the emulsion pump station to obtain the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence and the main supply pipeline pressure monitoring sequence set; Input the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence and the main supply pipeline pressure monitoring sequence set into the pre-trained BP neural network model, predict and output the main supply pipeline predicted pressure distribution of the next monitoring node, and calculate the predicted pressure deviation; If the predicted pressure deviation is smaller than the predetermined fluctuation scale, in order to eliminate the predicted pressure deviation, the valve opening and flow distribution ratio are optimized and analyzed according to the predicted pressure distribution of the main supply pipeline, and an optimized adjustment plan is output; According to the optimization adjustment scheme, the optimization control of the emulsion pump station of the next monitoring node is performed.

2. A method for suppressing pressure fluctuations in an emulsion pump station according to claim 1, characterized in that: Get the pump outlet pressure monitoring sequence, equipment inlet pressure monitoring sequence and main supply pipeline pressure monitoring sequence set, including: The pressure data of the predetermined position is monitored in real time by the pressure sensor deployed in the emulsion pump station, wherein the predetermined position includes the pump outlet position, the equipment inlet position and several pipeline positions in the main supply pipeline; Collect historical pressure data of K consecutive monitoring nodes before the next monitoring node, and construct pump outlet pressure monitoring sequence, equipment inlet pressure monitoring sequence and main supply pipeline pressure monitoring sequence set, where the main supply pipeline pressure monitoring sequence set includes the main supply pipeline pressure distribution under K consecutive monitoring nodes.

3. The method for suppressing pressure fluctuations in an emulsion pump station according to claim 1, characterized in that: The pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence and the main supply pipeline pressure monitoring sequence set are input into the pre-trained BP neural network model to predict and output the main supply pipeline predicted pressure distribution of the next monitoring node, including: According to the work log of the emulsion pump station in the historical time zone, collect the sample pump outlet pressure monitoring sequence, the sample equipment inlet pressure monitoring sequence, and the sample main supply pipeline pressure monitoring sequence set, and collect the main supply pipeline pressure distribution at the next historical monitoring node to obtain the sample main supply pipeline pressure distribution; The sample pump outlet pressure monitoring sequence, sample equipment inlet pressure monitoring sequence, and sample main supply pipeline pressure monitoring sequence set are used as input data for training, and the sample main supply pipeline pressure distribution is used as supervision data. The BP neural network is iteratively trained in combination with the gradient descent algorithm to obtain a main supply pipeline pressure prediction model that meets the convergence conditions. The main supply pipeline pressure prediction model is used to predict the main supply pipeline pressure distribution according to the pump outlet pressure monitoring sequence, the equipment inlet pressure monitoring sequence and the main supply pipeline pressure monitoring sequence set.

4. A method for suppressing pressure fluctuations in an emulsion pump station according to claim 3, characterized in that: The predicted pressure deviation is calculated, including: Based on the standard main supply pipeline pressure distribution, a mapping deviation calculation is performed on the predicted pressure distribution of the main supply pipeline to determine the main supply pipeline pressure deviation distribution; Performing deviation mean calculation and deviation distribution discrete analysis on the pressure deviation distribution of the main supply pipeline to obtain the pressure deviation mean and deviation distribution discreteness; A weight is set according to the deviation distribution dispersion, and the predicted pressure deviation is obtained by multiplying the weight by the pressure deviation mean value, wherein the weight is negatively correlated with the deviation distribution dispersion.

5. A method for suppressing pressure fluctuations in an emulsion pump station according to claim 4, characterized in that: If the predicted pressure deviation is greater than or equal to the predetermined fluctuation scale, the pump speed sequence, the mine dust density sequence, the emulsion temperature sequence, the ambient temperature sequence and the continuous working time are collected; The emulsion viscosity sequence is obtained according to the mine dust density sequence, the emulsion temperature sequence and the continuous working time prediction, wherein the emulsion viscosity is predicted using a BP neural network model; The predicted pressure distribution of the auxiliary main supply pipeline of the next monitoring node is obtained according to the pump speed sequence, the ambient temperature sequence, the emulsion viscosity sequence and the continuous working time prediction; The predicted pressure distribution of the main supply pipeline and the predicted pressure distribution of the auxiliary main supply pipeline are mapped and calculated according to a predetermined weight ratio to obtain a comprehensive predicted pressure distribution of the main supply pipeline, wherein the weight of the predicted data is 0.8 and the weight of the auxiliary predicted data is 0.2; Taking the standard main supply pipeline pressure distribution as a reference, an updated predicted pressure deviation is calculated according to the comprehensive main supply pipeline predicted pressure distribution.

6. A method for suppressing pressure fluctuations in an emulsion pump station according to claim 5, characterized in that: If the updated predicted pressure deviation is greater than or equal to a predetermined fluctuation scale, a deviation overflow ratio is calculated according to the updated predicted pressure deviation and the predetermined fluctuation scale; Obtaining a predetermined number of optimization iterations based on the deviation overflow ratio matching; With the purpose of eliminating the updated predicted pressure deviation and with the predetermined number of optimization iterations as a constraint, an optimization analysis of valve opening and flow distribution ratio is performed according to the predicted pressure distribution of the main supply pipeline, and an optimized adjustment plan is output.

7. A method for suppressing pressure fluctuations in an emulsion pump station according to claim 6, characterized in that: The valve opening and flow distribution ratio optimization analysis is performed according to the predicted pressure distribution of the main supply pipeline, including: Based on the valve opening space and the flow distribution ratio space, a plurality of valve opening parameters and a plurality of flow distribution ratios are randomly generated, and a plurality of initial adjustment parameters are obtained by integration; Rendering the predicted pressure distribution of the comprehensive main supply pipeline to the simulation space of the emulsion pump station, and performing simulation adjustment in the simulation space according to the multiple initial adjustment parameters to obtain multiple simulated pressure distributions of the main supply pipelines; Taking the standard main supply pipeline pressure distribution as a reference, multiple adjustment fitnesses are calculated according to the multiple main supply pipeline simulated pressure distributions, wherein the adjustment fitnesses are negatively correlated with the deviation mean; According to the multiple adjustment fitness and multiple initial adjustment parameters, with the valve opening space and the flow distribution ratio space as constraints, a genetic algorithm is used to optimize the adjustment parameters until the predetermined optimization iteration number is reached, and the output optimization adjustment scheme is output.

8. A pressure fluctuation suppression device for an emulsion pump station, characterized in that: The device is used to implement the method for suppressing pressure fluctuations in an emulsion pump station according to any one of claims 1 to 7, comprising: Real-time monitoring module: During the operation of the emulsion pump station, the pressure data at the predetermined position is monitored in real time through the pressure sensor deployed in the emulsion pump station to obtain the pump outlet pressure monitoring sequence, equipment inlet pressure monitoring sequence and main supply pipeline pressure monitoring sequence set; Model prediction module: input the pump outlet pressure monitoring sequence, equipment inlet pressure monitoring sequence and main supply pipeline pressure monitoring sequence set into the pre-trained BP neural network model, predict and output the main supply pipeline predicted pressure distribution of the next monitoring node, and calculate the predicted pressure deviation; Optimization analysis module: if the predicted pressure deviation is less than the predetermined fluctuation scale, the valve opening and flow distribution ratio are optimized and analyzed according to the predicted pressure distribution of the main supply pipeline in order to eliminate the predicted pressure deviation, and an optimized adjustment plan is output; Optimization control module: executes optimization control of the emulsion pump station of the next monitoring node according to the optimization adjustment scheme.

Images