Safety monitoring and remote scheduling system for mine air and water pipe network

Abstract

The invention discloses a mine air and water pipe network safety monitoring and remote scheduling system, which comprises an acquisition module, wherein the acquisition module is used for historical monitoring data of a plurality of monitoring substations in an underground looped network. The abnormal model is established according to the pressure data of the wind-water pipe network and the state data of the wind-water pipe network, the real-time monitoring data of the wind-water pipe network are substituted for calculation, the probability that the wind-water pipe network is abnormal is obtained, the historical parameter data can be trained, verified and tested by utilizing machine learning and a feedforward neural network model, and the reliability of the wind-water pipe network is improved. Whether the wind-water pipe network reaches the optimal operation state or not is judged, if the real-time monitoring data of the wind-water pipe network and the parameter data in the optimal operation state have high similarity and matching rate, the wind-water pipe network is inevitably not abnormal during operation, and if the abnormality predicted by the abnormality model is large, the pressure of the wind-water pipe network is optimized, so that the pressure of the wind-water pipe network is optimized. And the air-water pipe network reaches the optimal operation state.

Description

Technical Field

[0001] The present invention relates to the technical field of mine production safety, and in particular to a mine feng shui pipe network safety monitoring and remote dispatching system. Background Art

[0002] The underground water supply system of coal mines is a vital system for underground safety production activities. Whether it is mining and production activities or the "one ventilation and three defenses" of the mine, it needs to be supported by a sufficient amount of water with sufficient pressure. If the hydraulic requirements are not met, it will not only directly affect the operation and production of various users underground, but also bring very serious safety hazards.

[0003] The water source of the underground water supply system of coal mines comes from the ground on the one hand, and from the water source that is pumped up from the underground and treated on the other hand. The water supply pipeline supplies water to the underground in the form of gravity static pressure and is connected to the underground water supply network. Due to the static pressure of water, the water pressure difference in mining areas with different heights is very large, resulting in insufficient water supply in some places and excessive water pressure in some places. In addition, the laying and installation time of the main wind and water pipelines in coal mines is too long. Due to the harsh natural conditions underground, the pipelines have been eroded by water for a long time, the deformation of the tunnels, and the dynamic pressure of mine mining, which have caused leakage, bubbling, dripping, and leakage. When the coal mining working face and the excavation are affected by pipeline rupture or insufficient wind and water pressure, the production will be affected, which will cause underground water outage accidents, seriously affecting the progress of mining and production. Moreover, with the development of new mining areas, the pipeline network will become larger and more complex.

[0004] Due to the different locations, water consumption, water pressure and wind pressure requirements, and different operating methods, it is difficult to determine the period of water consumption and air volume in underground mine production activities, which brings great difficulties to the stability of the underground water supply system. Production practice experience shows that excessive or low pressure in the water supply pipeline and air supply pipeline will cause various accidents. If the water pressure is too high, the water consumption will be large, and it may cause a burst accident to the electromechanical equipment; excessive pipe pressure is easy to cause cracks in weak parts of the pipeline, which will not only cause accidental injuries to personnel or equipment, but also cause serious water leakage, resulting in the inability to raise the overall system pressure, resulting in the suspension of production in the entire mine or one wing; too low pressure is easy to cause insufficient water pressure or water outage in higher terrain, thereby affecting the production of one or several working faces; at the same time, if the water pressure is too low, the atomization effect is poor due to the small amount of water, the dust removal effect is poor, and the electromechanical equipment is not cooled enough and shuts down due to overheating.

[0005] At present, coal mines can only inspect the operation and use of underground Fengshui pipelines by manual inspection, which has many disadvantages: First, it is inefficient and consumes a lot of manpower and material resources; Second, it is impossible to monitor the status of Feng Shui pipelines in real time, and it is difficult to detect emergencies in time; Third, inspectors are prone to miss inspections, which poses a safety hazard. Summary of the invention

[0006] In order to solve the above-mentioned technical problems, a mine Feng Shui pipeline safety monitoring and remote dispatching system is provided. This technical solution solves the problems raised in the above-mentioned background technology, such as low efficiency, large consumption of manpower and material resources, inability to monitor Feng Shui pipelines in real time, difficulty in timely detection of emergencies, and easy omissions by inspectors, which pose safety hazards.

[0007] In order to achieve the above purpose, the technical solution adopted by the present invention is: A mine fengshui pipe network safety monitoring and remote dispatching system, comprising: An acquisition module, the acquisition module is used to acquire monitoring data of several monitoring substations in the historical underground ring network, the monitoring data includes pressure data of the Feng Shui pipe network and status data of the Feng Shui pipe, and the status data of the Feng Shui pipe includes the corrosion degree of the Feng Shui pipe; A data processing module, wherein the data processing module is used to preprocess the monitoring data, wherein the preprocessing includes data deduplication, missing value filling and outlier processing, and obtain the preprocessed monitoring data; A model building module, the model building module is used to build a pipe network abnormality prediction model based on the pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipes; An optimal state judgment module, which is used to learn the monitoring data based on a feedforward neural network model to determine whether the Feng Shui pipe network has reached an optimal operating state; A real-time monitoring module, which is used to obtain monitoring data in real time and perform similarity calculation and matching rate calculation between the monitoring data and the data of the Feng Shui pipe network in the optimal operating state; A scheduling module optimizes pipeline pressure based on similarity and matching rate.

[0008] Preferably, the data deduplication comprises the following steps: Arrange all data coordinates (x, y) into one column; Retrieve the duplication of the first coordinate and subsequent coordinates, and if there is duplication, delete the subsequent coordinates; After the search is completed, the second coordinate and the subsequent coordinates are searched for duplication. If there is duplication, the subsequent coordinates are deleted; Repeat the above steps until all data coordinates have been detected; The missing value filling comprises the following steps: Arrange all data coordinates (x, y) into one column; Retrieve all data coordinates (x, y). If x or y is missing, fill in the data at that position. The data collected is the mean of the adjacent data at the same position.

[0009] Preferably, the outlier processing comprises the following steps: Arrange all data coordinates (x, y) into one column; Calculate the deviation value k between the data coordinate (x, y) and the adjacent data coordinates (a, b) and (c, d). When the deviation value k is greater than 100%, the data coordinate (x, y) is an outlier, and the data coordinate (x, y) is replaced by the mean of the adjacent data. The calculation formula of the deviation value is: .

[0010] Preferably, the pipeline network anomaly prediction model is: ,

[0011] In the formula, Indicates that the pipe network is abnormal. It means the pipe network is operating normally; G is the predicted probability of the probability prediction model; It is the pressure abnormality index of Fengshui pipe network; It is an indicator of corrosion of Feng Shui pipes; are the coefficients of the pipeline network anomaly prediction model.

[0012] The pipeline network abnormality prediction model proposed in this scheme is based on the principle of Logistic regression model to establish an abnormal risk prediction model. Logistic regression model is a generalized linear regression analysis model, which is often used in data mining, result prediction and other fields; The pipeline abnormality prediction model proposed in this plan predicts the abnormal operating indicators of the Feng Shui pipeline in abnormal operating status and calculates the probability of abnormality in the Feng Shui pipeline. Based on the predicted probability of abnormality in the Feng Shui pipeline, it determines whether there is a risk of abnormality in the Feng Shui pipeline, and can promptly and quickly check for abnormal hidden dangers in the Feng Shui pipeline.

[0013] Preferably, the calculation formula of the pressure abnormality index is:

[0014] In the formula, is the offset of the real-time pressure data relative to the standard data, t is the Feng Shui pipe The running time in the state.

[0015] During the operation of the Feng Shui network, when abnormal operating data occurs, the greater the difference from the standard abnormal data and the longer the deviation operation time, the greater the risk of abnormality. Therefore, this scheme accumulates the product of the offset of all abnormal operating data and the abnormal operation time that occurs during the operation of the Feng Shui network as the abnormal operation indicator of the Feng Shui network.

[0016] Preferably, the The calculation method is: Classify the historical operation data according to whether the Feng Shui pipe network has abnormalities, and obtain several groups of historical operation data of Feng Shui pipe networks that eventually have abnormalities and several groups of historical operation data of Feng Shui pipe networks that eventually have no abnormalities; Based on several groups of historical operation data of Feng Shui pipe networks that eventually had abnormalities and several groups of historical operation data of Feng Shui pipe networks that eventually did not have abnormalities, the maximum likelihood method was used to Calculation of test The significance of the parameters of the pipeline network anomaly prediction model is judged Whether the significance requirement is met.

[0017] Preferably, the learning of monitoring data based on the feedforward neural network model specifically includes the following steps: Based on feedforward neural network and machine learning, the pressure data of multiple Feng Shui pipe networks and the status data of Feng Shui pipes are labeled and divided to obtain training sets, validation sets and test sets; The training set, validation set and test set are used to perform supervised training, validation and testing on the pipe network anomaly prediction model to determine whether the Feng Shui pipe network has reached the optimal operating state.

[0018] Preferably, the formula for calculating the similarity between the monitoring data and the data of the Feng Shui pipe network in the optimal operating state is:

[0019] In the formula, is the similarity between the real-time monitoring data and the i-th feature of the data under the historical best operating state, is the jth characteristic index value of the ith characteristic of the Feng Shui network real-time data, is the jth feature index value of the i-th feature of the data under the historical best operating state, is the total number of characteristic index values ​​of the ith feature.

[0020] Preferably, the calculation formula of the matching rate is:

[0021] In the formula, C is the adaptability between the characteristic parameters of real-time monitoring data and the data under the historical best operating state, is the weight value of the i-th feature parameter, and m is the total number of feature parameters.

[0022] Preferably, the optimization of pipeline pressure based on similarity and matching rate specifically includes the following steps: Compare the similarity and matching rate with the preset threshold; If the similarity and matching rate are greater than the preset threshold, no optimization is required; If the similarity and the matching rate are less than the preset threshold, the pressure of the pipeline is optimized by controlling the solenoid valves on the pipeline respectively.

[0023] Compared with the prior art, the present invention provides a mine wind and water pipe network safety monitoring and remote dispatching system, which has the following beneficial effects: The operation data of the Feng Shui pipe network is closely related to the pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipeline. The pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipeline are retrieved from the historical database, and the data are deduplicated, missing values ​​are filled and outliers are processed, so as to obtain complete data, that is, data with processing significance and reference value. The present invention can also establish an abnormal model based on the pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipeline, and substitute the real-time monitoring data of the Feng Shui pipe network for calculation to obtain the probability of abnormality in the Feng Shui pipe network. Using machine learning and feedforward neural network models, historical parameter data can be trained, verified and tested to determine whether the Feng Shui pipe network has reached the optimal operating state. If the real-time monitoring data of the Feng Shui pipe network and the parameter data under the optimal operating state have a high similarity and matching rate, the Feng Shui pipe network will inevitably not have abnormalities during operation. If the abnormality predicted by the abnormal model is large, the pressure of the Feng Shui pipe network is optimized until the Feng Shui pipe network reaches the optimal operating state. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the monitoring and remote scheduling method of the present invention; Figure 2 A schematic diagram of a method for deduplication of data in the present invention; Figure 3 A schematic diagram of a method for filling missing values ​​in the present invention; Figure 4 It is a schematic diagram of the method for processing outliers in the present invention; Figure 5 It is a schematic diagram of the internal integration of the monitoring and remote scheduling system in the present invention. DETAILED DESCRIPTION

[0025] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0026] Example 1 Please refer to Figure 1-Figure 5 As shown, a mine wind and water pipe network safety monitoring and remote dispatching system includes: An acquisition module is used to acquire monitoring data of several monitoring substations in the historical underground ring network, the monitoring data includes pressure data of the Feng Shui pipe network and status data of the Feng Shui pipe, and the status data of the Feng Shui pipe includes the corrosion degree of the Feng Shui pipe; Data processing module: The data processing module is used to preprocess the monitoring data, including data deduplication, missing value filling and outlier processing, to obtain the preprocessed monitoring data; A model building module, which is used to build a pipe network abnormality prediction model based on the pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipes; The optimal state judgment module is used to learn the monitoring data based on the feedforward neural network model to determine whether the Feng Shui pipe network has reached the optimal operating state; Real-time monitoring module, which is used to obtain monitoring data in real time, and calculate the similarity and matching rate between the monitoring data and the data of the Feng Shui pipe network in the best operating state; Scheduling Module,The scheduling module optimizes the pipeline pressure based on similarity and,matching rate.

[0027] It can be understood by those skilled in the art that the operating data of the Feng Shui network is closely related to the pressure data of the Feng Shui network and the status data of the Feng Shui pipelines. The pressure data of the Feng Shui network and the status data of the Feng Shui pipelines are retrieved from the historical database, and the data are deduplicated, missing values ​​are filled, and outliers are processed to obtain complete data, that is, data that is meaningful to process and has reference value. The present invention can also establish an abnormal model based on the pressure data of the Feng Shui network and the status data of the Feng Shui pipelines, and substitute the real-time monitoring data of the Feng Shui network for calculation to obtain the probability of abnormalities in the Feng Shui network. Using machine learning and feedforward neural network models, historical parameter data can be trained, verified, and tested to determine whether the Feng Shui network has reached the optimal operating state. If the real-time monitoring data of the Feng Shui network and the parameter data under the optimal operating state have a high similarity and matching rate, the Feng Shui network will inevitably not have abnormalities during operation. If the abnormality predicted by the abnormal model is large, the pressure of the Feng Shui network is optimized until the Feng Shui network reaches the optimal operating state.

[0028] Data deduplication includes the following steps: Arrange all data coordinates (x, y) into one column; Retrieve the duplication of the first coordinate and subsequent coordinates, and if there is duplication, delete the subsequent coordinates; After the search is completed, the second coordinate and the subsequent coordinates are searched for duplication. If there is duplication, the subsequent coordinates are deleted; Repeat the above steps until all data coordinates have been detected; Missing value filling includes the following steps: Arrange all data coordinates (x, y) into one column; Retrieve all data coordinates (x, y). If x or y is missing, fill in the data at that position. The data collected is the mean of the adjacent data at the same position.

[0029] Outlier processing includes the following steps: Arrange all data coordinates (x, y) into one column; Calculate the deviation value k between the data coordinate (x, y) and the adjacent data coordinates (a, b) and (c, d). When the deviation value k is greater than 100%, the data coordinate (x, y) is an outlier, and the data coordinate (x, y) is replaced by the mean of the adjacent data. The calculation formula of the deviation value is: .

[0030] The pipeline network anomaly prediction model is:

[0031] , where Indicates that the pipe network is abnormal. It means the pipe network is operating normally; G is the predicted probability of the probability prediction model; It is the pressure abnormality index of Fengshui pipe network; It is an indicator of corrosion of Feng Shui pipes; are the coefficients of the pipeline network anomaly prediction model.

[0032] The pipeline network abnormality prediction model proposed in this scheme is based on the principle of Logistic regression model to establish an abnormal risk prediction model. Logistic regression model is a generalized linear regression analysis model, which is often used in data mining, result prediction and other fields; The pipeline abnormality prediction model proposed in this plan predicts the abnormal operating indicators of the Feng Shui pipeline in abnormal operating status and calculates the probability of abnormality in the Feng Shui pipeline. Based on the predicted probability of abnormality in the Feng Shui pipeline, it determines whether there is a risk of abnormality in the Feng Shui pipeline, and can promptly and quickly check for abnormal hidden dangers in the Feng Shui pipeline.

[0033] The calculation formula of the pressure abnormality index is:

[0034] In the formula, is the offset of the real-time pressure data relative to the standard data, t is the Feng Shui pipe The running time in the state.

[0035] During the operation of the Feng Shui network, when abnormal operating data occurs, the greater the difference from the standard abnormal data and the longer the deviation operation time, the greater the risk of abnormality. Therefore, this scheme accumulates the product of the offset of all abnormal operating data and the abnormal operation time that occurs during the operation of the Feng Shui network as the abnormal operation indicator of the Feng Shui network.

[0036] The calculation method is: Classify the historical operation data according to whether the Feng Shui pipe network has abnormalities, and obtain several groups of historical operation data of Feng Shui pipe networks that eventually have abnormalities and several groups of historical operation data of Feng Shui pipe networks that eventually have no abnormalities; Based on several groups of historical operation data of Feng Shui pipe networks that eventually had abnormalities and several groups of historical operation data of Feng Shui pipe networks that eventually did not have abnormalities, the maximum likelihood method was used to Calculation of test The significance of the parameters of the pipeline network anomaly prediction model is judged Whether the significance requirement is met.

[0037] Based on the feedforward neural network model, learning the monitoring data specifically includes the following steps: Based on feedforward neural network and machine learning, the pressure data of multiple Feng Shui pipe networks and the status data of Feng Shui pipes are labeled and divided to obtain training sets, validation sets and test sets; The training set, validation set and test set are used to conduct supervised training, validation and testing of the pipe network anomaly prediction model to determine whether the Feng Shui pipe network has reached the optimal operating state.

[0038] The formula for calculating the similarity between the monitoring data and the data of the Fengshui pipe network in the optimal operating state is:

[0039] In the formula, is the similarity between the real-time monitoring data and the i-th feature of the data under the historical best operating state, is the jth characteristic index value of the ith characteristic of the Feng Shui network real-time data, is the jth feature index value of the i-th feature of the data under the historical best operating state, is the total number of characteristic index values ​​of the ith feature.

[0040] The formula for calculating the matching rate is:

[0041] In the formula, C is the adaptability between the characteristic parameters of real-time monitoring data and the data under the historical best operating state, is the weight value of the i-th feature parameter, and m is the total number of feature parameters.

[0042] Based on the similarity and matching rate, the optimization of pipeline pressure includes the following steps: Compare the similarity and matching rate with the preset threshold; If the similarity and matching rate are greater than the preset threshold, no optimization is required; If the similarity and the matching rate are less than the preset threshold, the pressure of the pipeline is optimized by controlling the solenoid valves on the pipeline respectively.

[0043] To sum up, the operation data of the Feng Shui pipe network is closely related to the pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipeline. The pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipeline are retrieved from the historical database, and the data are deduplicated, missing values ​​are filled and outliers are processed, so as to obtain complete data, that is, data with processing significance and reference value. The present invention can also establish an abnormal model based on the pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipeline, and substitute the real-time monitoring data of the Feng Shui pipe network for calculation to obtain the probability of abnormality in the Feng Shui pipe network. Using machine learning and feedforward neural network models, historical parameter data can be trained, verified and tested to determine whether the Feng Shui pipe network has reached the optimal operating state. If the real-time monitoring data of the Feng Shui pipe network and the parameter data under the optimal operating state have a high similarity and matching rate, the Feng Shui pipe network will inevitably not have abnormalities during operation. If the abnormality predicted by the abnormal model is large, the pressure of the Feng Shui pipe network is optimized until the Feng Shui pipe network reaches the optimal operating state.

[0044] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A mine fengshui pipe network safety monitoring and remote dispatching system, characterized in that: include: An acquisition module, the acquisition module is used to acquire monitoring data of several monitoring substations in the historical underground ring network, the monitoring data includes pressure data of the Feng Shui pipe network and status data of the Feng Shui pipe, and the status data of the Feng Shui pipe includes the corrosion degree of the Feng Shui pipe; A data processing module, wherein the data processing module is used to preprocess the monitoring data, wherein the preprocessing includes data deduplication, missing value filling and outlier processing, and obtain the preprocessed monitoring data; A model building module, the model building module is used to build a pipe network abnormality prediction model based on the pressure data of the Feng Shui pipe network and the status data of the Feng Shui pipes; An optimal state judgment module, which is used to learn the monitoring data based on a feedforward neural network model to determine whether the Feng Shui pipe network has reached an optimal operating state; A real-time monitoring module, which is used to obtain monitoring data in real time and perform similarity calculation and matching rate calculation between the monitoring data and the data of the Feng Shui pipe network in the optimal operating state; A scheduling module optimizes pipeline pressure based on similarity and matching rate.

2. A mine feng shui pipe network safety monitoring and remote dispatching system according to claim 1, characterized in that: The data deduplication comprises the following steps: Arrange all data coordinates (x, y) into one column; Retrieve the duplication of the first coordinate and subsequent coordinates, and if there is duplication, delete the subsequent coordinates; After the search is completed, the second coordinate and the subsequent coordinates are searched for duplication. If there is duplication, the subsequent coordinates are deleted; Repeat the above steps until all data coordinates have been detected; The missing value filling comprises the following steps: Arrange all data coordinates (x, y) into one column; Retrieve all data coordinates (x, y). If x or y is missing, fill in the data at that position. The data collected is the mean of the adjacent data at the same position.

3. A mine feng shui pipe network safety monitoring and remote dispatching system according to claim 2, characterized in that: The outlier processing comprises the following steps: Arrange all data coordinates (x, y) into one column; Calculate the deviation value k between the data coordinate (x, y) and the adjacent data coordinates (a, b) and (c, d). When the deviation value k is greater than 100%, the data coordinate (x, y) is an outlier, and the data coordinate (x, y) is replaced by the mean of the adjacent data. The calculation formula of the deviation value is: 。 4. A mine fengshui pipe network safety monitoring and remote dispatching system according to claim 3, characterized in that: The pipeline network anomaly prediction model is: ， In the formula, Indicates that the pipe network is abnormal. It means the pipe network is operating normally; G is the predicted probability of the probability prediction model; It is the pressure abnormality index of Fengshui pipe network; It is an indicator of corrosion of Feng Shui pipes; are the coefficients of the pipeline network anomaly prediction model.

5. A mine fengshui pipe network safety monitoring and remote dispatching system according to claim 4, characterized in that: The calculation formula of the pressure abnormality index is: In the formula, is the offset of the real-time pressure data relative to the standard data, t is the Feng Shui pipe The running time in the state.

6. A mine fengshui pipe network safety monitoring and remote dispatching system according to claim 5, characterized in that: Said The calculation method is: Classify the historical operation data according to whether the Feng Shui pipe network has abnormalities, and obtain several groups of historical operation data of Feng Shui pipe networks that eventually have abnormalities and several groups of historical operation data of Feng Shui pipe networks that eventually have no abnormalities; Based on several groups of historical operation data of Feng Shui pipe networks that eventually had abnormalities and several groups of historical operation data of Feng Shui pipe networks that eventually did not have abnormalities, the maximum likelihood method was used to Calculation of test The significance of the parameters of the pipeline network anomaly prediction model is judged Whether the significance requirement is met.

7. A mine fengshui pipe network safety monitoring and remote dispatching system according to claim 6, characterized in that: The learning of monitoring data based on the feedforward neural network model specifically includes the following steps: Based on feedforward neural network and machine learning, the pressure data of multiple Feng Shui pipe networks and the status data of Feng Shui pipes are labeled and divided to obtain training sets, validation sets and test sets; The training set, validation set and test set are used to perform supervised training, validation and testing on the pipe network anomaly prediction model to determine whether the Feng Shui pipe network has reached the optimal operating state.

8. A mine fengshui pipe network safety monitoring and remote dispatching system according to claim 7, characterized in that: The formula for calculating the similarity between the monitoring data and the data of the Feng Shui pipe network in the optimal operating state is: In the formula, is the similarity between the real-time monitoring data and the i-th feature of the data under the historical best operating state, is the jth characteristic index value of the ith characteristic of the Feng Shui network real-time data, is the jth feature index value of the i-th feature of the data under the historical best operating state, is the total number of characteristic index values ​​of the ith feature.

9. A mine fengshui pipe network safety monitoring and remote dispatching system according to claim 8, characterized in that: The calculation formula of the matching rate is: In the formula, C is the adaptability between the characteristic parameters of real-time monitoring data and the data under the historical best operating state, is the weight value of the i-th feature parameter, and m is the total number of feature parameters.

10. A mine fengshui pipe network safety monitoring and remote dispatching system according to claim 9, characterized in that: The optimization of pipeline pressure based on similarity and matching rate specifically includes the following steps: Compare the similarity and matching rate with the preset threshold; If the similarity and matching rate are greater than the preset threshold, no optimization is required; If the similarity and the matching rate are less than the preset threshold, the pressure of the pipeline is optimized by controlling the solenoid valves on the pipeline respectively.

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