Electric valve control method and system applied to fluid leakage sensing

Abstract

The invention discloses an electric valve control method and system applied to fluid leakage sensing, and the method comprises the steps: constructing a pipeline network diagram of a pipeline system in a target region, setting a sensor arrangement scheme, and combining a corresponding sensor region with the pipeline network diagram, thereby obtaining a final pipeline network diagram; the method comprises the following steps: acquiring internal monitoring information of a pipeline, performing data preprocessing, generating a monitoring data sequence, and performing data supplementation on the monitoring data sequence by adopting a spline function interpolation method to obtain an interpolated monitoring data sequence; an anomaly detection model is constructed, whether conveying pipeline anomaly exists in the current conveying task or not is detected through the monitoring data sequence obtained after interpolation, the degree of the abnormal condition is predicted, and abnormal condition prediction information is obtained; and a risk assessment rule is preset, whether conveying route adjustment needs to be carried out or not is judged through the risk assessment rule, and if conveying route adjustment needs to be carried out, an electric valve control scheme is formulated for route change. Therefore, the operation efficiency and safety of the whole pipeline network are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric valve control, and in particular to an electric valve control method and system for fluid leakage sensing. Background Art

[0002] As a key component of pipeline control, the control accuracy and response speed of electric valves are crucial to the perception and emergency response of fluid leakage. By accurately controlling the on-off state of electric valves, the affected area can be quickly isolated in the early stage of fluid leakage, thereby reducing the impact of leakage on the entire system. To achieve this goal, the control method of electric valves needs to have a highly sensitive leak detection mechanism and fast and accurate response capabilities. In modern intelligent pipeline systems, electric valves not only need to cooperate with traditional monitoring equipment, but also need to be combined with advanced data analysis technology and control algorithms to more effectively improve the automation and intelligence level of the entire system. Summary of the invention

[0003] The present invention overcomes the defects of the prior art and provides an electric valve control method and system for fluid leakage perception, an important purpose of which is to optimize the operating efficiency and safety of the entire pipeline network.

[0004] To achieve the above-mentioned purpose, the first aspect of the present invention provides an electric valve control method for fluid leakage sensing, comprising:

[0005] Acquire pipeline construction information, construct a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information, set a sensor layout plan and merge the corresponding sensor area with the pipeline network diagram to obtain a final pipeline network diagram;

[0006] The internal monitoring information of the pipeline during the current transportation task is obtained through the arranged sensor array, and the data is preprocessed to generate a monitoring data sequence. The monitoring data sequence is supplemented by the spline function interpolation method to obtain the interpolated monitoring data sequence;

[0007] Constructing an anomaly detection model, using the interpolated monitoring data sequence to detect whether there is an anomaly in the current transportation task, and predicting the degree of the abnormal condition to obtain abnormal condition prediction information;

[0008] Preset risk assessment rules, obtain abnormal condition prediction information and use risk assessment rules to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, formulate an electric valve control plan to change the route.

[0009] In this solution, the step of obtaining pipeline construction information and constructing a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information specifically includes:

[0010] Acquire pipeline construction information of the target area based on data retrieval, wherein the pipeline construction information includes pipeline structure information, pipeline distribution location information, pipeline attribute information and electric valve layout information;

[0011] Extracting pipeline structure information and pipeline distribution location information according to the pipeline construction information, performing pipeline node identification, defining the starting point, end point and bifurcation point of the pipeline as nodes, and defining connection relationships between nodes to obtain first analysis information;

[0012] Combining the first analysis information and the pipeline construction information, graph structure modeling is performed on the pipeline system of the target area to obtain an initial pipeline network graph;

[0013] The electric valve layout information is used to analyze the position, function and type of the electric valves in the pipelines in the target area, the layout position of the electric valve is used as a node and the valve function characteristics and type characteristics are associated, the nodes of the initial pipeline network diagram are updated, and an updated pipeline network diagram is obtained.

[0014] In this solution, the sensor layout scheme is set and the corresponding sensor area is merged with the pipeline network diagram to obtain the final pipeline network diagram, which specifically includes:

[0015] Acquire sensor setting requirement information, perform big data retrieval based on the sensor setting requirement information to acquire sensor types and corresponding functional characteristics that meet the setting requirements, and obtain first retrieval result information;

[0016] Obtaining an initial pipeline network diagram, introducing a genetic algorithm and presetting an objective function and constraint conditions, formulating a sensor layout plan in combination with the first search result information, randomly generating an initial population, and calculating the fitness of individuals in the initial population;

[0017] Preset the stopping criteria, select the best individual and the global optimal solution according to the calculated individual fitness, perform crossover and mutation operations, iterate and optimize until the stopping criteria are met, and then output the candidate layout plan;

[0018] Generate a sensor network corresponding to each candidate deployment scheme, obtain the deployment position of the sensor, including the acquisition sensor position and the monitoring sensor position, and calculate the Manhattan distance value between each monitoring sensor and the corresponding acquisition sensor;

[0019] Based on the calculated Manhattan distance value, the data collection energy consumption value in each candidate layout scheme is analyzed, and each candidate layout scheme is sorted as an evaluation index. According to the sorting result, the candidate layout scheme with the minimum energy consumption is selected as the final scheme to obtain the final layout scheme;

[0020] An updated pipeline network diagram is obtained, a sensor array is arranged according to the final arrangement scheme and a sensor network diagram is generated, and the sensor network diagram is associated with the updated pipeline network diagram to obtain a final pipeline network diagram.

[0021] In this solution, the internal monitoring information of the pipeline during the current transportation task is obtained by the arranged sensor array, data preprocessing is performed and a monitoring data sequence is generated, and the monitoring data sequence is supplemented by the spline function interpolation method to obtain the interpolated monitoring data sequence, which specifically includes:

[0022] Based on the arranged sensor array, the pipeline in the target area is monitored to obtain the internal monitoring information of the pipeline when the current transportation task is performed, and the internal monitoring information of the pipeline is preprocessed;

[0023] Calculate the data standard deviation of the internal monitoring information of the pipeline, compare the calculated data standard deviation with a preset standard deviation range, define the data corresponding to the data standard deviation that is not within the preset standard deviation range as an outlier, remove the outlier and compensate for the difference, and obtain preprocessing information;

[0024] Obtaining the pipeline route of the current delivery task, extracting sensor information on the current pipeline route using the final pipeline network diagram, and defining the sequence of each sensor based on the delivery flow direction of the pipeline to obtain second analysis information;

[0025] Sorting the monitoring data in the preprocessing information according to the second analysis information to form a monitoring data sequence based on the sensor sequence, and introducing a spline function interpolation method to supplement the data;

[0026] The interpolation interval is preset, and the interpolation interval is generated based on the constructed monitoring data sequence and a spline equation is established. The spline coefficients of each interpolation interval are solved and interpolation is performed through the corresponding spline equation to obtain the interpolated monitoring data sequence.

[0027] In this solution, the abnormality detection model is constructed, and the interpolated monitoring data sequence is used to detect whether there is an abnormality in the current transportation task, and the degree of abnormality is predicted to obtain abnormality prediction information, which specifically includes:

[0028] Building an anomaly detection model based on a generative adversarial network, obtaining historical delivery instances through historical data retrieval and building a monitoring data sequence during historical normal delivery, and training the anomaly detection model;

[0029] Obtaining a monitoring data sequence after interpolation, inputting it into the anomaly detection model for detection, taking the input monitoring data sequence after interpolation as the original sequence, obtaining a generated sequence through a generator, and performing reconstruction error calculation with the original sequence;

[0030] The calculated reconstruction error is compared with the preset threshold. If it is greater than the preset threshold, it means that there is an abnormal condition in the current transportation task, and the corresponding abnormal monitoring data is output to obtain abnormal detection information;

[0031] Extract historical abnormal transport instances based on the acquired historical transport instances, extract historical transport routes of each historical abnormal transport instance, calculate the Euclidean distance between each historical transport video, perform classification based on the calculated Euclidean distance, and obtain first classification information;

[0032] Introducing a Markov algorithm to construct a state transfer matrix of monitoring data in each historical abnormal transport instance category using the first category division information, and associating the state transfer matrix with the corresponding historical abnormal transport instance category to obtain first association information;

[0033] The transportation route of the current transportation task is obtained, the corresponding state transfer matrix is ​​obtained using the first association information, and the degree of change of the abnormal condition in the future time period is predicted in combination with the abnormal monitoring information to obtain abnormal condition prediction information.

[0034] In this solution, the risk assessment rules are used to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, an electric valve control plan is formulated to change the route, specifically including:

[0035] Acquire abnormal condition prediction information, perform abnormal area analysis based on the abnormal condition prediction information, determine the pipeline area where the transmission abnormality occurs, and obtain abnormal area analysis information;

[0036] Obtain the final pipeline network diagram, use the abnormal area analysis information to mark the abnormal pipeline, extract the transportation routes of other real-time pipeline transportation tasks, analyze the remaining enabled pipelines, and obtain the enabled pipeline information;

[0037] Generate an enabled pipeline map according to the enabled pipeline information, define the changed starting point and the changed end point of the transmission pipeline based on the abnormal area analysis information, set the transmission route distance as a restriction condition, and use the ant colony algorithm to perform route optimization;

[0038] The initial pheromone of the ant colony is preset, the position of the electric valve is defined as the route change node, the state transition probability of each node is set, the optimized route is obtained according to the state transition probability, the optimized route is penalized by the set restriction conditions, and iterative optimization is performed to obtain the candidate optimized route information;

[0039] Calculate the route distance of each route according to the candidate optimization route information, sort the candidate routes, select the one with the shortest distance as the final optimization route based on the sorting result, extract the corresponding route change node according to the final optimization route, and generate the electric valve control plan;

[0040] The electric valves in the pipeline system are controlled according to the generated electric valve control scheme to change the delivery route of the current delivery task.

[0041] A second aspect of the present invention provides an electric valve control system for fluid leakage perception, the system comprising: a memory, a processor, the memory containing an electric valve control method program for fluid leakage perception, the electric valve control method program for fluid leakage perception being executed by the processor to implement the following steps:

[0042] Acquire pipeline construction information, construct a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information, set a sensor layout plan and merge the corresponding sensor area with the pipeline network diagram to obtain a final pipeline network diagram;

[0043] The internal monitoring information of the pipeline during the current transportation task is obtained through the arranged sensor array, and the data is preprocessed to generate a monitoring data sequence. The monitoring data sequence is supplemented by the spline function interpolation method to obtain the interpolated monitoring data sequence;

[0044] Constructing an anomaly detection model, using the interpolated monitoring data sequence to detect whether there is an anomaly in the current transportation task, and predicting the degree of the abnormal condition to obtain abnormal condition prediction information;

[0045] Preset risk assessment rules, obtain abnormal condition prediction information and use risk assessment rules to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, formulate an electric valve control plan to change the route.

[0046] The present invention discloses an electric valve control method and system for fluid leakage perception, including: constructing a pipeline network diagram of a pipeline system in a target area, setting a sensor layout scheme and merging the corresponding sensor area with the pipeline network diagram to obtain a final pipeline network diagram; obtaining internal pipeline monitoring information, performing data preprocessing and generating a monitoring data sequence, using a spline function interpolation method to supplement the monitoring data sequence to obtain an interpolated monitoring data sequence; constructing an anomaly detection model, using the interpolated monitoring data sequence to detect whether there is an anomaly in the current transportation task, and predicting the degree of the abnormal condition to obtain abnormal condition prediction information; presetting a risk assessment rule, using the risk assessment rule to determine whether the transportation route needs to be adjusted, and if the transportation route needs to be adjusted, formulating an electric valve control scheme to change the route. This optimizes the operating efficiency and safety of the entire pipeline network. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments or exemplary embodiments of the present invention, the drawings required for use in the embodiments or exemplary descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained according to the drawings without paying creative work.

[0048] Figure 1 A flow chart of an electric valve control method for fluid leakage sensing provided by one embodiment of the present invention;

[0049] Figure 2 A flow chart of an electric valve control method provided by an embodiment of the present invention;

[0050] Figure 3 A block diagram of an electric valve control system for fluid leakage sensing provided by an embodiment of the present invention;

[0051] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0054] Figure 1 A flow chart of an electric valve control method for fluid leakage sensing provided by one embodiment of the present invention;

[0055] like Figure 1 As shown, the present invention provides a flow chart of an electric valve control method for fluid leakage sensing, including:

[0056] S102, obtaining pipeline construction information, constructing a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information, setting a sensor layout plan and merging the corresponding sensor area with the pipeline network diagram to obtain a final pipeline network diagram;

[0057] S104, obtaining internal monitoring information of the pipeline during the current transportation task through the arranged sensor array, performing data preprocessing and generating a monitoring data sequence, and supplementing the monitoring data sequence with data using a spline function interpolation method to obtain an interpolated monitoring data sequence;

[0058] S106, constructing an abnormality detection model, using the interpolated monitoring data sequence to detect whether there is an abnormality in the transportation pipeline in the current transportation task, and predicting the degree of the abnormality to obtain abnormality prediction information;

[0059] S108, preset risk assessment rules, obtain abnormal condition prediction information and use risk assessment rules to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, formulate an electric valve control plan to change the route.

[0060] It should be noted that the present invention provides an electric valve control method and system for fluid leakage perception. First, obtaining the construction information of the pipeline is the basis for pipeline monitoring and management. This information includes the geographical location, size, material, layout and interface conditions of each node of the pipeline. Based on these pipeline construction information, a pipeline network diagram of the pipeline system can be established in the target area. This graphical network structure can help to better understand the overall topology of the pipeline system. According to the network diagram and the operation requirements of the pipeline, the sensor layout plan is set, and the area covered by the sensor is merged with the pipeline network diagram to finally form a comprehensive pipeline network diagram. The internal monitoring information of the pipeline during the current transportation task is obtained through the arranged sensor array, and the collected monitoring data is preprocessed, noise is removed and standardized to ensure the consistency and accuracy of the data. Then, the monitoring data sequence is supplemented by the spline function interpolation method to enrich the pipeline monitoring data without the arrangement of sensors. The interpolated monitoring data sequence provides a reliable basis for subsequent anomaly detection. Subsequently, an anomaly detection model is constructed, and the interpolated monitoring data sequence is used to monitor the operation status of the pipeline system in real time, and detect whether there is an abnormality in the pipeline. By analyzing these monitoring data, the model can determine whether there are leaks, blockages or other faults in the pipeline, and predict the extent of these anomalies. The abnormal condition prediction information can be used to determine whether the current abnormal condition has reached the level that requires intervention. When the abnormal condition reaches the set risk threshold, the decision-making process for adjusting the delivery route is initiated. If the delivery route does need to be adjusted, the electric valve control solution will be activated to accurately regulate the valves in the pipeline network, thereby changing the path of fluid delivery and avoiding leakage or blockage that causes greater damage to the entire pipeline system. In this way, timely response measures can be taken before potential risks occur to ensure the smooth progress of the delivery task.

[0061] Further, in a preferred embodiment of the present invention, the step of obtaining pipeline construction information and constructing a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information specifically includes:

[0062] Acquire pipeline construction information of the target area based on data retrieval, wherein the pipeline construction information includes pipeline structure information, pipeline distribution location information, pipeline attribute information and electric valve layout information;

[0063] Extracting pipeline structure information and pipeline distribution location information according to the pipeline construction information, performing pipeline node identification, defining the starting point, end point and bifurcation point of the pipeline as nodes, and defining connection relationships between nodes to obtain first analysis information;

[0064] Combining the first analysis information and the pipeline construction information, graph structure modeling is performed on the pipeline system of the target area to obtain an initial pipeline network graph;

[0065] The electric valve layout information is used to analyze the position, function and type of the electric valves in the pipelines in the target area, the layout position of the electric valve is used as a node and the valve function characteristics and type characteristics are associated, the nodes of the initial pipeline network diagram are updated, and an updated pipeline network diagram is obtained.

[0066] It should be noted that, first, the pipeline construction information in the target area is obtained by data retrieval means, and the pipeline construction information contains multiple dimensions of content, including pipeline structure information, pipeline distribution location information, pipeline attribute information and electric valve layout information. The pipeline structure information and pipeline distribution location information are extracted according to the obtained pipeline construction information. Pipeline nodes are identified by analyzing these data. Nodes usually represent key positions of pipelines in pipeline systems, such as starting points, end points, and bifurcation points of pipelines. Therefore, the starting point, end point and bifurcation point of the pipeline can be defined as nodes, and the connection relationship between nodes needs to be determined according to the flow direction and connection method of the pipeline. In this process, the connection relationship between nodes is an important part of the pipeline network structure, which lays the foundation for subsequent pipeline network analysis and optimization, and obtains the first analysis information. Combined with the first analysis information and other relevant data of pipeline construction, the pipeline system in the target area is modeled by graph structure to form an initial pipeline network diagram. In graph structure modeling, the nodes in the pipeline network diagram represent the key positions of the pipeline, and the edges represent the connection relationship of the pipeline. This initial network diagram is the core data basis for pipeline monitoring and management, and can provide important support for subsequent intelligent analysis and decision-making. Furthermore, the electric valve layout information is used to analyze the location, function, and type of electric valves in the pipelines in the target area. The role of electric valves in pipeline systems is usually to regulate fluid flow, isolate fault areas, and respond to system emergencies. The layout of electric valves is regarded as a node, and the functional characteristics and type characteristics of the valves are associated, so that the information of electric valves can be integrated into the pipeline network diagram. The nodes of the pipeline network diagram are updated so that they not only represent the physical connection points of the pipelines, but also reflect the role of electric valves in the system.

[0067] Further, in a preferred embodiment of the present invention, the setting of the sensor arrangement scheme and merging the corresponding sensor area with the pipeline network diagram to obtain the final pipeline network diagram specifically includes:

[0068] Acquire sensor setting requirement information, perform big data retrieval based on the sensor setting requirement information to acquire sensor types and corresponding functional characteristics that meet the setting requirements, and obtain first retrieval result information;

[0069] Obtaining an initial pipeline network diagram, introducing a genetic algorithm and presetting an objective function and constraint conditions, formulating a sensor layout plan in combination with the first search result information, randomly generating an initial population, and calculating the fitness of individuals in the initial population;

[0070] Preset the stopping criteria, select the best individual and the global optimal solution according to the calculated individual fitness, perform crossover and mutation operations, iterate and optimize until the stopping criteria are met, and then output the candidate layout plan;

[0071] Generate a sensor network corresponding to each candidate deployment scheme, obtain the deployment position of the sensor, including the acquisition sensor position and the monitoring sensor position, and calculate the Manhattan distance value between each monitoring sensor and the corresponding acquisition sensor;

[0072] Based on the calculated Manhattan distance value, the data collection energy consumption value in each candidate layout scheme is analyzed, and each candidate layout scheme is sorted as an evaluation index. According to the sorting result, the candidate layout scheme with the minimum energy consumption is selected as the final scheme to obtain the final layout scheme;

[0073] An updated pipeline network diagram is obtained, a sensor array is arranged according to the final arrangement scheme and a sensor network diagram is generated, and the sensor network diagram is associated with the updated pipeline network diagram to obtain a final pipeline network diagram.

[0074] It should be noted that in the design and optimization process of the pipeline monitoring system, the setting requirements of the sensor is the first step to ensure effective monitoring and data collection of the system. The sensor setting requirement information usually includes requirements such as monitoring range, accuracy, response speed, etc. These requirements will directly affect the selection of sensor types and the formulation of layout plans. Based on these setting requirements, the sensor types and their corresponding functional characteristics that meet the requirements can be obtained through big data retrieval, thereby obtaining the first retrieval result information. After obtaining the initial pipeline network diagram, combined with the first retrieval result information, the genetic algorithm can be used to formulate the sensor layout plan. The genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms, and optimizes the layout of sensors by presetting objective functions and constraints. In this process, the initial population is first randomly generated, each individual represents a sensor layout plan, and the fitness of these individuals is calculated. The fitness measures the effect of the plan in meeting the requirements and constraints. In this way, multiple possible layout plans can be generated and preliminarily evaluated. According to the obtained candidate layout plan, the next step is to generate the corresponding sensor network. In this process, it is necessary to obtain the sensor layout position under each plan, including the location of the acquisition sensor and the location of the monitoring sensor. At the same time, it is necessary to calculate the Manhattan distance value between each monitoring sensor and the corresponding acquisition sensor. Manhattan distance reflects the shortest path between two points in a grid. Based on the data acquisition energy consumption value calculated based on the Manhattan distance value, energy consumption analysis is further performed on each candidate layout scheme. Since energy consumption is an important indicator for evaluating sensor layout schemes, the energy consumption values ​​of each candidate scheme can be sorted, and the scheme with the lowest energy consumption can be selected as the final layout scheme. Finally, based on the final selected layout scheme, the updated pipeline network diagram is obtained, and the sensor array is arranged according to the scheme. After the sensor layout is completed, a new sensor network diagram will be generated and associated with the updated pipeline network diagram to finally obtain the optimized pipeline network diagram. This diagram not only shows the overall structure of the pipeline, but also integrates the layout of the sensors, providing complete digital support for subsequent pipeline monitoring and intelligent decision-making.

[0075] Furthermore, in a preferred embodiment of the present invention, the sensor array is arranged to obtain the internal monitoring information of the pipeline during the current transportation task, perform data preprocessing and generate a monitoring data sequence, and use the spline function interpolation method to supplement the monitoring data sequence to obtain the interpolated monitoring data sequence, which specifically includes:

[0076] Based on the arranged sensor array, the pipeline in the target area is monitored to obtain the internal monitoring information of the pipeline when the current transportation task is performed, and the internal monitoring information of the pipeline is preprocessed;

[0077] Calculate the data standard deviation of the internal monitoring information of the pipeline, compare the calculated data standard deviation with a preset standard deviation range, define the data corresponding to the data standard deviation that is not within the preset standard deviation range as an outlier, and perform outlier elimination and difference compensation to obtain preprocessing information;

[0078] Obtaining the pipeline route of the current delivery task, extracting sensor information on the current pipeline route using the final pipeline network diagram, and defining the sequence of each sensor based on the delivery flow direction of the pipeline to obtain second analysis information;

[0079] Sorting the monitoring data in the preprocessing information according to the second analysis information to form a monitoring data sequence based on the sensor sequence, and introducing a spline function interpolation method to supplement the data;

[0080] The interpolation interval is preset, the interpolation interval is generated based on the constructed monitoring data sequence and the spline equation is established, the spline coefficient of each interpolation interval is solved and interpolation is performed through the corresponding spline equation to obtain the interpolated monitoring data sequence.

[0081] It should be noted that, first, the sensor array monitors various parameters inside the pipeline in real time and collects the status information inside the pipeline in the current transportation task. This information usually includes data such as pressure, flow, and temperature. Since the collected data may be affected by factors such as noise and transmission delay, it is crucial to preprocess the data. Next, the pipeline route of the current transportation task is obtained, and the sensor information on the route is extracted using the final pipeline network diagram. Each sensor is located at a specific position in the pipeline network, so it is necessary to define the sequence of each sensor on the path according to the pipeline transportation flow direction, and obtain the second analysis information, that is, the specific arrangement order and monitoring order of the sensors in the transportation pipeline. According to the second analysis information, the preprocessed monitoring data can be sorted to form a monitoring data sequence based on the sensor sequence. This sequence helps to accurately reflect the operating status of the pipeline system and provides a basis for subsequent interpolation compensation. In order to ensure the smoothness and continuity of the data, the spline function interpolation method is introduced for data supplementation. The spline function is a mathematical method that approximates complex data relationships by constructing piecewise polynomials. It can effectively fill in missing values ​​or irregular fluctuations in the data. In the interpolation process, the interpolation interval needs to be preset first, and the interpolation interval is generated based on the constructed monitoring data sequence. Each interpolation interval corresponds to a section of data that needs to be supplemented. Next, a spline equation is established, and the interpolation result is accurately determined by solving the spline coefficients of each interpolation interval. Finally, these spline equations are used to interpolate the missing or abnormal data to generate the interpolated monitoring data sequence. This further enriches the monitoring data and improves the perception of the inside of the pipeline.

[0082] Furthermore, in a preferred embodiment of the present invention, the construction of the abnormality detection model uses the interpolated monitoring data sequence to detect whether there is an abnormality in the current transportation task, and predicts the degree of the abnormal condition to obtain abnormal condition prediction information, which specifically includes:

[0083] Building an anomaly detection model based on a generative adversarial network, obtaining historical delivery instances through historical data retrieval and building a monitoring data sequence during historical normal delivery, and training the anomaly detection model;

[0084] Obtaining a monitoring data sequence after interpolation, inputting it into the anomaly detection model for detection, taking the input monitoring data sequence after interpolation as the original sequence, obtaining a generated sequence through a generator, and performing reconstruction error calculation with the original sequence;

[0085] The calculated reconstruction error is compared with the preset threshold. If it is greater than the preset threshold, it means that there is an abnormal condition in the current transportation task, and the corresponding abnormal monitoring data is output to obtain abnormal detection information;

[0086] Extract historical abnormal transport instances based on the acquired historical transport instances, extract historical transport routes of each historical abnormal transport instance, calculate the Euclidean distance between each historical transport video, perform classification based on the calculated Euclidean distance, and obtain first classification information;

[0087] Introducing a Markov algorithm to construct a state transfer matrix of monitoring data in each historical abnormal transport instance category using the first category division information, and associating the state transfer matrix with the corresponding historical abnormal transport instance category to obtain first association information;

[0088] The transportation route of the current transportation task is obtained, the corresponding state transfer matrix is ​​obtained using the first association information, and the degree of change of the abnormal condition in the future time period is predicted in combination with the abnormal monitoring information to obtain abnormal condition prediction information.

[0089] It should be noted that, first, historical transport instances are obtained through historical data retrieval, and monitoring data sequences of historical normal transport are extracted from them. These normal data sequences will be used to train the anomaly detection model to help the model learn the data features and patterns in the normal transportation process. Next, the monitoring data sequence after interpolation processing is obtained and input into the trained anomaly detection model for detection. In the model, the monitoring data sequence after interpolation is used as the original sequence input, and the corresponding generated sequence is generated by the generator. The goal of the generator is to generate a sequence as similar as possible to the input monitoring data, and then calculate the reconstruction error between the generated sequence and the original sequence. The reconstruction error reflects the difference between the input data and the generated data. If the reconstruction error is large, it means that the input sequence may contain anomalies, and the corresponding abnormal monitoring data will be output, and anomaly detection information will be generated and provided to the subsequent analysis and decision-making process. By retrieving the abnormal data in the historical transport instance, the relevant information of the historical abnormal transport instance can be extracted, especially the route of the historical abnormal transport. The Euclidean distance between each historical instance is calculated, and the Euclidean distance reflects the similarity between different abnormal transport instances. According to the calculated Euclidean distance, the historical abnormal transport instance will be classified to obtain the first classification information. Furthermore, combined with the first category classification information, the Markov algorithm is introduced to construct the state transition matrix of the monitoring data in each historical abnormal transportation instance category. The Markov algorithm helps the system predict abnormal state changes that may occur in the future by analyzing the state transition rules in the historical data. The state transition matrix is ​​associated with the corresponding historical abnormal transportation instance category to obtain the first association information. Through this association, the corresponding state transition matrix can be quickly obtained according to the transportation route of the current transportation task, and the degree of abnormal condition changes that may occur in the future time period can be predicted in combination with the abnormal monitoring information. This provides accurate abnormal condition prediction information, provides decision support for the pipeline monitoring system, and helps managers to adjust the transportation route or take other countermeasures in a timely manner to reduce potential risks.

[0090] Further, in a preferred embodiment of the present invention, the risk assessment rule is used to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, an electric valve control scheme is formulated to change the route, specifically including:

[0091] Acquire abnormal condition prediction information, perform abnormal area analysis based on the abnormal condition prediction information, determine the pipeline area where the transmission abnormality occurs, and obtain abnormal area analysis information;

[0092] Obtain the final pipeline network diagram, use the abnormal area analysis information to mark the abnormal pipeline, extract the transportation routes of other real-time pipeline transportation tasks, analyze the remaining enabled pipelines, and obtain the enabled pipeline information;

[0093] Generate an enabled pipeline map according to the enabled pipeline information, define the changed starting point and the changed end point of the transmission pipeline based on the abnormal area analysis information, set the transmission route distance as a restriction condition, and use the ant colony algorithm to perform route optimization;

[0094] The initial pheromone of the ant colony is preset, the position of the electric valve is defined as the route change node, the state transition probability of each node is set, the optimized route is obtained according to the state transition probability, the optimized route is penalized by the set restriction conditions, and iterative optimization is performed to obtain the candidate optimized route information;

[0095] Calculate the route distance of each route according to the candidate optimization route information, sort the candidate routes, select the one with the shortest distance as the final optimization route based on the sorting result, extract the corresponding route change node according to the final optimization route, and generate the electric valve control plan;

[0096] The electric valves in the pipeline system are controlled according to the generated electric valve control scheme to change the delivery route of the current delivery task.

[0097] It should be noted that by obtaining abnormal condition prediction information, the potential risks in the pipeline system are analyzed, and the pipeline areas where abnormal transportation may occur are identified and analyzed. Through abnormal area analysis, it is determined which specific pipeline areas have abnormal conditions. Once the abnormal areas are determined, these abnormal areas are marked using the final pipeline network diagram. By marking the abnormal pipeline, the specific location of the fault or abnormality is clearly identified. At the same time, the transportation routes of other ongoing real-time pipeline transportation tasks are extracted, which pipelines are still available in the current task, and the remaining enabled pipelines are calculated. These enabled pipeline information provides feasible alternative options for subsequent pipeline adjustments and risk response. After clarifying which pipelines can be enabled, an enabled pipeline map is generated based on these enabled pipeline information, and the change start and end points of the transportation pipeline are defined based on the abnormal area analysis information. The ant colony algorithm is introduced to perform route optimization. The ant colony algorithm optimizes the pipeline transportation route by simulating the foraging process of ants to ensure that the selected alternative path can both meet the transportation requirements and minimize the additional operating costs. When using the ant colony algorithm, finally, the corresponding route change nodes are extracted based on the selected final optimized route, and the electric valve control scheme is generated accordingly. According to the generated electric valve control scheme, the electric valve in the pipeline is controlled to change the delivery route of the current delivery task. By adjusting the switch state of the valve, the flow direction of the pipeline will be reset to avoid abnormal areas and ensure the smooth progress of the delivery task. Timely response to abnormal conditions in the pipeline system can also minimize production interruptions caused by abnormal pipelines and improve the operating efficiency and safety of the pipeline system.

[0098] Figure 2A flow chart of an electric valve control method provided by an embodiment of the present invention;

[0099] like Figure 2 As shown, the present invention provides a flow chart of an electric valve control method, comprising:

[0100] S202, monitoring the pipeline in the target area based on the arranged sensor array to obtain internal monitoring information of the pipeline during the current transportation task, performing data preprocessing and data supplementation, and obtaining an interpolated monitoring data sequence;

[0101] S204, inputting the interpolated monitoring data sequence into the anomaly detection model for detection to obtain an anomaly detection result, and obtaining the state transition matrix of the current transportation task to predict the degree of abnormal condition change in the future time period, and obtaining abnormal condition prediction information;

[0102] S206, based on the abnormal condition prediction information, using risk assessment rules to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, an electric valve control plan is formulated to change the route.

[0103] It should be noted that during the transportation task of the pipeline system, there may be problems such as pipeline leakage or blockage, which may affect the transportation task. Sensors arranged in the pipeline network are used for monitoring to obtain internal information of the pipeline in the transportation task, so as to detect whether there are abnormalities. Further based on the abnormality detection results, abnormal conditions are predicted to determine whether the current abnormalities will change, thereby having a significant impact on the transportation task. If there is a significant impact, the current transportation route is changed, and the electric valve is adjusted to change the corresponding delivery flow direction and flow rate indicators, thereby improving the operating efficiency and safety of the pipeline network and ensuring the normal progress of the transportation task.

[0104] Figure 3 An electric valve control system 3 for fluid leakage sensing is provided in one embodiment of the present invention. The system comprises: a memory 31 and a processor 32. The memory 31 contains an electric valve control method program for fluid leakage sensing. When the electric valve control method program for fluid leakage sensing is executed by the processor 32, the following steps are implemented:

[0105] Acquire pipeline construction information, construct a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information, set a sensor layout plan and merge the corresponding sensor area with the pipeline network diagram to obtain a final pipeline network diagram;

[0106] The internal monitoring information of the pipeline during the current transportation task is obtained through the arranged sensor array, and the data is preprocessed to generate a monitoring data sequence. The monitoring data sequence is supplemented by the spline function interpolation method to obtain the interpolated monitoring data sequence;

[0107] Constructing an anomaly detection model, using the interpolated monitoring data sequence to detect whether there is an anomaly in the current transportation task, and predicting the degree of the abnormal condition to obtain abnormal condition prediction information;

[0108] Preset risk assessment rules, obtain abnormal condition prediction information and use risk assessment rules to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, formulate an electric valve control plan to change the route.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0110] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0111] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0112] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0113] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0114] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An electric valve control method for fluid leakage sensing, characterized in that: include: Acquire pipeline construction information, construct a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information, set a sensor layout plan and merge the corresponding sensor area with the pipeline network diagram to obtain a final pipeline network diagram; The internal monitoring information of the pipeline during the current transportation task is obtained through the arranged sensor array, and the data is preprocessed to generate a monitoring data sequence. The monitoring data sequence is supplemented by the spline function interpolation method to obtain the interpolated monitoring data sequence; Constructing an anomaly detection model, using the interpolated monitoring data sequence to detect whether there is an anomaly in the current transportation task, and predicting the degree of the abnormal condition to obtain abnormal condition prediction information; Preset risk assessment rules, obtain abnormal condition prediction information and use risk assessment rules to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, formulate an electric valve control plan to change the route.

2. The electric valve control method for fluid leakage sensing according to claim 1, characterized in that: The step of acquiring pipeline construction information and constructing a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information specifically includes: Acquire pipeline construction information of the target area based on data retrieval, wherein the pipeline construction information includes pipeline structure information, pipeline distribution location information, pipeline attribute information and electric valve layout information; Extracting pipeline structure information and pipeline distribution location information according to the pipeline construction information, performing pipeline node identification, defining the starting point, end point and bifurcation point of the pipeline as nodes, and defining connection relationships between nodes to obtain first analysis information; Combining the first analysis information and the pipeline construction information, graph structure modeling is performed on the pipeline system of the target area to obtain an initial pipeline network graph; The electric valve layout information is used to analyze the position, function and type of the electric valves in the pipelines in the target area, the layout position of the electric valve is used as a node and the valve function characteristics and type characteristics are associated, the nodes of the initial pipeline network diagram are updated, and an updated pipeline network diagram is obtained.

3. The electric valve control method for fluid leakage sensing according to claim 1, characterized in that: The setting of the sensor arrangement scheme and merging the corresponding sensor area with the pipeline network diagram to obtain the final pipeline network diagram specifically includes: Acquire sensor setting requirement information, perform big data retrieval based on the sensor setting requirement information to acquire sensor types and corresponding functional characteristics that meet the setting requirements, and obtain first retrieval result information; Obtaining an initial pipeline network diagram, introducing a genetic algorithm and presetting an objective function and constraint conditions, formulating a sensor layout plan in combination with the first search result information, randomly generating an initial population, and calculating the fitness of individuals in the initial population; Preset the stopping criteria, select the best individual and the global optimal solution according to the calculated individual fitness, perform crossover and mutation operations, iterate and optimize until the stopping criteria are met, and then output the candidate layout plan; Generate a sensor network corresponding to each candidate deployment scheme, obtain the deployment position of the sensor, including the acquisition sensor position and the monitoring sensor position, and calculate the Manhattan distance value between each monitoring sensor and the corresponding acquisition sensor; Based on the calculated Manhattan distance value, the data collection energy consumption value in each candidate layout scheme is analyzed, and each candidate layout scheme is sorted as an evaluation index. According to the sorting result, the candidate layout scheme with the minimum energy consumption is selected as the final scheme to obtain the final layout scheme; An updated pipeline network diagram is obtained, a sensor array is arranged according to the final arrangement scheme and a sensor network diagram is generated, and the sensor network diagram is associated with the updated pipeline network diagram to obtain a final pipeline network diagram.

4. The electric valve control method for fluid leakage sensing according to claim 1, characterized in that: The sensor array is arranged to obtain the internal monitoring information of the pipeline during the current transportation task, perform data preprocessing and generate a monitoring data sequence, and use the spline function interpolation method to supplement the monitoring data sequence to obtain the interpolated monitoring data sequence, which specifically includes: Based on the arranged sensor array, the pipeline in the target area is monitored to obtain the internal monitoring information of the pipeline when the current transportation task is performed, and the internal monitoring information of the pipeline is preprocessed; Calculate the data standard deviation of the internal monitoring information of the pipeline, compare the calculated data standard deviation with a preset standard deviation range, define the data corresponding to the data standard deviation that is not within the preset standard deviation range as an outlier, remove the outlier and compensate for the difference, and obtain preprocessing information; Obtaining the pipeline route of the current delivery task, extracting sensor information on the current pipeline route using the final pipeline network diagram, and defining the sequence of each sensor based on the delivery flow direction of the pipeline to obtain second analysis information; Sorting the monitoring data in the preprocessing information according to the second analysis information to form a monitoring data sequence based on the sensor sequence, and introducing a spline function interpolation method to supplement the data; The interpolation interval is preset, and the interpolation interval is generated based on the constructed monitoring data sequence and a spline equation is established. The spline coefficients of each interpolation interval are solved and interpolation is performed through the corresponding spline equation to obtain the interpolated monitoring data sequence.

5. The electric valve control method for fluid leakage sensing according to claim 1, characterized in that: The construction of the anomaly detection model uses the interpolated monitoring data sequence to detect whether there is an anomaly in the current transportation task, and predicts the degree of the abnormal condition to obtain abnormal condition prediction information, specifically including: Building an anomaly detection model based on a generative adversarial network, obtaining historical delivery instances through historical data retrieval and building a monitoring data sequence during historical normal delivery, and training the anomaly detection model; Obtaining a monitoring data sequence after interpolation, inputting it into the anomaly detection model for detection, taking the input monitoring data sequence after interpolation as the original sequence, obtaining a generated sequence through a generator, and performing reconstruction error calculation with the original sequence; The calculated reconstruction error is compared with the preset threshold. If it is greater than the preset threshold, it means that there is an abnormal condition in the current transportation task, and the corresponding abnormal monitoring data is output to obtain abnormal detection information; Extract historical abnormal transport instances based on the acquired historical transport instances, extract historical transport routes of each historical abnormal transport instance, calculate the Euclidean distance between each historical transport video, perform classification based on the calculated Euclidean distance, and obtain first classification information; Introducing a Markov algorithm to construct a state transfer matrix of monitoring data in each historical abnormal transport instance category using the first category division information, and associating the state transfer matrix with the corresponding historical abnormal transport instance category to obtain first association information; The transportation route of the current transportation task is obtained, the corresponding state transfer matrix is ​​obtained using the first association information, and the degree of change of the abnormal condition in the future time period is predicted in combination with the abnormal monitoring information to obtain abnormal condition prediction information.

6. The electric valve control method for fluid leakage sensing according to claim 1, characterized in that: The risk assessment rules are used to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, an electric valve control plan is formulated to change the route, specifically including: Acquire abnormal condition prediction information, perform abnormal area analysis based on the abnormal condition prediction information, determine the pipeline area where the transmission abnormality occurs, and obtain abnormal area analysis information; Obtain the final pipeline network diagram, use the abnormal area analysis information to mark the abnormal pipeline, extract the transportation routes of other real-time pipeline transportation tasks, analyze the remaining enabled pipelines, and obtain the enabled pipeline information; Generate an enabled pipeline map according to the enabled pipeline information, define the changed starting point and the changed end point of the transmission pipeline based on the abnormal area analysis information, set the transmission route distance as a restriction condition, and use the ant colony algorithm to perform route optimization; The initial pheromone of the ant colony is preset, the position of the electric valve is defined as the route change node, the state transition probability of each node is set, the optimized route is obtained according to the state transition probability, the optimized route is penalized by the set restriction conditions, and iterative optimization is performed to obtain the candidate optimized route information; Calculate the route distance of each route according to the candidate optimization route information, sort the candidate routes, select the one with the shortest distance as the final optimization route based on the sorting result, extract the corresponding route change node according to the final optimization route, and generate the electric valve control plan; The electric valves in the pipeline system are controlled according to the generated electric valve control scheme to change the delivery route of the current delivery task.

7. An electric valve control system for fluid leakage sensing, characterized in that: The system includes: a memory and a processor, wherein the memory contains an electric valve control method program for fluid leakage perception, and when the electric valve control method program for fluid leakage perception is executed by the processor, the following steps are implemented: Acquire pipeline construction information, construct a pipeline network diagram of the pipeline system in the target area based on the pipeline construction information, set a sensor layout plan and merge the corresponding sensor area with the pipeline network diagram to obtain a final pipeline network diagram; The internal monitoring information of the pipeline during the current transportation task is obtained through the arranged sensor array, and the data is preprocessed to generate a monitoring data sequence. The monitoring data sequence is supplemented by the spline function interpolation method to obtain the interpolated monitoring data sequence; Constructing an anomaly detection model, using the interpolated monitoring data sequence to detect whether there is an anomaly in the current transportation task, and predicting the degree of the abnormal condition to obtain abnormal condition prediction information; Preset risk assessment rules, obtain abnormal condition prediction information and use risk assessment rules to determine whether the transportation route needs to be adjusted. If the transportation route needs to be adjusted, formulate an electric valve control plan to change the route.

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