Intelligent gas pipeline network valve remote control and supervision internet of things system and method

Abstract

The application provides a kind of wisdom gas pipe network valve remote control supervision Internet of Things system and method, it is related to wisdom gas control field.The method is realized based on the Internet of Things system, including: obtaining pipe network information and the performance parameter of control component and uploading the performance parameter to government safety supervision management platform;Parameter confirmation instruction is obtained, and the layout parameter is determined and uploaded to government safety supervision management platform;Layout confirmation instruction is obtained, and the layout parameter is sent to gas maintenance object platform;After completing layout, the control component is determined based on the correction period Control compensation and upload to government safety supervision management platform;Correction confirmation instruction is obtained, and the control component is corrected based on control compensation, and the control component is controlled based on control parameter group in the correction period Control valve, obtain control gas data pair, and determine control compensation.The application can reasonably determine the layout position of control component, accurately evaluate control deviation to ensure the effectiveness of valve remote control.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of smart gas control, in particular to a smart gas pipeline valve remote control and supervision Internet of Things system and method. BACKGROUND

[0002] A gas pipeline is the main carrier for transporting gas. In order to ensure the safety of gas operation, in the design of a gas pipeline network, pipeline valves are usually arranged at each pipeline network node to control the transmission and switching of gas, and by installing a remote control component, remote control of the pipeline valves is achieved, so that in the event of a gas accident, the valves can be remotely controlled to reduce gas leakage time and reduce safety risks.

[0003] Since the control component of the pipeline valve has a maximum communication distance, the reasonable installation position of the control component and the pairing relationship with the valve need to be determined before installation to ensure the effective coverage of the control component on the valve. In addition, after installation is completed and during use, error evaluation of the actual control result of the control component is also needed to timely correct the effectiveness of remote control.

[0004] Therefore, it is desirable to propose a smart gas pipeline valve remote control and supervision Internet of Things system and method that can reasonably determine the layout position of the control component to cover the valve while accurately evaluating the control deviation and timely correcting it to ensure the accuracy and effectiveness of the remote control and supervision of the valve, thereby improving the level of intelligent monitoring and control of the gas pipeline network. SUMMARY

[0005] In order to solve the problem of how to reasonably determine the layout position of the control component and accurately evaluate the control deviation to ensure the effectiveness of the remote control of the valve, the present application provides a smart gas pipeline valve remote control and supervision Internet of Things system and method.

[0006] The summary includes a smart gas pipeline valve remote control and supervision Internet of Things system, which comprises: a government safety supervision and management platform, a government safety supervision object platform, a gas equipment object platform, and a gas maintenance object platform; the government safety supervision object platform comprises a gas company management platform, the gas equipment object platform comprises at least one control component, and the gas maintenance object platform comprises at least one personnel interaction device; the gas company management platform is configured to execute a smart gas pipeline valve remote control and supervision method.

[0007] The invention includes a smart gas pipeline valve remote control supervision method, which is realized based on a smart gas pipeline valve remote control supervision Internet of Things system. The Internet of Things system includes a government safety supervision management platform, a government safety supervision object platform, a gas equipment object platform, and a gas maintenance object platform. The government safety supervision object platform includes a gas company management platform. The gas equipment object platform includes at least one control component. The gas maintenance object platform includes at least one personnel interaction device. The method is executed by the gas company management platform and includes: obtaining pipeline information and performance parameters of the control component, and uploading the performance parameters to the government safety supervision management platform; in response to obtaining a parameter confirmation instruction issued by the government safety supervision management platform, determining layout parameters based on the pipeline information and the performance parameters, and uploading them to the government safety supervision management platform; the layout parameters include the layout position of the control component and the covered valve; in response to obtaining a layout confirmation instruction issued by the government safety supervision management platform, sending the layout parameters to the gas maintenance object platform to arrange staff to lay out components; after the layout is completed, the compensation amount of the control component is determined based on the correction period and uploaded to the government safety supervision management platform; in response to obtaining a correction confirmation instruction issued by the government safety supervision management platform, correcting the control component based on the compensation amount; wherein the compensation amount of the control component is determined based on the correction period, which includes: within the correction period, controlling the control component to regulate the covered valve based on the regulation parameter group to obtain a regulation gas data pair; based on the regulation gas data pair and the regulation parameter group, the compensation amount is determined.

[0008] The beneficial effects brought by the above invention content include but are not limited to: (1) by uploading performance parameters and layout parameters to the government safety management platform, the government can promote the supervision and control of the layout process; based on the pipe network information and the performance parameters of the control component, the layout parameters are determined, which can make the control component connect as many valves as possible, improve the resource utilization rate; based on the correction period, the control component is determined, and based on the correction period, the control component is periodically corrected, which can timely avoid the problem that the control component is inaccurate in regulating the valve due to the failure of the control component or the valve, so as to ensure that the gas flow, pressure and temperature parameters are within the set range, reduce the risk of gas out of control, thereby improving the operation safety and realizing the automatic remote monitoring and intelligent control of the gas; (2) grouping based on the valve position, valve pipeline type and valve type of the valve can be carried out, and the valves can be independently managed according to the specific function or area, and the layout parameters are determined in combination with the performance parameters of the control component, so as to ensure that the control component can effectively regulate the covered valves, while avoiding resource waste, improving the regulation accuracy of the gas pipe network and the resource utilization efficiency; (3) based on the regulation and control of the gas data, the regulation and control offset distribution is determined, which can determine the deviation of the control component and lay the foundation for the correction process; by determining the regulation and control offset distribution, the Internet of Things system can accurately correct its regulation and control deviation during operation, which can reduce the problem of gas out of control caused by the failure of the control component or the valve, and is conducive to realizing the automatic remote regulation of the gas and improving the stability and safety of the gas pipe network. BRIEF DESCRIPTION OF DRAWINGS

[0009] The present application will be further illustrated in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0010] Figure 1 is a platform schematic diagram of a smart gas pipe network valve remote control monitoring Internet of Things system according to some embodiments of the present specification;

[0011] Figure 2 is an exemplary flowchart of a smart gas pipe network valve remote control monitoring method according to some embodiments of the present specification;

[0012] Figure 3 is an exemplary schematic diagram for determining layout parameters according to some embodiments of the present specification;

[0013] Figure 4 is an exemplary schematic diagram for determining regulation and control offset according to some embodiments of the present specification. DETAILED DESCRIPTION

[0014] Brief Description of the Drawings

[0015] In the embodiments of the present application, the operations performed in steps are described below. Unless otherwise specified, the order of the steps is interchangeable, the steps can be omitted, and other steps can be included in the operation process.

[0016] Figure 1 is a schematic diagram of the platform structure of the smart gas pipeline valve remote control and supervision Internet of Things system according to some embodiments of the present application.

[0017] In some embodiments, as shown in Figure 1 , the smart gas pipeline valve remote control and supervision Internet of Things system 100 can include a government safety supervision and management platform 110, a government safety supervision sensor network platform 120, a government safety supervision object platform 130, a gas company sensor network platform 140, a gas equipment object platform 150, and a gas maintenance object platform 160. Among them, the government safety supervision object platform 130 can include a gas company management platform 131, the gas equipment object platform 150 can include at least one control component, and the gas maintenance object platform 160 can include at least one personnel interaction device.

[0018] The government safety supervision and management platform 110 refers to the platform for government safety supervision and management, which can be configured as a processor and / or a server.

[0019] In some embodiments, the government safety supervision and management platform 110 can be connected in communication with the gas company management platform 131 through the government safety supervision sensor network platform 120.

[0020] The government safety supervision sensor network platform 120 refers to the platform for government safety supervision and management of sensor network information, which can be configured as a communication device and / or a server.

[0021] The government safety supervision object platform 130 refers to the object platform for generating sensing information and executing control information, which can be configured as a processor and / or a server. In some embodiments, the government safety supervision object platform 130 can include a gas company management platform 131.

[0022] The gas company management platform 131 refers to the comprehensive management platform for related information of the gas company, which can be configured as a processor and / or a server and a memory.

[0023] In some embodiments, the gas company management platform 131 can be configured to execute the smart gas pipeline valve remote control and supervision method. For more details about this method part, please refer to the relevant description of Figure 2 .

[0024] The gas company sensing network platform 140 refers to a comprehensive management platform of sensing information of the gas company, which can be configured as a communication device and / or a server. In some embodiments, the gas company sensing network platform 140 can be used for the communication interaction of the gas company management platform 131 with the gas equipment object platform 150 and the gas maintenance object platform 160.

[0025] The gas equipment object platform 150 refers to a functional platform for real-time remote regulation and control of the gas pipe network. In some embodiments, the gas equipment object platform 150 can include at least one control component.

[0026] The control component refers to a component for remotely controlling the valve of the pipe network. In some embodiments, one control component can remotely regulate one or more valves on the gas pipeline, and control the transmission of gas in the pipeline by controlling the opening degree of the valve.

[0027] The process of regulating the valve by the control component can include adjusting the opening degree of the valve from the current initial opening degree to the expected opening degree, and restoring the opening degree of the valve to the initial opening degree after monitoring the gas data. The expected opening degree can be set by a person according to the actual situation in advance.

[0028] The gas maintenance object platform 160 refers to a platform for interacting with gas users. The gas user refers to a person related to the use of gas. For example, an individual, an enterprise, a worker of the gas pipe network (such as a component installer, a pipeline monitor), etc. The gas maintenance object platform 160 can include at least one personnel interaction device. For example, a mobile phone, a computer, etc.

[0029] In some embodiments, the intelligent gas pipe network valve remote control and supervision Internet of Things system 100 can further include a processor. The processor can process data and / or information related to the intelligent gas pipe network valve remote control and supervision Internet of Things system 100. The processor can execute program instructions based on these data, information and / or processing results to perform one or more functions described in the present application. In some embodiments, the processor can include one or more sub-processing devices (for example, single-core processing devices, multi-core multi-core processing devices, etc.). For example only, the processor can include a central processing unit (CPU), an application specific integrated circuit (ASIC), etc. or any combination thereof.

[0030] In some embodiments, the processor can interact with and / or be configured in the multiple platforms included in the intelligent gas pipe network valve remote control and supervision Internet of Things system 100.

[0031] In some embodiments of the present specification, based on the smart gas pipeline valve remote control supervision Internet of Things system, an information operation closed loop can be formed between each functional platform, and coordinated and regularly operated under the unified management of the gas company management platform, so as to realize the informatization and smartization of the smart gas pipeline valve remote control supervision.

[0032] Figure 2 is an exemplary flowchart of the smart gas pipeline valve remote control supervision method according to some embodiments of the present specification.

[0033] In some embodiments, the flow 200 can be implemented based on the smart gas pipeline valve remote control supervision Internet of Things system 100 and can be executed by the gas company management platform 131 in the smart gas pipeline valve remote control supervision Internet of Things system 100. For example, executed by a processor in the gas company management platform 131. As shown in Figure 2 The flow 200 includes the following steps.

[0034] Step 210, obtaining pipeline information and performance parameters of control components from the gas equipment object platform via the gas company sensing network platform, and uploading the performance parameters to the government safety supervision management platform via the government safety supervision sensing network platform.

[0035] Pipeline information refers to information related to the current regional gas pipeline. The pipeline information can include valve positions. The valve can be a control valve arranged inside the gas pipeline in the current region to control the flow, flow rate and pressure of the gas in the pipeline, etc. The valve position can be represented by a position coordinate, etc.

[0036] The current region refers to the region currently requiring control component arrangement. One or more control components need to be arranged in the current region, and each control component can control one or more valves. In some embodiments, the control components required to be arranged in the current region are control components with the same performance parameters.

[0037] The performance parameters can include maximum communication distance and maximum number of valve connections.

[0038] The maximum communication distance refers to the maximum distance at which the control component can communicate with the valve.

[0039] The maximum number of valve connections refers to the maximum number of valves that the control component can communicate with. The control component can control the valves that are communicatively connected thereto.

[0040] In some embodiments, the gas equipment object platform can acquire the performance parameter of the control component based on the factory parameter of the control component, and acquire the pipe network information based on the installation record; the gas company management platform can acquire the pipe network information and the performance parameter of the control component from the gas equipment object platform via the gas company sensing network platform, and upload the performance parameter to the government safety supervision management platform via the government safety supervision sensing network platform, so that the government safety supervision management platform confirms the performance parameter.

[0041] In step 220, in response to acquiring the parameter confirmation instruction issued by the government safety supervision management platform, the layout parameter is determined based on the pipe network information and the performance parameter, and the layout parameter is uploaded to the government safety supervision management platform via the government safety supervision sensing network platform.

[0042] The parameter confirmation instruction refers to an instruction for determining that the performance parameter is correct.

[0043] In some embodiments, the parameter confirmation instruction can be generated by the government safety supervision management platform after confirming the performance parameter, and is issued to the gas company management platform via the government safety supervision sensing network platform.

[0044] The layout parameter can include a layout position of the control component and a covered valve.

[0045] The layout position refers to the position where the control component is laid out. The layout position can be represented by a position coordinate or the like.

[0046] The covered valve refers to a valve that can be controlled by the control component at the layout position. The distance between the valve position of the covered valve and the layout position of the control component is less than the maximum communication distance of the control component.

[0047] In some embodiments, the processor can determine the layout parameter in multiple ways based on the pipe network information and the performance parameter.

[0048] For example, the processor can construct a plurality of first reference vectors based on the pipe network information of a plurality of other regions and the performance parameters of the control components in the other regions, construct a first feature vector based on the pipe network information of the current region and the performance parameter of the control component, determine a first reference vector with the largest vector similarity with the first feature vector as a first target vector, and perform equivalent processing on the region corresponding to the first target vector and the current region, and project the actual layout parameter of the region corresponding to the first target vector into the current region as the layout parameter of the current region. The other regions refer to regions where the control component has been laid out. The equivalent processing can include coordinate system overlapping, etc.

[0049] In some embodiments, the processor can also determine the regulation association group based on the pipe network information, and determine the layout parameter based on the regulation association group and the performance parameter of the control component. More details about this part can be found in Figure 3 and related descriptions.

[0050] At step 230, in response to obtaining the layout confirmation instruction issued by the government safety supervision management platform, the layout parameter is sent to the gas maintenance object platform via the gas company sensing network platform to arrange the staff to perform component layout.

[0051] The layout confirmation instruction refers to an instruction for confirming the component layout according to the layout parameter. In some embodiments, the layout confirmation instruction can be generated by the government safety supervision management platform after confirming the layout parameter, and is issued to the gas company management platform through the government safety supervision sensing network platform.

[0052] In some embodiments, after the gas company management platform obtains the layout confirmation instruction, the layout parameter can be sent to the gas maintenance object platform via the gas company sensing network platform, and the staff can obtain the layout parameter through the personnel interaction device and perform the layout of the control component according to the layout parameter.

[0053] At step 240, after the layout is completed, the regulation compensation of the control component is determined based on the correction period, and the regulation compensation is uploaded to the government safety supervision management platform via the government safety supervision sensing network platform.

[0054] The correction period refers to the time interval for periodic correction of the control component.

[0055] In some embodiments, due to control component failure or valve failure (for example, control component calibration failure, valve rust, etc.), the control component may have a degree of deviation during the regulation of the valve.

[0056] The degree of deviation refers to the regulation deviation of the valve, which can be represented by the difference between the actual opening degree of the valve after regulation by the control component and the expected opening degree. The regulation compensation refers to the amount used to compensate for the degree of deviation of the control component during the regulation of the valve. For example, the expected opening degree of the valve is 20°, and the actual opening degree after regulation is 18°, so the degree of deviation of the valve is -2°, and therefore the expected opening degree of the valve needs to be set to 22° before the control component is regulated, so that the actual opening degree after regulation can reach 20°, and therefore the regulation compensation of the control component for the valve is +2°.

[0057] In some embodiments, the regulation compensation of the control component can be represented by a sequence composed of the opposites of the degrees of deviation of all the valves covered by the control component.

[0058] For example, the offsets of the cover valves 1, 2, …, n of the control component A are +1°, -3°, …, +2° respectively, and n represents the total number of the cover valves of the control component A. The regulation compensation of the control component A can be represented as (-1, +3, …, -2).

[0059] In some embodiments, the processor determines the regulation compensation of the control component based on the correction period, which can include the following steps 241 and 242.

[0060] Step 241, based on the regulation parameter set, control the control component to regulate the cover valves, and obtain the regulation gas data pair.

[0061] The regulation parameter set of a control component can include the expected opening degree of all cover valves of the control component. The regulation parameter set can be set by humans in advance according to actual conditions.

[0062] In some embodiments, the processor can control the control component based on the regulation parameter set to adjust the corresponding cover valve from the current initial opening degree to the expected opening degree, and restore the opening degree of the cover valve to the initial opening degree after monitoring and obtaining the gas data after valve regulation.

[0063] The regulation gas data pair can include the pipeline gas data before valve regulation and the pipeline gas data after valve regulation. The pipeline gas data can include gas flow, gas flow rate, and gas pressure in the pipeline. One regulation of a control component corresponds to the generation of multiple regulation gas data pairs of multiple cover valves of the control component.

[0064] In some embodiments, the processor can obtain the pipeline gas data before valve regulation and the pipeline gas data after valve regulation based on the sensors arranged in the pipeline, and then obtain the regulation gas data pair.

[0065] Step 242, determine the regulation compensation based on the regulation gas data pair and the regulation parameter set.

[0066] In some embodiments, the processor can determine the regulation compensation based on the regulation gas data pair and the regulation parameter set in multiple ways.

[0067] For example, the processor can construct multiple second reference vectors based on the historical regulation gas data pairs and historical regulation parameter sets corresponding to multiple historical control components in multiple historical regulations. For a control component in the current regulation, construct a second feature vector based on the regulation gas data pair and the regulation parameter set corresponding to the control component. Determine the second reference vector with the largest vector similarity with the second feature vector, and take it as the second target vector. Take the historical actual regulation compensation corresponding to the second target vector as the regulation compensation of the current control component.

[0068] In some embodiments, the processor can further determine a regulation offset distribution of the control component based on the regulation data pair and the regulation parameter group; and determine a regulation compensation based on the regulation offset distribution. More details about this part can be found in Figure 4 and related descriptions.

[0069] In some embodiments, the gas company management platform can upload the regulation compensation to the government safety supervision management platform via the government safety supervision sensing network platform. The staff of the government safety supervision management platform confirms the regulation compensation to generate a rectification confirmation instruction, and sends the rectification confirmation instruction to the gas company management platform via the government safety supervision sensing network platform.

[0070] Step 250, in response to obtaining the rectification confirmation instruction issued by the government safety supervision management platform, rectifying the control component based on the regulation compensation.

[0071] The rectification confirmation instruction refers to an instruction for confirming that the regulation compensation is correct and rectifying the control component.

[0072] In some embodiments, in response to obtaining the rectification confirmation instruction, the processor can rectify the control component based on the regulation compensation. For example, the regulation compensation corresponding to the cover valve 1 of the control component A is +2°, and the expected opening degree of the cover valve 1 of the control component A is increased by 2°.

[0073] In some embodiments of the present specification, by uploading performance parameters and layout parameters to the government safety management platform, the government can promote the supervision and control of the layout process; based on the pipe network information and the performance parameters of the control component, the layout parameters can be determined to connect as many valves as possible to the control component to improve resource utilization; based on the rectification period, the regulation compensation of the control component is determined, and the control component is periodically rectified based on the regulation compensation, which can timely avoid the problem of inaccurate regulation of the control component to the valve due to the failure of the control component or the valve, so as to ensure that the gas flow, pressure and temperature parameters are within the set range, reduce the risk of gas out of control, improve the operation safety and realize the automatic remote monitoring and intelligent control of gas.

[0074] Figure 3 is an exemplary schematic diagram for determining layout parameters according to some embodiments of the present specification.

[0075] In some embodiments, the pipe network information can also include valve types and valve pipe types. As Figure 3 shown, the processor can determine a regulation association group 320 based on the pipe network information 310; and determine the layout parameters 340 based on the regulation association group 320 and the performance parameters 330 of the control component.

[0076] The valve type can include a ball valve, a gate valve, a pressure reducing valve, etc. The valve pipeline type refers to the pipeline type of the gas pipeline where the valve is located. The pipeline type can be classified in various ways. For example, according to the pipeline purpose, the pipeline type is classified into a gas transmission pipeline, a gas distribution pipeline, a user access pipeline, etc.

[0077] The regulation association group refers to the grouping category corresponding to the valve. In some embodiments, each regulation association group can include one or more valves.

[0078] In some embodiments, the processor can determine the regulation association group based on the pipeline network information through a clustering algorithm. The processor can construct a plurality of clustering vectors based on the pipeline network information, a clustering vector being composed of the valve position, the valve pipeline type, and the valve type of one valve; cluster the plurality of clustering vectors through a clustering algorithm to form a preset number of clustering clusters; and divide the valves in one clustering cluster into one regulation association group. The clustering algorithm can be a clustering algorithm with a preset number of clustering clusters, such as a K-means clustering algorithm, etc.

[0079] In some embodiments, the preset number of clustering clusters can be related to the maximum communication distance of the performance parameter. The processor can grid the current area with the maximum communication distance as the grid side length, and determine the number of grids after gridding as the preset number of clustering clusters. For related content about the current area, the maximum communication distance of the performance parameter, etc., please refer to Figure 2 and related content thereof.

[0080] In some embodiments, the processor can determine the layout parameter in various ways based on the regulation association group and the performance parameter of the control component.

[0081] For example, the processor can count the number of valves in each regulation association group. For a regulation association group: in response to the number of valves in the regulation association group being greater than the maximum valve connection number of the control component, the area corresponding to the regulation association group is evenly divided into a plurality of sub-areas according to a preset division condition, and the centers of the plurality of sub-areas are respectively set as the layout positions of the plurality of control components, and the valves in the plurality of sub-areas are respectively taken as the covered valves of the respective corresponding control components. The preset division condition is that the number of sub-areas is the smallest under the premise that the number of valves in each sub-area is not greater than the maximum valve connection number of the control component.

[0082] For a regulation association group: in response to the number of valves in the regulation association group being less than or equal to the maximum valve connection number of the control component, the center of the area corresponding to the regulation association group is set as the layout position of one control component, and the valves in the area are taken as the covered valves of the corresponding control component; and the layout parameter is determined based on the layout position and the covered valves corresponding to each regulation association group.

[0083] The area corresponding to the regulation association group can refer to an area capable of covering all valves in the regulation association group. The area selection manner can be set by a human or set by a system, for example, a minimum rectangle capable of covering all valves in a regulation association group is set as the area corresponding to the regulation association group.

[0084] In some embodiments, as shown in FIG. 3, the processor can generate a layout position group 350 based on the regulation association group 320; construct a valve atlas 360 based on the layout position group 350 and the performance parameter 330; and determine the layout parameter 340 based on the valve atlas 360 through a parameter determination model 370, which is a machine learning model. Figure 3

[0085] In some embodiments, one layout position group can include multiple layout positions corresponding to multiple control components.

[0086] In some embodiments, for one regulation association group, the processor can randomly select multiple points from the area corresponding to the regulation association group as multiple layout positions of the control components corresponding to the regulation association group, randomly select one from the multiple layout positions corresponding to each regulation association group to form one layout position group, and generate multiple layout position groups based on multiple random selections.

[0087] The valve atlas refers to a graphical model showing valves and their mutual relationships. In some embodiments, the valve atlas can include multiple nodes and multiple edges.

[0088] The nodes of the valve atlas include a first node, a second node, and a third node.

[0089] The first node is a valve. The node features of the first node can include valve position, valve type, and pipeline type of the pipeline where the valve is located.

[0090] The second node is a layout position.

[0091] The third node is a communication device. The node features of the third node can include communication device position and communication parameters. The communication device can be used for pipeline communication and can also be used for remote communication between the control component and a remote device (such as a gas company management platform, etc.), and the communication device can be a network base station, etc. The communication device position is a known position uploaded by a human (such as the construction position of a network base station), and the communication parameters can include communication range, communication bandwidth, signal strength, etc.

[0092] ​In some embodiments, the edges of the valve graph can include first-type edges, second-type edges, third-type edges and fourth-type edges, the edge features of the first-type edges and the second-type edges include communication distances between connected nodes, and the edge feature of the third-type edges includes pipeline gas data, valve pipeline type and pipeline fluctuation feature.

[0093] The first-type edges are used to connect a first node and a second node that satisfy a preset connection condition. The preset connection condition is that the communication distance between the first node and the second node is less than a maximum communication distance. In some embodiments, if the communication distances between the first node and multiple second nodes are all less than the maximum communication distance, the first node is connected with the second node having the shortest communication distance, and the formed edge is taken as a first-type edge. One first node corresponds to only one first-type edge. The communication distance between the connected nodes can be represented by the straight-line distance between the nodes.

[0094] In some embodiments, when the first node M1 does not have a first-type edge with any second node, but the first node M2 in the same control association group as the first node M1 has a first-type edge with the second node W1, the first node M1 is connected with the second node W1, and a second-type edge is formed.

[0095] The third-type edges are used to connect two first nodes that have actual pipeline connections.

[0096] The fourth-type edges are used to connect a third node and a second node within the communication range of the third node. The communication range of the communication device corresponding to the third node is generally large, and thus it can be considered that the third node in a valve graph is connected with all second nodes by fourth-type edges.

[0097] The pipeline fluctuation feature refers to a feature reflecting the fluctuation of the gas data in the pipeline. In some embodiments, the processor can take the ratio of the standard deviation to the mean of the gas flow of the pipeline at multiple time points in a period of time as the pipeline fluctuation feature of the pipeline. The smaller the ratio is, the higher the stability of the pipeline is.

[0098] In some embodiments of the present specification, through the construction of the four types of edges, the information range covered by the valve graph is expanded, so that the valve graph can not only contain the position information of the valves, but also show the communication distances between the valves and the pipeline information connected with each valve, which is conducive to more accurately evaluating the communication efficiency between the control components and the valves and the pipeline network state, so as to optimize the layout and control strategy of the control components.

[0099] In some embodiments, the processor can determine the edges and nodes of the valve graph based on the pipeline network information, the layout location group and the performance parameters. One layout location group corresponds to one valve graph.

[0100] The parameter determination model refers to a model used to determine the layout parameters. In some embodiments, the parameter determination model can be a machine learning model, such as a graph neural network (GNN).

[0101] In some embodiments, the input of the parameter determination model can include the valve graph, and the output can include the communication delay of the first type of edge and the communication delay of the second type of edge in the valve graph.

[0102] In some embodiments, the communication delay can be represented by the time required for signal transmission.

[0103] In some embodiments, the parameter determination model can be trained in various ways. For example, it can be trained by a plurality of training samples with training labels.

[0104] The training samples and training labels can be obtained based on historical data. For example, the training samples can be sample valve graphs constructed based on historical data, including historical pipe network information, historical layout position groups, and historical performance parameters of historical control components. The construction method of the sample valve graph can refer to the construction process of the valve graph described above. The training labels corresponding to the training samples are the historical actual communication delays of the sample first type of edges and the historical actual communication delays of the sample second type of edges corresponding to the sample valve graph. The training labels can be manually labeled.

[0105] In some embodiments, the processor can input the sample valve graph into an initial parameter determination model, construct a loss function based on the communication delay of the first type of edge and the communication delay of the second type of edge output by the initial parameter determination model and the training labels, update the initial parameter determination model based on the loss function, and when a preset condition is met, the initial parameter determination model is trained to obtain a trained parameter determination model. The preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0106] In some embodiments, the training of the parameter determination model can include training based on a training set, verifying based on a verification set, and testing based on a test set; the training set, the verification set, and the test set are data sets composed of historical pipe network information, historical layout position groups, and historical performance parameters of historical control components; the data amount of the training set, the verification set, and the test set constitutes a preset proportion, and there is no data crossover among the training set, the verification set, and the test set; the sample learning rate in model training is related to the sample accident probability.

[0107] The training set refers to a data set used to train the internal parameters of the model.

[0108] The validation set refers to a data set used to verify the state of the model and the convergence during the training process. The validation set can be used to determine hyperparameters, monitor whether the model is overfitting, and determine when to stop training the model.

[0109] The test set refers to a data set used to test the generalization ability of the model. After determining the hyperparameters using the validation set and adjusting the internal parameters using the training set, the test set can be used to determine whether the model is running and the performance of the model.

[0110] In some embodiments, the historical pipe network information, the historical layout position group, and the historical performance parameters of the historical control components can constitute a data group, which is used to obtain a corresponding sample valve map. The training set, the validation set, and the test set are each composed of multiple data groups.

[0111] The preset ratio refers to a preset ratio of the amount of data included in the training set, the validation set, and the test set. In some embodiments, the preset ratio can be set by default by the system or by a technician according to experience. For example, the preset ratio can be 8:1:1.

[0112] Data intersection refers to the presence of the same data in different sets, i.e., the same data is used in multiple sets. In some embodiments, there is no data intersection in the training set, the validation set, and the test set, i.e., a data group is only included in one of the training set, the validation set, and the test set.

[0113] In some embodiments, the sample learning rate in model training can be related to the sample accident probability. For example, the larger the sample accident probability, the larger the sample learning rate.

[0114] In some embodiments, for each sample valve map, the processor can calculate the interval duration between the accident occurrence time and the regulation time of each accident occurring in the area corresponding to the sample valve map, and determine the sample accident probability as the normalized result of the ratio of the average of the interval durations corresponding to multiple accidents to the preset time length.

[0115] The accident occurrence time refers to the time point at which an accident occurs in the area corresponding to the sample valve map. The regulation time refers to the time point at which the valve in the sample valve map is regulated after the accident occurs. In some embodiments, the processor can directly obtain the accident occurrence time and the regulation time of each accident corresponding to the sample valve map from historical data. The preset time length can be set by default by the processor or set by a person according to experience. For example, the preset time length can be the average of the time intervals of multiple historical accident occurrence times in the historical data.

[0116] In some embodiments of the present specification, the model is trained in stages by dividing the historical data into a training set, a validation set and a test set, and dynamically adjusting the sample learning rate according to the sample accident probability, thereby avoiding overfitting caused by data cross and improving the accuracy and adaptability of the model in predicting the layout parameters.

[0117] In some embodiments, the processor can input the plurality of valve graphs corresponding to the plurality of layout position groups into the parameter determination model respectively, output the communication delay of the first type of edge and the communication delay of the second type of edge corresponding to each valve graph respectively, filter out the valve graphs that meet the filtering condition according to the output results of the model, and determine the second nodes included in the valve graphs as the layout positions of the control components. The first nodes connected to each second node through the first type of edge or the second type of edge are determined as the coverage valves of the corresponding control components, and the layout positions and the coverage valves are merged as the layout parameters.

[0118] The filtering condition can be that the communication delay of the first type of edge in the valve graph that exceeds the preset proportion is less than the communication delay threshold, and the communication delay of the second type of edge that exceeds the preset proportion is less than the communication delay threshold. The preset proportion and the communication delay threshold can be set by default by the processor or set by the technician according to experience.

[0119] In some embodiments of the present specification, the valve graphs are constructed by combining the performance parameters of the regulation associated groups and the control components, so that the valve relationship and characteristics in the pipe network are graphically presented, which helps to enhance the understanding of the overall structure of the pipe network when performing regulation work, and then the machine learning model is used to accurately determine the layout parameters, thereby improving the intelligent level of decision-making and ensuring the scientificity and effectiveness of the control component arrangement.

[0120] In some embodiments of the present specification, the grouping is based on the valve position, the valve pipe type and the valve type, which can be independently managed for specific functions or areas, and the layout parameters are determined in combination with the performance parameters of the control components, so that the control components can effectively regulate the covered valves while avoiding resource waste, thereby improving the regulation accuracy and resource utilization efficiency of the gas pipe network.

[0121] Figure 4 is an exemplary schematic diagram for determining the regulation compensation amount according to some embodiments of the present specification.

[0122] In some embodiments, as shown in Figure 4 the processor can determine the regulation offset distribution 430 of the control components based on the regulation gas data pair 410 and the regulation parameter group 420, and determine the regulation compensation amount 440 based on the regulation offset distribution 430. The regulation offset distribution includes the offset degree of the coverage valve of at least one control component. For more information about the regulation gas data pair, the regulation parameter group and the regulation compensation amount, please refer toFigure 2 the related description of

[0123] In some embodiments, the regulating offset distribution can include an offset degree of the cover valve of the control component. More description about the offset degree can refer to the related description of Figure 2 the related description of

[0124] In some embodiments, the processor can determine the regulating offset distribution of the control component based on the regulating gas data pair and the regulating parameter group in multiple ways.

[0125] For example, for each cover valve of the control component, the processor can determine the gas flow rate change rate and the gas flow speed change rate before and after the valve adjustment based on the regulating gas data pair, the gas flow rate change rate is represented by the absolute value of the difference between the pipe gas flow before the regulation and the pipe gas flow after the regulation and the pipe gas flow before the regulation, and the gas flow speed change rate is the same; based on the gas flow rate change rate and the gas flow speed change rate, the actual change range of the cover valve is determined by querying the preset relationship table, the actual change range refers to the actual change amount of the valve opening degree before and after the actual regulation, which is represented by the actual opening degree of the cover valve after the regulation minus the initial opening degree of the cover valve before the regulation; the actual opening degree of the valve after the regulation is obtained by adding the actual change range to the current opening degree of the valve; the expected opening degree of the valve is obtained based on the regulating parameter group; the difference between the actual opening degree and the expected opening degree is determined as the offset of the cover valve. The offset of each cover valve of each control component is calculated to determine the regulating offset distribution.

[0126] The preset relationship table can be constructed by manual experiment. For example, the technician can introduce gas into the experimental pipeline and regulate the opening degree of the experimental valve multiple times, the experimental valve is a valve that is not rusted, not aged, and regulated normally. During each experiment, record the valve change range (such as valve rotation angle, etc.) of the regulating experimental valve, and the pipe gas flow and pipe gas flow speed before regulation, and the pipe gas flow and pipe gas flow speed after regulation; based on the pipe gas flow before regulation and the pipe gas flow after regulation, the gas flow rate change rate is calculated, and the gas flow speed change rate is obtained in the same way; based on the corresponding relationship between the gas flow rate change rate, the gas flow speed change rate and the valve change range of the experimental valve in each experiment, the preset relationship table is constructed.

[0127] In some embodiments, as Figure 4As shown, for each control component, the processor can determine, based on the control gas data pair 410 and the control parameter set 420, an actual control degree 450 of the control component; in response to the actual control degree 450 being less than a control threshold 460, determine a fault instruction 470 and send the fault instruction to at least one personnel interaction device; in response to the actual control degree 450 being greater than or equal to the control threshold 460, determine, based on the control gas data pair 410 and the control parameter set 420, a control offset component 480 of the control component; and determine, based on the control offset component 480 of the at least one control component, a control offset distribution 430.

[0128] The actual control degree refers to a parameter for measuring the control effect of the control component. The greater the actual control degree, the better the control effect of the control component on the covered valve.

[0129] In some embodiments, the processor can determine, based on the control gas data pair, an actual change range of the covered valve of the control component; determine, based on the control parameter set, an expected change range of the covered valve of the control component; and determine, based on the actual change range and the expected change range, the actual control degree of the control component by weighting, the weight being related to the pipe fluctuation characteristics of the pipe where the valve is located.

[0130] For the description of determining the actual change range based on the control gas data pair, reference can be made to the relevant description above.

[0131] The expected change range refers to the expected change amount of the valve opening degree before and after the expected control.

[0132] In some embodiments, the processor can obtain, based on the control parameter set, an expected opening degree of the covered valve after the control, and take the difference between the expected opening degree after the control and the current actual opening degree before the control as the expected change range.

[0133] In some embodiments, the processor can determine, based on the actual change range and the expected change range of the plurality of covered valves of the control component, the actual control degree of the control component by weighting. For example, the actual control degree of the control component A can be calculated by the following formula (1):

[0134] (1)

[0135] Wherein, is the actual control degree of the control component A, is the weight of the i-th covered valve of the control component A, is the actual change range of the i-th covered valve of the control component A, is the expected change range of the i-th covered valve of the control component A, ​​​To control the number of covered valves of the control component.

[0136] In some embodiments, the weight of the covered valve is related to the pipeline fluctuation feature of the pipeline where the valve is located. The greater the pipeline fluctuation feature of the pipeline where the valve is located, the smaller the corresponding weight of the covered valve. For more information about the pipeline fluctuation feature, see the related description in Figure 3 .

[0137] In some embodiments of the present specification, based on the regulated gas data pair and the pairing relationship between the regulated gas data pair and the valve opening degree, the actual change range of the covered valve can be accurately determined; based on the actual change range and the expected change range, the actual regulation degree of the control component is determined by weighting, which realizes the quantification of the regulation effectiveness; based on the pipeline fluctuation feature to determine the weight, it can reduce the influence of the gas pipeline with large gas fluctuation on the gas data, and further improve the reliability of the actual regulation degree.

[0138] In some embodiments, the control threshold can be determined by the average value of the valve criticality of the plurality of covered valves of the control component. The greater the average value of the valve criticality, the greater the control threshold.

[0139] The valve criticality refers to the importance of the valve. In some embodiments, the valve criticality of a covered valve can be calculated by the following formula (2):

[0140] (2)

[0141] Wherein, is the valve criticality of the covered valve, is the number of associated downstream branches of the covered valve, is the historical maintenance timeliness of the covered valve, , is a preset constant.

[0142] The number of associated downstream branches refers to the number of downstream valves directly connected to the covered valve. The historical maintenance timeliness can be negatively related to the average response time. The average response time refers to the average value of the response time between the generation of the fault instruction and the start of maintenance in the plurality of historical faults of the covered valve.

[0143] The fault instruction can include the control component triggering the fault alarm and its corresponding covered valve.

[0144] In some embodiments, in response to the actual regulation degree of the control component being less than the control threshold, the processor can determine that there is a regulation anomaly or a performance decline of the control component, to generate a fault instruction and send the fault instruction to the personnel interaction device of the gas maintenance object platform via the gas company sensing network platform, to arrange the staff to perform fault diagnosis and maintenance.

[0145] The regulation offset component refers to a component corresponding to each control component in the regulation offset distribution.

[0146] In some embodiments, in response to the actual regulation degree of the control component being greater than or equal to the control threshold, the processor can determine the actual opening degree and the expected opening degree of each covered valve of the control component based on the regulation gas data pair and the regulation parameter group; determine the difference between the actual opening degree and the expected opening degree as the offset of the covered valve; and calculate the offset of each covered valve of the control component to determine the regulation offset component. More details about the above process can be found in the related description above.

[0147] In some embodiments, the processor can combine the regulation offset components of all control components without failure instructions to determine the regulation offset distribution.

[0148] In some embodiments of the present specification, the actual regulation degree is determined by the regulation gas data pair and the regulation parameter group, which quantifies the control effect of the control component. At the same time, by comparing the actual regulation degree with the control threshold, if the actual regulation degree is too small, it can be quickly judged that there is an abnormal situation, and a failure instruction can be issued in time, so as to speed up the failure response and maintenance process, and reduce potential safety hazards. For components that meet or exceed the control threshold, the regulation strategy is optimized by calculating the regulation offset component to ensure control accuracy and improve the operation efficiency and reliability of the entire gas pipe network.

[0149] In some embodiments, the processor can determine the offset degree of each covered valve of each control component based on the regulation offset distribution; and take the inverse of the offset degree as the regulation compensation.

[0150] In some embodiments of the present specification, the regulation offset distribution is determined based on the regulation gas data pair, which can determine the deviation of the control component and lay the foundation for the correction process. Through the way of determining the regulation compensation based on the regulation offset distribution, the Internet of Things system can accurately correct its own regulation deviation during operation, which can reduce the problem of gas out of control caused by control component failure or valve failure, and is conducive to realizing automatic remote regulation of gas and improving the stability and safety of the gas pipe network.

[0151] The embodiments in the present application are only for example and illustration, and do not limit the scope of application. Various modifications and changes made by those skilled in the art under the guidance of the present application are still within the scope of the present application.

[0152] In addition, some features, structures or characteristics in one or more embodiments of the present application can be properly combined.

[0153] If the use of the description, definitions and / or terms in the attached material of the present invention is inconsistent or in conflict with the description, definitions and / or terms of the present invention, the use of the description, definitions and / or terms of the present invention shall prevail.

Claims

1. A smart gas pipeline network valve remote control and monitoring IoT system, characterized in that, The Internet of Things system includes a government safety supervision and management platform, a government safety supervision object platform, a gas equipment object platform, and a gas maintenance object platform. The government safety supervision platform includes a gas company management platform, the gas equipment platform includes at least one control component, and the gas maintenance platform includes at least one human interaction device. The gas company management platform is configured as follows: The system acquires pipeline network information and performance parameters of the control components, and uploads the performance parameters to the government safety supervision and management platform; the performance parameters include maximum communication distance and maximum number of valve connections. In response to receiving the parameter confirmation command issued by the government security supervision and management platform, Based on the pipeline network information, control association groups are determined; the control association groups are group categories corresponding to valves, and each control association group includes one or more valves; Based on the control association group and the performance parameters, the deployment parameters are determined and uploaded to the government safety supervision and management platform; the deployment parameters include the deployment location of the control component and the covered valves; the pipeline information includes valve location, valve type, and valve pipeline type; the covered valves are valves that the control component can control at the deployment location; the distance between the valve location of the covered valve and the deployment location of the control component is less than the maximum communication distance of the control component; In response to receiving the deployment confirmation instruction issued by the government safety supervision and management platform, the deployment parameters are sent to the gas maintenance target platform to arrange staff to deploy the components; After deployment is completed, the adjustment compensation amount of the control component is determined based on the correction cycle and uploaded to the government safety supervision and management platform; the correction cycle is the time interval for periodically correcting the control component; the adjustment compensation amount is the amount used to compensate for the deviation of the control component in the process of regulating the valve. In response to receiving a correction confirmation instruction issued by the government safety supervision and management platform, the control component is corrected based on the adjustment compensation amount; The gas company management platform performs the following during the correction period: Based on the set of control parameters, the control component is controlled to regulate the covered valve to obtain a pair of regulated gas data; the set of control parameters of a control component includes the expected opening degree of all covered valves of the control component. Based on the controlled gas data pair and the controlled parameter group, the controlled offset distribution of the control component is determined; the controlled offset distribution includes the offset of the covering valve of the at least one control component; The amount of regulatory compensation is determined based on the aforementioned regulatory offset distribution.

2. The system according to claim 1, characterized in that, The gas company management platform is further configured as follows: Based on the aforementioned control association grouping, a deployment location group is generated; Based on the deployment location group and the performance parameters, a valve map is constructed; Based on the valve diagram, the layout parameters are determined by a parameter determination model, which is a machine learning model.

3. The system according to claim 1, characterized in that, The gas company management platform is further configured as follows: For each of the aforementioned control components, Based on the controlled gas data pair and the controlled parameter group, the actual control degree of the control component is determined; In response to the actual control degree being less than the control threshold, a fault command is determined and the fault command is sent to the at least one human interaction device; In response to the actual degree of regulation being greater than or equal to the control threshold, the regulation offset component of the control component is determined based on the regulated gas data pair and the regulation parameter group. The control offset distribution is determined based on the control offset component of the at least one control component.

4. A method for remote control and monitoring of valves in a smart gas pipeline network, characterized in that, The method is based on a smart gas pipeline valve remote control and monitoring IoT system, which includes a government safety supervision and management platform, a government safety supervision object platform, a gas equipment object platform, and a gas maintenance object platform. The government safety supervision platform includes a gas company management platform, the gas equipment platform includes at least one control component, and the gas maintenance platform includes at least one human interaction device. The method is executed by the gas company's management platform and includes: The system acquires pipeline network information and performance parameters of the control components, and uploads the performance parameters to the government safety supervision and management platform; the performance parameters include maximum communication distance and maximum number of valve connections. In response to receiving the parameter confirmation command issued by the government security supervision and management platform, Based on the pipeline network information, control association groups are determined; the control association groups are group categories corresponding to valves, and each control association group includes one or more valves; Based on the control association group and the performance parameters, the deployment parameters are determined and uploaded to the government safety supervision and management platform; the deployment parameters include the deployment location of the control component and the covered valves; the pipeline information includes valve location, valve type, and valve pipeline type; the covered valves are valves that the control component can control at the deployment location; the distance between the valve location of the covered valve and the deployment location of the control component is less than the maximum communication distance of the control component; In response to receiving the deployment confirmation instruction issued by the government safety supervision and management platform, the deployment parameters are sent to the gas maintenance target platform to arrange staff to deploy the components; After deployment is completed, the adjustment compensation amount of the control component is determined based on the correction cycle and uploaded to the government safety supervision and management platform; the correction cycle is the time interval for periodically correcting the control component; the adjustment compensation amount is the amount used to compensate for the deviation of the control component in the process of regulating the valve. In response to receiving a correction confirmation instruction issued by the government safety supervision and management platform, the control component is corrected based on the adjustment compensation amount; The step of determining the adjustment compensation amount of the control component based on the correction cycle includes: Performed during the said correction cycle: Based on the set of control parameters, the control component is controlled to regulate the covered valve to obtain a pair of regulated gas data; the set of control parameters of a control component includes the expected opening degree of all covered valves of the control component. Based on the controlled gas data pair and the controlled parameter group, the controlled offset distribution of the control component is determined; the controlled offset distribution includes the offset of the covering valve of the at least one control component; The amount of regulatory compensation is determined based on the aforementioned regulatory offset distribution.

5. The method according to claim 4, characterized in that, The determination of deployment parameters based on the control association grouping and the performance parameters includes: Based on the aforementioned control association grouping, a deployment location group is generated; Based on the deployment location group and the performance parameters, a valve map is constructed; Based on the valve diagram, the layout parameters are determined by a parameter determination model, which is a machine learning model.

6. The method according to claim 4, characterized in that, Determining the control offset distribution of the control component based on the controlled gas data pair and the control parameter group includes: For each of the aforementioned control components, Based on the controlled gas data pair and the controlled parameter group, the actual control degree of the control component is determined; In response to the actual control degree being less than the control threshold, a fault command is determined and the fault command is sent to the at least one human interaction device; In response to the actual degree of regulation being greater than or equal to the control threshold, the regulation offset component of the control component is determined based on the regulated gas data pair and the regulation parameter group. The control offset distribution is determined based on the control offset component of the at least one control component.

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