Intelligent control internet of things system and method for intelligent gas pipeline cutoff valve

Abstract

This invention provides an intelligent control IoT system and method for smart gas pipeline shut-off valves, relating to the field of gas valve technology. The method includes: a smart gas company management platform determining the estimated opening and closing time of the shut-off valve, the valve's control methods (including automatic and remote control), and the remote-controlled and automatic shut-off valve groups based on shut-off valve data and historical gas data from the pipeline; adjusting the set of receiving objects for remote control commands so that the issued remote control commands are only sent to the remote-controlled shut-off valve; generating an automatic control opening command and sending it to the automatic control shut-off valve. The system includes a smart gas government safety supervision and management platform, a smart gas government safety supervision sensor network platform, a smart gas government safety supervision object platform, a smart gas company sensor network platform, and a smart gas equipment object platform. This invention can intelligently determine the initial opening and closing mode of the shut-off valve and intelligently determine whether the opening and closing state of shut-off valves in different gas pipelines needs to be changed.

Description

Technical Field

[0001] This specification relates to the field of gas valve technology, and in particular to an intelligent control Internet of Things system and method for intelligent gas pipeline shut-off valves. Background Technology

[0002] In the field of gas valve technology, shut-off valves are widely used in pipeline systems to ensure effective shut-off of gas flow during emergencies or routine maintenance. Traditional shut-off valves typically rely on remote control commands or automated systems to automatically open and close based on external conditions. However, traditional solutions cannot intelligently determine the initial opening and closing mode of the shut-off valve, nor do they address how to intelligently determine whether shut-off valves in different gas pipelines need to change their opening and closing status. With the increasing complexity of urban gas pipeline networks, relying solely on the operation of traditional shut-off valves is no longer sufficient to meet the requirements of modern smart cities and safety management. Therefore, developing a smart gas shut-off valve system and method integrating Internet of Things (IoT) technology is crucial to more intelligently and efficiently manage the operation of shut-off valves and enhance their reliability in gas networks. Summary of the Invention

[0003] A smart gas pipeline shut-off valve intelligent control IoT method, executed by the smart gas company management platform of the smart gas pipeline shut-off valve intelligent control IoT system, includes: determining the estimated opening and closing time of the shut-off valve based on the shut-off valve data and historical gas data of the gas pipeline where the shut-off valve is located; determining the control mode of the shut-off valve based on the estimated opening and closing time and gas pipeline data, including automatic control and remote control; determining the remote control shut-off valve group and the automatic control shut-off valve group based on the control mode of the shut-off valve; adjusting the receiving object set of the remote control command based on the remote control shut-off valve group so that the issued remote control command is only sent to the remote control shut-off valve; generating an automatic control opening command based on the automatic control shut-off valve group and sending it to the automatic control shut-off valve to enable the automatic control shut-off valve to open / close it. Automatic control is performed; in response to the gas data in the gas pipeline where the remote-controlled shut-off valve is located not meeting a first preset condition, the remote-controlled shut-off valve is controlled to close, thereby cutting off the gas supply to the remote-controlled shut-off valve node; in response to the gas data in the gas pipeline where the remote-controlled shut-off valve is located meeting a second preset condition, the remote-controlled shut-off valve is controlled to open, thereby restoring the gas supply to the remote-controlled shut-off valve node; the first preset condition and the second preset condition are related to different gas data thresholds; based on historical accident data of the gas pipeline where the automatic shut-off valve is located, the initial opening and closing conditions of the automatic shut-off valve are determined and sent to the automatic shut-off valve, so that the automatic shut-off valve sets initial opening and closing parameters based on the initial opening and closing conditions, and automatically performs opening and / or closing operations based on the initial opening and closing parameters; the opening and closing parameters include the gas data threshold when the automatic shut-off valve needs to perform opening and closing operations.

[0004] In order to intelligently determine the initial opening and closing mode of the shut-off valve, and to intelligently determine whether the shut-off valves of different gas pipelines need to change their opening and closing states, this specification provides an intelligent gas pipeline shut-off valve intelligent control IoT system and method.

[0005] The invention relates to an intelligent control IoT system for smart gas pipeline shut-off valves. It includes a smart gas company management platform and an equipment platform; the smart gas company management platform is configured to execute the aforementioned intelligent control IoT method for smart gas pipeline shut-off valves.

[0006] The beneficial effects of the above invention include, but are not limited to: (1) By dividing the remote control and automatic shut-off valve groups into different operating modes, the shut-off valve can be controlled more sensitively, the gas flow can be cut off in time, and the pipeline can be kept safe; (2) By determining the estimated risk value through risk map and risk prediction model to control the opening and closing, the risk can be accurately identified and blocked in time, thereby improving safety and emergency efficiency; (3) The opening and closing parameters are updated regularly using environmental conditions and future gas data to make them conform to future gas transmission conditions, thereby improving the pertinence and effectiveness of the automatic shut-off valve. Attached Figure Description

[0007] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0008] Figure 1 This is an exemplary schematic diagram of an intelligent control IoT system for smart gas pipeline shut-off valves according to some embodiments of this specification;

[0009] Figure 2 This is an exemplary flowchart of an IoT method for intelligent control of smart gas pipeline shut-off valves according to some embodiments of this specification;

[0010] Figure 3 This is an exemplary schematic diagram of a risk assessment model shown according to some embodiments of this specification;

[0011] Figure 4 This is an exemplary flowchart illustrating the updating of start / stop conditions according to some embodiments of this specification; Detailed Implementation

[0012] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0013] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0014] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0015] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0016] Figure 1 This is a schematic diagram of an exemplary platform for an intelligent control IoT system for a smart gas pipeline shut-off valve, as shown in some embodiments of this specification.

[0017] In some embodiments, such as Figure 1 As shown, the intelligent gas pipeline shut-off valve intelligent control IoT system 100 may include an intelligent gas government safety supervision and management platform 110, an intelligent gas government safety supervision sensor network platform 120, an intelligent gas government safety supervision object platform 130, an intelligent gas company sensor network platform 140, and an intelligent gas equipment object platform 150.

[0018] The Smart Gas Government Safety Supervision and Management Platform 110 refers to a comprehensive management platform for government management information. In some embodiments, the Smart Gas Government Safety Supervision and Management Platform may include a comprehensive government supervision database 111 for querying. The comprehensive government supervision database 111 refers to a database that stores data related to the Smart Gas Government Safety Supervision and Management Platform 110. For example, the comprehensive government supervision database 111 may store information related to gas business and information related to gas safety. In some embodiments, the Smart Gas Government Safety Supervision and Management Platform 110 may be implemented based on a processor or server.

[0019] In some embodiments, the smart gas government safety supervision and management platform 110 can receive information related to gas business and information related to gas safety. For example, information such as shut-off valve data of gas pipelines and historical gas data of the gas pipelines where the shut-off valves are located, and store the information related to gas business and gas safety in the government supervision comprehensive database 111 for supervision and management of gas business and gas safety.

[0020] The Smart Gas Government Safety Supervision Sensor Network Platform 120 is a functional platform for managing government sensor communications. In some embodiments, the Smart Gas Government Safety Supervision Sensor Network Platform 120 can be implemented based on communication equipment or servers, etc. In some embodiments, the Smart Gas Government Safety Supervision Sensor Network Platform 120 can realize the functions of sensing and communication of perception information and control information.

[0021] In some embodiments, the smart gas government safety supervision sensor network platform 120 can interact with the smart gas government safety supervision management platform 110 and the smart gas government safety supervision object platform 130.

[0022] The Smart Gas Government Safety Supervision Platform 130 is a platform used by the government to generate regulatory information and implement control information. In some embodiments, the Smart Gas Government Safety Supervision Platform 130 may include a Smart Gas Company Management Platform 131. In some embodiments, the Smart Gas Company Management Platform 131 may include a data center 132.

[0023] In some embodiments, the smart gas company management platform 131 can interact with the smart gas government safety supervision sensor network platform 120. For example, the smart gas company management platform 131 can obtain historical data on gas pipeline operation based on the smart gas government safety supervision sensor network platform 120, such as historical gas data of the gas pipeline where the shut-off valve is located. In some embodiments, the smart gas company management platform 131 can be configured on a server.

[0024] In some embodiments, the smart gas company management platform 131 can read data from the data center 132, such as gas pipeline data of the gas pipeline where the shut-off valve is located.

[0025] For more information about the Smart Gas Company Management Platform 131, please refer to [link / reference needed]. Figure 2 And related content.

[0026] The smart gas company sensor network platform 140 is a platform for the comprehensive management of sensor information of a gas company. In some embodiments, the smart gas company sensor network platform 140 can realize the functions of sensing and communication of perception information and sensing and communication of control information. In some embodiments, the smart gas company sensor network platform 140 includes a communication base station, a router, and a wireless WIFI device.

[0027] In some embodiments, the smart gas company sensor network platform 140 can communicate with the smart gas government safety supervision object platform 130 and the smart gas equipment object platform 150.

[0028] The Smart Gas Equipment Object Platform 150 is a platform used to monitor gas pipelines and their components.

[0029] In some embodiments, the smart gas equipment object platform 150 may be configured to include various gas pipelines and components disposed on (internal or external) the gas pipelines, such as valves, sensors, etc.

[0030] In some embodiments, the smart gas equipment object platform 150 can interact with the smart gas company management platform 131 through the smart gas company sensor network platform 140.

[0031] Figure 2 This is an exemplary flowchart of an intelligent control method for a smart gas pipeline shut-off valve according to some embodiments of this specification. In some embodiments, process 200 is executed by the smart gas company management platform 131 of the smart gas pipeline shut-off valve intelligent control IoT system 100. Figure 2 As shown, process 200 includes the following steps:

[0032] Step 210: Determine the estimated opening and closing time of the shut-off valve based on the shut-off valve data and the historical gas data of the gas pipeline where the shut-off valve is located.

[0033] A shut-off valve is a valve device used to cut off the flow of fluid in a pipeline. For example, a valve installed in a gas pipeline to cut off the flow of gas.

[0034] In some embodiments, shut-off valve data may include the usage distribution, size, type, and actuation force type of the shut-off valve. The usage distribution may include information such as the time, duration, and frequency of opening and / or closing the shut-off valve. The type of shut-off valve may include ball valves, butterfly valves, and gate valves. The actuation force type of the shut-off valve may include electric and pneumatic actuation.

[0035] In some embodiments, the smart gas company management platform can send a read command to the smart gas equipment object platform 150 when needed to read the latest shut-off valve data.

[0036] In some embodiments, historical gas data may include gas temperature data and gas pressure data of gas passing through the shut-off valve during a historical period.

[0037] In some embodiments, the smart gas company management platform can send a read command to the smart gas equipment object platform 150 to read historical gas data stored in gas flow meters, gas meters, etc.

[0038] Estimated opening / closing time refers to the estimated time required to open or close a shut-off valve. For example, it is the time required for the shut-off valve to start opening (closing) and for it to complete opening (closing).

[0039] In some embodiments, the smart gas company management platform can determine the estimated opening and closing time of the shut-off valve using various methods. For example, the platform can obtain the estimated opening and closing time by performing cluster analysis on historical opening and closing records of historical shut-off valves. These historical records include historical shut-off valve data, historical gas data, and historical opening and closing times.

[0040] Historical opening and closing duration refers to the actual opening and closing duration of the historical shut-off valve during a historical period. In some embodiments, the historical opening and closing duration can be obtained by acquiring the duration between the start and end times of the displacement of the electric / pneumatic actuator of the shut-off valve using a displacement sensor placed on the electric / pneumatic actuator of the shut-off valve.

[0041] Cluster analysis can include: constructing cluster vectors based on historical shut-off valve data and historical gas data; performing cluster analysis on multiple historical opening and closing records based on the vector distance between cluster vectors to obtain multiple clusters; constructing a target vector based on the current shut-off valve data and historical gas data; selecting the cluster containing the cluster vector with the highest similarity to the target vector as the target cluster; and using the average historical opening and closing duration of multiple historical opening and closing records in the target cluster as the estimated opening and closing duration. Cluster analysis algorithms can include K-means clustering, etc. The number of clusters K in the K-means clustering algorithm can be preset and determined by technical personnel.

[0042] In some embodiments, the smart gas company management platform can also determine the estimated opening and closing time of the shut-off valve based on the shut-off valve data and the historical gas data of the gas pipeline where the shut-off valve is located, through a duration prediction model; the duration prediction model is a machine learning model.

[0043] A duration prediction model is a model used to determine the estimated opening and closing time of a shut-off valve. In some embodiments, the duration prediction model can be a machine learning model, such as a neural network (NN) model, a deep neural network (DNN) model, or any combination thereof.

[0044] In some embodiments, the inputs to the duration prediction model include shut-off valve data and historical gas data of the gas pipeline where the shut-off valve is located; the output includes the estimated opening and closing duration of the shut-off valve.

[0045] In some embodiments, the duration prediction model can be trained using a large number of first training samples with a first label. The first training samples may include sample shut-off valve data and sample historical gas data of the gas pipeline where the sample shut-off valve is located. In some embodiments, the first training samples may be determined based on historical data. In some embodiments, the first training label may be the historical opening and closing duration of the sample shut-off valve.

[0046] In some embodiments, the smart gas company management platform can input one or more first training samples into the initial prediction model to obtain the estimated start-up and shutdown duration output by the initial prediction model; based on the estimated start-up and shutdown duration output by the initial prediction model and the first label corresponding to one or more first training samples, substitute them into the formula of a predefined loss function to calculate the value of the loss function; based on the value of the loss function, update the model parameters in the initial prediction model in reverse, and the update method for the model parameters may include gradient descent, etc.; when the iteration completion condition is met, the model training ends, and the trained duration prediction model is obtained. The iteration completion condition may include the loss value being less than the loss threshold, the number of iterations reaching the maximum number of iterations, etc.

[0047] In some embodiments of this specification, the estimated opening and closing time of the shut-off valve is predicted by a duration prediction model, which can quickly and accurately determine the estimated opening and closing time of the shut-off valve.

[0048] Step 220: Determine the control method of the shut-off valve based on the estimated opening and closing time and the gas pipeline data of the gas pipeline where the shut-off valve is located.

[0049] Gas pipeline data can include the height difference between the two ends of the gas pipeline section where the shut-off valve is located, the number of bends in the gas pipeline section, and the number of components in the pipeline section.

[0050] In some embodiments, the smart gas company management platform can read gas pipeline data of the gas pipeline where the shut-off valve is located from the data center of the smart gas company management platform.

[0051] Operating method refers to the way the shut-off valve is opened and / or closed.

[0052] In some embodiments, the control methods include automatic control and remote control.

[0053] Automatic control means that the shut-off valve can automatically open or close when certain conditions are met.

[0054] Remote control can be achieved by the smart gas company management platform sending control commands to the shut-off valve to control its opening or closing.

[0055] In some embodiments, the smart gas company management platform can determine the operation mode of the shut-off valve in multiple ways. For example, the platform can determine the pipeline complexity of the gas pipeline where the shut-off valve is located based on the pipeline data; a weighted sum is calculated on the pipeline complexity and the estimated opening / closing time, with the weights preset by technical personnel; if the weighted sum is greater than or equal to a preset automatic control threshold, the shut-off valve operates in automatic control mode; if it is less than the preset automatic control threshold, the shut-off valve operates in remote control mode.

[0056] It is understandable that although the smart gas company management platform can remotely control the shut-off valve more precisely based on the gas data in the entire gas pipeline network, for shut-off valves whose weighted summation results exceed the preset automatic control threshold, remote control is more likely to cause high delays and gas accidents due to their long estimated opening and closing time and high pipeline complexity. Therefore, an automatic operation mode is adopted to enable sensitive operation of the shut-off valve.

[0057] Pipeline complexity refers to the degree of complexity of a gas pipeline structure. In some embodiments, pipeline complexity can be obtained by weighted summation of the height difference between the two ends of the gas pipeline segment where the shut-off valve is located, the number of bends within the pipeline segment, and the number of components within the pipeline segment. The weights can be preset by technical personnel.

[0058] In some embodiments, the preset automatic control threshold can be determined in various ways. For example, the preset automatic control threshold can be determined by technicians based on experience. Another example is that the preset automatic control threshold can be negatively correlated with the data transmission delay between the shut-off valve and the smart gas company management platform. The data transmission delay can be obtained statistically based on historical data. When the data transmission delay is large, a smaller preset automatic control threshold needs to be set to lower the threshold for automatic control of the shut-off valve and prevent the shut-off valve from closing untimely due to large transmission delays.

[0059] In some embodiments, the smart gas company management platform can also determine the control method of the shut-off valve based on the estimated opening and closing time, the gas pipeline data of the gas pipeline where the shut-off valve is located, and the opening and closing status of the shut-off valve.

[0060] The open / closed state of a shut-off valve refers to whether the shut-off valve is currently open or closed.

[0061] In some embodiments, the smart gas company management platform can determine the pipeline complexity of the gas pipeline based on the gas pipeline data of the gas pipeline where the shut-off valve is located; determine the weights of the estimated opening and closing time and pipeline complexity when performing a weighted summation based on the opening and closing status of the shut-off valve; and determine the control method of the shut-off valve based on the result of the weighted summation.

[0062] In some embodiments, when the shut-off valve is in the open state, the weight of pipeline complexity is greater than the weight of the estimated opening and closing time; when the shut-off valve is in the closed state, the weight of pipeline complexity is less than the weight of the estimated opening and closing time.

[0063] It is understandable that when the shut-off valve is in the open state, it is necessary to determine whether the pipeline has returned to normal. At this time, the structure of the gas pipeline needs to be considered more to avoid turbulence in the pipeline. Therefore, a greater weight should be given to the pipeline complexity. When the shut-off valve is in the closed state, it means that an accident has occurred in the pipeline and the gas transportation needs to be cut off. At this time, the timeliness of the shut-off valve opening and closing is more important. Therefore, a greater weight should be given to the estimated opening and closing time.

[0064] In some embodiments of this specification, the operation mode of the shut-off valve is determined by the opening and closing status of the shut-off valve, gas pipeline data, estimated opening and closing time, and the opening and closing status of the shut-off valve. The operation mode can be set according to the actual needs of the shut-off valve, thereby improving operation efficiency.

[0065] Step 230: Determine the remote-controlled shut-off valve group and the automatic shut-off valve group according to the control method of the shut-off valve.

[0066] A remote-controlled shut-off valve assembly refers to a collection of shut-off valves that are operated remotely.

[0067] An automatic shut-off valve assembly refers to a collection of shut-off valves whose operation mode is automatic.

[0068] Step 240: Adjust the set of recipients of remote control commands according to the remote control shut-off valve group so that the issued remote control commands are sent only to the remote control shut-off valve.

[0069] Remote control commands refer to computer commands that remotely control shut-off valves. For example, computer commands issued by a smart gas company's management platform for remotely controlling the opening and / or closing of shut-off valves.

[0070] The receiving object set refers to the collection of shut-off valves that receive remote control commands.

[0071] A remote-controlled shut-off valve is a shut-off valve whose opening and / or closing state is controlled by remote control commands.

[0072] In some embodiments, the smart gas company management platform can adjust the set of recipients for remote control commands in various ways. For example, the smart gas company management platform can directly delete the current set of recipients and set the latest remote-controlled shut-off valve group as the set of recipients for remote control commands.

[0073] In some embodiments, in response to a change in the open / closed state of the shut-off valve, the smart gas company management platform can also determine the updated control method of the shut-off valve; adjust the set of recipients of the remote control command according to the updated control method; and / or generate an automatic control opening command and send it to the shut-off valve so that the shut-off valve can automatically control its open / closed state.

[0074] A change in the open / closed state refers to the shut-off valve changing from the open state to the closed state or from the closed state to the open state.

[0075] In some embodiments, when the opening and closing state of the shut-off valve changes, the smart gas company management platform can redetermine the operation mode of the shut-off valve based on the method in step 230 above, thereby obtaining an updated operation mode.

[0076] It is understandable that the change in the opening and closing state of the shut-off valve will cause changes in the operational weight allocation of the gas pipeline complexity and the estimated opening and closing time when determining the operation mode of the shut-off valve. In addition, the operation mode determined by the method in step 230 may also be different due to the differences in historical gas data at different times.

[0077] In some embodiments, the smart gas company management platform can redetermine the remote-controlled shut-off valve group and the automatic shut-off valve group based on the updated operating mode, thereby readjusting the set of recipients of remote control commands.

[0078] In some embodiments of this specification, the operating mode of the shut-off valve is redefined when the open / closed state of the shut-off valve changes, thereby adjusting the set of recipients of the remote control command. This enables dynamic adjustment of the shut-off valve's operating mode to ensure the valve's working sensitivity.

[0079] Step 250: Generate an automatic opening command based on the automatic shut-off valve group and send it to the automatic shut-off valve so that the automatic shut-off valve can automatically control its opening / closing operation.

[0080] An automatic control opening command is a computer command that grants a shut-off valve the authority to automatically control its opening and closing status. In some embodiments, when a shut-off valve receives an automatic control opening command, it can control its own opening and closing status.

[0081] In some embodiments, the smart gas company management platform can generate a corresponding automatic control opening command based on the shut-off valve data of the automatic shut-off valve in the automatic shut-off valve group, and send it to the corresponding automatic shut-off valve.

[0082] Step 260: In response to the gas data of the gas pipeline where the remote shut-off valve is located not meeting the first preset condition, control the remote shut-off valve to close, so as to cut off the gas supply to the remote shut-off valve node.

[0083] In some embodiments, the first preset condition may include gas temperature data being less than a first preset temperature threshold or gas pressure data being less than a first preset pressure threshold.

[0084] In some embodiments, the first preset temperature threshold and the first preset pressure threshold can be preset by a technician.

[0085] In some embodiments, the first preset temperature threshold and the first preset pressure threshold can be adjusted based on the estimated opening and closing time. For example, the smart gas company management platform can proportionally lower the first preset temperature threshold and / or the first preset pressure threshold based on the difference between the estimated opening and closing time and the standard opening and closing time. The standard opening and closing time can be determined based on the actual opening and closing time of the shut-off valve when it is first put into use.

[0086] It is understandable that when the estimated opening and closing time differs significantly from the standard opening and closing time, it indicates that the shut-off valve has aged to some extent. In this case, lowering the first preset temperature threshold and / or the first preset pressure threshold can reduce the judgment conditions for closing the shut-off valve, thereby responding more sensitively to abnormal situations in the gas pipeline and preventing gas accidents.

[0087] Step 270: In response to the gas data in the gas pipeline where the remote-controlled shut-off valve is located meeting the second preset condition, control the remote-controlled shut-off valve to open so that the gas supply to the remote-controlled shut-off valve node is restored.

[0088] In some embodiments, the first preset condition and the second preset condition are related to different gas data thresholds.

[0089] For example, the second preset condition may include gas temperature data being less than a second preset temperature threshold and gas pressure data being less than a second preset pressure threshold. The second preset temperature threshold and the second preset pressure threshold may be preset and determined by a technician. In some embodiments, the second preset temperature threshold is less than a first preset temperature threshold, and the second preset pressure threshold is less than the first preset pressure threshold.

[0090] In some embodiments, when the gas data of the gas pipeline where the remote-controlled shut-off valve is located meets the second preset condition, the smart gas company management platform can generate a remote control command to open the shut-off valve and send it to the remote-controlled shut-off valve to control the opening of the remote-controlled shut-off valve and realize the restoration of gas supply to the remote-controlled shut-off valve node.

[0091] Step 280: Based on the historical accident data of the gas pipeline where the automatic shut-off valve is located, determine the initial opening and closing conditions of the automatic shut-off valve and send them to the automatic shut-off valve so that the automatic shut-off valve can set the initial opening and closing parameters based on the initial opening and closing conditions and automatically perform opening and / or closing operations based on the initial opening and closing parameters.

[0092] Historical accident data refers to the gas data at the shut-off valve when a gas accident occurred within a historical period.

[0093] In some embodiments, the smart gas company management platform can obtain historical accident data from the data center.

[0094] Initial opening and closing conditions refer to the conditions used by the self-controlled shut-off valve to determine whether it needs to be opened or closed. In some embodiments, initial opening and closing conditions may include initial opening conditions and initial closing conditions. Initial opening conditions may include gas temperature data being less than an opening temperature threshold and gas pressure data being less than an opening pressure threshold; initial closing conditions may include gas temperature data being higher than a closing temperature threshold or gas pressure data being higher than a closing pressure threshold.

[0095] In some embodiments, the smart gas company management platform can calculate the average gas temperature data and the average gas pressure data during stable gas operation as the opening temperature threshold and the opening pressure threshold, respectively, thereby obtaining the initial opening conditions.

[0096] In some embodiments, the smart gas company management platform can calculate the average gas temperature data at the time of a gas accident in historical accident data as the shut-off temperature threshold, and calculate the average gas pressure data at the time of a gas accident as the shut-off pressure threshold, thereby obtaining the initial shut-off conditions.

[0097] Initial opening and closing parameters refer to the opening and closing parameters used by the self-controlled shut-off valve to determine whether to automatically open or close.

[0098] In some embodiments, the opening and closing parameters include gas data thresholds when the self-controlled shut-off valve needs to perform opening and closing operations. These gas data thresholds may include an opening temperature threshold and an opening pressure threshold for the self-controlled shut-off valve to determine whether it needs to automatically open, and a closing temperature threshold and a closing pressure threshold for the self-controlled shut-off valve to determine whether it needs to automatically close.

[0099] In some embodiments, the smart gas company management platform can determine the initial start-up and shut-down parameters based on the initial start-up and shut-down conditions.

[0100] In some embodiments, when the gas temperature data in the gas pipeline where the self-controlled shut-off valve is located is lower than the opening temperature threshold and the gas pressure data is lower than the opening pressure threshold, the self-controlled shut-off valve automatically opens.

[0101] In some embodiments, when the gas temperature data in the gas pipeline where the self-controlled shut-off valve is located is higher than the shut-off temperature threshold, or the gas pressure data is higher than the shut-off pressure threshold, the self-controlled shut-off valve automatically closes.

[0102] In some embodiments of this specification, the estimated opening and closing time of the shut-off valve is determined by the shut-off valve data and historical gas data, thereby dividing the shut-off valve into a remote-controlled shut-off valve group and an automatic shut-off valve group. Different operating methods are adopted for different shut-off valves, which can more sensitively control the operation of the shut-off valve. The opening and closing of the remote-controlled shut-off valve is controlled by the first preset condition and the second preset condition, which can accurately control the operation of the shut-off valve when needed, avoiding gas safety hazards caused by emergencies or gas maintenance. The automatic control of the shut-off valve is enabled by the initial opening and closing condition, which can respond more promptly and cut off the gas flow in the event of an emergency in the gas pipeline, ensuring the safe operation of the gas pipeline.

[0103] It should be noted that the above description of process 200 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 200 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0104] Figure 3 This is an exemplary schematic diagram of a risk assessment model shown in some embodiments of this specification.

[0105] In some embodiments, the first preset condition further includes that the estimated risk value of the gas pipeline where the remote-controlled shut-off valve is located in a preset future period is not greater than a preset risk threshold; the smart gas company management platform determines the estimated risk value by: constructing a risk map 330 based on the gas data 310 and gas pipeline data 320 of the gas pipeline where the remote-controlled shut-off valve is located; and determining the estimated risk value 350 of the gas pipeline where the remote-controlled shut-off valve is located in a preset future period through a risk prediction model 340 based on the risk map 330, wherein the risk prediction model is a machine learning model.

[0106] The preset future time period can be within a week or a month after the current time, and can be determined based on presets.

[0107] The estimated risk value can characterize the likelihood of a gas pipeline experiencing a gas accident in the future.

[0108] The preset risk threshold refers to the maximum acceptable estimated risk value, which can be preset by managers based on prior experience.

[0109] In some embodiments, the preset risk threshold is related to environmental operating condition data within a preset historical period.

[0110] The preset historical time period can be the past week, the past month, etc., and can be preset and determined by technical personnel.

[0111] In some embodiments, the smart gas company management platform can statistically analyze the temperature range and humidity range of environmental operating condition data from a preset historical time period, and calculate a weighted sum of the temperature and humidity ranges. A preset risk threshold is then determined based on this weighted sum; a larger weighted sum results in a smaller preset risk threshold. The weights used in the weighted summation are preset by technical personnel. For more information on environmental operating condition data, please refer to [link / reference]. Figure 4 And its related descriptions.

[0112] In some embodiments of this specification, a preset risk threshold is determined by environmental operating condition data over a historical period. When the environmental operating condition data changes significantly, the preset risk threshold can be lowered to close the remote-controlled shut-off valve in a timely manner, thereby ensuring the safety of gas transportation.

[0113] A risk graph is a graph structure constructed based on data related to gas accidents. In some embodiments, the risk graph 330 is a directed graph structure. Nodes 331 of the risk graph can correspond to shut-off valves in a gas pipeline network. The characteristics of a node can include gas data at the shut-off valve, shut-off valve data, and shut-off valve type. The shut-off valve type can include remote-controlled shut-off valves or automatic shut-off valves. Edges 332 of the risk graph 330 can correspond to gas pipelines that directly connect two shut-off valves. The gas flow direction in the gas pipeline is the direction of edge 332. The characteristics of an edge can include the distance between the two shut-off valves and gas pipeline data. For a description of shut-off valve data, gas data, and gas pipeline data, see [link to documentation]. Figure 2 And its related descriptions.

[0114] In some embodiments, the smart gas company management platform can obtain gas data, shut-off valve data, gas pipeline data, and distances between shut-off valves in the gas pipeline network from the data center to construct a risk map 330. The smart gas company management platform can also obtain the latest gas data, shut-off valve data, or gas pipeline data to update the risk map 330.

[0115] A risk prediction model is a model used to determine the predicted risk value. In some embodiments, the risk prediction model is a machine learning model, such as a graph neural network (GNN) model.

[0116] In some embodiments, the input of the risk prediction model 340 includes a risk map 330, and the output includes the estimated risk value 350 of the gas pipeline corresponding to the edge connected to the node of each remote shut-off valve in a preset future time period.

[0117] In some embodiments, the risk prediction model can be trained based on a large number of second training samples with second labels. The second training samples include a sample risk map, and the second label includes the historical risk value of the gas pipeline corresponding to the edge connected to the remote-controlled shut-off valve in the sample risk map.

[0118] The second training sample can be constructed based on historical gas data, historical shut-off valve data, historical gas pipeline data, and the distances between historical shut-off valves within the first historical period. The second label can be determined based on the gas accident data of the sample gas pipelines of the sample remote-controlled shut-off valve nodes in the second training sample during the second historical period; for example, the number of historical accidents can be directly used as the second label for the edges corresponding to the sample gas pipelines. The first historical period is earlier than the second historical period.

[0119] In some embodiments, the training dataset for training the risk prediction model includes multiple training subsets, each training subset corresponding to at least one gas pipeline classification, and the number of training subsets corresponding to each gas pipeline classification is greater than a threshold for the number of subsets corresponding to that gas pipeline classification; the threshold for the number of subsets corresponding to each gas pipeline classification is determined based on the sample gas pipelines included in the training subsets corresponding to the gas pipeline classification and the number of opening and closing times of the corresponding sample shut-off valves.

[0120] The training dataset refers to the set of second training samples. A training subset may include one or more second training samples.

[0121] Gas pipeline classification refers to the categorization of gas pipelines. For example, gas pipeline classification can be derived by dividing gas pipelines based on gas data and gas pipeline data.

[0122] In some embodiments, the smart gas company management platform can construct pipeline feature vectors corresponding to gas pipelines based on gas data and gas pipeline data; cluster the gas pipelines based on the pipeline feature vectors to obtain multiple pipeline clusters; and treat each pipeline cluster as a gas pipeline category. The clustering methods include, but are not limited to, K-means clustering, DBSCAN clustering, and density peak clustering. In some embodiments, the smart gas company management platform can determine the subset size threshold corresponding to each gas pipeline category based on the statistical value of the number of opening and closing times of sample shut-off valves in the sample gas pipelines corresponding to each gas pipeline category. For example, the statistical value of the number of opening and closing times may include the standard deviation of the number of opening and closing times of multiple sample shut-off valves on the sample gas pipelines corresponding to that gas pipeline category; the larger the standard deviation of the number of opening and closing times, the larger the subset size threshold corresponding to that gas pipeline category.

[0123] It is understandable that the larger the standard deviation of the number of opening and closing times of shut-off valves in the sample gas pipelines corresponding to the gas pipeline classification, the greater the difference in gas data at each shut-off valve in the corresponding gas pipeline network. This will, to some extent, increase the complexity of the risk prediction model when predicting the risk value. Therefore, it is necessary to ensure that the sample gas pipelines for each gas pipeline classification have a sufficient number of samples in the second training sample to ensure the accuracy and generalization ability of the trained risk prediction model.

[0124] In some embodiments of this specification, by limiting the lower limit of the number of samples corresponding to each gas pipeline category in the second training samples, the risk prediction model can avoid overfitting or underfitting some training data due to the unevenness of gas pipeline types, thereby improving the accuracy and generalization of the model.

[0125] In some embodiments, the smart gas company management platform can train the initial risk prediction model based on the second training samples and their corresponding second labels to obtain a trained risk prediction model. The specific training process is similar to that of the duration prediction model; please refer to [link to relevant documentation]. Figure 2 The relevant description includes step 210, which describes the training process of the duration prediction model.

[0126] In some embodiments of this specification, a risk map is constructed using gas pipeline data, gas data, shut-off valve data, etc., in the gas pipeline network. The risk map is then processed by a risk prediction model to obtain a predicted risk value, which is used as a first preset condition to control the opening and closing of the remote-controlled shut-off valve. This allows for more accurate identification of gas accident risks in the gas pipeline network and timely blocking, thereby improving the safety of gas pipeline network operation and the efficiency of emergency response.

[0127] Figure 4This is an exemplary flowchart illustrating the updating of opening and closing conditions according to some embodiments of this specification. In some embodiments, process 400 is executed by the smart gas pipeline shut-off valve intelligent control IoT system 100's smart gas company management platform 131. Figure 4 As shown, process 400 includes the following steps:

[0128] In some embodiments, the smart gas company management platform can update the initial start-up and shut-down parameters by performing the following steps 410 to 420 to obtain the updated start-up and shut-down parameters.

[0129] Step 410: Determine the update cycle of the opening and closing conditions of the automatic shut-off valve based on the shut-off valve data and the usage time of the gas pipeline where the automatic shut-off valve is located.

[0130] The usage duration of a gas pipeline refers to the time elapsed from its installation and commissioning to the present moment. In some embodiments, the usage duration of a gas pipeline can be expressed in units of time such as days.

[0131] The update cycle refers to the time interval between two consecutive updates of the start and stop conditions. For example, one week.

[0132] In some embodiments, the smart gas company management platform can determine the update cycle of the automatic shut-off valve by querying a preset cycle table based on the shut-off valve data of the automatic shut-off valve and the usage time of the gas pipeline where the automatic shut-off valve is located.

[0133] The preset cycle table records the update cycle corresponding to the data of different shut-off valves and the usage time of the gas pipeline they are located in.

[0134] In some embodiments, for multiple historical self-controlled shut-off valves located in different gas pipeline networks, the smart gas company management platform can cluster them based on the historical shut-off valve data of each historical self-controlled shut-off valve and the historical usage time of the gas pipeline where the historical self-controlled shut-off valve is located, resulting in multiple self-controlled clusters. Within each self-controlled cluster, the historical update cycle with the most adjustments to the opening and closing parameters during updates is taken as the update cycle corresponding to that self-controlled cluster and recorded in a preset cycle table. The clustering methods that can be used here can be found in [reference needed]. Figure 3 The relevant instructions explain the clustering method used to determine the classification of gas pipelines.

[0135] In some embodiments, in response to meeting preset periodic conditions, the smart gas company management platform can adjust the update cycle; the preset periodic conditions include that the opening and closing parameters of the self-controlled shut-off valve are updated for N consecutive update cycles, or that the opening and closing parameters of the self-controlled shut-off valve are not updated for M consecutive update cycles.

[0136] In some embodiments, the values ​​of N and M may also be set by technicians based on experience.

[0137] In some embodiments, the value of M can also be related to the distribution density of self-controlled shut-off valves within the gas pipeline network; the higher the distribution density of self-controlled shut-off valves, the larger the value of M. The distribution density of self-controlled shut-off valves can include the average number of self-controlled shut-off valves in multiple unit areas within the gas pipeline network. A unit area can be a residential area, etc.

[0138] It is understandable that the greater the distribution density of self-controlled shut-off valves, the more complex the connection relationships or gas data distribution at various points within the gas pipeline network. In this case, the M value can be increased to stabilize the gas pipeline network before adjusting the update cycle, thereby improving the stability of the gas pipeline network operation.

[0139] In some embodiments, if the opening and closing parameters of the self-controlled shut-off valve are updated for N consecutive update cycles, the smart gas company management platform can shorten the update cycle by a preset adjustment amount. If the opening and closing parameters of the self-controlled shut-off valve are not updated for M consecutive update cycles, the smart gas company management platform will extend the update cycle by a preset adjustment amount.

[0140] In some embodiments, the preset adjustment amount can be set by a technician based on experience. For example, one day.

[0141] In some embodiments of this specification, the update cycle is adjusted by checking whether the opening and closing parameters of the shut-off valve are updated within multiple update cycles. This ensures the rationality of the update cycle and thus guarantees the timeliness and sensitivity of the opening and closing parameters of the automatic shut-off valve.

[0142] Step 420: Based on the update cycle, periodically update the opening and closing conditions of the automatic shut-off valve so that the automatic shut-off valve sets the updated opening and closing parameters based on the updated opening and closing conditions, and automatically controls the opening and closing state based on the updated opening and closing parameters.

[0143] In some embodiments, the smart gas company management platform can update the opening and closing conditions of the self-controlled shut-off valve every update cycle.

[0144] In some embodiments, every update cycle, the smart gas company management platform can send the updated opening and closing parameters to the corresponding self-controlled shut-off valve in the smart gas equipment object platform 150 through the smart gas company sensor network platform 140, so as to periodically update the opening and closing conditions of the self-controlled shut-off valve.

[0145] The updated on / off parameters refer to the on / off parameters obtained by updating the initial on / off parameters.

[0146] In some embodiments, the smart gas company management platform can determine the updated opening and closing parameters in various ways. For example, the smart gas company management platform can redetermine the opening and closing parameters of the automatic shut-off valve based on the updated opening and closing conditions, using the same method as determining the initial opening and closing parameters, to obtain the updated opening and closing parameters. For details on determining the initial opening and closing parameters, please refer to [link to relevant documentation]. Figure 2 The content of step 280.

[0147] In some embodiments, the smart gas company management platform can acquire environmental operating condition data within a preset historical period at the beginning of each update cycle; determine the opening and closing conditions of the automatic shut-off valve in the next cycle based on the shut-off valve data, environmental operating condition data, and future gas data in the next cycle; and generate parameter update instructions based on the opening and closing conditions to control the automatic shut-off valve to update its opening and closing parameters.

[0148] Environmental operating condition data refers to data related to the working environment of the shut-off valve. For example, this includes temperature and humidity data sequences of the external environment of the pipeline, as well as vibration intensity sequences of the pipeline. The temperature, humidity, and vibration intensity sequences can each include temperature data, humidity data, and vibration intensity data at multiple time points.

[0149] In some embodiments, the smart gas company management platform can obtain environmental operating condition data for a preset historical period from the data center. The environmental operating condition data in the data center is collected and uploaded in real time by the environmental monitoring devices (such as temperature sensors, humidity sensors, and vibration sensors) of the smart gas equipment object platform 150 during the preset historical period. For more information on the preset historical period, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0150] Future gas data refers to gas data for future periods, such as gas data within the next update cycle.

[0151] In some embodiments, the smart gas company management platform can determine future gas data based on the gas company's gas transportation plan for the next update cycle.

[0152] In some embodiments, the smart gas company management platform can construct a feature vector based on the shut-off valve data of the self-controlled shut-off valve, environmental operating condition data, and future gas data in the next update cycle; retrieve the reference feature vector with the highest similarity from the gas vector database based on the feature vector; and use the reference opening and closing conditions corresponding to the reference feature vector with the highest similarity as the opening and closing conditions for the next update cycle.

[0153] The gas vector database includes shut-off valve data from multiple historical self-controlled shut-off valves within the third historical period, environmental operating condition data from multiple historical gas pipelines, and reference feature vectors constructed from gas data within the corresponding fourth historical period; as well as reference opening and closing conditions corresponding to each reference feature vector. The third historical period is earlier than the fourth historical period. The reference opening and closing conditions are the historical opening and closing conditions among the multiple conditions corresponding to the reference feature vectors in the historical data, where triggering the historical opening and closing condition causes a change in the opening and closing state of the historical shut-off valve, resulting in the shortest time required for the gas pressure on both sides of the shut-off valve to reach a stable state.

[0154] In some embodiments, the smart gas company management platform can determine the opening and closing conditions of the automatic shut-off valve in the next cycle based on the shut-off valve data, environmental operating condition data, and future gas data in the next cycle, using a threshold prediction model.

[0155] A threshold prediction model is a model used to determine the on / off conditions for the next cycle. In some embodiments, the threshold prediction model is a machine learning model, such as a Convolutional Neural Network (CNN) model, a Deep Neural Network (DNN) model, or any combination thereof. In some embodiments, the threshold evaluation model may include a feature extraction layer and a threshold determination layer.

[0156] The input to the feature extraction layer can include environmental operating condition data, and the output can include environmental operating condition features.

[0157] Environmental operating condition characteristics refer to data extracted from environmental operating condition data. In some embodiments, environmental operating condition characteristics may include a data sequence consisting of temperature data, humidity data, and pipeline vibration intensity at one or more time points.

[0158] In some embodiments, the feature extraction layer can be trained using a large number of third training samples with third labels. The third training samples may include sample environmental condition data of multiple sample self-controlled shut-off valves located in sample gas pipelines. The third label may include environmental condition characteristics of the sample gas pipeline where the sample self-controlled shut-off valve is located, corresponding to the third training sample. In some embodiments, the third training samples may be constructed based on historical environmental condition data of gas pipelines where historical self-controlled shut-off valves are located; determining the third label may include: multiple different candidate environmental condition features composed of temperature data, humidity data, and vibration intensity selected from multiple time points in the third training samples; inputting each candidate environmental condition feature, the shut-off valve data of the sample self-controlled shut-off valve, and sample future gas data into a threshold determination layer to obtain candidate opening and closing conditions; and using the candidate environmental condition feature corresponding to the candidate opening and closing condition with the smallest difference from the initial opening and closing conditions as the third label.

[0159] The training process for the feature extraction layer is similar to that for the duration prediction model. For details on the training process of the duration prediction model, please refer to [link to training process]. Figure 2 The relevant description of step 210.

[0160] In some embodiments, the input to the threshold determination layer may include environmental operating condition characteristics, future gas data, and shut-off valve data of the self-controlled shut-off valve, and the output may include the opening and closing parameters of the self-controlled shut-off valve for the next cycle.

[0161] In some embodiments, the threshold determination layer can be trained using a large number of fourth training samples with fourth labels. The fourth training samples include sample environmental conditions, sample future gas data, and sample self-controlled shut-off valve data; the fourth label can include the opening and closing conditions of the sample self-controlled shut-off valve in the next cycle. In some embodiments, the fourth training samples can be constructed based on the historical environmental conditions of historical gas pipelines containing multiple historical self-controlled shut-off valves within a fifth historical period, historical gas data within a sixth historical period, and historical shut-off valve data; the fourth label can be constructed based on the historical opening and closing conditions corresponding to multiple historical self-controlled shut-off valves within the sixth historical period, where the time required for the gas pressure on both sides of the shut-off valve to reach a stable state after the shut-off valve's opening and closing state changes. The fifth historical period is earlier than the sixth historical period.

[0162] The training process for the threshold determination layer is similar to that for the duration prediction model. For details on the training process of the duration prediction model, please refer to [link to training process]. Figure 2 The relevant description of step 210.

[0163] In some embodiments of this specification, the opening and closing conditions of the self-controlled shut-off valve in the next cycle are obtained by processing environmental operating condition data, shut-off valve data and future gas data through a threshold evaluation model, which can yield more accurate and reasonable opening and closing conditions for the next cycle.

[0164] Parameter update commands are computer commands that update the opening and closing parameters of an automatic shut-off valve. For example, commands that replace or update the initial opening and closing conditions of an automatic shut-off valve.

[0165] In some embodiments, the smart gas company management platform can use the start-up and shut-down conditions of the next cycle as the updated start-up and shut-down conditions, and generate corresponding parameter update instructions.

[0166] In some embodiments, the smart gas company management platform can send parameter update instructions to the corresponding self-controlled shut-off valve in the smart gas equipment object platform through the smart gas company sensor network platform 140 to update the opening and closing parameters of the self-controlled shut-off valve.

[0167] In some embodiments of this specification, the opening and closing conditions for the next update cycle are determined by pre-setting environmental operating condition data, shut-off valve data, and future gas data within the next update cycle. This allows the updated opening and closing parameters to better match the gas transportation situation in the future, thereby improving the relevance and effectiveness of the automatic shut-off valve opening and closing parameters.

[0168] In some embodiments of this specification, the update cycle of the opening and closing conditions is determined by the shut-off valve data and the usage time of the shut-off valve, thereby periodically updating the opening and closing parameters of the shut-off valve and ensuring the timeliness and accuracy of the shut-off valve.

[0169] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0170] Furthermore, this specification uses specific terms to describe embodiments thereof, such as "an embodiment". "An Example" The terms "and / or some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification can be appropriately combined.

[0171] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0172] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0173] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0174] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0175] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A smart gas pipeline shut-off valve intelligent control Internet of Things system, characterized in that, This includes a smart gas company management platform; the smart gas company management platform is configured as follows: Based on the shut-off valve data and the historical gas data of the gas pipeline where the shut-off valve is located, the estimated opening and closing time of the shut-off valve is determined. The estimated opening and closing time refers to the estimated time required to open or close the shut-off valve. Based on the estimated opening and closing time and the gas pipeline data of the gas pipeline where the shut-off valve is located, the control mode of the shut-off valve is determined, and the control mode includes automatic control and remote control. Based on the control method of the shut-off valve, determine the remote-controlled shut-off valve group and the automatic shut-off valve group; According to the remote control shut-off valve group, adjust the set of recipients of the remote control command so that the issued remote control command is sent only to the remote control shut-off valve; Based on the self-controlled shut-off valve group, a self-controlled opening command is generated and sent to the self-controlled shut-off valve so that the self-controlled shut-off valve can automatically control its opening or closing operation.

2. The system of claim 1, wherein, The intelligent gas company management platform is further configured as follows: In response to the gas data of the gas pipeline where the remote-controlled shut-off valve is located not meeting the first preset condition, the remote-controlled shut-off valve is controlled to close, thereby cutting off the gas supply to the remote-controlled shut-off valve node; In response to the gas data in the gas pipeline where the remote-controlled shut-off valve is located meeting a second preset condition, the remote-controlled shut-off valve is controlled to open so that the gas supply to the remote-controlled shut-off valve node is restored; the first preset condition and the second preset condition are related to different gas data thresholds; Based on historical accident data of the gas pipeline where the self-controlled shut-off valve is located, the initial opening and closing conditions of the self-controlled shut-off valve are determined and sent to the self-controlled shut-off valve, so that the self-controlled shut-off valve sets initial opening and closing parameters based on the initial opening and closing conditions, and automatically performs opening and / or closing operations based on the initial opening and closing parameters; the opening and closing parameters include the gas data threshold when the self-controlled shut-off valve needs to perform opening and closing operations.

3. The system of claim 2, wherein, The first preset condition also includes that the estimated risk value of the gas pipeline where the remote-controlled shut-off valve is located is not greater than a preset risk threshold within a preset future time period; The intelligent gas company management platform is further configured as follows: A risk map is constructed based on the gas data and the gas pipeline data of the gas pipeline where the remote-controlled shut-off valve is located; Based on the risk map, the estimated risk value of the gas pipeline where the remote-controlled shut-off valve is located is determined in the preset future time period through a risk prediction model, wherein the risk prediction model is a machine learning model.

4. The system of claim 2, wherein, The intelligent gas company management platform is further configured as follows: For the aforementioned self-controlled shut-off valve: The update cycle of the opening and closing conditions of the automatic shut-off valve is determined based on the shut-off valve data of the automatic shut-off valve and the usage time of the gas pipeline where the automatic shut-off valve is located. Based on the update cycle, the opening and closing conditions of the self-controlled shut-off valve are updated periodically, so that the self-controlled shut-off valve sets updated opening and closing parameters based on the updated opening and closing conditions, and automatically controls the opening and closing state based on the updated opening and closing parameters.

5. The system of claim 4, wherein, The intelligent gas company management platform is further configured as follows: At the start of each update cycle, environmental operating condition data within a preset historical time period is acquired; Based on the shut-off valve data, the environmental operating condition data, and the future gas data for the next cycle, the opening and closing conditions of the self-controlled shut-off valve in the next cycle are determined. Based on the opening and closing conditions, a parameter update command is generated to control the automatic shut-off valve to update its opening and closing parameters.

6. A smart gas pipeline shut-off valve intelligent control Internet of Things method, characterized in that, The method is executed by the smart gas company management platform of the smart gas pipeline shut-off valve intelligent control IoT system, including: Based on the shut-off valve data and the historical gas data of the gas pipeline where the shut-off valve is located, the estimated opening and closing time of the shut-off valve is determined. The estimated opening and closing time refers to the estimated time required to open or close the shut-off valve. Based on the estimated opening and closing time and the gas pipeline data of the gas pipeline where the shut-off valve is located, the control mode of the shut-off valve is determined, and the control mode includes automatic control and remote control. Based on the control method of the shut-off valve, determine the remote-controlled shut-off valve group and the automatic shut-off valve group; According to the remote control shut-off valve group, adjust the set of recipients of the remote control command so that the issued remote control command is sent only to the remote control shut-off valve; Based on the self-controlled shut-off valve group, a self-controlled opening command is generated and sent to the self-controlled shut-off valve so that the self-controlled shut-off valve can automatically control its opening or closing operation.

7. The method of claim 6, wherein, The method further includes: in response to the gas data of the gas pipeline where the remote-controlled shut-off valve is located not meeting a first preset condition, controlling the remote-controlled shut-off valve to close, thereby cutting off the gas supply to the remote-controlled shut-off valve node; In response to the gas data in the gas pipeline where the remote-controlled shut-off valve is located meeting a second preset condition, the remote-controlled shut-off valve is controlled to open so that the gas supply to the remote-controlled shut-off valve node is restored; the first preset condition and the second preset condition are related to different gas data thresholds; Based on historical accident data of the gas pipeline where the self-controlled shut-off valve is located, the initial opening and closing conditions of the self-controlled shut-off valve are determined and sent to the self-controlled shut-off valve, so that the self-controlled shut-off valve sets initial opening and closing parameters based on the initial opening and closing conditions, and automatically performs opening and / or closing operations based on the initial opening and closing parameters; the opening and closing parameters include the gas data threshold when the self-controlled shut-off valve needs to perform opening and closing operations.

8. The method of claim 7, wherein, The first preset condition also includes that the estimated risk value of the gas pipeline where the remote-controlled shut-off valve is located is not greater than a preset risk threshold within a preset future time period; Determining the estimated risk value includes: A risk map is constructed based on the gas data and the gas pipeline data of the gas pipeline where the remote-controlled shut-off valve is located; Based on the risk map, the estimated risk value of the gas pipeline where the remote-controlled shut-off valve is located is determined in the preset future time period through a risk prediction model, wherein the risk prediction model is a machine learning model.

9. The method of claim 7, wherein, The method further includes: For the aforementioned self-controlled shut-off valve: The update cycle of the opening and closing conditions of the automatic shut-off valve is determined based on the shut-off valve data of the automatic shut-off valve and the usage time of the gas pipeline where the automatic shut-off valve is located. Based on the update cycle, the opening and closing conditions of the self-controlled shut-off valve are updated periodically, so that the self-controlled shut-off valve sets updated opening and closing parameters based on the updated opening and closing conditions, and automatically controls the opening and closing state based on the updated opening and closing parameters.

10. The method of claim 9, wherein, The method further includes: At the start of each update cycle, environmental operating condition data within a preset historical time period is acquired; Based on the shut-off valve data, the environmental operating condition data, and the future gas data for the next cycle, the opening and closing conditions of the self-controlled shut-off valve in the next cycle are determined. Based on the opening and closing conditions, a parameter update command is generated to control the automatic shut-off valve to update its opening and closing parameters.

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