Pipeline valve verification Internet of Things system and method based on intelligent gas safety supervision

Abstract

The invention provides a pipeline valve verification Internet of Things system and method based on intelligent gas safety supervision, and the method comprises the steps: generating a second verification instruction based on a flexible verification batch and a first verification instruction; performing second verification on the gas pipeline and the pipeline valve to obtain a second verification result; based on the first verification result and the second verification result, a to-be-maintained valve is determined; based on the to-be-maintained valve, work order information is determined; determining a work order distribution instruction based on the work order information; and generating a parameter adjusting instruction. The Internet of Things system comprises an intelligent gas government safety supervision management platform, an intelligent gas government safety supervision sensing network platform, an intelligent gas government safety supervision object platform, a gas company management platform, a gas company sensing network platform, a gas equipment object platform and a gas equipment maintenance object platform. According to the method, the to-be-maintained valve with potential safety hazards can be quickly and accurately positioned, and the monitoring frequency of the valve is enhanced before the valve is maintained, so that the gas use safety is ensured.

Description

Technical Field

[0001] This specification relates to the field of gas management, and in particular to an Internet of Things system and method for pipeline valve verification based on smart gas safety supervision. Background Art

[0002] For valves on gas pipelines, in addition to repairs when they fail, daily maintenance is also required to reduce risks. Gas companies usually conduct regular inspections of gas pipelines in various regions. While determining the maintenance and replacement strategies for each valve on the gas pipeline to reduce risks, it is necessary to minimize the impact on the daily transportation of gas in the gas pipeline and to promptly identify gas pipelines with a high frequency of failures.

[0003] In order to detect the fault conditions of gas pipelines, CN105741186A discloses a pipeline gas data processing method and system based on user levels, which classifies pipeline gas users, obtains actual gas usage conditions, and classifies different types of pipeline gas users according to corresponding preset gas management indicators, so as to set the most suitable differentiated pipeline gas data processing method according to gas users of different levels, thereby detecting and reducing the safety hazards of gas pipelines. However, the above method only considers the relevant data of the gas pipeline. Although the gas users are classified, it does not combine the fault conditions of the gas pipeline valves and the priority of valve maintenance to conduct a more targeted investigation of the safety hazards of gas pipelines and valves.

[0004] Therefore, it is necessary to provide an IoT system and method for pipeline valve verification based on smart gas safety supervision to reasonably set valve maintenance and replacement strategies, thereby maintaining stable and safe transportation of gas. Summary of the invention

[0005] The invention content includes a pipeline valve verification Internet of Things system based on smart gas safety supervision, which can execute any one of the pipeline valve verification methods based on smart gas safety supervision, including: a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, a smart gas government safety supervision object platform, a gas company sensor network platform, a gas equipment object platform, and a gas equipment maintenance object platform that are sequentially connected in communication; the smart gas government safety supervision object platform includes a gas company management platform; the smart gas government safety supervision management platform is configured to generate a first verification instruction and send it to the gas company management platform; the gas company management platform is configured to: in response to receiving the first verification instruction Or if it is monitored that the historical calibration time of the gas pipeline exceeds the calibration time threshold, a first calibration is performed on the gas pipeline and the pipeline valves on the gas pipeline to determine the first calibration result; based on the sensing parameters and the operating parameters, a flexible calibration batch is determined; based on the flexible calibration batch and the first calibration instruction, a second calibration instruction is generated, and a second calibration is performed on the gas pipeline and the pipeline valve to obtain a second calibration result; based on the first calibration result and the second calibration result, the valves to be maintained of the gas pipeline are determined; based on the valves to be maintained, work order information is determined; based on the work order information, a work order dispatching instruction is determined and sent to the gas equipment maintenance object platform; before maintaining the valves to be maintained, a parameter adjustment instruction is generated, and based on the parameter adjustment instruction, the working parameters of the auxiliary devices supporting the operation of the valves to be maintained are adjusted.

[0006] The invention content includes a pipeline valve calibration method based on smart gas safety supervision, the method comprising: in response to receiving a first calibration instruction, or monitoring that the historical calibration time of the gas pipeline exceeds a calibration time threshold, performing a first calibration on the gas pipeline and the pipeline valves on the gas pipeline, and determining a first calibration result; determining a flexible calibration batch based on sensing parameters and operating parameters; generating a second calibration instruction based on the flexible calibration batch and the first calibration instruction, performing a second calibration on the gas pipeline and the pipeline valve to obtain a second calibration result; determining the valve to be maintained of the gas pipeline based on the first calibration result and the second calibration result; determining work order information based on the valve to be maintained; determining a work order dispatching instruction based on the work order information and sending it to a gas equipment maintenance object platform; before maintaining the valve to be maintained, generating a parameter adjustment instruction, and adjusting the working parameters of the auxiliary device supporting the operation of the valve to be maintained based on the parameter adjustment instruction.

[0007] The beneficial effects brought by the above invention include but are not limited to: (1) When the average feedback interval and the average maintenance interval are both less than the preset interval length, it means that the frequency of failure of the gas pipeline is high. In order to ensure timely detection of gas pipeline failures, the verification time threshold can be appropriately shortened; and when the average feedback interval and the average maintenance interval are both greater than the verification time threshold of the gas pipeline, it means that the possibility of failure of the current pipeline is small. Frequent verification will lead to waste of manpower and financial resources. The verification time threshold can be appropriately increased to save resources. (2) By determining the gas pipelines and pipeline valves that need to be second-checked through the machine learning model, the data processing and analysis capabilities of the machine learning model can be fully utilized to quickly and accurately obtain the required gas pipelines and pipeline valves that need to be second-checked, thereby saving manpower and material resources. (3) By setting the order in which each valve is repaired, the maintenance priority of the valve with a large number of failures is increased. By predicting the second candidate valve through the fault prediction model, the reasonable allocation of maintenance resources can be ensured, the valve with a fault can be more accurately determined, and the valve with a higher potential failure risk can be repaired in time, thereby improving the safety of the gas pipeline system. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a schematic diagram of the platform structure of the pipeline valve verification Internet of Things system based on smart gas safety supervision shown in some embodiments of this specification; Figure 2 is an exemplary flow chart of a pipeline valve verification method based on smart gas safety supervision according to some embodiments of this specification; Figure 3 is an exemplary flow chart of obtaining a second verification result according to some embodiments of this specification; Figure 4 is an exemplary schematic diagram of a batch determination model according to some embodiments of this specification; Figure 5 is an exemplary flow chart for determining a valve to be maintained according to some embodiments of the present specification. DETAILED DESCRIPTION

[0009] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly introduces the drawings required for describing the embodiments.

[0010] Figure 1 It is a schematic diagram of the platform structure of a pipeline valve verification Internet of Things system based on smart gas safety supervision as shown in some embodiments of this specification.

[0011] In some embodiments, a pipeline valve verification IoT system based on smart gas safety supervision may include a smart gas government safety supervision management platform 110, a smart gas government safety supervision sensor network platform 120, a smart gas government safety supervision object platform 130, a gas company management platform 131, a gas company sensor network platform 140, a gas equipment object platform 150 and a gas equipment maintenance object platform 160, which are connected in sequence for communication.

[0012] The smart gas government safety supervision management platform 110 refers to a comprehensive management platform for government management information. In some embodiments, the smart gas government safety supervision management platform may include a government supervision comprehensive database for query. The government supervision comprehensive database refers to a database that stores data related to the smart gas government safety supervision management platform 110. For example, the government supervision comprehensive database may store information related to gas business and information related to gas safety, etc.

[0013] In some embodiments, the smart gas government safety supervision and management platform 110 can receive information related to gas business and gas safety, such as the usage information sequence of the gas pipeline, the sensor parameters and operating parameters on the gas pipeline, and store the information related to gas business and gas safety in the government supervision comprehensive database to supervise and manage gas business and gas safety. For the content of the information such as the usage information sequence of the gas pipeline, the sensor parameters and operating parameters on the gas pipeline, please refer to the relevant description below.

[0014] In some embodiments, the smart gas government safety supervision management platform 110 may be configured to generate a first verification instruction and send it to the gas company management platform 131. For the content of the first verification instruction, please refer to the relevant description below.

[0015] The smart gas government safety supervision sensor network platform 120 can be a functional platform for managing sensor communication. In some embodiments, the smart gas government safety supervision sensor network platform 120 can realize the functions of sensing information sensor communication and controlling information sensor communication. 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.

[0016] The smart gas government safety supervision object platform 130 refers to a platform for generating government supervision information and controlling information execution.

[0017] In some embodiments, the gas company management platform 131 can interact with the smart gas government safety supervision sensor network platform 120. For example, the gas company management platform 131 can obtain historical data of gas pipeline operation based on the smart gas government safety supervision sensor network platform 120, such as historical verification time and other data. In some embodiments, the gas company management platform 131 can be configured on the gas company management server. In some embodiments, the gas company management platform 131 includes a gas work order management center. For information about historical verification time, please refer to Figure 2 and its related instructions.

[0018] The gas work order management center refers to a platform for comprehensive management of gas work orders. In some embodiments, the gas work order management center can be configured to determine work order information based on the pipeline valve to be maintained.

[0019] In some embodiments, the gas company management platform 131 and the smart gas government safety supervision management platform 110 are communicated with the gas equipment maintenance object platform through the network.

[0020] For more information about the gas company management platform 131, please refer to Figure 2 and its related instructions.

[0021] The gas company sensor network platform 140 refers to a platform for comprehensive management of sensor information of the gas company. In some embodiments, the gas company sensor network platform 140 includes a communication base station, a router, and a wireless WIF device. In some embodiments, the gas company sensor network platform 140 can realize the functions of sensing information sensor communication and controlling information sensor communication. In some embodiments, the gas company sensor network platform 140 can exchange data with the gas company management platform 131, the gas equipment object platform 150, and the gas equipment maintenance object platform 160.

[0022] In some embodiments, the gas company sensor network platform 140 includes multiple groups of communication sub-devices. In some embodiments, the gas company sensor network platform 140 can obtain the sensing parameters and operating parameters of the pipeline valves on the gas pipeline based on the gas equipment object platform 150.

[0023] In some embodiments, the gas equipment object platform 150 is configured to obtain sensing parameters and operating parameters of pipeline valves on the gas pipeline.

[0024] In some embodiments, the gas equipment object platform 150 may interact with the gas company management platform 131 through the gas company sensor network platform 140 .

[0025] In some embodiments, the gas equipment object platform 150 is configured to send the sensing parameters and operating parameters of the pipeline valves on the gas pipeline to the gas company sensor network platform 140 .

[0026] The gas equipment maintenance object platform 160 is a platform for receiving work order information and performing maintenance and replacement on valves. In some embodiments, the gas equipment maintenance object platform 160 can interact with the gas company management platform 131 through the gas company sensor network platform 140. In some embodiments, the gas equipment maintenance object platform 160 can be configured in the terminal of the gas operation and maintenance personnel.

[0027] In some embodiments, the gas equipment maintenance object platform 160 is configured to dispatch the work order information to the gas maintenance personnel terminal for pipeline valve verification in response to receiving a work order dispatch instruction.

[0028] In some embodiments, pipeline valve verification may include testing the sealing performance, pressure bearing capacity, and operational flexibility of each valve of the gas pipeline.

[0029] In some embodiments, the gas company sensor network platform 140 can perform data transmission with the gas equipment maintenance object platform 160 and the gas company management platform 131. For example, the gas equipment maintenance object platform 160 can obtain the valve maintenance record uploaded by the gas maintenance personnel and send it to the gas company management platform 131 through the gas company sensor network platform 140.

[0030] In some embodiments of the present specification, in a pipeline valve verification IoT system based on smart gas safety supervision, various functional platforms coordinate and operate regularly to form an information operation closed loop, thereby realizing real-time detection of gas pipeline valves and informatization and intelligence of valve maintenance strategies or valve maintenance strategy formulation.

[0031] Figure 2 It is an exemplary flow chart of a pipeline valve verification Internet of Things system based on smart gas safety supervision as shown in some embodiments of this specification.

[0032] Step 210, in response to receiving a first verification instruction or monitoring that the historical verification time of the gas pipeline exceeds a verification time threshold, a first verification is performed on the gas pipeline and the pipeline valves on the gas pipeline to determine a first verification result.

[0033] The first verification instruction refers to a verification instruction for performing a first verification on the gas pipeline and the pipeline valve. The first verification instruction may include the gas pipeline and the pipeline valve that need to be inspected. In some embodiments, the smart gas government safety supervision and management platform can generate a corresponding first verification instruction based on the gas pipeline valve verification notification of the government department.

[0034] The first check refers to a regular check of the gas pipeline and the pipeline valve. In some embodiments, the first check may include a pressure test of the gas pipeline, a gas leakage test, and a valve availability test.

[0035] The first verification result refers to the verification result obtained after the first verification. In some embodiments, the first verification result may include a pressure resistance test result, a gas leakage test result, a valve availability test result, and the like.

[0036] In some embodiments, the pressure resistance test results may include the stress distribution and strain distribution of the gas pipeline; the gas leakage test results may include whether there is a leak in the gas pipeline and the leakage point; the valve availability test results may include whether the pipeline valve is functioning normally.

[0037] In some embodiments, the verification time threshold may also be determined based on sensing parameters and operating parameters of the pipeline valve.

[0038] For example, the gas company management platform 131 can obtain the sensing parameters and operating parameters of the pipeline valve within a preset time range, and obtain the sensing parameter mean and the operating parameter mean by statistics. The period in which the sensing parameters and the operating parameters deviate from the sensing parameter mean and the operating parameter mean by more than the preset deviation amplitude is determined as the sensing parameter fluctuation period and the operating parameter fluctuation period.

[0039] The gas company management platform 131 can determine the corresponding period between each adjacent fluctuation period, select the period with the shortest interval between the fluctuation durations, and determine it as the verification time threshold. Among them, the values ​​of the preset time range and the preset deviation range can be determined based on prior experience. For example, the preset time range can be 1 minute, and the preset deviation range can be ±10% or ±20%.

[0040] For more information about the sensing parameters and operating parameters, please refer to the relevant content in step 220.

[0041] In some embodiments of the present specification, the verification time threshold is determined by the sensing parameters and operating parameters of the pipeline valve, so that timely verification can be performed when the sensing parameters and operating parameters of the pipeline valve fluctuate, thereby promptly discovering faulty valves for maintenance, thereby ensuring the safety of the gas pipeline and the stability of gas transportation.

[0042] In some embodiments, for the gas pipeline including the first alternative valve, the gas company management platform 131 may also adjust the verification time threshold based on the frequency of feedback information and the maintenance frequency of the gas pipeline.

[0043] The first alternative valve refers to the valve to be maintained that has failed and is reported by gas users. For details about the first alternative valve, please refer to Figure 5and its related contents.

[0044] Feedback information refers to the information related to gas pipeline and / or pipeline valve failures fed back by gas users to government departments, and the feedback information frequency refers to the frequency of gas users sending feedback information to government departments. In some embodiments, the gas company management platform 131 can obtain the time point of gas user feedback information from the smart gas government safety supervision management platform, obtain the interval length between different feedback information, and obtain the average feedback interval after averaging.

[0045] Maintenance frequency refers to the frequency of maintenance of gas pipelines and / or pipeline valves by relevant gas maintenance personnel. In some embodiments, the gas company management platform 131 can obtain the feedback information frequency and the maintenance frequency of the gas pipeline through the government supervision comprehensive database.

[0046] In some embodiments, the gas company management platform 131 may determine the average feedback interval between different feedback information and the average maintenance interval of the gas pipeline based on the feedback information frequency and the maintenance frequency of the gas pipeline.

[0047] When both the average feedback interval and the average maintenance interval are greater than the preset interval duration, the gas company management platform 131 may determine the longest time interval between the average feedback interval and the average maintenance interval as the verification time threshold.

[0048] When both the average feedback interval and the average maintenance interval are shorter than the preset interval duration, the gas company management platform 131 may determine the shortest time interval between the average feedback interval and the average maintenance interval as the verification time threshold.

[0049] The preset interval duration may be determined based on prior experience. In some embodiments, the preset interval duration may be a check time threshold determined for the first time.

[0050] In some embodiments of the present specification, when the average feedback interval and the average maintenance interval are both shorter than the preset interval length, it indicates that the frequency of failures in the gas pipeline is high. In order to ensure that the failures in the gas pipeline are discovered in time, the verification time threshold can be appropriately shortened. When the average feedback interval and the average maintenance interval are both longer than the verification time threshold of the gas pipeline, it indicates that the possibility of failure in the current pipeline is low. Frequent verification will lead to a waste of manpower and financial resources. The verification time threshold can be appropriately increased to save resources.

[0051] Step 220, determining a flexible verification batch based on the sensing parameters and the operating parameters.

[0052] The sensing parameters refer to the parameters collected by the sensors on the pipeline valves. In some embodiments, the sensing parameters may include force sensing parameters when the valve rotates or twists and the stress state of the structural units around the valve (for example, the pipeline near the valve, the fixed bracket, etc.). The gas company management platform 131 can obtain the force sensing parameters and stress state through devices such as force sensors, piezoelectric sensors and strain gauges installed on the pipeline valves.

[0053] In some embodiments, since a gas pipeline often has multiple pipeline valves, a gas pipeline may correspond to multiple sets of sensing parameters.

[0054] Operation parameters refer to indicator parameters when the gas pipeline is in operation. In some embodiments, the operation parameters may include the pipe material of the gas pipeline (e.g., steel, copper, and composite materials, etc.), gas flow rate, and gas pressure. The gas company management platform 131 can determine the gas flow rate and gas pressure through the flow meter and pressure gauge on the gas pipeline. The pipe material is obtained by querying the recorded data when the gas pipeline is installed.

[0055] Flexible verification batches refer to batches of risk pipeline valves that are verified for potential safety hazards. The flexible verification batch may include a gas pipeline that undergoes a second verification and multiple pipeline valves in the corresponding gas pipeline. In some embodiments, the gas company management platform 131 can determine the flexible verification batch in a variety of ways. For example, for a gas pipeline, if the gas flow rate of the gas pipeline is lower than a preset flow rate threshold or the gas pressure is higher than a preset pressure threshold, it indicates that there may be an abnormality, and it is necessary to determine the risk pipeline valves in the gas pipeline for additional inspection.

[0056] For a gas pipeline, the gas company management platform 131 can determine the flexible verification batch based on the sensing parameters and the operating parameters by querying the first vector database.

[0057] For a gas pipeline, the gas company management platform 131 can construct a to-be-matched vector of the gas pipeline based on operating parameters and several groups of sensor parameters as elements. The gas company management platform 131 can search in the vector database based on the to-be-matched vector, obtain a reference vector whose vector distance to the to-be-matched vector is less than a distance threshold, and determine the label corresponding to the reference vector as the risky pipeline valve of the current gas pipeline. The risky pipeline valves of multiple gas pipelines are determined as flexible verification batches.

[0058] Among them, the vector database stores several reference vectors and their corresponding labels. The reference vector is constructed based on the historical operating parameters of the gas pipeline and the historical sensing parameters of the pipeline valve. The label of the reference vector is whether the historical risk pipeline valve is abnormal (for example, the probability of failure or failure in the short future is high). The gas company management platform 131 can determine the pipeline valves that are actually determined to be abnormal or to fail in the short future in the pipeline valves corresponding to the feature vector in the historical gas pipeline calibration and maintenance records as historical risk pipeline valves as labels of the reference vector.

[0059] In some embodiments, the gas company management platform 131 can also determine flexible verification batches based on the usage information sequence, sensor parameters, and operating parameters. For more information about this section, please refer to Figure 3 and related instructions.

[0060] In some embodiments, the gas company management platform 131 can also determine the gas pipelines and pipeline valves that need to be second-checked through the batch determination model, and adjust the flexible check batch. For more information about this part, please refer to Figure 4 and related instructions.

[0061] Step 230, based on the flexible verification batch and the first verification instruction, a second verification instruction is generated, and a second verification is performed on the gas pipeline and the pipeline valve to obtain a second verification result.

[0062] The second verification instruction refers to a verification instruction for the gas company to perform a second verification on the gas pipeline and pipeline valve that are judged to have risks. In some embodiments, the specific composition form of the second verification instruction is the same as the first verification instruction, and the relevant part of the first verification instruction can be referred to above. The second verification refers to an additional verification performed by the gas company on the gas pipeline and pipeline valve that are judged to have risks to eliminate hidden dangers.

[0063] The second verification result refers to the verification result obtained after the second verification. The second verification result has the same composition as the first verification result, and the relevant part can be referred to above.

[0064] The historical verification time refers to the time period during which the gas pipeline was last verified. The verification time threshold refers to the maximum interval allowed between two inspection tests of the gas pipeline. The verification time thresholds corresponding to multiple gas pipelines can constitute a verification time threshold set. For more information about the first verification, please refer to the following. In some embodiments, the gas company management platform 131 can determine the verification time threshold in a variety of ways. For example, the gas company management platform 131 can determine the verification time threshold by querying the first preset table based on the pipeline material, the service life, and the statutory service life of the gas pipeline. The gas company management platform 131 can construct a first preset table based on the historical pipeline material, the historical service life, the historical statutory service life, and the historical verification time threshold. The first preset table contains the correspondence between different historical gas pipeline materials, historical service life, historical statutory service life, and historical verification time thresholds. In some embodiments, when the wall thickness of the gas pipeline is thin and the material is brittle, the verification time threshold of the gas pipeline can be appropriately shortened.

[0065] In some embodiments, the gas company management platform 131 may exclude the pipeline valves included in the first verification instruction from the flexible verification batch, and determine the remaining risky pipeline valves after the exclusion as pipeline valves that need to be inspected in the second verification instruction.

[0066] Step 240: Determine the valve to be maintained on the gas pipeline based on the first verification result and the second verification result.

[0067] In some embodiments, the gas company management platform 131 can determine the scores of the first verification result and the second verification result based on whether the pressure test results in the first verification result and the second verification result meet the pipeline pressure standard, whether the gas leakage test results meet the pipeline sealing standard, and whether the valve availability test results can be used normally.

[0068] For example, when the above-mentioned pressure resistance test results, gas leakage test results and valve availability test results are all qualified, the score is 3, and when only one is qualified, the score is 1. Among them, the pipeline pressure standard and pipeline sealing standard can be determined based on relevant laws and regulations and mandatory inspection specifications.

[0069] In some embodiments, the gas company management platform 131 may determine pipeline valves with scores less than a first preset threshold in the first verification result and pipeline valves with scores less than a second preset threshold in the second verification result as valves to be maintained.

[0070] In some embodiments, since the pipeline valve in the first verification result is a periodic verification, the sensing parameters and operating parameters of the pipeline valve are not taken into account, and it is difficult to timely perceive the real-time changes of the pipeline valve, so the qualification standard of the first verification result can be stricter than that of the second verification result to ensure that the pipeline valve in the first verification result will not fail in a short time. Therefore, the first preset threshold value can be greater than the second preset threshold value.

[0071] In some embodiments, the gas company management platform 131 can also determine the valve to be maintained based on the third candidate valve and the first candidate valve. For more information about this part, please refer to Figure 5 Relevant part.

[0072] Step 250, determining work order information based on the valve to be maintained.

[0073] The work order information refers to the record information used for pipeline valve maintenance. In some embodiments, the work order information may include the gas pipeline to which the valve to be maintained belongs, the location of the valve to be maintained, and the model of the valve to be maintained.

[0074] In some embodiments, the gas company management platform 131 can determine the work order information in a variety of ways. Exemplarily, the gas company management platform 131 can determine the work order information by querying the work order information preset table. The gas company management platform 131 can construct a work order information preset table based on historical valves to be maintained, historical gas pipelines, historical valve positions to be maintained, and historical valve models to be maintained. The work order information preset table contains the correspondence between different historical valves to be maintained and historical gas pipelines, historical valve positions to be maintained, and historical valve models to be maintained. The processor can determine the current work order information by querying the work order information preset table based on the current valve to be maintained.

[0075] In some embodiments, the gas company management platform 131 can also obtain the installation records of gas pipelines and pipeline valves based on the government supervision comprehensive database, and then obtain the gas pipeline to which the valve to be maintained belongs and the model of the valve to be maintained. The location of the valve to be maintained and other information can be obtained through the positioning module installed on the valve to be maintained.

[0076] Step 260: Based on the work order information, determine the work order dispatch instruction and send it to the gas equipment maintenance object platform.

[0077] The work order dispatching instruction is an instruction to dispatch a work order to the relevant gas maintenance personnel. In some embodiments, the work order dispatching instruction may include the gas pipeline to which the valve to be maintained belongs, the location of the valve to be maintained, the model of the valve to be maintained, and a list of personnel to be dispatched.

[0078] In some embodiments, the gas company management platform 131 can determine the list of personnel to be dispatched in a variety of ways. For example, the gas company management platform 131 can select the required number of gas maintenance personnel closest to the valve to be repaired, and determine the list of personnel to be dispatched.

[0079] Step 270: before maintaining the valve to be maintained, generate a parameter adjustment instruction, and adjust the working parameters of the auxiliary device supporting the valve to be maintained based on the parameter adjustment instruction.

[0080] The parameter adjustment instruction is an instruction for adjusting the working parameters of the auxiliary device. In some embodiments, in order to ensure that the valve to be maintained can operate normally and reliably during the window period before maintenance and avoid the expansion of potential hidden dangers, the gas company management platform 131 can appropriately increase the monitoring frequency of the corresponding sensor through the parameter adjustment instruction.

[0081] In some embodiments, the auxiliary device supporting the valve to be maintained may include a force sensor, a piezoelectric sensor, a strain gauge and other devices installed on the valve to be maintained. The working parameter of the auxiliary device may be the frequency of the monitoring scan performed by the auxiliary device.

[0082] In some embodiments, the gas company management platform 131 can determine the stress distribution difference and strain distribution difference of the valve to be maintained based on the first verification result and the second verification result. The working parameters of the auxiliary device supporting the valve to be maintained can be positively correlated with the stress distribution difference and strain distribution difference.

[0083] For example, the greater the stress distribution difference and strain distribution difference of the valve to be maintained, the more serious the damage to the valve is and the greater the safety risk of continued use. The gas company management platform 131 can increase parameters such as the detection frequency of the corresponding auxiliary devices for timely monitoring.

[0084] The stress distribution difference may be the difference between the valve to be tested and the standard stress distribution, and the strain distribution difference may be the difference between the valve to be tested and the standard strain distribution. The standard stress distribution and the standard strain distribution may be determined based on prior experience.

[0085] In some embodiments of the present specification, based on the first verification result and the second verification result, the valve to be maintained of the gas pipeline is determined, and the working parameters of the auxiliary device in supporting operation are adjusted. The valve to be maintained with abnormalities and potential hidden dangers can be quickly and accurately located from the numerous pipeline valves included in the gas pipeline network, and the monitoring frequency of the valve can be increased before the valve is maintained, thereby ensuring the safety of gas use and avoiding the expansion of hidden dangers due to untimely monitoring.

[0086] Figure 3This is an exemplary flowchart for obtaining the second verification result according to some embodiments of this specification.

[0087] Step 310: Obtain the usage information sequence uploaded by the gas user from the gas equipment object platform.

[0088] The usage information sequence is an information sequence used to represent the change of gas consumption over time. One gas pipeline corresponds to one usage information sequence. In some embodiments, the usage information sequence may include gas consumption in different time periods, for example, as [( , ), , ),……,( , )].in, For the time period, is the gas consumption during this time period.

[0089] In some embodiments, the gas equipment object platform can determine the gas consumption in different time periods and generate a usage information sequence through a flow meter and a pressure gauge installed in the gas user's room.

[0090] Step 320, determining a flexible verification batch based on the usage information sequence, the sensing parameters, and the operating parameters.

[0091] In some embodiments, the gas company management platform 131 may determine the delivery flow and gas consumption based on the usage information sequence and the operating parameters of the gas pipeline.

[0092] For example, if the difference between the delivery flow of the gas pipeline in the corresponding period and its gas consumption is greater than the preset difference threshold, it means that the gas pipeline may have a leak; if the difference between the delivery flow of the gas pipeline in the corresponding period and the gas consumption in the corresponding period is less than the preset difference threshold, but the delivery pressure in the preset time period is significantly greater than the normal value, it means that the gas pipeline may be blocked.

[0093] The gas company management platform 131 may determine the gas pipeline where gas leakage or pipeline blockage occurs as the gas pipeline that needs to be subjected to the second calibration. The gas leakage threshold and the gas blockage threshold may be determined based on prior experience.

[0094] In some embodiments, for each gas pipeline that needs to be second-checked, the gas company management platform 131 can determine the risk pipeline valve on the gas pipeline based on the operating parameters and sensor parameters by querying the first vector database, and then determine the flexible check batch. For more information about this part, please refer to Figure 2 and its related contents.

[0095] Step 330, based on the flexible verification batch, generate a second verification instruction, perform a second verification on the gas pipeline and the pipeline valve to obtain a second verification result.

[0096] In some embodiments, the gas company management platform 131 can remove the pipeline valves included in the first verification instruction from the flexible verification batch. The remaining risky pipeline valves after the removal are determined as pipeline valves that need to be inspected in the second verification instruction. For more information about this part, please refer to Figure 2 Relevant part.

[0097] In some embodiments of the present specification, by comparing the difference between the delivery flow and the gas consumption, it can be determined whether there is a gas leak or blockage in the gas pipeline, which helps to determine a flexible calibration batch and perform a second calibration.

[0098] Figure 4 is an exemplary schematic diagram of a batch determination model according to some embodiments of the present specification.

[0099] In some embodiments, for a gas pipeline, the gas company management platform 131 may determine a consistency sequence 430 between the usage information sequence and the operating parameters based on the usage information sequence 410 and the operating parameters 420 .

[0100] The consistency can reflect the degree of data matching between the gas consumption and the delivery flow of the gas pipeline. In some embodiments, the consistency of a gas pipeline in different time periods can be represented by a consistency sequence 430. For example, it can be represented as [( , ), , ),……,( , )].in, For the time period, is the consistency within the time period. In some embodiments, the time period in the consistency sequence to Can be used with the time period in the information sequence to same.

[0101] In some embodiments, the consistency can be determined based on the gas consumption and the delivery flow in different time periods. For example, the consistency can be obtained by the following formula (1):

[0102] in, Indicates time period The consistency of Indicates time period Gas consumption in Indicates time period The flow rate inside.

[0103] In some embodiments, based on the consistency sequence 430 and the sensor parameters 440, the gas company management platform 131 can determine the gas pipelines and pipeline valves 480 that need to be subjected to a second calibration through the batch determination model 470, and adjust the flexible calibration batch.

[0104] The batch determination model 470 is a prediction model for determining the flexible verification batch. In some embodiments, the batch determination model is a machine learning model, such as a deep neural network (DNN) model or any combination of other custom model structures.

[0105] In some embodiments, the input of the batch determination model 470 may be a consistency sequence and sensor parameters of a plurality of gas pipelines, and the output may be the gas pipelines and pipeline valves that require a second calibration.

[0106] In some embodiments, the inputs to the batch determination model 470 also include environmental parameters 450 .

[0107] Environmental parameters 450 refer to parameters reflecting environmental information near the sample gas pipeline. In some embodiments, environmental parameters 450 may include a temperature sequence and a humidity sequence, and different gas pipelines may correspond to different temperature sequences and humidity sequences. The temperature sequence and humidity sequence may include temperature and humidity in different time periods. The composition of the temperature sequence and humidity sequence is similar to that of the usage information sequence, and the relevant description of the usage information sequence can be found above.

[0108] In some embodiments, the gas company management platform 131 can determine the environmental parameters 450 in a variety of ways. For example, the gas company management platform 131 can determine the collected data (such as moisture content, gas composition, etc.) as environmental parameters through gas detectors installed in gas corridors, geological detectors buried near gas pipelines, and other equipment.

[0109] In some embodiments, the gas company management platform 131 can also obtain temperature / humidity data of different time periods based on the thermometer / hygrometer on the gas pipeline, thereby obtaining a temperature / humidity sequence.

[0110] In some embodiments of the present specification, by inputting a temperature / humidity sequence into a batch determination model, the input data dimension of the batch determination model can be improved, which helps the batch determination model consider the impact of different temperatures and humidities on gas pipelines and pipeline valves, thereby improving the reliability and accuracy of the judgment results.

[0111] In some embodiments, the input to the batch determination model 470 also includes the pipeline adjacency matrix 460 .

[0112] The pipeline adjacency matrix 460 is a matrix representing the connection relationship between gas pipelines. In some embodiments, the pipeline adjacency matrix may be composed of at least one vertex and at least one edge. In some embodiments, the vertices may be divided into bifurcation vertices and valve vertices. The bifurcation vertices correspond to the bifurcations between gas pipelines. The valve vertices correspond to the pipeline valves between gas pipelines. The vertex attributes of the bifurcation vertices and the valve vertices may include the position coordinates of the bifurcation and the pipeline valve.

[0113] In some embodiments, an edge represents a connection relationship between vertices, and the distance between two vertices in the pipeline adjacency matrix can reflect the path distance between the two vertices. When there is a pipeline connecting two vertices, the vertices are connected by an edge.

[0114] In some embodiments, the gas company management platform 131 can obtain the construction records of the gas pipeline based on the government regulatory comprehensive database, obtain the location coordinates of the pipeline valves and pipeline bifurcations, and the connection relationship between the gas pipelines, and then construct a pipeline adjacency matrix 460.

[0115] In some embodiments of the present specification, by inputting the pipeline adjacency matrix 460 into the batch determination model 470, the input data dimension of the batch determination model can be improved, which helps the batch determination model to consider the impact of the connection relationship between different gas pipelines, thereby improving the reliability and accuracy of the judgment results.

[0116] In some embodiments, the gas company management platform 131 can obtain the batch determination model 470 through training based on the first training sample and the first label. In some embodiments, the first training sample may include a sample consistency sequence, a sample sensor parameter, a sample environmental parameter, and a sample pipeline adjacency matrix. In some embodiments, the first training sample may also include a sample environmental parameter and a sample pipeline adjacency matrix. The first label may be a sample gas pipeline and a sample pipeline valve corresponding to the first training sample that need to be subjected to a second calibration.

[0117] In some embodiments, the first training sample can be obtained based on historical data, and the first label corresponding to the first training sample can be obtained from the historical fault conditions of the historical gas pipeline. For example, the gas company management platform 131 can determine the historical gas pipeline and the historical pipeline valve at the first historical time point in the historical data as the sample gas pipeline and the sample pipeline valve. The gas company management platform 131 can obtain the historical temperature / humidity information of the historical gas pipeline and construct the sample environmental parameters. The gas company management platform 131 can obtain the historical position coordinates of the pipeline valve and the pipeline bifurcation at the first historical time point, as well as the historical connection relationship between the gas pipelines based on the government supervision comprehensive database, and then determine the sample pipeline adjacency matrix.

[0118] In some embodiments, the gas company management platform 131 can obtain historical fault conditions of historical gas pipelines and historical pipeline valves. If the historical gas pipelines and historical pipeline valves have failed at the second historical time point, the label is 0, otherwise it is 1. The first historical time point is earlier than the second historical time point.

[0119] In some embodiments, the gas company management platform 131 can be trained by a variety of methods based on the first training sample and the first label. For example, the gas company management platform 131 can select one or more first training samples from the training data set, input the one or more first training samples into the initial batch determination model, and obtain the model prediction output corresponding to the one or more first training samples; substitute the model prediction output corresponding to the one or more training samples and the labels of the one or more first training samples into the formula of the predefined loss function to calculate the value of the loss function; according to the value of the loss function, use various feasible methods to reversely update the model parameters in the initial batch determination model, for example, it can be updated based on the gradient descent method. When the iteration end condition is met, the iteration ends and the trained batch determination model is obtained. Among them, the iteration end condition can be the convergence of the loss function, the number of iterations reaches a threshold, etc.

[0120] In some embodiments, after a preset number of rounds of training, the gas company management platform 131 may update the learning rate of the batch determination model based on the attenuation factor; the preset number of rounds is determined based on the ratio of pipeline valves.

[0121] The learning rate refers to the parameter used to determine the update step size during the batch determination model training process. The decay factor is a coefficient used to reduce the learning rate. In some embodiments, during the training process of the primary batch determination model, multiple iterative updates are often required. At this time, the learning rate can be used to adjust the step size of the loss function movement.

[0122] In some embodiments, when the learning rate is large, the model can respond quickly to training data and speed up training, but it may cause the model to miss the optimal solution and fail to converge. When the learning rate is small, the loss function can change more smoothly, which helps to improve the stability and generalization ability of the model, but it may slow down the training speed and cause the loss function to fall into the dilemma of local optimality. Therefore, the gas company management platform 131 can determine the learning rate of the model by updating the batch and control the training speed and convergence amplitude of the model.

[0123] In some embodiments, for the primary batch determination model, the gas company management platform 131 may determine the product of the attenuation factor and the learning rate as the updated learning rate after a preset number of rounds of training. The attenuation factor has a value range of 0 to 1, and the value of the attenuation factor may be determined based on prior experience. The preset number of rounds may be related to the pipeline valve ratio.

[0124] The pipeline valve ratio may reflect the number of sample pipeline valves in the first training sample. In some embodiments, the gas company management platform 131 may divide the first training sample into three levels: less, medium, and more according to the number of sample pipeline valves. Each level may correspond to a different number of preset rounds. The preset rounds corresponding to different levels may be preset based on prior experience.

[0125] In some embodiments, in order to ensure that the model can fully learn the data features in the training samples before the end of training, the preset number of rounds for higher levels is greater than the preset number of rounds for medium levels, thereby ensuring that the learning rate of the first training sample with a high pipeline valve ratio decays later.

[0126] In some embodiments of this specification, the more the number of pipeline valves, the more complex the entire gas pipeline network, and the greater the amount of information in the training samples. Using a smaller learning rate often takes a lot of time, while using a larger learning rate will lead to a decrease in the generalization of the model. Therefore, in order to ensure a balance between the training accuracy and training time of the model, a higher learning rate can be used first to improve the training efficiency, and the learning rate can be appropriately reduced when the preset number of rounds is reached to improve the training accuracy of the model, thereby helping the model to converge better.

[0127] In some embodiments, the gas company management platform 131 can obtain the gas pipelines and pipeline valves 480 that need to undergo a second verification output by the batch determination model, and add the gas pipelines and / or pipeline valves that are not included in the flexible verification batch to the flexible verification batch, thereby achieving adjustment of the flexible verification batch.

[0128] In some embodiments of the present specification, by using a machine learning model to determine the gas pipelines and pipeline valves that require a second calibration, the data processing and analysis capabilities of the machine learning model can be fully utilized to quickly and accurately obtain the required gas pipelines and pipeline valves that require a second calibration, thereby saving manpower and material resources.

[0129] Figure 5 is an exemplary flow chart for determining a valve to be maintained according to some embodiments of the present specification.

[0130] In some embodiments, the valve to be maintained includes a first candidate valve and a third candidate valve. The third candidate valve is a second candidate valve whose maintenance sequence meets a preset sorting condition. The maintenance priority of the first candidate valve is higher than that of the second candidate valve. For more information about the first candidate valve, please refer to Figure 2 Related content.

[0131] Step 510, based on the feedback result of the smart gas government safety supervision and management platform, determine the first alternative valve.

[0132] In some embodiments, the feedback results of the smart gas government safety supervision management platform may include damaged valves and / or valves that need to be repaired. In some embodiments, the gas company management platform can obtain feedback results through the smart gas government safety supervision management platform.

[0133] In some embodiments, the gas company management platform may determine the first candidate valve based on the feedback result. For example, the gas company management platform may read the relevant information of the damaged valve and / or the valve that needs to be repaired in the feedback result.

[0134] Step 520: Determine a second candidate valve based on the first verification result, the second verification result and the first candidate valve.

[0135] The second candidate valve is a valve that has been detected or predicted to have a fault. In some embodiments, the maintenance priority of the second candidate valve is lower than that of the first candidate valve.

[0136] In some embodiments, the gas company management platform can determine the second candidate valve in a variety of ways based on the first verification result, the second verification result, and the first candidate valve. For example, the gas company management platform can predict the second candidate valve through a fault valve prediction model based on the first verification result and the second verification result.

[0137] The faulty valve prediction model refers to a model used to predict the second candidate valve. In some embodiments, the faulty valve prediction model can be a machine learning model, such as a deep neural network.

[0138] In some embodiments, the input of the faulty valve prediction model may include a first verification result of pipeline valves in a plurality of gas pipelines and a second verification result of pipeline valves in a plurality of gas pipelines.

[0139] In some embodiments, the output of the faulty valve prediction model may include a second candidate valve, etc.

[0140] In other embodiments, the input of the faulty valve prediction model may further include a first candidate valve and a local pipeline adjacency matrix.

[0141] The local pipeline adjacency matrix refers to the adjacency matrix that represents the positional relationship between the first candidate valve and other valves. For more information about the adjacency matrix, see Figure 4 The corresponding description.

[0142] In some embodiments, the rows and columns of the local pipeline adjacency matrix may correspond to each valve (i.e., vertex) in the region where the first candidate valve is located, and the elements of the local pipeline adjacency matrix correspond to whether there is a connection relationship between the valves in the rows and columns. For example, the element in the mth row and nth column (or the element in the nth row and mth column) of the local pipeline adjacency matrix may represent the connection relationship between valve No. m and valve No. n. If the element in the mth row and nth column (or the element in the nth row and mth column) of the local pipeline adjacency matrix is ​​a non-zero value, it indicates that there is a connection relationship between valve No. m and valve No. n, otherwise there is no connection relationship.

[0143] In some embodiments, the gas company management platform can construct a local pipeline adjacency matrix of the first candidate valve based on the first candidate valve and the other valves in the area where the first candidate valve is located. For example, in the local pipeline adjacency matrix, the gas company management platform can set the elements corresponding to the valves with a connection relationship to 1 (or other non-zero values), otherwise set them to 0.

[0144] By predicting the second candidate valve through the connection relationship between the first candidate valve and the remaining valves, the model can simultaneously predict the valves around the first candidate valve, thereby improving the accuracy of the fault valve prediction model.

[0145] In some embodiments, the gas company management platform may obtain a faulty valve training data set. The faulty valve training data set may include multiple groups of second training samples and multiple groups of second labels corresponding to the multiple groups of second training samples.

[0146] In some embodiments, the gas company management platform can obtain a faulty valve training data set in a variety of ways. In some embodiments, the gas company management platform can determine the faulty valve training data set based on the first verification result and the second verification result in the historical data. For example, the gas company management platform can set a group of first verification results and a group of second verification results in the historical maintenance records as a group of second training samples, and set the actual second candidate valves corresponding to the group of second training samples as the second labels of the group of second training samples through automatic or manual labeling. Among them, the actual second candidate valves can include the second candidate valves corresponding to each group of second training samples in the historical maintenance records.

[0147] In some embodiments, the gas company management platform can determine the fault valve training data set based on the first candidate valves in the historical data and their corresponding local pipeline adjacency matrix. For example, the gas company management platform can set a group of first candidate valves in the historical maintenance records and their corresponding local pipeline adjacency matrix as a group of second training samples, and set the actual second candidate valves corresponding to the group of second training samples as the second labels of the group of second training samples through automatic or manual labeling.

[0148] In some embodiments, the gas company management platform can train the faulty valve prediction model through multiple rounds of iterations, wherein at least one round of iteration includes: the gas company management platform selects one or more groups of second training samples from the faulty valve training data set, inputs the faulty valve prediction model, and obtains model prediction outputs corresponding to one or more groups of second training samples; the gas company management platform substitutes the model prediction outputs corresponding to one or more groups of second training samples and one or more groups of second labels corresponding to the one or more second training samples into the formula of a predefined loss function to calculate the value of the loss function.

[0149] The gas company management platform reversely updates the model parameters in the fault valve prediction model based on the value of the loss function and optimization algorithms such as the gradient descent method. When the iteration end conditions are met (for example, the loss function converges, the number of iterations reaches the preset iteration threshold, etc.), the gas company management platform ends the iteration and obtains the trained fault valve prediction model.

[0150] By predicting the second alternative valve through the faulty valve prediction model, the valve that needs to be replaced or maintained first can be determined more accurately; by using the connection relationship between the first alternative valve and the remaining valves as the input of the faulty valve prediction model, the faulty valve prediction model can use the contextual information around the first alternative valve as a reference factor for prediction, which in turn helps the faulty valve prediction model to more accurately identify potential faulty valves, thereby improving the prediction accuracy.

[0151] Step 530: Determine the maintenance order of the second candidate valve based on the first verification result and the second verification result.

[0152] In some embodiments, the gas company management platform may determine the maintenance order of the second candidate valve through the second vector database based on the first verification result and the second verification result.

[0153] The second vector database refers to a database for storing the maintenance order of the second alternative valve. In some embodiments, the second vector database may store a mapping relationship between the maintenance order of the second alternative valve and a verification result (e.g., a first verification result and a second verification result, etc.). For example, the feature vector of the second vector database may include the first verification result and the second verification result, and the label of the second vector database may include the maintenance order of the second alternative valve corresponding to the first verification result and the second verification result. In some embodiments, the label of the second vector database may be preset based on manual experience.

[0154] In some embodiments, the gas company management platform can construct the second vector database in a variety of ways based on the historical maintenance results. For example, the gas company management platform can use the maintenance order of the second candidate valve corresponding to the best historical maintenance result as the label of the second vector database, and associate the label of the second vector database with its corresponding feature vector (for example, the feature vector corresponding to the best historical maintenance result, etc.) and store it in the second vector database.

[0155] In some embodiments, the best maintenance effect can be determined based on feedback from gas users. In some embodiments, the best maintenance effect can include at least one of no secondary failure events during maintenance, best feedback from gas users after maintenance, and the lowest frequency of valve failures.

[0156] In some embodiments, the gas company management platform can determine the maintenance order of the second candidate valve through the second vector database. The second vector database refers to a database used to store, index and query vectors. For example, the gas company management platform can retrieve an index matching the vector composed of the first verification result and the second verification result in the second vector database. If there is a matching index, the label corresponding to the retrieved index is used as the maintenance order of the second candidate valve and fed back to the gas company management platform.

[0157] Step 540: Determine a third candidate valve based on the maintenance order of the second candidate valve.

[0158] The third candidate valve refers to a second candidate valve that needs to be maintained first and whose maintenance order meets the preset sorting condition. In some embodiments, the preset sorting condition can be determined based on manual experience. In some embodiments, the preset sorting condition can include that the maintenance order of the second candidate valve is in the first N. In some embodiments, N can be an integer greater than 0, and the value of N can be determined based on manual experience.

[0159] In some embodiments, the gas company management platform can determine the third candidate valve in a variety of ways based on the maintenance sequence of the second candidate valve. For example, the gas company management platform can move the maintenance sequence of the first N valves in the maintenance sequence of the second candidate valve to the post-sequence of the first candidate valve, and set the first N valves in the maintenance sequence of the second candidate valve together with the first candidate valve as the third candidate valve.

[0160] In some embodiments, N may be related to the consistency sequence of the corresponding gas pipeline in the second alternative valve. For example, the gas company management platform may set the number of gas pipelines whose consistency sequence mean is less than the consistency threshold as the value of N. In some embodiments, the consistency threshold may be determined based on manual experience. In some embodiments, the consistency sequence mean may include the average value of the consistency sequence in one or more historical time periods. For more information about the consistency sequence, please refer to the relevant description above.

[0161] By determining the valves that need priority maintenance through the mean of the consistency sequence, it can be ensured that valves in the gas pipeline with a higher maintenance frequency can be repaired or maintained first, and valves with more serious faults can be repaired in time, thereby reducing the abnormal gas transportation caused by damage to such valves that are prone to failure.

[0162] Step 550: Determine the valve to be maintained based on the third candidate valve and the first candidate valve.

[0163] In some embodiments, the gas company management platform can determine the valves to be maintained based on the third alternative valve and the first alternative valve in a variety of ways. For example, the gas company management platform can generate a list of valves to be maintained, and can store the first alternative valve and the third alternative valve in the list of valves to be maintained in sequence. In some embodiments, the list of valves to be maintained includes one or more valves to be maintained, and the order of the valves in the list of valves to be maintained represents the order in which the valves to be maintained are maintained. The order of the valves in the list of valves to be maintained is determined based on the first verification result, the second verification result and / or the maintenance frequency. For related instructions, see Figure 2 And the corresponding content below.

[0164] In some embodiments, the gas company management platform can determine the maintenance order of the first candidate valves in a variety of ways based on the feedback results of the smart gas government safety supervision management platform and the maintenance frequency of the gas pipeline. For example, the gas company management platform can sort the damaged valves and / or valves that need to be repaired in the feedback results in descending order according to the maintenance frequency, and set the maintenance order of the first candidate valves to the sorting result of the aforementioned descending order.

[0165] In some embodiments, the gas company management platform can obtain the historical maintenance information of each gas pipeline through the gas user platform, and obtain the maintenance frequency of each gas pipeline based on the historical maintenance information of each gas pipeline.

[0166] By combining the feedback results and the maintenance frequency of the gas pipeline, the urgency and failure risk of the first alternative valve can be assessed more accurately, and the maintenance order of the first alternative valve can be determined more accurately; by maintaining valves with a higher maintenance frequency first, it can ensure timely maintenance or replacement of aging or severely worn valves, which can reduce downtime caused by valve failure, thereby improving the reliability and stability of the gas pipeline.

[0167] In some embodiments of the present specification, by setting the order in which the valves are to be repaired and increasing the maintenance priority of valves with more failures, and predicting the second alternative valve through a fault prediction model, it is possible to ensure the rational allocation of maintenance resources, more accurately identify the faulty valves, and promptly repair valves with higher potential failure risks, thereby improving the safety of the gas pipeline system.

[0168] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. The embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A pipeline valve verification Internet of Things system based on smart gas safety supervision, characterized in that: Including smart gas government safety supervision management platform, smart gas government safety supervision sensor network platform, smart gas government safety supervision object platform, gas company management platform, gas company sensor network platform, gas equipment object platform and gas equipment maintenance object platform; The smart gas government safety supervision management platform is configured to generate a first verification instruction and send it to the gas company management platform; The gas company management platform is configured as follows: In response to receiving the first verification instruction or monitoring that the historical verification time of the gas pipeline exceeds the verification time threshold, performing a first verification on the gas pipeline and the pipeline valve on the gas pipeline, and determining a first verification result; Determine flexible verification batches based on sensing parameters and operating parameters; Based on the flexible verification batch and the first verification instruction, a second verification instruction is generated, and a second verification is performed on the gas pipeline and the pipeline valve to obtain a second verification result; Based on the first verification result and the second verification result, determining the valve to be maintained of the gas pipeline; Based on the valve to be maintained, determining work order information; Based on the work order information, determine the work order dispatch instruction and send it to the gas equipment maintenance object platform; Before maintaining the valve to be maintained, a parameter adjustment instruction is generated, and based on the parameter adjustment instruction, the working parameters of the auxiliary device supporting the valve to be maintained are adjusted.

2. The system according to claim 1, characterized in that The gas company management platform is further configured as follows: Acquire a usage information sequence uploaded by a gas user from the gas equipment object platform; determining the flexible verification batch based on the usage information sequence, the sensing parameter, and the operating parameter; Based on the flexible verification batch, the second verification instruction is generated, and a second verification is performed on the gas pipeline and the pipeline valve to obtain the second verification result.

3. The system according to claim 2, characterized in that The gas company management platform is further configured as follows: For a gas pipeline, based on the usage information sequence and the operating parameter, determining the consistency between the usage information sequence and the operating parameter; Based on the consistency sequence and the sensing parameters, the gas pipelines and pipeline valves that need to undergo a second calibration are determined through a batch determination model, and the flexible calibration batch is adjusted; wherein the consistency sequence is composed of the consistency of multiple gas pipelines, and the batch determination model is a machine learning model.

4. The system according to claim 1, characterized in that The valve to be maintained includes a first candidate valve and a third candidate valve, the third candidate valve is a second candidate valve whose maintenance sequence meets a preset sorting condition, and the maintenance priority of the first candidate valve is higher than that of the second candidate valve; the gas company management platform is further configured as follows: Based on the feedback result of the smart gas government safety supervision and management platform, determine the first candidate valve, where the first candidate valve includes the valve to be maintained that is reported to be faulty by the gas user; and determining the second candidate valve based on the first verification result, the second verification result and the first candidate valve; Determining a maintenance order of the second candidate valve based on the first verification result and the second verification result; determining the third candidate valve based on the maintenance order of the second candidate valve; The valve to be maintained is determined based on the third candidate valve and the first candidate valve.

5. The system according to claim 4, characterized in that The gas company management platform is further configured as follows: Based on the feedback results of the smart gas government safety supervision and management platform and the maintenance frequency of the gas pipeline, the maintenance order of the first alternative valve is determined.

6. A pipeline valve calibration method based on smart gas safety supervision, characterized in that: The method is executed based on the gas company management platform of the pipeline valve verification Internet of Things system based on smart gas safety supervision in any one of claims 1 to 5 above, and the method comprises: In response to receiving the first verification instruction or monitoring that the historical verification time of the gas pipeline exceeds the verification time threshold, performing a first verification on the gas pipeline and the pipeline valve on the gas pipeline, and determining a first verification result; Determine flexible verification batches based on sensing parameters and operating parameters; Based on the flexible verification batch and the first verification instruction, a second verification instruction is generated, and a second verification is performed on the gas pipeline and the pipeline valve to obtain a second verification result; Based on the first verification result and the second verification result, determining the valve to be maintained of the gas pipeline; Based on the valve to be maintained, determining work order information; Based on the work order information, determine the work order dispatch instruction and send it to the gas equipment maintenance object platform; Before maintaining the valve to be maintained, a parameter adjustment instruction is generated, and based on the parameter adjustment instruction, the working parameters of the auxiliary device supporting the valve to be maintained are adjusted.

7. The method according to claim 6, characterized in that The method further comprises: Acquire a usage information sequence uploaded by a gas user from the gas equipment object platform; determining the flexible verification batch based on the usage information sequence, the sensing parameter, and the operating parameter; Based on the flexible verification batch, the second verification instruction is generated, and a second verification is performed on the gas pipeline and the pipeline valve to obtain the second verification result.

8. The method according to claim 7, characterized in that The determining the flexible verification batch based on the usage information sequence, the sensing parameter and the operating parameter comprises: for a gas pipeline, based on the usage information sequence and the operating parameter, determining the consistency between the usage information sequence and the operating parameter; Based on the consistency sequence and the sensing parameters, the gas pipelines and pipeline valves that need to undergo a second calibration are determined through a batch determination model, and the flexible calibration batch is adjusted; wherein the consistency sequence is composed of the consistency of multiple gas pipelines, and the batch determination model is a machine learning model.

9. The method according to claim 6, characterized in that The valve to be maintained includes a first candidate valve and a third candidate valve, the third candidate valve is a second candidate valve whose maintenance sequence meets a preset sorting condition, the maintenance priority of the first candidate valve is higher than that of the second candidate valve, and the determination of the valve to be maintained of the gas pipeline based on the first verification result and the second verification result includes: Based on the feedback result of the smart gas government safety supervision and management platform, determine the first candidate valve, where the first candidate valve includes the valve to be maintained that is reported to be faulty by the gas user; and determining the second candidate valve based on the first verification result, the second verification result and the first candidate valve; Determining a maintenance order of the second candidate valve based on the first verification result and the second verification result; determining the third candidate valve based on the maintenance order of the second candidate valve; The valve to be maintained is determined based on the third candidate valve and the first candidate valve.

10. The method according to claim 9, characterized in that The determining of the first alternative valve based on the feedback result of the smart gas government safety supervision and management platform includes: determining the maintenance order of the first alternative valve based on the feedback result of the smart gas government safety supervision and management platform and the maintenance frequency of the gas pipeline.

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