Pipeline impurity monitoring Internet of Things system and method based on intelligent gas Internet of Things

Abstract

The invention provides a pipeline impurity monitoring Internet of Things system and method based on an intelligent gas Internet of Things, and the system comprises a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company sensing network platform, and a gas equipment object platform. The government safety supervision object platform comprises a gas company management platform, and the gas company management platform is configured to determine the impurity gathering degree of at least one gas pipeline based on pressure monitoring data and temperature monitoring data of the valve device; determining a temperature control adjustment parameter based on the impurity removal instruction and the impurity aggregation degree; and generating a temperature control instruction based on the confirmation parameter, and sending the temperature control instruction to the temperature control device. According to the invention, the adhesion condition of impurities in the gas pipeline can be accurately monitored in real time, so that the corrosion problem of the pipeline can be timely found and effectively treated, and the overall safety performance of the gas pipeline is further guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of pipeline impurity monitoring, and particularly to an Internet of Things system and method for pipeline impurity monitoring based on the intelligent gas Internet of Things. Background Art

[0002] Gas is mainly transported through pipelines. During this process, secondary non-gas impurities will be generated in the pipelines. These impurities may deposit on the inner wall of the pipeline due to changes in pipeline structure, temperature or pressure. The presence of non-gas impurities not only corrodes local parts of the pipeline, leading to potential safety hazards, but also may have an adverse impact on the operation safety and regulation stability of pipeline accessory facilities (such as valves, monitoring devices, etc.).

[0003] Therefore, it is desired to propose an Internet of Things system and method for pipeline impurity monitoring based on the intelligent gas Internet of Things to monitor the attachment of impurities in the gas pipeline in real time and accurately, so as to be able to timely detect and effectively handle pipeline corrosion problems, and thus ensure the overall safety performance of the gas pipeline. Summary of the Invention

[0004] In order to achieve real-time monitoring of pipeline impurities, the present invention proposes an Internet of Things system and method for pipeline impurity monitoring based on the intelligent gas Internet of Things, which can monitor the attachment of impurities in the gas pipeline in real time and accurately, so as to be able to timely detect and effectively handle pipeline corrosion problems, and thus ensure the overall safety performance of the gas pipeline.

[0005] The invention content includes a pipeline impurity monitoring Internet of Things system based on the intelligent gas Internet of Things. The Internet of Things system includes a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company sensing network platform, and a gas equipment object platform. The government safety supervision object platform includes a gas company management platform. The gas equipment object platform includes a valve device and a temperature control device deployed at at least one pipeline connection. The valve device includes a pressure monitoring component and a temperature monitoring component. The gas company management platform is configured to: obtain the pressure monitoring data and temperature monitoring data of the valve device from the gas equipment object platform through the gas company sensing network platform; determine the impurity aggregation degree of at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data, and send the impurity aggregation degree to the government safety supervision management platform through the government safety supervision sensing network platform; receive the impurity removal instruction sent by the government safety supervision management platform, determine the temperature control adjustment parameter based on the impurity removal instruction and the impurity aggregation degree, and send the temperature control adjustment parameter to the government safety supervision management platform; and receive the confirmation parameter returned by the government safety supervision management platform, generate a temperature control instruction based on the confirmation parameter, and send the temperature control instruction to the temperature control device of the gas equipment object platform through the gas company sensing network platform.

[0006] The invention content includes providing a method for monitoring pipeline impurities based on the intelligent gas Internet of Things. The method is executed by the gas company management platform of the pipeline impurity monitoring Internet of Things system based on the intelligent gas Internet of Things. The method includes: obtaining the pressure monitoring data and temperature monitoring data of the valve device from the gas equipment object platform through the gas company sensing network platform; determining the impurity aggregation degree of at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data, and sending the impurity aggregation degree to the government safety supervision management platform through the government safety supervision sensing network platform; receiving the impurity removal instruction sent by the government safety supervision management platform, determining the temperature control adjustment parameter based on the impurity removal instruction and the impurity aggregation degree, and sending the temperature control adjustment parameter to the government safety supervision management platform; and receiving the confirmation parameter returned by the government safety supervision management platform, generating a temperature control instruction based on the confirmation parameter, and sending the temperature control instruction to the temperature control device through the gas company sensing network platform and the gas equipment object platform.

[0007] The beneficial effects of the present invention include but are not limited to: (1) The pipeline impurity monitoring Internet of Things system based on the intelligent gas Internet of Things can form a closed-loop information operation among various functional platforms, realizing the informatization and intelligence of pipeline impurity monitoring. (2) By monitoring the pressure and temperature of the valve device, the adhesion of impurities in the gas pipeline can be monitored in real time and accurately, so that pipeline corrosion problems can be discovered and effectively treated in a timely manner, the gas transmission environment of the gas pipeline can be effectively maintained, thereby ensuring the purity of the gas and the normal transmission of the gas, and significantly improving the overall safety performance of the gas pipeline. (3) By constructing a pipeline atlas that can well describe the structural characteristics of the gas pipeline network, considering the topological structure under the entire gas pipeline network, the efficiency and accuracy of determining the adjustment effect can be effectively improved through the effect evaluation model, which is beneficial to determining the optimal temperature control adjustment parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is a schematic diagram of the platform structure of the pipeline impurity monitoring Internet of Things system based on the intelligent gas Internet of Things shown in some embodiments of this specification; Figure 2 is an exemplary flowchart of the pipeline impurity monitoring method based on the intelligent gas Internet of Things shown in some embodiments of this specification; Figure 3 is an exemplary flowchart of determining the degree of impurity aggregation shown in some embodiments of this specification; Figure 4 is an exemplary schematic diagram of the effect evaluation model shown in some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below. The drawings do not represent all embodiments.

[0010] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the said words can be replaced by other expressions.

[0011] Unless the context clearly indicates otherwise, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the specifically identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0012] When the operations performed in the embodiments of this specification are described step by step, unless otherwise specified, the order of the steps can be adjusted, steps can be omitted, and other steps may also be included during the operation process.

[0013] Figure 1 It is a schematic diagram of the platform structure of the pipeline impurity monitoring Internet of Things system based on the intelligent gas Internet of Things shown in some embodiments of this specification.

[0014] Such as Figure 1 As shown, the pipeline impurity monitoring Internet of Things system 100 based on the intelligent gas Internet of Things may include a government safety supervision management platform 110, a government safety supervision sensor network platform 120, a government safety supervision object platform 130, a gas company sensor network platform 140, and a gas equipment object platform 150.

[0015] The government safety supervision management platform 110 refers to the platform for the government to conduct information supervision and management.

[0016] The government safety supervision sensor network platform 120 refers to the platform for the government to supervise and manage the sensor network information. In some embodiments, the government safety supervision sensor network platform 120 may interact with the gas company management platform 131, key gas-using enterprises 132, and the government safety supervision management platform 110.

[0017] The government safety supervision object platform 130 refers to the platform for generating government supervision information and executing control information. In some embodiments, the government supervision object platform 130 may include the gas company management platform 131 and key gas-using enterprises 132.

[0018] The gas company management platform 131 refers to the comprehensive management platform for gas company information. The key gas-using enterprises 132 refer to the enterprises that use gas and are of key concern.

[0019] The gas company sensor network platform 140 refers to the platform for comprehensively managing the sensor information of the gas company. In some embodiments, the gas company sensor network platform may be configured as a communication network or a gateway, etc. In some embodiments, the gas company sensor network platform may interact with the gas company management platform 131 and the gas equipment object platform 150.

[0020] The gas equipment object platform 150 refers to a functional platform for generating sensing information and executing control information.

[0021] In some embodiments, the gas equipment object platform 150 may further include a valve device and a temperature control device deployed at at least one pipeline connection. A pipeline connection refers to the location where two or more gas pipelines are connected. The valve device may include a pressure monitoring component and a temperature monitoring component.

[0022] The pressure monitoring component is used to collect pressure monitoring data. In some embodiments, the pressure monitoring component may include a pressure sensor, a pressure detector, etc. The temperature monitoring component is used to collect temperature monitoring data. In some embodiments, the temperature monitoring component may include a temperature sensor, a thermometer, etc.

[0023] In some embodiments, the frequency at which the pressure monitoring component collects pressure monitoring data and the frequency at which the temperature monitoring component collects temperature monitoring data can be preset based on historical experience.

[0024] The temperature control device is used to adjust the temperature inside the gas pipeline. For example, the temperature control device can raise or lower the temperature inside the gas pipeline. In some embodiments, the temperature control device may include a pipeline heater, a thermal heater, etc. In some embodiments, the temperature control device may be deployed on the outer wall of the pipeline at at least one pipeline connection.

[0025] In some embodiments, the pipeline impurity monitoring Internet of Things system 100 based on the intelligent gas Internet of Things may further include a processor. In some embodiments, the processor can process information and / or data related to the pipeline impurity monitoring Internet of Things system 100 based on the intelligent gas Internet of Things to perform one or more functions described in this application. In some embodiments, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), etc. or any combination of the above.

[0026] For the foregoing detailed description, reference may be made to Figures 2 to 4 the relevant description.

[0027] In some embodiments of this specification, the pipeline impurity monitoring Internet of Things system 100 based on the intelligent gas Internet of Things can form an information operation closed loop between each functional platform to realize the informatization and intelligentization of pipeline impurity monitoring.

[0028] Figure 2 is an exemplary flowchart of a pipeline impurity monitoring method based on the intelligent gas Internet of Things shown in some embodiments of this specification. In some embodiments, process 200 is executed by the gas company management platform (hereinafter referred to as the management platform) of the pipeline impurity monitoring Internet of Things system based on the intelligent gas Internet of Things. As Figure 2As shown, process 200 includes the following steps: For the relevant content of each platform of the pipeline impurity monitoring Internet of Things system based on the intelligent gas Internet of Things, reference can be made to Figure 1 the corresponding description.

[0029] Step 210, through the gas company sensing network platform, obtain the pressure monitoring data and temperature monitoring data of the valve device from the gas equipment object platform.

[0030] The pressure monitoring data refers to the data obtained by monitoring the gas pressure at at least one valve device. In some embodiments, the pressure monitoring data may include a sequence composed of the gas pressures of the valve devices within a preset monitoring period.

[0031] The preset monitoring period refers to the period during which the valve device is monitored. The preset monitoring period can be set in advance based on historical experience. For example, the preset monitoring period can be the past 5 minutes of the current time, etc. The gas pressure refers to the gas pressure in the gas pipeline.

[0032] In some embodiments, the pressure monitoring data can be obtained by a pressure monitoring component. For the description of the pressure monitoring component, reference can be made to Figure 1 and its relevant description.

[0033] The temperature monitoring data refers to the data obtained by monitoring the temperature at at least one valve device. In some embodiments, the temperature monitoring data may include a sequence composed of the temperature values of the valve devices within a preset monitoring period.

[0034] In some embodiments, the temperature monitoring data can be obtained by a temperature monitoring component. For the description of the temperature monitoring component, reference can be made to Figure 1 and its relevant description.

[0035] Step 220, based on the pressure monitoring data and temperature monitoring data, determine the impurity aggregation degree of at least one gas pipeline, and send the impurity aggregation degree to the government safety supervision management platform through the government safety supervision sensing network platform.

[0036] The impurity aggregation degree refers to the data characterizing the degree of formation and aggregation of impurities in the gas pipeline. The impurity aggregation degree can be represented by numerical values, etc. For example, the impurity aggregation degree can be represented by 0 - 1. The closer the value is to 1, the higher the impurity aggregation degree, indicating a greater possibility of forming qualitatively changed impurities in the gas pipeline.

[0037] Qualitatively changed impurities refer to impurities visible to the naked eye or impurities that may affect gas transportation.

[0038] In some embodiments, the management platform may determine the degree of impurity aggregation of at least one gas pipeline in various ways based on pressure monitoring data and temperature monitoring data. For example, the management platform may determine the degree of impurity aggregation of the valve devices at both ends of the gas pipeline based on the pressure monitoring data and temperature monitoring data of the valve devices at both ends of the gas pipeline, and determine the average value of the degree of impurity aggregation of the valve devices at both ends of the gas pipeline as the degree of impurity aggregation of the gas pipeline.

[0039] In some embodiments, the degree of impurity aggregation of the valve device may be related to the difference between the pressure monitoring data and the comprehensive pressure data, and related to the difference between the temperature monitoring data and the comprehensive temperature data, etc. For example, the degree of impurity aggregation of the valve device may be positively correlated with the difference between the pressure monitoring data and the comprehensive pressure data, etc., and positively correlated with the difference between the temperature monitoring data and the comprehensive temperature data, etc. In some embodiments, the management platform may determine the degree of impurity aggregation of the valve device based on the pressure monitoring data and temperature monitoring data of the valve device through a preset formula. Exemplarily, the preset formula may be as shown in formula (1): (1) Where, represents the degree of impurity aggregation of the valve device, 、 respectively represent the fluctuations of the pressure monitoring data and the temperature monitoring data, 、 respectively represent the change trends of the pressure monitoring data and the temperature monitoring data, represents the fluctuation of the comprehensive pressure data, represents the change trend of the comprehensive pressure data, represents the fluctuation of the comprehensive temperature data, represents the change trend of the comprehensive temperature data; represents the coefficient of the difference between the fluctuation of the pressure monitoring data and the fluctuation of the comprehensive pressure data, represents the coefficient of the difference between the change trend of the pressure monitoring data and the change trend of the comprehensive pressure data, represents the coefficient of the difference between the fluctuation of the temperature monitoring data and the fluctuation of the comprehensive temperature data, represents the coefficient of the difference between the change trend of the temperature monitoring data and the change trend of the comprehensive temperature data. Where, can be a constant of an order of magnitude, etc., which can be set manually in advance or set by default by the system.

[0040] In some embodiments, the management platform may calculate the fluctuations and change trends of the pressure monitoring data of each valve device in various ways. For example, the management platform may calculate the range or variance, etc. of the pressure monitoring data of each valve device, and represent the fluctuations of the pressure monitoring data through the range or variance, etc.

[0041] For another example, the management platform can utilize a processing algorithm to obtain the change trend of the pressure monitoring data of each valve device. The processing algorithm can include, but is not limited to, a linear regression algorithm, etc. Only as an example, for the pressure monitoring data of a valve device, the management platform can plot a fitting line for it, calculate the slope of the fitting line, and use this slope as the change trend of the pressure monitoring data corresponding to this valve device. Among them, the horizontal axis of the fitting line corresponds to time, and the vertical axis corresponds to the gas pressure.

[0042] In some embodiments, the method for calculating the fluctuation and change trend of the temperature monitoring data of the valve device is the same as that for calculating the fluctuation and change trend of the pressure monitoring data of the valve device, and will not be elaborated here.

[0043] In some embodiments, the management platform can calculate the pressure average value of each valve device among multiple valve devices, and form comprehensive pressure data from the pressure average values of multiple valve devices, and calculate the fluctuation and change trend of the comprehensive pressure data. At the same time, the management platform can calculate the temperature average value of each valve device, and form comprehensive temperature data from the temperature average values of all valve devices, and calculate the fluctuation and change trend of the comprehensive temperature data. Among them, the management platform can average the pressure monitoring data and temperature monitoring data of a single valve device to obtain the pressure average value and temperature average value of this valve device.

[0044] In some embodiments, the method for calculating the fluctuation and change trend of the comprehensive pressure data and the fluctuation and change trend of the comprehensive temperature data is similar to the method for calculating the fluctuation and change trend of the pressure monitoring data and the fluctuation and change trend of the temperature monitoring data described above, and the implementation method can refer to the method for calculating the fluctuation and change trend of the pressure monitoring data and the fluctuation and change trend of the temperature monitoring data described above.

[0045] It can be understood that when the fluctuation of the pressure monitoring data and / or temperature monitoring data of a certain valve device is significantly different from the fluctuation of the comprehensive pressure data and / or comprehensive temperature data, it indicates that impurities with qualitative changes may have formed or impurities may have been generated at the location of this valve device. When the change trend of the pressure monitoring data and / or temperature monitoring data of a certain valve device is different from the change trend of the comprehensive pressure data and / or comprehensive temperature data, it indicates that the pressure change and / or temperature change of this valve device is abnormal, and impurities with qualitative changes may have formed at the location of this valve device. By determining the difference between the pressure and / or temperature in a single gas pipeline and the pressure and / or temperature in the entire gas pipeline network, the degree of impurity aggregation in the gas pipeline can be determined more effectively.

[0046] In some embodiments, the management platform may determine a pressure gradient value based on first pressure data and second pressure data; determine a temperature gradient value based on first temperature data and second temperature data; and determine the degree of impurity aggregation based on the pressure gradient value and the temperature gradient value. For more content on this part, reference can be made to Figure 3 the corresponding description.

[0047] Step 230: Receive an impurity removal instruction sent by the government safety supervision management platform, determine a temperature control adjustment parameter based on the impurity removal instruction and the degree of impurity aggregation, and send the temperature control adjustment parameter to the government safety supervision management platform.

[0048] The impurity removal instruction refers to an instruction indicating whether impurities need to be removed. In some embodiments, the impurity removal instruction can be represented in various ways. For example, the impurity removal instruction can be represented using a Boolean value, where 0 represents no need to remove impurities and 1 represents the need to remove impurities. Another example is that the impurity removal instruction can be represented using text, including "impurities need to be removed", "impurities do not need to be removed", etc.

[0049] In some embodiments, the impurity removal instruction can be determined by the government safety supervision management platform. For example, the impurity removal instruction can be manually determined by the supervisors of the government safety supervision management platform. Another example is that in response to the degree of impurity aggregation being greater than the removal threshold, the impurity removal instruction is determined to be that impurities need to be removed. The removal threshold is used to determine whether impurities need to be removed and can be preset based on historical experience.

[0050] The temperature control adjustment parameter refers to the relevant parameters used to adjust the operation of the temperature control device. In some embodiments, the temperature control adjustment parameter may include at least one of the start time and the start temperature, etc. The start time may include the time point of start and the duration of continuation, etc. The start temperature refers to the temperature that the temperature control device needs to rise / to fall.

[0051] In some embodiments, the temperature control adjustment parameter may include multiple sets of data, each set of data may correspond to a temperature control device, and each temperature control device corresponds to a set of data.

[0052] It can be understood that when there are impurities in the gas pipeline and the impurities need to be removed, the impurities can be degraded by controlling the temperature control device to increase the temperature, so as to achieve the purpose of removing impurities. For the description of the temperature control device, reference can be made to Figure 1 its related description.

[0053] In some embodiments, in response to the impurity removal instruction received from the government safety supervision and management platform being to remove impurities (such as 1), the management platform can determine the temperature control adjustment parameters in various ways based on the impurity removal instruction and the degree of impurity aggregation. For example, for each of multiple gas pipelines, the management platform can query a parameter comparison table based on the impurity level of the impurity aggregation degree in the gas pipeline, and determine the temperature control adjustment parameter corresponding to the temperature control device of the gas pipeline as the reference adjustment parameter corresponding to the impurity level in the parameter comparison table.

[0054] In some embodiments, the management platform can determine the impurity level of the impurity aggregation degree through an impurity level table. The impurity level table can include the corresponding relationship between the impurity aggregation degree and the impurity level. The impurity level table can be preset based on historical experience. Exemplarily, the impurity level table can include four or more levels, corresponding to 0 - 0.25, 0.25 - 0.5, 0.5 - 0.75, and 0.75 - 1 of the impurity aggregation degree, etc.

[0055] In some embodiments, the management platform can construct a parameter comparison table based on historical data. Only as an example, the management platform can count the historical impurity aggregation degree and the corresponding historical temperature control adjustment parameters in the historical data as sample data, and determine the sample data with the impurity removal effect meeting the preset standard as the target sample data. At the same time, the management platform can classify the target sample data based on the impurity level to obtain multiple target sample data under each impurity level. For the multiple target sample data under each impurity level, the management platform can count the historical temperature control adjustment parameter that appears the most times in the multiple target sample data, use it as the reference adjustment parameter and record it in the table to obtain a parameter comparison table including multiple impurity levels and the reference adjustment parameter corresponding to each impurity level.

[0056] The impurity removal effect refers to the effect of removing impurities from the gas pipeline based on the sample data. In some embodiments, the impurity removal effect can be represented by the degree of impurity aggregation after impurity removal. The degree of impurity aggregation after impurity removal can be obtained by means such as manual measurement or robot detection of the actual impurity removal situation in the gas pipeline.

[0057] In some embodiments, the preset standard can be preset based on historical experience. An exemplary preset standard can be that the impurity aggregation degree is less than 0.1.

[0058] In some embodiments, the management platform can determine the adjustment effect of each candidate adjustment parameter among multiple candidate adjustment parameters through an effect evaluation model based on the pipeline map and the multiple candidate adjustment parameters; and determine the temperature control adjustment parameter based on the adjustment effect. For more content in this part, reference can be made to Figure 4 the corresponding description.

[0059] Step 240: Receive the confirmation parameters returned by the government safety supervision and management platform, generate a temperature control instruction based on the confirmation parameters, and send the temperature control instruction to the temperature control device through the gas company sensing network platform and the gas equipment object platform.

[0060] The temperature control instruction refers to an instruction for controlling the operation of the temperature control device.

[0061] In some embodiments, the management platform may generate a temperature control instruction based on the confirmation parameters.

[0062] The confirmation parameter refers to information used to confirm the operation of the temperature control device. In some embodiments, the confirmation parameter may include at least one of whether to use the temperature control device to adjust the pipeline temperature for impurity removal and the temperature control adjustment parameter, etc. In some embodiments, the confirmation parameter may be issued by the supervisors of the government safety supervision and management platform.

[0063] In some embodiments, the supervisors of the government safety supervision and management platform may adjust the temperature control adjustment parameter based on their own judgment to obtain a new temperature control adjustment parameter, and add the new temperature control adjustment parameter to the confirmation parameter.

[0064] In some embodiments, in response to the confirmation parameter received by the management platform being to use the temperature control device to adjust the pipeline temperature for impurity removal, the management platform may generate a temperature control instruction based on the temperature control adjustment parameter. In response to the confirmation parameter received by the management platform being not to use the temperature control device to adjust the pipeline temperature for impurity removal, the management platform may continue to use the previous temperature control instruction.

[0065] In some embodiments of this specification, since non-gas impurities will be generated in the gas pipeline, by monitoring the pressure and temperature of the valve device, the adhesion of impurities in the gas pipeline can be monitored in real time and accurately, so that the pipeline corrosion problem can be discovered and effectively processed in time, the gas transmission environment of the gas pipeline can be effectively maintained, thereby ensuring the purity of the gas and the normal transmission of the gas, and significantly improving the overall safety performance of the gas pipeline.

[0066] It should be noted that the above description of process 200 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various corrections and changes can be made to the process hand-eye calibration under the guidance of this specification. However, these corrections and changes are still within the scope of this specification.

[0067] Figure 3 is an exemplary flowchart for determining the degree of impurity aggregation shown in some embodiments of this specification. In some embodiments, process 300 is executed by the management platform. As Figure 3 shown, process 300 includes the following steps: In some embodiments, the pressure monitoring component may include a first pressure component and a second pressure component, and the pressure monitoring data may include first pressure data and second pressure data. The temperature monitoring component may include a first temperature component and a second temperature component, and the temperature monitoring data may include first temperature data and second temperature data. The management platform may determine a pressure gradient value based on the first pressure data and the second pressure data; determine a temperature gradient value based on the first temperature data and the second temperature data; and determine the degree of impurity aggregation based on the pressure gradient value and the temperature gradient value.

[0068] For more descriptions of the pressure monitoring component and the temperature monitoring component, reference may be made to Figure 1 and its related descriptions. For more descriptions of the pressure monitoring data, the temperature monitoring data, and the degree of impurity aggregation, reference may be made to Figure 2 and its related descriptions.

[0069] Both sides of the valve device include an upstream side and a downstream side. The upstream side of the valve device refers to the side where the valve device receives gas. The downstream side of the valve device refers to the side where the valve device outputs gas.

[0070] The first pressure component refers to a component that monitors the gas pressure on the upstream side of the valve device. The first pressure data refers to the data obtained by monitoring the gas pressure on the upstream side of the valve device. In some embodiments, the first pressure data may include a sequence of gas pressures on the upstream side of the valve device within a preset monitoring period. For more content about the preset monitoring period, reference may be made to Figure 2 the corresponding description.

[0071] In some embodiments, the first pressure component may be deployed on the upstream side of the valve device.

[0072] The second pressure component refers to a component that monitors the gas pressure on the downstream side of the valve device. The second pressure data refers to the data obtained by monitoring the gas pressure on the downstream side of the valve device. In some embodiments, the second pressure data may include a sequence of gas pressures on the downstream side of the valve device within a preset monitoring period.

[0073] In some embodiments, the second pressure component may be deployed on the downstream side of the valve device.

[0074] The first temperature component refers to a component that monitors the temperature on the upstream side of the valve device. The first temperature data is the data obtained by monitoring the temperature on the upstream side of the valve device. In some embodiments, the first temperature data may include a sequence of temperature values on the upstream side of the valve device within a preset monitoring period.

[0075] In some embodiments, the first temperature component may be deployed on the upstream side of the valve device.

[0076] The second temperature component refers to a component that monitors the temperature on the downstream side of the valve device. The second temperature data refers to the data obtained by monitoring the temperature on the downstream side of the valve device. In some embodiments, the second temperature data may include a sequence of temperature values on the downstream side of the valve device within a preset monitoring period.

[0077] In some embodiments, the second temperature component may be deployed on the downstream side of the valve device.

[0078] Step 310, determine the pressure gradient value based on the first pressure data and the second pressure data.

[0079] The pressure gradient value is data used to measure the pressure change situation on both sides of the valve device.

[0080] In some embodiments, the management platform may determine the pressure gradient value based on the first pressure data and the second pressure data. For example, for each valve device, the management platform may calculate the difference between the first pressure data and the second pressure data to obtain the pressure difference data on both sides of the valve device, and calculate the fluctuation of the pressure difference data, and determine the difference between the fluctuation of the pressure difference data and the pressure fluctuation threshold as the pressure gradient value. Among them, the method of calculating the fluctuation of the pressure difference data is similar to Figure 2 the method of calculating the fluctuation of the pressure monitoring data in, which will not be elaborated here.

[0081] The pressure fluctuation threshold refers to the critical value of the gas pressure fluctuation on both sides of the valve device. In some embodiments, the pressure fluctuation threshold corresponds to the valve device, and one valve device corresponds to one pressure fluctuation threshold.

[0082] In some embodiments, the pressure fluctuation threshold of a single valve device is related to the comprehensive pressure fluctuation value, the average in-out degree difference, and the in-out degree difference of the valve device. For example, the pressure fluctuation threshold of a single valve device is directly proportional to the comprehensive pressure fluctuation value and the in-out degree difference of the valve device, and inversely proportional to the average in-out degree difference. In some embodiments, the management platform may calculate the pressure fluctuation threshold corresponding to the valve device based on the comprehensive pressure fluctuation value, the in-out degree difference of the valve device, and the average in-out degree difference through a preset pressure formula. Exemplarily, the preset pressure formula may be as shown in formula (2): (2) where, represents the pressure fluctuation threshold, represents the comprehensive pressure fluctuation value, represents the in-out degree difference of the valve device, represents the average in-out degree difference.

[0083] The comprehensive pressure fluctuation value refers to the data used to reflect the fluctuation of the pressure difference data of multiple valve devices in the gas pipeline network. In some embodiments, the management platform can calculate the pressure difference data of all valve devices, calculate the standard deviation and mean of the multiple pressure difference data, and determine the ratio of the standard deviation to the mean as the comprehensive pressure fluctuation value.

[0084] The in-out degree difference of the valve device refers to the data characterizing the gap between the out-degree and in-degree of the valve device. In some embodiments, the management platform can determine the absolute value of the difference between the in-degree and out-degree of the valve device as the in-out degree difference of the valve device. Among them, the in-degree refers to the gas volume input to the valve device. The out-degree refers to the gas volume output by the valve device.

[0085] In some embodiments, the management platform can obtain the in-degree and out-degree of the valve device through multiple gas metering devices, and the gas metering devices can be deployed on the upstream side and downstream side of the valve device.

[0086] In some embodiments, the management platform can calculate the in-out degree difference of all valve devices in the gas pipeline network, and determine the mean of the multiple in-out degree differences as the average in-out degree difference.

[0087] Step 320, based on the first temperature data and the second temperature data, determine the temperature gradient value.

[0088] The temperature gradient value is the data used to measure the temperature change situation on both sides of the valve device.

[0089] In some embodiments, the management platform can determine the temperature gradient value based on the first temperature data and the second temperature data in various ways. For example, for each valve device, the management platform can calculate the difference between the first temperature data and the second temperature data to obtain the temperature difference data on both sides of the valve device, and calculate the fluctuation of the temperature difference data, and determine the difference between the fluctuation of the temperature difference data and the temperature fluctuation threshold as the temperature gradient value. Among them, the method of calculating the fluctuation of the temperature difference data is similar to the method of calculating the fluctuation of the temperature monitoring data, and will not be elaborated here.

[0090] The temperature fluctuation threshold refers to the critical value of the temperature fluctuation on both sides of the valve. In some embodiments, the temperature fluctuation threshold of a single valve device is related to the comprehensive temperature fluctuation value, the average in-out degree difference, the in-out degree difference of the valve device, and the thermal conductivity of the valve material. For example, the temperature fluctuation threshold of a single valve device is proportional to the comprehensive temperature fluctuation value and the in-out degree difference of the valve device, and inversely proportional to the average in-out degree difference and the thermal conductivity of the valve material. In some embodiments, the management platform can calculate the temperature fluctuation threshold corresponding to the valve device based on the comprehensive temperature fluctuation value, the in-out degree difference of the valve device, the average in-out degree difference, and the thermal conductivity of the valve material through a preset temperature formula. Exemplarily, the preset temperature formula can be as shown in formula (3): (3) Among them, represents the temperature fluctuation threshold, represents the comprehensive temperature fluctuation value, represents the difference in the degree of entry and exit of the valve device, represents the average difference in the degree of entry and exit, represents the thermal conductivity of the valve material.

[0091] The comprehensive temperature fluctuation value refers to the data reflecting the fluctuation of the temperature difference data of multiple valve devices in the gas pipeline network. In some embodiments, the method for calculating the comprehensive temperature fluctuation value is similar to the method for calculating the comprehensive pressure fluctuation value, which will not be elaborated here.

[0092] The thermal conductivity of the valve material refers to the physical quantity characterizing the magnitude of the heat conduction ability of the material used to manufacture the valve device. The management platform can obtain the thermal conductivity of the valve material through the gas company management platform.

[0093] Step 330, based on the pressure gradient value and the temperature gradient value, determine the degree of impurity aggregation.

[0094] In some embodiments, the management platform can determine the degree of impurity aggregation of at least one gas pipeline based on the pressure gradient value and the temperature gradient value in various ways. For example, the management platform can first determine the degree of impurity aggregation of the valve devices at both ends of the gas pipeline based on the pressure gradient value and the temperature gradient value, and determine the average value of the degree of impurity aggregation of the valve devices at both ends of the gas pipeline as the degree of impurity aggregation of the gas pipeline.

[0095] In some embodiments, the degree of impurity aggregation of the valve device is related to the pressure gradient value and the temperature gradient value. For example, the degree of impurity aggregation of the valve device can be positively correlated with the pressure gradient value and the temperature gradient value. In some embodiments, the management platform can determine the degree of impurity aggregation of the valve device through a preset aggregation formula based on the pressure fluctuation threshold and the temperature fluctuation threshold. Exemplarily, the preset aggregation formula can be as shown in formula (4): (4) Among them, represents the degree of impurity aggregation of the valve device, represents the pressure fluctuation threshold, represents the temperature fluctuation threshold, respectively represent the coefficients of the pressure fluctuation threshold and the temperature fluctuation threshold. Among them, can be a constant of an order of magnitude, etc., which can be set artificially in advance or set by default by the system.

[0096] In some embodiments, the valve device further includes a flow rate regulating component and a pressure regulating component. The management platform may determine the degree of impurity aggregation based on the flow rate regulation accuracy of the flow rate regulating component, the pressure regulation accuracy of the pressure regulating component, the temperature gradient value, and the pressure gradient value.

[0097] The flow rate regulating component refers to a component that regulates the gas flow rate in the gas pipeline. For example, a flow-limiting valve, an orifice plate, etc. The gas flow rate refers to the volume of gas flowing through the gas pipeline per unit time. For example, 100 m³ / h, etc.

[0098] The flow rate regulation accuracy refers to the regulation sensitivity of the flow rate regulating component. For example, the flow rate regulation accuracy can reach 0.01.

[0099] The pressure regulating component refers to a component that regulates the gas pressure in the gas pipeline. For example, a pressure regulator, etc.

[0100] The pressure regulation accuracy refers to the regulation sensitivity of the pressure regulating component. For example, the pressure regulation accuracy can reach 0.01.

[0101] In some embodiments, the management platform may obtain the flow rate regulation accuracy and the pressure regulation accuracy through the gas equipment object platform. The gas equipment object platform may obtain and store the flow rate regulation accuracy and the pressure regulation accuracy through user input and other means.

[0102] In some embodiments, the management platform may determine the degree of impurity aggregation in various ways based on the flow rate regulation accuracy, the pressure regulation accuracy, the temperature gradient value, and the pressure gradient value. For example, the management platform may construct a target feature vector based on the flow rate regulation accuracy, the pressure regulation accuracy, the temperature gradient value, and the pressure gradient value, match a reference vector that satisfies a preset matching condition with the target feature vector in the vector database, and determine the label of the reference vector that satisfies the preset matching condition as the degree of impurity aggregation. Among them, the target feature vector may be a feature vector constructed based on the flow rate regulation accuracy, the pressure regulation accuracy, the temperature gradient value, and the pressure gradient value. In some embodiments, the preset matching condition may include the highest vector similarity. The vector similarity is negatively correlated with the vector distance, and the vector distance may include the Euclidean distance, the cosine distance, etc., and the distance threshold may be set in advance.

[0103] In some embodiments, the vector database may be preset based on historical data. The vector database may include multiple reference vectors and the label of each reference vector.

[0104] In some embodiments, the management platform may construct a clustering vector based on historical samples and the actual impurity aggregation degree of the gas pipeline corresponding to the historical samples, cluster multiple clustering vectors, construct a reference vector based on the historical samples corresponding to the clustering centers formed by clustering, and use the actual impurity aggregation degree corresponding to the clustering centers as the label of the reference vector. Among them, the historical samples refer to a set of historical flow regulation accuracy, historical pressure regulation accuracy, historical temperature gradient value, and historical pressure gradient value in historical data.

[0105] In some embodiments, the actual impurity aggregation degree can be determined through multiple experiments. For example, technicians can extract and analyze the gas in the gas pipeline to obtain experimental gas, conduct multiple experiments based on the experimental gas, count the number of experiments in which impurities condense in multiple experiments, and determine the ratio of the number of experiments in which impurities condense to the total number of experiments as the actual impurity aggregation degree. Among them, the experimental gas is consistent with the gas composition in the extracted gas pipeline. The process of the experiment may include injecting the experimental gas into a simulated gas pipeline environment for the experiment and observing whether impurities condense in the simulated gas pipeline environment.

[0106] Exemplarily, if technicians conduct 100 experiments based on the experimental gas and count that the number of experiments in which impurities condense in multiple experiments is 90 times, then the actual impurity aggregation degree is 0.9.

[0107] In some embodiments of this specification, by considering the regulation accuracy of the flow regulation component and the pressure regulation component, thus considering the system errors that may be caused by the two components, and considering the system errors when determining the impurity aggregation degree, the accuracy of determining the impurity aggregation degree can be further improved.

[0108] In some embodiments, the management platform may determine the impurity aggregation degree through an aggregation prediction model based on the flow regulation accuracy, pressure regulation accuracy, temperature gradient value, and pressure gradient value.

[0109] The aggregation prediction model refers to a model used to determine the impurity aggregation degree. In some embodiments, the aggregation prediction model may be a machine learning model. For example, the aggregation prediction model may include any one or a combination of a convolutional neural network (CNN) model, a neural network (NN) model, or other custom model structures, etc.

[0110] In some embodiments, the management platform may train an aggregation prediction model based on a training sample set through methods such as gradient descent. The training sample set includes a large number of labeled training samples. Each set of training samples in the training sample set may include sample flow regulation accuracy, sample pressure regulation accuracy, sample temperature gradient value, and sample pressure gradient value. The label for each set of training samples may be the actual impurity aggregation degree. In some embodiments, the training samples and labels may be obtained based on historical data. For the description of the actual impurity aggregation degree, refer to the above and its related descriptions.

[0111] In some embodiments, the aggregation prediction model may be trained as follows: Input multiple labeled training samples into an initial aggregation prediction model, construct a loss function based on the labels and the prediction results of the initial aggregation prediction model, iteratively update the initial aggregation prediction model based on the loss function, and when the loss function of the initial aggregation prediction model meets a preset condition, the aggregation prediction model training is completed. Among them, the preset condition may be that the loss function converges, the number of iterations reaches a set value, etc.

[0112] In some embodiments, the learning rate of the training samples may be related to the historical accident frequency of the training samples. For example, the learning rate of the training samples may be negatively correlated with the historical accident frequency. The higher the historical accident frequency, the smaller the learning rate of the training samples.

[0113] The historical accident frequency refers to the occurrence frequency of historical accidents. Historical accidents refer to pipeline accidents caused by impurities in the gas pipeline. For example, blockage, pipeline deformation, etc.

[0114] In some embodiments, the management platform may determine the gas pipeline corresponding to the training samples, and determine the historical accidents that occurred in the gas pipeline within a preset time period and the duration of the preset time period as the historical accident frequency of the training samples. The preset time period may be set in advance based on historical experience.

[0115] In some embodiments of this specification, the larger the historical accident frequency, the more historical accidents are caused by impurities in the gas pipeline, and the greater the impact of the impurities in the gas pipeline on the gas pipeline. Therefore, reducing the learning rate of such training samples makes the aggregation prediction model converge more slowly and better learn the implicit rules of such training samples.

[0116] In some embodiments of this specification, by processing the flow regulation accuracy, pressure regulation accuracy, temperature gradient value, and pressure gradient value through the aggregation prediction model, the self-learning ability of the machine learning model can be utilized to find patterns from a large amount of data, improving the accuracy and efficiency of determining the impurity aggregation degree.

[0117] In some embodiments of the present specification, the degree of impurity aggregation is determined by the pressure gradient value and the temperature gradient value. When determining the degree of impurity aggregation, the pressure change and temperature change conditions upstream and downstream of the valve can be considered to determine the degree of impurity aggregation that is closer to the actual situation of the environment where the valve is located.

[0118] It should be noted that the above description of process 300 is only for illustration and explanation, and does not limit the scope of application of the present specification. For those skilled in the art, various modifications and changes can be made to the hand-eye calibration of the process under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification.

[0119] In some embodiments, the management platform can determine the adjustment effect 450 of each candidate adjustment parameter among the multiple candidate adjustment parameters based on the pipeline map 411 and the multiple candidate adjustment parameters 412 through the effect evaluation model 420, and determine the temperature control adjustment parameter based on the adjustment effect 450. For the description of the temperature control adjustment parameter, reference can be made to step 230 and its related description.

[0120] The adjustment effect can be used to characterize the cleaning effect of the impurities in the gas pipeline after the temperature control device heats up or cools down the gas pipeline based on the candidate adjustment parameter. In some embodiments, the adjustment effect can be represented by a numerical value or the like. For example, the larger the numerical value, the higher the adjustment effect.

[0121] The pipeline map refers to a graph structure that characterizes the association relationship between the valve device and the temperature control device. The graph structure is a data structure composed of nodes and edges. The edges connect the nodes, and the nodes and edges can have characteristics.

[0122] In some embodiments, the management platform can construct a pipeline map based on the connection relationship between the temperature control devices and valve devices in the gas pipeline network. The nodes of the pipeline map can include valve devices and temperature control devices. The node characteristics of the valve device node can include pressure monitoring data and temperature monitoring data, and the node characteristics of the temperature control device node can include the temperature control adjustment parameter corresponding to the temperature control device.

[0123] The edges of the pipeline map can represent the connectivity between nodes. In some embodiments, the edges of the pipeline map can include the gas pipelines connecting the nodes. The characteristics of the edges can include the length of the gas pipeline, the edge type, and the degree of impurity aggregation. Among them, the edge type refers to the type of the gas pipeline connecting the nodes. The edge type can include the gas pipeline between temperature control devices, the gas pipeline between valve devices, and the gas pipeline between valve devices and temperature control devices. The length of the gas pipeline can be obtained through the gas company management platform.

[0124] The candidate adjustment parameter refers to the temperature control adjustment parameter to be determined.

[0125] In some embodiments, the management platform can determine multiple candidate adjustment parameters in various ways. For example, the management platform can count the temperature control adjustment parameters used in historical data, sort the temperature control adjustment parameters from largest to smallest based on the number of times of use, and determine a preset number of the temperature control adjustment parameters with the highest rankings as the multiple candidate adjustment parameters.

[0126] In some embodiments, the preset number can be related to the amount of computing resources. For example, the preset number can be positively correlated with the amount of computing resources. The more the computing resources, the larger the preset number. Herein, the computing resources refer to the resources related to the calculation used by the management platform. In some embodiments, the computing resources can include at least one of storage resources, network resources, etc. In some embodiments, the management platform can count its own computing resources.

[0127] In some embodiments, the management platform can determine multiple candidate adjustment parameters through a frequent item database.

[0128] The frequent item database refers to a database that includes multiple frequent items and the support degree corresponding to each frequent item. A frequent item refers to a temperature control adjustment parameter frequently used in historical data. The support degree is used to represent the reliability of the frequency.

[0129] In some embodiments, the frequent item database can be preset based on historical data. For example, the management platform can determine a positive sample database and a negative sample database based on historical data, count the number of times n of a single temperature control adjustment parameter in the positive sample database and the number of times m in the negative sample database in historical data, determine the temperature control adjustment parameter with n greater than a first threshold and m less than a second threshold as a frequent item, and record n / m as the support degree of this frequent item. The management platform can determine multiple frequent items and the support degree corresponding to each frequent item in the above manner. Among them, the first threshold and the second threshold can be preset based on historical experience.

[0130] In some embodiments, the management platform can screen the temperature control adjustment parameters in historical data whose adjustment difference meets the positive screening conditions as positive samples and add them to the positive sample database. Among them, the positive screening conditions can include that the adjustment difference is greater than a third threshold. The adjustment difference refers to the difference between the impurity aggregation degree before the temperature control device adjusts and the impurity aggregation degree after the adjustment.

[0131] In some embodiments, the third threshold may be preset based on historical experience. The management platform may also count multiple adjustment differences in the historical data, sort the multiple adjustment differences from largest to smallest, and determine the adjustment difference ranked at the preset upper quantile as the third threshold. The preset upper quantile may be determined based on the number of adjustment differences. For example, the larger the number of adjustment differences, the larger the preset upper quantile may be. Exemplarily, the preset upper quantile may include the upper quartile or the upper quintile, etc. The upper quintile is greater than the upper quartile. The upper quartile represents the data in the top 25% of the sorted adjustment differences, and the upper quintile represents the data in the top 20% of the sorted adjustment differences.

[0132] In some embodiments, the management platform may screen the temperature control adjustment parameters in the historical data whose adjustment differences meet the negative screening conditions as negative samples and add them to the negative sample database. Among them, the negative screening conditions may include that the adjustment difference is less than the fourth threshold.

[0133] In some embodiments, the fourth threshold may be preset based on historical experience. The management platform may also count multiple adjustment differences in the historical data, sort the multiple adjustment differences from largest to smallest, and determine the adjustment difference ranked at the preset lower quantile as the fourth threshold. The preset lower quantile may be determined based on the number of adjustment differences. For example, the larger the number of adjustment differences, the larger the preset lower quantile may be. Exemplarily, the preset lower quantile may include the lower quartile or the lower quintile, etc. The lower quintile is greater than the lower quartile. The lower quartile represents the data in the bottom 25% of the sorted adjustment differences, and the lower quintile represents the data in the bottom 20% of the sorted adjustment differences.

[0134] In some embodiments, the management platform may sort the frequent items in the frequent item database according to the support degree from largest to smallest, and select the top preset candidate number of frequent items as multiple candidate adjustment parameters.

[0135] In some embodiments, the number of multiple candidate adjustment parameters may be related to the historical accident frequency. For example, the number of multiple candidate adjustment parameters may be positively correlated with the historical accident frequency. The historical accident frequency here may be the average value of the historical accident frequencies of all gas pipelines in the gas pipeline network. For more descriptions about the historical accident frequency, reference may be made to step 330 and its related descriptions.

[0136] In some embodiments of this specification, many of the existing temperature adjustment parameters in the historical data may not be the best adjustment parameters. There is no good solution to solve the problem of impurity degradation through the temperature adjustment parameters; it is necessary to determine more candidate temperature adjustment parameters to find the temperature adjustment parameter with the best effect from a wider range of temperature adjustment parameters.

[0137] In some embodiments of this specification, determining the temperature control adjustment parameters with better adjustment effects and more usage times in historical data as candidate adjustment parameters helps to more easily find the temperature control adjustment parameters with the optimal adjustment effect.

[0138] The effect evaluation model refers to a model used to determine the adjustment effect of candidate adjustment parameters. In some embodiments, the effect evaluation model can be a machine learning model. For example, the effect evaluation model can include any one or a combination of a Graph Neural Network (GNN) model, a Neural Networks (NN) model, or other custom model structures, etc.

[0139] In some embodiments, the management platform can train the effect evaluation model based on a large number of adjustment training samples with adjustment labels through methods such as gradient descent. The adjustment training samples can include a sample pipeline map and multiple sample candidate adjustment parameters, and the adjustment label can be the actual adjustment effect. In some embodiments, the training samples can be obtained based on historical data. Among them, the sample pipeline map can include a historical pipeline map determined based on historical data, and the nodes and their features, edges and their features of the historical pipeline map are similar to the descriptions of the above pipeline map.

[0140] In some embodiments, the management platform can count the degree of impurity aggregation of all gas pipelines in the sample pipeline map, and form a historical impurity sequence based on these degrees of impurity aggregation. At the same time, it can count the actual degree of impurity aggregation of these gas pipelines and form an actual impurity sequence based on these actual degrees of impurity aggregation. The management platform can further calculate the difference between each data in the historical impurity sequence and the corresponding data in the actual impurity sequence to obtain a result sequence. In response to the number of positive values in the result sequence being greater than the label threshold, calculate the similarity value between the historical impurity sequence and the actual impurity sequence, and determine the difference between the value 1 and the similarity value as the adjustment label. Among them, the similarity value can include cosine similarity. The label threshold can be preset based on historical experience. For example, if it is estimated that the degree of impurity aggregation of more than half of the gas pipelines in the sample pipeline map will decrease, the label threshold can be 50%.

[0141] The similarity value between each data in the historical impurity sequence and the corresponding data in the actual impurity sequence can be used to characterize the similarity between the actual impurity sequence and the historical impurity sequence. The greater the similarity, the more similar the situation after impurity cleaning is to the situation before impurity cleaning, and the worse the actual adjustment effect.

[0142] In some embodiments, in response to the number of positive values in the result sequence not being greater than the label threshold, the adjustment label can be -1. The -1 here has no actual meaning and only represents the worst actual adjustment effect.

[0143] In some embodiments, the effect evaluation model 420 may include a safety evaluation layer 421, an ablation evaluation layer 422, and an effect determination layer 423.

[0144] In some embodiments, the safety evaluation layer 421, the ablation evaluation layer 422, and the effect determination layer 423 may be trained separately.

[0145] The safety evaluation layer 421 is used to determine the pipeline safety degree 430 of each edge in the pipeline graph.

[0146] In some embodiments, the management platform may input the pipeline graph and candidate adjustment parameters into the safety evaluation layer to determine the pipeline safety degree of each edge in the pipeline graph under the candidate adjustment parameters.

[0147] The pipeline safety degree 430 can be used to measure the safety risk of temperature adjustment for each gas pipeline in the pipeline graph. In some embodiments, the pipeline safety degree can be represented by numerical values, etc. For example, the pipeline safety degree can be represented by 0 - 1. The closer it is to 1, the lower the safety risk of each gas pipeline.

[0148] In some embodiments, the pipeline platform may input the adjustment training samples with safety labels into the initial safety evaluation layer, construct a loss function through the safety labels and the prediction results of the initial safety evaluation layer, iteratively update the initial safety evaluation layer based on the loss function, and complete the training of the safety evaluation layer when the loss function of the initial safety evaluation layer meets the preset conditions. Among them, the preset conditions may be that the loss function converges, the number of iterations reaches a set value, etc.

[0149] In some embodiments, the safety labels can be obtained based on manual annotation. For example, the safety label can be the ratio of the numerical value 1 to the number of subsequent actual historical accidents corresponding to the adjustment training samples. If the number of subsequent actual historical accidents is 0, the safety label is 1.

[0150] The ablation evaluation layer 422 is used to determine the impurity ablation degree 440 of each candidate adjustment parameter among multiple candidate adjustment parameters. In some embodiments, the management platform may input the pipeline graph and candidate adjustment parameters into the ablation evaluation layer to determine the impurity ablation degree of each edge in the pipeline graph under the candidate adjustment parameters.

[0151] The impurity ablation degree 440 can be used to measure the speed of impurity ablation. In some embodiments, the impurity ablation degree can be represented by numerical values, etc. For example, the larger the numerical value, the faster the speed of impurity ablation.

[0152] In some embodiments, the pipeline platform may input the adjustment training samples with ablation labels into the initial ablation evaluation layer, construct loss functions respectively through the ablation labels and the prediction results of the initial ablation evaluation layer, iteratively update the initial ablation evaluation layer based on the loss functions, and complete the training of the ablation evaluation layer when the loss function of the initial ablation evaluation layer meets the preset conditions.

[0153] In some embodiments, the ablation label may be the actual impurity degradation rate corresponding to the adjustment training sample. The actual impurity degradation rate can be obtained by means such as manual measurement. The impurity degradation rate can be the ratio of the amount of degraded impurities to the time taken for impurity degradation.

[0154] The effect determination layer 423 is used to determine the adjustment effect of each candidate adjustment parameter among a plurality of candidate adjustment parameters. In some embodiments, the management platform may input a pipeline atlas with the pipeline safety degree and impurity ablation degree marked on each edge into the effect determination layer to determine the adjustment effect of the candidate adjustment parameters.

[0155] In some embodiments, the pipeline platform may, based on the output results of the initial safety evaluation layer and the initial ablation evaluation layer, mark the pipeline safety degree and impurity ablation degree on each edge of the sample pipeline atlas in the adjustment training sample, and input the adjustment training sample with adjustment labels into the initial effect determination layer, construct a loss function through the adjustment labels and the prediction results of the initial effect determination layer, iteratively update the initial effect determination layer based on the loss function, and complete the training of the effect determination layer when the loss function of the initial effect determination layer meets the preset conditions.

[0156] In some embodiments of this specification, by improving the pipeline atlas through the safety evaluation layer and the ablation evaluation layer, the pipeline atlas can have more effective information, and thus a more comprehensive adjustment effect can be obtained through the effect determination layer, which is beneficial to determining the optimal candidate adjustment parameter.

[0157] In some embodiments, the management platform may, based on the adjustment effect of each candidate adjustment parameter among a plurality of candidate adjustment parameters, determine the candidate adjustment parameter corresponding to the adjustment effect with the largest value as the temperature control adjustment parameter.

[0158] In some embodiments of this specification, by constructing a pipeline atlas that can well describe the structural characteristics of the gas pipeline network, considering the topological structure under the entire gas pipeline network, the efficiency and accuracy of determining the adjustment effect can be effectively improved through the effect evaluation model, which is beneficial to determining the optimal temperature control adjustment parameter.

[0159] Some embodiments of this specification also provide a computer-readable storage medium, and the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method described in any one of the above embodiments.

[0160] In addition, certain features, structures, or characteristics in one or more embodiments of this specification may be appropriately combined.

[0161] If there are any inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the materials cited in this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

Claims

1. The pipeline impurity monitoring IoT system based on smart gas IoT is characterized by: The Internet of Things system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform and a gas equipment object platform, and the government safety supervision object platform includes a gas company management platform; The gas equipment object platform includes a valve device and a temperature control device deployed at at least one pipeline connection, and the valve device includes a pressure monitoring component and a temperature monitoring component; The government safety supervision object platform includes the gas company management platform and key gas-using enterprises; The gas company management platform is configured as follows: Obtaining pressure monitoring data and temperature monitoring data of the valve device from the gas equipment object platform through the gas company sensor network platform; Determine the impurity accumulation degree of at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data, and send the impurity accumulation degree to the government safety supervision management platform through the government safety supervision sensor network platform; Receiving an impurity removal instruction sent by the government safety supervision management platform, determining a temperature control adjustment parameter based on the impurity removal instruction and the degree of impurity aggregation, and sending the temperature control adjustment parameter to the government safety supervision management platform; as well as, Receive the confirmation parameters returned by the government safety supervision management platform, generate a temperature control instruction based on the confirmation parameters, and send the temperature control instruction to the temperature control device through the gas company sensor network platform and the gas equipment object platform.

2. The pipeline impurity monitoring Internet of Things system based on the smart gas Internet of Things according to claim 1 is characterized in that: The pressure monitoring component includes a first pressure component and a second pressure component, and the pressure monitoring data includes first pressure data and second pressure data; the temperature monitoring component includes a first temperature component and a second temperature component, and the temperature monitoring data includes first temperature data and second temperature data; the gas company management platform is further configured as follows: determining a pressure gradient value based on the first pressure data and the second pressure data; determining a temperature gradient value based on the first temperature data and the second temperature data; and, The impurity aggregation degree is determined based on the pressure gradient value and the temperature gradient value.

3. The pipeline impurity monitoring Internet of Things system based on the smart gas Internet of Things as claimed in claim 2 is characterized in that: The valve device further includes a flow regulating component and a pressure regulating component, and the gas company management platform is further configured as follows: The impurity aggregation degree is determined based on the flow regulation accuracy of the flow regulation component, the pressure regulation accuracy of the pressure regulation component, the temperature gradient value, and the pressure gradient value.

4. The pipeline impurity monitoring Internet of Things system based on the smart gas Internet of Things according to claim 1 is characterized in that: The gas company management platform is further configured as follows: Based on the pipeline map and the multiple candidate adjustment parameters, determining the adjustment effect of each of the multiple candidate adjustment parameters through an effect evaluation model, wherein the effect evaluation model is a machine learning model; and Based on the adjustment effect, the temperature control adjustment parameter is determined.

5. The pipeline impurity monitoring Internet of Things system based on the smart gas Internet of Things as claimed in claim 4 is characterized in that: The gas company management platform is further configured as follows: The plurality of candidate adjustment parameters are determined through a frequent item database.

6. A pipeline impurity monitoring method based on smart gas Internet of Things, characterized in that: The method is executed by the gas company management platform of the pipeline impurity monitoring Internet of Things system based on the smart gas Internet of Things according to claim 1, and the method includes: Obtain the pressure monitoring data and temperature monitoring data of the valve device from the gas equipment object platform through the gas company's sensor network platform; Determine the degree of impurity accumulation of at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data, and send the degree of impurity accumulation to the government safety supervision management platform through the government safety supervision sensor network platform; receiving an impurity removal instruction sent by the government safety supervision management platform, determining a temperature control adjustment parameter based on the impurity removal instruction and the impurity aggregation degree, and sending the temperature control adjustment parameter to the government safety supervision management platform; and, Receive the confirmation parameters returned by the government safety supervision management platform, generate a temperature control instruction based on the confirmation parameters, and send the temperature control instruction to the temperature control device through the gas company sensor network platform and the gas equipment object platform.

7. The pipeline impurity monitoring method based on the smart gas Internet of Things according to claim 6 is characterized in that: The determining the degree of impurity accumulation of at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data comprises: determining a pressure gradient value based on the first pressure data and the second pressure data; determining a temperature gradient value based on the first temperature data and the second temperature data; and, The impurity aggregation degree is determined based on the pressure gradient value and the temperature gradient value.

8. The pipeline impurity monitoring method based on the smart gas Internet of Things according to claim 7 is characterized in that: The determining the impurity aggregation degree based on the pressure gradient value and the temperature gradient value comprises: The impurity aggregation degree is determined based on the flow regulation accuracy of the flow regulation component, the pressure regulation accuracy of the pressure regulation component, the temperature gradient value, and the pressure gradient value.

9. The pipeline impurity monitoring method based on the smart gas Internet of Things according to claim 6 is characterized in that: The method further comprises: Based on the pipeline map and the multiple candidate adjustment parameters, determining the adjustment effect of each of the multiple candidate adjustment parameters through an effect evaluation model, wherein the effect evaluation model is a machine learning model; and Based on the adjustment effect, the temperature control adjustment parameter is determined.

10. The pipeline impurity monitoring method based on the smart gas Internet of Things according to claim 9 is characterized in that: The method further comprises: The plurality of candidate adjustment parameters are determined through a frequent item database.

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