Pipeline impurity monitoring IoT system and method based on smart gas IoT

Abstract

The present invention provides a pipeline impurity monitoring IoT system and method based on a smart gas IoT. The system comprises a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision target platform, a gas company sensor network platform, and a gas equipment target platform. The government safety supervision target platform includes a gas company management platform. The gas company management platform is configured to: determine the degree of impurity accumulation in at least one gas pipeline based on pressure and temperature monitoring data from a valve device; determine temperature control adjustment parameters based on impurity removal instructions and the degree of impurity accumulation; generate temperature control instructions based on the confirmed parameters, and send the temperature control instructions to the temperature control device. This system can accurately monitor impurity adhesion in gas pipelines in real time, thereby enabling timely detection and effective treatment of pipeline corrosion issues, thereby ensuring the overall safety of the gas pipeline.

Description

Technical Field

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

[0002] Gas is primarily transported through pipelines, during which secondary non-gas impurities are generated within the pipelines. These impurities may deposit on the pipeline walls due to fluctuations in pipeline structure, temperature, or pressure. The presence of non-gas impurities not only corrodes localized pipelines, posing safety risks, but can also adversely affect the operational safety and control stability of pipeline ancillary facilities (such as valves and monitoring devices).

[0003] Therefore, we hope to propose a pipeline impurity monitoring IoT system and method based on the smart gas IoT, which can monitor the adhesion of impurities in the gas pipeline in real time and accurately, so as to timely detect and effectively deal with pipeline corrosion problems, thereby ensuring 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 a pipeline impurity monitoring Internet of Things system and method based on the smart gas Internet of Things, which can monitor the adhesion of impurities in the gas pipeline in real time and accurately, so as to promptly discover and effectively deal with pipeline corrosion problems, thereby ensuring the overall safety performance of the gas pipeline.

[0005] The invention includes a pipeline impurity monitoring IoT system based on a smart gas IoT. The IoT 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. 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 pressure monitoring data and temperature monitoring data of the valve device from the gas equipment object platform via the gas company sensor network platform. Based on the pressure monitoring data and the temperature monitoring data, determine the degree of impurity accumulation of at least one gas pipeline, and send the impurity accumulation degree to the government safety supervision and management platform through the government safety supervision sensor network platform; receive the impurity removal instruction sent by the government safety supervision and management platform, determine the temperature control adjustment parameters based on the impurity removal instruction and the impurity accumulation degree, and send the temperature control adjustment parameters to the government safety supervision and management platform; and receive the confirmation parameters returned by the government safety supervision and 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 sensor network platform.

[0006] The invention content includes providing a pipeline impurity monitoring method based on a smart gas Internet of Things, which is executed by a gas company management platform of a pipeline impurity monitoring Internet of Things system based on the smart gas Internet of Things. The method includes: obtaining pressure monitoring data and temperature monitoring data of a valve device from a gas equipment object platform through a gas company sensor network platform; determining the impurity accumulation degree of at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data, and sending the impurity accumulation degree to a government safety supervision management platform through a 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 accumulation degree, and sending the temperature control adjustment parameter to the government safety supervision management platform; and receiving a 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 sensor 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 IoT system based on the smart gas IoT can form an information operation closed loop between 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 handled in a timely manner, and the gas transportation environment of the gas pipeline can be effectively maintained, thereby ensuring the purity of the gas and the normal transportation of the gas, and significantly improving the overall safety performance of the gas pipeline. (3) By constructing a pipeline map that can well describe the structural characteristics of the gas pipeline network and considering the topological structure of the entire gas pipeline network, the effect evaluation model can effectively improve the efficiency and accuracy of determining the regulation effect, which is conducive to determining the optimal temperature control adjustment parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0009] Figure 1 This is a schematic diagram of the platform structure of a pipeline impurity monitoring IoT system based on a smart gas IoT according to some embodiments of this specification;

[0010] Figure 2 This is an exemplary flow chart of a pipeline impurity monitoring method based on a smart gas Internet of Things according to some embodiments of this specification;

[0011] Figure 3 is an exemplary flow chart for determining the degree of impurity aggregation according to some embodiments of this specification;

[0012] Figure 4 It is an exemplary schematic diagram of the effect evaluation model shown in some embodiments of this specification. DETAILED DESCRIPTION

[0013] 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. The drawings do not represent all implementation methods.

[0014] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. If other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0015] Unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0016] When operations are performed according to the step descriptions in the embodiments of this specification, unless otherwise specified, the order of the steps is interchangeable, steps can be omitted, and other steps can be included in the operation process.

[0017] Figure 1 This is a schematic diagram of the platform structure of a pipeline impurity monitoring Internet of Things system based on a smart gas Internet of Things according to some embodiments of this specification.

[0018] like Figure 1 As shown, the pipeline impurity monitoring IoT system 100 based on the smart gas IoT 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.

[0019] The government security supervision and management platform 110 refers to a platform for the government to conduct information supervision and management.

[0020] The government safety supervision sensor network platform 120 is a platform for the government to supervise and manage sensor network information. In some embodiments, the government safety supervision sensor network platform 120 can interact with the gas company management platform 131, key gas-using enterprises 132, and the government safety supervision management platform 110.

[0021] The government safety supervision platform 130 is a platform for generating government supervision information and controlling information execution. In some embodiments, the government supervision platform 130 may include a gas company management platform 131 and key gas-using enterprises 132.

[0022] The gas company management platform 131 is a comprehensive management platform for gas company information. The key gas-using enterprises 132 are gas-using enterprises that are the focus of attention.

[0023] The gas company sensor network platform 140 is a platform that comprehensively manages gas company sensor information. In some embodiments, the gas company sensor network platform can be configured as a communication network or gateway. In some embodiments, the gas company sensor network platform can interact with the gas company management platform 131 and the gas equipment object platform 150.

[0024] The gas equipment object platform 150 refers to a functional platform for sensing information generation and executing control information.

[0025] 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 a location where two or more gas pipelines connect. The valve device may include a pressure monitoring component and a temperature monitoring component.

[0026] The pressure monitoring component is used to collect pressure monitoring data. In some embodiments, the pressure monitoring component may include a pressure sensor and a pressure detector. The temperature monitoring component is used to collect temperature monitoring data. In some embodiments, the temperature monitoring component may include a temperature sensor and a thermometer.

[0027] 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.

[0028] The temperature control device is used to regulate the temperature within the gas pipeline. For example, the temperature control device can increase or decrease the temperature within the gas pipeline. In some embodiments, the temperature control device may include a pipeline heater or a thermal heater. In some embodiments, the temperature control device may be deployed on the outer wall of at least one pipeline connection.

[0029] In some embodiments, the smart gas IoT-based pipeline impurity monitoring IoT system 100 may further include a processor. In some embodiments, the processor may process information and / or data related to the smart gas IoT-based pipeline impurity monitoring IoT system 100 to perform one or more of the functions described herein. In some embodiments, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a graphics processing unit (GPU), or any combination thereof.

[0030] For detailed description of the above, please refer to Figures 2 to 4 Related description.

[0031] In some embodiments of this specification, the pipeline impurity monitoring IoT system 100 based on the smart gas IoT can form an information operation closed loop between various functional platforms to realize the informatization and intelligence of pipeline impurity monitoring.

[0032] Figure 2 This is an exemplary flow chart of a pipeline impurity monitoring method based on the smart gas Internet of Things according to some embodiments of this specification. In some embodiments, process 200 is executed by a gas company management platform (hereinafter referred to as the management platform) of a pipeline impurity monitoring Internet of Things system based on the smart gas Internet of Things. Figure 2As shown, process 200 includes the following steps:

[0033] For more information about the various platforms of the pipeline impurity monitoring IoT system based on the smart gas IoT, please refer to Figure 1 The corresponding description.

[0034] Step 210: 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.

[0035] The pressure monitoring data refers to data obtained by monitoring the gas pressure at at least one valve device. In some embodiments, the pressure monitoring data may include a sequence of gas pressures of the valve device within a preset monitoring period.

[0036] The preset monitoring period refers to the period during which the valve device is monitored. The preset monitoring period can be pre-set based on historical experience. For example, the preset monitoring period can be the past 5 minutes of the current time. Gas pressure refers to the gas pressure in the gas pipeline.

[0037] In some embodiments, pressure monitoring data can be obtained by a pressure monitoring component. For a description of the pressure monitoring component, see Figure 1 and its related descriptions.

[0038] The temperature monitoring data refers to data obtained by monitoring the temperature of at least one valve device. In some embodiments, the temperature monitoring data may include a sequence of temperature values ​​of the valve device within a preset monitoring period.

[0039] In some embodiments, the temperature monitoring data can be obtained by a temperature monitoring component. For a description of the temperature monitoring component, see Figure 1 and its related descriptions.

[0040] Step 220: 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.

[0041] The degree of impurity accumulation refers to data that characterizes the extent to which impurities form and accumulate within a gas pipeline. The degree of impurity accumulation can be represented by a numerical value. For example, the degree of impurity accumulation can be represented on a scale of 0 to 1. The closer the value is to 1, the higher the degree of impurity accumulation, indicating a greater likelihood of the formation of impurities with potentially detrimental properties within the gas pipeline.

[0042] Impurities that cause qualitative changes refer to impurities that are visible to the naked eye or impurities that may affect the transportation of gas.

[0043] In some embodiments, the management platform may determine the degree of impurity accumulation in at least one gas pipeline based on pressure monitoring data and temperature monitoring data using various methods. For example, the management platform may determine the degree of impurity accumulation in the valve devices at both ends of the gas pipeline based on pressure monitoring data and temperature monitoring data of the valve devices at both ends of the gas pipeline, and determine the average of the impurity accumulation degrees of the valve devices at both ends of the gas pipeline as the impurity accumulation degree of the gas pipeline.

[0044] In some embodiments, the degree of impurity accumulation of the valve device may be correlated with the difference between the pressure monitoring data and the integrated pressure data, and may be correlated with the difference between the temperature monitoring data and the integrated temperature data. For example, the degree of impurity accumulation of the valve device may be positively correlated with the difference between the pressure monitoring data and the integrated pressure data, and may be positively correlated with the difference between the temperature monitoring data and the integrated temperature data. In some embodiments, the management platform may determine the degree of impurity accumulation of the valve device using a preset formula based on the pressure monitoring data and the temperature monitoring data of the valve device. For example, the preset formula may be as shown in formula (1):

[0045] (1)

[0046] in, Indicates the degree of impurity accumulation in the valve device, 、 Respectively represent the fluctuation of pressure monitoring data and temperature monitoring data, 、 Respectively represent the changing trends of pressure monitoring data and temperature monitoring data, represents the fluctuation of the comprehensive pressure data, Indicates the changing trend of comprehensive pressure data, represents the fluctuation of the comprehensive temperature data, Indicates the changing trend of comprehensive temperature data; The coefficient representing the difference between the fluctuation of pressure monitoring data and the fluctuation of integrated pressure data, The coefficient that represents the difference between the changing trend of pressure monitoring data and the changing trend of comprehensive pressure data, The coefficient that represents the difference between the fluctuation of temperature monitoring data and the fluctuation of comprehensive temperature data, The coefficient that represents the difference between the changing trend of temperature monitoring data and the changing trend of comprehensive temperature data. It can be a constant of an order of magnitude, etc., and can be preset manually or set by system default.

[0047] In some embodiments, the management platform can calculate the fluctuation and trend of the pressure monitoring data of each valve device in various ways. For example, the management platform can calculate the range or variance of the pressure monitoring data of each valve device, and use the range or variance to represent the fluctuation of the pressure monitoring data.

[0048] For another example, the management platform can use a processing algorithm to obtain the changing trend of the pressure monitoring data for each valve device. The processing algorithm may include, but is not limited to, linear regression algorithms. As an example, for the pressure monitoring data of a valve device, the management platform can draw a fitted straight line and calculate the slope of the fitted line, using this slope as the changing trend of the pressure monitoring data corresponding to the valve device. The horizontal axis of the fitted line corresponds to time, and the vertical axis corresponds to gas pressure.

[0049] 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 the method for calculating the fluctuation and change trend of the pressure monitoring data of the valve device, which will not be described in detail here.

[0050] In some embodiments, the management platform can calculate the average pressure of each valve device among multiple valve devices, combine the pressure averages of the multiple valve devices into comprehensive pressure data, and calculate the fluctuation and change trend of the comprehensive pressure data. Simultaneously, the management platform can calculate the average temperature of each valve device, combine the temperature averages of all valve devices into comprehensive temperature data, and calculate the fluctuation and change trend of the comprehensive temperature data. The management platform can average the pressure monitoring data and temperature monitoring data of a single valve device to obtain the average pressure and temperature values ​​of that valve device.

[0051] In some embodiments, the method of calculating the fluctuation and changing trend of the comprehensive pressure data and the fluctuation and changing trend of the comprehensive temperature data is similar to the above-mentioned method of calculating the fluctuation and changing trend of the pressure monitoring data and the fluctuation and changing trend of the temperature monitoring data. The implementation method can refer to the above-mentioned method of calculating the fluctuation and changing trend of the pressure monitoring data and the fluctuation and changing trend of the temperature monitoring data.

[0052] It is understood that when the fluctuations in the pressure monitoring data and / or temperature monitoring data of a valve device differ significantly from the fluctuations in the comprehensive pressure data and / or comprehensive temperature data, it indicates that impurities with qualitative changes may have formed or are being generated at the location of the valve device. When the changing trend of the pressure monitoring data and / or temperature monitoring data of a valve device differs from the changing trend of the comprehensive pressure data and / or comprehensive temperature data, it indicates that the pressure and / or temperature changes of the valve device are abnormal, and impurities with qualitative changes may have formed at the location of the valve device. By determining the difference between the pressure and / or temperature in a single gas pipeline and the pressure and / or temperature of the entire gas pipeline network, the degree of impurity accumulation in the gas pipeline can be more effectively determined.

[0053] In some embodiments, the management platform can 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 accumulation based on the pressure gradient value and the temperature gradient value. For more information about this section, please refer to Figure 3 The corresponding description.

[0054] Step 230: Receive the impurity removal instruction sent by the government safety supervision and management platform, determine the temperature control adjustment parameters based on the impurity removal instruction and the degree of impurity accumulation, and send the temperature control adjustment parameters to the government safety supervision and management platform.

[0055] An impurity removal instruction is an instruction indicating whether impurities need to be removed. In some embodiments, the impurity removal instruction can be expressed in various ways. For example, the impurity removal instruction can be expressed using a Boolean value, where 0 indicates that impurities do not need to be removed and 1 indicates that impurities need to be removed. In another example, the impurity removal instruction can be expressed using text, including "impurity removal is required" and "impurity removal is not required."

[0056] In some embodiments, the impurity removal instruction can be determined by a government safety supervision and management platform. For example, the impurity removal instruction can be manually determined by supervisors of the government safety supervision and management platform. In another example, in response to the impurity accumulation level exceeding a removal threshold, the impurity removal instruction is determined to require impurity removal. The removal threshold is used to determine whether impurity removal is necessary, and the removal threshold can be pre-set based on historical experience.

[0057] Temperature control parameters refer to parameters used to adjust the operation of a temperature control device. In some embodiments, these parameters may include at least one of a start time and a start temperature. The start time may include the start time and duration of the start. The start temperature refers to the temperature at which the temperature control device is required to rise or fall.

[0058] In some embodiments, the temperature control adjustment parameters may include multiple groups of data, each group of data may correspond to a temperature control device, and each temperature control device corresponds to a group of data.

[0059] It is understandable that when there are impurities in the gas pipeline and they need to be removed, the impurities can be degraded by controlling the temperature control device to increase the temperature, thereby achieving the purpose of removing the impurities. Figure 1 and its related descriptions.

[0060] In some embodiments, in response to an impurity removal instruction received from a government safety supervision management platform indicating that impurities need to be removed (e.g., step 1), the management platform may determine temperature control adjustment parameters in various ways based on the impurity removal instruction and the degree of impurity accumulation. For example, for each of a plurality of gas pipelines, the management platform may query a parameter comparison table based on the impurity level of the gas pipeline's impurity accumulation level, and determine the reference adjustment parameter corresponding to the impurity level in the parameter comparison table as the temperature control adjustment parameter for the temperature control device corresponding to the gas pipeline.

[0061] In some embodiments, the management platform can determine the impurity level of the impurity concentration degree through an impurity level table. The impurity level table can include a correspondence between the impurity concentration degree and the impurity level. The impurity level table can be pre-set based on historical experience. For example, the impurity level table can include four or more levels, corresponding to impurity concentration degrees of 0-0.25, 0.25-0.5, 0.5-0.75, and 0.75-1, etc.

[0062] In some embodiments, the management platform can construct a parameter comparison table based on historical data. As an example only, 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 whose impurity removal effect meets 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 multiple target sample data under each impurity level, the management platform can count the historical temperature control adjustment parameters that appear the most times in the multiple target sample data, use them as reference adjustment parameters and enter them into the table to obtain a parameter comparison table including multiple impurity levels and the reference adjustment parameters corresponding to each impurity level.

[0063] The impurity removal effect refers to the effect of impurity removal from the gas pipeline based on sample data. In some embodiments, the impurity removal effect can be represented by the degree of impurity accumulation after impurity removal. The degree of impurity accumulation after impurity removal can be obtained by manual measurement or robotic detection of the actual impurity removal status within the gas pipeline.

[0064] In some embodiments, the preset standard may be pre-set based on historical experience. An exemplary preset standard may be that the impurity aggregation degree is less than 0.1.

[0065] In some embodiments, the management platform can determine the adjustment effect of each of the multiple candidate adjustment parameters through the effect evaluation model based on the pipeline map and multiple candidate adjustment parameters; and determine the temperature control adjustment parameter based on the adjustment effect. For more information about this part, please refer to Figure 4 The corresponding description.

[0066] Step 240: 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.

[0067] Temperature control instructions refer to instructions for controlling the operation of temperature control devices.

[0068] In some embodiments, the management platform may generate temperature control instructions based on the confirmed parameters.

[0069] Confirmation parameters refer to information used to confirm the operation of the temperature control device. In some embodiments, the confirmation parameters may include at least one of whether the temperature control device is used to adjust the pipeline temperature for impurity removal and temperature control adjustment parameters. In some embodiments, the confirmation parameters may be issued by supervisors on the government safety supervision and management platform.

[0070] In some embodiments, supervisors of the government safety supervision management platform can adjust the temperature control adjustment parameters based on their own judgment, obtain new temperature control adjustment parameters, and add the new temperature control adjustment parameters to the confirmation parameters.

[0071] In some embodiments, in response to the management platform receiving confirmation parameters indicating that a temperature control device is used to regulate the pipe temperature for impurity removal, the management platform may generate a temperature control instruction based on the temperature control adjustment parameters. In response to the management platform receiving confirmation parameters indicating that a temperature control device is not used to regulate the pipe temperature for impurity removal, the management platform may continue to use the previous temperature control instruction.

[0072] In some embodiments of the present specification, since non-gas impurities may 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 pipeline corrosion problems can be discovered and effectively dealt with in a timely manner, and the gas transportation environment of the gas pipeline can be effectively maintained, thereby ensuring the purity of the gas and the normal transportation of the gas, and significantly improving the overall safety performance of the gas pipeline.

[0073] It should be noted that the above description of process 200 is for illustrative purposes only and does not limit the scope of application of this specification. Those skilled in the art may, under the guidance of this specification, make various modifications and alterations to the hand-eye calibration process. However, such modifications and alterations remain within the scope of this specification.

[0074] Figure 3 is an exemplary flow chart for determining the degree of impurity accumulation according to some embodiments of this specification. In some embodiments, process 300 is executed by a management platform. Figure 3 As shown, process 300 includes the following steps:

[0075] 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 accumulation based on the pressure gradient value and the temperature gradient value.

[0076] For more information about pressure monitoring components and temperature monitoring components, please refer to Figure 1 For more information on pressure monitoring data, temperature monitoring data, and impurity accumulation, see Figure 2 and its related descriptions.

[0077] The two sides of the valve device include the upstream side and the downstream side. The upstream side of the valve device refers to the side of the valve device that receives the gas. The downstream side of the valve device refers to the side of the valve device that outputs the gas.

[0078] The first pressure component is a component that monitors the gas pressure on the upstream side of the valve device. The first pressure data is 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 during a preset monitoring period. For more information about the preset monitoring period, please refer to Figure 2 The corresponding description.

[0079] In some embodiments, the first pressure member may be disposed on an upstream side of the valve arrangement.

[0080] The second pressure component is a component that monitors the gas pressure downstream of the valve device. The second pressure data is data obtained by monitoring the gas pressure downstream of the valve device. In some embodiments, the second pressure data may include a sequence of gas pressures downstream of the valve device during a predetermined monitoring period.

[0081] In some embodiments, the second pressure member may be disposed on a downstream side of the valve arrangement.

[0082] The first temperature component is a component that monitors the temperature of the upstream side of the valve device. The first temperature data is data obtained by monitoring the temperature of the upstream side of the valve device. In some embodiments, the first temperature data may include a sequence of temperature values ​​of the upstream side of the valve device within a preset monitoring period.

[0083] In some embodiments, the first temperature component may be disposed on an upstream side of the valve arrangement.

[0084] The second temperature component is a component that monitors the temperature of the downstream side of the valve device. The second temperature data is data obtained by monitoring the temperature of the downstream side of the valve device. In some embodiments, the second temperature data may include a sequence of temperature values ​​of the downstream side of the valve device within a preset monitoring period.

[0085] In some embodiments, the second temperature component may be disposed on a downstream side of the valve arrangement.

[0086] Step 310: Determine a pressure gradient value based on the first pressure data and the second pressure data.

[0087] The pressure gradient value is a data used to measure the pressure changes on both sides of the valve device.

[0088] In some embodiments, the management platform can 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 can 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. The method of calculating the fluctuation of the pressure difference data is the same as that of Figure 2 The method for calculating the fluctuation of pressure monitoring data is similar and will not be described here.

[0089] 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.

[0090] 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 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 can 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 using a preset pressure formula. For example, the preset pressure formula can be shown as formula (2):

[0091] (2)

[0092] in, Indicates the pressure fluctuation threshold, Indicates the comprehensive pressure fluctuation value, Indicates the difference in inlet and outlet of the valve device. Indicates the average in-degree difference.

[0093] The comprehensive pressure fluctuation value refers to data used to reflect the fluctuation of pressure differential data of multiple valve devices in the gas pipeline network. In some embodiments, the management platform can calculate the pressure differential data of all valve devices, calculate the standard deviation and mean of the multiple pressure differential data, and determine the comprehensive pressure fluctuation value as the ratio of the standard deviation to the mean.

[0094] The valve device's in-and-out degree difference refers to data that characterizes the difference between the valve device's in- and out-degrees. In some embodiments, the management platform may determine the absolute value of the difference between the valve device's in- and out-degrees as the valve device's in- and out-degree difference. The in-degree refers to the amount of gas input into the valve device, while the out-degree refers to the amount of gas output from the valve device.

[0095] In some embodiments, the management platform may obtain the in-degree and out-degree of the valve device through multiple gas metering devices, and the gas metering devices may be deployed on the upstream and downstream sides of the valve device.

[0096] In some embodiments, the management platform may calculate the in-and-out degree differences of all valve devices in the gas pipeline network, and determine the average of the multiple in-and-out degree differences as the average in-and-out degree difference.

[0097] Step 320: Determine a temperature gradient value based on the first temperature data and the second temperature data.

[0098] The temperature gradient value is a data used to measure the temperature change on both sides of the valve device.

[0099] In some embodiments, the management platform can determine the temperature gradient value based on the first temperature data and the second temperature data using various methods. 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 temperature difference data across the valve device, calculate the fluctuation of the temperature difference data, and determine the temperature gradient value as the difference between the fluctuation of the temperature difference data and a temperature fluctuation threshold. The method for calculating the fluctuation of the temperature difference data is similar to the method for calculating the fluctuation of the temperature monitoring data and is not further described here.

[0100] 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-and-out degree difference, the in-and-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-and-out degree difference of the valve device, and inversely proportional to the average in-and-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 through a preset temperature formula based on the comprehensive temperature fluctuation value, the in-and-out degree difference of the valve device, the average in-and-out degree difference, and the thermal conductivity of the valve material. For example, the preset temperature formula can be shown as formula (3):

[0101] (3)

[0102] in, Indicates the temperature fluctuation threshold, Indicates the comprehensive temperature fluctuation value, Indicates the difference in inlet and outlet of the valve device. represents the average in-and-out degree difference, Indicates the thermal conductivity of the valve material.

[0103] The integrated temperature fluctuation value refers to data used to reflect the fluctuation of temperature difference data of multiple valve devices in the gas pipeline network. In some embodiments, the method for calculating the integrated temperature fluctuation value is similar to the method for calculating the integrated pressure fluctuation value, which will not be repeated here.

[0104] The thermal conductivity of valve materials is a physical quantity that characterizes the heat conduction capacity of the material used to manufacture the valve. The management platform can obtain the thermal conductivity of valve materials through the gas company's management platform.

[0105] Step 330: Determine the degree of impurity accumulation based on the pressure gradient value and the temperature gradient value.

[0106] In some embodiments, the management platform may determine the degree of impurity accumulation in at least one gas pipeline based on the pressure gradient value and the temperature gradient value using various methods. For example, the management platform may first determine the degree of impurity accumulation in the valve devices at both ends of the gas pipeline based on the pressure gradient value and the temperature gradient value, and then determine the average of the impurity accumulation degrees of the valve devices at both ends of the gas pipeline as the impurity accumulation degree of the gas pipeline.

[0107] In some embodiments, the degree of impurity accumulation in the valve device is correlated with the pressure gradient value and the temperature gradient value. For example, the degree of impurity accumulation in the valve device may be positively correlated with the pressure gradient value and the temperature gradient value. In some embodiments, the management platform may determine the degree of impurity accumulation in the valve device using a preset accumulation formula based on a pressure fluctuation threshold and a temperature fluctuation threshold. For example, the preset accumulation formula may be as shown in Formula (4):

[0108] (4)

[0109] in, Indicates the degree of impurity accumulation in the valve device, Indicates the pressure fluctuation threshold, Indicates the temperature fluctuation threshold, They represent the coefficients of the pressure fluctuation threshold and the temperature fluctuation threshold, respectively. It can be a constant of an order of magnitude, etc., and can be preset manually or set by system default.

[0110] In some embodiments, the valve device further includes a flow regulating component and a pressure regulating component. The management platform can determine the degree of impurity accumulation based on the flow regulating accuracy of the flow regulating component, the pressure regulating accuracy of the pressure regulating component, the temperature gradient value, and the pressure gradient value.

[0111] Flow regulating components are those that regulate the flow of gas within a gas pipeline. Examples include flow restrictor valves and orifice plates. Gas flow rate refers to the volume of gas flowing through a gas pipeline per unit time. For example, 100 m³ / h.

[0112] Flow regulation accuracy refers to the adjustment sensitivity of the flow regulation component. For example, the flow regulation accuracy can reach 0.01.

[0113] Pressure regulating components refer to components that regulate the gas pressure in gas pipelines, such as pressure regulators.

[0114] Pressure regulation accuracy refers to the adjustment sensitivity of the pressure regulating component. For example, the pressure regulation accuracy can reach 0.01.

[0115] In some embodiments, the management platform can obtain the flow regulation accuracy and the pressure regulation accuracy through the gas equipment object platform. The gas equipment object platform can obtain and store the flow regulation accuracy and the pressure regulation accuracy through user input or other methods.

[0116] In some embodiments, the management platform can determine the degree of impurity aggregation in a variety of ways based on flow regulation accuracy, pressure regulation accuracy, temperature gradient value and pressure gradient value. For example, the management platform can construct a target feature vector based on flow regulation accuracy, pressure regulation accuracy, temperature gradient value and pressure gradient value, match the reference vector that meets the preset matching conditions with the target feature vector in the vector database, and determine the label of the reference vector that meets the preset matching conditions as the degree of impurity aggregation. Among them, the target feature vector can be a feature vector constructed based on flow regulation accuracy, pressure regulation accuracy, temperature gradient value and pressure gradient value. In some embodiments, the preset matching condition may include the highest vector similarity. Vector similarity is negatively correlated with vector distance, and vector distance may include Euclidean distance, cosine distance, etc., and the distance threshold can be pre-set.

[0117] In some embodiments, the vector database may be pre-set based on historical data.The vector database may include multiple reference vectors and a label for each reference vector.

[0118] In some embodiments, the management platform can construct cluster vectors based on historical samples and the actual impurity concentration levels in the gas pipeline corresponding to the historical samples, cluster multiple cluster vectors, and construct a reference vector based on the historical samples corresponding to the cluster centers formed by clustering, using the actual impurity concentration levels corresponding to the cluster centers as labels for the reference vectors. A historical sample refers to a set of historical flow regulation accuracy, historical pressure regulation accuracy, historical temperature gradient values, and historical pressure gradient values ​​in the historical data.

[0119] In some embodiments, the actual level of impurity accumulation can be determined through multiple experiments. For example, a technician can extract and analyze gas from a gas pipeline to obtain a test gas. Multiple experiments can be conducted based on the test gas. The number of experiments in which impurities condensed can be counted. The ratio of the number of experiments in which impurities condensed to the total number of experiments can be used to determine the actual level of impurity accumulation. The test gas has the same composition as the gas extracted from the gas pipeline. The experimental process can include injecting the test gas into a simulated gas pipeline environment to observe whether impurities condense in the simulated gas pipeline environment.

[0120] For example, a technician conducts 100 experiments based on the experimental gas, and finds that the number of experiments in which impurities are condensed is 90, and the actual impurity aggregation degree is 0.9.

[0121] In some embodiments of the present specification, by considering the adjustment accuracy of the flow regulating component and the pressure regulating component, thereby considering the systematic errors that may be caused by the two components, and considering the systematic errors when determining the degree of impurity accumulation, the accuracy of determining the degree of impurity accumulation can be further improved.

[0122] In some embodiments, the management platform may determine the degree of impurity aggregation through an aggregation prediction model based on flow regulation accuracy, pressure regulation accuracy, temperature gradient value, and pressure gradient value.

[0123] The aggregation prediction model refers to a model used to determine the degree of impurity aggregation. In some embodiments, the aggregation prediction model can be a machine learning model. For example, the aggregation prediction model can include any one or a combination of a convolutional neural network (CNN) model, a neural network (NN) model, or other custom model structures.

[0124] In some embodiments, the management platform can train an aggregation prediction model based on a training sample set using a gradient descent method or the like. 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 rate adjustment accuracy, sample pressure adjustment accuracy, sample temperature gradient value, and sample pressure gradient value. The label for each set of training samples may be the actual impurity aggregation level. In some embodiments, the training samples and labels can be obtained based on historical data. For an explanation of the actual impurity aggregation level, please refer to the above and related descriptions.

[0125] In some embodiments, the clustering prediction model can be trained by inputting a plurality of labeled training samples into an initial clustering prediction model, constructing a loss function based on the labels and the prediction results of the initial clustering prediction model, iteratively updating the initial clustering prediction model based on the loss function, and completing the clustering prediction model training when the loss function of the initial clustering prediction model satisfies a preset condition. The preset condition may be that the loss function converges, the number of iterations reaches a set value, etc.

[0126] In some embodiments, the learning rate of a training sample may be related to the historical accident frequency of the training sample. For example, the learning rate of a training sample may be negatively correlated with the historical accident frequency, where the higher the historical accident frequency, the smaller the learning rate of the training sample.

[0127] The historical accident frequency refers to the frequency of historical accidents. Historical accidents refer to pipeline accidents caused by impurities in the gas pipeline, such as blockages and pipeline deformations.

[0128] In some embodiments, the management platform can determine the gas pipeline corresponding to the training sample, and determine the historical accidents of the gas pipeline within a preset period and the duration of the preset period as the historical accident frequency of the training sample. The preset period can be pre-set based on historical experience.

[0129] In some embodiments of this specification, a greater frequency of historical accidents indicates that there are more historical accidents caused by impurities in the gas pipeline, and a greater impact of the impurities in the gas pipeline on the gas pipeline. Therefore, the learning rate of such training samples is reduced, so that the aggregation prediction model converges more slowly and the implicit rules of such training samples are better learned.

[0130] In some embodiments of this specification, the flow regulation accuracy, pressure regulation accuracy, temperature gradient value and pressure gradient value are processed through the aggregation prediction model, and the self-learning ability of the machine learning model can be utilized to find patterns from a large amount of data, thereby improving the accuracy and efficiency of determining the degree of impurity aggregation.

[0131] In some embodiments of this specification, the degree of impurity accumulation is determined by the pressure gradient value and the temperature gradient value. When determining the degree of impurity accumulation, the pressure changes and temperature changes upstream and downstream of the valve can be taken into consideration to determine the degree of impurity accumulation that is closer to the actual situation of the environment in which the valve is located.

[0132] It should be noted that the above description of process 300 is for illustrative purposes only and does not limit the scope of application of this specification. Those skilled in the art may, under the guidance of this specification, make various modifications and alterations to the hand-eye calibration process. However, such modifications and alterations remain within the scope of this specification.

[0133] In some embodiments, the management platform can determine an adjustment effect 450 for each of the multiple candidate adjustment parameters using an effect evaluation model 420 based on the pipeline map 411 and multiple candidate adjustment parameters 412, and determine a temperature control adjustment parameter based on the adjustment effect 450. For an explanation of the temperature control adjustment parameter, see step 230 and its related description.

[0134] The adjustment effect can be used to represent the effect of the temperature control device on removing impurities from the gas pipeline after the temperature is increased or decreased based on the candidate adjustment parameters. In some embodiments, the adjustment effect can be represented by a numerical value, for example, where a larger numerical value indicates a higher adjustment effect.

[0135] A pipeline graph is a graph structure that represents the relationship between valve devices and temperature control devices. A graph structure is a data structure composed of nodes and edges. Edges connect nodes, and nodes and edges can have features.

[0136] In some embodiments, the management platform can construct a pipeline map based on the connection relationships between temperature control devices and valve devices in the gas pipeline network. Nodes in the pipeline map can include valve devices and temperature control devices. Node features of valve device nodes can include pressure monitoring data and temperature monitoring data, while node features of temperature control device nodes can include temperature control adjustment parameters corresponding to the temperature control device.

[0137] Edges in a pipeline graph can represent connections between nodes. In some embodiments, edges in a pipeline graph can include gas pipelines connecting nodes. Edge characteristics can include the length of the gas pipeline, edge type, and the degree of impurity accumulation. The edge type refers to the type of gas pipeline connecting nodes. These edge types can include gas pipelines between temperature control devices, gas pipelines between valve devices, and gas pipelines between valve devices and temperature control devices. The length of the gas pipeline can be obtained from the gas company's management platform.

[0138] Candidate adjustment parameters refer to temperature control adjustment parameters to be determined.

[0139] In some embodiments, the management platform may determine multiple candidate adjustment parameters in various ways. For example, the management platform may collect data on temperature control adjustment parameters that have been used in historical data, sort the temperature control adjustment parameters from largest to smallest based on the number of times they have been used, and determine a preset number of temperature control adjustment parameters that are ranked top as the multiple candidate adjustment parameters.

[0140] In some embodiments, the preset number may be related to the amount of computing resources. For example, the preset number may be positively correlated with the amount of computing resources, with the greater the amount of computing resources, the larger the preset number. Computing resources refer to resources related to computing used by the management platform. In some embodiments, computing resources may include at least one of storage resources and network resources. In some embodiments, the management platform may calculate its own computing resources.

[0141] In some embodiments, the management platform may determine multiple candidate adjustment parameters through a frequent item database.

[0142] A frequent item database includes multiple frequent items and the corresponding support for each frequent item. Frequent items are temperature control parameters that are frequently used in historical data. Support is used to indicate the reliability of the frequency.

[0143] In some embodiments, the frequent item database can be pre-set 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 a single temperature control adjustment parameter in the positive sample database and the number of times m in the negative sample database in the 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 of the frequent item. The management platform can determine multiple frequent items and the support corresponding to each frequent item in the above manner. The first threshold and the second threshold can be pre-set based on historical experience.

[0144] In some embodiments, the management platform can filter historical data for temperature control adjustment parameters whose adjustment differences meet positive screening criteria and add them to the positive sample database as positive samples. The positive screening criteria can include an adjustment difference greater than a third threshold. The adjustment difference refers to the difference between the degree of impurity accumulation before and after the temperature control device is adjusted.

[0145] In some embodiments, the third threshold value can be preset based on historical experience. The management platform can also count multiple adjustment differences in historical data, sort the multiple adjustment differences from large to small, and determine the adjustment difference value sorted as the preset upper quartile as the third threshold value. The preset upper quartile can be determined based on the number of adjustment differences. For example, the more the number of adjustment differences, the larger the preset upper quartile can be. Exemplarily, the preset upper quartile can 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 sorting of the adjustment differences, and the upper quintile represents the data in the top 20% of the sorting of the adjustment differences.

[0146] In some embodiments, the management platform can filter the temperature control adjustment parameters in the historical data whose adjustment difference satisfies a negative screening condition and add them as negative samples to the negative sample database. The negative screening condition may include that the adjustment difference is less than a fourth threshold.

[0147] In some embodiments, the fourth threshold value can be preset based on historical experience. The management platform can also count multiple adjustment differences in historical data, sort the multiple adjustment differences from large to small, and determine the adjustment difference value sorted as the preset lower quartile as the fourth threshold value. The preset lower quartile can be determined based on the number of adjustment differences. For example, the larger the number of adjustment differences, the larger the preset lower quartile can be. Exemplarily, the preset lower quartile can 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 last 25% of the sorting of the adjustment differences, and the lower quintile represents the data in the last 20% of the sorting of the adjustment differences.

[0148] In some embodiments, the management platform may sort the frequent items in the frequent item database from large to small according to support, and select a preset candidate number of frequent items with high rankings as multiple candidate adjustment parameters.

[0149] In some embodiments, the number of candidate adjustment parameters may be related to the historical accident frequency. For example, the number of candidate adjustment parameters may be positively correlated with the historical accident frequency. The historical accident frequency here may be the average of the historical accident frequencies of all gas pipelines in the gas pipeline network. For more information on the historical accident frequency, see step 330 and its related description.

[0150] In some embodiments of this specification, many of the temperature adjustment parameters in the existing historical data may not be optimal adjustment parameters, and solving the problem of impurity degradation by temperature adjustment parameters does not provide a good solution; it is necessary to determine more candidate temperature adjustment parameters to find the most effective temperature adjustment parameters from a wider range of temperature adjustment parameters.

[0151] In some embodiments of this specification, temperature control adjustment parameters with better adjustment effects and more usage times in historical data are determined as candidate adjustment parameters, which helps to more easily find temperature control adjustment parameters with the best adjustment effects.

[0152] The effect evaluation model refers to a model used to determine the adjustment effect of the candidate adjustment parameters. In some embodiments, the effect evaluation model may be a machine learning model. For example, the effect evaluation model may include any one or a combination of a Graph Neural Network (GNN) model, a Neural Network (NN) model, or other custom model structures.

[0153] In some embodiments, the management platform can use a training effect evaluation model, such as a gradient descent method, based on a large number of adjustment training samples with adjustment labels. The adjustment training samples can include a sample pipeline graph and multiple sample candidate adjustment parameters. The adjustment labels can be actual adjustment effects. In some embodiments, the training samples can be obtained based on historical data. The sample pipeline graph can include a historical pipeline graph determined based on historical data. The nodes and their features, and the edges and their features of the historical pipeline graph are similar to those described above for the pipeline graph.

[0154] In some embodiments, the management platform can count the impurity aggregation levels of all gas pipelines in the sample pipeline map, and form a historical impurity sequence based on these impurity aggregation levels. At the same time, the management platform can count the actual impurity aggregation levels of these gas pipelines, and form an actual impurity sequence based on these actual impurity aggregation levels. 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, the similarity value between the historical impurity sequence and the actual impurity sequence is calculated, and the difference between the value 1 and the similarity value is determined as the adjustment label. The similarity value may include cosine similarity. The label threshold can be pre-set based on historical experience. For example, if it is estimated that the impurity aggregation level of more than half of the gas pipelines in the sample pipeline map will decrease, the label threshold can be 50%.

[0155] 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 similarity between the situation before and after impurity cleaning, and the poorer the actual adjustment effect.

[0156] In some embodiments, in response to the number of positive values ​​in the result sequence being no greater than the label threshold, the adjustment label may be -1. -1 here has no practical meaning and only represents the worst actual adjustment effect.

[0157] 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 .

[0158] In some embodiments, the safety assessment layer 421 , the ablation assessment layer 422 , and the effect determination layer 423 may be trained separately.

[0159] The safety assessment layer 421 is used to determine the pipeline safety 430 of each edge in the pipeline graph.

[0160] In some embodiments, the management platform may input the pipeline map and candidate adjustment parameters into the safety assessment layer to determine the pipeline safety of each edge in the pipeline map under the candidate adjustment parameters.

[0161] The pipeline safety degree 430 can be used to measure the safety risk of temperature regulation for each gas pipeline in the pipeline map. In some embodiments, the pipeline safety degree can be represented by a numerical value, for example, the pipeline safety degree can be represented by a scale of 0-1, where the closer to 1, the lower the safety risk of each gas pipeline.

[0162] In some embodiments, the pipeline platform can input regulated training samples with safety labels into the initial safety assessment layer, construct a loss function based on the safety labels and the prediction results of the initial safety assessment layer, and iteratively update the initial safety assessment layer based on the loss function. Training of the safety assessment layer is completed when the loss function of the initial safety assessment layer meets preset conditions. The preset conditions may include convergence of the loss function and a set number of iterations.

[0163] In some embodiments, the safety label can be obtained based on manual annotation. For example, the safety label can be the ratio of a value of 1 to the number of subsequent historical accidents corresponding to the adjusted training sample. If the number of subsequent historical accidents is 0, the safety label is 1.

[0164] The ablation evaluation layer 422 is used to determine the impurity ablation degree 440 of each candidate adjustment parameter in a plurality of candidate adjustment parameters. In some embodiments, the management platform can input the pipeline map and the candidate adjustment parameters into the ablation evaluation layer to determine the impurity ablation degree of each edge in the pipeline map under the candidate adjustment parameters.

[0165] 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 a numerical value, for example, a larger numerical value indicates a faster impurity ablation speed.

[0166] In some embodiments, the pipeline platform can input the adjusted training samples with ablation labels into the initial ablation evaluation layer, construct loss functions through the ablation labels and the prediction results of the initial ablation evaluation layer respectively, and update the initial ablation evaluation layer based on the iteration of the loss function. When the loss function of the initial ablation evaluation layer meets the preset conditions, the ablation evaluation layer training is completed.

[0167] In some embodiments, the ablation label may be the actual impurity degradation rate corresponding to the adjusted training sample. The actual impurity degradation rate may be obtained by manual measurement or other methods. The impurity degradation rate may be the ratio of the amount of degraded impurities to the time taken for impurity degradation.

[0168] The effect determination layer 423 is used to determine the adjustment effect of each candidate adjustment parameter among multiple candidate adjustment parameters. In some embodiments, the management platform can input a pipeline map with each edge labeled with pipeline safety and impurity ablation degree into the effect determination layer to determine the adjustment effect of the candidate adjustment parameter.

[0169] In some embodiments, the pipeline platform can label the pipeline safety degree and impurity ablation degree for each edge of the sample pipeline map in the adjustment training sample based on the output results of the initial safety assessment layer and the initial ablation assessment layer, and input the adjustment training sample with the adjustment label into the initial effect determination layer, construct a loss function through the adjustment label and the prediction result of the initial effect determination layer, update the initial effect determination layer based on the iteration of 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.

[0170] In some embodiments of this specification, the pipeline map is improved through the safety assessment layer and the ablation assessment layer so that the pipeline map can have more effective information, and then a more comprehensive adjustment effect can be obtained through the effect determination layer, which is conducive to determining the optimal candidate adjustment parameters.

[0171] In some embodiments, the management platform may determine the candidate adjustment parameter corresponding to the maximum adjustment effect as the temperature control adjustment parameter based on the adjustment effect of each candidate adjustment parameter among the multiple candidate adjustment parameters.

[0172] In some embodiments of this specification, by constructing a pipeline map that can well describe the structural characteristics of the gas pipeline network and considering the topological structure of the entire gas pipeline network, the efficiency and accuracy of determining the regulation effect can be effectively improved through the effect evaluation model, which is conducive to determining the optimal temperature control adjustment parameters.

[0173] Some embodiments of this specification further provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any one of the methods in the above embodiments.

[0174] Furthermore, certain features, structures, or characteristics in one or more embodiments of this specification may be appropriately combined.

[0175] If there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the materials cited in this specification and the contents described in this specification, the descriptions, definitions, and / or usage 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. 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 platform includes the gas company management platform and key gas-using enterprises; The gas company management platform is configured to: Obtaining pressure monitoring data and temperature monitoring data of the valve device from the gas equipment object platform via the gas company sensor network platform; Determining the degree of impurity accumulation in at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data, and sending the degree of impurity accumulation to the government safety supervision management platform via the government safety supervision sensor network platform; receiving an impurity removal instruction sent by the government safety supervision and management platform, and determining, based on a pipeline map and a plurality of candidate adjustment parameters, an adjustment effect of each of the plurality of candidate adjustment parameters using an effect evaluation model, wherein the effect evaluation model is a machine learning model; the nodes of the pipeline map include the valve device and the temperature control device, the edges of the pipeline map include the gas pipelines connected between the nodes, and the characteristics of the edges include the length of the gas pipeline, the edge type, and the degree of impurity aggregation; Determining temperature control adjustment parameters based on the adjustment effect, and sending the temperature control adjustment parameters to the government safety supervision and 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 IoT system based on the smart gas IoT 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 accumulation 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 according to 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 accumulation 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 IoT system based on the smart gas IoT according to claim 1 is characterized in that: The gas company management platform is further configured to: The multiple candidate adjustment parameters are determined through a frequent item database; the frequent item database is determined based on a positive sample database and a negative sample database, and at least one of the multiple candidate adjustment parameters includes at least one of the start time and the start temperature of the temperature control device.

5. The pipeline impurity monitoring method based on the smart gas Internet of Things is characterized by: The method is executed by the gas company management platform of the pipeline impurity monitoring IoT system based on the smart gas IoT according to claim 1, and the method includes: Obtain valve device pressure and temperature monitoring data from the gas equipment object platform through the gas company's sensor network platform; Determining the degree of impurity accumulation in at least one gas pipeline based on the pressure monitoring data and the temperature monitoring data, and sending the degree of impurity accumulation to the government safety supervision management platform via the government safety supervision sensor network platform; receiving an impurity removal instruction sent by the government safety supervision and management platform, and determining, based on a pipeline map and a plurality of candidate adjustment parameters, an adjustment effect of each of the plurality of candidate adjustment parameters using an effect evaluation model, wherein the effect evaluation model is a machine learning model; the nodes of the pipeline map include the valve device and the temperature control device, the edges of the pipeline map include the gas pipelines connected between the nodes, and the characteristics of the edges include the length of the gas pipeline, the edge type, and the degree of impurity aggregation; Determining temperature control adjustment parameters based on the adjustment effect, and sending the temperature control adjustment parameters to the government safety supervision and 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.

6. The pipeline impurity monitoring method based on the smart gas Internet of Things according to claim 5 is characterized in that: The determining, based on the pressure monitoring data and the temperature monitoring data, the degree of impurity accumulation in at least one gas pipeline 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 accumulation degree is determined based on the pressure gradient value and the temperature gradient value.

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 impurity accumulation degree based on the pressure gradient value and the temperature gradient value includes: The impurity accumulation 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.

8. The pipeline impurity monitoring method based on the smart gas Internet of Things according to claim 5 is characterized in that: The method further comprises: The multiple candidate adjustment parameters are determined through a frequent item database; the frequent item database is determined based on a positive sample database and a negative sample database, and at least one of the multiple candidate adjustment parameters includes at least one of the start time and the start temperature of the temperature control device.

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