A smart gas pipeline network energy recovery Internet of Things system and method

Abstract

The present invention provides an Internet of Things system and method for energy recovery of a smart gas pipeline network, which is executed by the Internet of Things system for energy recovery of a smart gas pipeline network. The Internet of Things system includes a government regulatory management platform, a government regulatory sensor network platform, a government regulatory object platform, a gas company sensor network platform, and a gas equipment object platform. The method includes: determining future pressure regulation parameters and future output pressure energy based on future external information sequences, as well as current pressure regulation parameters and sensor data information of the target pressure regulating station; determining recovery parameters of a target cleaning station corresponding to the target pressure regulating station based on the future output pressure energy; generating recovery instructions based on the recovery parameters; and sending the recovery instructions to the target cleaning station, and controlling the operation of the cleaning equipment and / or energy storage equipment of the target cleaning station. The system and method can improve energy utilization and the economy of natural gas pipeline operation, and avoid energy waste.

Description

Technical Field

[0001] The present invention relates to the field of smart gas, and in particular to an Internet of Things system and method for energy recovery from a smart gas pipeline network. Background Art

[0002] Natural gas is an essential energy source for people's lives. Its transportation requires a pressure-boosting or pressure-reducing process at a regulating station before it can be used by end users. For example, during long transportation, to improve transmission efficiency, power is often required to drive the boosting equipment in the regulating station to increase the gas transmission pressure. Meanwhile, at the gas user end, which typically requires natural gas at a lower output pressure, the pressure-reducing equipment in the regulating station is used to reduce the high-pressure gas transmission pressure to meet the low-pressure gas demand of the gas user.

[0003] During the long transportation of natural gas, the high-pressure natural gas transported in gas pipelines is converted to low-pressure natural gas delivered to the user, releasing pressure energy. Currently, the recovery and utilization of this pressure energy cannot be accurately estimated in the future. Therefore, it is impossible to set recovery parameters for equipment that utilizes this energy (such as equipment in pigging stations) based on this estimated output pressure energy, resulting in low energy recovery rates.

[0004] Therefore, it is hoped to provide an intelligent gas pipeline network energy recovery Internet of Things system and method, which can accurately predict the output pressure energy of the pressure regulating station in the future period, and determine the recovery parameters of the target pigging station located downstream of the pressure regulating station in advance based on the size of the output pressure energy, and then control the operation of the pigging equipment and / or energy storage equipment of the downstream target pigging station to improve the energy utilization rate and the economy of the natural gas pipeline network operation and avoid energy waste. Summary of the Invention

[0005] One or more embodiments of the present invention provide a smart gas pipeline network energy recovery Internet of Things system, including: a government supervision management platform, a government supervision sensor network platform, a government supervision object platform, a gas company sensor network platform and a gas equipment object platform; the government supervision object platform includes a gas company management platform; the gas company management platform and the government supervision management platform are respectively configured on different servers; the gas company management platform and the government supervision management platform exchange data through the government supervision sensor network platform; the gas company management platform exchanges data with the gas equipment object platform through the gas company sensor network platform; the government supervision sensor network platform and the gas company sensor network platform operate based on data communication equipment; the gas equipment object platform includes a pressure regulating station and a pipe cleaning station, the pressure regulating station includes pressure regulating equipment, and the pipe cleaning station includes pipe cleaning equipment and energy storage equipment; the gas company management platform is configured to execute a smart gas pipeline network energy recovery method.

[0006] One or more embodiments of the present invention provide a smart gas pipeline energy recovery method, comprising: determining future pressure regulation parameters and future output pressure energy based on a future external information sequence, and current pressure regulation parameters and sensor data information of a target pressure regulating station; the future pressure regulation parameters include pressure regulation parameters of at least one unit time interval within the future preset time period, and the length of the at least one unit time interval is determined based on the target pipeline characteristics between the target pressure regulating station and a target pigging station; determining recovery parameters of a target pigging station corresponding to the target pressure regulating station based on the future output pressure energy, the recovery parameters including at least one of pigging parameters and energy storage device parameters; generating a recovery instruction based on the recovery parameters; and sending the recovery instruction to the target pigging station, and controlling the operation of the pigging equipment and / or the energy storage device of the target pigging station.

[0007] Through this smart gas pipeline network energy recovery Internet of Things system and method, the output pressure energy of the pressure regulating station in the future period can be accurately predicted, and based on the size of the output pressure energy, the recovery parameters of the target pigging station located downstream of the pressure regulating station can be determined in advance. Then, the operation of the pigging equipment and / or energy storage equipment of the downstream target pigging station can be controlled to improve energy utilization and the economy of natural gas pipeline network operation, and avoid energy waste. 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 1This is a module diagram of a smart gas network energy recovery IoT system according to some embodiments of the present invention;

[0010] Figure 2 is an exemplary flow chart of a smart gas network energy recovery method according to some embodiments of the present invention;

[0011] Figure 3 is an exemplary flow chart for determining future pressure regulation parameters and future output pressure energy according to some embodiments of the present invention;

[0012] Figure 4 is an exemplary flow chart for determining future gas change characteristics of a target pressure regulating station according to some embodiments of the present invention;

[0013] Figure 5 is an exemplary flow chart of determining recovery parameters of a target pigging station corresponding to a target pressure regulating station according to some embodiments of the present invention. DETAILED DESCRIPTION

[0014] Figure 1 2 is a block diagram of a smart gas pipeline network energy recovery IoT system according to some embodiments of the present invention.

[0015] like Figure 1 As shown, the smart gas pipeline energy recovery Internet of Things system 100 may include a government supervision management platform 110 , a government supervision sensor network platform 120 , a government supervision object platform 130 , a gas company sensor network platform 140 and a gas equipment object platform 160 .

[0016] The government supervision and management platform 110 refers to a platform for government supervision and management.

[0017] In some embodiments, the government regulatory management platform 110 is configured as a first server and a database.

[0018] In some embodiments, the government regulatory management platform 110 may determine an alternative recycling strategy based on the recycled pressure energy data.

[0019] The government supervision sensor network platform 120 refers to a connection platform for realizing data interaction between the government supervision management platform 110 and the government supervision object platform 130 .

[0020] In some embodiments, the government regulatory sensor network platform 120 operates based on data communication equipment and is configured with a gateway and a data interface.

[0021] In some embodiments, the government regulatory sensor network platform 120 may obtain the recovered pressure energy data generated by the gas company management platform 131 and send the recovered pressure energy data to the government regulatory management platform 110 .

[0022] In some embodiments, the government supervision sensor network platform 120 can also obtain the backup recovery strategy generated by the government supervision management platform 110 and send the backup recovery strategy to the gas company management platform 131. Figure 5 and related descriptions.

[0023] The government supervision object platform 130 refers to a functional platform for sensing information generation and controlling information execution.

[0024] In some embodiments, the government regulatory object platform 130 may exchange information with the government regulatory sensor network platform 120 and the gas company sensor network platform 140 .

[0025] In some embodiments, the government regulatory object platform 130 may include a gas company management platform 131 .

[0026] The gas company management platform 131 refers to a platform for sensing information generation and controlling information execution.

[0027] In some embodiments, the gas company management platform 131 is configured as a second server and database.

[0028] In some embodiments, the gas company management platform 131 is configured to execute steps 210 to 240 of the smart gas network energy recovery method. For more information, see Figure 2 and related descriptions.

[0029] In some embodiments, the gas company management platform 131 and the government supervision management platform 110 are configured on different servers. That is, the first server and the second server can be different servers. Each of the different servers is configured with a processor.

[0030] The gas company sensor network platform 140 is a connection platform for data exchange between the government supervision object platform 130 and the gas equipment object platform 160. The gas company sensor network platform 140 operates based on data communication equipment.

[0031] In some embodiments, the gas company sensor network platform 140 may obtain a recycling instruction generated by the gas company management platform 131 and send the recycling instruction to a target pigging station.

[0032] The gas equipment object platform 160 refers to a functional platform for generating perception information and executing control information.

[0033] In some embodiments, the gas equipment object platform 160 may include a pressure regulating station and a pigging station, which are connected via a gas pipeline.

[0034] A pressure regulating station is a station used to regulate the pressure of natural gas during transportation. A pressure regulating station can increase or decrease the pressure of natural gas. A pressure regulating station can be installed on a gas transportation pipeline network to increase or decrease the pressure of natural gas and transport it. In some embodiments, the pressure regulating station includes pressure regulating equipment.

[0035] Pressure regulating equipment refers to equipment used to increase or decrease gas pressure.

[0036] In some embodiments, the pressure regulating device includes a pressure-boosting device and a pressure-reducing device. For example, the pressure-boosting device may include at least one of a screw compressor, a centrifugal compressor, a supercharger, etc. The pressure-reducing device may include a pressure regulating valve, etc.

[0037] A pigging station is a facility used to clean and maintain gas pipelines. A pigging station can be installed on a gas pipeline network to facilitate the transportation of natural gas and to clean and maintain gas pipelines. In some embodiments, a pigging station can include pigging equipment and energy storage devices.

[0038] Pipe cleaning equipment refers to equipment used to clean gas pipelines, such as pipe cleaning devices. Pipe cleaning devices can include pigs, etc.

[0039] Energy storage equipment refers to equipment used to store energy, such as generators and batteries.

[0040] In the process of transporting high-pressure natural gas produced from the wellhead to the gas user end through the gas pipeline, at least one pressure regulating station and at least one pigging station can be installed on the gas pipeline network. The pigging stations and the pressure regulating stations are arranged at intervals and correspond to each other one by one.

[0041] Different outlet pressures result in different amounts of released output pressure energy. The higher the outlet pressure at the regulating station, the greater the pressure drop, and the more output pressure energy released. This output pressure energy can be recovered by downstream pigging stations for use in gas pipeline cleaning, maintenance, and electrical energy storage. This eliminates the need for external energy to clean gas pipelines, thus avoiding waste of output pressure energy.

[0042] For example, when the gas pipeline needs to be cleaned and maintained, the cleaning equipment (such as a cleaning ball) of the cleaning station located downstream of the pressure regulating station can be placed in the gas pipeline between the pressure regulating station and the cleaning station located downstream of the pressure regulating station. The cleaning equipment can flow from one end of the gas pipeline to the other end under the action of kinetic energy converted from the output pressure energy released by the natural gas, and then the cleaning equipment can be recovered by a device for recovering the cleaning equipment set at the other end of the gas pipeline. Therefore, there is no need to provide additional energy to the cleaning station, and the cleaning station can clean the gas pipeline.

[0043] If the output pressure energy released by the natural gas output from the pressure regulating station is large, the excess output pressure energy can be converted into electrical energy and stored through the energy storage equipment in the pigging station when the natural gas flows to the pigging station downstream of the pressure regulating station for subsequent use by the pigging balls in the pigging station.

[0044] For more information about the various platforms in the smart gas network energy recovery IoT system 100, see the present invention. Figure 2-Figure 5 Related description in .

[0045] Figure 2 is an exemplary flow chart of a smart gas network energy recovery method according to some embodiments of the present invention. In some embodiments, the first process 200 can be executed by the gas company management platform 131 in the smart gas network energy recovery IoT system 100. Figure 2 As shown, the first process 200 includes the following steps 210 to 240.

[0046] Step 210 : Determine future pressure regulation parameters and future output pressure energy based on the future external information sequence, and current pressure regulation parameters and sensor data information of the target pressure regulation station.

[0047] Pressure regulating parameters refer to the set parameters of the pressure regulating device. For example, the pressure regulating parameters may include at least one of the set output pressure, set output temperature, compression ratio, and operating power of the pressure regulating device. The set output pressure and set output temperature refer to the preset output pressure and output temperature of the natural gas output by the pressure regulating device, respectively.

[0048] Future output pressure energy refers to the output pressure energy for a predetermined period in the future. This energy is the energy released by pressure loss during the flow of high-pressure natural gas from the high-pressure area at the wellhead to the low-pressure area at the gas user's end. The longer the distance the output pressure energy is transmitted and the larger the diameter of the pipe used, the greater the probability of energy loss. Therefore, timely prediction of future output pressure energy and its timely allocation to targeted pigging stations can avoid wasting future output pressure energy.

[0049] The future preset time period refers to the time period from the current moment to a preset moment in the future. The future preset time period can be preset by those skilled in the art based on experience.

[0050] The future external information sequence refers to a sequence composed of multiple external information in a future preset period. In some embodiments, the external information in the future preset period may include future weather information and future environmental information of the target pressure regulating station.

[0051] The future weather information may include information such as atmospheric pressure and temperature during a preset future period of time in the weather forecast.

[0052] In some embodiments, the gas company management platform may obtain future weather information from a third party (eg, a meteorological bureau).

[0053] Future environmental information refers to information such as the type of pipeline connecting the target pressure regulating station and the target pigging station located downstream of the target pressure regulating station, the ambient temperature during a preset future time period, and the ambient pressure during a preset future time period. Information such as the ambient temperature and ambient pressure during a preset future time period may differ from the corresponding information in the future weather information.

[0054] The pipeline type may include one of an open-air pipeline, a buried pipeline, a submarine pipeline, etc. The pipeline type is preset by those skilled in the art based on the pipeline actually laid.

[0055] In some embodiments, the gas company management platform uses the following first algorithm to determine the ambient temperature for a preset time period in the future:

[0056] The ambient temperature in the future preset time period T=T1×a+T2×b; where T1 is the currently measured ambient temperature, T2 is the weather temperature in the future preset time period in the weather forecast, a and b are coefficients greater than 0 and less than 1, and a and b are preset by technical personnel in this field based on experience.

[0057] In some embodiments, the gas company management platform may obtain the currently actually measured ambient temperature through a temperature sensor installed outside the pipeline.

[0058] In some embodiments, coefficient a is positively correlated with the buried depth of the gas pipeline, and coefficient b is negatively correlated with the buried depth of the gas pipeline. The buried depth of the gas pipeline refers to the depth of the buried gas pipeline from the ground surface.

[0059] For example, for surface gas pipelines, since the weight of future environmental information is very small, the gas company management platform can directly use future weather information as the future external information sequence.

[0060] For example, for deeply buried gas pipelines, since they are not easily affected by external future weather information, the weight of future weather information is very small. The gas company management platform can directly use future environmental information as a future external information sequence.

[0061] The ambient pressure for the future preset period of time may be determined using a method similar to determining the ambient temperature for the future preset period of time.

[0062] Pressure regulating stations and pigging stations can be arranged alternately. For more information about pressure regulating stations and pigging stations, please refer to Figure 1 See the relevant instructions in .

[0063] The target pressure regulating station refers to a pressure regulating station that is connected to the target pigging station and is located upstream of the target pigging station.

[0064] The target pigging station is the pigging station for which the operating parameters of the pigging equipment and / or energy storage equipment need to be determined at the current moment. For a description of pigging equipment and energy storage equipment, see Figure 1 In some embodiments, the operating parameters of the pigging device may include pigging parameters.

[0065] Pipe cleaning parameters may include the launch speed of the pig ball, the compressed gas displacement of the air compressor, etc. The operating parameters of the energy storage device may include energy storage device parameters.

[0066] Energy storage device parameters refer to the parameters of the energy storage equipment used for power generation at the target pigging station. Energy storage device parameters can include generator set parameters, the number of charge and discharge cycles of the energy storage device, etc.

[0067] The current pressure regulating parameters of the target pressure regulating station refer to the pressure regulating parameters of the pressure regulating equipment in the target pressure regulating station in the current state.

[0068] In some embodiments, the gas company management platform retrieves the current pressure regulation parameters of the target pressure regulating station through a known database.

[0069] In some embodiments, the sensor data includes at least one of gas temperature and gas pressure collected at the inlet or outlet of the target pressure regulating station during a current time period. The current time period refers to a period of time that is a short period of time from the current time (e.g., from the current time point to the previous day). The current time period can be preset by those skilled in the art based on experience.

[0070] In some embodiments, the gas company management platform obtains sensor data information through sensors (eg, temperature sensors, pressure sensors, etc.) installed at the inlet or outlet of the target pressure regulating station.

[0071] The future pressure regulation parameters may be pressure regulation parameters involved in boosting the pressure of high-pressure natural gas for a predetermined future period during the long-distance transportation of high-pressure natural gas. In some embodiments, the future pressure regulation parameters include pressure regulation parameters for at least one unit time interval within the predetermined future period. The pressure regulation parameters include output information and compression parameters of the target pressure regulating station.

[0072] The output information includes at least one of the set output pressure and set output temperature of the natural gas outputted by the target pressure regulating station. The set output pressure and set temperature of the natural gas outputted by the target pressure regulating station are hereinafter referred to as the set output pressure and set output temperature, respectively. The compression parameter includes at least one of the compression ratio and operating power of the natural gas outputted by the target pressure regulating station.

[0073] The compression ratio refers to the ratio of the natural gas pressure at the regulator's outlet to the natural gas pressure at the regulator's inlet after compression. Operating power refers to the power required to operate the regulator itself. Operating power is positively correlated with the compression ratio.

[0074] A unit time interval refers to a time interval. In some embodiments, the set output pressure and set output temperature may differ for different unit time intervals. For example, if a unit time interval is displayed as the evening time period, the set output pressure for that unit time interval may be higher than for other unit time intervals due to the high gas demand at that time.

[0075] In some embodiments, the gas company management platform may evenly divide the future preset period into a preset number of unit time intervals. The preset number may be preset by those skilled in the art based on experience.

[0076] In some embodiments, the gas company management platform determines the length of at least one unit time interval based on target pipeline characteristics of the pipeline between the target pressure regulating station and the target pigging station.

[0077] The target pipeline characteristics refer to the characteristics of the pipeline connecting the target pressure regulating station and the target pigging station. For example, the target pipeline characteristics include at least one of pipeline length, pipeline material, pipeline diameter, and pipeline burial depth.

[0078] In some embodiments, the length of at least one unit time interval is positively correlated with the pipeline length and pipeline diameter. For example, the longer the pipeline length and the larger the pipeline diameter, the longer the length of each unit interval set by the gas company management platform. It can be understood that the longer the pipeline length and the larger the pipeline diameter, the longer it takes for the output pressure energy released by the output natural gas to be transmitted to the target pigging station, and the greater the probability of energy loss. In this case, if the length of the unit time interval is set to be shorter, the output pressure energy released by the output natural gas from the target pressure regulating station in the future preset period may not be transmitted to the target pigging station. Therefore, setting the length of the unit time interval to be longer can avoid invalid calculations.

[0079] In some embodiments, the length of at least one unit time interval is also related to the first pigging probability of the target pigging station. For example, the higher the first pigging probability of the target pigging station, the shorter the length of the unit time interval.

[0080] The first pigging probability refers to the probability that the target pigging station is currently performing pigging work.

[0081] In some embodiments, the gas company management platform obtains a fluctuation frequency of the first upstream sensor data; and determines a first pigging probability of the target pigging station based on the fluctuation frequency.

[0082] First upstream sensor data refers to data collected by sensors at a preset location during the current time period, including gas temperature and pressure. The preset location can be midway along the pipeline connecting the first upstream pigging station and the target pressure regulating station. The first upstream pigging station is located upstream of and directly connected to the target pressure regulating station.

[0083] In some embodiments, the fluctuation frequency can be represented by the sum of the number of times the gas flow rate exceeds a flow threshold, the number of times the gas pressure exceeds a pressure threshold, and the number of times the gas temperature exceeds a temperature threshold in the sensor data information of the target pressure regulating station during the current time period. The flow threshold, pressure threshold, and temperature threshold can be preset by those skilled in the art based on experience.

[0084] In some embodiments, the gas company management platform uses the ratio of the fluctuation frequency to the total data volume as the first pigging probability of the target pigging station.

[0085] The total data volume refers to the sum of the number of times the gas flow, gas pressure, and gas temperature are collected during the current period.

[0086] In some embodiments of the present invention, the length of at least one unit time interval is negatively correlated with the first pigging probability of the target pigging station. When the first pigging probability is higher, the length of the unit time interval is set to be shorter, so as to divide the future pressure regulation parameters into more pressure regulation parameters of unit time intervals, so that the future output pressure released by the natural gas output by the target pressure regulating station can meet the needs of the target pigging station in each unit time interval.

[0087] In some embodiments, the gas company management platform determines the adjustment amount through a first preset comparison table based on the future external information sequence and the sensor data information of the target pressure regulating station; and uses the sum of the adjustment amount and the current pressure regulating parameter of the target pressure regulating station as the future pressure regulating parameter. The first preset comparison table contains a correspondence between the reference external information sequence and the reference sensor data information of the target pressure regulating station and the reference adjustment amount. The first preset comparison table can be constructed based on prior knowledge or historical data. Through the setting of the first preset comparison table, the future pressure regulating parameters are adjusted along with the future external information sequence to ensure that the target pressure regulating station works with the future pressure regulating parameters, and the future output pressure energy released by the output natural gas can meet the energy demand of the target cleaning station.

[0088] In some embodiments, the gas company management platform determines the future output pressure energy based on the future pressure regulation parameters through a second preset comparison table. The future output pressure energy may include the future output pressure energy of multiple unit time intervals.

[0089] The second preset comparison table contains a correspondence between reference future pressure regulation parameters and reference output pressure energy. The second preset comparison table can be constructed based on prior knowledge or historical data.

[0090] In some embodiments, the gas company management platform determines the future gas change characteristics of the target pressure regulating station based on the future external information sequence, the current pressure regulating parameters and sensor data information of the target pressure regulating station; and determines the future pressure regulating parameters and future output pressure energy based on the gas change characteristics and the preset pressure regulating plan. Figure 3 Instructions in .

[0091] Step 220 : Determine the recovery parameters of the target pigging station corresponding to the target pressure regulating station based on the future output pressure energy.

[0092] In some embodiments, the recovery parameter includes at least one of a pigging parameter and an energy storage device parameter.

[0093] For details on pigging parameters and energy storage device parameters, see Figure 2 Description in step 210.

[0094] In some embodiments, the gas company management platform determines the recovery parameters based on the future output pressure energy using a third preset comparison table. The third preset comparison table contains a correspondence between reference output pressure energy and reference recovery parameters. The third preset comparison table can be constructed based on prior knowledge or historical data.

[0095] In some embodiments, the gas company management platform further determines recovery pressure energy data based on the future output pressure energy; and determines recovery parameters of a target pigging station corresponding to the target pressure regulating station based on the recovered pressure energy data.

[0096] Recovered pressure energy data refers to the future output pressure energy that can be recovered and utilized by the target pigging station. Recovered pressure energy data can be used to drive pigging equipment, assist in compressing gas, and power energy storage devices.

[0097] In some embodiments, the gas company management platform uses the product of the future output pressure energy and a specific coefficient c as the recovered pressure energy data. The specific coefficient c is any number between 0 and 1. The specific coefficient c can be preset by those skilled in the art based on experience. The magnitude of the specific coefficient c is inversely correlated with the pipeline length between the target pressure regulating station and the target pigging station.

[0098] In some embodiments, the gas company management platform determines the transmission loss coefficient based on the target pipeline characteristics and future environmental information of the pipeline between the target pressure regulating station and the target pigging station; determines the recovery pressure energy data based on the future output pressure energy and the transmission loss coefficient; sends the recovery pressure energy data to the government supervision and management platform, and obtains the backup recovery strategy issued by the government supervision and management platform; and determines the recovery parameters of the target pigging station corresponding to the target pressure regulating station based on the backup recovery strategy. Figure 5 Instructions in .

[0099] Step 230: Generate a recycling instruction based on the recycling parameters.

[0100] The recovery instruction refers to an instruction including pigging parameters and energy storage device parameters.

[0101] In some embodiments, the gas company management platform generates a recycling instruction including the recycling parameters based on the recycling parameters.

[0102] Step 240: Send the recovery instruction to the target pigging station, and control the pigging equipment and / or energy storage equipment of the target pigging station to operate.

[0103] In some embodiments, the gas company management platform sends a recovery instruction to a target pigging station, and controls the pigging equipment of the target pigging station to operate according to pigging parameters, and / or controls the energy storage equipment to operate according to energy storage device parameters.

[0104] In some embodiments of the present invention, by estimating in advance the future pressure regulating parameters and future output pressure energy of the target pressure regulating station, the recovery parameters of the target pigging station corresponding to the target pressure regulating station are determined based on the future output pressure energy, and the operation of the pigging equipment and / or energy storage equipment of the target pigging station is controlled, so that the future output pressure energy can be used for the operation of the equipment in the target pigging station, thereby improving the energy utilization rate of the target pressure regulating station and the economy of the natural gas pipeline network operation, and avoiding energy waste.

[0105] Figure 3 is an exemplary flow chart for determining future pressure regulation parameters and future output pressure energy according to some embodiments of the present invention. In some embodiments, the second process 300 can be executed by the gas company management platform 131 in the smart gas network energy recovery IoT system 100. Figure 3 As shown, the second process 300 includes the following steps 310 to 320.

[0106] Step 310 , based on the future external information sequence 311 , and the current pressure regulation parameters 313 and sensor data information 312 of the target pressure regulation station, determines the future gas change characteristics 314 of the target pressure regulation station.

[0107] For a description of the future external information sequence, the current pressure regulation parameters of the target pressure regulating station, and the sensor data information, see Figure 2 Description in step 210.

[0108] The future gas change characteristics refer to the characteristics that can reflect the gas changes at the target pressure regulating station within a preset future period of time.

[0109] Future gas variation characteristics can be characterized by a sequence consisting of the output pressure, output flow rate, and output temperature of natural gas outputted from a target pressure regulating station at multiple future time points. The multiple future time points are within a preset future time period. The output pressure, output flow rate, and output temperature of the natural gas outputted from the target pressure regulating station are hereinafter referred to as output pressure, output flow rate, and output temperature, respectively.

[0110] In some embodiments, the gas company management platform uses the following steps a11 to a14 to determine the gas change characteristics.

[0111] Step a11: Based on the future external information sequence, determine the impact range of the external information sequence through the fourth preset comparison table.

[0112] In some embodiments, the impact amplitude of the external information sequence may include the impact amplitude of future gas pressure and the impact amplitude of future gas temperature.

[0113] The impact range of future gas pressure and the impact range of future gas temperature refer to the impact range of future external information sequence on the set gas pressure and set gas temperature of the target pressure regulating station respectively.

[0114] The ambient temperature and ambient pressure in a future preset period of time may simultaneously affect the future gas pressure impact range and the future gas temperature impact range.

[0115] The fourth preset comparison table contains a correspondence between a reference external information sequence and a reference future gas pressure impact amplitude and a reference future gas temperature impact amplitude. The fourth preset comparison table can be constructed based on prior knowledge or historical data. For example, if the atmospheric pressure in the future preset time period in the future external information sequence is 0.2 MPa, the fourth preset comparison table shows that the future gas pressure impact amplitude can be based on the set output pressure in the current pressure regulation parameters of the target pressure regulating station, with an increase of 0.2 MPa in the output pressure, and the future gas temperature impact amplitude can be based on the set output temperature in the current pressure regulation parameters of the target pressure regulating station, with an increase of 5°C in the output temperature.

[0116] Step a12: determining the output pressure and output temperature of each unit time interval within a future preset period based on the impact amplitude of the external information sequence and the current pressure regulation parameters of the target pressure regulating station.

[0117] In some embodiments, for a unit time interval within a preset time period in the future, the gas company management platform uses the sum of the future gas pressure impact amplitude of the unit time interval and the set output pressure in the current pressure regulation parameters of the target pressure regulating station as the output pressure of the unit time interval; and uses the sum of the future gas temperature impact amplitude of the unit time interval and the set output temperature in the current pressure regulation parameters of the target pressure regulating station as the output temperature of the unit time interval.

[0118] Step a13: taking the output pressure and output temperature of each unit time interval as the gas variation characteristics.

[0119] In some embodiments, the gas equipment object platform includes multiple pressure regulating stations and at least one pigging station. The gas company management platform may further use the following steps b11 and b12 to determine the future gas change characteristics 314 of the target pressure regulating station:

[0120] Step b11: constructing a gas pipeline network map based on the site characteristics of multiple pressure regulating stations and at least one pigging station, and the pipeline characteristics of the connecting pipelines between the pressure regulating stations and the pigging stations.

[0121] Site characteristics refer to characteristics related to a pressure regulating station or a pigging station. In some embodiments, the site characteristics include the type of site, current pressure regulating parameters and sensor data information of the target pressure regulating station, historical pigging parameters of the pigging station, and future external information sequences.

[0122] The type of station includes whether the station is a pressure regulating station or a pigging station. The historical pigging parameters refer to the actual pigging parameters of the pigging station located upstream of the pressure regulating station and directly connected to the pressure regulating station through a pipeline. For an explanation of the pigging parameters, see Figure 2 Description in step 210.

[0123] The pipeline characteristics may include at least one of the pipeline length, pipeline material, pipeline diameter, pipeline burial depth, etc. of the gas pipeline.

[0124] A gas pipeline network graph is a graph that can show the connection relationship between pressure regulating stations, pigging stations, and gas pipelines. In some embodiments, the gas pipeline network graph is a data structure composed of nodes and edges, where edges connect nodes, and nodes and edges can have attributes.

[0125] In some embodiments, nodes in the gas pipeline network map may correspond to individual pressure regulating stations or pigging stations. Node attributes may reflect relevant characteristics of the corresponding pressure regulating station or pigging station. For example, node attributes include: station type, current pressure regulating parameters and sensor data information of the target pressure regulating station, historical pigging parameters of the pigging station, and future external information sequences.

[0126] In some embodiments, the node attributes corresponding to the pigging station node further include a pigging work plan of the pigging station.

[0127] A pigging work plan is a plan for cleaning gas pipelines at a pre-set timeframe. For example, a pigging work plan might include the number of gas pipelines to be cleaned, the length of the pipelines, and the level of impurity accumulation within the pipelines. The gas company's management platform can estimate the level of impurity accumulation within the pipelines based on the cleaning intervals. Pigging work plans can also be pre-set for government regulatory management platforms.

[0128] In some embodiments, the gas company management platform obtains the pipeline cleaning work plan from the government regulatory management platform.

[0129] In some embodiments of the present invention, the node attributes of the gas network map also include the pigging work plan of the pigging station, thereby reflecting the impact of the pigging work plan on future gas change characteristics.

[0130] In some embodiments, edges in a gas pipeline network graph may correspond to gas pipelines between nodes. Edges are directed and can reflect the direction of gas flow. Edge attributes can reflect relevant characteristics of the corresponding gas pipelines. For example, edge attributes include pipeline characteristics.

[0131] Step b12: Based on the gas network map, determine the future gas change characteristics through the feature prediction model.

[0132] For more information about this part, please see Figure 4 Instructions in .

[0133] Step 320 , based on the future gas variation characteristics 314 of the target pressure regulating station and the preset pressure regulating plan 321 , determines the future pressure regulating parameters 322 and the future output pressure energy 323 .

[0134] A preset pressure regulation plan refers to a pre-set gas pressure regulation plan. This plan does not consider the impact of future external information sequences. A preset pressure regulation plan can be preset by those skilled in the art based on experience. For example, the preset pressure regulation plan may include set output pressures and set output temperatures for each unit interval within a preset future time period.

[0135] In some embodiments, the gas company management platform obtains the preset pressure regulation plan through input from gas management personnel. Gas management personnel refer to personnel in the gas company who manage gas operations.

[0136] In some embodiments, the gas company management platform adjusts the pressure regulation parameters in the preset pressure regulation plan based on future gas change characteristics to obtain adjusted pressure regulation parameters; and uses the adjusted pressure regulation parameters as future pressure regulation parameters.

[0137] In some embodiments, for a unit time interval in a future preset period of time, the gas company management platform averages the future gas change characteristics (e.g., output pressure, output temperature) and the set output pressure and set output temperature within the unit time interval in the preset pressure regulation plan, and the obtained values ​​are used as the set output pressure and set output temperature for the unit time interval in the adjusted pressure regulation parameters.

[0138] In some embodiments, the gas company management platform further uses the ratio of the set output pressure in the second unit time interval of the adjusted pressure regulation parameter to the set output pressure in the first unit time interval as the compression ratio in the adjusted pressure regulation parameter. The second unit time interval is later than the first unit time interval.

[0139] In some embodiments, after determining the future pressure regulation parameters, the gas company management platform further adjusts the future pressure regulation parameters based on future gas change characteristics, a preset pressure regulation plan, and future pressure energy requirements of the target pigging station.

[0140] The future pressure energy demand refers to the output pressure energy required for the target pigging station to complete the pigging work during a preset period in the future.

[0141] In some embodiments, the gas company management platform determines the total energy consumption demand based on the future preset time period cleaning work plan; and determines the future pressure energy demand based on the total energy consumption demand.

[0142] Total energy demand refers to the total energy required to complete the pigging work plan.

[0143] In some embodiments, the gas company management platform determines the total energy consumption requirement based on the pigging work plan for a preset future time period using a fifth preset comparison table. The fifth preset comparison table contains a correspondence between a reference pigging work plan and a reference total energy consumption requirement. The fifth preset comparison table can be constructed based on prior knowledge or historical data.

[0144] In some embodiments, the gas company management platform uses the difference between the total energy demand and the existing energy storage at the target pigging station as the future pressure energy demand. The existing energy storage at the target pigging station includes energy stored through other energy sources. For example, other energy sources include energy stored through solar power generation at the target pigging station or energy stored through stored electrical energy.

[0145] In some embodiments, in response to the future pressure energy demand being less than or equal to the future output pressure energy, the future pressure regulation parameter remains unchanged.

[0146] In some embodiments, in response to the future pressure energy demand being greater than the future output pressure energy, the gas company management platform adjusts the future pressure regulation parameters (e.g., compression parameters) based on a preset step size; based on the adjusted future pressure regulation parameters, the future output pressure energy is re-determined until the future pressure energy demand is less than or equal to the future output pressure energy, and the adjustment is stopped; and the future pressure regulation parameters obtained by the last adjustment are used as the final future pressure regulation parameters.

[0147] The preset step size refers to the value of increasing the compression parameter, and can be preset by those skilled in the art based on experience.

[0148] The method of adjusting the future voltage regulation parameters may include increasing the compression ratio of the compression parameter once or multiple times according to a preset step size.

[0149] Regarding the specific implementation method of re-determining the future output pressure energy based on the adjusted future pressure regulation parameters, it can be adopted Figure 2 The method for determining future output pressure energy recorded in step 210 is determined.

[0150] In some embodiments of the present invention, the pressure regulation parameters are adjusted based on the future pressure energy demand and future output pressure energy of the target pigging station. Therefore, when the gas company management platform finds that the future pressure energy demand of the target pigging station is large, the current pressure regulation parameters of the target pressure regulation station can be adjusted (for example, the compression ratio can be increased) to increase the future output pressure energy to meet the future pressure energy demand of the target pigging station without affecting the use of the gas user end.

[0151] Since the first upstream pigging station of the target pressure regulating station is performing pigging operations, the pressure of the natural gas arriving at the inlet of the target pressure regulating station will be reduced, thereby reducing the future output pressure energy released by the natural gas output by the target pressure regulating station.

[0152] In some embodiments, the future production pressure energy is negatively correlated with historical pigging parameters of a first upstream pigging station located upstream of the target pressure regulating station. For example, the greater the historical pigging parameters (e.g., pigging frequency, pig ball speed) of the first upstream pigging station, the lower the future production pressure energy.

[0153] In some embodiments, the gas company management platform can determine a specific coefficient based on historical cleaning parameters through a preset coefficient comparison table; and use the product of the future output pressure energy and the specific coefficient as the final future output pressure energy.

[0154] The coefficient preset comparison table contains a correspondence between historical pigging parameters and reference specific coefficients. The coefficient preset comparison table can be constructed based on prior knowledge or historical data. For example, when historical pigging parameters are close to 0, the specific coefficient is close to 1; the larger the historical pigging parameters, the closer the specific coefficient is to 0.

[0155] For a description of historical pigging parameters, see Figure 3 Relevant instructions in step 310.

[0156] In some embodiments, the gas company management platform determines historical pigging parameters based on the following steps S1 to S3:

[0157] Step S1: obtaining second upstream sensor data of a target pressure regulating station based on a preset frequency.

[0158] Second upstream sensor data refers to gas temperature, gas pressure, and other data obtained by sensors at the midpoint of the pipeline connecting the second upstream pigging station and the upstream pressure regulating station within a specific period of time prior to the current time (e.g., one day prior to the current time). The second upstream pigging station is a pigging station located upstream of and directly connected to the upstream pressure regulating station. The upstream pressure regulating station is a pressure regulating station located upstream of the target pressure regulating station and separated by one pigging station from the target pressure regulating station.

[0159] For example, if the gas flow direction is: Pigging Station 0 - Pressure Regulating Station 0 - Pigging Station 1 - Pressure Regulating Station 1 - Pigging Station 2; Pressure Regulating Station 1 is the target pressure regulating station, then Pigging Station 0 is the second upstream pigging station of the target pressure regulating station, Pigging Station 1 is the first upstream pigging station of the target pressure regulating station, Pressure Regulating Station 0 is the upstream pigging station of the target pressure regulating station, and Pigging Station 2 is the target pigging station; then the second upstream sensor data is: the gas temperature, gas pressure, etc. collected by the sensor at the middle position of the pipeline connecting Pigging Station 0 and Pressure Regulating Station 0, or at the inlet position of Pressure Regulating Station 0.

[0160] The preset frequency refers to a preset frequency for acquiring the second upstream sensor data of the target pressure regulating station.

[0161] In some embodiments, the predetermined frequency is inversely related to the length of the upstream pipeline characteristic.

[0162] Upstream pipeline characteristics refer to the pipeline characteristics of the gas pipeline connecting the upstream pressure regulating station and the second upstream pigging station. For an explanation of pipeline characteristics, please refer to the above Figure 3 Relevant instructions in step 310.

[0163] In some embodiments of the present invention, the reliability of collecting the second upstream sensing data can be ensured by negatively correlating the preset frequency with the pipe length in the upstream pipe characteristic.

[0164] Step S2: determining a second pigging probability of a second upstream pigging station located upstream of the target pressure regulating station based on the second upstream sensor data.

[0165] The second pigging probability refers to the probability that the second upstream pigging station is currently pigging. The gas company management platform can use a similar method to determine the first pigging probability to determine the second pigging probability. Figure 2 Description of the determination of the first pigging probability in step 210.

[0166] Step S3: in response to the second pigging probability being greater than a preset threshold, obtaining historical pigging parameters from a government supervision and management platform.

[0167] The preset threshold refers to a critical value of the second pigging probability and can be preset by those skilled in the art based on experience.

[0168] In some embodiments of the present invention, when it is found that the future pressure energy demand of the target pigging station is greater than the future output pressure energy, the future pressure regulation parameters of the target pressure regulating station can be adjusted without affecting the use of the target pigging station, so that the future output pressure released by the natural gas output by the target pressure regulating station in a preset future time period can meet the future pressure energy demand of the target pigging station.

[0169] In addition, if pigging is in progress in the pipeline, it may affect the gas flow rate and cause abnormalities in the upstream sensor data. In this case, when the second upstream pigging station upstream of the target pressure regulating station is likely to be pigging, considering the impact of historical pigging parameters on future output pressure energy can further improve the accuracy of the determined future output pressure energy.

[0170] Figure 4 is an exemplary flow chart of determining the future gas change characteristics of a target pressure regulating station according to some embodiments of the present invention. In some embodiments, Figure 4 It can be executed by the gas company management platform 131 in the smart gas pipeline energy recovery Internet of Things system 100.

[0171] In some embodiments, the gas company management platform determines future gas change characteristics 314 of the target pressure regulating station based on the gas pipeline network map 410-1 using a feature prediction model 420. Node attributes of the gas pipeline network map 410-1 include future external information sequences 311, and current pressure regulation parameters 313 and sensor data information 312 of the target pressure regulating station.

[0172] For a description of the future external information sequence, the current pressure regulation parameters of the target pressure regulating station, and the sensor data information, see Figure 2 For details about future gas change characteristics and gas network maps, see Figure 3 Relevant instructions in step 310.

[0173] In some embodiments, the feature prediction model 420 is a machine learning model. For example, the feature prediction model can be a graph neural network (GNN) model. Future gas change features can be output by nodes of the GNN.

[0174] In some embodiments, the gas company management platform determines a training sample set based on the distribution of pigging stations.

[0175] A training sample set may include multiple first training samples, each of which corresponds to a first training label.

[0176] Pigging station distribution refers to the location distribution of each pigging station on the gas pipeline network map.

[0177] In some embodiments, due to different historical gas pipeline network maps, the diversity of the first training samples can be guaranteed, and the training effect of the feature prediction model can be guaranteed. The gas company management platform can divide the historical data into the same training sample set, multiple cleaning stations located in the same preset range, and multiple different historical gas pipeline network maps constructed by the pressure regulating stations connected to the multiple cleaning stations.

[0178] The preset range can be a range divided by administrative units such as provinces, towns, and cities. The preset range can be set by those skilled in the art based on their experience. Different historical gas pipeline network maps within the same training sample set can have the same pressure regulating station and pigging station corresponding to a node, but different historical time periods, external information sequences, and other features. This setting ensures the diversity of the first training samples within the same training sample set.

[0179] In some embodiments, different training sample sets have different learning rates during the training process. The learning rate of the training sample set is determined based on the training sample features of the training sample set.

[0180] The learning rate is a hyperparameter used to adjust the weight of the training sample set in the feature prediction model learning process.

[0181] The training sample feature may reflect the characteristics of the first training sample in the training sample set.

[0182] In some embodiments, the training sample features may include the gas input pressure of the pressure regulating station and the reliability of the training sample set.

[0183] Gas input pressure refers to the pressure at the inlet of the gas regulating station. It can be understood that for the first training sample with the same gas input pressure, the current pressure regulating parameters of the target regulating station are also closer. For the description of the current pressure regulating parameters of the target regulating station, please refer to Figure 2 Relevant instructions in step 210.

[0184] In some embodiments, the gas company management platform obtains the gas input pressure of the pressure regulating station through historical data.

[0185] The reliability of the training sample set can reflect the quality of the training effect of the training sample set. For example, the higher the reliability of the training sample set, the better the training effect of the training sample set.

[0186] In some embodiments, the higher the degree of consistency of the first labels of the first training samples in the same training sample set, the more similar the actual conditions of the first training samples in the training sample set are, and the better the training effect of the feature prediction model trained using the training sample set. The gas company management platform counts the number of consistent first labels corresponding to multiple first training samples in the same training sample set. In response to the number of consistent first labels corresponding to multiple first training samples in the same training sample set being greater than a preset number, the gas company management platform determines that the reliability of the training sample set is high; otherwise, the reliability of the training sample set is determined to be low. The preset number can be preset by those skilled in the art based on experience.

[0187] In some embodiments, the gas company management platform determines the learning rate of the training sample set based on the training sample features of the training sample set using a sixth preset comparison table. The sixth preset comparison table contains a correspondence between the training sample features of the reference training sample set and the learning rate of the reference training sample set. The sixth preset comparison table can be constructed based on prior knowledge or historical data.

[0188] In some embodiments, the feature prediction model can be trained based on multiple training sample sets.

[0189] In some embodiments, each first training sample in each training sample set includes a historical gas network map. In some embodiments, the historical gas network map can be constructed based on historical data. The method for constructing the historical gas network map is similar to the method for constructing the gas network map, see Figure 3 Relevant instructions in step 310.

[0190] In some embodiments, the first training label can be actual gas change characteristics of a pressure regulating station on a historical gas pipeline network map during a second preset historical period following the first preset historical period. In some embodiments, the gas company management platform obtains the first training label from historical data. The pressure regulating station can be any pressure regulating station on the historical gas pipeline network map.

[0191] In some embodiments, the gas company management platform may input multiple first training samples with first training labels into an initial feature prediction model, construct a loss function based on the first training labels and the results of the initial feature prediction model, and iteratively update the parameters of the initial feature prediction model based on the loss function using gradient descent or other methods. When preset conditions are met, the initial feature prediction model training is completed, resulting in a trained feature prediction model. The preset conditions may include convergence of the loss function, a threshold number of iterations, and the like.

[0192] In some embodiments of the present invention, the learning rate of each training sample set is determined by the training sample features of the training sample set, and a feature prediction model is performed, which can improve the accuracy of the prediction results of the feature training model obtained through training.

[0193] In addition, the feature prediction model obtained through training sample sets determined by pigging station distribution is applicable to gas systems in different regions, thereby improving the pertinence and practicality of the feature prediction model obtained through training.

[0194] In addition, the feature prediction model can quickly and accurately determine the characteristics of gas changes, saving human resources.

[0195] Figure 5 is an exemplary flow chart of determining the recovery parameters of a target pigging station corresponding to a target pressure regulating station according to some embodiments of the present invention. In some embodiments, the third process 500 can be executed by the gas company management platform 131 in the smart gas network energy recovery IoT system 100. Figure 5 As shown, the third process 500 includes the following steps 510 to 540.

[0196] Step 510 : determining a transmission loss coefficient based on target pipeline characteristics and future environmental information of the pipeline between the target pressure regulating station and the target pigging station.

[0197] For a description of target pressure regulating stations, target pigging stations, target pipeline characteristics, and future environmental information, see Figure 2 Relevant instructions in step 210.

[0198] The transmission loss coefficient refers to the proportion of the future output pressure released by natural gas that can be lost in the process of being transported from the target pressure regulating station to the target pigging station.

[0199] The transmission loss coefficient can be characterized by a value obtained by weighted summation of the pressure transmission loss coefficient and the temperature transmission loss coefficient, wherein the weight of the pressure transmission loss coefficient is much greater than the weight of the temperature transmission loss coefficient.

[0200] The pressure transmission loss coefficient is the ratio of the input pressure of the natural gas at the target pigging station to the output pressure of the natural gas at the target pressure regulating station during a preset historical period. The temperature transmission loss coefficient is the ratio of the input temperature of the natural gas at the target pigging station to the output temperature of the natural gas at the target pressure regulating station during a preset historical period. The preset historical period can be preset by those skilled in the art based on experience.

[0201] In some embodiments, the weight of the pressure transmission loss coefficient and the weight of the temperature transmission loss coefficient can be preset by those skilled in the art based on experience, for example, the weight of the pressure transmission loss coefficient is 0.9, and the weight of the temperature transmission loss coefficient is 0.1.

[0202] In some embodiments, the transmission loss coefficient may also be represented by an average value of the pressure transmission loss coefficient and the temperature transmission loss coefficient of the sensing data information in a plurality of different historical preset time periods.

[0203] In some embodiments, the gas company management platform determines the transmission loss coefficient based on the target pipeline characteristics and future environmental information of the pipeline between the target pressure regulating station and the target pigging station through the vector database.

[0204] In some embodiments, the gas company management platform determines a target feature vector based on the target pipeline characteristics and future environmental information of the pipeline between the target pressure regulating station and the target cleaning station; based on the target feature vector, determines an associated feature vector through a vector database; and determines a reference transmission loss coefficient corresponding to the associated feature vector as the transmission loss coefficient of the pipeline between the target pressure regulating station and the target cleaning station.

[0205] The vector database contains multiple reference feature vectors, each with a corresponding reference transmission loss coefficient. Reference feature vectors are constructed based on historical data. This data includes pipeline characteristics, future environmental information, and sensor data from the pressure regulating stations and pigging stations at both ends of the pipeline.

[0206] The gas company management platform can use the ratio of sensor data from the pressure regulating station to the pigging station in historical data as a reference transmission loss coefficient. For example, the gas company management platform can obtain the output pressure and output temperature of the natural gas output from the pressure regulating station and the input pressure and input temperature of the natural gas input to the pigging station during a preset historical period; determine the ratio of the input pressure of the natural gas input to the pigging station to the output pressure of the natural gas output from the pressure regulating station during the preset historical period as the reference pressure transmission loss coefficient; determine the ratio of the input temperature of the natural gas input to the pigging station to the output temperature of the natural gas output from the pressure regulating station during the preset historical period as the reference temperature transmission loss coefficient; and finally, determine the value obtained by weighted summation of the pressure transmission loss coefficient and the temperature transmission loss coefficient as the reference transmission loss coefficient.

[0207] In some embodiments, the gas company may determine, based on the target feature vector, a reference feature vector that meets a preset condition in a vector database, and determine the reference feature vector that meets the preset condition as the associated feature vector. In some embodiments, the preset condition may include a minimum vector distance from the target feature vector.

[0208] Step 520: Determine recovered pressure energy data based on the future output pressure energy and the transmission loss coefficient.

[0209] For details on future output pressure energy and recovered pressure energy data, please refer to Figure 2 Relevant instructions in step 220.

[0210] In some embodiments, the gas company management platform uses the product of future output pressure energy and the transmission loss coefficient as the recovered pressure energy data.

[0211] Step 530: Send the recovered pressure energy data to the government supervision and management platform, and obtain the backup recovery strategy issued by the government supervision and management platform.

[0212] For instructions on the government regulatory management platform, see Figure 1 See the relevant instructions in .

[0213] The backup recovery strategy is a distribution strategy for recovered pressure energy data. It reflects the external site conditions corresponding to the pigging energy interval and the second energy allocation value. The backup recovery strategy is preset by the government regulatory management platform.

[0214] The pigging energy interval refers to the energy range defined by the upper and lower limits of the first energy allocation value. The first energy allocation value represents the amount of recovered pressure energy data allocated to the target pigging station. Understandably, different pigging operations may require different first energy allocation values. The pigging energy interval can be preset by the government regulatory management platform.

[0215] In some embodiments, the gas company management platform determines the pigging energy interval based on the pigging work plan. For example, the gas company management platform can determine the maximum and average values ​​of the total energy consumption demand determined by the pigging work plan as the upper and lower limits of the pigging energy interval, respectively. For a description of the pigging work plan, see Figure 3 See the relevant instructions in .

[0216] External sites refer to other pigging stations or power generation equipment other than the target pigging station. Power generation equipment can be located within a preset range of the target pigging station. This preset range can be determined by those skilled in the art based on experience. Power generation equipment can be located within the target pigging station or separately.

[0217] Step 540 : Determine the recovery parameters of the target pigging station corresponding to the target pressure regulating station based on the backup recovery strategy.

[0218] In some embodiments, the gas company management platform uses the following steps k1 to k3 to determine the recovery parameters:

[0219] Step k1: determining a first energy allocation value based on the pigging energy interval in the backup recovery strategy and by using a preset rule.

[0220] The preset rule may be to use the middle value or the maximum value of the pigging energy interval as the first energy allocation value.

[0221] Step k2: in response to the recovery pressure energy data being greater than the first energy allocation value, determining a second energy allocation value based on the first energy allocation value and the future output pressure energy, and allocating the second energy allocation value to the external site in the backup recovery strategy.

[0222] In some embodiments, in response to the recovered pressure energy data being greater than the first energy allocation value, the gas company management platform uses the difference between the future output pressure energy and the first energy allocation value as the second energy allocation value.

[0223] Step k3: using the first energy allocation value as the future output pressure energy of the pigging work plan, and re-determining the recovery parameter of the target pigging station corresponding to the target pressure regulating station.

[0224] For instructions on re-determining the recovery parameters of the target pigging station corresponding to the target pressure regulating station based on future output pressure energy, see Figure 2 Relevant instructions in step 220.

[0225] In some embodiments of the present invention, recovery parameters are prioritized to meet the pigging schedule, and excess energy is transferred to other sites to ensure efficient utilization of future output pressure energy.

[0226] In some embodiments, the gas company management platform further determines the recycling parameters using the following steps 541 to 543:

[0227] Step 541 : generating multiple sets of candidate allocation parameters based on the recovery pressure energy data and the pigging energy interval of the backup recovery strategy issued by the government regulatory management platform.

[0228] In some embodiments, the set of candidate allocation parameters includes a first energy allocation value for the target pigging station, a second energy allocation value for the external site, and the external site corresponding to the second energy allocation value.

[0229] The first energy allocation value in the set of candidate allocation parameters may be determined based on the pigging energy interval. For example, the first energy allocation value in the set of candidate allocation parameters may be randomly generated in the pigging energy interval.

[0230] Since the sum of the first energy allocation value and the second energy allocation value in a set of candidate allocation parameters is the recovered pressure energy data, the gas company management platform can use the difference between the recovered pressure energy data and the above-mentioned randomly determined first energy allocation value as the second energy allocation value in a set of candidate allocation parameters.

[0231] The external site corresponding to the second energy allocation value may be randomly selected from a plurality of selectable external sites.

[0232] The multiple selectable external sites can be determined based on the target pigging station using a seventh preset comparison table. The seventh preset comparison table contains a correspondence between the reference target pigging station and the multiple reference selectable external sites. For example, the reference selectable external site can be an external site that can receive recovered pressure energy data from the reference target pigging station. The seventh preset comparison table can be constructed based on prior knowledge or historical data.

[0233] Step 542 : For a set of candidate allocation parameters, determine the estimated abnormal risk and the estimated energy loss rate of the recycling process corresponding to the set of candidate allocation parameters through the recycling model.

[0234] The estimated abnormal risk refers to the risk or probability of an abnormal situation occurring after allocating recovered pressure energy data according to candidate allocation parameters. Abnormal situations can include reports of pipeline leaks from the target pigging station or a neighboring pigging station, or reports of energy shortages or equipment downtime from neighboring power generation equipment. A neighboring pigging station is one within a preset straight-line distance from the target pigging station. A neighboring power generation equipment is one within a preset straight-line distance from the target pigging station. The preset range can be determined by those skilled in the art based on experience.

[0235] The estimated energy loss rate refers to the estimated energy loss rate after allocating the recovered pressure energy data according to the candidate allocation parameters.

[0236] The estimated energy loss rate can be represented by (1 - (total energy utilization / recovered pressure energy data)). The total energy utilization can be the sum of the energy required by the target pigging station, the energy required by adjacent pigging stations, and the energy required by adjacent power generation equipment during a preset future time period. The preset future time period can be predetermined by those skilled in the art based on experience.

[0237] In some embodiments, the recycling model is a machine learning model. For example, the recycling model may include a graph neural network (GNN) model and a recurrent neural network (RNN) model.

[0238] In some embodiments, the input of the recycling model includes a set of candidate allocation parameters and a gas network map, and the output includes the estimated abnormal risk and estimated energy loss rate of the recycling process corresponding to the set of candidate allocation parameters. For a description of the gas network map, see Figure 3 Description in step 310.

[0239] In some embodiments, the recovery model can be trained based on a plurality of second training samples with second training labels.

[0240] In some embodiments, each set of second training samples in the second training samples includes a set of historical distribution parameters and a historical gas network map. For a method of obtaining a historical gas network map, see Figure 4 Related instructions in [1]. Historical allocation parameters can be obtained based on historical data. For example, the gas company management platform determines historical allocation parameters based on the operating power of the pigging station and the operating power of the external site in historical data.

[0241] In some embodiments, the second training label may be the actual abnormal risk and the actual energy loss rate corresponding to the second training sample.

[0242] The actual abnormal risk can be expressed as 0 or 1, where 0 indicates that the second training sample has no abnormal risk, and 0 indicates that the second training sample has an abnormal risk. The actual abnormal risk can be obtained through manual annotation based on historical data. In response to the presence of an abnormality in the historical data, the gas company management platform can determine the presence of an abnormal risk and set the abnormal risk to 1. For a description of abnormal conditions, please refer to the description of step 542.

[0243] The actual energy loss rate can be represented by (1-((actual total energy utilization / actual recovered pressure energy data))). The actual total energy utilization can be the sum of the actual energy required by the target pigging station, the actual energy required by neighboring pigging stations, and the actual energy required by neighboring power generation equipment during a historical preset period. The actual recovered pressure energy data refers to the future output pressure energy actually recovered and utilized by the target pigging station.

[0244] In some embodiments, the gas company management platform may calculate the actual energy loss rate using the above formula based on historical data.

[0245] In some embodiments, the gas company management platform can use a similar training method as the feature prediction model to obtain a trained recovery model. For the training process of the feature prediction model, please refer to Figure 4 See the relevant instructions in .

[0246] Step 543 : Determine the recovery parameters based on the estimated abnormal risks and estimated energy loss rates corresponding to the multiple sets of candidate allocation parameters.

[0247] In some embodiments, the gas company management platform performs a weighted summation of the estimated abnormal risk and the estimated energy loss rate corresponding to each set of candidate allocation parameters to obtain multiple first values; sorts the multiple first values ​​to determine the smallest first value; and uses the first energy allocation value of the candidate allocation parameter corresponding to the smallest first value as the future output pressure energy to determine the recovery parameter. The weights of the estimated abnormal risk and the estimated energy loss rate are numbers greater than 0 and less than 1, and can be preset by those skilled in the art based on experience. For example, the weight of the estimated abnormal risk can be set to be greater than the weight of the estimated energy loss rate.

[0248] For instructions on determining recovery parameters based on future output pressure energy, see Figure 2 Instructions in step 220.

[0249] In some embodiments of the present invention, multiple sets of candidate allocation parameters are generated based on the recovered pressure energy data and the pigging energy range. Then, through the recovery model, candidate allocation parameters with lower estimated abnormal risks and estimated energy loss rates are found to determine the optimal recovery parameters and control the operation of the pigging equipment and / or energy storage equipment at the target pigging station.

Claims

1. A smart gas network energy recovery Internet of Things system, characterized by: Including government supervision management platform, government supervision sensor network platform, government supervision object platform, gas company sensor network platform and gas equipment object platform; The government supervision object platform includes the gas company management platform; The gas company management platform and the government supervision management platform are respectively configured on different servers; The gas company management platform and the government supervision management platform exchange data via the government supervision sensor network platform; The gas company management platform exchanges data with the gas equipment object platform through the gas company sensor network platform; The government regulatory sensor network platform and the gas company sensor network platform are operated based on data communication equipment; The gas equipment object platform includes a pressure regulating station and a pigging station. The pressure regulating station includes pressure regulating equipment, and the pigging station includes pigging equipment and energy storage equipment. The gas company management platform is configured to: Determining future pressure regulation parameters and future output pressure energy based on a future external information sequence, current pressure regulation parameters, and sensor data information of a target pressure regulating station; the future pressure regulation parameters include pressure regulation parameters for at least one unit time interval within a future preset period of time, the length of the at least one unit time interval being determined based on characteristics of a target pipeline between the target pressure regulating station and a target pigging station; determining, based on the future output pressure energy, a recovery parameter of the target pigging station corresponding to the target pressure regulating station, the recovery parameter comprising at least one of a pigging parameter and an energy storage device parameter; generating a recycling instruction based on the recycling parameters; as well as The recovery instruction is sent to the target pigging station, and the pigging equipment and / or the energy storage equipment of the target pigging station are controlled to operate.

2. The Internet of Things system according to claim 1, characterized in that The length of the at least one unit time interval is also related to the first pigging probability of the target pigging station.

3. The Internet of Things system according to claim 1, characterized in that: The gas company management platform is further configured to: Determining future gas change characteristics of the target pressure regulating station based on the future external information sequence, the current pressure regulating parameters of the target pressure regulating station, and the sensor data information; Determining the future pressure regulation parameters and the future output pressure energy based on the future gas change characteristics and the preset pressure regulation plan; determining recovered pressure energy data based on the future output pressure energy; as well as The recovery parameter of the target pigging station corresponding to the target pressure regulating station is determined based on the recovered pressure energy data.

4. The Internet of Things system according to claim 3, characterized in that: The gas equipment object platform includes a plurality of the pressure regulating stations and at least one pigging station; the gas company management platform is further configured as follows: Constructing a gas pipeline network map based on site characteristics of the plurality of pressure regulating stations and at least one pigging station, and pipeline characteristics of connecting pipelines between the pressure regulating stations and the pigging stations; the site characteristics include current pressure regulating parameters and sensor data information of the pressure regulating stations, and the future external information sequence; and Based on the gas network map, the future gas change characteristics of the target pressure regulating station are determined through a feature prediction model, and the feature prediction model is a machine learning model.

5. The Internet of Things system according to claim 3, characterized in that: The gas company management platform is further configured to: adjusting the future pressure regulation parameters based on the future gas variation characteristics, the preset pressure regulation plan, and the future pressure energy demand of the target pigging station; The future pressure energy demand is determined by the following method: Determining the total energy consumption requirement based on the pigging work plan for the future preset time period; and Based on the total energy consumption requirement, a future pressure energy requirement is determined.

6. The Internet of Things system according to claim 1, characterized in that: The gas company management platform is further configured to: determining a transmission loss coefficient based on the target pipeline characteristics and future environmental information of the pipeline between the target pressure regulating station and the target pigging station; determining recovered pressure energy data based on the future output pressure energy and the transmission loss coefficient; Sending the recovered pressure energy data to the government supervision and management platform, and obtaining a backup recovery strategy issued by the government supervision and management platform; as well as Based on the backup recovery strategy, recovery parameters of the target pigging station corresponding to the target pressure regulating station are determined.

7. A smart gas network energy recovery method, characterized in that: The method is executed by a gas company management platform of a smart gas network energy recovery Internet of Things system, wherein the Internet of Things system includes the gas company management platform, a gas company sensor network platform, and a gas equipment object platform; The gas company management platform exchanges data with the gas equipment object platform through the gas company sensor network platform; The gas equipment object platform includes a pressure regulating station and a pigging station. The pressure regulating station includes pressure regulating equipment, and the pigging station includes pigging equipment and energy storage equipment. The method comprises: Determining future pressure regulation parameters and future output pressure energy based on a future external information sequence, current pressure regulation parameters, and sensor data information of a target pressure regulating station; the future pressure regulation parameters include pressure regulation parameters for at least one unit time interval within a future preset period of time, the length of the at least one unit time interval being determined based on characteristics of a target pipeline between the target pressure regulating station and a target pigging station; determining, based on the future output pressure energy, a recovery parameter of a target pigging station corresponding to the target pressure regulating station, the recovery parameter comprising at least one of a pigging parameter and an energy storage device parameter; generating a recycling instruction based on the recycling parameters; and The recovery instruction is sent to the target pigging station, and the pigging equipment and / or the energy storage equipment of the target pigging station are controlled to operate.

8. The method according to claim 7, characterized in that The smart gas pipeline network energy recovery IoT system also includes a government supervision management platform, a government supervision sensor network platform and a government supervision object platform; The government supervision object platform includes the gas company management platform; The gas company management platform and the government supervision management platform are respectively configured on different servers; The gas company management platform and the government supervision management platform exchange data via the government supervision sensor network platform; The government regulatory sensor network platform and the gas company sensor network platform operate based on data communication equipment.

9. The method according to claim 7, characterized in that Determining the future pressure regulating parameters and the future output pressure based on the future external information sequence and the current pressure regulating parameters and sensor data information of the target pressure regulating station can include: Determining future gas change characteristics of the target pressure regulating station based on the future external information sequence, the current pressure regulating parameters of the target pressure regulating station, and the sensor data information; and Determining the future pressure regulation parameters and the future output pressure energy based on the future gas change characteristics and the preset pressure regulation plan; The determining, based on the future output pressure energy, the recovery parameters of the target pigging station corresponding to the target pressure regulating station includes: Determining recovered pressure energy data based on the future produced pressure energy; and The recovery parameter of the target pigging station corresponding to the target pressure regulating station is determined based on the recovered pressure energy data.

10. The method according to claim 9, characterized in that The gas equipment object platform includes a plurality of the pressure regulating stations and at least one pigging station; determining the future gas change characteristics of the target pressure regulating station based on the future external information sequence, the current pressure regulating parameters of the target pressure regulating station, and the sensor data information includes: Constructing a gas pipeline network map based on site characteristics of the plurality of pressure regulating stations and at least one pigging station, and pipeline characteristics of connecting pipelines between the pressure regulating stations and the pigging stations; the site characteristics include current pressure regulating parameters and sensor data information of the pressure regulating stations, and the future external information sequence; and Based on the gas network map, the future gas change characteristics of the target pressure regulating station are determined through a feature prediction model, and the feature prediction model is a machine learning model.

Images