Gas mixing station equipment monitoring method based on smart gas and Internet of Things system

Abstract

The invention provides a gas mixing station equipment monitoring method based on intelligent fuel gas and an Internet of Things system.The method is executed by a fuel gas company management platform of the gas mixing station equipment monitoring Internet of Things system based on the intelligent fuel gas, and the method comprises the steps that gas mixing parameters are determined based on state data and the mixing proportion of gas to be mixed; acquiring gas component data; in response to the condition that the gas component data does not meet the preset condition, performing at least one round of iterative update on the gas mixing parameters based on the gas component data, and determining updated gas mixing parameters; gas transportation parameters are determined based on the output component data of the output gas and the downstream pipeline data; and updating a gas database based on the gas mixing parameters and / or the updated gas mixing parameters, the gas transportation parameters, the downstream pipeline data, the output component data and the receiving component data of the mixed gas received by the downstream gas pipeline. By means of the method, the uniformity of the mixed gas can be monitored in time, and it is ensured that the mixed gas is kept uniform in the mixing process and the transportation process.

Description

Technical Field

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

[0002] The gas mixing station is equipped with storage equipment, gasification equipment and gas mixing equipment, which can convert liquid natural gas or petroleum gas into gaseous state, and mix it with air or other combustible gases in a certain proportion to prepare mixed gas, and finally supply gas to users. However, in the process of mixing gas at the gas mixing station, there is often a problem of uneven mixed gas, and in the process of transporting mixed gas, there may be a problem that the mixed gas becomes uneven again, which in turn affects the gas use of users.

[0003] Therefore, it is hoped to provide a gas mixing station equipment monitoring method and an Internet of Things system based on smart gas, which can timely monitor the uniformity of the mixed gas and ensure that the mixed gas remains relatively uniform during the mixing and transportation processes. Summary of the invention

[0004] The invention content includes a method for monitoring mixing station equipment based on smart gas, which is executed by a gas company management platform based on a smart gas mixing station equipment monitoring Internet of Things system, and the method includes: obtaining state data of the gas to be mixed through a gas company sensor network platform and a device object platform, and the device object platform includes a state monitoring device; determining a mixing parameter based on the state data and a mixing ratio; generating a mixing instruction based on the mixing parameter, and sending the mixing instruction to the device object platform, and the device object platform also includes a mixing device; in the process of the mixing device mixing gases based on the mixing instruction, obtaining gas composition data of at least one preset point of the mixing device; responding to the gas composition data If the preset condition is not met, the gas mixing parameters are iteratively updated for at least one round based on the gas composition data to determine the updated gas mixing parameters; an updated gas mixing instruction is generated based on the updated gas mixing parameters, and the updated gas mixing instruction is sent to the gas mixing device, and the gas mixing device performs gas mixing based on the updated gas mixing instruction to obtain output gas; based on the output component data of the output gas and the downstream pipeline data, the gas transportation parameters are determined through a gas database, and the gas database is configured in a government data center; and based on the gas mixing parameters and / or the updated gas mixing parameters, the gas transportation parameters, the downstream pipeline data, the output component data, and the receiving component data of the mixed gas received by the downstream gas pipeline, the gas database is updated.

[0005] The invention content includes an Internet of Things system for monitoring mixing station equipment based on smart gas, the Internet of Things system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform and an equipment object platform, the government safety supervision management platform includes a government data center, the government safety supervision object platform includes a gas company management platform; the gas company management platform is configured to: obtain state data of the gas to be mixed through the gas company sensor network platform and the equipment object platform, the equipment object platform includes a state monitoring device; determine the mixing parameters based on the state data and the mixing ratio; generate a mixing instruction based on the mixing parameters, and send the mixing instruction to the equipment object platform, the equipment object platform also includes a mixing device; in the mixing device, perform the gas mixing process based on the mixing instruction During the process, gas composition data of at least one preset point of the gas mixing device is obtained; in response to the gas composition data not satisfying the preset condition, the gas mixing parameters are iteratively updated for at least one round based on the gas composition data to determine the updated gas mixing parameters; an updated gas mixing instruction is generated based on the updated gas mixing parameters, and the updated gas mixing instruction is sent to the gas mixing device, and the gas mixing device performs gas mixing based on the updated gas mixing instruction to obtain output gas; based on the output composition data of the output gas and the downstream pipeline data, a gas transportation parameter is determined through a gas database, and the gas database is configured to be obtained in the government data center; and based on the gas mixing parameters and / or the updated gas mixing parameters, the gas transportation parameters, the downstream pipeline data, the output composition data, and the receiving composition data of the mixed gas received by the downstream gas pipeline, the gas database is updated.

[0006] The beneficial effects of the present invention include but are not limited to: (1) Based on the intelligent gas mixing station equipment monitoring Internet of Things system, an information operation closed loop can be formed between various functional platforms to realize the informatization and intelligence of gas mixing supervision. (2) Based on the state data and mixing ratio of the gas to be mixed, the pretreatment parameters of the gas to be mixed are determined, and the mixed gas can be preliminarily treated before mixing, which is conducive to the subsequent better mixing effect of the gas to be mixed. (3) By timely monitoring the changes in the composition of the mixed gas during the mixing process, the mixing parameters are continuously updated iteratively to ensure that the mixed gas remains relatively uniform during the mixing process, so that the mixed gas achieves a better mixing effect. Considering the transportation conditions of the output gas in the downstream gas pipeline and the actual conditions of the downstream gas pipeline, more reasonable gas transportation parameters can be determined so that the output gas remains relatively uniform in the downstream gas pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a schematic diagram of the platform structure of the Internet of Things system for monitoring the mixing station equipment based on smart gas as shown in some embodiments of this specification; Figure 2 is an exemplary flow chart of a method for monitoring a gas mixing station device based on smart gas according to some embodiments of this specification; Figure 3 is a schematic diagram of a process for determining and updating mixing gas parameters according to some embodiments of this specification; Figure 4 is an exemplary schematic diagram of a transportation model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0008] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings do not represent all implementation methods.

[0009] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the words can be replaced by other expressions.

[0010] Unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0011] When the operations are performed according to the step description in the embodiments of this specification, unless otherwise specified, the order of the steps is interchangeable, the steps may be omitted, and other steps may be included in the operation process.

[0012] Figure 1 It is a schematic diagram of the platform structure of the Internet of Things system for monitoring mixing station equipment based on smart gas as shown in some embodiments of this specification.

[0013] like Figure 1 As shown, the smart gas-based gas mixing station equipment monitoring Internet of Things system 100 includes a government safety supervision management platform 110, a government safety supervision sensor network platform 120, a government safety supervision object platform 130, a gas company sensor network platform 140 and an equipment object platform 150.

[0014] The government security supervision management platform 110 refers to a comprehensive management platform for government management information. In some embodiments, the government security supervision management platform 110 includes a government data center 111 .

[0015] The government data center 111 refers to a data center for storing data of the smart gas-based gas mixing station equipment monitoring Internet of Things system 100. For example, the government data center stores a gas database. The gas database can be a database management system that supports high concurrent access, such as MySQL, PostgreSQL, or Oracle.

[0016] The government security supervision sensor network platform 120 refers to a platform for comprehensive management of government sensor information. In some embodiments, the government security supervision sensor network platform interacts with the government security supervision object platform and the government security supervision management platform. The government security supervision sensor network platform can be configured as a communication network or a gateway, etc.

[0017] The government safety supervision object platform 130 refers to a platform for generating government supervision information and controlling information execution. In some embodiments, the government safety supervision object platform 130 includes a gas company management platform 131. The gas company management platform 131 refers to a comprehensive management platform for gas company information. The gas company management platform 131 can be configured to process data of the smart gas-based mixing station equipment monitoring Internet of Things system 100.

[0018] The gas company sensor network platform 140 refers to a platform for comprehensively managing the sensor information of the gas company. In some embodiments, the gas company sensor network platform can be configured as a communication network or a gateway, etc. The gas company sensor network platform interacts with the government safety supervision object platform and the device object platform.

[0019] The device object platform 150 refers to a functional platform for sensing information generation and controlling information execution. In some embodiments, the device object platform may include at least one of a state monitoring device, a gas mixing device, and a component detection device. The state monitoring device and the gas mixing device are deployed in the gas mixing station. The component detection device is deployed in the gas mixing station, the gas mixing device, and / or the gas pipeline.

[0020] A gas mixing station refers to a gas supply facility in a gas output pipeline network that mixes gaseous petroleum gas with air and / or other combustible gases to form a mixed gas and supplies gas to users. Users refer to residents and / or enterprises that use gas.

[0021] The state monitoring device is used to collect state data of the gas to be mixed. In some embodiments, the state monitoring device may include a temperature sensor, a pressure sensor, and multiple component sensors, etc. A component sensor is used to collect component data of a gas to be mixed.

[0022] Gas mixing equipment refers to equipment used to mix the gases to be mixed.

[0023] The component detection device refers to a device used to obtain gas component data of the mixed gas. In some embodiments, the component detection device can be deployed at at least one preset point of the gas mixing device, and can also be deployed at the gas outlet of the gas mixing station, etc.

[0024] In some embodiments, the component detection device may include a sampling device and a component sensor. The sampling device refers to a device for sampling the mixed gas. The component sensor refers to a sensor for detecting gas component data. The component sensor can detect the mixed gas sampled by the sampling device to obtain gas component data. The mixed gas refers to the gas obtained by mixing the gas to be mixed by the gas mixing device.

[0025] In some embodiments, the smart gas mixing station equipment monitoring IoT system 100 may further include a server. The server may process information and / or data related to the smart gas mixing station equipment monitoring IoT system 100 to perform one or more functions described in this application.

[0026] In some embodiments, the server may include a processor, a memory, a storage device, and a network. The storage device may include a high-speed SSD to support database operations and data backup. The processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), etc., or any combination thereof.

[0027] For detailed description of the above, please refer to Figures 2 to 4 Related description.

[0028] In some embodiments of the present specification, the Internet of Things system 100 for monitoring the mixing station equipment based on smart gas can form an information operation closed loop between various functional platforms to realize the informatization and intelligence of gas mixing supervision.

[0029] Figure 2 It is an exemplary flow chart of a method for monitoring a gas mixing station device based on smart gas according to some embodiments of this specification. In some embodiments, process 200 is executed by a gas company management platform of a gas mixing station device monitoring Internet of Things system based on smart gas.

[0030] For more information about the various platforms of the IoT system for monitoring gas mixing station equipment based on smart gas, please refer to Figure 1 The corresponding description.

[0031] like Figure 2 As shown, process 200 includes the following steps: Step 210: Acquire the state data of the gas to be mixed through the gas company's sensor network platform and the device object platform.

[0032] The gas to be mixed refers to the gas to be mixed by the gas mixing device. In some embodiments, the gas to be mixed may include at least one of gaseous petroleum gas, hydrogen, etc.

[0033] The state data refers to data related to the state of the gas to be mixed. In some embodiments, the state data may include one or more groups, and each group of state data corresponds to a gas to be mixed.

[0034] In some embodiments, a set of state data includes at least one of temperature, pressure and composition data of a gas to be mixed, etc. Composition data refers to data related to the composition of the gas to be mixed.

[0035] In some embodiments, the component data may include at least one of the purity and impurity information of the gas to be mixed. Purity refers to the content of the main component of the gas to be mixed. Impurity information refers to the content of other components in the gas to be mixed except the main component.

[0036] In some embodiments, the gas company management platform can obtain the status data of the gas to be mixed through the gas company sensor network platform and the device object platform.

[0037] Step 220, determining the mixing parameters based on the state data and the mixing ratio.

[0038] The mixing ratio refers to the ideal ratio of multiple gases to be mixed when mixed. In some embodiments, the mixing ratio includes the volume ratio of the multiple gases to be mixed. In some embodiments, the mixing ratio of the gases to be mixed can be pre-set based on industry specifications for gas transportation.

[0039] The mixing parameter refers to the working parameter of the gas mixing device for mixing gas. In some embodiments, the mixing parameter may include at least one of the pressure, temperature and flow rate when the gas mixing device is working.

[0040] In some embodiments, the gas company management platform can determine the gas mixing parameters in a variety of ways based on the state data and the mixing ratio. For example, the gas company management platform can construct a gas mixing vector based on the state data and the mixing ratio of the gas to be mixed. The gas company management platform matches the reference gas mixing vector that is the same as the gas mixing vector in the gas database, and determines the actual gas mixing parameters in the historical gas mixing record corresponding to the reference gas mixing vector as the current gas mixing parameters. The gas mixing vector refers to a feature vector constructed based on the state data and the mixing ratio of the gas to be mixed. The actual gas mixing parameters refer to the historical gas mixing parameters ultimately used by the gas mixing equipment in the historical gas mixing record.

[0041] In some embodiments, the gas company management platform can obtain the gas database through the government data center.

[0042] In some embodiments, the gas company management platform can build a gas database based on historical gas mixing records. The gas database may include multiple historical gas mixing records and multiple vectors corresponding to each historical gas mixing record. In some embodiments, the multiple vectors may include a reference gas mixing vector. The reference gas mixing vector refers to a feature vector built by the gas company management platform based on historical state data and mixing ratio.

[0043] Historical gas mixing records refer to data related to gas mixing performed by gas mixing equipment at historical times. In some embodiments, the gas company management platform may record historical status data and mixing ratios, historical gas mixing parameters, historical output component data, historical downstream pipeline data, historical gas transportation parameters, historical receiving component data, historical round gas data, and historical compliance expectations corresponding to gas mixing equipment at historical times as a historical gas mixing record, and include the historical gas mixing record in the gas database. For instructions on output component data, downstream pipeline data, gas transportation parameters, round gas data, compliance expectations, and receiving component data, please refer to the relevant description below.

[0044] In some embodiments, the gas company management platform may determine pre-processing parameters of the gas to be mixed based on the state data and the mixing ratio, and determine the gas mixing parameters based on the mixing ratio and the processing state data.

[0045] The pretreatment parameters refer to the parameters that the gas mixing device uses to pretreatment the gas to be mixed before mixing the gas to be mixed. In some embodiments, the pretreatment may include heating and pressurizing the gas to be mixed. The pretreatment parameters may include the temperature and pressure values ​​that need to be increased or decreased.

[0046] In some embodiments, the gas company management platform may determine the pre-processing parameters of the gas to be mixed in a variety of ways based on the state data and the mixing ratio.

[0047] For example, the gas company management platform can determine the pre-processing parameters of the gas to be mixed through the following steps: S11, determining a reference state range based on historical gas mixing records and / or mixing ratios.

[0048] The reference state range refers to the range of ideal state data of the gas to be mixed. The reference state range may include one or more groups of data, each group of data corresponding to state data of a gas to be mixed. In some embodiments, the reference state range may be preset based on prior experience.

[0049] In some embodiments, the gas company management platform can query multiple historical gas mixing records based on the status data and the mixing ratio, and find historical gas mixing records in which the mixed gas meets the mixing ratio during the gas mixing process, and determine the range of status data corresponding to such historical gas mixing records as the reference status range.

[0050] In some embodiments, the gas company management platform can determine the reference state range of multiple gases to be mixed based on historical gas mixing records. For example, for each of the multiple gases to be mixed, the gas company management platform can select a historical gas mixing record in which the number of iterations used to update the mixing parameters is less than a preset number threshold, and the reference state range is formed based on the maximum and minimum values ​​of multiple historical state data in such historical gas mixing records. The maximum values ​​of multiple historical state data constitute the upper limit of the reference state range, and the minimum values ​​constitute the lower limit of the reference state range. The preset number threshold can be pre-set based on historical experience. For instructions on determining the updated mixing parameters, please refer to the relevant description below.

[0051] In some embodiments, when the state data of a gas to be mixed is within the corresponding reference state range, the gas company management platform may determine that the gas to be mixed does not need to be preprocessed, and set the preprocessing parameters to empty or zero.

[0052] S12, determining a state difference based on a reference state range.

[0053] In some embodiments, when the state data of the gas to be mixed is outside the corresponding reference state range, the gas company management platform can calculate the state difference based on the state data and the reference state range. The state difference refers to the difference between the state data and the center value of the reference state range. The state difference may include one or more groups of data, each group of data corresponding to a gas to be mixed.

[0054] S13, determining preprocessing parameters based on the state difference.

[0055] In some embodiments, the gas company management platform can determine the pre-processing parameters based on the state difference. For example, if the state difference of the temperature of a gas to be mixed is -4°C, the temperature adjustment temperature of the gas to be mixed is increased by 4°C to raise the temperature of the gas to be mixed to the same as the center value of the state range.

[0056] Processing state data refers to the state data of the gas to be mixed after pre-processing. The processing state data can be obtained through various component sensors deployed in the gas mixing station.

[0057] In some embodiments, the gas company management platform can determine the gas mixing parameters by vector matching based on the mixing ratio and the processing state data. The process of the gas company management platform determining the gas mixing parameters based on the mixing ratio and the processing state data is similar to the process of determining the gas mixing parameters based on the mixing ratio and the state data. The implementation process can refer to the process of determining the gas mixing parameters above.

[0058] In some embodiments of the present specification, pretreatment parameters of the gas to be mixed are determined based on the state data and mixing ratio of the gas to be mixed, and the gas to be mixed can be preliminarily treated before mixing, which is beneficial for achieving a better mixing effect of the gas to be mixed later.

[0059] Step 230: Generate a mixing instruction based on the gas mixing parameters, and send the mixing instruction to the device object platform.

[0060] The mixing instruction refers to a related instruction for instructing the gas mixing device to mix the gas to be mixed. In some embodiments, the gas company management platform can generate the mixing instruction based on the gas mixing parameters and the gas mixing device corresponding to the gas mixing parameters.

[0061] In some embodiments, the gas company management platform can generate a mixing instruction based on the gas mixing parameters, send the mixing instruction to the gas mixing device corresponding to the mixing instruction in the device object platform, and then control the gas mixing device to mix the gas based on the mixing instruction.

[0062] Step 240 , when the gas mixing device performs gas mixing based on the mixing instruction, gas composition data of at least one preset point of the gas mixing device is obtained.

[0063] The gas composition data refers to data related to the composition of the mixed fuel gas. In some embodiments, the gas composition data may include the content of each gas component in the mixed fuel gas.

[0064] In some embodiments, the gas composition data may include multiple sets of data, each set of data may correspond to gas composition data of at least one preset point at a sampling time point. The sampling time point may be preset based on historical experience.

[0065] The preset point refers to the point where the gas mixing device performs gas sampling. In some embodiments, the preset point may include multiple points, and may be pre-set based on detection requirements and / or historical experience.

[0066] In some embodiments, the gas composition data can be obtained by a composition detection device deployed at at least one preset point of the gas mixing device and uploaded to the device object platform. For more information about the composition detection device, see Figure 1 The above description.

[0067] Step 250 , in response to the gas composition data not satisfying a preset condition, performing at least one round of iterative updating on the gas mixing parameters based on the gas composition data to determine and update the gas mixing parameters.

[0068] The preset condition refers to the condition used to determine whether the gas composition data meets the mixing ratio. For example, the preset condition may be that the composition difference between the gas composition data at multiple preset points and the theoretical composition data is less than or equal to the preset difference threshold. The theoretical composition data refers to the content of each gas component in the mixed fuel gas calculated based on the mixing ratio. The composition difference is represented by the absolute value of the difference between the gas composition data and the theoretical composition data. The difference between the gas composition data and the theoretical composition data may include the difference between each item of data in the gas composition data and the data of the same item in the theoretical composition data. The preset difference threshold may include multiple sub-difference thresholds, each of which may correspond to one item of data in the composition difference. The preset difference threshold may be determined based on prior experience.

[0069] The updated mixing parameters refer to the mixing parameters obtained after iterative updating.

[0070] In some embodiments, in response to the gas composition data not satisfying the preset condition, the gas company management platform may determine to update the gas mixing parameters in a variety of ways based on the gas composition data. For example, in response to the gas composition data not satisfying the preset condition, the gas company management platform may perform at least one round of iterative updates on the gas mixing parameters based on the gas composition data to determine to update the gas mixing parameters. The gas composition data not satisfying the preset condition may be that the difference between the gas composition data of any one of the multiple preset points and the theoretical composition data is greater than a preset difference threshold.

[0071] Exemplarily, one round of iterative updating in the at least one round of iterative updating includes: The gas company management platform can generate a mixing instruction based on the gas mixing parameters and / or the round gas mixing parameters and send it to the gas mixing device to control the gas mixing device to mix the gas based on the mixing instruction. The gas mixing parameters are the gas mixing parameters during the first round of iterative updates. The round gas mixing parameters are the gas mixing parameters during the second and subsequent rounds of iterative updates.

[0072] In response to the round gas data not satisfying the preset conditions, based on the gas mixing parameters or the round gas mixing parameters and the composition difference between the round gas data and the theoretical composition data, the gas mixing parameters and / or the round gas mixing parameters are adjusted according to the preset rules, the round gas mixing parameters for the next round of iterative update are determined, and the next round of iterative update is performed. The round gas mixing parameters are the gas mixing parameters according to which the gas mixing device of the next round performs gas mixing. The composition difference refers to the difference between the round gas data and the theoretical composition data. The difference between the round gas data and the theoretical composition data may include the difference between each item of data in the round gas data and the data of the same item in the theoretical composition data.

[0073] The round gas data is the gas composition data acquired in the current round. In some embodiments, the round gas data may include multiple groups of data, each group of data corresponding to the round gas data at at least one preset point at a sampling time point.

[0074] In response to the round gas data satisfying a preset condition, the iterative update ends, and the gas mixing parameter and / or the round gas mixing parameter is determined as the updated gas mixing parameter.

[0075] In the next round of iterative update, the gas company management platform can generate a mixing instruction based on the round gas mixing parameters and send it to the gas mixing equipment, control the gas mixing equipment to mix the gas based on the mixing instruction, and repeat the above steps until the iterative update is completed, and determine the round gas mixing parameters as the updated gas mixing parameters.

[0076] The preset rule refers to a rule for adjusting the mixing parameters or the round mixing parameters. In some embodiments, the preset rule may include a corresponding relationship between the component difference and the adjustment parameter. The adjustment parameter may include the adjustment direction and adjustment amplitude of the state data of the gas to be mixed.

[0077] In some embodiments, the gas company management platform can determine preset rules based on historical gas mixing records. For example, the gas company management platform can construct preset rules based on the gas mixing parameters before multiple historical iteration updates and the round gas mixing parameters after historical iteration updates in the historical gas mixing records, as well as the composition difference after the gas mixing equipment uses the updated round gas mixing parameters to mix the gas. For example, the temperature in the gas mixing parameters before the update is 20°C, the temperature in the updated round gas mixing parameters is 24°C, and the composition difference between the round gas data and the theoretical composition data changes from -5L / mol before the update to 0. It can be determined that the adjustment direction corresponding to the composition difference is to increase the temperature, and the adjustment range is 4°C.

[0078] In some embodiments, the gas company management platform can determine the next round of iterative update of the gas mixing parameters through the gas mixing model. For an explanation of this part, please refer to Figure 3 and its related description.

[0079] Step 260: Generate an updated gas mixing instruction based on the updated gas mixing parameter, and send the updated gas mixing instruction to the gas mixing device. The gas mixing device mixes the gas based on the updated gas mixing instruction to obtain output gas.

[0080] In some embodiments, if the gas composition data continues to meet preset conditions, the gas company management platform can generate a mixing instruction based on the gas mixing parameters and send the mixing instruction to the gas mixing device. The gas mixing device mixes the gas based on the mixing instruction to obtain output gas.

[0081] The updated gas mixing instruction refers to the updated mixing instruction based on the updated gas mixing parameters. In some embodiments, the gas company management platform can generate an updated gas mixing instruction based on the updated gas mixing parameters, send the updated gas mixing instruction to the corresponding gas mixing device in the device object platform, and the gas mixing device mixes the gas based on the updated gas mixing instruction to obtain the output gas.

[0082] Output gas refers to the gas output after the gas mixing station has completed mixing the gas to be mixed.

[0083] Step 270, based on the output composition data of the output gas and the downstream pipeline data, determine the gas transportation parameters through the gas database.

[0084] Output component data refers to component data of the output gas. In some embodiments, the output component data may include the content of various gas components in the output gas. In some embodiments, the output component data may be obtained by a component detection device deployed at the gas outlet of the gas mixing station and uploaded to the device object platform.

[0085] The received component data refers to the component data of the output gas received by the downstream gas pipeline. In some embodiments, the received component data may include the contents of various gas components in the output gas received by the downstream gas pipeline.

[0086] In some embodiments, the received component data may be acquired by a component detection device disposed in a downstream gas pipeline and uploaded to the device object platform.

[0087] Downstream pipeline data refers to data related to the downstream gas pipeline. In some embodiments, the downstream pipeline data may include at least one of the length, inner diameter change, pipeline structure, cleanliness, etc. of the downstream gas pipeline. The inner diameter change refers to the change of the inner diameter at different positions of the downstream gas pipeline. The pipeline structure refers to the structure of the downstream gas pipeline. For example, the shape, bends and branches of the downstream gas pipeline. The cleanliness can be represented by the amount of impurities, etc.

[0088] In some embodiments, the downstream pipeline data may include one or more groups of data, each group of data corresponding to a section of the downstream gas pipeline.

[0089] In some embodiments, the downstream pipeline data may be uploaded by the gas company to the gas company management platform.

[0090] Gas transportation parameters refer to parameters related to the delivery of output gas to multiple downstream gas pipelines. In some embodiments, the gas transportation parameters may include at least one of the parameters such as pressure, flow rate and flow rate of the output gas delivered by the gas mixing station to multiple downstream gas pipelines. The downstream gas pipeline refers to the gas pipeline through which the gas mixing station transports the output gas to the destination. The destination may include downstream stations, etc. For an explanation of the downstream stations, please refer to Figure 4 and its related description.

[0091] In some embodiments, the gas company management platform can determine the gas transportation parameters in a variety of ways based on the output component data of the output gas and the downstream pipeline data. For example, the gas company management platform can construct a transportation vector based on the output component data of the output gas and the downstream pipeline data, and match the reference transportation vector that meets the first matching condition in the gas database. The gas company management platform extracts the historical output component data and the historical received component data corresponding to the reference transportation vector, takes the reference transportation vector with the smallest difference between the historical output component data and the historical received component data as the target vector, and takes the historical gas transportation parameters corresponding to the target vector as the current gas transportation parameters. The transportation vector refers to a feature vector constructed based on the output component data of the output gas and the downstream pipeline data. For an explanation of the received component data, see step 280 and its related description.

[0092] The difference between the historical output component data and the historical received component data may include the difference between each item of data in the historical output component data and the same item of data in the historical received component data. It can be understood that the smaller the difference between the historical output component data and the historical received component data, the more uniform the mixed gas is during transportation, and the more reasonable the corresponding gas transportation parameters are.

[0093] The first matching condition may include that the similarity exceeds a first similarity threshold. The similarity of the vectors is negatively correlated to the distance of the vectors. The distance of the vectors includes the Euclidean distance, etc. The first similarity threshold may be preset based on historical experience.

[0094] In some embodiments, the multiple vectors corresponding to the historical gas mixing records in the gas database may include a reference transport vector. The gas company management platform may construct a reference transport vector based on the historical output component data and the historical downstream pipeline data in the historical gas mixing records. The reference transport vector refers to a feature vector constructed based on the historical output component data and the historical downstream pipeline data. For more information about the gas database, see step 220 and its related description.

[0095] In some embodiments, the gas company management platform may determine the component impact data through the gas database based on the output component data of the output gas and the downstream pipeline data; and determine the gas transportation parameters based on the component impact data and the output component data.

[0096] The composition impact data refers to the data that characterizes the impact of the downstream gas pipeline on the output gas composition after the output gas is transported under the gas transportation parameters of the gas mixing station. The larger the composition impact data, the greater the impact of the downstream gas pipeline on the output gas composition.

[0097] In some embodiments, the composition influence data may include one or more sets of data, each set of data corresponding to a gas transportation parameter.

[0098] In some embodiments, the gas company management platform can determine the component impact data in a variety of ways based on the output component data and downstream pipeline data of the output gas. For example, the gas company management platform can query the gas database for historical output component data and historical downstream pipeline data that are the same as the output component data and downstream pipeline data based on the output component data and downstream pipeline data of the output gas, and determine the historical component impact data corresponding to the historical output component data and historical downstream pipeline data as the current component impact data.

[0099] In some embodiments, the gas company management platform may determine downstream pipeline characteristics based on downstream pipeline data, and determine component impact data through a gas database based on the downstream pipeline characteristics and output component data.

[0100] Downstream pipeline characteristics refer to data characterizing the structural conditions of the downstream pipeline. In some embodiments, the downstream pipeline characteristics may include at least one of the lengths of pipelines with different inner diameters in the downstream gas pipeline, the number of branches and turns of the downstream gas pipeline, etc.

[0101] In some embodiments, the gas company management platform may obtain downstream pipeline characteristics based on statistics of downstream pipeline data of historical gas mixing records in the gas database.

[0102] In some embodiments, the gas company management platform can construct an influence vector based on the downstream pipeline characteristics and output component data, match multiple reference influence vectors that meet the second matching condition in the gas database, and determine the historical component influence data and historical gas transportation parameters in the historical gas mixing record corresponding to each reference influence vector in the multiple reference influence vectors as a set of component influence data. The influence vector refers to a feature vector constructed based on the downstream pipeline characteristics and output component data. The second matching condition may include a similarity exceeding a second similarity threshold. The second similarity threshold may be pre-set based on historical experience. The historical component influence data may be represented by the difference between the historical output component data and the historical received component data. For an explanation of the difference between the historical output component data and the historical received component data, see step 270 and its related description.

[0103] In some embodiments, the gas database may include a reference influence vector corresponding to a plurality of vectors in the historical gas mixing record. The gas company management platform may construct a reference influence vector based on the historical downstream pipeline characteristics and the historical output component data in the historical gas mixing record. The reference influence vector refers to a feature vector constructed based on the historical downstream pipeline characteristics and the historical output component data. For more information about the gas database, see step 220 and its related description.

[0104] In some embodiments of the present specification, a vector is constructed based on downstream pipeline characteristics and output component data, and component influence data is determined through a gas database. This can avoid the difficulty in searching for component influence data corresponding to the downstream gas pipeline in the gas database due to the downstream pipeline data being too complex, thereby determining the component influence data more quickly.

[0105] In some embodiments, for each downstream gas pipeline in the downstream pipeline data, the gas company management platform can calculate the difference between each group of component influence data and the theoretical difference based on the multiple groups of component influence data corresponding to the downstream gas pipeline, and determine the gas transportation parameter with the smallest difference as the current gas transportation parameter. A group of component influence data corresponds to a group of gas transportation parameters.

[0106] The theoretical difference can be used to characterize the degree of difference between the output component data and the theoretical component data. In some embodiments, the gas company management platform can determine the difference between the output component data and the theoretical component data as the theoretical difference. The difference between the output component data and the theoretical component data can include the difference between each item of data in the output component data and the same item of data in the theoretical component data.

[0107] In some embodiments, the gas company management platform can also determine gas transportation parameters through the transportation model. For more information on determining gas transportation parameters, see Figure 4 The corresponding description.

[0108] In some embodiments of the present specification, by considering the impact of the downstream gas pipeline on the composition of the output gas when transporting gas under different gas transportation parameters, and then determining more reasonable gas transportation parameters, the stability of the composition of the output gas during transportation can be ensured.

[0109] Step 280, updating the gas database based on the gas mixing parameters and / or updated gas mixing parameters, gas transportation parameters, downstream pipeline data, output component data, and received component data of the mixed gas received by the downstream gas pipeline.

[0110] In some embodiments, the gas company management platform can record the gas mixing parameters and / or updated gas mixing parameters, gas transportation parameters, downstream pipeline data, output component data, and received component data of the mixed gas received by the downstream gas pipeline as a set of historical gas mixing records, and store them in the gas database. The gas database is continuously updated by storing the historical gas mixing records of each gas mixing at multiple gas mixing stations in the gas database.

[0111] It can be understood that storing the mixed gas data of different gas mixing stations in the gas database can expand the amount of reference data when determining the gas transportation parameters, so as to obtain more reasonable gas transportation parameters.

[0112] In some embodiments of the specification, by timely monitoring the composition changes of the mixed gas during the gas mixing process, the gas mixing parameters are continuously updated iteratively to ensure that the mixed gas remains relatively uniform during the mixing process, so that the mixed gas achieves a better mixing effect. Considering the transportation conditions of the output gas in the downstream gas pipeline and the actual conditions of the downstream gas pipeline, more reasonable gas transportation parameters can be determined so that the output gas remains relatively uniform in the downstream gas pipeline.

[0113] It should be noted that the above description of process 200 is only for illustration and description, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process hand-eye calibration under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0114] Figure 3 It is a schematic diagram of a flow chart for determining and updating mixing parameters according to some embodiments of this specification.

[0115] In some embodiments, one of the iterative updates in at least one round of iterative updates includes: the gas company management platform can generate a mixing instruction based on the gas mixing parameters and / or the round gas mixing parameters and send it to the gas mixing device, and control the gas mixing device to mix the gas based on the mixing instruction; in response to the round gas data not meeting the preset conditions, determine the expected compliance of the gas mixing parameters and / or the round gas mixing parameters; adjust the gas mixing parameters and / or the round gas mixing parameters based on the expected compliance, determine the round gas mixing parameters for the next round of iterative updates, and perform the next round of iterative updates; and in response to the round gas data meeting the preset conditions, the iterative update ends, and the gas mixing parameters and / or the round gas mixing parameters are determined as the updated gas mixing parameters. For instructions on round gas mixing parameters, round gas data, and preset conditions, see Figure 2 and its related description.

[0116] like Figure 3 As shown, process 300 includes the following steps: Step 310: Generate a mixing instruction based on the mixing parameters and / or the round mixing parameters and send it to the mixing device, and control the mixing device to mix the gas based on the mixing instruction. Figure 2 and its related description.

[0117] In some embodiments, in response to the round gas data not satisfying the preset condition, step 320 is performed, and in response to the round gas data satisfying the preset condition, step 340 is performed.

[0118] Step 320: Determine the expected achievement of the mixing parameters and / or round mixing parameters.

[0119] The expectation of reaching the standard refers to the condition required for the round gas data to meet the preset condition under the mixing parameters and / or round gas parameters when the mixing device does not perform other operations. In some embodiments, the expectation of reaching the standard may include the time required for the round gas data to meet the preset condition.

[0120] It is understandable that due to the random movement of gas molecules, the uniformity of the mixed gas will gradually change over time, and a certain amount of time is required to continue mixing the gas to ensure that the round gas data in the first round of iterative update meets the preset conditions, or the mixing parameters need to be iteratively updated to obtain the round gas parameters to ensure that the round gas data in the next round of iterative update meets the preset conditions.

[0121] In some embodiments, in response to the round gas data not satisfying the preset condition, the gas company management platform may determine the gas mixing parameters and / or the target target of the round gas mixing parameters in a variety of ways. For example, the gas company management platform may select the same historical round gas data as the round gas data and the corresponding historical target target in the gas database, and determine the historical target target as the current target target.

[0122] In some embodiments, in response to the round gas data not satisfying a preset condition, the gas company management platform may determine the expectation of meeting the standard based on the changing trend of the round gas data.

[0123] The change trend refers to the trend of the round gas data changing over time. In some embodiments, the change trend may include the trend of the round gas data of multiple preset points in the gas mixing device changing over time. The change trend of the round gas data of each preset point can be represented by a fitting curve or the like.

[0124] In some embodiments, for each of the multiple preset points, the gas company management platform can perform curve fitting based on the round gas data at multiple sampling time points to obtain a fitting curve. The horizontal axis of the fitting curve can be time, and the vertical axis can be round gas data. The multiple sampling time points refer to the time points within the period when the gas mixing device executes the mixing instruction or updates the gas mixing instruction.

[0125] It is understandable that since the gas data obtained in each round of iterative update is different, the change trend needs to be re-determined in each round of iterative update.

[0126] In some embodiments, for the fitting curve corresponding to each preset point, the gas company management platform can calculate the difference between the time when the round gas data meets the preset conditions and the current time, determine the obtained difference as the target expectation corresponding to the point, and use the maximum value of the target expectations corresponding to all preset points as the target expectation of the mixing parameter or the round mixing parameter.

[0127] In some embodiments of the present specification, by analyzing the changing trend of the round gas data, the accuracy of determining the expected target can be improved, which is conducive to determining more efficient round gas mixing parameters and improving the efficiency of gas mixing.

[0128] Step 330: Adjust the gas mixing parameters and / or the round gas mixing parameters based on the target-reaching expectation, determine the round gas mixing parameters for the next round of iterative update, and perform the next round of iterative update.

[0129] In some embodiments, the gas company management platform can adjust the gas mixing parameters based on the expected compliance of the gas mixing parameters in the first round of update iterations to obtain round gas mixing parameters, and in the next round of iterative updates, adjust the round gas mixing parameters based on the expected compliance of the round gas mixing parameters to obtain the round gas mixing parameters for the next round.

[0130] In some embodiments, the gas company management platform can adjust the gas mixing parameters or round gas mixing parameters in a variety of ways based on the target expectations of the gas mixing parameters or round gas mixing parameters. For example, the management platform can select the historical target expectations in the gas database that are the same as the current target expectations based on the target expectations, and adjust the historical gas mixing parameters or historical round gas mixing parameters based on the historical target expectations to determine the historical round gas mixing parameters as the round gas mixing parameters for the next round of iterative updates.

[0131] In some embodiments, the gas company management platform can determine the next round of iterative update of the round mixing parameters through the gas mixing model based on the expected target, gas mixing parameters and / or round mixing parameters, processing status data and delivery saturation. For an explanation of the processing status data, see Figure 2 and its related description.

[0132] The gas mixing model is a model used to determine the round gas mixing parameters. In some embodiments, the gas mixing model can be a machine learning model. For example, the gas mixing model can include any one or combination of a convolutional neural network (CNN) model, a neural network (NN) model, or other custom model structures.

[0133] The delivery saturation refers to the degree to which the gas outputted by the downstream station meets the preset gas usage conditions. The preset gas usage conditions can be used to characterize the gas usage requirements of users supplied by the downstream station. In some embodiments, the preset gas usage conditions may include the delivery saturation that does not affect the user's use of gas. The preset gas usage conditions can be preset based on historical experience. For an explanation of the downstream station, see Figure 4 and its related description.

[0134] In some embodiments, the gas company management platform can obtain the gas pressure of the gas output by the downstream station in the gas pipeline through the device object platform, and determine the ratio of the gas pressure to the standard gas pressure as the transmission saturation corresponding to the downstream station. The standard gas pressure can be pre-set based on the gas transportation specification.

[0135] It is understandable that the gas pressure is lower than the standard gas pressure, which means that the gas supply from the downstream station is small and cannot meet the gas demand of users.

[0136] In some embodiments, the gas company management platform can train the gas mixing model based on a large number of first training samples with first labels by gradient descent method, etc. The first training sample can include sample compliance expectations, sample gas mixing parameters or sample round gas mixing parameters, sample processing status data, sample delivery saturation, and the first label of the first training sample can be a historical updated gas mixing parameter.

[0137] In some embodiments, the gas company management platform may determine the first training sample and the first label based on the historical gas mixing records. For example, the gas company management platform may select the historical gas mixing records whose historical output component data meets the preset conditions, and use the historical target expectations, historical gas mixing parameters or historical round gas mixing parameters, historical processing status data, and historical delivery saturation in such historical gas mixing records as the first training sample, and use the historical updated gas mixing parameters in the historical gas mixing records as the first label.

[0138] In some embodiments, the gas mixture model can be trained by: inputting a plurality of first training samples with first labels into the initial gas mixture model, constructing a loss function through the training labels and the prediction results of the initial gas mixture model, iteratively updating the initial gas mixture model based on the loss function, and completing the gas mixture model training when the loss function of the initial gas mixture model satisfies a preset iteration condition. The preset iteration condition can be the convergence of the loss function, the number of iterations reaching a set value, etc.

[0139] In some embodiments of the present specification, by introducing a gas mixing model, the self-learning ability of the machine learning model can be used to find patterns from a large amount of data, thereby improving the accuracy and efficiency of determining the round mixing parameters.

[0140] Step 340, the iterative update ends, and the gas mixing parameter and / or the round gas mixing parameter are determined as the updated gas mixing parameter. For this part, please refer to step 250 and its related description.

[0141] In some embodiments of the present specification, the mixing gas parameters are updated by multiple rounds of iterative updates, so that the uniformity of the mixed gas in the entire mixing process can be ensured, thereby obtaining output gas that is closer to the ideal mixing ratio.

[0142] It should be noted that the above description of process 300 is only for illustration and description, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process hand-eye calibration under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0143] Figure 4 is an exemplary schematic diagram of a transportation model according to some embodiments of the present specification.

[0144] In some embodiments, the gas company management platform can construct a gas transportation map 440 based on the component impact data 410, the downstream pipeline data 420, and the output component data 430; based on the gas transportation map 440, determine the gas transportation parameters 460 through the transportation model 450. For more information about the component impact data, the downstream pipeline data, the output component data, and the gas transportation parameters, see Figure 2 , Figure 3 and its related description.

[0145] The gas transportation graph 440 refers to a graph structure that characterizes the association between the gas mixing station and the downstream station. The graph structure is a data structure composed of nodes and edges, the edges connect the nodes, and the nodes and edges may have features. In some embodiments, the gas transportation graph may be a directed graph, the starting point of the directed graph may be the current gas mixing station node, and the direction of the edge may be the transportation direction of the gas.

[0146] In some embodiments, the gas company management platform can construct a gas transportation map based on the connection relationship between the gas mixing station and the downstream station in the gas pipeline network. The nodes of the gas transportation map (such as node 440-1) can include the gas mixing station node, the pipeline key node and the downstream station node. The downstream station refers to the gas station that receives the mixed gas output by the gas mixing station. For example, a gas pressure regulating station, etc.

[0147] Pipeline key nodes refer to nodes that represent key locations in the gas pipeline. In some embodiments, the gas company management platform can obtain historical composition data of multiple locations in the gas pipeline through the government data center, determine the composition change of the gas between two adjacent locations based on the historical composition data, and determine the key locations based on the composition change. Multiple locations may include turning points, branch points, and confluence points of the gas pipeline or the gas pipeline itself. The historical composition data of each location can be obtained by the composition detection equipment deployed at each location. Historical composition data refers to received composition data obtained at historical times. For instructions on receiving composition data, please refer to Figure 2 and its related description.

[0148] In some embodiments, for two adjacent locations, the gas company management platform may calculate the difference between the historical composition data of the upstream location and the historical composition data of the downstream location, and use the difference as the composition change of the gas between the two adjacent locations. The difference between the historical composition data of the two locations may include the difference between each item of the historical composition data of the two locations.

[0149] In some embodiments, if the change in the composition of the gas between two adjacent locations exceeds a change threshold, the gas company management platform may determine the two adjacent locations as key nodes.

[0150] The node characteristics of the gas mixing station node may include output composition data.

[0151] The node characteristics of the downstream station node may include the station scale. The station scale of the downstream station may be represented by data such as the amount of gas that can be received every day.

[0152] Different types of pipeline key nodes have different node features. For example, the node features of a node representing a turning point of a gas pipeline may include the model and size of the turning joint, etc. For another example, the node features of a node representing a branch point of a gas pipeline may include the number of branches and the inner diameter of the pipeline before and after the branch, etc.

[0153] In some embodiments, the node characteristics of the downstream station node may also include branch gas mixing parameters and branch transportation parameters of other gas mixing stations corresponding to the downstream station. Other gas mixing stations corresponding to the downstream station refer to gas mixing stations that supply mixed gas to the downstream station in addition to the current gas mixing station.

[0154] Branch gas mixing parameters refer to the gas mixing parameters followed by other gas mixing stations when performing gas mixing operations. Branch transportation parameters refer to the gas transportation parameters followed by other gas mixing stations when transporting mixed gases.

[0155] In some embodiments, the gas company management platform can obtain branch gas mixing parameters and branch transportation parameters of other gas mixing stations through the government data center.

[0156] In some embodiments of the present specification, adding branch gas mixing parameters and branch transportation parameters of other gas mixing stations to the node characteristics of the downstream station node can provide a reference when determining the gas transportation parameters, thereby improving the accuracy of determining the gas transportation parameters.

[0157] The edges of the gas transportation graph may characterize the connectivity between nodes. In some embodiments, the edges of the gas transportation graph (eg, edge 440-2) may include gas pipelines connecting between nodes. The characteristics of the edges may include composition impact data of the gas pipelines, etc.

[0158] In some embodiments, the gas company management platform may input the gas transportation map into the transportation model to obtain the gas transportation parameters output by the transportation model.

[0159] The transportation model is a model for determining gas transportation parameters. In some embodiments, the transportation model may be a machine learning model. For example, the transportation model may include any one or combination of a graph neural network (GNN) model or other custom model structures. The edge outputs of the transportation model correspond to the gas transportation parameters of the gas pipeline.

[0160] In some embodiments, the gas company management platform may train the transportation model based on the training sample set by a gradient descent method or the like. The training sample set includes a large number of second training samples with second labels. The second training samples may include a sample gas transportation map, and the second label of the second training sample may be an actual gas transportation parameter.

[0161] In some embodiments, the gas company management platform can determine the second training sample and the second label based on the historical gas mixing records. For example, the gas company management platform can select historical gas mixing records in which each item of historical component impact data is greater than a preset component threshold, and construct a sample gas transportation map based on the historical component impact data, historical downstream pipeline data, and historical output component data in such historical gas mixing records, and use the actual gas transportation parameters of each gas pipeline in the sample gas transportation map as the second label of the sample gas transportation map. The preset component threshold may include multiple sub-component thresholds, each of which may correspond to an item of data in the historical component impact data. The preset difference threshold may be determined based on prior experience.

[0162] In some embodiments, the training process of the transport model is similar to the training process of the gas mixing model, and its implementation method can refer to the training method of the gas mixing model.

[0163] In some embodiments, in response to the transmission saturation of the downstream station not meeting the preset gas usage conditions, the gas company management platform can determine the target training sample in the training sample set based on the station data of the downstream station; adjust the learning rate of the target training sample, and perform intensive training on the transportation model of the mixing station corresponding to the downstream station based on the adjusted learning rate. Each mixing station can correspond to a transportation model. For instructions on transmission saturation and preset gas usage conditions, please refer to Figure 3 and its related description.

[0164] The station data refers to data related to the downstream station. In some embodiments, the station data may include the station scale of the downstream station and the number of connected gas mixing stations, etc. For an explanation of the station scale, please refer to the above description.

[0165] The target training sample refers to a second training sample that is more suitable for the downstream station. In some embodiments, the gas company management platform can select sample gas transportation maps whose station differences meet preset difference conditions in the training sample set based on the station data of the downstream station, and determine the sample gas transportation maps whose transmission saturation of the downstream station in such sample gas transportation maps meets the preset gas use conditions as target training samples. The station difference meeting the preset difference condition may include that the station difference is less than a preset station threshold. The preset station threshold may be preset based on historical experience.

[0166] The station difference may represent the difference between the station data and the sample station data. The sample station data refers to the station data of the downstream station in the sample gas transportation map. In some embodiments, the gas company management platform may determine the difference between the station data and the sample station data as the station difference.

[0167] In some embodiments, the gas company management platform can adjust the learning rate of the target training sample, for example, setting a higher learning rate for the target training data. The gas company management platform can also perform intensive training on the transportation model of the gas mixing station corresponding to the downstream station based on the adjusted learning rate and the target training sample, and re-determine the gas transportation parameters of each gas mixing station through the transportation model after intensive training.

[0168] It is understandable that in the process of using the transportation model, the transportation saturation of other downstream stations may not meet the preset gas consumption conditions, and then the transportation model is strengthened. Training, resulting in different model parameters of the transportation model, therefore, it is necessary to strengthen the training of the transportation parameters of each gas mixing station separately.

[0169] In some embodiments of the present specification, when the delivery saturation of the downstream station does not meet the preset gas usage conditions, the transportation model of each gas station is retrained using the target training samples so that the transportation model can determine more appropriate gas transportation parameters, thereby improving the delivery saturation of the downstream station.

[0170] In some embodiments of the present specification, through the construction of a gas transportation map, various scattered and large data and their characteristics can be effectively organized. At the same time, the use of a transportation model can better capture the topological structure and relationship information in the gas transportation map, thereby improving the accuracy of determining gas transportation parameters.

[0171] Some embodiments of the present specification also provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any of the methods in the above embodiments.

[0172] Furthermore, certain features, structures or characteristics in one or more embodiments of this specification may be appropriately combined.

[0173] If there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the materials cited in this specification and the contents described in this specification, the descriptions, definitions, and / or usage of terms in this specification shall prevail.

Claims

1. A method for monitoring gas mixing station equipment based on smart gas, characterized in that: The method is executed by a gas company management platform based on a smart gas mixing station equipment monitoring Internet of Things system, and the method includes: Acquiring state data of the gas to be mixed through the gas company's sensor network platform and the device object platform, wherein the device object platform includes a state monitoring device; Determining mixing parameters based on the state data and the mixing ratio; generating a mixing instruction based on the gas mixing parameter, and sending the mixing instruction to the device object platform, wherein the device object platform also includes a gas mixing device; In a process in which the gas mixing device performs gas mixing based on the mixing instruction, obtaining gas composition data of at least one preset point of the gas mixing device; In response to the gas composition data not satisfying a preset condition, performing at least one round of iterative updating on the gas mixing parameter based on the gas composition data to determine an updated gas mixing parameter; generating an updated gas mixing instruction based on the updated gas mixing parameter, and sending the updated gas mixing instruction to the gas mixing device, wherein the gas mixing device performs gas mixing based on the updated gas mixing instruction to obtain output gas; Based on the output composition data of the output gas and the downstream pipeline data, determining gas transportation parameters through a gas database, wherein the gas database is configured in a government data center; and The gas database is updated based on the gas mixing parameter and / or the updated gas mixing parameter, the gas transportation parameter, the downstream pipeline data, the output composition data, and the received composition data of the mixed gas received by the downstream gas pipeline.

2. The method according to claim 1, characterized in that The determining of the mixing parameters based on the state data and the mixing ratio includes: Determining pretreatment parameters of the gas to be mixed based on the state data and the mixing ratio; and The gas mixing parameters are determined based on the mixing ratio and the processing state data, wherein the processing state data is state data after the gas to be mixed is preprocessed by the device object platform based on the preprocessing parameters.

3. The method according to claim 1, characterized in that In response to the gas composition data not satisfying a preset condition, performing at least one round of iterative updating on the gas mixing parameter based on the gas composition data, and determining to update the gas mixing parameter comprises: One of the at least one round of iterative updates includes: Generate a mixing instruction based on the gas mixing parameter and / or the round gas mixing parameter and send it to the gas mixing device, and control the gas mixing device to mix the gas based on the mixing instruction, wherein the round gas mixing parameter is the gas mixing parameter according to which the gas is mixed in the current round; In response to the round gas data not satisfying the preset condition, determining the gas mixing parameter and / or the expected compliance of the round gas mixing parameter, the round gas data being gas composition data acquired in the current round; adjusting the gas mixing parameter and / or the round gas mixing parameter based on the target-reaching expectation, determining the round gas mixing parameter for the next round of iterative update, and performing the next round of iterative update; and, In response to the round gas data satisfying the preset condition, the iterative update ends, and the gas mixing parameter and / or the round gas mixing parameter is determined as the updated gas mixing parameter.

4. The method according to claim 1, characterized in that The determining of the gas transportation parameters through the gas database based on the output composition data of the output gas and the downstream pipeline data comprises: Determining composition influence data through the gas database based on the output composition data and the downstream pipeline data; and, The gas transport parameter is determined based on the composition influence data and the output composition data.

5. The method according to claim 4, characterized in that The determining the gas transportation parameter based on the component influence data and the output component data comprises: constructing a gas transportation map based on the component impact data, the downstream pipeline data, and the output component data; and, Based on the gas transportation map, the gas transportation parameters are determined by a transportation model, and the transportation model is a machine learning model.

6. A gas mixing station equipment monitoring Internet of Things system based on smart gas, characterized in that: The Internet of Things system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform and an equipment object platform. The government safety supervision management platform includes a government data center, and the government safety supervision object platform includes a gas company management platform; the gas company management platform is configured as follows: Acquiring state data of the gas to be mixed through the gas company sensor network platform and the device object platform, wherein the device object platform includes a state monitoring device; Determining mixing parameters based on the state data and the mixing ratio; generating a mixing instruction based on the gas mixing parameter, and sending the mixing instruction to the device object platform, wherein the device object platform also includes a gas mixing device; In a process in which the gas mixing device performs gas mixing based on the mixing instruction, obtaining gas composition data of at least one preset point of the gas mixing device; In response to the gas composition data not satisfying a preset condition, performing at least one round of iterative updating on the gas mixing parameter based on the gas composition data to determine an updated gas mixing parameter; generating an updated gas mixing instruction based on the updated gas mixing parameter, and sending the updated gas mixing instruction to the gas mixing device, wherein the gas mixing device performs gas mixing based on the updated gas mixing instruction to obtain output gas; Based on the output composition data of the output gas and the downstream pipeline data, determining gas transportation parameters through a gas database, wherein the gas database is configured in the government data center; and The gas database is updated based on the gas mixing parameter and / or the updated gas mixing parameter, the gas transportation parameter, the downstream pipeline data, the output composition data, and the received composition data of the mixed gas received by the downstream gas pipeline.

7. The system according to claim 6, characterized in that The gas company management platform is further configured as follows: Determining pretreatment parameters of the gas to be mixed based on the state data and the mixing ratio; and The gas mixing parameters are determined based on the mixing ratio and the processing state data, wherein the processing state data is state data after the gas to be mixed is preprocessed by the device object platform based on the preprocessing parameters.

8. The system according to claim 6, characterized in that The gas company management platform is further configured as follows: One of the at least one round of iterative updates includes: Generate a mixing instruction based on the gas mixing parameter and / or the round gas mixing parameter and send it to the gas mixing device, and control the gas mixing device to mix the gas based on the mixing instruction, wherein the round gas mixing parameter is the gas mixing parameter according to which the gas is mixed in the current round; In response to the round gas data not satisfying the preset condition, determining the gas mixing parameter and / or the expected compliance of the round gas mixing parameter, the round gas data being gas composition data acquired in the current round; adjusting the gas mixing parameter and / or the round gas mixing parameter based on the target-reaching expectation, determining the round gas mixing parameter for the next round of iterative update, and performing the next round of iterative update; and, In response to the round gas data satisfying the preset condition, the iterative update ends, and the gas mixing parameter and / or the round gas mixing parameter is determined as the updated gas mixing parameter.

9. The system according to claim 6, characterized in that The gas company management platform is further configured as follows: Determining composition influence data through the gas database based on the output composition data and the downstream pipeline data; and, The gas transport parameter is determined based on the composition influence data and the output composition data.

10. The system according to claim 9, characterized in that The gas company management platform is further configured as follows: constructing a gas transportation map based on the component impact data, the downstream pipeline data, and the output component data; and, Based on the gas transportation map, the gas transportation parameters are determined by a transportation model, and the transportation model is a machine learning model.

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