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

Abstract

The present invention provides a method for monitoring the equipment of a gas mixing station based on intelligent gas and an Internet of Things system. This method is executed by the gas company management platform of the Internet of Things system for monitoring the equipment of the gas mixing station based on intelligent gas. The method includes: determining gas mixing parameters based on the state data and mixing ratio of the gas to be mixed; obtaining gas composition data; in response to the gas composition data not meeting the preset conditions, performing at least one round of iterative update on the gas mixing parameters based on the gas composition data to determine the updated gas mixing parameters; determining gas transportation parameters based on the output composition data of the output gas and the downstream pipeline data; updating the 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 composition data, and the received composition data of the mixed gas received by the downstream gas pipeline. This method can timely monitor the uniformity of the mixed gas and ensure that the mixed gas remains uniform during the mixing process and the transportation process.

Description

Technical Field

[0001] The present invention relates to the field of monitoring of gas mixing stations, and particularly to a method for monitoring equipment of a gas mixing station based on intelligent gas and an Internet of Things system. 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 liquefied petroleum gas into gas 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 gases in the gas mixing station, the mixed gas is often uneven, and in the process of transporting the mixed gas, the mixed gas may become uneven again, thus affecting the gas use of users.

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

[0004] The summary of the invention includes a method for monitoring equipment of a gas mixing station based on intelligent gas, which is executed by a gas company management platform of an Internet of Things system for monitoring equipment of a gas mixing station based on intelligent gas. The method includes: obtaining status data of the gas to be mixed through a gas company sensing network platform and an equipment object platform, where the equipment object platform includes status monitoring equipment; determining gas mixing parameters based on the status data and the mixing ratio; generating a mixing instruction based on the gas mixing parameters, and sending the mixing instruction to the equipment object platform, where the equipment object platform further includes gas mixing equipment; obtaining gas component data at at least one preset point of the gas mixing equipment during the process of gas mixing by the gas mixing equipment based on the mixing instruction; in response to the gas component data not meeting the preset conditions, performing at least one round of iterative update on the gas mixing parameters based on the gas component data to determine updated gas mixing parameters; generating an updated mixing instruction based on the updated gas mixing parameters, and sending the updated mixing instruction to the gas mixing equipment, where the gas mixing equipment performs gas mixing based on the updated mixing instruction to obtain output gas; determining gas transportation parameters through a gas database based on the output component data of the output gas and downstream pipeline data, where the gas database is configured in a government data center; and updating the 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 received component data of the mixed gas received by the downstream gas pipeline.

[0005] The invention content includes an Internet of Things system for monitoring gas mixing station equipment based on intelligent gas. The Internet of Things system includes a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company sensing network platform, and 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 to: obtain the status data of the gas to be mixed through the gas company sensing network platform and the equipment object platform, and the equipment object platform includes status monitoring equipment; determine the gas mixing parameters based on the status data and the mixing ratio; generate a mixing instruction based on the gas mixing parameters, and send the mixing instruction to the equipment object platform, and the equipment object platform further includes gas mixing equipment; during the process of gas mixing by the gas mixing equipment based on the mixing instruction, obtain the gas component data of at least one preset point of the gas mixing equipment; in response to the gas component data not meeting the preset conditions, perform at least one round of iterative update on the gas mixing parameters based on the gas component data to determine the updated gas mixing parameters; generate an updated mixing instruction based on the updated gas mixing parameters, and send the updated mixing instruction to the gas mixing equipment, and the gas mixing equipment performs gas mixing based on the updated mixing instruction to obtain the output gas; determine the gas transportation parameters through the gas database based on the output component data of the output gas and the downstream pipeline data, and the gas database is configured to be obtained from the government data center; and update the 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 received component data of the mixed gas received by the downstream gas pipeline.

[0006] The beneficial effects of the present invention include but are not limited to: (1) The Internet of Things system for monitoring gas mixing station equipment based on intelligent gas can form an information operation closed-loop among various functional platforms, realizing the informatization and intelligentization of gas mixing supervision. (2) Based on the status data and the mixing ratio of the gas to be mixed, determining the pretreatment parameters of the gas to be mixed can preliminarily process the gas to be mixed before mixing, which is beneficial to achieving a better mixing effect of the gas to be mixed subsequently. (3) By timely monitoring the component changes of the mixed gas during the gas mixing process and continuously iteratively updating the gas mixing parameters, it is ensured that the mixed gas remains relatively uniform during the mixing process, enabling the mixed gas to achieve a better mixing effect. Considering the transportation situation of the output gas in the downstream gas pipeline and the actual situation 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. Description of the Drawings

[0007] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same reference numerals represent the same structures, where:

[0008] Figure 1 is a schematic diagram of the platform structure of the Internet of Things system for monitoring the equipment of a gas mixing station based on intelligent gas as shown in some embodiments of this specification;

[0009] Figure 2 is an exemplary flowchart of a method for monitoring the equipment of a gas mixing station based on intelligent gas as shown in some embodiments of this specification;

[0010] Figure 3 is a schematic diagram of the process for determining and updating the gas mixing parameters as shown in some embodiments of this specification;

[0011] Figure 4 is an exemplary schematic diagram of a transportation model as shown in some embodiments of this specification. Detailed implementation manners

[0012] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. The accompanying drawings do not represent all implementation manners.

[0013] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the said words can be replaced by other expressions.

[0014] Unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically 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.

[0015] In the embodiments of this specification, when the operations are described step by step, unless otherwise specified, the order of the steps can be adjusted, the steps can be omitted, and other steps can also be included during the operation process.

[0016] Figure 1 is a schematic diagram of the platform structure of the Internet of Things system for monitoring the equipment of a gas mixing station based on intelligent gas as shown in some embodiments of this specification.

[0017] Such as Figure 1As shown, the Internet of Things system 100 for monitoring the equipment of a gas mixing station based on intelligent gas includes a government safety supervision management platform 110, a government safety supervision sensing network platform 120, a government safety supervision object platform 130, a gas company sensing network platform 140, and an equipment object platform 150.

[0018] The government safety supervision management platform 110 refers to the comprehensive management platform for government management information. In some embodiments, the government safety supervision management platform 110 includes a government data center 111.

[0019] The government data center 111 refers to the data center for storing the data of the Internet of Things system 100 for monitoring the equipment of a gas mixing station based on intelligent gas. For example, the government data center stores a gas database. The gas database can be a database management system that supports high-concurrency access, such as MySQL, PostgreSQL, or Oracle, etc.

[0020] The government safety supervision sensing network platform 120 refers to the platform for comprehensive management of government sensing information. In some embodiments, the government safety supervision sensing network platform interacts with the government safety supervision object platform and the government safety supervision management platform. The government safety supervision sensing network platform can be configured as a communication network or a gateway, etc.

[0021] The government safety supervision object platform 130 refers to the platform for generating government supervision information and executing control information. In some embodiments, the government safety supervision object platform 130 includes a gas company management platform 131. The gas company management platform 131 refers to the comprehensive management platform for gas company information. The gas company management platform 131 can be configured to process the data of the Internet of Things system 100 for monitoring the equipment of a gas mixing station based on intelligent gas.

[0022] The gas company sensing network platform 140 refers to the platform for comprehensively managing the sensing information of the gas company. In some embodiments, the gas company sensing network platform can be configured as a communication network or a gateway, etc. The gas company sensing network platform interacts with the government safety supervision object platform and the equipment object platform.

[0023] The equipment object platform 150 refers to the functional platform for generating perception information and executing control information. In some embodiments, the equipment object platform can include at least one of a status monitoring device, a gas mixing device, and a composition detection device, etc. The status monitoring device and the gas mixing device are deployed in the gas mixing station. The composition detection device is deployed in the gas mixing station, the gas mixing device, and / or the gas pipeline.

[0024] The gas mixing station refers to a gas supply facility in the gas output pipe network that mixes gaseous liquefied petroleum gas with air and / or other combustible gases to prepare a mixed gas and supply gas to users. Users refer to residents and / or enterprises that use gas, etc.

[0025] The status monitoring device is used to collect the status data of the gas to be mixed. In some embodiments, the status monitoring device may include a temperature sensor, a pressure sensor, and various component sensors, etc. One component sensor is used to collect the component data of one gas to be mixed.

[0026] The gas mixing device refers to the device used to mix the gas to be mixed.

[0027] The component detection device refers to the device used to obtain the gas component data of the mixed gas. In some embodiments, the component detection device may be deployed at at least one preset point of the gas mixing device, and may also be deployed at the gas outlet of the gas mixing station, etc.

[0028] In some embodiments, the component detection device may include a sampling device and a component sensor. The sampling device refers to the device used to sample the mixed gas. The component sensor refers to the sensor used to detect the gas component data. The component sensor can detect the mixed gas sampled by the sampling device to obtain the gas component data. The mixed gas refers to the gas obtained by the gas mixing device mixing the gas to be mixed.

[0029] In some embodiments, the Internet of Things system 100 for monitoring the equipment of the gas mixing station based on smart gas may further include a server. The server can process the information and / or data related to the Internet of Things system 100 for monitoring the equipment of the gas mixing station based on smart gas to perform one or more functions described in this application.

[0030] 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 of the above.

[0031] For the foregoing detailed description, reference may be made to Figures 2 to 4 the relevant description.

[0032] In some embodiments of this specification, the Internet of Things system 100 for monitoring the equipment of the gas mixing station based on smart gas can form a closed-loop information operation among various functional platforms, realizing the informatization and intelligentization of gas mixing supervision.

[0033] Figure 2 is an exemplary flowchart of the method for monitoring the equipment of the gas mixing station based on smart gas shown in some embodiments of this specification. In some embodiments, process 200 is executed by the gas company management platform of the Internet of Things system for monitoring the equipment of the gas mixing station based on smart gas.

[0034] For the relevant content of each platform of the Internet of Things system for monitoring the equipment of the gas mixing station based on intelligent gas, reference can be made to Figure 1 the corresponding description.

[0035] As Figure 2 shown, process 200 includes the following steps:

[0036] Step 210, obtain the status data of the gas to be mixed through the gas company sensing network platform and the device object platform.

[0037] The gas to be mixed refers to the gas that needs to be mixed by the gas mixing equipment. In some embodiments, the gas to be mixed may include at least one of gaseous petroleum gas, hydrogen, etc.

[0038] The status data refers to the data related to the status of the gas to be mixed. In some embodiments, the status data may include one or more groups, and each group of status data corresponds to a gas to be mixed.

[0039] In some embodiments, a group of status data includes at least one of the temperature, pressure, and composition data of a gas to be mixed. The composition data refers to the data related to the composition of the gas to be mixed.

[0040] In some embodiments, the composition data may include at least one of the purity and impurity information of the gas to be mixed. The purity refers to the content of the main component of the gas to be mixed. The impurity information refers to the content of other components other than the main component in the gas to be mixed.

[0041] In some embodiments, the gas company management platform can obtain the status data of the gas to be mixed through the gas company sensing network platform and the device object platform.

[0042] Step 220, determine the gas mixing parameters based on the status data and the mixing ratio.

[0043] The mixing ratio refers to the ideal ratio of multiple gases to be mixed during mixing. In some embodiments, the mixing ratio includes the volume ratio of multiple gases to be mixed. In some embodiments, the mixing ratio of the gases to be mixed can be preset based on the industry specifications of gas transportation.

[0044] The mixing parameters refer to the working parameters of the gas mixing equipment for mixing gases. In some embodiments, the mixing parameters may include at least one of the pressure, temperature, and flow rate during the operation of the gas mixing equipment.

[0045] In some embodiments, the gas company management platform can determine the gas mixing parameters in various ways based on the status data and the mixing ratio. For example, the gas company management platform can construct a gas mixing vector based on the status data and the mixing ratio of the gas to be mixed. The gas company management platform matches the reference gas mixing vector identical to 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 the feature vector constructed based on the status data and the mixing ratio of the gas to be mixed. The actual gas mixing parameters refer to the historical gas mixing parameters finally used by the gas mixing equipment in the historical gas mixing record.

[0046] In some embodiments, the gas company management platform can obtain the gas database through the government data center.

[0047] In some embodiments, the gas company management platform can construct the gas database based on the historical gas mixing records. The gas database can include multiple historical gas mixing records and various vectors corresponding to each historical gas mixing record. In some embodiments, the various vectors can include the reference gas mixing vector. The reference gas mixing vector refers to the feature vector constructed by the gas company management platform based on the historical status data and the mixing ratio.

[0048] The historical gas mixing record refers to the data related to the gas mixing of the gas mixing equipment at a historical time. In some embodiments, the gas company management platform can record the historical status data and the mixing ratio, the historical gas mixing parameters, the historical output component data, the historical downstream pipeline data, the historical gas transportation parameters, the historical received component data, the historical round gas data, and the historical compliance expectation, etc. corresponding to the gas mixing of the gas mixing equipment at a historical time as a historical gas mixing record, and record the historical gas mixing record in the gas database. For the descriptions of the output component data, the downstream pipeline data, the gas transportation parameters, the round gas data, the compliance expectation, and the received component data, please refer to the relevant descriptions below.

[0049] In some embodiments, the gas company management platform can determine the pretreatment parameters of the gas to be mixed based on the status data and the mixing ratio, and determine the gas mixing parameters based on the mixing ratio and the processing status data.

[0050] The pretreatment parameters refer to the parameters for pretreating the gas to be mixed before the gas mixing equipment mixes the gas to be mixed. In some embodiments, the pretreatment can include processes such as heating and pressurizing the gas to be mixed. The pretreatment parameters can include the temperature value and the pressure value that need to be increased or decreased, etc.

[0051] In some embodiments, the gas company management platform can determine the pretreatment parameters of the gas to be mixed in various ways based on the status data and the mixing ratio.

[0052] For example, the gas company management platform can determine the pretreatment parameters of the gas to be mixed through the following steps:

[0053] S11. Determine the reference state range based on historical gas mixing records and / or mixing ratios.

[0054] The reference state range refers to the range of ideal state data of the gas to be mixed. The reference state range can include one or more sets of data, and each set of data corresponds to the state data of a gas to be mixed. In some embodiments, the reference state range can be preset based on prior experience.

[0055] In some embodiments, the gas company management platform can, based on the state data and the mixing ratio, query the historical gas mixing records in which the mixed gas meets the mixing ratio during the gas mixing process among multiple historical gas mixing records, and determine the range of the state data corresponding to such historical gas mixing records as the reference state range.

[0056] In some embodiments, the gas company management platform can determine the reference state ranges of multiple gases to be mixed based on historical gas mixing records. For example, for each gas to be mixed among multiple gases to be mixed, the gas company management platform can select historical gas mixing records in which the number of iterations used to determine the updated mixing parameters is less than the preset number threshold, and form the reference state range based on the maximum and minimum values of multiple historical state data in such historical gas mixing records. The maximum value of multiple historical state data constitutes the upper limit of the reference state range, and the minimum value constitutes the lower limit of the reference state range. The preset number threshold can be preset based on historical experience. For the description of determining the updated mixing parameters, please refer to the relevant description below.

[0057] 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 can determine that the gas to be mixed does not need to be pretreated, and set the pretreatment parameters to be empty or 0.

[0058] S12. Determine the state difference based on the reference state range.

[0059] 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 central value of the reference state range. The state difference can include one or more sets of data, and each set of data corresponds to a gas to be mixed.

[0060] S13. Determine the pretreatment parameters based on the state difference.

[0061] In some embodiments, the gas company management platform may determine pretreatment parameters based on the state difference. For example, if the state difference in the temperature of a gas to be mixed is -4°C, then the temperature adjustment for this gas to be mixed is to increase the temperature by 4°C to raise the temperature of this gas to be mixed to the same as the central value of the reference state range.

[0062] Processing state data refers to the state data of the gas to be mixed after pretreatment. The processing state data can be obtained through various component sensors deployed in the gas mixing station.

[0063] In some embodiments, the gas company management platform may determine gas mixing parameters by means of vector matching based on the mixing ratio and the processing state data. The process by which the gas company management platform determines 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, and the implementation process can refer to the process of determining the gas mixing parameters above.

[0064] In some embodiments of this specification, determining the pretreatment parameters of the gas to be mixed based on the state data and the mixing ratio of the gas to be mixed can perform preliminary processing on the gas to be mixed before mixing, which is beneficial for the subsequent better mixing effect of the gas to be mixed.

[0065] Step 230: Generate a mixing instruction based on the gas mixing parameters and send the mixing instruction to the device object platform.

[0066] The mixing instruction refers to the relevant instruction for instructing the gas mixing device to mix the gas to be mixed. In some embodiments, the gas company management platform may generate a mixing instruction based on the gas mixing parameters and the gas mixing device corresponding to the gas mixing parameters.

[0067] In some embodiments, the gas company management platform may 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 perform gas mixing based on the mixing instruction.

[0068] Step 240: During the process of the gas mixing device performing gas mixing based on the mixing instruction, obtain the gas component data at at least one preset point of the gas mixing device.

[0069] The gas component data refers to the data related to the components of the mixed gas. In some embodiments, the gas component data may include the content of each gas component in the mixed gas.

[0070] In some embodiments, the gas component data may include multiple sets of data, and each set of data may correspond to the gas component data at at least one preset point at a sampling time point. The sampling time point can be preset based on historical experience.

[0071] The preset points refer to the points where the gas mixing device performs gas sampling. In some embodiments, there can be multiple preset points, which can be preset based on detection requirements and / or historical experience.

[0072] In some embodiments, the gas component data can be obtained by the component detection devices deployed at at least one preset point of the gas mixing device and uploaded to the device object platform. For more information about the component detection devices, reference can be made to Figure 1 the relevant descriptions above.

[0073] Step 250, in response to the gas component data not meeting the preset conditions, perform at least one round of iterative update on the gas mixing parameters based on the gas component data to determine the updated gas mixing parameters.

[0074] The preset conditions refer to the conditions used to determine whether the gas component data meets the mixing ratio. For example, the preset conditions can be that the component differences between the gas component data at multiple preset points and the theoretical component data are all less than or equal to the preset difference threshold. The theoretical component data refers to the content of each gas component in the mixed gas calculated based on the mixing ratio. The component difference is represented by the absolute value of the difference between the gas component data and the theoretical component data. The difference between the gas component data and the theoretical component data can include the differences between each item of data in the gas component data and the same item of data in the theoretical component data. The preset difference threshold can include multiple sub-difference thresholds, and each sub-difference threshold can correspond to one item of data in the component difference. The preset difference threshold can be determined based on prior experience.

[0075] The updated gas mixing parameters refer to the gas mixing parameters obtained through iterative update.

[0076] In some embodiments, in response to the gas component data not meeting the preset conditions, the gas company management platform can determine the updated gas mixing parameters based on the gas component data in various ways. For example, in response to the gas component data not meeting the preset conditions, the gas company management platform can perform at least one round of iterative update on the gas mixing parameters based on the gas component data to determine the updated gas mixing parameters. The gas component data not meeting the preset conditions can be that the component difference between the gas component data at any one of the multiple preset points and the theoretical component data is greater than the preset difference threshold.

[0077] Exemplarily, one of the rounds of iterative update in the at least one round of iterative update includes:

[0078] The gas company management platform can generate a mixing instruction based on the gas mixing parameters and / or the round-based gas mixing parameters and send it to the gas mixing device to control the gas mixing device to perform gas mixing based on the mixing instruction. The gas mixing parameters are the gas mixing parameters for the first round of iterative update. The round-based gas mixing parameters are the gas mixing parameters for the second and subsequent rounds of iterative update.

[0079] In response to the round gas data not meeting 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, adjust the gas mixing parameters and / or the round gas mixing parameters according to the preset rules, determine the round gas mixing parameters for the next round of iterative update, and perform the next round of iterative update. The round gas mixing parameters are the gas mixing parameters followed by the gas mixing equipment for the next round of 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 same item of data in the theoretical composition data.

[0080] The round gas data is the gas composition data obtained in the current round. In some embodiments, the round gas data may include multiple sets of data, and each set of data corresponds to the round gas data of at least one preset point at a sampling time point.

[0081] 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.

[0082] In the next round of iterative update, the gas company management platform may 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 perform gas mixing based on the mixing instruction, and repeat the above steps until the iterative update ends and the round gas mixing parameters are determined as the updated gas mixing parameters.

[0083] The preset rules refer to the rules for adjusting the gas mixing parameters or the round gas mixing parameters. In some embodiments, the preset rules may include the correspondence between the composition difference and the adjustment parameters. The adjustment parameters may include the adjustment direction and adjustment amplitude of the state data of the gas to be mixed, etc.

[0084] In some embodiments, the gas company management platform may determine the preset rules based on the historical gas mixing records. For example, the gas company management platform may construct the preset rules based on the gas mixing parameters before multiple historical iterative updates, the round gas mixing parameters after the historical iterative updates, and the composition difference after the gas mixing equipment uses the updated round gas mixing parameters for gas mixing in the historical gas mixing records. For example, the temperature in the gas mixing parameters before the update is 20°C, the temperature in the round gas mixing parameters after the update is 24°C, and the composition difference between the round gas data and the theoretical composition data changes from -5 L / mol before the update to 0, then it can be determined that the adjustment direction corresponding to the composition difference is to increase the temperature, and the adjustment amplitude is 4°C.

[0085] In some embodiments, the gas company management platform may determine the round gas mixing parameters for the next round of iterative update through a gas mixing model. For the description of this part of the content, reference can be made to Figure 3 And its related descriptions.

[0086] Step 260: Generate an updated gas mixing instruction based on the updated gas mixing parameters, and send the updated gas mixing instruction to the gas mixing device. The gas mixing device performs gas mixing based on the updated gas mixing instruction to obtain the output gas.

[0087] In some embodiments, if the gas composition data continuously meets the preset conditions, the gas company management platform may generate a mixing instruction based on the gas mixing parameters and send the mixing instruction to the gas mixing device. The gas mixing device performs gas mixing based on the mixing instruction to obtain the output gas.

[0088] The updated gas mixing instruction refers to the mixing instruction updated based on the updated gas mixing parameters. In some embodiments, the gas company management platform may 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 performs gas mixing based on the updated gas mixing instruction to obtain the output gas.

[0089] The output gas refers to the gas output after the gas mixing station mixes the gases to be mixed.

[0090] Step 270: Determine the gas transportation parameters through the gas database based on the output composition data of the output gas and the downstream pipeline data.

[0091] The output composition data refers to the composition data of the output gas. In some embodiments, the output composition data may include the content of various gas components in the output gas. In some embodiments, the output composition data may be obtained by the composition detection device deployed at the gas outlet of the gas mixing station and uploaded to the device object platform.

[0092] The received composition data refers to the composition data of the output gas received by the downstream gas pipeline. In some embodiments, the received composition data may include the content of various gas components in the output gas received by the downstream gas pipeline.

[0093] In some embodiments, the received composition data may be obtained by the composition detection device set in the downstream gas pipeline and uploaded to the device object platform.

[0094] The downstream pipeline data refers to the 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 level, etc. of the downstream gas pipeline. The inner diameter change refers to the change in the inner diameter of different positions of the downstream gas pipeline. The pipeline structure refers to the structure of the downstream gas pipeline. For example, the shape, turns, and branches of the downstream gas pipeline. The cleanliness level may be represented by the number of impurities, etc.

[0095] In some embodiments, the downstream pipeline data may include one or more groups of data, and each group of data corresponds to a section of the downstream gas pipeline.

[0096] In some embodiments, the downstream pipeline data can be uploaded by the gas company to the gas company management platform.

[0097] The gas transportation parameters refer to the relevant parameters for transporting and outputting gas to multiple downstream gas pipelines. In some embodiments, the gas transportation parameters can include at least one of parameters such as the pressure, flow rate, and velocity of the gas transported and output 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 can include downstream stations, etc. For the description of the downstream station, reference can be made to Figure 4 and its related description.

[0098] In some embodiments, the gas company management platform can determine the gas transportation parameters in various 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 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 the feature vector constructed based on the output component data of the output gas and the downstream pipeline data. For the description of the received component data, reference can be made to step 280 and its related description.

[0099] The difference between the historical output component data and the historical received component data can 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 during transportation, and the more reasonable the corresponding gas transportation parameters.

[0100] The first matching condition can include that the similarity exceeds the first similarity threshold. The similarity of vectors is negatively correlated with the distance between vectors. The distance between vectors includes the Euclidean distance, etc. The first similarity threshold can be preset based on historical experience.

[0101] In some embodiments, multiple vectors corresponding to the historical gas mixing records in the gas database can include reference transportation vectors. The gas company management platform can construct reference transportation vectors based on the historical output component data and historical downstream pipeline data in the historical gas mixing records. The reference transportation vector refers to the feature vector constructed based on the historical output component data and historical downstream pipeline data. For more descriptions of the gas database, reference can be made to step 220 and its related description.

[0102] In some embodiments, the gas company management platform may determine component impact data through a gas database based on the output component data of the output gas and the downstream pipeline data; and determine gas transportation parameters based on the component impact data and the output component data.

[0103] Component impact data refers to data that characterizes the impact of the downstream gas pipeline on the output gas components after transporting the output gas under the gas transportation parameters of the gas mixing station. The greater the component impact data, the greater the impact of the downstream gas pipeline on the output gas components.

[0104] In some embodiments, the component impact data may include one or more sets of data, and each set of data corresponds to a gas transportation parameter.

[0105] In some embodiments, the gas company management platform may determine component impact data in various ways based on the output component data of the output gas and the downstream pipeline data. For example, the gas company management platform may query the historical output component data and historical downstream pipeline data in the gas database that are the same as the output component data and downstream pipeline data, 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.

[0106] In some embodiments, the gas company management platform may determine the downstream pipeline characteristics based on the downstream pipeline data, and determine the component impact data through the gas database based on the downstream pipeline characteristics and the output component data.

[0107] Downstream pipeline characteristics refer to data that characterizes 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 of the downstream gas pipeline, the number of turns, etc.

[0108] In some embodiments, the gas company management platform may obtain the downstream pipeline characteristics through statistics based on the downstream pipeline data in the historical gas mixing records in the gas database.

[0109] In some embodiments, the gas company management platform may construct an influence vector based on 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 records corresponding to each reference influence vector in the multiple reference influence vectors as a set of component influence data. An influence vector refers to a feature vector constructed based on downstream pipeline characteristics and output component data. The second matching condition may include that the similarity exceeds a second similarity threshold. The second similarity threshold may be preset 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 the description of the difference between the historical output component data and the historical received component data, refer to step 270 and its related description.

[0110] In some embodiments, multiple vectors corresponding to the historical gas mixing records in the gas database may include reference influence vectors. The gas company management platform may construct a reference influence vector based on the historical downstream pipeline characteristics and historical output component data in the historical gas mixing record. A reference influence vector refers to a feature vector constructed based on historical downstream pipeline characteristics and historical output component data. For more descriptions of the gas database, refer to step 220 and its related description.

[0111] In some embodiments of this specification, constructing a vector based on downstream pipeline characteristics and output component data and determining component influence data through the gas database can avoid the difficulty of searching for component influence data corresponding to the downstream gas pipeline in the gas database due to the overly complex downstream pipeline data, thereby determining the component influence data more quickly.

[0112] In some embodiments, for each downstream gas pipeline in the downstream pipeline data, the gas company management platform may calculate the difference between each set of component influence data and the theoretical difference based on the multiple sets 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 set of component influence data corresponds to a set of gas transportation parameters.

[0113] The theoretical difference may 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 may 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 may include the difference between each item of data in the output component data and the same item of data in the theoretical component data.

[0114] In some embodiments, the gas company management platform may also determine the gas transportation parameter through a transportation model. For more content on determining the gas transportation parameter, refer to Figure 4 the corresponding description.

[0115] In some embodiments of the present specification, by considering the influence of the downstream gas pipeline on the composition of the output gas when transporting gas under different gas transportation parameters, more reasonable gas transportation parameters can be determined, ensuring the stability of the composition of the output gas during transportation.

[0116] Step 280: Update the gas database based on the gas mixing parameters and / or updated gas mixing parameters, gas transportation parameters, downstream pipeline data, output composition data, and received composition data of the mixed gas received by the downstream gas pipeline.

[0117] In some embodiments, the gas company management platform can record data such as the gas mixing parameters and / or updated gas mixing parameters, gas transportation parameters, downstream pipeline data, output composition data, and received composition data of the mixed gas received by the downstream gas pipeline for this gas mixing as a set of historical gas mixing records and store them in the gas database. By storing the historical gas mixing records of each gas mixing at multiple gas mixing stations in the gas database, the gas database can be continuously updated.

[0118] 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 gas transportation parameters, so as to obtain more reasonable gas transportation parameters.

[0119] In some embodiments of the present specification, by timely monitoring the composition change of the mixed gas during gas mixing and continuously iteratively updating the gas mixing parameters, it is ensured that the mixed gas remains relatively uniform during the mixing process, enabling the mixed gas to achieve a better mixing effect. Considering the transportation situation of the output gas in the downstream gas pipeline and the actual situation 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.

[0120] It should be noted that the above description of process 200 is only for illustration and example, and does not limit the scope of application of the present specification. For those skilled in the art, various corrections and changes can be made to the process hand-eye calibration under the guidance of the present specification. However, these corrections and changes are still within the scope of the present specification.

[0121] Figure 3 It is a schematic diagram of the process for determining and updating gas mixing parameters shown in some embodiments of the present specification.

[0122] In some embodiments, one of the 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-based gas mixing parameters and send it to the gas mixing device, controlling the gas mixing device to perform gas mixing based on the mixing instruction; in response to the round-based gas data not meeting the preset conditions, determining the compliance expectation of the gas mixing parameters and / or the round-based gas mixing parameters; adjusting the gas mixing parameters and / or the round-based gas mixing parameters based on the compliance expectation, determining the round-based gas mixing parameters for the next round of iterative update, and performing the next round of iterative update; and in response to the round-based gas data meeting the preset conditions, ending the iterative update and determining the gas mixing parameters and / or the round-based gas mixing parameters as the updated gas mixing parameters. For the description of the round-based gas mixing parameters, the round-based gas data, and the preset conditions, reference can be made to Figure 2 and its related descriptions.

[0123] As Figure 3 shown, process 300 includes the following steps:

[0124] Step 310, generating a mixing instruction based on the gas mixing parameters and / or the round-based gas mixing parameters and sending it to the gas mixing device, controlling the gas mixing device to perform gas mixing based on the mixing instruction. For the description of this part of the content, reference can be made to Figure 2 and its related descriptions.

[0125] In some embodiments, in response to the round-based gas data not meeting the preset conditions, step 320 is performed, and in response to the round-based gas data meeting the preset conditions, step 340 is performed.

[0126] Step 320, determining the compliance expectation of the gas mixing parameters and / or the round-based gas mixing parameters.

[0127] The compliance expectation refers to the conditions required for the round-based gas data to meet the preset conditions under the gas mixing parameters and / or the round-based gas parameters when no other operations are performed on the gas mixing device. In some embodiments, the compliance expectation may include the duration required for the round-based gas data to meet the preset conditions.

[0128] It can be understood that due to the random movement of gas molecules, the uniformity of the mixed gas will gradually change over time. It is necessary to continue mixing the gas for a certain period of time to ensure that the round-based gas data in the first round of iterative update meets the preset conditions, or it is necessary to iteratively update the gas mixing parameters to obtain the round-based gas parameters to ensure that the round-based gas data in the next round of iterative update meets the preset conditions.

[0129] In some embodiments, in response to the round-based gas data not meeting the preset conditions, the gas company management platform can determine the compliance expectation of the gas mixing parameters and / or the round-based gas mixing parameters in various ways. For example, the gas company management platform can select the historical round-based gas data identical to the round-based gas data and the corresponding historical compliance expectation in the gas database, and determine the historical compliance expectation as the current compliance expectation.

[0130] In some embodiments, in response to the round gas data not meeting the preset conditions, the gas company management platform may determine the compliance expectation based on the change trend of the round gas data.

[0131] 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 at multiple preset points in the gas mixing device changing over time. The change trend of the round gas data at each preset point may be represented by means such as a fitting curve.

[0132] In some embodiments, for each preset point among the multiple preset points, the gas company management platform may 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 may be time, and the vertical axis may be the round gas data. The multiple sampling time points refer to the time points within the period when the gas mixing device executes or updates the mixing instruction.

[0133] It can be understood that since the round gas data obtained by each round of iterative update is different, the change trend needs to be re-determined for each round of iterative update.

[0134] In some embodiments, for the fitting curve corresponding to each preset point, the gas company management platform may 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 compliance expectation corresponding to this point, and take the maximum value of the compliance expectations corresponding to all preset points as the compliance expectation of the gas mixing parameter or the round gas mixing parameter.

[0135] In some embodiments of this specification, by analyzing the change trend of the round gas data, the accuracy of determining the compliance expectation can be improved, which is conducive to determining more efficient round gas mixing parameters and improving the efficiency of gas mixing.

[0136] Step 330: Adjust the gas mixing parameter and / or the round gas mixing parameter based on the compliance expectation, determine the round gas mixing parameter for the next round of iterative update, and perform the next round of iterative update.

[0137] In some embodiments, the gas company management platform may adjust the gas mixing parameter based on the compliance expectation of the gas mixing parameter in the first round of update iteration to obtain the round gas mixing parameter, and in the next round of iterative update, adjust the round gas mixing parameter based on the compliance expectation of the round gas mixing parameter to obtain the round gas mixing parameter for the next round.

[0138] In some embodiments, the gas company management platform may adjust the mixing gas parameters or round-based mixing gas parameters in various ways based on the compliance expectations of the mixing gas parameters or round-based mixing gas parameters. For example, the management platform may, based on the compliance expectations, select a historical compliance expectation in the gas database that is the same as the current compliance expectation, and determine the historical round-based mixing gas parameters obtained by adjusting the historical mixing gas parameters or historical round-based mixing gas parameters based on the historical compliance expectation as the round-based mixing gas parameters for the next iteration update.

[0139] In some embodiments, the gas company management platform may determine the round-based mixing gas parameters for the next iteration update through a mixing gas model based on the compliance expectations, mixing gas parameters and / or round-based mixing gas parameters, processing status data, and delivery saturation. For the description of the processing status data, reference can be made to Figure 2 and its related descriptions.

[0140] The mixing gas model is a model used to determine the round-based mixing gas parameters. In some embodiments, the mixing gas model may be a machine learning model. For example, the mixing gas model may include any one or a combination of a Convolutional Neural Networks (CNN) model, a Neural Networks (NN) model, or other custom model structures, etc.

[0141] The delivery saturation refers to the degree to which the gas output from the downstream station satisfies the preset gas usage conditions. The preset gas usage conditions can be used to characterize the gas usage requirements of the users supplied with gas at the downstream station. In some embodiments, the preset gas usage conditions may include a delivery saturation that does not affect the users' use of gas. The preset gas usage conditions can be pre-set based on historical experience. For the description of the downstream station, reference can be made to Figure 4 and its related descriptions.

[0142] In some embodiments, the gas company management platform may obtain the gas pressure of the gas output from 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 delivery saturation corresponding to the downstream station. The standard gas pressure can be pre-set based on gas transportation specifications.

[0143] It can be understood that if the gas pressure is lower than the standard gas pressure, it means that the gas supply volume of the downstream station is small and cannot meet the gas usage requirements of the users.

[0144] In some embodiments, the gas company management platform may train the mixing gas model based on a large number of first training samples with a first label through methods such as gradient descent. The first training samples may include sample compliance expectations, sample mixing gas parameters or sample round-based mixing gas parameters, sample processing status data, sample delivery saturation, and the first label of the first training samples may be the historical updated mixing gas parameters.

[0145] In some embodiments, the gas company management platform may determine a first training sample and a first label based on historical gas mixing records. For example, the gas company management platform may select historical gas mixing records whose historical output composition data meets preset conditions, and use the historical compliance expectations, historical gas mixing parameters or historical round gas mixing parameters, historical processing status data, and historical transmission 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.

[0146] In some embodiments, the gas mixing model can be trained in the following way: input multiple first training samples with first labels into the initial gas mixing model, construct a loss function through the training labels and the prediction results of the initial gas mixing model, iteratively update the initial gas mixing model based on the loss function, and when the loss function of the initial gas mixing model meets the preset iteration conditions, the gas mixing model training is completed. The preset iteration conditions can be that the loss function converges, the number of iterations reaches a set value, etc.

[0147] In some embodiments of this specification, by introducing the gas mixing model, the self-learning ability of the machine learning model can be utilized to find patterns from a large amount of data, improving the accuracy and efficiency of determining the round gas mixing parameters.

[0148] Step 340, when the iterative update ends, determine the gas mixing parameters and / or the round gas mixing parameters as the updated gas mixing parameters. For the relevant content of this part, reference can be made to Step 250 and its related descriptions.

[0149] In some embodiments of this specification, by determining the updated gas mixing parameters through multiple rounds of iterative updates, the uniformity of the mixed gas during the entire gas mixing process can be ensured, and thus the output gas closer to the ideal mixing ratio can be obtained.

[0150] It should be noted that the above description of Process 300 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various corrections and changes can be made to the process hand-eye calibration under the guidance of this specification. However, these corrections and changes are still within the scope of this specification.

[0151] Figure 4 It is an exemplary schematic diagram of the transportation model shown in some embodiments of this specification.

[0152] In some embodiments, the gas company management platform may construct a gas transportation map 440 based on the composition influence data 410, the downstream pipeline data 420, and the output composition data 430; based on the gas transportation map 440, determine the gas transportation parameters 460 through the transportation model 450. For more descriptions of the composition influence data, the downstream pipeline data, the output composition data, and the gas transportation parameters, reference can be made toFigure 2 , Figure 3 and its related descriptions.

[0153] The gas transportation map 440 refers to a graph structure that characterizes the association relationship between the gas mixing station and the downstream stations. The graph structure is a data structure composed of nodes and edges. The edges connect the nodes, and the nodes and edges can have features. In some embodiments, the gas transportation map can be a directed graph. The starting point of the directed graph can be the current gas mixing station node, and the direction of the edge can be the gas transportation direction.

[0154] In some embodiments, the gas company management platform can construct a gas transportation map based on the connection relationship between the gas mixing stations and the downstream stations in the gas pipeline network. The nodes of the gas transportation map (such as node 440-1) can include gas mixing station nodes, pipeline key nodes, and downstream station nodes. The downstream station refers to a gas station that receives the mixed gas output from the gas mixing station. For example, a gas pressure regulating station, etc.

[0155] The pipeline key node refers to a node that represents a key position in the gas pipeline. In some embodiments, the gas company management platform can obtain the historical composition data of multiple positions in the gas pipeline through the government data center, determine the composition change amount of the gas between two adjacent positions based on the historical composition data, and determine the key positions based on the composition change amount. The multiple positions can include turning points, branch points, and confluence points of the gas pipeline or the gas pipeline itself, etc. The historical composition data of each position can be obtained by the composition detection equipment deployed at each position. The historical composition data refers to the received composition data obtained at historical times. For the description of the received composition data, please refer to Figure 2 and its related descriptions.

[0156] In some embodiments, for two adjacent positions, the gas company management platform can calculate the difference between the historical composition data of the upstream position and the historical composition data of the downstream position, and use this difference as the composition change amount of the gas between these two adjacent positions. The difference between the historical composition data of the two positions can include the difference of each item of data in the historical composition data of the two positions.

[0157] In some embodiments, if the composition change amount of the gas between two adjacent positions exceeds the change amount threshold, the gas company management platform can determine these two adjacent positions as key nodes.

[0158] The node features of the gas mixing station node can include output composition data.

[0159] The node features of the downstream station node can include the station scale. The station scale of the downstream station can be represented by data such as the daily gas reception volume.

[0160] The node characteristics of different types of pipeline key nodes are different. For example, the node characteristics 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 characteristics of a node representing a branch point of a gas pipeline may include the number of branches and the inner diameters of the pipelines before and after the branch, etc.

[0161] In some embodiments, the node characteristics of the downstream station node may further include the 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 except the current gas mixing station.

[0162] The branch gas mixing parameters refer to the gas mixing parameters followed by other gas mixing stations during gas mixing operations. The branch transportation parameters refer to the gas transportation parameters followed by other gas mixing stations when transporting the mixed gas.

[0163] In some embodiments, the gas company management platform can obtain the branch gas mixing parameters and branch transportation parameters of other gas mixing stations through the government data center.

[0164] In some embodiments of this specification, adding the 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.

[0165] The edges of the gas transportation map can represent the connectivity between nodes. In some embodiments, the edges of the gas transportation map (such as edge 440-2) may include the gas pipelines connecting the nodes. The characteristics of the edges may include the component influence data of the gas pipelines, etc.

[0166] In some embodiments, the gas company management platform can input the gas transportation map into the transportation model to obtain the gas transportation parameters output by the transportation model.

[0167] The transportation model is a model for determining gas transportation parameters. In some embodiments, the transportation model can be a machine learning model. For example, the transportation model can include any one or combination of a graph neural network (GNN) model or other custom model structures, etc. The edge output of the transportation model corresponds to the gas transportation parameters of the gas pipeline.

[0168] In some embodiments, the gas company management platform can train the transportation model based on the training sample set by methods such as gradient descent. The training sample set includes a large number of second training samples with second labels. The second training samples can include sample gas transportation maps, and the second labels of the second training samples can be the actual gas transportation parameters.

[0169] In some embodiments, the gas company management platform may determine the second training samples and the second labels based on historical gas mixing records. For example, the gas company management platform may select historical gas mixing records where each item of the historical component impact data is greater than a preset component threshold, 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, and each sub-component threshold may correspond to one item of data in the historical component impact data. The preset difference threshold may be determined based on prior experience.

[0170] In some embodiments, the training process of the transportation model is similar to that of the gas mixing model, and the implementation method can refer to the training method of the gas mixing model.

[0171] In some embodiments, in response to the delivery saturation of the downstream station not meeting the preset gas usage conditions, the gas company management platform may determine target training samples from the training sample set based on the station data of the downstream station; adjust the learning rate of the target training samples, and perform reinforcement training on the transportation model of the gas mixing station corresponding to the downstream station based on the adjusted learning rate. Each gas mixing station may correspond to a transportation model. For the description of the delivery saturation and the preset gas usage conditions, refer to Figure 3 its related descriptions.

[0172] The station data refers to the 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 the description of the station scale, refer to the related descriptions above.

[0173] The target training samples refer to the second training samples that are more suitable for the downstream station. In some embodiments, the gas company management platform may select sample gas transportation maps in the training sample set where the station differences meet the preset difference conditions based on the station data of the downstream station, and determine the sample gas transportation maps where the delivery saturation of the downstream station in such sample gas transportation maps meets the preset gas usage conditions as the target training samples. The station differences meeting the preset difference conditions may include that the station differences are less than a preset station threshold. The preset station threshold may be set in advance based on historical experience.

[0174] 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.

[0175] In some embodiments, the gas company management platform can adjust the learning rate of the target training samples. For example, a higher learning rate can be set for the target training data. The gas company management platform can also perform reinforcement 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 samples, and re-determine the gas transportation parameters of each gas mixing station through the transportation model after reinforcement training.

[0176] It can be understood that during the use of the transportation model, the delivery saturation of other downstream stations may not meet the preset gas usage conditions, thereby performing reinforcement training on the transportation model, resulting in different model parameters of the transportation model. Therefore, it is necessary to perform reinforcement training on the transportation parameters of each gas mixing station separately.

[0177] In some embodiments of this 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.

[0178] In some embodiments of this specification, through the construction of the gas transportation map, various scattered and huge data and their features can be effectively organized. At the same time, using the transportation model can better capture the topological structure and relationship information in the gas transportation map, improving the accuracy of determining gas transportation parameters.

[0179] Some embodiments of this specification also provide a computer-readable storage medium storing computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the method described in any one of the above embodiments.

[0180] In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0181] If there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the materials cited in this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

Claims

1. 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 security supervision management platform, a government security supervision sensor network platform, a government security supervision object platform, a gas company sensor network platform and an equipment object platform. The government security supervision management platform includes a government data center, and the government security supervision object platform includes a gas company management platform. The government security supervision management platform refers to a comprehensive management platform for government management information, the government security supervision sensor network platform refers to a platform for comprehensive management of government sensor information, the government security supervision object platform refers to a platform for government supervision information generation and control information execution, the gas company sensor network platform refers to a platform for comprehensive management of gas company sensor information, the equipment object platform refers to a functional platform for sensor information generation and control information execution, the government data center refers to a data center for storing data of the Internet of Things system, and the gas company management platform refers to a comprehensive management platform for gas company information; the gas company management platform is configured as: 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 and a gas mixing device for collecting the state data of the gas to be mixed; Determine a gas mixing parameter based on the state data and the mixing ratio, wherein the mixing ratio refers to an ideal ratio of the multiple gases to be mixed when mixed, and the gas mixing parameter refers to a working parameter of the gas mixing device for mixing the gases; generate a mixing instruction based on the gas mixing parameter, and send the mixing instruction to the device object platform; 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, wherein the preset condition is used to determine whether the gas composition data satisfies the mixing ratio; 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; determining gas transportation parameters through a gas database based on output component data of the output gas and downstream pipeline data, wherein the gas database is configured in the government data center; and updating the 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.

2. The system according to claim 1, 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.

3. The system according to claim 1, 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 the 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 gas mixing parameter is used in the first round of iterative update, and the round gas mixing parameter is used in the second and subsequent rounds of iterative update, and the round gas mixing parameter is the gas mixing parameter according to which the gas mixing device performs gas mixing in the next round after the iterative update; 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, wherein the round gas data is gas composition data acquired in the current round, and the expected compliance includes the time required for the round gas data to satisfy the preset condition; 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 system according to claim 1, characterized in that The gas company management platform is further configured as follows: Based on the output composition data and the downstream pipeline data, determining composition influence data through the gas database, the composition influence data representing the influence data of the downstream gas pipeline on the composition of the output gas after the output gas is transported based on the gas transportation parameters of the gas mixing station; and Based on the component influence data and the output component data, the gas transportation parameter is determined, where the gas transportation parameter refers to a parameter related to transporting the output gas to a plurality of downstream gas pipelines.

5. The system according to claim 4, characterized in that The gas company management platform is further configured as follows: Based on the component influence data, the downstream pipeline data and the output component data, a gas transportation map is constructed, wherein the gas transportation map is a graph structure representing the association relationship between the gas mixing station and the gas field station downstream of the gas mixing station; as well as, 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 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 according to any one of claims 1 to 5, and the method comprises: The state data of the gas to be mixed is obtained through the gas company's sensor network platform and the device object platform, wherein the device object platform includes a state monitoring device and a gas mixing device for collecting the state data of the gas to be mixed; based on the state data and the mixing ratio, a gas mixing parameter is determined, wherein the mixing ratio refers to an ideal ratio of multiple gases to be mixed when mixed, and the gas mixing parameter refers to a working parameter of the gas mixing device for mixing gas; a mixing instruction is generated based on the gas mixing parameter, and the mixing instruction is sent to the device object platform; In the process of the gas mixing device performing gas mixing based on the mixing instruction, 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 a preset condition, at least one round of iterative updating of the gas mixing parameters is performed based on the gas composition data to determine and update the gas mixing parameters, wherein the preset condition is used to determine whether the gas composition data satisfies the mixing ratio; 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 performs gas mixing based on the updated gas mixing instruction to obtain output gas; determine the gas transportation parameters through a gas database based on the output component data of the output gas and the downstream pipeline data, the gas database is configured in a government data center; and update the 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.

7. The method according to claim 6, 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.

8. The method according to claim 6, 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 the 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 gas mixing parameter is used in the first round of iterative update, and the round gas mixing parameter is used in the second and subsequent rounds of iterative update, and the round gas mixing parameter is the gas mixing parameter according to which the gas mixing device performs gas mixing in the next round after the iterative update; 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, wherein the round gas data is gas composition data acquired in the current round, and the expected compliance includes the time required for the round gas data to satisfy the preset condition; 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 method according to claim 6, 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: Based on the output composition data and the downstream pipeline data, determining composition influence data through the gas database, the composition influence data representing the influence data of the downstream gas pipeline on the composition of the output gas after the output gas is transported based on the gas transportation parameters of the gas mixing station; and Based on the component influence data and the output component data, the gas transportation parameter is determined, where the gas transportation parameter refers to a parameter related to transporting the output gas to a plurality of downstream gas pipelines.

10. The method according to claim 9, characterized in that The determining the gas transportation parameter based on the component influence data and the output component data comprises: Based on the component influence data, the downstream pipeline data and the output component data, a gas transportation map is constructed, wherein the gas transportation map is a graph structure representing the association relationship between the gas mixing station and the gas field station downstream of the gas mixing station; 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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