Intelligent gas field station noise reduction method, internet of things system and storage medium

Abstract

The embodiment of the specification provides a smart gas station noise reduction method, an Internet of Things system and a storage medium. The method comprises the following steps: obtaining related data of a target station, wherein the related data comprises at least one of operation data of the target station, noise data of the target station and pressure regulating parameters of an associated station; based on the related data, predicting noise enhancement data of the target station; and determining noise reduction control parameters based on the noise enhancement data and the pressure regulating parameters. The Internet of Things system comprises a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas pipe network equipment sensing network platform and a smart gas pipe network equipment object platform. The smart gas safety management platform is configured to execute the smart gas station noise reduction method.

Description

Technical Field

[0001] This specification relates to the field of gas equipment technology, specifically to a method for noise reduction in smart gas stations, an Internet of Things system, and a storage medium. Background Technology

[0002] Noise sources at gas stations mainly include mechanical noise, pneumatic noise, and electromagnetic noise. Noise can have serious impacts on gas station staff and those in the surrounding environment (e.g., health hazards, including hearing loss, neurasthenia, and various neurological and psychological damages). With the increase in users and natural gas consumption, gas stations are facing increasingly frequent high-load operations, making noise problems increasingly prominent. Therefore, effectively solving this problem has become an urgent need for gas companies. Installing noise-reducing pipes or sound insulation panels often has limited lifespan and poor noise reduction effects. Actively applying noise reduction signals often has significant limitations and is subject to lag, failing to provide timely warnings and adjustments when noise levels change, potentially harming personnel's health.

[0003] Therefore, it is desirable to provide a smart gas station noise reduction method, an Internet of Things system, and a storage medium that can predict noise changes in gas stations, make timely adjustments and provide early warnings, and reduce harm to people's health. Summary of the Invention

[0004] One embodiment of this specification provides a method for noise reduction in smart gas stations. The method includes: acquiring relevant data of a target gas station, the relevant data including at least one of the target gas station's operational data, noise data of the target gas station, and pressure regulation parameters of an associated gas station, wherein the associated gas station is a gas station in a gas pipeline network that jointly regulates pressure with the target gas station; predicting noise enhancement data for at least one future time period of the target gas station based on the relevant data; and determining noise reduction control parameters based on the noise enhancement data and the pressure regulation parameters in response to the noise enhancement data meeting preset conditions, the noise reduction control parameters including at least the pressure regulation update parameters of the target gas station and / or the associated gas station for the at least one future time period.

[0005] One embodiment of this specification provides a smart gas station noise reduction IoT system. The system includes: a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas pipeline equipment sensor network platform, and a smart gas pipeline equipment object platform. The smart gas safety management platform includes a smart gas pipeline safety management sub-platform and a smart gas data center. The smart gas pipeline equipment sensor network platform is configured to interact with the smart gas data center and the smart gas pipeline equipment object platform. The smart gas safety management platform is configured to acquire relevant data from the target gas station, including the operation data of the target gas station. The system comprises at least one of pipeline data, noise data of the target gas station, and pressure regulation parameters of associated gas stations, wherein the associated gas stations are gas stations in the gas pipeline network that jointly regulate pressure with the target gas station; based on the relevant data, noise enhancement data for at least one future time period of the target gas station is predicted; in response to the noise enhancement data meeting preset conditions, noise reduction control parameters are determined based on the noise enhancement data and the pressure regulation parameters, wherein the noise reduction control parameters include at least the pressure regulation update parameters of the target gas station and / or the associated gas station for the at least one future time period; the smart gas service platform is configured to send the noise reduction control parameters to the smart gas user platform.

[0006] This specification provides one or more embodiments of a smart gas station noise reduction device, the device including at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least a portion of the computer instructions to implement a smart gas station noise reduction method.

[0007] One embodiment of this specification provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes a smart gas station noise reduction method. Attached Figure Description

[0008] 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 limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is an exemplary platform structure diagram of a smart gas station noise reduction IoT system according to some embodiments of this specification;

[0010] Figure 2 This is an exemplary flowchart of a noise reduction method for smart gas stations according to some embodiments of this specification;

[0011] Figure 3This is an exemplary schematic diagram illustrating the prediction of noise-enhanced data using a first prediction model according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart illustrating the determination of noise reduction control parameters according to some embodiments of this specification;

[0013] Figure 5 This is an exemplary schematic diagram illustrating the determination of estimated augmented data using a second prediction model according to some embodiments of this specification. Detailed Implementation

[0014] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0015] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0016] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0017] Figure 1 This is an exemplary platform structure diagram of a smart gas station noise reduction IoT system according to some embodiments of this specification. It should be noted that the following embodiments are for illustrative purposes only and do not constitute a limitation thereof.

[0018] like Figure 1 As shown, the smart gas station noise reduction IoT system includes an interactive smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas pipeline equipment sensor network platform, and a smart gas pipeline equipment object platform. In some embodiments, the smart gas station noise reduction IoT system may be part of a processing device or implemented by the processing device.

[0019] A smart gas user platform can refer to a platform used for interacting with users. In some embodiments, a smart gas user platform can be configured as a terminal device.

[0020] In some embodiments, the smart gas user platform may include a gas user sub-platform and a regulatory user sub-platform. The gas user sub-platform can be used to interact with gas users. The regulatory user sub-platform can be used to interact with regulatory users (e.g., relevant personnel and / or departments responsible for ensuring gas safety).

[0021] A smart gas service platform can be a platform for receiving and transmitting data and / or information. For example, a smart gas service platform can receive instructions sent by a smart gas user platform and, after processing the instructions, send them to a smart gas safety management platform. As another example, a smart gas service platform can obtain information required by the user from the smart gas safety management platform and send it to the smart gas user platform. In some embodiments, the smart gas service platform is configured to send noise reduction control parameters to the smart gas user platform.

[0022] In some embodiments, the smart gas service platform may include a smart gas consumption service sub-platform and a smart regulatory service sub-platform.

[0023] In some embodiments, the smart gas service sub-platform can interact with the gas user sub-platform, and the smart supervision service sub-platform can interact with the supervision user sub-platform.

[0024] The intelligent gas safety management platform can provide sensing, management, and control functions for the intelligent gas station noise reduction IoT system. It can coordinate and integrate the connections and collaboration between various functional platforms, and aggregate all information from the intelligent gas station noise reduction IoT system.

[0025] In some embodiments, the smart gas safety management platform may include a smart gas data center and a smart gas pipeline safety management sub-platform.

[0026] The smart gas data center can aggregate and store all operational data from the smart gas station noise reduction IoT system. The smart gas safety management platform can interact with the smart gas service platform and the smart gas pipeline equipment sensor network platform through the smart gas data center.

[0027] The intelligent gas pipeline safety management sub-platform can be an independent data usage platform. It can acquire relevant data from the intelligent gas data center and send management operation data to it. In some embodiments, the intelligent gas pipeline safety management sub-platform may include functions such as pipeline inspection safety management, pipeline gas leak monitoring, pipeline equipment safety monitoring, emergency management, pipeline geographic information management, station inspection safety management, station gas leak monitoring, station equipment safety monitoring, pipeline risk assessment management, and pipeline simulation management.

[0028] For example, the smart gas data center can receive operation and management information query instructions for gas stations issued by the smart gas service platform. The smart gas data center can also issue instructions to the smart gas pipeline equipment sensor network platform to retrieve relevant data (e.g., gas flow and gas pressure), or receive relevant data from the gas equipment uploaded by the smart gas pipeline equipment sensor network platform. The smart gas data center then sends the gas equipment-related data to the smart gas management sub-platform for analysis and processing.

[0029] A smart gas pipeline network equipment sensor network platform can refer to a functional platform that manages sensor communication. In some embodiments, the smart gas pipeline network equipment sensor network platform can be configured as a communication network and a gateway. The smart gas pipeline network equipment sensor network platform can receive relevant data uploaded by the object platform and issue instructions to the smart gas pipeline network equipment object platform to retrieve relevant data. Alternatively, it can receive instructions from the smart gas data center to retrieve relevant data and upload relevant data to the smart gas data center. In some embodiments, the smart gas pipeline network equipment sensor network platform is configured to interact with the smart gas data center and the smart gas pipeline network equipment object platform.

[0030] In some embodiments, the intelligent gas pipeline network equipment sensor network platform can realize one or more functions such as network management, protocol management, command management, and data parsing.

[0031] The intelligent gas pipeline network equipment object platform can refer to a functional platform for generating sensing information. This platform can be configured with various gas-related devices, including pipeline equipment. Pipeline equipment may include gas gate stations, sections of gas pipelines, gas valve control equipment, gas meters, flow meters, pressure gauges, noise monitoring equipment, pressure regulating equipment, temperature sensors, humidity sensors, etc. The intelligent gas pipeline network equipment object platform can acquire information including, but not limited to, gas flow information and gas pressure information. This collected information can be transmitted to the intelligent gas safety management platform via the intelligent gas pipeline network equipment sensor network platform.

[0032] For more information on the above, please refer to the following: Figures 2 to 5 Related descriptions.

[0033] The smart gas station noise reduction IoT system can form an information operation closed loop between the smart gas pipeline network equipment object platform and the smart gas user platform, and operate in a coordinated and regular manner under the unified management of the smart gas safety management platform, so as to realize the informatization and intelligence of gas station noise reduction.

[0034] Figure 2 This is an exemplary flowchart illustrating a noise reduction method for smart gas stations according to some embodiments of this specification. In some embodiments, process 200 may be executed by a smart gas safety management platform. Figure 2 As shown, process 200 includes the following steps:

[0035] Step 210: Obtain relevant data for the target site.

[0036] The target station refers to a gas station that requires noise reduction treatment.

[0037] Relevant data refers to the data involved in the noise reduction processing of the target site. In some embodiments, relevant data includes at least one of the following: the operating data of the target site, the noise data of the target site, and the voltage regulation parameters of the associated site.

[0038] Operational data refers to data related to the gas delivery at the target site. For example, operational data may include gas flow rate, gas pressure, etc., at the target site and its various input gas pipelines. In some embodiments, the smart gas safety management platform can acquire operational data from the smart gas pipeline equipment object platform based on the smart gas pipeline equipment sensor network platform, and transmit the operational data to the smart gas data center within the smart gas safety management platform.

[0039] Noise data refers to noise-related data during the operation of the target gas station. In some embodiments, noise data may include basic noise data and historical noise enhancement data. Historical noise enhancement data refers to noise enhancement data of the target gas station at different times over a past period. In some embodiments, the intelligent gas safety management platform may filter out the basic noise data from the acquired noise data to determine the historical noise enhancement data. For further explanation of noise enhancement data, see step 220 and its related description.

[0040] In some embodiments, the smart gas safety management platform can acquire noise data from the smart gas pipeline equipment object platform based on the smart gas pipeline equipment sensor network platform, and transmit the noise data to the smart gas data center in the smart gas safety management platform.

[0041] Associated stations are gas stations within a gas pipeline network that work together with target stations to regulate pressure. Multiple associated and target stations can exist within the same gas pipeline network. These associated and target stations work in concert. This concerted action enables functions such as pressure balancing and adjustment, fault handling, gas quality assurance, gas supply flexibility, and monitoring and communication, thereby ensuring the stable and safe operation of the entire gas pipeline network, optimizing gas supply, and guaranteeing users' gas needs.

[0042] Pressure regulation parameters refer to relevant parameters of the pressure regulation process at associated stations. For example, pressure regulation parameters may include planned pressure regulation time, pressure regulation load data, and the pipeline where pressure regulation occurs. Pressure regulation load data refers to the pressure difference before and after pressure regulation at the associated station. A plan refers to the relevant pressure regulation schedule for the associated station in at least one future time period.

[0043] The intelligent gas safety management platform can obtain pressure regulation parameters in various ways. For example, it can directly obtain pre-stored pressure regulation parameters from the intelligent gas station noise reduction IoT system. In some embodiments, the platform can obtain pressure regulation parameters from the intelligent gas pipeline equipment object platform based on the intelligent gas pipeline equipment sensor network platform and transmit them to the intelligent gas data center within the platform. In other embodiments, the platform can directly retrieve pressure regulation parameters from the intelligent gas data center based on the intelligent gas pipeline safety management sub-platform.

[0044] Step 220: Based on relevant data, predict noise enhancement data for at least one future time period of the target site.

[0045] At least one future time period refers to at least one future time period. In some embodiments, the intelligent gas safety management platform may select a time period in which people are more sensitive to noise as at least one future time period (e.g., lunch break, after get off work, etc.). In some embodiments, the intelligent gas safety management platform may select a time period with higher baseline noise data as at least one future time period (e.g., a time period very close to the noise limit). The noise limit refers to the maximum noise level of the target site set by relevant regulations.

[0046] Noise enhancement data refers to data showing an increase in noise level compared to baseline noise data. For example, noise enhancement data can include the increments in parameters such as sound frequency and intensity compared to baseline noise data. Noise enhancement data can be detected across the entire target site, or it can be specific noise enhancement data detected at multiple points of interest within the gas station. For more information on points of interest, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0047] A smart gas safety management platform can predict noise enhancement data for at least one future time period through various methods. In some embodiments, the platform can construct a query feature vector based on the target station's operational data, basic noise data, and pressure regulation parameters of associated stations, and determine the noise enhancement data by vector matching based on a first vector database. The first vector database contains historical vectors composed of multiple historical operational data, historical noise data, and historical pressure regulation parameters of associated stations, as well as the historical noise enhancement data corresponding to these historical vectors. In some embodiments, the platform can determine historical vectors that meet preset conditions in the first vector database based on the query feature vectors. The platform can then determine historical vectors that meet the preset conditions as reference vectors and identify the historical noise enhancement data corresponding to these reference vectors as noise enhancement data for at least one future time period. The preset conditions can refer to the judgment conditions used to determine the historical vectors. In some embodiments, the preset conditions may include vector distance less than a distance threshold, minimum vector distance, etc.

[0048] In some embodiments, the intelligent gas safety management platform can determine the pressure regulation load data of the target station for at least one future period based on the pressure regulation parameters of the associated station; and predict the noise enhancement data of the target station for at least one future period based on the relevant data and the pressure regulation load data.

[0049] The intelligent gas safety management platform can determine the pressure regulation load data of a target gas station for at least one future time period through various methods. In some embodiments, the intelligent gas safety management platform can determine the gas pressure output by upstream associated gas stations based on the pressure regulation parameters of associated gas stations, and use the difference between the gas pressure output by upstream associated gas stations of each pressure regulating device at the target gas station and the target gas pressure of the target gas station as the pressure regulation load data. The target gas pressure refers to the gas pressure that the target gas station needs to output. The upstream associated gas stations of the pressure regulating devices refer to gas stations located upstream of the target gas station in the gas pipeline network. In some embodiments, the intelligent gas safety management platform can obtain the pre-defined gas pressure output by upstream associated gas stations and the target gas pressure from the intelligent gas pipeline network equipment object platform based on the intelligent gas pipeline network equipment sensor network platform.

[0050] In some embodiments, the intelligent gas safety management platform can assess the impact of gas usage on gas pressure based on upstream and downstream gas consumption data of the target station. Based on the usage impact value and the pressure regulation parameters of the associated stations, the intelligent gas safety management platform determines the pre-regulation pressure and target pressure of the target station. Based on the pre-regulation pressure and target pressure, the intelligent gas safety management platform can determine the pressure regulation load data of the target station for at least one future time period.

[0051] Gas consumption data upstream and downstream of the target station refers to the relevant data on gas usage by gas equipment between the target station and its associated gas input and output stations. This includes gas consumption data upstream and downstream of the target station. Upstream gas consumption data refers to the relevant data on gas usage by gas equipment between the target station and its upstream associated stations. Downstream gas consumption data refers to the relevant data on gas usage by gas equipment between the target station and its downstream associated stations. In some embodiments, the intelligent gas safety management platform can obtain upstream and downstream gas consumption data from the intelligent gas pipeline equipment object platform based on the intelligent gas pipeline equipment sensor network platform.

[0052] The usage impact value refers to the change in gas pressure caused by gas usage. The usage impact value includes both upstream and downstream usage impact values. After gas usage, the amount of gas in the pipeline decreases, and the gas pressure output by the upstream associated stations naturally decreases as well. Therefore, the actual gas pressure input to the target station is less than the gas pressure output by the upstream associated stations.

[0053] In some embodiments, the intelligent gas safety management platform can use upstream and downstream gas consumption data from the same period to replace future gas consumption data to determine the amount of gas consumed in the gas pipeline; and based on the amount of gas consumed, determine the decrease in gas pressure in the gas pipeline as the usage impact value. In some embodiments, the intelligent gas safety management platform can collect and statistically analyze the pressure values ​​of gas before and after passing through the gas inlet pipeline using flow meters and pressure gauges in the gas pipeline network. The intelligent gas safety management platform can correlate historical gas consumption data corresponding to the inlet pipeline with the difference in historical gas pressure values ​​before and after passing through the inlet pipeline to create a preset table. After obtaining the amount of gas consumed, the intelligent gas safety management platform can obtain the decrease in gas pressure in the gas pipeline by looking up the preset table or other methods.

[0054] Pre-regulation pressure refers to the gas pressure at the target gas station before pressure regulation in at least one future time period. In some embodiments, the intelligent gas safety management platform can obtain the pre-regulation pressure of the target gas station by subtracting the upstream usage impact value from the gas pressure output by the upstream associated gas station. In some embodiments, the gas pressure output by the upstream associated gas station can be preset.

[0055] The target pressure refers to the gas pressure at the target gas station after pressure regulation for at least one future time period. In some embodiments, the intelligent gas safety management platform can use the gas pressure required by the downstream gas pipeline plus the downstream usage impact value as the target pressure. In some embodiments, the intelligent gas safety management platform can use the result of adding half of the downstream usage impact value to the gas pressure required by the downstream gas pipeline as the target pressure, so that the pressure in the downstream pipeline fluctuates around the required gas pressure. In some embodiments, the amount of adding the downstream usage impact value to the gas pressure required by the downstream gas pipeline can be determined according to actual needs. In some embodiments, the gas pressure required by the gas pipeline can be preset.

[0056] In some embodiments, the intelligent gas safety management platform can use the difference between the pressure before pressure regulation and the target pressure at the target gas station in a future period as the pressure regulation load data of the target gas station in the future period.

[0057] Some embodiments in this specification indirectly determine the pressure regulation load data of the target station in future periods by using the usage impact value determined based on upstream and downstream gas consumption data. This can make the predicted pressure regulation load data more accurate and lay a good foundation for the subsequent prediction of noise enhancement data.

[0058] In some embodiments, the intelligent gas safety management platform can construct a problem feature vector based on the pressure regulation load data of the target station, basic noise data, and pressure regulation parameters of associated stations, and determine the noise enhancement data by vector matching based on a second vector database. For further explanation of vector matching, please refer to the relevant description above. The second vector database contains historical vectors composed of pressure regulation load data of multiple historical target stations, historical noise data, and pressure regulation parameters of historical associated stations, as well as historical noise enhancement data for at least one time period corresponding to each historical vector. In some embodiments, the intelligent gas safety management platform can, based on the problem feature vector, determine historical vectors in the second vector database that meet preset vector conditions as conforming vectors, and determine the historical noise enhancement data for at least one time period corresponding to the conforming vectors as noise enhancement data.

[0059] In some embodiments, the intelligent gas safety management platform can determine noise enhancement data for at least one future time period at the target site using a first prediction model. Further details can be found in [link to relevant documentation]. Figure 3 And its related descriptions.

[0060] Some embodiments in this specification, by simultaneously considering relevant data and voltage regulation load data, can more accurately predict the noise enhancement data of the target station in future periods, which is beneficial to improving the accuracy of the noise reduction control parameters subsequently determined.

[0061] Step 230: In response to the noise enhancement data meeting the preset conditions, determine the noise reduction control parameters based on the noise enhancement data and the voltage regulation parameters.

[0062] Preset conditions refer to the conditions that must be met for noise reduction to take effect. For example, a preset condition could be that the noise enhancement data exceeds a maximum threshold. The maximum threshold is the difference between the noise limit of the target site and the baseline noise data. In some embodiments, preset conditions can be determined based on prior knowledge. For further explanation of noise limits, see step 220 and its related description.

[0063] Noise reduction control parameters refer to relevant parameters that are adjusted for noise reduction purposes. In some embodiments, noise reduction control parameters include at least voltage regulation update parameters for the target site and / or associated sites for at least one future time period.

[0064] Voltage regulation update parameters refer to the updated voltage regulation parameters.

[0065] In some embodiments, the intelligent gas safety management platform can determine noise reduction control parameters in various ways. For example, the platform can appropriately reduce the pressure regulation parameters of the target station based on noise enhancement data, thereby reducing the noise enhancement data at the target station. The platform can also identify associated stations corresponding to gas pipelines with lower gas flow and pressure based on the target station's operational data. Furthermore, the platform can increase the pressure regulation parameters of these associated stations to meet the gas pressure demand.

[0066] In some embodiments, noise reduction control parameters can be determined based on candidate parameters; for further details, please refer to [link to relevant documentation]. Figure 4 And related content.

[0067] Some embodiments in this specification predict noise enhancement data for future periods based on relevant data of the target site and determine noise reduction control parameters. This allows for personalized control of noise changes at the target site, real-time adjustment of voltage regulation parameters, ensuring that noise levels meet requirements, and reducing the harm of noise to personnel health.

[0068] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0069] Figure 3 This is an exemplary schematic diagram illustrating the prediction of noise-enhanced data using a first prediction model according to some embodiments of this specification.

[0070] In some embodiments, such as Figure 3As shown, the intelligent gas safety management platform can predict noise enhancement data 330 for at least one future time period of the target gas station based on the station sub-map 310 and a first prediction model 320, which is a machine learning model. More information on noise enhancement data for at least one future time period of the target gas station can be found in [link to relevant documentation]. Figure 2 Related descriptions.

[0071] The target site sub-map 310 refers to a directed graph used to represent the relationships between pressure regulating equipment and points of interest within the target site. The target site sub-map 310 can include information on multiple pressure regulating devices and their corresponding pipelines, points of interest, and other features within the target site. The sub-map can, on the one hand, demonstrate the noise superposition between multiple pressure regulating devices and points of interest; on the other hand, it can supplement noise data generated by gas transmission in pipelines, making the predicted noise enhancement data more complete.

[0072] Noise from different locations has varying impacts on the surrounding environment. At least one point of concern can refer to at least one location where noise requires focused attention. For example, a location facing densely populated residential areas may have a greater noise impact, and the smart gas safety management platform can designate this location as a point of concern. In some embodiments, the smart gas safety management platform can determine the number and location of points of concern based on actual needs.

[0073] The sub-graph 310 of the target site can include nodes and edges.

[0074] In some embodiments, a node may include voltage regulating equipment within the target site (e.g., Figure 3 The voltage regulating equipment A-1, A-2, A-3, A-4, etc. within the target site A shown), and at least one point of interest within the target site (e.g., Figure 3 (Points of interest within target site A, such as B-1 and B-2).

[0075] In some embodiments, the node characteristics of a nodal voltage regulator may include voltage regulation parameters, operating data, noise limits, voltage regulation load data, and basic noise data. For further information on voltage regulation parameters, operating data, noise limits, voltage regulation load data, and basic noise data, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0076] In some embodiments, the node characteristics of a point of interest may include the point's location.

[0077] In some embodiments, edges can be directed edges. Edges can include first-type edges and second-type edges. In some embodiments, first-type edges can include gas pipelines between any nodes, and the direction of the first-type edges indicates the flow direction of gas within the gas pipelines. In some embodiments, first-type edges can include internal edges within the target station (e.g., edge 311, etc.) and external edges connected to other associated stations (e.g., edges 312 and 313, i.e., edges in the station pressure regulation map). In some embodiments, the characteristics of first-type edges can include pipeline parameters (e.g., pipeline material, pipe diameter, pipe length, residual impurities in the pipeline, etc.). In some embodiments, second-type edges can include the noise propagation direction between any nodes (e.g., edge 314, etc.). The characteristics of second-type edges can include the relative positional relationship between each pressure regulating device and the point of interest (e.g., the distance and direction between each pressure regulating device and the point of interest, etc.).

[0078] In some embodiments, the intelligent gas safety management platform can construct a sub-map 310 of the target gas station based on the pressure regulating equipment and / or relevant data of at least one point of interest at the target gas station.

[0079] The first prediction model 320 refers to the model used to determine the noise-enhanced data. The first prediction model 320 can be a machine learning model (e.g., any one or a combination of neural networks (NN), graph neural networks (GNN), etc.).

[0080] In some embodiments, the input of the first prediction model 320 may include a sub-map 310 of the target station, and the output may be noise enhancement data 330 for at least one future time period of the target station, wherein the noise enhancement data for at least one future time period corresponding to at least one point of interest in the node output of the first prediction model is noise enhancement data for at least one future time period.

[0081] In some embodiments, the first prediction model can be trained using multiple first training samples with first labels. Multiple first training samples with first labels can be input into the initial first prediction model. A loss function is constructed using the first labels and the results of the initial first prediction model. The parameters of the initial first prediction model are iteratively updated based on the loss function. Model training is complete when the loss function of the initial first prediction model satisfies a preset condition, resulting in a trained first prediction model. The preset condition may be loss function convergence, the number of iterations reaching a threshold, etc.

[0082] In some embodiments, the first training sample can be a historical site sub-map constructed based on historical data from a first time period, and the first label can be the actual noise enhancement data of different points of interest in the historical data from a second time period. The first time period is earlier than the second time period, and the second time period is a future time period of the first time period. In some embodiments, the first training sample can be obtained based on historical data. The first label of the first training sample can be obtained through manual annotation.

[0083] Using the sub-map of the target gas station as input to the first prediction model can reflect the superposition of noise among multiple pressure regulating devices. Simultaneously, it can supplement noise data generated by gas transmission in pipelines, making the predicted noise enhancement data more accurate and complete.

[0084] Figure 4 This is an exemplary flowchart illustrating the determination of noise reduction control parameters according to some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps. In some embodiments, process 400 may be executed by a smart gas safety management platform.

[0085] Step 410: Based on the noise enhancement data, determine the adjustment range of the voltage regulation parameters.

[0086] The adjustment range refers to the magnitude of the pressure regulating parameter adjustment. In some embodiments, the adjustment range includes the adjustment range of each of the at least one associated station. In some embodiments, the smart gas safety management platform can determine the adjustment range in various ways. In some embodiments, the smart gas safety management platform can determine the adjustment range of the pressure regulating parameter for at least one future time period of the target station based on noise enhancement data (regulating the noise data of the target station to below the noise limit). The smart gas safety management platform can determine the total pressure regulating load that at least one associated station needs to share based on the adjustment range of the pressure regulating parameter of the target station to ensure that the gas pressure in the gas pipeline network can meet the gas supply demand. The smart gas safety management platform can evenly distribute the total pressure regulating load that needs to be shared to at least one associated station to determine the adjustment range of the pressure regulating parameter for each associated station. For more information on noise limits, see [link to relevant documentation]. Figure 2 And its related descriptions.

[0087] In some embodiments, the intelligent gas safety management platform can determine the adjustment range for associated gas stations based on their noise tolerance and associated enhancement data. In some embodiments, the lower the noise tolerance, the smaller the adjustment range for the associated gas station, in order to reduce the possibility of excessive noise at the associated gas station.

[0088] Noise tolerance refers to the maximum noise level that an associated gas station can tolerate. In some embodiments, noise tolerance is determined based on population distribution information around the associated gas station and the weather information for the day. In sunny weather, noise propagation is good, resulting in lower noise tolerance; in inclement weather (e.g., rain), rain noise interferes with noise propagation, resulting in higher noise tolerance. When closer to the associated gas station, noise volume attenuation is small, resulting in lower noise tolerance; conversely, noise volume attenuation is large, resulting in higher noise tolerance. In some embodiments, the intelligent gas safety management platform can, based on population distribution information, identify multiple high-population areas of concern and collect noise data from these areas under different weather conditions using noise collection devices. The noise data is then filtered to remove other sound waves (e.g., rain sounds, human voices), yielding voltage regulation noise data. The noise tolerance is then determined based on the magnitude of this voltage regulation noise data.

[0089] Correlation enhancement data refers to noise enhancement data associated with specific gas stations. In some embodiments, the intelligent gas safety management platform can identify correlation enhancement data from historical time periods as correlation enhancement data for corresponding future time periods.

[0090] In some embodiments, the intelligent gas safety management platform can, based on the total pressure regulating load that needs to be shared by at least one associated station, proportionally allocate the total pressure regulating load according to the difference between the noise tolerance and the associated enhancement data of each associated station, and determine the adjustment range of at least one associated station. For example, the adjustment range of each associated station is positively correlated with the difference between the noise tolerance and the associated enhancement data of that associated station.

[0091] Some embodiments in this specification determine the adjustment range of at least one associated station by using noise tolerance and associated enhancement data. This can better take into account the noise tolerance of different associated stations and the impact of environmental factors on noise, thereby more accurately determining the adjustment range of the voltage regulation parameters of the associated stations, which helps to improve the accuracy and adaptability of voltage regulation control.

[0092] Step 420: Adjust the voltage regulation parameters based on the adjustment range to determine candidate parameters.

[0093] Candidate parameters refer to parameters to be confirmed as pressure regulation update parameters. In some embodiments, the intelligent gas safety management platform can adjust the pressure regulation parameters of the target station and / or at least one associated station based on the adjustment range determined above, thereby obtaining a set of candidate pressure regulation update parameters for the target station and / or at least one associated station as candidate parameters. Candidate pressure regulation update parameters refer to data to be determined as pressure regulation update parameters.

[0094] Step 430: Based on the evaluation data of the candidate parameters, the noise reduction control parameters are determined through iterative updates.

[0095] Evaluation data refers to the scores used to assess the merits of candidate parameters. The greater the adjustment of the pressure regulating parameters at the target station and / or at least one associated station, the greater the potential unknown risks (e.g., increased noise, insufficient gas supply pressure, etc.), and the lower the evaluation data. In some embodiments, the evaluation data is negatively correlated with the sum of the percentage of the adjustment range of the target station relative to the original pressure regulating parameter and / or the percentage of the adjustment range of at least one associated station relative to the original pressure regulating parameter.

[0096] In some embodiments, the intelligent gas safety management platform can determine the estimated enhancement data for the target station and associated stations based on candidate parameters; and determine the evaluation data for the candidate parameters based on the estimated enhancement data.

[0097] Predicted enhancement data refers to noise enhancement data predicted based on candidate parameters. In some embodiments, the intelligent gas safety management platform can determine the corresponding sub-map of associated stations based on information such as candidate parameters, and determine the predicted enhancement data of the associated stations corresponding to the candidate parameters through a first prediction model. For more information on the first prediction model, see [link to relevant documentation]. Figure 3 And its related descriptions.

[0098] In some embodiments, the intelligent gas safety management platform can determine the estimated enhancement data based on a second predictive model. Further details can be found in [link to relevant documentation]. Figure 5 And its related descriptions.

[0099] In some embodiments, the intelligent gas safety management platform can determine whether the noise level of a gas station exceeds the noise limit based on the estimated augmentation data of the target station and associated stations, thereby determining the assessment data. For example, if the noise level of each gas station does not exceed the noise limit, the assessment score is higher; if the noise level of any gas station exceeds the noise limit, the more gas stations that exceed the limit, the lower the assessment score.

[0100] Some embodiments of this specification determine the estimated enhancement data of the target station and associated stations based on candidate parameters, and determine the evaluation data of the candidate parameters based on the estimated enhancement data. This can assess the potential impact of different candidate parameters on gas stations, thereby better guiding the subsequent iterative updates of candidate parameters and facilitating the faster determination of noise reduction and control parameters.

[0101] In some embodiments, the intelligent gas safety management platform can determine noise reduction control parameters through iterative updates based on the evaluation data of candidate parameters. The noise reduction control parameters are determined based on whether the iteration meets a termination condition. For example, the iteration termination condition could be that the evaluation data of a candidate parameter is greater than an evaluation threshold. When the evaluation data of a candidate parameter in the current round is greater than the evaluation threshold, the intelligent gas safety management platform can determine the candidate pressure regulation update parameters from the candidate parameters in the current round as the target station and / or associated station's pressure regulation update parameters for at least one future time period in the noise reduction control parameters. The evaluation threshold can be determined based on prior knowledge. When the evaluation data of a candidate parameter in the current round is less than the evaluation threshold, the intelligent gas safety management platform can redetermine the candidate parameters for the next round and continue iterative updates until the iteration meets the termination condition, at which point the iteration stops, and the noise reduction control parameters are determined. The updated candidate parameters can be obtained by randomly adjusting the pressure regulating parameters of a specified number of associated gas stations in the candidate parameters according to the preset update rate. For example, if the preset update rate is 5% and the specified number is 3, the smart gas safety management platform can randomly select 3 associated stations and increase or decrease their pressure regulating parameters by 5% to obtain the updated candidate parameters.

[0102] Some embodiments of this specification determine the adjustment range of the voltage regulation parameter based on noise enhancement data, and adjust the voltage regulation parameter based on the adjustment range to determine candidate parameters. Then, based on the evaluation data of the candidate parameters, the noise reduction control parameter is determined through iterative updates. This enables dynamic adjustment and optimization of the voltage regulation parameter to better adapt to noise changes, which helps to improve the accuracy and efficiency of noise reduction control.

[0103] Figure 5 This is an exemplary schematic diagram illustrating the determination of estimated augmented data using a second prediction model according to some embodiments of this specification.

[0104] In some embodiments, the intelligent gas safety management platform can construct a station pressure regulation map 510; based on the station pressure regulation map 510, the estimated enhancement data 530 is determined through a second prediction model 520. For further explanation regarding the estimated enhancement data, see [link to relevant documentation]. Figure 4 And its related descriptions.

[0105] The station pressure regulation map 510 refers to a map that can characterize the pressure regulation process of the target station and related stations. The station pressure regulation map can be a data structure composed of nodes and edges, with edges connecting nodes, and nodes and edges can have features.

[0106] In some embodiments, the intelligent gas safety management platform can construct a station pressure regulation map 510 based on candidate parameters, operating data, pressure regulation load data, noise limits, environmental characteristics, at least one future time period requiring prediction, and at least one point of interest for the target station and / or associated stations. A second prediction model 520 processes the station pressure regulation map 510 to determine the predicted enhancement data 530. For further explanation of the candidate parameters, see [link to relevant documentation]. Figure 4 And its related description. For more information on future periods, see [link to relevant documentation / description]. Figure 2 And its related descriptions.

[0107] Environmental characteristics may include environmentally related attributes. For example, environmental characteristics may include ambient temperature and ambient humidity. In some embodiments, the intelligent gas safety management platform can acquire environmental characteristics from temperature and humidity sensors configured on the intelligent gas pipeline network equipment object platform, based on the intelligent gas pipeline network equipment sensor network platform. For more information on operating data, pressure regulation load data, and noise limits, please refer to [link to relevant documentation]. Figure 2 And its related descriptions. For more information on the points of interest, please see [link / reference]. Figure 3 And its related descriptions.

[0108] In some embodiments, the nodes of the power station voltage regulation map 510 may correspond to target power stations and associated power stations. Node features may reflect the relevant characteristics of the corresponding power stations. For example, node features may include parameters involved in constructing the power station voltage regulation map.

[0109] In some embodiments, the edges of the station pressure regulating map 510 may correspond to gas pipelines. In some embodiments, the edges are directed edges, and the direction of the edges can be determined according to the gas delivery direction. For example, the direction of the edges may be the gas delivery direction. The edge features may reflect the relevant features of the corresponding gas pipeline. In some embodiments, the edge features may include pipeline length, pipeline diameter, etc.

[0110] In some embodiments, the station pressure regulating map 510 includes at least one station sub-map. The intelligent gas safety management platform can adjust the station pressure regulating map 510 based on the station sub-map, replacing the nodes represented by the corresponding stations in the station pressure regulating map 510 with the station sub-map.

[0111] For example only, Figure 5 In the station pressure regulation diagram 510, A represents the target station, and A1, A2, A3, and A4 represent associated stations. Figure 3 In the sub-map of the target gas station, A-1, A-2, A-3, and A-4 represent pressure regulating equipment included in node A of the target gas station, while B-1 and B-2 represent points of interest included in node A of the target gas station. The intelligent gas safety management platform can... Figure 5 Node A in the middle passes through Figure 3Replacement of field station sub-maps.

[0112] In some embodiments, at least one site submap is constructed based on at least one pressure regulating device within the target site and / or associated sites, piping connected to the at least one pressure regulating device, and points of interest. For further explanation of site submaps, see [link to documentation]. Figure 3 And its related descriptions.

[0113] In some embodiments of this specification, the station pressure regulation map includes at least one station sub-map. On the one hand, it can reflect the superposition of noise between multiple pressure regulation devices and points of interest. On the other hand, it can supplement the noise data generated by the transmission of gas in the pipeline, making the predicted enhancement data for at least one future period more complete and accurate.

[0114] In some embodiments, the second prediction model 520 may be a graph neural network (GNN) model, etc. In some embodiments, the input to the second prediction model 520 may be a power station voltage regulation map 510, and the output may be estimated augmented data 530 for at least one future time period for the target power station and associated power stations, wherein the nodes in the second prediction model output estimated augmented data 530 for at least one future time period for the corresponding power station. In some embodiments, the input to the second prediction model 520 may be a power station voltage regulation map 510 adjusted based on a power station sub-map, and the output may be estimated augmented data 530 for at least one future time period for the target power station, associated power stations, and points of interest, wherein the nodes in the second prediction model output estimated augmented data 530 for at least one future time period for the corresponding power station.

[0115] The second prediction model can be trained based on a second training sample with a second label. Multiple second training samples with second labels can be input into the initial second prediction model. A loss function is constructed using the second labels and the results of the initial second prediction model. The parameters of the initial second prediction model are then iteratively updated based on the loss function. Training is complete when the loss function of the initial second prediction model meets preset conditions, resulting in a trained second prediction model. These preset conditions can include loss function convergence, reaching a threshold number of iterations, etc.

[0116] In some embodiments, the second training sample can be a historical gas station pressure regulation map constructed based on historical data from a third time period, and the second label can be the actual noise enhancement data monitored by the gas station in historical data from a fourth time period. The third time period is earlier than the fourth time period, and the fourth time period is a future time period of the third time period. In some embodiments, the second training sample can be obtained based on historical data. The second label of the second training sample can be obtained through manual annotation.

[0117] When the station pressure regulation map includes at least one station sub-map, the second training sample can be a historical station pressure regulation map, a historical station sub-map, and a historical point of interest constructed based on historical data from the third time period. The second label can be noise enhancement data monitored at gas stations and at least one point of interest in the historical data from the fourth time period.

[0118] The method shown in some embodiments of this specification takes into account the flow of gas in each gas station and gas pipeline, as well as the mutual influence between each gas station, when determining the estimated enhancement data for at least one future time period of each gas station. This makes the determined estimated enhancement data for at least one future time period of each gas station more consistent with the actual situation and improves the accuracy of the estimated enhancement data.

[0119] This specification provides a smart gas station noise reduction device in some embodiments. The device includes at least one processor and at least one memory. The at least one memory is used to store computer instructions. The at least one processor is used to execute at least a portion of the computer instructions to implement the smart gas station noise reduction method described in any one of the embodiments of this specification.

[0120] Some embodiments of this specification provide a computer-readable storage medium that stores computer instructions, which, when executed by a computer, implement the intelligent gas station noise reduction method described in any one of the embodiments of this specification.

[0121] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0122] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0123] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0124] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for noise reduction in intelligent gas stations, characterized in that, The method includes: Acquire relevant data of the target gas station, including at least one of the target gas station's operating data, the target gas station's noise data, and pressure regulation parameters of associated gas stations, wherein the associated gas stations are gas stations in the gas pipeline network that jointly regulate pressure with the target gas station; Based on the relevant data, predict the noise enhancement data for at least one future time period of the target site; The prediction of noise enhancement data for at least one future time period of the target site based on the relevant data includes: Based on the pressure regulation parameters, determine the pressure regulation load data of the target station in the at least one future time period; Based on the relevant data and the voltage regulation load data, the noise enhancement data is predicted; In response to the noise enhancement data meeting preset conditions, noise reduction control parameters are determined based on the noise enhancement data and the voltage regulation parameters. The noise reduction control parameters include at least the voltage regulation update parameters of the target station and / or the associated station in the at least one future time period.

2. The method according to claim 1, characterized in that, The step of determining noise reduction control parameters based on the noise enhancement data and the voltage regulation parameters in response to the noise enhancement data meeting preset conditions includes: Based on the noise enhancement data, the adjustment range of the voltage regulation parameter is determined; The voltage regulation parameters are adjusted based on the adjustment range to determine candidate parameters; Based on the evaluation data of the candidate parameters, the noise reduction control parameters are determined through iterative updates.

3. The method according to claim 2, characterized in that, The evaluation data used to determine the candidate parameters includes: Based on the candidate parameters, the estimated augmentation data for the target site and the associated site are determined; The evaluation data for the candidate parameters is determined based on the predicted enhancement data.

4. A smart gas station noise reduction IoT system, characterized in that, The system includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas pipeline equipment sensor network platform, and a smart gas pipeline equipment object platform. The intelligent gas safety management platform includes an intelligent gas pipeline safety management sub-platform and an intelligent gas data center; The intelligent gas pipeline network equipment sensor network platform is configured to interact with the intelligent gas data center and the intelligent gas pipeline network equipment object platform; The intelligent gas safety management platform is configured as follows: Acquire relevant data of the target gas station, including at least one of the target gas station's operating data, the target gas station's noise data, and pressure regulation parameters of associated gas stations, wherein the associated gas stations are gas stations in the gas pipeline network that jointly regulate pressure with the target gas station; Based on the relevant data, predict the noise enhancement data for at least one future time period of the target site; The prediction of noise enhancement data for at least one future time period of the target site based on the relevant data includes: Based on the pressure regulation parameters, determine the pressure regulation load data of the target station in the at least one future time period; Based on the relevant data and the voltage regulation load data, the noise enhancement data is predicted; In response to the noise enhancement data meeting preset conditions, noise reduction control parameters are determined based on the noise enhancement data and the voltage regulation parameters. The noise reduction control parameters include at least the voltage regulation update parameters of the target station and / or the associated station in the at least one future time period. The smart gas service platform is configured to send the noise reduction and control parameters to the smart gas user platform.

5. The system according to claim 4, characterized in that, The intelligent gas safety management platform is further configured as follows: Based on the noise enhancement data, the adjustment range of the voltage regulation parameter is determined; The voltage regulation parameters are adjusted based on the adjustment range to determine candidate parameters; Based on the evaluation data of the candidate parameters, the noise reduction control parameters are determined through iterative updates.

6. The system according to claim 5, characterized in that, The intelligent gas safety management platform is further configured as follows: Based on the candidate parameters, the estimated augmentation data for the target site and the associated site are determined; The evaluation data for the candidate parameters is determined based on the predicted enhancement data.

7. A smart gas station noise reduction device, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is configured to execute at least a portion of the computer instructions to implement the method as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the method as described in any one of claims 1 to 3.

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