Intelligent Gas Pipeline Waste Gas Safety Treatment Method and System Based on the Internet of Things

Abstract

The present invention provides a method and system for safely processing waste gas from intelligent gas pipelines based on the Internet of Things. The system includes an intelligent gas government safety supervision and management platform, an intelligent gas government safety supervision sensor network platform, a gas company management platform, a gas company sensor network platform, and a gas equipment object platform; the gas company management platform is configured to determine the waste gas emission standard of the pipeline to be processed based on the waste gas treatment level of the pipeline to be processed; in response to the start of waste gas emission from the pipeline to be processed, obtain the instant processing parameters of the waste gas treatment equipment in the pipeline to be processed; determine the expected processing parameters based on at least one of the waste gas emission standard, the instant processing parameters, and the waste gas characteristics; generate an adjustment instruction according to the expected processing parameters to adjust the processing parameters of the waste gas treatment equipment based on the adjustment instruction. Through the foregoing method and system, the waste gas treatment effect can be ensured, and the processed waste gas can more efficiently and accurately meet the emission standard.

Description

Technical Field

[0001] This specification relates to the field of gas safety, and particularly to an intelligent gas pipeline waste gas safety treatment method and system based on the Internet of Things. Background Art

[0002] During the renovation and emergency repair process of gas pipelines, it is necessary to treat the gas inside local gas pipelines, such as discharging pressure, flaring and igniting, evacuating, etc., so as to reduce the gas concentration in the gas pipeline to a safe range. However, the existing discharge equipment only meets qualitative requirements, such as reducing the gas pressure to a specific degree, or reducing the gas concentration in the pipeline to a specific level. The current method lacks the evaluation of treatment efficiency and the stability of the treatment process.

[0003] CN216259902U provides a natural gas waste gas treatment device applied to long-distance natural gas pipelines. The natural gas waste gas treatment device is used to treat the natural gas discharged from each pressure relief point of the long-distance pipeline, absorbs gases such as methane by the adsorption method, and discharges the natural gas after the pressure relief is treated cleanly into the atmosphere.

[0004] The above method uses a natural gas waste gas treatment device to treat the gas discharged from the pipeline, but does not systematically evaluate the waste gas discharge time and discharge risk, and there are potential safety hazards.

[0005] Therefore, it is hoped to provide an intelligent gas pipeline waste gas safety treatment method and system based on the Internet of Things, which can systematically manage and evaluate the pipeline waste gas discharge, so as to ensure the safety of waste gas discharge. Summary of the Invention

[0006] In order to better systematically manage and evaluate the pipeline waste gas discharge and ensure the safety of waste gas discharge, it is hoped to provide an intelligent gas pipeline waste gas safety treatment method and system based on the Internet of Things.

[0007] The invention content includes an intelligent gas pipeline waste gas safety treatment system based on the Internet of Things. The system includes an intelligent gas government safety supervision and management platform, an intelligent gas government safety supervision sensor network platform, a gas company management platform, a gas company sensor network platform, and an intelligent gas equipment object platform. The gas company management platform includes a processor, and the gas company management platform is configured to: determine the waste gas emission standard of the pipeline to be treated based on the waste gas treatment level of the pipeline to be treated; in response to the start of waste gas emission from the pipeline to be treated, obtain the immediate treatment parameters of the waste gas treatment equipment in the pipeline to be treated from the gas equipment object platform through the gas company sensor network platform; determine the expected treatment parameters corresponding to a future preset time point based on at least one of the waste gas emission standard, the immediate treatment parameters, and the waste gas characteristics; generate an adjustment instruction according to the expected treatment parameters, and send the adjustment instruction to the gas equipment object platform through the gas company sensor network platform to control the gas equipment object platform to adjust the treatment parameters of the waste gas treatment equipment based on the adjustment instruction.

[0008] The invention content also includes an intelligent gas pipeline waste gas safety treatment method based on the Internet of Things. The method is executed by the processor of the gas company management platform in the gas pipeline waste gas treatment system based on the Internet of Things, and includes: determining the waste gas emission standard of the pipeline to be treated based on the waste gas treatment level of the pipeline to be treated; in response to the start of waste gas emission from the pipeline to be treated, obtain the immediate treatment parameters of the waste gas treatment equipment in the pipeline to be treated from the intelligent gas equipment object platform through the gas company sensor network platform; determining the expected treatment parameters corresponding to a future preset time point based on at least one of the waste gas emission standard, the immediate treatment parameters, and the waste gas characteristics; generating an adjustment instruction according to the expected treatment parameters, and sending the adjustment instruction to the intelligent gas equipment object platform through the gas company sensor network platform to control the intelligent gas equipment object platform to adjust the treatment parameters of the waste gas treatment equipment based on the adjustment instruction.

[0009] Through the foregoing method and system, the waste gas treatment effect can be guaranteed, and the treated waste gas can reach the emission standard more efficiently and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] This specification will further illustrate in the form of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0011] Figure 1 is an exemplary platform structure diagram of an intelligent gas pipeline waste gas safety treatment system according to some embodiments of this specification;

[0012] Figure 2 is an exemplary flowchart of an Internet of Things-based intelligent gas pipeline waste gas safety treatment method shown in some embodiments of this specification;

[0013] Figure 3 is a schematic diagram of determining expected treatment parameters based on expected treatment efficiency shown in some embodiments of this specification;

[0014] Figure 4 is a schematic diagram of determining expected treatment parameters based on waste gas treatment time shown in some embodiments of this specification;

[0015] Figure 5 is a schematic diagram of determining expected treatment parameters based on emission risk shown in some embodiments of this specification.

[0016] Explanation of reference numerals: 100 - Internet of Things-based intelligent gas pipeline waste gas safety treatment system; 110 - Intelligent gas government safety supervision management platform; 120 - Intelligent gas government safety supervision sensing network platform; 130 - Intelligent gas government safety supervision object platform; 140 - Gas company sensing network platform; 150 - Intelligent gas equipment object platform; 310 - Waste gas emission standard; 320 - Instantaneous treatment parameter; 330 - Waste gas characteristic; 340 - Standard treatment efficiency; 350 - Environmental characteristic; 360 - Target treatment efficiency; 370 - Expected treatment parameter; 410 - Pipeline parameter; 420 - Historical pressure regulating parameter sequence; 430 - Waste gas volume to be treated; 440 - Waste gas treatment time; 450 - Duration condition; 510 - Pipeline characteristic; 520 - Historical environmental characteristic; 530 - Historical waste gas characteristic; 540 - Risk assessment model; 550 - Emission risk; 560 - Temperature sequence of the target period. Detailed implementation manners

[0017] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0018] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0019] Unless the context clearly indicates otherwise, the words "a", "an", "one" and / or "the" are not specific to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" 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.

[0020] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the operations before or after may not necessarily be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. Also, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0021] Figure 1 It is an exemplary platform structure diagram of an Internet of Things-based intelligent gas pipeline waste gas safety treatment system shown in some embodiments of this specification. Hereinafter, the Internet of Things-based intelligent gas pipeline waste gas safety treatment system 100 (also referred to as the Internet of Things system) involved in the embodiments of this specification will be described in detail. It should be noted that the following embodiments are only used to explain this specification and do not constitute a limitation to this specification.

[0022] In some embodiments, the Internet of Things system includes an intelligent gas government safety supervision management platform 110, an intelligent gas government safety supervision sensing network platform 120, an intelligent gas government safety supervision object platform 130, a gas company sensing network platform 140, and an intelligent gas equipment object platform 150.

[0023] The intelligent gas government safety supervision management platform 110 is a comprehensive management platform for the government department to supervise the gas pipeline network and gas companies. In some embodiments, the intelligent gas government safety supervision management platform can be configured to supervise the gas pipeline waste gas treatment process based on the Internet of Things, and process and / or store the relevant data of the gas pipeline waste gas treatment process. Among them, the relevant data of the gas pipeline waste gas treatment process may include, but is not limited to, the environmental characteristics of the pipeline to be treated, the historical waste gas treatment time of the pipeline to be treated, and the corresponding historical scores, etc.

[0024] The intelligent gas government safety supervision sensing network platform 120 is a platform for comprehensive management of government sensing information. In some embodiments, the intelligent gas government safety supervision sensing network platform 120 can realize the functions of sensing information sensing communication and control information sensing communication between the intelligent gas government safety supervision management platform 110 and the intelligent gas government safety supervision object platform 130.

[0025] The Smart Gas Government Safety Supervision Object Platform 130 is a platform for generating government supervision information and executing control information. For example, the Smart Gas Government Safety Supervision Object Platform 130 can obtain safety supervision requirement information to enable government safety supervisors to conduct safety supervision on relevant tasks of gas pipeline waste gas treatment.

[0026] In some embodiments, the Smart Gas Government Safety Supervision Object Platform 130 may include a gas company management platform.

[0027] In some embodiments, the gas company management platform can interact with the Smart Gas Government Safety Supervision Management Platform 110 through the Smart Gas Government Safety Supervision Sensor Network Platform 120. For example, the gas company management platform can obtain data such as the environmental characteristics of the pipeline to be processed, the historical waste gas treatment time of the pipeline to be processed, and the corresponding historical scores from the Smart Gas Government Safety Supervision Management Platform 110 via the Smart Gas Government Safety Supervision Sensor Network Platform 120.

[0028] In some embodiments, the gas company management platform may include a communication module and a processor.

[0029] In some embodiments, the communication module can be used for communication and information transfer between various platforms and devices in the Internet of Things system.

[0030] In some embodiments, the processor can process information and / or data related to the Internet of Things system to perform the functions described in this specification.

[0031] The processor can be used to collect, analyze, and process data, generate corresponding control instructions based on the data, and send the control instructions to the actuator to make the actuator perform corresponding actions or functions. For example, send the control instructions to the communication module to make the communication module perform at least one of the functions of starting, relating, obtaining data, and transmitting data.

[0032] In some embodiments, the processor can determine the waste gas emission standard of the pipeline to be processed based on the waste gas treatment level of the pipeline to be processed; in response to the pipeline to be processed starting to emit waste gas, obtain the immediate processing parameters of the waste gas treatment equipment in the pipeline to be processed from the Smart Gas Equipment Object Platform through the gas company sensor network platform; determine the expected processing parameters corresponding to a future preset time point based on at least one of the waste gas emission standard, the immediate processing parameters, and the waste gas characteristics; generate an adjustment instruction according to the expected processing parameters, and send the adjustment instruction to the Smart Gas Equipment Object Platform through the gas company sensor network platform to control the Smart Gas Equipment Object Platform to adjust the processing parameters of the waste gas treatment equipment based on the adjustment instruction. For more detailed descriptions, please refer to the relevant descriptions in this specification Figures 3 - 5 of this specification.

[0033] By adopting a processor and a communication module, functions such as automatic data transmission, data processing, and automatic control can be realized, which improves the automation level of the gas pipeline waste gas treatment system based on the Internet of Things and can improve the efficiency and accuracy of gas dispatching.

[0034] In some embodiments, the gas company management platform can send an execution plan containing the processing parameters of the waste gas treatment equipment to the intelligent gas government safety supervision management platform 110, and execute the execution plan only when receiving a confirmation instruction from the gas government safety supervision management platform.

[0035] The gas company sensor network platform 140 is a platform for managing sensor communication. In some embodiments, the gas company sensor network platform 140 can interact with the gas company sensor network platform and the government safety supervision sensor network platform respectively.

[0036] In some embodiments, the gas company sensor network platform 140 can realize the functions of sensing information sensor communication and control information sensor communication.

[0037] The intelligent gas equipment object platform 150 is a functional platform for generating sensing information and executing control information.

[0038] In some embodiments, the intelligent gas equipment object platform 150 can at least include waste gas treatment equipment, monitoring equipment, etc.

[0039] In some embodiments, the intelligent gas equipment object platform 150 can interact bidirectionally with the gas company sensor network platform. For example, the intelligent gas equipment object platform 150 can receive an adjustment instruction issued by the gas company management platform via the gas company sensor network platform 140, and send the adjustment instruction to the waste gas treatment equipment to control the processing parameters of the waste gas treatment equipment.

[0040] The intelligent gas pipeline waste gas safety treatment system 100 based on the Internet of Things in some embodiments of this specification can form information communication among various platforms, and operate coordinately and regularly under the unified management of the intelligent gas government safety supervision object platform, realizing the intelligence and standardization of the gas pipeline waste gas treatment process, effectively supervising the process of treating waste gas in the gas pipeline, and avoiding potential safety hazards.

[0041] It should be noted that the above description of the system and platform is only for convenience of description and does not limit this specification within the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make any combination of each module, or form a subsystem and connect it with other modules.

[0042] Figure 2is an exemplary flowchart of an Internet of Things-based gas pipeline waste gas treatment method shown in some embodiments of this specification. As Figure 2 shown, process 200 includes the following steps. In some embodiments, process 200 can be executed by a gas company management platform.

[0043] Step 210, based on the waste gas treatment level of the pipeline to be treated, determine the waste gas emission standard of the pipeline to be treated.

[0044] The pipeline to be treated refers to a pipeline that needs to carry out gas treatment. Among them, gas treatment can include discharging the waste gas in the pipeline, treating the waste gas in the pipeline, etc. Waste gas refers to the gas in the gas pipeline that needs to be discharged or treated.

[0045] The waste gas treatment level can indicate the urgency of waste gas treatment. The higher the waste gas treatment level, the higher the urgency of waste gas treatment required for the pipeline to be treated, and the more urgent it is to complete the waste gas treatment as soon as possible.

[0046] In some embodiments, the waste gas treatment level can be determined by the reasons for waste gas treatment corresponding to the pipeline to be treated. In some embodiments, the reasons for waste gas treatment can include at least one of easy explosion, gas leakage, pipeline renovation, pressure regulation, and emergency repair. The gas company management platform can assign corresponding weights to different reasons, and based on at least one reason corresponding to the pipeline to be treated and its corresponding weight, determine the treatment evaluation value corresponding to the pipeline to be treated by means of weighted summation; based on the foregoing treatment evaluation value and the pre-set treatment level division standard, determine the waste gas treatment level corresponding to the pipeline to be treated.

[0047] In some embodiments, the reasons for waste gas treatment corresponding to the pipeline to be treated can be determined based on user feedback. For example, the gas company management platform can obtain the input data of gas management users in the smart gas government safety supervision management platform through the smart gas government safety supervision sensor network platform, and then determine the reasons for waste gas treatment corresponding to the pipeline to be treated.

[0048] In some embodiments, the weights corresponding to different reasons can be set based on prior experience and / or actual needs. For example, the weight ranges of emergency situations such as gas leakage and emergency repair are within a first preset range, and the weight ranges of non-emergency situations such as pipeline renovation and pressure regulation are within a second preset range. Among them, both the first preset range and the second preset range are greater than 0, and the minimum value of the first preset range is not less than the maximum value of the second preset range.

[0049] The pre-set treatment level division standard can be set based on at least one of prior experience, historical data, and actual needs.

[0050] In some embodiments, the processing level classification criteria can be determined based on the historical data of waste gas treatment. For example, the gas company management platform can determine the historical evaluation value corresponding to the pipeline to be processed in the historical data based on the aforementioned weights and the reasons for waste gas treatment corresponding to different waste gas treatment operations in the historical data; based on the maximum and minimum values of the historical evaluation values, determine the evaluation value range, and evenly divide the evaluation value range into several levels. The gas company management platform can determine the processing level classification criteria based on the aforementioned several levels and the evaluation value ranges corresponding to different levels.

[0051] The waste gas emission standard refers to the restrictive regulations on the concentration and / or total amount of waste gas discharged into the atmosphere. For example, the waste gas emission standard can include the maximum value of the concentration of each component in the waste gas discharged from the pipeline to be processed.

[0052] In some embodiments, the gas company management platform can determine the waste gas emission standard of the pipeline to be processed through the emission standard reference table based on the waste gas treatment level of the pipeline to be processed.

[0053] The emission standard reference table can include the reference waste gas treatment level and its corresponding reference emission standard. In some embodiments, the gas company management platform can determine it based on the historical data that meets the requirements, and the historical data can be obtained from the intelligent gas government safety supervision management platform. Only as an example, the gas company management platform can obtain the target historical data that meets the requirements from the intelligent gas government safety supervision management platform through the intelligent gas government safety supervision sensor network platform, determine the reference waste gas treatment level based on the historical waste gas treatment level corresponding to the target historical data, and determine the reference emission standard based on the average value of the historical emission standards corresponding to at least one target historical data with the same historical waste gas treatment level. Among them, meeting the requirements means that no danger occurred during the historical gas emission process corresponding to the historical data.

[0054] In some embodiments, the gas company management platform can determine the waste gas emission standard of the pipeline to be processed through preset rules based on the waste gas treatment level of the pipeline to be processed. Among them, the preset rules can include the waste gas emission standards corresponding to different waste gas treatment levels, which can be determined based on the input of gas supervision users.

[0055] Step 220, in response to the pipeline to be processed starting to emit waste gas, obtain the instant processing parameters of the waste gas treatment equipment in the pipeline to be processed from the intelligent gas equipment object platform through the intelligent gas company sensor network platform.

[0056] The waste gas treatment equipment refers to the equipment installed in the gas pipeline to treat the pipeline waste gas. In some instances, the waste gas treatment equipment can include induced draft fans, filtering equipment, regulating valves, etc.

[0057] The induced draft fan can be used to extract the exhaust gas after combustion. In some embodiments, the gas company management platform can send instructions to the intelligent gas equipment object platform through the gas company sensing network platform to adjust the rotational speed of the induced draft fan, thereby controlling the emission speed of the exhaust gas.

[0058] The filtering equipment can include equipment for purifying exhaust gas such as dust removal equipment, desulfurization equipment, and denitrification equipment.

[0059] The regulating valve is a valve installed in the gas pipeline. In some embodiments, the gas company management platform can send instructions to the intelligent gas equipment object platform through the gas company sensing network platform to control the opening degree of the regulating valve, thereby controlling the emission speed of the gas in the pipeline.

[0060] The immediate processing parameter is a parameter that characterizes the working efficiency of the exhaust gas treatment equipment at the current time.

[0061] In some embodiments, the gas company management platform can obtain the immediate processing parameter from the intelligent gas equipment object platform through the gas company sensing network platform. By way of example only, the intelligent gas equipment object platform can communicate with the exhaust gas treatment equipment to obtain the immediate processing parameter of the exhaust gas treatment equipment.

[0062] Step 230, based on at least one of the exhaust gas emission standard, the immediate processing parameter, and the exhaust gas characteristics, determine the expected processing parameter corresponding to a future preset time point.

[0063] The exhaust gas characteristics can include the components of the exhaust gas and the concentration of each component. In some embodiments, the exhaust gas characteristics can be represented in the form of a vector, and the vector includes at least one element, and each element characterizes a component in the exhaust gas and its corresponding concentration.

[0064] In some embodiments, the gas company management platform can obtain the exhaust gas characteristics from the intelligent gas equipment object platform through the gas company sensing network platform. By way of example only, the intelligent gas equipment object platform can determine the exhaust gas characteristics through sensors installed in the gas pipeline to be processed.

[0065] The future preset time point refers to the time when the working efficiency of the exhaust gas treatment equipment is planned to be adjusted in advance.

[0066] In some embodiments, the future preset time can be set based on prior experience and / or actual requirements.

[0067] The expected processing parameter represents the working efficiency of the adjusted exhaust gas treatment equipment.

[0068] In some embodiments, the gas company management platform may determine the expected processing parameters corresponding to a future preset time point based on at least one of the waste gas emission standards, technical processing parameters, and waste gas characteristics. Merely by way of example, the gas company management platform may determine the expected processing parameters by querying a reference processing parameter table based on at least one of the waste gas emission standards, immediate processing parameters, and waste gas characteristics. The reference processing parameter table includes reference characteristics and their corresponding reference processing parameters. In some embodiments, determined according to prior experience, the gas company management platform may obtain the recommended processing parameters input by the user and determine the recommended processing parameters as the expected processing parameters.

[0069] In some embodiments, the gas company management platform may determine the standard processing efficiency according to the waste gas emission standards, immediate processing parameters, and waste gas characteristics, determine the target processing efficiency according to the standard processing efficiency and the environmental characteristics of the pipeline to be processed, and determine the expected processing parameters corresponding to a future preset time point according to the target processing efficiency. For detailed description, please refer to Figure 3 its related description.

[0070] In some embodiments, the gas company management platform may also evaluate the emission risk of waste gas emissions from the pipeline to be processed through a risk assessment model based on at least one of the pipeline characteristics of the pipeline to be processed, immediate processing parameters, historical environmental characteristics within a preset historical time period, and historical waste gas characteristics. In response to the emission risk not meeting the safety conditions, determine the expected processing parameters corresponding to a future preset time point. For detailed description, please refer to Figure 5 its related description.

[0071] Step 240, generate an adjustment instruction according to the expected processing parameters, and send the adjustment instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the intelligent gas equipment object platform to adjust the processing parameters of the waste gas treatment equipment based on the adjustment instruction.

[0072] The adjustment instruction refers to an instruction for adjusting the input quantity and working efficiency of the waste gas treatment equipment.

[0073] In some embodiments, the gas company management platform may generate an adjustment instruction according to the expected processing parameters. For example, if the expected processing parameters are {( ), …, ( ), then it may be determined that the adjustment instruction is "adjust each waste gas treatment equipment in the pipeline to be processed according to the expected processing parameters {( ), …, ( ). Among them, ( ) is the processing parameter of the nth waste gas treatment equipment in the pipeline to be processed, characterizes the on state of the nth waste gas treatment equipment. The on state can be represented by 1 or 0, where 1 is on and 0 is off. Characterize the working efficiency of the nth waste gas treatment device.

[0074] In some embodiments, the gas company management platform may send the foregoing adjustment instruction to the intelligent gas device object platform through the gas company sensing network platform; the intelligent gas device object platform may control at least one waste gas treatment device in the pipeline to be processed based on the adjustment instruction, and adjust the processing parameters of the foregoing at least one waste gas treatment device according to the expected processing parameters included in the adjustment instruction.

[0075] In some embodiments of the present specification, by at least one of the waste gas emission standard, the instant processing parameter, and the waste gas characteristics of the pipeline to be processed, the expected processing parameter is determined, which can fully consider the current waste gas treatment situation and waste gas characteristics, and is beneficial to determining more appropriate processing parameters based on the actual processing situation, so as to ensure the waste gas treatment effect and more efficiently and accurately meet the waste gas emission standard.

[0076] It should be noted that the above description of process 200 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0077] Figure 3 It is a schematic diagram of determining the expected processing parameter based on the expected processing efficiency shown in some embodiments of this specification. As Figure 3 shown, determining the expected processing parameter based on the expected processing efficiency may include the following content. In some embodiments, determining the expected processing parameter based on the expected processing efficiency may be executed by the processor of the gas company management platform.

[0078] In some embodiments, the gas company management platform may determine the standard processing efficiency 340 according to the waste gas emission standard 310, the instant processing parameter 320, and the waste gas characteristics 330; determine the target processing efficiency 360 according to the environmental characteristics 350 and the standard processing efficiency 340; and determine the expected processing parameter 370 corresponding to a future preset time point according to the target processing efficiency 360.

[0079] For more content about the waste gas emission standard, the instant processing parameter, and the waste gas characteristics, reference can be made to Figure 2 and its related description.

[0080] The standard processing efficiency is used to characterize the actual emission amount of waste gas in the pipeline to be processed per unit time. For example, when the standard processing efficiency is 500 m 3 / h, it means that the waste gas treatment device in the pipeline to be processed can actually process 500 cubic meters of waste gas per hour.

[0081] In some embodiments, the gas company management platform may determine the standard treatment efficiency in various ways based on the waste gas emission standards, the immediate treatment parameters, and the waste gas characteristics.

[0082] In some embodiments, the waste gas emission standards may include the maximum values of the concentrations of the respective components in the discharged waste gas. The immediate treatment parameters may include the number of waste gas treatment devices in the waste gas treatment process of the pipeline to be treated and the current working efficiency of the corresponding waste gas treatment devices. The waste gas characteristics may include the concentrations of the respective waste gas components. For more information on the waste gas emission standards, the immediate treatment parameters, and the waste gas characteristics, reference may be made to Figure 2 and its related descriptions.

[0083] In some embodiments, the gas company management platform may determine the standard treatment efficiency of the pipeline to be treated through the following formula (1) based on the relationship that the standard treatment efficiency of the pipeline to be treated is positively correlated with the standard working efficiency of each waste gas treatment device and negatively correlated with the maximum threshold of the concentrations of the respective waste gas components and the concentrations of the respective waste gas components.

[0084] (1)

[0085] Wherein, is the standard treatment efficiency of the pipeline to be treated, is the waste gas emission volume per unit time of the i-th waste gas treatment device in the pipeline to be treated, is the corresponding standard working efficiency of the i-th waste gas treatment device, is the inlet concentration of the waste gas components in the pipeline to be treated, is the maximum threshold of the concentrations of the respective waste gas components in the waste gas emission standards of the pipeline to be treated.

[0086] The standard working efficiency is a characterization of the ability of the waste gas treatment device in the pipeline to be treated to treat waste gas. For example, the standard working efficiency of the waste gas treatment device may be expressed by the volume of waste gas treated per unit time. In some embodiments, the standard working efficiency of the waste gas treatment device may be determined based on the specifications of the waste gas treatment device. In some embodiments, the standard working efficiencies of at least one waste gas treatment device may be different.

[0087] The environmental characteristics are the characteristics that characterize the surrounding environment of the pipeline to be treated. For example, the environmental characteristics may include, but are not limited to, at least one of the pedestrian flow, the traffic congestion index, and the air fluidity around the pipeline to be treated. In some embodiments, the environmental characteristics may be represented in the form of a vector, and the elements in the vector may include at least one of the pedestrian flow, the traffic congestion index, and the air fluidity around the pipeline to be treated.

[0088] In some embodiments, the gas company management platform can obtain the environmental characteristics of the pipeline to be processed from the intelligent gas government safety supervision management platform. For example, the intelligent gas government safety supervision management platform can collect the pedestrian flow, traffic congestion index, and air mobility around the pipeline to be processed through devices such as sensors and cameras, and transmit them to the gas company management platform.

[0089] The pedestrian flow refers to the number of pedestrians passing by the pipeline to be processed per unit time. For example, 200 people pass by the pipeline to be processed per hour. In some embodiments, the area around the pipeline to be processed can be restricted by professional technicians or the system default. Only as an example, it can be an area of 100 square meters near the pipeline to be processed, etc., which can be specifically set based on prior experience and / or actual needs.

[0090] The traffic congestion index is a value used to characterize the traffic congestion degree of the road around the pipeline to be processed. For example, the traffic congestion index can be a value from 0 to 100, and the larger the value, the higher the congestion degree.

[0091] The air mobility refers to the data related to the air flow velocity around the pipeline to be processed. For example, the air mobility around the pipeline to be processed can be a wind speed of 2 m / s and a wind direction of northeast wind, etc.

[0092] The target processing efficiency is the value obtained by adjusting the standard processing efficiency, which is used to characterize the expected waste gas emission that the pipeline to be processed can achieve per unit time.

[0093] In some embodiments, the processing efficiency of the waste gas treatment equipment will change due to the influence of environmental factors and the state of the waste gas treatment equipment itself. By determining the target processing efficiency, it can better adapt to the actual environment, and then better improve the ability and efficiency of the waste gas treatment system to treat waste gas.

[0094] In some embodiments, the gas company management platform can determine the target processing efficiency based on the environmental characteristics and the standard processing efficiency. Only as an example, the gas company management platform can determine the adjustment ratio based on the vector distance between the environmental characteristics and the standard environmental characteristics, and the weighted sum of each sub-parameter in the environmental characteristics (such as the pedestrian flow, traffic congestion index, and air mobility around the pipeline to be processed); and adjust the standard processing efficiency based on the adjustment ratio to determine the target processing efficiency. Among them, the vector distance can be determined based on the cosine distance between the environmental characteristics and the standard environmental characteristics.

[0095] In some embodiments, the target processing efficiency is positively correlated with the standard processing efficiency. Only as an example, the gas company management platform can determine the target processing efficiency based on the following formula (2).

[0096] (2)

[0097] Among them, is the target processing efficiency of the pipeline to be processed, is the standard processing efficiency of the pipeline to be processed, is the cosine distance between the environmental characteristics and the standard environmental characteristics, is the pedestrian flow around the pipeline to be processed in the environmental characteristics, is the traffic congestion index around the pipeline to be processed in the environmental characteristics, is the air fluidity around the pipeline to be processed in the environmental characteristics, , , are the weight coefficients of pedestrian flow, traffic congestion index, and air fluidity respectively.

[0098] In some embodiments, the weight coefficients of the pedestrian flow, traffic congestion index, and air fluidity around the pipeline to be processed can be determined based on prior experience. In some embodiments, the greater the influence of a certain sub-parameter of the environmental characteristics on waste gas treatment, the greater its corresponding weight coefficient. Only as an example, the influence of air fluidity on waste gas emission diffusion may be greater than that of pedestrian flow and traffic congestion index. Therefore, the weight coefficient of air fluidity can be greater than the weight coefficients of the pedestrian flow and traffic congestion index around the pipeline to be processed.

[0099] The standard environmental characteristics refer to the parameters related to the surrounding environment of the pipeline to be processed under standard conditions. For example, the standard environmental characteristics can include, but are not limited to, pedestrian flow, traffic congestion index, and air fluidity under standard conditions. In some embodiments, the standard environmental characteristics can be represented in the form of a vector, and the vector can include elements such as pedestrian flow, traffic congestion index, and air fluidity around the pipeline to be processed under standard conditions.

[0100] The standard conditions refer to an idealized setting of the environmental characteristic parameters of the surrounding environment of the pipeline to be processed in a specific environment. For example, the standard conditions can be ideal weather, no abnormal events occurring, normal operation of equipment, etc.

[0101] In some embodiments, the gas company management platform can determine the standard environmental characteristics through the following steps:

[0102] S1. Obtain the historical environmental characteristic set of the pipeline to be processed. Each environmental characteristic in the historical environmental characteristic set can include multiple sub-parameters. For example, the pedestrian flow, traffic congestion index, and air fluidity of the pipeline to be processed, etc.

[0103] S2. Cluster the historical environmental feature set according to each sub-parameter (pedestrian flow, traffic congestion index, and air mobility) respectively, and obtain multiple clusters A1, A2, A3,... clustered by pedestrian flow; multiple clusters B1, B2, B3,... clustered by traffic congestion index; and multiple clusters C1, C2, C3,... clustered by air mobility. In some embodiments, the type of clustering algorithm can include multiple types. For example, the clustering algorithm can include K-Means clustering, density-based clustering method (DBSCAN), etc.

[0104] S3. For the multiple clusters obtained by clustering each sub-parameter in the historical environmental feature set, calculate the mean value of the sub-parameter in the corresponding cluster respectively; and then obtain multiple mean values of pedestrian flow, multiple mean values of traffic congestion index, and multiple mean values of air mobility in the historical environmental feature set respectively.

[0105] S4. Calculate the weighted sum of the mean values of each sub-parameter in the historical environmental feature set obtained in S3 respectively, and obtain the weighted sum of pedestrian flow, the weighted sum of traffic index, and the weighted sum of air mobility in the historical environmental feature set.

[0106] Exemplarily, taking the pedestrian flow in the historical environmental feature set as an example, the gas company management platform can determine the weighted sum corresponding to the sub-parameter through the following formula (3) based on the relationship that the weighted sum of each sub-parameter is positively correlated with the mean value of each cluster of each sub-parameter.

[0107] (3)

[0108] Wherein, is the weighted sum of pedestrian flow, is the average value of pedestrian flow in each cluster, is the weight coefficient of the mean value of pedestrian flow corresponding to cluster i.

[0109] In some embodiments, the weight coefficient k si of cluster i is the number of environmental features in cluster i divided by the size of the historical environmental feature set.

[0110] In some embodiments, the weighted sum of traffic congestion index and air mobility, as well as the weight calculation method, can be determined in a similar manner.

[0111] In some embodiments, the weight of the weighted value of each mean value of each sub-parameter is the number of environmental features of the cluster corresponding to the sub-parameter divided by the total number of environmental features in the historical environmental feature set.

[0112] S5. The gas company management platform can determine the standard environmental features based on the weighted sum of each sub-parameter determined in the foregoing S4.

[0113] For example, the weighted sum of the pedestrian flow obtained in the aforementioned S4 is used as the pedestrian flow in the standard environmental features, the weighted sum of the traffic index is used as the traffic congestion index in the standard environmental features, and the weighted sum of the air mobility is used as the air mobility in the standard environmental features; thus, the standard environmental features of the pipeline to be processed are obtained.

[0114] Exemplarily, taking the calculation of the pedestrian flow of the standard environmental features as an example. Assume that there are multiple environmental features in the historical environmental feature set, and the pedestrian flows in the historical environmental feature set are clustered to obtain clusters A1, A2, and A3. Then, calculate the average pedestrian flow a1 of the environmental features in cluster A1, the average pedestrian flow a2 of the environmental features in cluster A2, and the average pedestrian flow a3 of the environmental features in cluster A3. And use the above formula (3) to obtain the weighted sum of the pedestrian flow in the historical environmental feature set as the pedestrian flow of the standard environmental features. In some embodiments, the obtaining methods of the traffic congestion index and air mobility of the standard environmental features are the same as the calculation method of the pedestrian flow, and reference can be made to the relevant descriptions above.

[0115] The historical environmental feature set refers to a set composed of environmental features at multiple moments in historical data. For example, the historical environmental feature set can be represented by {X1, X2, …, X i , …X n}. Among them, X i represents the environmental feature corresponding to the i-th moment.

[0116] In some embodiments, the gas company management platform can uniformly increase the working efficiency of each waste gas treatment device (for example, increase the rotational speed of the induced draft fan, increase the opening degree of the regulating valve, and increase the power of the filtering device) until the maximum power (or maximum rotational speed, maximum opening degree). If the current waste gas treatment efficiency is still less than the target treatment efficiency, then increase the number of working waste gas treatment devices (add an induced draft fan or add a filtering device), and thus obtain the expected treatment parameters.

[0117] In some embodiments, in response to the pipeline to be processed starting to discharge waste gas, the gas company management platform can, through the gas company sensor network platform, based on the data acquisition period, obtain the monitoring parameter sequence of the pipeline to be processed within a preset historical time period from the intelligent gas equipment object platform.

[0118] The data acquisition period refers to the frequency of regularly acquiring data from the intelligent gas equipment object platform at a preset time interval. For example, the data acquisition period can be 1 day, 1 hour, etc.

[0119] In some embodiments, the data acquisition period can be determined in various ways. Exemplarily, the data acquisition period can be set based on prior experience and / or actual requirements.

[0120] In some embodiments, the data acquisition period is related to the target treatment efficiency.

[0121] In some embodiments, the data acquisition period may be negatively correlated with the target processing efficiency of the current pipeline to be processed.

[0122] In some embodiments of the present specification, the higher the waste gas emission efficiency, the more waste gas treatment devices there are and the higher their working efficiency, and the more drastic the change in waste gas concentration. When a waste gas treatment device fails, the impact is greater. Therefore, by appropriately shortening the interval of the data acquisition period, it helps to take corresponding measures in a timely manner when the risk increases.

[0123] The preset historical time period refers to a preset historical time range calculated forward from the current moment. For example, the preset historical time period may be the historical time of 1 week or 1 month from the current moment.

[0124] In some embodiments, the preset historical time period can be set based on prior experience and / or actual needs.

[0125] The monitoring parameter refers to a parameter used to reflect the operating state and safety of the pipeline to be processed. For example, the monitoring parameter may include, but is not limited to, at least one of the temperature, pressure, and gas flow rate in the pipeline to be processed.

[0126] The monitoring parameter sequence refers to a sequence composed of monitoring parameters at multiple moments. For example, the monitoring parameter sequence can be represented by {(t1, Y1), (t2, Y2), …, (t i , Y i ), …, (t n , Y n ).} Where Y i represents the monitoring parameter corresponding to the moment t i .

[0127] In some embodiments, the gas company management platform can obtain the monitoring parameters of devices such as temperature sensors, pressure sensors, and flow sensors in the smart gas device object platform through the gas company sensor network platform.

[0128] In some embodiments, in response to the monitoring parameter sequence not meeting the preset conditions, the gas company management platform can predict the remaining processing time.

[0129] The preset condition refers to a condition used to determine that the parameters in the monitoring parameter sequence are lower than the safe situation. For example, the preset condition may be that the temperature, pressure, and gas flow rate in the pipeline to be processed in the monitoring parameter sequence are all lower than the threshold. In some embodiments, the threshold can be set by professional technicians or the system default.

[0130] The remaining processing time refers to the time required for the pipeline to be processed to continue waste gas treatment. For example, 10 minutes, etc.

[0131] In some embodiments, the gas company management platform may determine the remaining processing time based on the waste gas emission time and historical data. For example, the gas company management platform may obtain the average time for processing the waste gas in the pipeline to be processed in the historical data, determine an estimated value of the time used for processing the waste gas in the pipeline to be processed, and determine the remaining processing time based on the estimated value of the waste gas processing time and the waste gas emission time.

[0132] In some embodiments, in response to the pipeline to be processed starting to emit waste gas, the gas company management platform may obtain, through the gas company sensing network platform, a sequence of monitoring parameters of the pipeline to be processed within a preset historical time period from the intelligent gas device object platform; in response to the sequence of monitoring parameters not meeting the preset conditions, predict the remaining processing time. For more detailed descriptions, reference can be made to Figure 4 and its related descriptions.

[0133] In some embodiments, the gas company management platform may update the target processing efficiency according to the waste gas emission time, the remaining processing time, and the environmental characteristics to determine the expected processing efficiency.

[0134] The waste gas emission time refers to the time for which the waste gas has been emitted.

[0135] The expected processing efficiency refers to the data after the target processing efficiency is updated.

[0136] In some embodiments, in response to the remaining processing time being greater than the difference between the time threshold and the waste gas emission time, the gas company management platform may construct a vector to be matched based on the environmental characteristics of the pipeline to be processed. Match based on the vector to be matched in the processing efficiency reference database to determine the waste gas processing efficiency of the pipeline to be processed, and determine the waste gas processing efficiency as the expected processing efficiency of the pipeline to be processed.

[0137] The processing efficiency reference database is a database used to determine the waste gas processing efficiency. The database includes at least one reference scenario feature vector and its corresponding reference waste gas processing efficiency.

[0138] In some embodiments, the reference vectors in the processing efficiency reference database may be determined based on historical data. For example, the gas company management platform may construct a historical vector based on the historical environmental parameters of the pipeline to be processed in the historical data, perform clustering on the historical vector to form a preset number of clustering centers, construct reference vectors based on the historical environmental characteristics corresponding to the clustering centers. And determine the reference waste gas processing efficiency corresponding to the reference vectors based on the historical waste gas processing efficiency.

[0139] In some embodiments, the gas company management platform may obtain the actual exhaust gas emission time corresponding to multiple historical vectors in each cluster from the intelligent gas government safety supervision and management platform. During the exhaust gas treatment process corresponding to multiple historical vectors in the cluster, the exhaust gas treatment efficiency corresponding to the exhaust gas project with the shortest exhaust gas emission time and no exhaust gas accident is used as the historical exhaust gas treatment efficiency.

[0140] In some embodiments, the gas company management platform may calculate the similarity between the vector to be matched and at least one reference vector respectively, and use the reference acquisition parameters corresponding to the reference vector with the highest similarity to the vector to be matched as the exhaust gas treatment efficiency. Among them, the similarity may be determined negatively correlated with the vector distance between the vector to be matched and the reference vector, and the vector distance may be determined based on the cosine distance, etc.

[0141] The time threshold is a value used to determine whether the remaining treatment time is too long. For more information about the time threshold, reference can be made to Figure 4 and its related descriptions.

[0142] The exhaust gas treatment efficiency refers to the maximum emission volume of exhaust gas treatment per unit time for the pipeline to be treated.

[0143] In some embodiments, in response to the remaining treatment time being less than or equal to the difference between the time threshold and the exhaust gas emission time, the gas company management platform may use the current target treatment efficiency as the expected treatment efficiency.

[0144] In some embodiments of this specification, when the remaining treatment time is less than or equal to the time threshold, it indicates that the exhaust gas emission may not be carried out according to the predicted treatment efficiency. At this time, it is necessary to adjust the exhaust gas emission efficiency in a timely manner to complete the exhaust gas treatment within the specified time.

[0145] In some embodiments, the gas company management platform may determine the expected treatment parameters corresponding to a future preset time point according to the expected treatment efficiency.

[0146] In some embodiments, the gas company management platform adjusts the expected treatment parameters in response to the emission risk of the pipeline to be treated for exhaust gas emission meeting the safety conditions.

[0147] The emission risk is a value used to characterize the risk existing in the exhaust gas treatment process. For example, the larger the value, the higher the emission risk of the pipeline to be treated. For a detailed description of determining the emission risk, reference can be made to this specification Figure 5 in the relevant descriptions.

[0148] The safety condition is a condition used to determine whether the exhaust gas emission is safe. For example, the safety condition may include that the emission risk is not greater than the risk threshold.

[0149] The risk threshold refers to the threshold used to determine whether the emission risk is too high. For more information on how to determine the risk threshold, please refer to Figure 5 and its related descriptions.

[0150] In some embodiments, when the emission risk is lower than the risk threshold, the gas company management platform can adjust the expected processing parameters. For example, uniformly increase the working efficiency of each waste gas treatment device (e.g., increase the rotational speed of the induced draft fan, increase the opening degree of the regulating valve, and increase the power of the filtration device) until the maximum power (or maximum rotational speed, maximum opening degree). If the current waste gas treatment efficiency is still less than the target treatment efficiency, increase the number of working waste gas treatment devices (increase the induced draft fan or increase the filtration device).

[0151] In some embodiments of this specification, when the emission risk is relatively high, by giving priority to controlling the risk of waste gas emissions and then considering the efficiency of waste gas emissions, it helps to ensure the safety of waste gas treatment.

[0152] In some embodiments of this specification, through the cooperation of the gas company management platform and the sensing network platform, the system can, when the pipeline to be processed starts to emit waste gas, obtain a historical monitoring parameter sequence from the intelligent gas equipment object platform based on the data acquisition period, and predict the remaining processing time when the monitoring parameters do not meet the preset conditions. The system can also update the target treatment efficiency according to the waste gas emission time, the remaining processing time, and the environmental characteristics to determine the expected treatment efficiency, and further determine the expected processing parameters corresponding to the future preset time points according to the expected treatment efficiency, which can further improve the accuracy and real-time performance of waste gas treatment and optimize the overall efficiency of waste gas treatment.

[0153] In some embodiments of this specification, through the collaborative work of the gas company management platform and the gas government safety supervision sensing network platform, the system can determine the standard treatment efficiency based on the waste gas emission standard, the instant processing parameters, and the waste gas characteristics, and obtain the environmental characteristics of the pipeline to be processed from the intelligent gas government safety supervision management platform. By comprehensively considering the environmental characteristics and the standard treatment efficiency, determine the target treatment efficiency, and further determine the expected processing parameters corresponding to the future preset time points according to the target treatment efficiency, which can effectively improve the accuracy and reliability of the waste gas treatment process and ensure that the system can maintain a high-efficiency and safe waste gas treatment level under various environmental conditions.

[0154] Figure 4 is a schematic diagram of determining the expected processing parameters based on the waste gas treatment time as shown in some embodiments of this specification. As Figure 4 shown, determining the expected processing parameters based on the waste gas treatment time may include the following. In some embodiments, determining the expected processing parameters based on the waste gas treatment time can be executed by the processor of the gas company management platform.

[0155] In some embodiments, the gas company management platform may also determine the volume of waste gas to be processed 430 according to the pipeline parameters 410 and the historical pressure regulation parameter sequence 420; determine the waste gas treatment time 440 according to the volume of waste gas to be processed 430 and the target treatment efficiency 360; and in response to the waste gas treatment time 440 not meeting the duration condition 450, determine the expected treatment parameters 370 corresponding to a future preset time point.

[0156] Pipeline parameters are parameters that describe the characteristics and related conditions of a pipeline. For example, the pipeline parameters may include at least one of the diameter of the pipeline, the temperature inside the pipeline, and the flow rate of the pipeline gas. Among them, the temperature inside the pipeline and the flow rate of the gas inside the pipeline can be obtained by sensors, and the diameter of the pipeline can be obtained based on the specification parameters of the pipeline to be processed.

[0157] The historical pressure regulation parameter sequence characterizes the pressures at multiple historical moments before and after the pressure regulation of the pipeline to be processed.

[0158] In some embodiments, the gas company management platform may obtain the pipeline parameters of the pipeline to be processed and the historical pressure regulation parameter sequence for a preset historical time period from the intelligent gas device object platform through the gas company sensing network platform.

[0159] The preset historical time period refers to the time period from a certain historical moment to the current moment, which can be set according to prior experience and / or actual needs.

[0160] The volume of waste gas to be processed refers to the volume of waste gas that needs to be processed inside the pipeline.

[0161] In some embodiments, the gas company management platform may determine the volume of waste gas to be processed by querying the reference database according to the pipeline parameters of the pipeline to be processed and the historical pressure regulation parameter sequence.

[0162] In some embodiments, the reference database may include reference feature vectors and their corresponding reference waste gas volumes. Among them, the reference feature vectors can be constructed based on the historical pipeline parameters and the historical pressure regulation parameter sequences of multiple historical pipelines to be processed in historical data; the reference waste gas volumes can be determined based on the actual waste gas treatment volumes of the historical pipelines to be processed.

[0163] In some embodiments, the processor may construct a vector to be matched according to the pipeline parameters of the pipeline to be processed and the pressure regulation parameter sequence. Based on the vector to be matched, perform a match in the reference database, and determine the reference feature vector with the highest similarity to the vector to be matched as the target vector; determine the reference waste gas volume corresponding to the target vector as the volume of waste gas to be processed. Among them, the similarity can be calculated based on the cosine distance, Euclidean distance, etc.

[0164] In some embodiments, the gas company management platform may also determine at least one reference feature vector whose similarity to the vector to be matched is higher than the similarity threshold, and use the average value of the reference waste gas volumes corresponding to the at least one reference feature vector as the waste gas volume to be processed corresponding to the vector to be matched.

[0165] The waste gas treatment time refers to the time required to treat the waste gas in the treatment pipeline. In some embodiments, the waste gas treatment time may be determined according to the waste gas volume to be processed and the target waste gas treatment efficiency.

[0166] In some embodiments, the gas company management platform may determine the waste gas treatment time based on the waste gas volume to be processed and the target waste gas treatment efficiency. By way of example only, the waste gas treatment time is positively correlated with the waste gas volume to be processed and negatively correlated with the target waste gas treatment efficiency. The gas company management platform may determine the waste gas treatment time through the following formula (4):

[0167] (4)

[0168] Where T represents the waste gas treatment time; V represents the waste gas volume to be processed; and P represents the target waste gas treatment efficiency.

[0169] In some embodiments, in response to the waste gas treatment time not meeting the duration condition, the gas company management platform may determine the expected treatment parameters corresponding to a future preset time point.

[0170] In some embodiments, the duration condition may include a time threshold. For example, the duration condition may be that the waste gas treatment time is not greater than the time threshold. The time threshold refers to the maximum value of the set waste gas treatment time.

[0171] In some embodiments, the time threshold may be determined based on prior experience and / or actual requirements.

[0172] In some embodiments, the gas company management platform may determine the time threshold based on the historical waste gas treatment time and the historical score.

[0173] The historical waste gas treatment time refers to the time taken for the historical waste gas treatment process.

[0174] The historical score represents the evaluation of the historical waste gas treatment process. The higher the historical score, the better the effect of treating the waste gas based on the historical waste gas treatment time.

[0175] In some embodiments, the gas company management platform may obtain the historical waste gas treatment time and the corresponding historical score of the pipeline to be processed from the intelligent gas government safety supervision management platform through the intelligent gas government safety supervision sensor network platform.

[0176] In some embodiments, the gas company management platform may screen the foregoing historical waste gas treatment times based on historical scores to determine a reference waste gas treatment time. For example, historical waste gas treatment times with historical scores not meeting the score requirements are excluded, such as historical scores less than the minimum threshold, historical scores ranked in the last 30% in descending order, etc.

[0177] In some embodiments, the gas company management platform may determine the average value of the reference waste gas treatment times as the time threshold.

[0178] In some embodiments, by determining the time threshold according to the historical waste gas treatment times and historical scores, more practical requirements can be set, enabling the waste gas treatment to achieve the expected goal of meeting the exhaust requirements, effectively managing the waste gas treatment, controlling costs effectively, and avoiding unnecessary investments or problems with unqualified treatment.

[0179] In some embodiments, the processor may uniformly increase the working efficiency of each waste gas treatment device (for example, increasing the rotational speed of the induced draft fan, increasing the power of the filtration device) until the rated power is reached; if the current waste gas treatment time is still greater than the time threshold, the number of waste gas treatment devices is increased (for example, adding an induced draft fan or adding a filtration device, etc.).

[0180] In some embodiments, determining the expected processing parameters corresponding to a future preset time point helps with advance planning and resource allocation, more timely treatment of the waste gas in the pipeline, improving the treatment efficiency and accuracy, and ensuring the orderliness and controllability of the waste gas treatment process.

[0181] Figure 5 It is a schematic diagram of determining expected processing parameters based on emission risk according to some embodiments of this specification. As Figure 5 shown, determining expected processing parameters based on emission risk may include the following. In some embodiments, determining expected processing parameters based on emission risk may be executed by the processor of the gas company management platform.

[0182] In some embodiments, as Figure 5 shown, the gas company management platform may evaluate the emission risk 550 of waste gas emissions from the pipeline to be treated through the risk assessment model 540 based on the evaluation period; the inputs of the risk assessment model 540 include at least one of the pipeline characteristics 510, the immediate processing parameters 320, the historical environmental characteristics 520 within a preset historical time period, and the historical waste gas characteristics 530 within a preset historical time period.

[0183] For more information about emission risk, immediate processing parameters, and the preset historical time period, reference can be made to Figure 3 and its related descriptions.

[0184] Pipeline features refer to features related to the attributes of the pipeline to be processed. For example, pipeline features may include, but are not limited to, the type of the pipeline to be processed, the adjacency matrix of the pipeline to be processed, etc.

[0185] The type of the pipeline to be processed refers to the classification result of the pipeline based on features such as the location of the pipeline to be processed. For example, the type of the pipeline to be processed can be a main pipeline, a branch pipeline, a feeder pipeline, etc.

[0186] The adjacency matrix of the pipeline to be processed refers to a matrix used to describe the connection relationships between the pipelines to be processed in the gas pipeline waste gas treatment system of the Internet of Things.

[0187] In some embodiments, the rows and columns of the adjacency matrix of the pipeline to be processed can respectively represent the pipelines to be processed in the waste gas treatment system. Each element of the adjacency matrix indicates whether there is a connection between two pipelines to be processed. For example, when the element in the adjacency matrix is 0, it means there is no connection between the two pipelines to be processed; when the element in the adjacency matrix is 1, it means there is a connection between the two pipelines to be processed.

[0188] In some embodiments, the gas company management platform can obtain the pipeline features of the pipeline to be processed from the intelligent gas device object platform through the gas company sensing network platform.

[0189] The evaluation period refers to the time interval for evaluating the emission risk of waste gas emissions from the pipeline to be processed. For example, 1 week, etc.

[0190] In some embodiments, the evaluation period can be set by professional technicians or the system default.

[0191] Historical environmental features refer to parameters composed of environmental features within a preset historical time period. In some embodiments, historical environmental features can be represented in a sequential manner. For example, historical environmental features can be represented by {(t1, P1), (t2, P2), …, (t i , P i ), …, (t n , P n ). Where P i represents the historical environmental feature corresponding to the time t i . For more descriptions about the preset historical time period, reference can be made to the relevant descriptions in this specification Figure 3 in the relevant part.

[0192] In some embodiments, the gas company management platform can determine the historical environmental features by obtaining the historical data of the environmental features of the pipeline to be processed and the time when the environmental features were historically obtained from the intelligent gas government safety supervision management platform.

[0193] For more content about environmental features, reference can be made to Figure 3and its related descriptions.

[0194] The historical exhaust gas characteristics refer to the parameters composed of the exhaust gas characteristics within a preset historical time period.

[0195] In some embodiments, the historical exhaust gas characteristics can be represented in a sequential manner, similar to the historical environmental characteristics, except that the historical exhaust gas characteristics include the exhaust gas characteristics at each moment within the preset historical time period.

[0196] In some embodiments, the gas company management platform can determine the historical exhaust gas characteristics by obtaining the exhaust gas characteristics in the historical data and the moments when the exhaust gas characteristics are obtained.

[0197] For more information about the exhaust gas characteristics, please refer to Figure 2 and its related descriptions.

[0198] The risk assessment model refers to a model used to evaluate the emission risk of exhaust gas from the pipeline to be processed. In some embodiments, the risk assessment model can be a machine learning model. For example, a Deep Neural Networks (DNN) model, etc.

[0199] In some embodiments, the input of the risk assessment model can include the pipeline characteristics of the pipeline to be processed, the immediate processing parameters of the exhaust gas treatment equipment in the pipeline to be processed, the historical environmental characteristics within the preset historical time period of the pipeline to be processed, and the historical exhaust gas characteristics within the preset historical time period. The output of the risk assessment model can be the emission risk of exhaust gas from the pipeline to be processed.

[0200] In some embodiments, the risk assessment model can be trained based on a large number of first training samples with a first label. The gas company management platform can input multiple first training samples with a first label into the initial risk assessment model, construct a loss function through the first label and the results of the initial risk assessment model, and iteratively update the initial risk assessment model based on the loss function. When the preset conditions are met, the model training is completed, and a trained risk assessment model is obtained. Among them, the preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0201] In some embodiments, the first training samples can be the sample pipeline characteristics corresponding to the sample pipelines to be processed in the historical data, the sample immediate processing parameters of the sample exhaust gas treatment equipment in the sample pipelines to be processed, the sample historical environmental characteristics within the sample preset historical time period, and the sample historical exhaust gas characteristics within the sample preset historical time period.

[0202] In some embodiments, the first label may be the emission risk of exhaust gas discharged from the pipeline to be processed for the sample. In some embodiments, the gas company management platform may cluster the first training samples and count the number of emission accidents occurring in the exhaust gas emission project in a subsequent period of time for each cluster of corresponding training samples, and divide the number by the number of training samples corresponding to the cluster as the first label corresponding to the samples of this class. Among them, the aforementioned subsequent period of time is the period of time after the preset historical period of the sample, which can be determined based on prior experience and / or actual needs; the number of emission accidents refers to the number of adverse events and / or unexpected situations occurring during the exhaust gas emission process.

[0203] In some embodiments, the input of the risk assessment model 540 may further include the temperature sequence 560 of the target period.

[0204] The target period refers to the period of time for obtaining the temperature sequence. For example, 1 hour, etc.

[0205] The temperature sequence refers to the sequence composed of the temperatures around the pipeline to be processed within the target time period. In some embodiments, the temperature sequence is similar to the structure of the historical environmental characteristics, and the difference is that the temperature sequence includes the temperatures around the pipeline to be processed at each moment.

[0206] Correspondingly, when the input of the risk assessment model includes the temperature sequence of the target period, the first training sample may further include the temperature sequence of the sample target period on the basis of the foregoing content.

[0207] In some embodiments of the present specification, temperature affects the diffusion and transmission rate of exhaust gas. A higher temperature usually accelerates the gas diffusion rate, making it dilute and disperse into the surrounding environment faster, thereby affecting the concentration distribution of pollutants and the maintenance time of the concentration peak. Therefore, considering the temperature around the pipeline to be processed when evaluating the emission risk can improve the accuracy of the risk assessment model.

[0208] In some embodiments, the risk assessment model may be obtained by training an initial risk assessment model; the training samples for training the risk assessment model include at least one acquisition classification, and the number of samples in each acquisition classification is greater than the quantity threshold corresponding to the acquisition classification; the quantity threshold is related to the connection information of the gas pipeline in the acquisition classification.

[0209] The acquisition classification refers to dividing the sampling area into multiple regions according to longitude and latitude, and each region is used as an acquisition classification.

[0210] The quantity threshold refers to the threshold used to characterize whether the amount of training sample data in this acquisition classification is rich.

[0211] In some embodiments, each acquisition classification corresponds to a quantity threshold.

[0212] Exemplarily, the gas company management platform may divide the area with longitude ranging from 0 to 10 and latitude ranging from 0 to 10 in the sampling area into collection category 1, and its corresponding quantity threshold is A; the area with longitude ranging from 0 to 10 and latitude ranging from 10 to 20 in the sampling area is divided into collection category 2, and its corresponding quantity threshold is B.

[0213] In some embodiments, the more complex the connection information of the gas pipeline in the collection category, the larger the quantity threshold. For example, complex connection information means the complex and diverse connection relationships between the pipelines to be processed in the waste gas treatment system. For example, complex connection information can be that the connection methods between the pipelines to be processed are diverse, or the number of connections is large, etc.

[0214] The connection information is information characterizing the connection relationship between the pipeline to be processed and other pipelines, and the connection information can be represented by the adjacency matrix of the pipeline to be processed in the collection category. For more content about the adjacency matrix, reference can be made to Figure 5 the relevant description above.

[0215] In some embodiments of this specification, the more complex the connection situation of the pipeline to be processed, the greater the impact on the surrounding pipelines when discharging waste gas, and the greater the complexity of the emission risk assessment. Therefore, by collecting more training samples, the training effect of the risk assessment model can be guaranteed.

[0216] In some embodiments, as Figure 5 shown, the gas company management platform may also determine the expected processing parameter 370 at a future preset time point in response to the emission risk 550 not meeting the safety condition.

[0217] In some embodiments, the gas company management platform may uniformly reduce the working efficiency of each waste gas treatment device (for example, reduce the rotation speed of the induced draft fan and lower the power of the filtering device) until the emission risk is less than the risk threshold.

[0218] In some embodiments, the risk threshold is related to the environmental characteristics of the pipeline to be processed.

[0219] For more content about the risk threshold and environmental characteristics, reference can be made to Figure 3 and its related description.

[0220] In some embodiments, the risk threshold may have a negative correlation with the weighted sum of the pedestrian flow, traffic congestion index, and air mobility in the environmental characteristics.

[0221] For more content about the weighted sum of the pedestrian flow, traffic congestion index, and air mobility in the environmental characteristics, reference can be made to Figure 3 in and its related description.

[0222] In some embodiments of this specification, the greater the weighted sum of the human flow, traffic congestion index, and air mobility in the environmental characteristics, the more people and vehicles there are near the pipeline to be processed. At this time, appropriately reducing the risk threshold can ensure the safety of the exhaust project.

[0223] In some embodiments of this specification, through the gas company management platform and the sensing network platform, the pipeline characteristics of the pipeline to be processed are obtained from the intelligent gas equipment object platform, which can ensure that the system grasps the detailed information of the pipeline and provides an accurate data basis for subsequent risk assessment. In addition, based on the evaluation period, through the risk assessment model constructed by the machine learning model, integrating the pipeline characteristics, immediate processing parameters, historical environmental characteristics, and historical waste gas characteristics, the emission risk of the pipeline to be processed is evaluated, which is beneficial to improving the accuracy and reliability of the model evaluation. When the emission risk is greater than the risk threshold, by determining the expected processing parameters corresponding to a future preset time point, the waste gas treatment process can be optimized, and the safety and overall treatment efficiency of the waste gas treatment can be improved.

[0224] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, 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, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0225] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment" or "one embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0226] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical and alphabetical characters, or the use of other names described in this specification are not used to limit the order of the processes and methods in this specification. Although some currently useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details are for illustrative purposes only. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0227] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, multiple features are sometimes grouped into one embodiment, drawing, or description thereof. However, this method of disclosure does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0228] In some embodiments, numbers are used to describe components and attribute quantities. It should be understood that such numbers used to describe the embodiments are modified by the modifiers "about", "approximately", or "substantially" in some examples. Unless otherwise stated, "about", "approximately", or "substantially" indicate that the stated number allows a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used to confirm the breadth of the scope in some embodiments of this specification are approximate values, in specific embodiments, such numerical settings are made as precise as possible within the feasible range.

[0229] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes the application history documents that are inconsistent with or conflict with the content of this specification, and also excludes the documents that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or the use of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or the use of terms in this specification shall prevail.

[0230] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly presented and described in this specification.

Claims

1. A smart gas pipeline waste gas safety treatment system based on the Internet of Things, characterized in that: The system includes a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, a gas company management platform, a gas company sensor network platform and a smart gas equipment object platform; The gas company management platform includes a processor, and the gas company management platform is configured to: Determining the exhaust gas emission standard of the pipeline to be treated based on the exhaust gas treatment level of the pipeline to be treated; In response to the pipeline to be treated starting to discharge waste gas, obtaining real-time processing parameters of the waste gas treatment equipment in the pipeline to be treated from the smart gas equipment object platform through the gas company sensor network platform; Determining a standard treatment efficiency based on the exhaust gas emission standard, the instant treatment parameter and the exhaust gas characteristics; Obtaining environmental characteristics of the pipeline to be processed from the smart gas government safety supervision management platform through the smart gas government safety supervision sensor network platform; Determining a target processing efficiency according to the environmental characteristics and the standard processing efficiency; Determining expected processing parameters corresponding to a preset time point in the future according to the target processing efficiency; An adjustment instruction is generated according to the expected processing parameters, and the adjustment instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the smart gas equipment object platform to adjust the processing parameters of the exhaust gas treatment equipment based on the adjustment instruction.

2. The system according to claim 1, characterized in that The gas company management platform is also configured to: Obtaining the pipeline parameters of the pipeline to be processed and the historical pressure regulation parameter sequence of a preset historical time period from the smart gas equipment object platform through the gas company sensor network platform; Determining the amount of waste gas to be treated according to the pipeline parameters and the historical pressure regulation parameter sequence; Determining the waste gas treatment time according to the waste gas volume to be treated and the target treatment efficiency; In response to the exhaust gas treatment time not satisfying a duration condition, the expected treatment parameter corresponding to the future preset time point is determined; the duration condition includes a duration threshold.

3. The system according to claim 1, characterized in that The gas company management platform is also configured to: Obtaining the pipeline characteristics of the pipeline to be processed from the smart gas equipment object platform through the gas company sensor network platform; Based on the evaluation cycle, the emission risk of the waste gas emission from the pipeline to be treated is evaluated by a risk assessment model; the input of the risk assessment model includes at least one of the pipeline characteristics, the immediate treatment parameters, the historical environmental characteristics within a preset historical time period, and the historical waste gas characteristics within the preset historical time period, and the risk assessment model is a machine learning model; In response to the emission risk not satisfying a safety condition, determining the expected processing parameter corresponding to the future preset time point; the safety condition includes a risk threshold.

4. The system according to claim 3, characterized in that The risk threshold is related to the environmental characteristics of the pipeline to be processed.

5. A smart gas pipeline waste gas safety treatment method based on the Internet of Things, characterized in that: The method is executed by a processor of a gas company management platform in a gas pipeline exhaust treatment system based on the Internet of Things, and includes: Determining the exhaust gas emission standard of the pipeline to be treated based on the exhaust gas treatment level of the pipeline to be treated; In response to the pipeline to be treated starting to discharge waste gas, obtaining real-time processing parameters of the waste gas treatment equipment in the pipeline to be treated from the smart gas equipment object platform through the gas company sensor network platform; Determining a standard treatment efficiency based on the exhaust gas emission standard, the instant treatment parameter and the exhaust gas characteristics; Obtaining environmental characteristics of the pipeline to be processed from the smart gas government safety supervision management platform through the smart gas government safety supervision sensor network platform; Determining a target processing efficiency according to the environmental characteristics and the standard processing efficiency; Determining expected processing parameters corresponding to a preset time point in the future according to the target processing efficiency; An adjustment instruction is generated according to the expected processing parameters, and the adjustment instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the smart gas equipment object platform to adjust the processing parameters of the exhaust gas treatment equipment based on the adjustment instruction.

6. The method according to claim 5, characterized in that The determining, according to the target processing efficiency, the expected processing parameter corresponding to the future preset time point includes: Obtaining the pipeline parameters of the pipeline to be processed and the historical pressure regulation parameter sequence of a preset historical time period from the smart gas equipment object platform through the gas company sensor network platform; Determining the amount of waste gas to be treated according to the pipeline parameters and the historical pressure regulation parameter sequence; Determining the waste gas treatment time according to the waste gas volume to be treated and the target treatment efficiency; In response to the exhaust gas treatment time not satisfying a duration condition, the expected treatment parameter corresponding to the future preset time point is determined; the duration condition includes a duration threshold.

7. The method according to claim 5, characterized in that The determining of the expected processing parameters corresponding to a preset time point in the future based on at least one of the exhaust emission standard, the immediate processing parameters and the exhaust characteristics includes: Obtaining the pipeline characteristics of the pipeline to be processed from the smart gas equipment object platform through the gas company sensor network platform; Based on the evaluation cycle, the emission risk of the waste gas emission from the pipeline to be treated is evaluated by a risk assessment model; the input of the risk assessment model includes at least one of the pipeline characteristics, the immediate treatment parameters, the historical environmental characteristics within a preset historical time period, and the historical waste gas characteristics within the preset historical time period, and the risk assessment model is a machine learning model; In response to the emission risk not satisfying a safety condition, determining the expected processing parameter corresponding to the future preset time point; the safety condition includes a risk threshold.

8. The method according to claim 7, characterized in that The risk threshold is related to the environmental characteristics of the pipeline to be processed.

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