A pipeline network safety linkage treatment method and internet of things system for smart gas

Abstract

The embodiment of the present specification provides a pipeline network safety linkage treatment method for smart gas, which is realized based on a smart gas safety management platform of a smart gas pipeline network safety management Internet of Things system, and the method comprises the following steps: acquiring detection information of a gas pipeline in a target area; determining an abnormal pipeline in the target area based on the detection information; and determining an abnormal treatment scheme based on the detection information corresponding to the abnormal pipeline.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of intelligent detection of gas pipelines, and in particular to a pipeline network safety linkage disposal method and Internet of Things system for smart gas. BACKGROUND

[0002] Gas safety is a topic of high social concern because it relates to people's life and property safety. Common gas safety hazards currently include problems such as deformation, corrosion, rupture and gas leakage of gas pipelines. Conventional inspection methods include manually inspecting key parts of gas pipelines, daily maintenance of gas pipeline network equipment, and targeted repair upon receiving emergency notification. Using the above manual inspection method, due to limited manual resources, the inspection range may not cover all gas pipelines in time, thus causing safety hazards in gas pipeline inspection and problems of not discovering safety hazards in time.

[0003] Therefore, it is necessary to provide a pipeline network safety linkage disposal method and Internet of Things system for smart gas for timely discovering safety hazards of gas pipelines and notifying relevant personnel to arrive at a specified area to eliminate safety hazards and ensure user gas safety. SUMMARY

[0004] One or more embodiments of the present specification provide a pipeline network safety linkage disposal method for smart gas, which is implemented based on a smart gas safety management platform of a smart gas pipeline network safety management Internet of Things system. The method comprises: acquiring detection information of gas pipelines in a target area; determining abnormal pipelines in the target area based on the detection information; and determining an abnormal processing scheme based on the detection information corresponding to the abnormal pipelines. In some embodiments, the method further comprises: transmitting the abnormal pipelines and / or the abnormal processing scheme to a smart gas user platform based on a smart gas service platform.

[0005] One or more embodiments of the present specification provide a smart gas pipeline network safety management Internet of Things system, which comprises a smart gas safety management platform configured to perform the following operations: acquiring detection information of gas pipelines in a target area; determining abnormal pipelines in the target area based on the detection information; and determining an abnormal processing scheme based on the detection information corresponding to the abnormal pipelines. In some embodiments, the Internet of Things system further comprises a smart gas user platform, a smart gas service platform, a smart gas pipeline network equipment sensing network platform and a smart gas pipeline network equipment object platform; the smart gas pipeline network equipment object platform is used to acquire the detection information of the gas pipelines in the target area; the smart gas pipeline network equipment sensing network platform is used to transmit the detection information of the gas pipelines in the target area to the smart gas safety management platform; and the smart gas service platform is used to transmit the abnormal pipelines and / or the abnormal processing scheme to the smart gas user platform.

[0006] The one or more embodiments of the specification provide a computer readable storage medium, the storage medium stores computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes a pipeline safety linkage handling method for smart gas. BRIEF DESCRIPTION OF DRAWINGS

[0007] The specification will be further illustrated in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, wherein:

[0008] Figure 1 is an intelligent gas pipeline safety management Internet of Things system diagram according to some embodiments of the specification;

[0009] Figure 2 is a pipeline safety linkage handling method flowchart for smart gas according to some embodiments of the specification;

[0010] Figure 3 is a flowchart for determining an abnormal processing scheme according to some embodiments of the specification;

[0011] Figure 4 is a flowchart of at least one round of iteration in multi-round iteration update according to some embodiments of the specification;

[0012] Figure 5 is a flowchart for determining the evaluation value of the first candidate processing scheme according to some embodiments of the specification;

[0013] Figure 6 is a schematic diagram of a smart gas pipeline network diagram according to some embodiments of the specification. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the specification, and for those skilled in the art, the specification can also be applied to other similar scenarios without creative labor. Unless it is obvious from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0015] 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 words can be replaced by other expressions.

[0016] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0018] Figure 1 This is a diagram of an IoT system for intelligent gas pipeline safety management, based on some embodiments of this specification.

[0019] like Figure 1 As shown, the Internet of Things (IoT) system 100 may include a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas pipeline equipment sensor network platform, and a smart gas pipeline equipment object platform that interact sequentially. These platforms interact through sequential communication connections.

[0020] The smart gas user platform is configured as a terminal device to receive abnormal pipeline information and / or abnormal handling solutions transmitted by the smart gas service platform. It can also transmit gas pipeline abnormality information query commands to the smart gas service platform. The terminal device may include intelligent electronic devices such as desktop computers, tablets, laptops, and mobile phones that enable data processing and communication; no further limitations are imposed here. In some embodiments, the smart gas user platform may include a gas user sub-platform and a regulatory user sub-platform, so that both gas users and regulatory users can receive abnormal pipeline information and / or abnormal handling solution information transmitted by the smart gas service platform.

[0021] In some embodiments, the gas user sub-platform can be configured to receive information related to safe gas use transmitted by the smart gas use service sub-platform, such as a reminder of safe gas use at home, and can also be configured to transmit a gas use query instruction or gas pipe network anomaly information to the smart gas use service sub-platform for anomaly warranty, etc. In some embodiments, the regulatory user sub-platform can be configured to receive anomaly pipe information and / or anomaly treatment scheme transmitted by the smart regulatory service sub-platform and / or gas safety operation information in the jurisdictional area, and can also be configured to transmit a gas pipe network anomaly information query instruction to the smart regulatory service sub-platform.

[0022] The smart gas service platform is configured to receive anomaly pipe information and / or anomaly treatment scheme transmitted by the smart gas data center in the smart gas safety management platform, and transmit the anomaly pipe information and / or anomaly treatment scheme to the smart gas user platform. The smart gas service platform is also configured to receive a gas pipe network anomaly information query instruction issued by the smart gas user platform and transmit it to the smart gas data center in the smart gas safety management platform. In some embodiments, the smart gas service platform can include a smart gas use service sub-platform and a smart regulatory service sub-platform.

[0023] In some embodiments, the smart regulatory service sub-platform can receive anomaly pipe information and / or anomaly treatment scheme transmitted by the smart gas safety management platform, and transmit the anomaly pipe information and / or anomaly treatment scheme to the regulatory user sub-platform. The smart regulatory service sub-platform is also configured to receive a gas pipe network anomaly information query instruction issued by the regulatory user sub-platform and transmit it to the smart gas data center in the smart gas safety management platform.

[0024] The smart gas safety management platform is configured to receive detection information of a gas pipe in a target area transmitted by the smart gas pipe network equipment sensing network platform, determine an anomaly pipe in the target area based on the detection information, and determine an anomaly treatment scheme based on the detection information corresponding to the anomaly pipe. In some embodiments, the smart gas safety management platform can include a smart gas data center and a smart gas pipe network safety management sub-platform. The smart gas pipe network safety management sub-platform can include a pipe network patrol safety management, a pipe network gas leakage monitoring, a pipe network equipment safety monitoring, etc. functional modules, respectively configured to manage patrol safety, monitor pipe network gas leakage, monitor pipe network equipment safety, etc.

[0025] In some embodiments, the smart gas data center can receive detection information of gas pipelines in a target area transmitted by the smart gas pipeline equipment sensing network platform. Based on the detection information of gas pipelines in the target area obtained by the smart gas data center, the smart gas pipeline safety management sub-platform can determine abnormal pipelines and abnormal treatment schemes, and based on the smart gas data center transmitting the smart supervision service sub-platform and the gas user sub-platform to the smart gas service platform. The smart gas data center can also receive the gas pipeline anomaly information query instruction transmitted by the smart supervision service sub-platform and transmit it to the smart gas pipeline equipment sensing network platform.

[0026] The smart gas pipeline equipment sensing network platform is configured as a communication network and a gateway, for receiving detection information of gas pipelines in a target area obtained by the smart gas pipeline equipment object platform, and transmitting it to the smart gas safety management platform. The smart gas pipeline equipment sensing network platform can also receive the gas pipeline anomaly information query instruction transmitted by the smart gas pipeline safety management platform and transmit it to the smart gas pipeline equipment object platform.

[0027] In some embodiments, the smart gas pipeline equipment sensing network platform can include network management, protocol management, instruction management, data analysis and other modules. Network management and protocol management correspond to the network communication function and communication protocol conversion function of the smart gas pipeline equipment sensing network platform respectively, realizing the interaction of the smart gas pipeline equipment sensing network platform, the smart gas pipeline equipment object platform and the smart gas safety management platform. The instruction management and data analysis module in the smart gas pipeline equipment sensing network platform can analyze, classify, transform and other processes for the gas pipeline anomaly information query instruction and the detection information of gas pipelines in the target area, so as to complete the transmission of the gas pipeline anomaly information query instruction and the detection information of gas pipelines in the target area.

[0028] The smart gas pipeline equipment object platform is used to obtain detection information of gas pipelines in a target area, and can also be used to receive the gas pipeline anomaly information query instruction transmitted by the smart gas pipeline equipment sensing network platform. Pipeline equipment is the equipment contained in the gas pipeline system, which can include pressure sensors, flow meters, temperature sensors and the like, which are respectively used to detect the pressure, flow, temperature and the like of each point in the gas pipeline network.

[0029] Based on the pipeline safety linkage handling method for smart gas, the detection information of the gas pipeline in the target area is transmitted by the smart gas pipeline equipment object platform and the smart gas pipeline equipment sensing network platform in turn, the smart gas safety management platform processes the detection information of the gas pipeline in the target area to obtain an abnormal pipeline and / or an abnormal handling scheme, the abnormal pipeline and / or the abnormal handling scheme are transmitted by the steps of the smart gas safety management platform, the smart gas service platform and the smart gas user platform in turn, and the gas pipeline abnormal information query instruction is transmitted by the steps of the smart gas user platform, the smart gas service platform, the smart gas safety management platform, the smart gas pipeline equipment sensing network platform and the smart gas pipeline equipment object platform in turn. When the gas pipeline abnormal information query instruction is transmitted, the receiving and processing of the instruction by each platform is specifically processing into a data packet format that is set for the next level of receiving object for easy identification.

[0030] It should be understood that Figure 1 The Internet of Things system and its modules shown can be implemented in various ways.

[0031] It should be noted that the above description of the smart gas pipeline safety management Internet of Things system and its modules is for convenience of description only and cannot limit the scope of the embodiments. It can be understood that, for those skilled in the art, after understanding the principle of the Internet of Things system, the modules can be combined arbitrarily or connected to form a subsystem without departing from the principle. In some embodiments, Figure 1 The smart gas user platform, the smart gas service platform, the smart gas safety management platform, the smart gas pipeline equipment sensing network platform and the smart gas pipeline equipment object platform disclosed in the embodiments can be different modules in a system, or one module can implement the functions of two or more modules. For example, each module can share a storage module, and each module can have its own storage module. Such variations are within the scope of the present disclosure.

[0032] Figure 2 is a flowchart of a pipeline safety linkage handling method for smart gas according to some embodiments of the present disclosure. In some embodiments, the flow 200 can be executed by the Internet of Things system 100. As shown in Figure 2 The flow 200 can include the following steps:

[0033] Step 210, obtaining detection information of a gas pipeline in a target area.

[0034] The target area refers to an area that needs to be detected for the gas pipeline, such as a street, a community, etc. The target area can be determined by manual designation or pre-delimitation. For example, if a gas user reports that the gas usage in a certain area is abnormal or a regulatory user finds that the gas usage data in a certain area is abnormal, the area can be manually designated as a target area. For another example, if it is pre-delimited that the gas pipeline in a certain area needs to be checked regularly for three months, when the area reaches the checking time, the area is determined as a target area.

[0035] The detection information refers to relevant information obtained when detecting the gas pipeline, such as the pressure, flow rate, temperature, etc. of each point in the gas pipeline segment. The point refers to a position in the gas pipeline where a corresponding detection device (such as a sensor, etc.) is installed for obtaining the detection information. Multiple points can be set in the same gas pipeline.

[0036] In some embodiments, the intelligent gas data center in the intelligent gas safety management platform can obtain the detection information of the gas pipeline in the target area based on the intelligent gas pipeline equipment object platform. The intelligent gas pipeline equipment object platform can be configured as a detection device of the pipeline equipment for obtaining the detection information of the gas pipeline in the target area. For example, the intelligent gas pipeline equipment object platform respectively uses a pressure sensor, a flow meter, and a temperature sensor to obtain the pressure, flow rate, and temperature of each point in the gas pipeline segment.

[0037] In some embodiments, the regulatory user can call the latest detection information of the gas pipeline in the target area stored in the Internet of Things system as the detection information of the gas pipeline in the target area. For example, if the regulatory user finds that the gas usage in a certain area is abnormal, the regulatory user can preferentially call the latest detection information of the gas pipeline in the target area stored in the Internet of Things system, so as to quickly analyze the detection information of the gas pipeline.

[0038] In step 220, based on the detection information, an abnormal pipeline in the target area is determined.

[0039] The abnormal pipeline refers to a gas pipeline that may have abnormal usage. For example, the gas flow rate in the gas pipeline is abnormal, the pressure of the gas pipeline is abnormal, the temperature of the gas pipeline is abnormal, etc.

[0040] In some embodiments, the smart gas data center transmits the detection information to the smart gas safety management sub-platform, which can determine abnormal pipelines in the target region based on the detection information in various ways. For example, the smart gas safety management sub-platform can determine whether a gas pipeline is an abnormal pipeline by comparing the detection information of the gas pipeline in the target region with reference detection information. The reference detection information is related information of the gas pipeline in a normal working condition, for example, the reference detection information can be the gas delivery speed, temperature, etc. of the gas pipeline in the normal working condition. If the detection information of the gas pipeline in the target region does not conform to the reference detection information, the gas pipeline is determined to be an abnormal pipeline. For example, the reference detection information includes that the gas flow rate in the low-pressure gas pipeline is not greater than 3 m / s, and if the detection information of the gas pipeline A in the target region includes that the gas flow rate in the low-pressure gas pipeline of the gas pipeline A is 5 m / s, the detection information does not conform to the reference detection information, so the gas pipeline A in the target region is an abnormal pipeline.

[0041] In step 230, an abnormal processing scheme is determined based on the detection information corresponding to the abnormal pipeline.

[0042] The abnormal processing scheme is a scheme for solving the abnormality of the abnormal pipeline. For example, if the detection information corresponding to the abnormal pipeline is that the gas flow rate in the abnormal pipeline is lower than the normal value, the abnormal processing scheme is to increase the gas flow rate. For another example, if the detection information corresponding to the abnormal pipeline is that the pressure in the abnormal pipeline is too large, the abnormal pipeline is subjected to pressure reduction processing, etc.

[0043] In some embodiments, the abnormal processing scheme can include schemes for processing various abnormal situations, for example, the abnormal processing scheme can include a processing scheme for gas flow rate abnormality, a processing scheme for pressure abnormality, and a processing scheme for temperature abnormality, which are respectively represented by English letters A, B, and C to represent the categories of the processing schemes, for example, increasing the gas flow rate by 1 m / s can be represented as A+1, increasing the pressure by 5 Pa can be represented as B+5, and reducing the temperature by 1°C can be represented as C-1. The abnormal processing scheme is represented as (A+1, B+5, C-1). Other abnormal situations and processing schemes for the abnormal situations can be represented in the above manner.

[0044] In some embodiments, the abnormal processing scheme can be determined by manual analysis. For example, the detection information of the gas pipeline in the target region includes that the gas flow rate in the low-pressure gas pipeline is 5 m / s, which exceeds the normal value of 3 m / s, and the gas company gives a processing scheme to reduce the gas flow rate after analyzing the detection information. The specific adjustment value can be set based on experience, such as using a gradual reduction method, etc.

[0045] In some embodiments, the detection information of the gas pipeline in the target area can be obtained based on the smart gas data center, the smart gas data center transmits the detection information of the gas pipeline in the target area to the smart gas pipeline safety management sub-platform, and the smart gas pipeline safety management sub-platform determines the abnormal processing scheme. Wherein, the smart gas pipeline safety management sub-platform can preset a plurality of abnormal conditions corresponding to the abnormal processing scheme, for example, when the preset gas flow rate value exceeds the preset range, the corresponding gas flow rate adjustment scheme is preset. When the preset gas temperature value is higher than the corresponding range, different cooling schemes corresponding to each high temperature range are preset. The smart gas pipeline safety management sub-platform can determine the corresponding abnormal condition according to the detection information of the gas pipeline in the target area, and then match the corresponding abnormal processing scheme for the abnormal condition.

[0046] In some embodiments, the abnormal processing scheme can also be determined based on a preset algorithm. For more information about the preset algorithm, see Figure 3 、 Figure 4 and related descriptions.

[0047] Step 240, based on the smart gas service platform, the abnormal pipeline and / or abnormal processing scheme is transmitted to the smart gas user platform.

[0048] In some embodiments, after the smart gas pipeline safety management sub-platform determines the abnormal pipeline and / or abnormal processing scheme, the smart gas data center can transmit the abnormal pipeline information and / or abnormal processing scheme to the smart gas service platform, and the smart gas service platform transmits the abnormal pipeline information and / or abnormal processing scheme to the supervision user sub-platform of the smart gas user platform. The supervision user can obtain the abnormal pipeline information and / or abnormal processing scheme on the smart gas user platform.

[0049] Based on the smart gas pipeline safety management Internet of Things system, the determination and transmission of the abnormal pipeline and / or abnormal processing scheme in the gas pipeline network are realized, which facilitates the supervision user to obtain the abnormal processing scheme in time to quickly repair the abnormal pipeline, and can more accurately and conveniently supervise and dynamically investigate the abnormal pipeline of the gas pipeline to protect the gas safety of the user.

[0050] Figure 3 is a flowchart for determining an abnormal processing scheme according to some embodiments of the present specification. In some embodiments, flow 300 can be executed by a smart gas safety management platform. As shown in Figure 3 , the flow 300 can include the following steps:

[0051] Step 310, based on the detection information corresponding to the abnormal pipeline, a plurality of initial candidate schemes are randomly generated for the abnormal pipeline. The initial candidate scheme includes the processing parameter of the abnormal pipeline.

[0052] The initial candidate scheme is a processing scheme randomly generated for the detection information corresponding to the abnormal pipeline. For example, the detection information corresponding to the abnormal pipeline is that the gas flow rate is lower than the normal value, and the initial candidate scheme can include increasing the gas flow rate by 0.5 m / s, increasing the gas flow rate by 1 m / s, etc. Referring to the above, A represents the processing scheme for the gas flow rate anomaly, and the initial candidate scheme can be represented as a vector (A+0.5), (A+1), etc. For another example, the detection information corresponding to the abnormal pipeline is that the gas flow rate is lower than the normal value and the pipeline pressure is lower than the normal value, and the initial candidate scheme can include increasing the gas flow rate and increasing the pipeline pressure. Referring to the above, B represents the processing scheme for the pressure anomaly, and the initial candidate scheme can be represented as a vector (A+0.5, B+0.5), (A+1, B+0.5), etc. in a similar manner as described above.

[0053] The processing parameter refers to the processing scheme corresponding to the pipeline anomaly. For example, A+0.5 in the initial candidate scheme (A+0.5, B+0.5) is a processing parameter, A+0.5 indicates that the gas flow rate anomaly is for the pipeline anomaly; the gas flow rate is increased by 0.5 m / s, and B+0.5 is also a processing parameter, B+0.5 indicates that the pipeline pressure anomaly is for the pipeline anomaly, and the pressure is increased by 0.5 Pa.

[0054] In some embodiments, the intelligent gas safety management platform randomly generates a plurality of initial candidate schemes for the abnormal pipeline based on the detection information corresponding to the abnormal pipeline. The values of the processing parameters in the randomly generated initial candidate schemes can be set to have an upper limit and a lower limit. For example, for the detection information that the gas flow rate is lower than the normal value, the gas flow rate can be randomly increased by n m / s, n is greater than 0 and not greater than 10.

[0055] In some embodiments, the intelligent gas safety management platform can call a plurality of initial candidate schemes preset in the platform based on the detection information corresponding to the abnormal pipeline. For example, a plurality of initial candidate schemes corresponding to various possible abnormal situations can be stored in the intelligent gas safety management platform in advance for calling.

[0056] In step 320, based on a preset algorithm, a plurality of initial candidate schemes are updated in multiple rounds until a preset iteration condition is met; and an abnormal processing scheme is determined.

[0057] The preset algorithm refers to an algorithm for updating a plurality of initial candidate schemes in multiple rounds to determine an abnormal processing scheme, which will be described in detail in Figure 4 .

[0058] The process of multiple rounds of iterative updating on the multiple initial candidate solutions comprises determining multiple first candidate processing solutions based on the multiple initial candidate solutions, determining second candidate processing solutions from the first candidate processing solutions based on evaluation values of the first candidate processing solutions, determining third candidate processing solutions by performing change processing on the second candidate processing solutions, determining first candidate processing solutions of the next round based on reference values of the third candidate processing solutions, and repeating the above iterative updating process for the newly determined first candidate processing solutions. When a preset iteration condition is met, the third candidate processing solution is determined as the abnormal processing solution. For details of the multiple rounds of iterative updating on the multiple initial candidate solutions, see Figure 4 and the related description.

[0059] The preset iteration condition refers to a condition for stopping the multiple rounds of iteration. In some embodiments, the preset iteration condition can include that the number of iteration rounds is not less than a preset round number value, or the evaluation value of the first candidate processing solution is not less than a preset evaluation value, or in at least two consecutive rounds of iteration, the change range of the evaluation value of the first candidate processing solution is less than a preset change value. For more details of the preset iteration condition, see Figure 4 and the related description.

[0060] The abnormal processing solution is determined based on a preset algorithm, and the process of multiple rounds of iterative updating on the initial candidate solution can make the determined abnormal processing solution an optimal solution, thereby shortening the time length for determining the abnormal processing solution and saving the labor cost, and thus improving the processing efficiency of the abnormal pipeline of the gas pipeline network.

[0061] It should be noted that the above description of the flow 300 is only for example and illustration, and does not limit the scope of the present specification. Those skilled in the art can make various modifications and changes to the flow 300 under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification.

[0062] Figure 4 is a flowchart of at least one round of iteration in the multiple rounds of iterative updating according to some embodiments of the present specification. In some embodiments, the flow 400 can be performed by the intelligent gas safety management platform. As Figure 4 indicated, the flow 400 can include the following steps:

[0063] Step 410, determining an evaluation value of each candidate processing solution in the multiple first candidate processing solutions; wherein when the number of iteration rounds = 1, the first candidate processing solution is one of the multiple initial candidate solutions; and when the number of iteration rounds > 1, the first candidate processing solution is the third candidate processing solution of the last round of iteration.

[0064] The first candidate processing scheme can refer to a candidate processing scheme that needs to be iteratively processed. For example, in the first iteration, a first candidate processing scheme can be one of the initial candidate processing scheme 1, the initial candidate processing scheme 2, …, and the initial candidate processing scheme n.

[0065] In some embodiments, the first candidate processing scheme can be represented in a vector-based manner. For example, a first candidate processing scheme can include a candidate processing scheme for gas flow rate anomaly, a candidate processing scheme for pressure anomaly, and a candidate processing scheme for temperature anomaly, which are represented by English letters A, B, and C, respectively, to represent the type of processing scheme. For example, increasing the gas flow rate by 1 m / s can be represented as A+1, increasing the pressure by 5 Pa can be represented as B+5, and decreasing the temperature by 1℃ can be represented as C-1. Thus, the first candidate processing scheme can be represented as (A+1, B+5, C-1).

[0066] The first candidate processing scheme can be determined based on the iteration result of the previous round or based on the initial candidate processing scheme. For example, in the first iteration, the first candidate processing scheme can be the initial candidate processing scheme, and in subsequent iterations, the first candidate processing scheme can be determined based on the third candidate processing scheme of the previous iteration. For a detailed description of the third candidate processing scheme, please refer to the following.

[0067] The evaluation value can refer to a parameter for evaluating the pros and cons of the candidate processing scheme. The evaluation value can be positively correlated with the pros and cons of the candidate processing scheme. That is, the better the effect of the candidate processing scheme in handling the anomaly, the larger the evaluation value.

[0068] In some embodiments, the evaluation value can be determined in multiple ways. For example, it can be determined manually or by using an algorithm model.

[0069] In some embodiments, the intelligent gas safety management platform can determine the fitness of the first candidate processing scheme based on the fitness determination model for the attribute information of the abnormal pipeline and the processing of the first candidate processing scheme, and determine the evaluation value of the first candidate processing scheme based on the fitness of the first candidate processing scheme.

[0070] The fitness determination model can be used to determine the fitness of the first candidate processing scheme. In some embodiments, the fitness model can be a machine learning model.

[0071] In some embodiments, the fitness determination model can process the attribute information of the abnormal pipeline and the first candidate processing scheme to determine the fitness of the first candidate processing scheme. The input of the fitness determination model can include the attribute information of the abnormal pipeline and the first candidate processing scheme, and the output can include the fitness of the first candidate processing scheme.

[0072] The attribute information of the abnormal pipeline can be related information of the pipeline with the abnormality. For example, the location and length of the pipeline with the abnormality, and the specific abnormality detection situation of each pipeline with the abnormality, which can include information such as the detected pressure and temperature in the pipeline section.

[0073] In some embodiments, the fitness value is similar to the evaluation value and can also be used to evaluate the pros and cons of the candidate processing scheme. The greater the fitness value, the better the effect of the candidate processing scheme in handling the abnormality, and the greater the success rate of solving the abnormality.

[0074] In some embodiments, the intelligent gas safety management platform can directly use the fitness value of the first candidate processing scheme as the evaluation value of the first candidate processing scheme or use the value obtained by equi-proportionally scaling the fitness value of the first candidate processing scheme as the evaluation value of the first candidate processing scheme.

[0075] In some embodiments, the intelligent gas safety management platform can determine the evaluation value of the first candidate processing scheme based on the fitness value of the first candidate processing scheme and the linkage influence prediction model. For more information about the linkage influence prediction model, see Figure 5 and the related description thereof.

[0076] In some embodiments of the present specification, the fitness value of the first candidate processing scheme is determined by using the fitness determination model, and the evaluation value of the first candidate processing scheme is determined based on the fitness value. This not only improves the operation efficiency, but also guarantees the accuracy of the result.

[0077] Step 420: determining a second candidate processing scheme from the plurality of first candidate processing schemes based on the evaluation value of the first candidate processing scheme.

[0078] The second candidate processing scheme can refer to a candidate processing scheme selected based on the evaluation value of the first candidate processing scheme.

[0079] In some embodiments, the intelligent gas safety management platform can determine a plurality of second candidate processing schemes from the plurality of first candidate processing schemes based on the corresponding evaluation value of each of the plurality of first candidate processing schemes. For example, the first candidate processing scheme with an evaluation value greater than a preset evaluation value can be determined as the second candidate processing scheme. The preset evaluation value can be a parameter set in advance. For example, from three first candidate processing schemes: first candidate processing scheme 1 (A+1, B+5, C-1), first candidate processing scheme 2 (A+1, B+4, C-0.5), and first candidate processing scheme 3 (A+0.5, B+4, C-1), the first candidate processing scheme 1 (A+1, B+5, C-1) with an evaluation value greater than the preset evaluation value is selected as the second candidate processing scheme.

[0080] At step 430, the second candidate processing scheme is transformed to determine a third candidate processing scheme.

[0081] The third candidate processing scheme can refer to a candidate processing scheme obtained by further processing the second candidate processing scheme.

[0082] In some embodiments, the third candidate processing scheme can be determined by transforming the second candidate processing scheme. The transformation can include a first transformation and a second transformation.

[0083] In some embodiments, the first transformation can include selecting two second candidate processing schemes from a plurality of second candidate processing schemes, exchanging one or more processing parameters in the selected two candidate processing schemes to generate at least two third candidate processing schemes, and determining the third candidate processing scheme based on the third candidate processing schemes.

[0084] In some embodiments, the first transformation can be to exchange processing parameters of the same abnormal pipeline or the same abnormal area in different second candidate processing schemes.

[0085] For example, the second candidate processing scheme 1 and the second candidate processing scheme 2 are both processing schemes for the abnormality of area A, where the second candidate processing scheme 1 is (A+1, B+5, C-1), and the second candidate processing scheme 2 is (A+1, B+4, C-0.5). The second processing parameter in the second candidate processing scheme 1 and the second candidate processing scheme 2 can be exchanged to generate third candidate processing schemes, such as the third candidate processing scheme 1 (A+1, B+4, C-1) and the third candidate processing scheme 2 (A+1, B+5, C-0.5).

[0086] In some embodiments, the smart gas safety management platform can also preferentially exchange the processing parameter with poor effect in the two second candidate processing schemes to improve the efficiency of determining the abnormal processing scheme. The processing parameter with poor effect in the scheme can be obtained based on the test, for example, adjusting a certain type of processing parameter can greatly improve the processing effect of the entire scheme, and the processing parameter can be considered as the processing parameter with poor effect.

[0087] The third candidate processing scheme can refer to a candidate processing scheme obtained by the first transformation of the second candidate processing scheme. In some embodiments, the smart gas safety management platform can directly use the third candidate processing scheme as the third candidate processing scheme.

[0088] The second transformation can refer to an operation for adjusting the processing parameter. In some embodiments, the second transformation can include updating at least one processing parameter in the preliminary processing scheme to generate at least one third candidate processing scheme.

[0089] The preliminary processing scheme can refer to a processing scheme to be subjected to a second transformation. In some embodiments, the preliminary processing scheme is a second candidate processing scheme or a third candidate processing scheme.

[0090] In some embodiments, for each of the plurality of preliminary processing schemes, the intelligent gas safety management platform can adjust at least one processing parameter in the preliminary processing scheme to generate at least one third candidate processing scheme. For example, the preliminary processing scheme 1 is (A+1, B+4, C-1), the third processing parameter in the preliminary processing scheme 1 can be adjusted, i.e., C-1 is modified to C-0.8, and the third candidate processing scheme generated after the modification is (A+1, B+4, C-0.8).

[0091] In some embodiments, the intelligent gas safety management platform can also preferentially adjust the processing parameters with poor processing effects in the preliminary processing scheme to improve the efficiency of determining the abnormal processing scheme.

[0092] It should be noted that the adjusted parameters can not exceed the maximum values that the related devices can withstand. For example, the maximum temperature and the maximum pressure in the pipe section.

[0093] In some embodiments, the intelligent gas safety management platform can take the third candidate processing scheme obtained based on the foregoing steps as the first candidate processing scheme of the next round of iteration, repeat the iteration steps until the preset iteration condition is met. In some embodiments, the intelligent gas safety management platform can further process the obtained third candidate processing scheme based on the following steps:

[0094] Step 440, determining a reference value of the third candidate processing scheme.

[0095] The reference value can refer to the probability of any one of the third candidate processing schemes being selected as the first candidate processing scheme of the next round of iteration or the probability of being selected as the final abnormal processing scheme.

[0096] In some embodiments, the reference value of a certain candidate processing scheme in the third candidate processing scheme can be the ratio of the fitness value of the scheme to the total fitness value of the third candidate processing scheme. For example, the total number of the third candidate processing schemes is 2, the fitness value of the third candidate processing scheme 1 is 0.4, and the fitness value of the third candidate processing scheme 2 is 0.1, then the reference value of the third candidate processing scheme 1 is 0.4 / (0.4+0.1)=0.8, i.e., the reference value of the third candidate processing scheme 1 is 0.8.

[0097] In some embodiments, the smart gas safety management platform can obtain the fitness of the third candidate processing scheme based on the fitness determination model, and based on the fitness value, calculate through programs, algorithms and various ways to determine the reference value of the third candidate processing scheme.

[0098] At step 450, the third candidate processing scheme is screened based on the reference value of the third candidate processing scheme, and the screened third candidate processing scheme is used as the first candidate processing scheme of the next round or used to determine the abnormal processing scheme.

[0099] In some embodiments, the smart gas safety management platform can determine the first candidate processing scheme entering the next round from the plurality of third candidate processing schemes based on the size of the reference value. For example, the reference values can be sorted from large to small, and the top several third candidate processing schemes are determined as the first candidate processing scheme entering the next round.

[0100] In some embodiments, the smart gas safety management platform can determine the third candidate processing scheme with a reference value greater than a preset reference value as the first candidate processing scheme of the next round. For example, the total number of third candidate processing schemes is 4, the reference value of the third candidate processing scheme 1 is 0.8, the reference value of the third candidate processing scheme 2 is 0.6, the reference value of the third candidate processing scheme 3 is 0.5, and the reference value of the third candidate processing scheme 4 is 0.9, and the preset reference value is 0.7. Since the reference values of the third candidate processing scheme 1 and the third candidate processing scheme 4 are greater than the preset reference value, the third candidate processing scheme 1 and the third candidate processing scheme 4 can be determined as the first candidate processing scheme of the next round. The preset reference value can be a probability parameter set in advance.

[0101] The smart gas safety management platform can determine the third candidate processing scheme with the largest reference value as the abnormal processing scheme based on the screened third candidate processing scheme as the first candidate processing scheme of the next round, and repeat the execution of steps 410-450 to continue the iterative update until the preset iteration condition is met.

[0102] In some embodiments, the preset iteration condition can include that the iteration round number is not less than a preset round number value. The preset round number value can be directly determined according to past experience, or can be determined by trial and error. For example, a small value (e.g., 50) can be set first, and then gradually expanded to a reasonable range according to the iteration results.

[0103] In some embodiments, the preset iteration condition can include that the evaluation value of the first candidate processing scheme is not less than a preset evaluation value. The preset evaluation value can be the minimum evaluation value corresponding to the scheme that can successfully solve the abnormality based on experience. When the evaluation value of the first candidate processing scheme is not less than the preset evaluation value, it means that a processing scheme that can solve the abnormality has been generated.

[0104] In some embodiments, the preset iteration condition can further include that, in at least two consecutive iterations, a variation range of the evaluation value of the first candidate processing scheme is less than a preset variation value. The preset variation value can be a minimum variation requirement that the evaluation value of the first candidate processing scheme needs to meet before and after iteration. If, in at least two consecutive iterations, the variation range of the evaluation value of the first candidate processing scheme is less than the preset variation value, it can be considered that the candidate processing scheme before and after iteration has little or no change, and iteration can be stopped at this time.

[0105] The preset iteration condition can be preset by a user. In some embodiments, the preset iteration condition can include at least one of the above conditions.

[0106] In some embodiments of the present specification, the fitness of the first candidate processing scheme is determined by using the fitness determination model, which can ensure the accuracy of the result and improve the operation efficiency; based on the exchange processing or the adjustment of the processing parameter with poor processing effect in the candidate processing scheme, and multiple iterations, the efficiency of iteration can be effectively improved to quickly determine the abnormal processing scheme.

[0107] Figure 5 FIG. 5 is a flow diagram illustrating a process of determining an evaluation value of a first candidate processing scheme according to some embodiments of the present specification. In some embodiments, the process 500 can be performed by the intelligent gas safety management platform. As shown in FIG. 5, the process 500 can include the following steps: Figure 5

[0108] Step 510, based on the pipe network information of the target area, a pipe network diagram of the target area is constructed; the nodes of the pipe network diagram correspond to the intersection points or end points of the gas pipelines in the target area, and the edges of the pipe network diagram correspond to the gas pipelines in the target area.

[0109] Figure 6 FIG. 6 is a schematic diagram of an intelligent gas pipe network diagram according to some embodiments of the present specification. As shown in FIG. 6, the pipe network diagram 600 can be composed of multiple nodes and multiple edges. Figure 6

[0110] The pipe network diagram can refer to a graph reflecting the connection relationship between the gas pipeline segments.

[0111] The node of the pipe network diagram can refer to a node generated at the intersection of the gas pipeline segments. The types of the nodes can include inlaid nodes, storage nodes, and gas use nodes, etc. The inlaid node can refer to a node generated at the inlaid point of two pipelines, the storage node can refer to a node generated at the gas storage point (for example, a gate station, etc.), and the gas use node can refer to a node generated at the gas use point (for example, a residential building, etc.).

[0112] As shown in FIG. 5, the process 500 can include the following steps: Figure 6 ​​As shown, the splicing nodes may include "Node 3", "Node 5" and "Node 6", the gas storage node may include "Node 1" and the gas consumption node may include multiple nodes such as "Node 2" and "Node 4".

[0113] Different types of nodes can include different node characteristics. For example, the node characteristics of a splicing node can include pressure loss at the splicing bend, the node characteristics of a gas storage node can include standard flow rate, inlet pressure, outlet pressure, etc., and the node characteristics of a gas consumption node can include gas consumption demand, etc.

[0114] Pressure loss can refer to the energy loss caused by the increased flow resistance due to vortices and velocity redistribution in areas where gas flows through bends. In some embodiments, the pressure loss of a bend can be determined using the formula for calculating the local pressure loss of gas in a pipeline, or by installing pressure testing instruments at both ends of the bend to obtain the pressure at each end and calculating the pressure difference.

[0115] Standard flow rate can refer to the flow rate of gas entering and exiting a gas point under standard conditions (standard temperature and standard pressure). In some embodiments, the standard flow rate can be determined directly based on the configuration of the gas point.

[0116] Inlet pressure and outlet pressure refer to the gas pressure at the inlet and outlet ends of the gas pressure regulator, respectively. In some embodiments, the inlet and outlet pressures can be obtained based on monitoring equipment built into the regulator. This monitoring equipment can be used to monitor the pressure in the gas pipeline in real time.

[0117] Gas consumption demand can refer to the total gas demand of all residences in a building or the average gas demand of each household in the building. In some embodiments, gas consumption demand can be determined by calculating weekly or monthly average gas consumption.

[0118] Multiple nodes are connected by edges, which can refer to gas pipeline segments, meaning that there is an edge between two nodes that are directly connected by a gas pipeline.

[0119] In some embodiments, the edges of the pipeline diagram are directed edges, used to indicate the direction of gas delivery. For example... Figure 6 As shown, the edge between "Node 1" and "Node 2" points from "Node 1" to "Node 2", which means that in this diagram, gas is transported from "Node 1" to "Node 2", that is, gas is transported from the gas storage point to the residential building.

[0120] The characteristics of the edges in a pipe network diagram can include pipe segment length, pressure within the pipe segment, and temperature within the pipe segment. Different edges have different characteristics; that is, different pipe segments have different characteristics. In some embodiments, the edges of a pipe network diagram may have markers (...).Figure 6 In the figure, the nodes are labeled as “Node 1”, “Node 2”, etc., and the edges are labeled as “Edge 1”, “Edge 2”, etc., to distinguish different pipe segments.

[0121] In some embodiments, the edges of the pipe network graph can have weights (not shown in the figure) to represent the importance of the edges between two nodes. For example, the weight of “Edge 4” is 0.4, and the weight of “Edge 1” is 0.1, which means that the importance of “Edge 4” is higher than that of “Edge 1”.

[0122] It can be understood that, Figure 6 The graph in the figure is only used to simply illustrate the connection network of the gas pipe segments, and the actual gas pipe network is more complex and can include more nodes and more edges.

[0123] In step 520, based on the linkage influence prediction model, the abnormal evaluation value of the target area corresponding to the first candidate processing scheme is determined by processing the pipe network graph, the detection information corresponding to the abnormal pipe, and the processing of the first candidate processing scheme. The abnormal evaluation value is determined based on the abnormal probability value output by the node and edge of the pipe network graph; the linkage influence prediction model is a machine learning model.

[0124] The linkage influence prediction model can be used to predict the abnormal evaluation value of the target area corresponding to the first candidate processing scheme. In some embodiments, the linkage influence prediction model is a machine learning model. For example, a convolutional neural network model, a graph neural network model, etc.

[0125] In some embodiments, the linkage influence prediction model can be a graph neural network model.

[0126] In some embodiments, the smart gas safety management platform can input the pipe network graph, the detection information corresponding to the abnormal pipe, and the first candidate processing scheme into the linkage influence prediction model. The linkage influence prediction model processes the detection information corresponding to the abnormal pipe and the first candidate processing scheme based on the nodes and edges of the pipe network graph, and determines the abnormal probability value corresponding to the nodes and edges of the pipe network graph.

[0127] In some embodiments, the linkage influence prediction model can be a graph neural network model.

[0128] In some embodiments, the linkage influence prediction model can be trained based on multiple groups of training samples with labels. Specifically, the training samples with labels are input into the linkage influence prediction model, and the parameters of the linkage influence prediction model are updated through training.

[0129] In some embodiments, the training samples can include sample pipe network graphs, detection information corresponding to sample historical abnormal pipes, and sample processing schemes.

[0130] In some embodiments, the label can be an abnormal probability value corresponding to the node and edge of the pipe network graph. In some embodiments, the label can be obtained according to the actual implementation of the sample processing scheme, for example, implementing the sample processing scheme, obtaining the detection information corresponding to each level of pipe and node, and monitoring whether it is abnormal. The label can also be obtained in other ways, for example, based on historical data, etc.

[0131] In some embodiments, the linkage influence prediction model can be trained based on the training sample by various methods to update the parameters of the linkage influence prediction model. For example, the training can be based on the gradient descent method.

[0132] In some embodiments, the training ends when the trained linkage influence prediction model meets the preset condition. The preset condition can be that the loss function result converges or is less than a preset threshold, etc.

[0133] The abnormal probability value can refer to the probability of abnormality of the joint node, gas storage node, and gas consumption node and gas pipeline corresponding to the node and edge of the pipe network graph when the first candidate processing scheme is implemented. For example, if the abnormal probability value output by "edge 2" is 0.8, it means that the probability of abnormality of the gas pipeline corresponding to "edge 2" is 80% when the first candidate processing scheme is implemented. In some embodiments, the node and edge of the pipe network graph can output specific abnormal conditions at the same time as the output of the abnormal probability value, for example, the output of "edge 2" is (a, 0.8), which means that the probability of a type of abnormality is 0.8, and through the pre-set abnormal type table, it is known that the a type of abnormality is flow rate abnormality.

[0134] The abnormal evaluation value refers to the evaluation value of the abnormality of the entire pipe network graph corresponding to the target region caused by the implementation of the first candidate processing scheme input into the model. In some embodiments, the abnormal evaluation value can be obtained by statistically processing the abnormal probability values output by the nodes and edges of the pipe network graph. For example, if there are 10 nodes and edges in the pipe network graph, based on the detection information corresponding to the pipe network graph and abnormal pipeline and the processing of the first candidate processing scheme by the linkage influence prediction model, the abnormal probability values output by all nodes and edges of the pipe network graph are less than a preset value (for example, 0.2), it can be considered that the abnormal evaluation value of the first candidate processing scheme is full score, such as 100 points; if one or more edges or nodes output an abnormal probability value greater than the preset value, the evaluation value of the first candidate processing scheme can be reduced, for example, if one edge or node outputs an abnormal probability value greater than the preset value, the abnormal evaluation value of the first candidate processing scheme is 90 points, and so on. The representation of the abnormal evaluation value can also be other ways, such as grades or other ways.

[0135] At step 530, the evaluation value of the first candidate processing scheme is determined based on the fitness of the first candidate processing scheme and the abnormality evaluation value of the target region corresponding to the first candidate processing scheme.

[0136] In some embodiments, the intelligent gas safety management platform can determine the evaluation value of the first candidate processing scheme by directly taking the fitness of the first candidate processing scheme as the evaluation value, or by comprehensively considering the fitness of the first candidate processing scheme and the abnormality evaluation value of the target region corresponding to the first candidate processing scheme. For example, the fitness of the first candidate processing scheme and the abnormality evaluation value of the target region corresponding to the first candidate processing scheme can be given different weights, and the evaluation value of the first candidate processing scheme can be determined by weighted summation. Alternatively, the evaluation value of the first candidate processing scheme can be determined by averaging or other methods.

[0137] In some embodiments of the present specification, the abnormality evaluation value of the target region corresponding to the first candidate processing scheme is determined based on the predicted result of the linkage effect generated by the first candidate processing scheme after being processed by the trained linkage effect prediction model, and the evaluation value of the first candidate processing scheme is determined based on the abnormality evaluation value and the fitness of the first candidate processing scheme, which can make the evaluation value more accurate.

[0138] The above has described the basic concept. Obviously, the above detailed disclosure is only used as an example for those skilled in the art, and does not limit the present specification. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0139] Meanwhile, specific words are used in the present specification to describe the embodiments of the present specification. For example, “one embodiment”, “an embodiment”, and / or “some embodiments” means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the “one embodiment” or “one alternative embodiment” mentioned in the present specification twice or more in different positions does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0140] Furthermore, the order of the processing elements and sequences described in this specification are not intended to be construed as a limitation, unless specifically stated, but are included to provide a complete description of one or more embodiments of the present specification. Regardless of the particular sequence of processing elements and sequences, however, the description herein of a process should be understood to include any and all combinations of one or more elements of a process independently selected from each sequence. For example, although the system components described above can be implemented by hardware devices, they can also be implemented by software solutions, such as installing the described system on an existing server or mobile device.

[0141] Similarly, it is to be noticed that the term "comprising", used in the description, is not intended to exclude other elements or steps. It is to be understood that the description and the examples are intended to be illustrative, but not limiting, of the scope of the present specification. Thus, the scope of the present specification should be given by the appended claims, along with their full scope of equivalents, and not by an restricting interpretation of the description or the examples.

[0142] Some embodiments use numerical designations to describe components, quantities of attributes. It is to be understood that such numerical designations used in the description of embodiments are, in some examples, modified by the adjectives "about", "approximately", or "generally". Unless otherwise stated, "about", "approximately", or "generally" indicates that the stated numerical value is allowed ±20% variation. Accordingly, numerical values used in the description and claims are approximations that can vary depending on the desired properties of the individual embodiments. In some embodiments, numerical values should be considered in the context of the number of significant figures used in the description and claims. Although the numerical ranges and parameters setting forth the broadest scope of the embodiments herein are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values set forth in the specific examples are provided to give a general understanding of the embodiments.

[0143] Each patent, patent application, patent publication, and other material cited in this specification is incorporated herein by reference in its entirety. In the event of inconsistencies between the disclosure of this specification and the documents, articles, or other materials incorporated by reference, the disclosure of this specification will prevail. In the event of inconsistencies between the disclosure of this specification and the claims, the claims will prevail. It is specifically intended that the description, definitions, and / or terminology used in the materials incorporated by reference into this specification be interpreted and used in accordance with the description, definitions, and / or terminology used in the present specification. In the event of inconsistencies between the disclosure of this specification and the incorporated by reference materials, the disclosure of this specification will prevail.

[0144] Finally, it should be understood that the embodiments described herein are only given by way of example and that other modifications can occur to persons skilled in the art. Therefore, the scope of the present description is not intended to be limited to the embodiments described herein but is only limited by the claims that follow.

Claims

1. A pipeline safety linkage handling method for smart gas, realized based on a smart gas safety management platform of a smart gas pipeline safety management Internet of Things system, comprising: obtaining detection information of a gas pipeline in a target area; determining an abnormal pipeline in the target area based on the detection information; generating a plurality of initial candidate schemes based on the detection information corresponding to the abnormal pipeline; the initial candidate schemes include treatment parameters of the abnormal pipeline; based on a preset algorithm, performing multiple rounds of iterative updates on the plurality of initial candidate schemes until a preset iteration condition is met to determine an abnormal treatment scheme, at least one round of iteration in the multiple rounds of iterative updates comprising: based on a fitness determination model, processing attribute information of the abnormal pipeline and a first candidate treatment scheme to determine the fitness of the first candidate treatment scheme; based on the fitness, determining an evaluation value of each first candidate treatment scheme; based on the evaluation value, determining a second candidate treatment scheme from a plurality of first candidate treatment schemes; performing transformation processing on the second candidate treatment scheme to determine a third candidate treatment scheme, wherein when the iteration round number = 1, the first candidate treatment scheme is one of the plurality of initial candidate schemes, and when the iteration round number > 1, the first candidate treatment scheme is a third candidate treatment scheme generated in the last round of iteration; the transformation processing includes first transformation and second transformation, the first transformation exchanges one or more treatment parameters in a plurality of different second candidate treatment schemes, and the second transformation updates at least one treatment parameter in a preliminary treatment scheme, the preliminary treatment scheme being the second candidate treatment scheme or a third candidate scheme.

2. The method of claim 1, wherein the smart gas pipeline safety management Internet of Things system further comprises a smart gas user platform, a smart gas service platform, a smart gas pipeline equipment sensing network platform, and a smart gas pipeline equipment object platform; the detection information of the gas pipeline in the target area is obtained based on the smart gas pipeline equipment object platform and transmitted to the smart gas safety management platform based on the smart gas pipeline equipment sensing network platform; the method further comprises: transmitting the abnormal pipeline and / or the abnormal treatment scheme to the smart gas user platform based on the smart gas service platform.

3. The method of claim 2, wherein the smart gas user platform comprises a gas user sub-platform and a regulatory user sub-platform; the smart gas service platform comprises a smart gas service sub-platform and a smart regulatory service sub-platform; the smart gas safety management platform comprises a smart gas data center and a smart gas pipeline safety management sub-platform; the smart gas pipeline equipment sensing network platform transmits the detection information of the gas pipeline in the target area to the smart gas data center; the smart gas pipeline safety management sub-platform obtains the detection information of the gas pipeline in the target area based on the smart gas data center. The abnormal pipeline and the abnormal treatment scheme are determined by the intelligent gas pipeline network safety management sub-platform, and are transmitted to the intelligent gas service platform based on the intelligent gas data center; The intelligent gas service platform transmits the abnormal pipeline and / or the abnormal treatment scheme to the intelligent gas user platform based on the intelligent gas service platform; The intelligent gas service platform transmits the abnormal pipeline and / or the abnormal treatment scheme to the intelligent gas user platform based on the intelligent gas service platform.

4. The method of claim 1, wherein at least one of the multiple rounds of iterative updates further comprises: determining a reference value of the third candidate treatment scheme; screening the third candidate treatment scheme based on the reference value of the third candidate treatment scheme, and taking the screened third candidate treatment scheme as a first candidate treatment scheme of the next round or for determining the abnormal treatment scheme.

5. The method of claim 1, The first transform further comprises: selecting two second candidate treatment schemes from the multiple second candidate treatment schemes, exchanging one or more treatment parameters in the selected two second candidate treatment schemes to generate at least two third candidate schemes, and determining the third candidate treatment scheme based on the third candidate schemes.

6. An intelligent gas pipeline network safety management Internet of Things system, comprising an intelligent gas user platform, an intelligent gas service platform, an intelligent gas safety management platform, an intelligent gas pipeline network device sensing network platform, and an intelligent gas pipeline network device object platform. The intelligent gas pipeline network device object platform is configured to obtain detection information of a gas pipeline in a target area. The intelligent gas pipeline network device sensing network platform is configured to transmit the detection information of the gas pipeline in the target area to the intelligent gas safety management platform. The intelligent gas safety management platform is configured to: determine an abnormal pipeline in the target area based on the detection information; generate multiple initial candidate schemes based on the detection information corresponding to the abnormal pipeline; the initial candidate schemes include treatment parameters of the abnormal pipeline; perform multiple rounds of iterative updates on the multiple initial candidate schemes based on a preset algorithm until a preset iteration condition is met, to determine an abnormal treatment scheme, wherein at least one of the multiple rounds of iterative updates comprises: determine the fitness of the first candidate treatment scheme based on a fitness determination model; determine an evaluation value of each first candidate treatment scheme based on the fitness; determine a second candidate treatment scheme from the multiple first candidate treatment schemes based on the evaluation value; and determine a reference value of the third candidate treatment scheme. screen the third candidate treatment scheme based on the reference value of the third candidate treatment scheme, and take the screened third candidate treatment scheme as a first candidate treatment scheme of the next round or for determining the abnormal treatment scheme. transforming the second candidate processing scheme to determine a third candidate processing scheme, wherein, when the iteration round number is 1, the first candidate processing scheme is one of the plurality of initial candidate schemes; when the iteration round number is greater than 1, the first candidate processing scheme is the third candidate processing scheme of the last iteration round; the transforming includes a first transforming and a second transforming, the first transforming exchanges one or more processing parameters in a plurality of different second candidate processing schemes, and the second transforming updates at least one processing parameter in a preliminary processing scheme, the preliminary processing scheme being the second candidate processing scheme or a third candidate scheme; The intelligent gas service platform is used for delivering the abnormal pipeline and / or the abnormal processing scheme to the intelligent gas user platform. 7.A computer readable storage medium, the storage medium storing computer instructions, when a computer reads the computer instructions in the storage medium, the computer executes the pipeline safety linkage treatment method for intelligent gas according to any one of claims 1-5.

8. An apparatus for intelligent gas pipeline network safety linkage treatment, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is used for storing computer instructions; The at least one processor is used for executing at least part of the computer instructions to implement the pipeline safety linkage treatment method for intelligent gas according to any one of claims 1-5.

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