Pipeline protection assembly laying method and system based on intelligent gas internet of things and medium

Abstract

The embodiment of the invention provides a pipeline protection assembly laying method and system based on an intelligent gas internet of things and a medium, and belongs to the field of gas pipeline management. The method comprises the following steps: acquiring environment information of a target area in a plurality of first preset time periods; acquiring biological information, climate information and facility information of the target area in a plurality of first preset time periods; determining a vibration risk value, a corrosion risk value and a temperature risk value of the target area in a plurality of first preset time periods; determining target risk distribution of the target area; based on the target risk distribution, determining target protection component information of a collection point position laid in the target area; based on the target risk distribution and a pipeline laying map of the target area, determining laying density distribution of the target protection component; and based on the laying density distribution, generating a valve control instruction to adjust the gas conveying pressure of at least one gas pipeline in the target area. The method can guarantee the service life of the gas pipeline and reduce the replacement cost of the pipeline.

Description

Technical Field

[0001] The present invention relates to the field of gas pipeline management, and particularly to a method, system and medium for arranging pipeline protection components based on an intelligent gas Internet of Things. Background Art

[0002] Gas pipelines are the main transportation facilities for gas. During operation, external interferences, including climate temperature changes, stress, vibration, freeze-thaw cycles, biological corrosion, etc., may reduce the service life of gas pipelines, and even cause pipeline failures and potential safety hazards. The existing technologies for the management and maintenance of gas pipelines mostly combine regular inspections and after-the-fact repairs, lacking means for early prevention during the laying of gas pipelines.

[0003] Therefore, it is necessary to propose a method, system and medium for arranging pipeline protection components based on an intelligent gas Internet of Things, which arrange corresponding pipeline protection components in key protection areas according to the actual situation during pipeline laying, so as to improve the service life of gas pipelines and ensure the safety of gas management and operation. Summary of the Invention

[0004] One or more embodiments of the present invention provide a method for arranging pipeline protection components based on the intelligent gas Internet of Things. The method is executed by the gas company management platform of the pipeline protection component arrangement system, and the method includes: obtaining environmental information of a target area in a plurality of first preset time periods from a sensing device set in the gas equipment object platform through the gas company sensing network platform, where the environmental information includes temperature information, humidity information, geological information, and vibration information; obtaining biological information, climate information, and facility information of the target area in the plurality of first preset time periods from the government safety supervision management platform through the government safety supervision sensing network platform; determining the vibration risk value of the target area in each of the plurality of first preset time periods based on the geological information, the facility information, and the vibration information in the plurality of first preset time periods; determining the corrosion risk value of the target area in each of the plurality of first preset time periods based on the geological information, the biological information, and the facility information in the plurality of first preset time periods; determining the temperature risk value of the target area in each of the plurality of first preset time periods based on the climate information in the plurality of first preset time periods; determining the target risk distribution of the target area based on the vibration risk value, the corrosion risk value, and the temperature risk value in each of the plurality of first preset time periods; determining the target protection component information of the acquisition points laid in the target area based on the target risk distribution, where the target protection component information includes the target protection component type and the corresponding first target protection level; determining the laying density distribution of the target protection components based on the target risk distribution and the pipeline laying map of the target area; and generating a valve control instruction and sending it to the gas equipment object platform based on the laying density distribution before and / or during the execution of the protection component arrangement operation to adjust the gas transmission pressure of at least one gas pipeline in the target area.

[0005] One or more embodiments of the present invention provide a pipeline protection component layout system based on the intelligent gas Internet of Things. The pipeline protection component layout system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, and a gas equipment object platform. The government safety supervision object platform includes a gas company management platform; the gas company management platform includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to implement: through the gas company sensor network platform, obtain environmental information of a target area in a plurality of first preset time periods from a sensing device set in the gas equipment object platform, where the environmental information includes temperature information, humidity information, geological information, and vibration information; through the government safety supervision sensor network platform, obtain biological information, climate information, and facility information of the target area in the plurality of first preset time periods from the government safety supervision management platform; based on the geological information, the facility information, and the vibration information in the plurality of first preset time periods, determine the vibration risk value of the target area in each of the plurality of first preset time periods; based on the geological information, the biological information, and the facility information in the plurality of first preset time periods, determine the corrosion risk value of the target area in each of the plurality of first preset time periods; based on the climate information in the plurality of first preset time periods, determine the temperature risk value of the target area in each of the plurality of first preset time periods; based on the vibration risk value, the corrosion risk value, and the temperature risk value of each of the plurality of first preset time periods, determine the target risk distribution of the target area; based on the target risk distribution, determine the target protection component information of the acquisition points laid in the target area, where the target protection component information includes the target protection component type and the corresponding first target protection level; based on the target risk distribution and the pipeline laying map of the target area, determine the laying density distribution of the target protection components; and before and / or during the execution of the protection component layout operation, based on the laying density distribution, generate a valve control instruction and send it to the gas equipment object platform to adjust the gas transmission pressure of at least one gas pipeline in the target area.

[0006] One or more embodiments of the present invention provide a computer-readable storage medium storing computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes a method for laying pipeline protection components based on the intelligent gas Internet of Things as described in any embodiment of the present invention.

[0007] The beneficial effects of the present invention include, but are not limited to: during pipeline laying, corresponding pipeline protection components can be arranged in key protection areas according to actual situations, which can ensure the service life of gas pipelines; by adjusting the gas transmission pressure of at least one gas pipeline in the target area according to valve control instructions, failures can be avoided, thereby ensuring the service life of gas pipelines and reducing the pipeline replacement cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is an exemplary schematic diagram of an intelligent gas Internet of Things shown according to some embodiments of the present invention; Figure 2 is an exemplary flowchart of a method for arranging pipeline protection components based on an intelligent gas Internet of Things shown according to some embodiments of the present invention; Figure 3 is an exemplary schematic diagram of determining target protection component information shown according to some embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in 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 the present invention. For those of ordinary skill in the art, without creative efforts, the present invention 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 structures or operations.

[0010] 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.

[0011] As shown in the present invention and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0012] In the present invention, flowcharts are used to illustrate the operations performed by the system according to the embodiments of the present invention. It should be understood that the preceding or subsequent operations do not necessarily have to be executed precisely in sequence. Instead, the various 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.

[0013] Figure 1 It is an exemplary schematic diagram of the intelligent gas Internet of Things shown in some embodiments of the present invention. It should be noted that the following embodiments are only used to explain the present invention and do not constitute a limitation to the present invention.

[0014] As Figure 1 shown, in some embodiments, the intelligent gas Internet of Things 100 may include a government safety supervision and management platform 110, a government safety supervision sensor network platform 120, a government safety supervision object platform 130, a gas company sensor network platform 140, and a gas equipment object platform 150.

[0015] In some embodiments, information and / or data can be exchanged between one or more platforms in the intelligent gas Internet of Things 100 through a network. In some embodiments, the network can be any one or more of a wired network or a wireless network.

[0016] The government safety supervision and management platform 110 refers to a comprehensive management platform for the government to conduct safety supervision and management. In some embodiments, the government safety supervision and management platform 110 can be configured to process and store the data of the intelligent gas Internet of Things 100.

[0017] In some embodiments, the government safety supervision and management platform 110 may include a government supervision comprehensive database 110-1. Among them, the government supervision comprehensive database 110-1 is a database for realizing data storage.

[0018] In some embodiments, the government safety supervision and management platform 110 can be configured to obtain the biological information, climate information, and facility information of multiple gas pipeline areas in multiple time periods in real time or at regular time intervals, and store the obtained information in the government supervision comprehensive database 110-1. In some embodiments, more descriptions about the government safety supervision and management platform 110 can be found in the relevant descriptions later.

[0019] The government safety supervision sensor network platform 120 is a functional platform for sensing communication between the government safety supervision and management platform 110 and the government safety supervision object platform 130. The government safety supervision sensor network platform 120 can be configured as a communication network and a gateway.

[0020] The government safety supervision object platform 130 refers to a platform used for safety supervision of gas-related objects. For example, gas-related objects may include gas companies, etc. In some embodiments, the government safety supervision object platform 130 may interact with the government safety supervision sensing network platform 120 and the gas company sensing network platform 140.

[0021] In some embodiments, the government safety supervision object platform 130 may include a gas company management platform 130-1. Among them, the gas company management platform 130-1 refers to an information comprehensive management platform of the gas company. For example, the gas company management platform 130-1 may be a processor or a server of the gas company. In some embodiments, the gas company management platform 130-1 may interact with the gas company sensing network platform 140 and the government safety supervision sensing network platform 120.

[0022] In some embodiments, the gas company management platform 130-1 may be configured to execute a method for arranging pipeline protection components based on the intelligent gas Internet of Things according to any embodiment of the present invention. For more content, please refer to Figures 2 - 3 。

[0023] The gas company sensing network platform 140 is a functional platform for sensing communication between the government safety supervision object platform 130 and the gas equipment object platform 150. The gas company sensing network platform 140 may be configured as a communication network and a gateway. In some embodiments, the gas company sensing network platform 140 may also be a server located in the gas pipeline laying area.

[0024] In some embodiments, the gas company sensing network platform 140 may interact with the gas equipment object platform 150 and the gas company management platform 130-1. For example, the gas company sensing network platform 140 may obtain the environmental information sent by the gas equipment object platform 150 and transmit the environmental information to the gas company management platform 130-1.

[0025] The gas equipment object platform 150 is related to gas equipment and is a functional platform for sensing information generation and control information execution.

[0026] In some embodiments, the gas equipment object platform 150 may be configured to interact with multiple devices. The multiple devices may include gas equipment (such as gas meters), gas monitoring equipment (such as gas pressure monitoring devices, temperature monitoring devices, flow monitoring devices), environmental protection facilities (such as waste gas treatment equipment, environmental protection monitoring equipment), maintenance equipment (such as inspection equipment, cleaning equipment, etc.). In some embodiments, the gas equipment object platform 150 may include multiple gas pipelines and a sensing device for collecting environmental information in the area where the gas pipelines are located.

[0027] In some embodiments, the gas equipment object platform 150 may interact with the gas company sensing network platform 140. For example, the gas equipment object platform 150 may obtain the valve control instructions issued by the gas company management platform 130-1 through the gas company sensing network platform 140, and adjust the gas transmission pressure of at least one gas pipeline in the target area according to the valve control instructions.

[0028] It should be noted that the above description of the intelligent gas Internet of Things 100 is only for convenience of description and does not limit the present invention to the scope of the exemplified embodiments.

[0029] Figure 2 It is an exemplary flowchart of a method for laying pipeline protection components based on an intelligent gas Internet of Things according to some embodiments of the present invention. In some embodiments, the process 200 may be executed by the gas company management platform of the pipeline protection component laying system. For the content of each platform, see Figure 1 and its related descriptions. As Figure 2 shown, the process 200 includes the following steps.

[0030] Step 210, obtain the environmental information of the target area in a plurality of first preset time periods from the sensing devices set in the gas equipment object platform through the gas company sensing network platform.

[0031] For the related descriptions of the gas company sensing network platform and the gas equipment object platform, see Figure 1 .

[0032] The target area refers to the area where the gas pipeline protection components are to be laid. The gas company management platform may determine the target area based on the construction information pre-stored in the database (for example, the government supervision comprehensive database 110-1).

[0033] The first preset time period refers to a preset period of time. The first preset time period may be a historical period before the current moment. In some embodiments, the first preset time period may include multiple time points.

[0034] The environmental information refers to the information related to the environment of the target area. The environmental information in the first preset time period may include the information related to the environment of the target area obtained at multiple time points in the first preset time period. In some embodiments, the environmental information may include temperature information, humidity information, geological information, and vibration information.

[0035] Temperature information refers to information related to the temperature of the target area. In some embodiments, the temperature information may include the average temperature, the maximum temperature, and the minimum temperature. The average temperature refers to the mean value of the temperatures collected at multiple time points within a first preset time period. The maximum temperature refers to the highest temperature collected at multiple time points within the first preset time period. The minimum temperature refers to the lowest temperature collected at multiple time points within the first preset time period.

[0036] Humidity information refers to information related to the humidity of the target area. In some embodiments, the humidity information may include the average humidity, the maximum humidity, and the minimum humidity. The average humidity, the maximum humidity, and the minimum humidity are similar to the average temperature, the maximum temperature, and the minimum temperature, and will not be elaborated here.

[0037] Geological information refers to information related to the geology of the target area. In some embodiments, the geological information may include the soil density distribution and the soil pH distribution. Among them, the soil density distribution includes the mean value of the soil densities at multiple collection points collected at each time point within a first preset time period and the corresponding location information. The soil pH distribution includes the mean value of the soil pH values at multiple collection points collected at each time point within the first preset time period and the corresponding location information.

[0038] A collection point refers to a point where information is collected. The collection points can be determined by user input.

[0039] Only by way of example, for geological information, within the first preset time period, there are time points t1, t2, ……, t m , , n , n , , n ,

[0040] , n ,

[0039] , m , and the multiple collection points in the preset collection path are p1, ……, p m , if data is collected along the preset path at each time point within the first preset time period, then the soil density distribution includes the mean value of the soil densities collected at collection point p1 at time points t1, t2, ……, t n collected, the mean value of the soil densities collected at collection point p2 at time points t1, t2, ……, t n collected, ……, and the mean value of the soil densities collected at collection point p m at time points t1, t2, ……, t n collected.

[0040] Vibration information refers to information reflecting the land vibration of the target area. In some embodiments, the vibration information may include the vibration intensity distribution and the vibration frequency distribution. Among them, the vibration intensity distribution includes the mean value of the vibration intensities at multiple collection points collected at each time point within a first preset time period and the corresponding location information. The vibration frequency distribution includes the mean value of the vibration frequencies at multiple collection points collected at each time point within the first preset time period and the corresponding location information.

[0041] In some embodiments, the gas equipment object platform can obtain the environmental information of the target area in multiple first preset time periods through the set sensing device. The environmental information of the multiple first preset time periods obtained by the gas equipment object platform can be transmitted to the gas company management platform through the gas company sensing network platform.

[0042] A sensing device refers to a detection device that can measure environmental information. In some embodiments, the sensing device may include a temperature acquisition device (e.g., a temperature sensor), a humidity acquisition device (e.g., a humidity sensor), a geology acquisition device (e.g., a soil detection instrument), a vibration acquisition device (e.g., a vibration sensor), etc.

[0043] In some embodiments, the sensing device can work in various ways. For example, the sensing device can be set at a fixed position for data detection or loaded on a carrier (e.g., a mobile carrier) for data detection, etc.

[0044] In some embodiments, the sensing device can be loaded on a crawling robot. The crawling robot can be configured to perform data acquisition along a preset acquisition path.

[0045] A crawling robot refers to a robot that can crawl and can load items (e.g., a sensing device). The crawling robot can be composed of a robot body and a control system, with its own power drive system, and it can walk automatically. In some embodiments, the crawling robot can be set inside the gas pipeline or above the ground corresponding to the gas pipeline.

[0046] In some embodiments, the crawling robot can also carry an image sensor, which can transmit detection data (e.g., detection images) to the control system in real time.

[0047] In some embodiments, the crawling robot can collect the environmental information in the target area according to the preset acquisition path.

[0048] The preset acquisition path refers to the path for the crawling robot to collect information set in advance. In some embodiments, the preset acquisition path may include multiple acquisition points.

[0049] In some embodiments, there can be various ways to determine the preset acquisition path.

[0050] In some embodiments, the gas company management platform can determine the preset acquisition path based on multiple gas pipeline transportation paths in the pipeline laying atlas.

[0051] The pipeline laying atlas refers to the schematic diagram of laying gas pipelines. The pipeline laying atlas can represent the pipeline routing, dimensions, and other conditions. In some embodiments, the pipeline laying atlas can be input by the user or obtained from the information pre-stored in the government supervision integrated database 110-1. The gas pipeline transportation path is the path where the gas pipeline is laid underground and can be directly determined through the pipeline laying atlas. In some embodiments, the gas company management platform can determine the gas pipeline transportation path as the preset acquisition path.

[0052] In some embodiments, the gas company management platform can determine the acquisition parameters of the crawling robot based on the pipeline laying atlas and the pipeline importance levels of multiple gas pipeline segments; and determine the preset acquisition path based on the acquisition parameters of the crawling robot.

[0053] A gas pipeline segment refers to a section of the gas pipeline obtained by dividing the gas pipeline. The pipeline laying atlas may include multiple gas pipeline segments. In some embodiments, the gas pipeline segments can be divided by relevant staff. In some embodiments, the gas company management platform can also divide the gas pipeline into multiple gas pipeline segments according to the length of the gas pipeline. For example, equally spaced division, that is, dividing the gas pipeline into multiple gas pipeline segments with the same length.

[0054] The pipeline importance level refers to the level reflecting the importance of the gas pipeline segment. In some embodiments, the pipeline importance level can be preset by technicians based on prior knowledge or historical data.

[0055] The acquisition parameters of the crawling robot refer to the parameters when the crawling robot conducts data acquisition. In some embodiments, the acquisition parameters of the crawling robot may include the acquisition points, sampling frequency, sampling volume, etc. during data acquisition.

[0056] In some embodiments, the gas company management platform can determine the number of acquisition points, sampling frequency, and corresponding sampling volume corresponding to the gas pipeline segments in the target area through the first preset table based on the pipeline importance levels of the gas pipeline segments. The first preset table includes multiple pipeline importance levels and the acquisition parameters of the crawling robot corresponding to each pipeline importance level (for example, including the number of acquisition points, sampling frequency, and sampling volume). In some embodiments, the first preset table can be set by technicians according to experience. In some embodiments, the first preset table can be constructed based on prior knowledge or historical data (for example, the historical acquisition parameters when the historical crawling robot conducts data acquisition on gas pipeline segments with different pipeline importance levels, etc.).

[0057] In some embodiments, the gas company management platform may use a preset algorithm to obtain the shortest path covering all the collection points as the preset collection path. For example, the preset algorithm may be a path planning algorithm (such as Dijkstra algorithm, BFS (Best-First-Search) algorithm, A algorithm, etc.).

[0058] After determining the preset collection path, the gas company management platform may generate a movement instruction and send the movement instruction to the gas equipment object platform. The gas equipment object platform may send the movement instruction to the crawling robot.

[0059] The movement instruction is an instruction for instructing the crawling robot to move. In some embodiments, the gas company management platform may generate a collection instruction based on the preset collection path of the crawling robot and send it to the sensing device installed on the crawling robot. When the crawling robot moves on the preset collection path, the sensing device installed on the crawling robot may perform data collection based on the collection instruction.

[0060] In some embodiments of the present invention, the sensing device is installed on the crawling robot, and the preset collection path is determined based on the pipeline laying map and the pipeline importance level of each gas pipeline section. More information can be collected for the gas pipeline sections with a higher pipeline importance level, and less information can be appropriately collected for the gas pipeline sections with a lower pipeline importance level, so as to collect environmental information more reasonably and efficiently.

[0061] Step 220: Obtain the biological information, climate information, and facility information of the target area in multiple first preset time periods from the government safety supervision management platform through the government safety supervision sensing network platform.

[0062] Biological information refers to the information related to the organisms in the target area. In some embodiments, the biological information may include the distribution of microorganism density, the distribution of termite density, and the number of rodents, etc. Among them, the distribution of microorganism density includes the mean value of the microorganism density distribution at multiple collection points collected at each time point within the multiple time points in the first preset time period and the corresponding location information. The distribution of termite density includes the mean value of the termite density distribution at multiple collection points collected at each time point within the multiple time points in the first preset time period and the corresponding location information. The number of rodents refers to the mean value of the number of rodents at multiple collection points collected at each time point within the multiple time points in the first preset time period.

[0063] Climate information refers to the information related to the climate of the target area. In some embodiments, the climate information may include the weather types at multiple time points within the first preset time period and the number of occurrences of each weather type.

[0064] Facility information refers to information related to the facilities and equipment in the target area. In some embodiments, the facility information may include the factory type, the number of factories corresponding to each factory type, and the number of facilities. Facilities may include vehicles, mechanical equipment, sites, lines, communication equipment, signal signs, houses, etc.

[0065] The factory type may include chemical factories, metallurgical processing factories, heavy machinery processing factories, etc. In some embodiments, the government safety supervision and management platform may obtain the factory types of all factories at multiple time points within a first preset time period, take the average value of the number of each factory type obtained at multiple time points within the first preset time period as the number of factories corresponding to the factory type, and take the average value of the number of facilities obtained at multiple time points within the first preset time period as the number of facilities.

[0066] In some embodiments, the government safety supervision and management platform may obtain biological information, climate information, and facility information in the target area within multiple first preset time periods, and transmit them to the gas company management platform through the government safety supervision sensor network platform.

[0067] In some embodiments, the government safety supervision and management platform may regularly obtain biological information, climate information, and facility information in the target area and store them in the government supervision comprehensive database. For example, the government safety supervision and management platform may regularly conduct biometric statistics, industrial facility statistics, or facility statistics, etc., and determine biological information and facility information based on the statistical results. The gas company management platform may call the biological information, climate information, and facility information corresponding to the first preset time period through the government safety supervision sensor network platform.

[0068] Step 230, based on the geological information, facility information, and vibration information within multiple first preset time periods, determine the vibration risk value of the target area in each of the multiple first preset time periods.

[0069] The multiple first preset time periods may be multiple historical time periods before the current moment. The durations of the multiple first preset time periods may be the same. There may be no interval between the multiple first preset time periods, that is, the end time point of the previous first preset time period and the start time point of the next first preset time period among adjacent two first preset time periods coincide.

[0070] The vibration risk value refers to a numerical value that measures the degree of influence of ground vibration on gas pipelines. In some embodiments, the vibration risk value of the target area in each of the multiple first preset time periods may include the vibration risk value of multiple collection points in the target area in each of the multiple first preset time periods.

[0071] In some embodiments, the gas company management platform may determine the vibration risk value of the target area in each of the multiple first preset time periods based on the geological information, facility information, and vibration information within the multiple first preset time periods through various methods.

[0072] In some embodiments, for each first preset time period, the gas company management platform may determine the vibration amplification factor corresponding to each of the multiple collection points based on the average soil density of each of the multiple collection points. For example, for each collection point, the gas company management platform may divide the vibration coefficient by the average soil density of that collection point to obtain the vibration amplification factor corresponding to that collection point. Here, the vibration coefficient refers to the amplification factor of vibration under standard soil conditions. In some embodiments, the vibration coefficient may be set according to experience. The vibration amplification factor is the amplification factor of vibration under the actual soil conditions corresponding to the collection point. The average soil density may be the mean value of the soil densities collected at each time point in the soil density distribution of the first preset time period.

[0073] In some embodiments, for each first preset time period, the gas company management platform may determine the vibration risk value corresponding to each of the multiple collection points based on parameters such as the vibration amplification factor, average vibration intensity, average vibration frequency, average soil pH value of each of the multiple collection points, and the facility information of the target area. Here, the average soil pH value may be the mean value of the soil pH values collected at each time point within the first preset time period in the soil pH value distribution. The average vibration intensity and average vibration frequency may be the mean values of the vibration intensity and vibration frequency collected at each time point within the first preset time period in the vibration information.

[0074] For example, for each collection point, the gas company management platform may determine the vibration risk value corresponding to that collection point according to formula (1): (1) Wherein, represents the vibration risk value of this collection point in the first preset time period, represents the vibration amplification factor of this collection point, represents the average vibration intensity of this collection point, represents the average soil pH value of this collection point, represents the average vibration frequency of this collection point, represents the number of factories, represents the number of facilities. The number of factories and the number of facilities can be obtained from the facility information. 、 、 、 are the weight coefficients of multiple parameters when calculating the vibration risk value. 、 、 、 can be set according to experience.

[0075] In some embodiments, the gas company management platform can determine the weight coefficients of multiple parameters when calculating the vibration risk value in various ways , , , . For example, the gas company management platform can determine the weight coefficients of multiple parameters when calculating the vibration risk value through a first preset regression algorithm

[0076] In some embodiments, the gas company management platform can determine the labels of multiple parameters; based on the multiple parameters and the corresponding labels, through the first preset regression algorithm, the weight coefficients of each parameter when calculating the vibration risk value are obtained by fitting

[0077] For each parameter, the gas company management platform can use the number of times the gas pipeline corresponding to the parameter fails due to vibration as the corresponding label. The first preset regression algorithm is similar to the third preset regression algorithm. For more details, see step 250

[0078] Step 240, based on the geological information, biological information, and facility information within multiple first preset time periods, determine the corrosion risk value of the target area in each of the multiple first preset time periods

[0079] The corrosion risk value refers to a numerical value that measures the degree of risk of a gas pipeline being corroded. In some embodiments, the corrosion risk value of the target area in each of the multiple first preset time periods may include the corrosion risk value of multiple collection points in the target area in each of the multiple first preset time periods

[0080] In some embodiments, the gas company management platform can determine the corrosion risk value of the target area in each of the multiple first preset time periods in various ways based on the geological information, biological information, and facility information within the multiple first preset time periods

[0081] In some embodiments, for each first preset time period, the gas company management platform can determine the soil acidity coefficient of multiple collection points based on the average soil acidity of each collection point. For example, for each collection point, the gas company management platform can subtract the average soil acidity of the collection point from 14 (the range of the ph acid-base value) to obtain the soil acidity coefficient of the collection point. Among them, the soil acidity coefficient is a coefficient related to the soil acidity. The average soil acidity can be the average value of the soil acidity collected at each time point in the soil acidity distribution of the first preset time period

[0082] In some embodiments, for each first preset time period, the gas company management platform may determine the corrosion risk value of each of the multiple collection points based on parameters such as the soil acidity coefficient, the number of factories generating corrosive gases and liquids, the average microbial density, the average termite density, and the average rodent number of each of the multiple collection points. Among them, the average microbial density may be the mean value of the microbial densities collected at each time point within the first preset time period in the microbial density distribution. The average termite density may be the mean value of the termite densities collected at each time point within the first preset time period in the termite density distribution. The average rodent number may be the mean value of the rodent numbers collected at each time point within the first preset time period in the rodent number. The number of factories generating corrosive gases and liquids may be the number of factories corresponding to those generating corrosive gases and liquids in the factory type.

[0083] For example, for each collection point, the gas company management platform may determine the corrosion risk value corresponding to the collection point according to formula (2): (2) Wherein, represents the corrosion risk value of the collection point in the first preset time period, represents the soil acidity coefficient of the collection point, represents the number of factories generating corrosive gases and liquids, represents the average microbial density, represents the average termite density, represents the average rodent number. 、 、 are the weight coefficients of multiple parameters when calculating the corrosion risk value. 、 、 can be set according to experience.

[0084] In some embodiments, the gas company management platform may determine the weight coefficients of multiple parameters when calculating the corrosion risk value in various ways 、 、 . For example, the gas company management platform may determine the weight coefficients of multiple parameters when calculating the corrosion risk value through a second preset regression algorithm.

[0085] In some embodiments, the gas company management platform may determine the labels of multiple parameters; according to the multiple parameters and the corresponding labels, through the second preset regression algorithm, the weight coefficients of each parameter when calculating the corrosion risk value are obtained by fitting.

[0086] For each parameter, the gas company management platform can use the number of failures of the gas pipeline corresponding to the parameter due to corrosion as the corresponding label. The second preset regression algorithm is similar to the third preset regression algorithm. For more details, see step 250.

[0087] Step 250: Based on the climate information within multiple first preset time periods, determine the temperature risk value of the target area for each of the multiple first preset time periods.

[0088] The temperature risk value refers to a numerical value that measures the risk degree of a gas pipeline bursting or exploding due to high temperature, low temperature, etc. In some embodiments, the temperature risk value of the target area for each of the multiple first preset time periods may include the temperature risk values of multiple collection points in the target area for each of the multiple first preset time periods.

[0089] In some embodiments, the gas company management platform can determine the temperature risk value of the target area for each of the multiple first preset time periods based on the climate information within multiple first preset time periods in various ways.

[0090] In some embodiments, for each first preset time period, the gas company management platform can determine the temperature risk value of each of the multiple collection points based on parameters such as the average temperature, highest temperature, lowest temperature, average humidity, and weather risk coefficient of each of the multiple collection points.

[0091] For example, for each collection point, the gas company management platform can determine the temperature risk value of the collection point according to formula (3): (3) Where, represents the temperature risk value of the collection point in the first preset time period, represents the average temperature of the collection point in the first preset time period, represents the highest temperature of the collection point in the first preset time period, represents the lowest temperature of the collection point in the first preset time period, represents the average humidity of the collection point in the first preset time period, represents the weather risk coefficient of the collection point. 、 、 、 are the weight coefficients of multiple parameters when calculating the temperature risk value. 、 、 、 can be set according to experience.

[0092] The weather risk coefficient refers to the mean value of the product of the risk degrees of each weather type and the corresponding frequencies. In some embodiments, the risk degrees of each weather type can be set based on experience.

[0093] For another example, the gas company management platform can set the weight coefficients based on experience.

[0094] In some embodiments, the gas company management platform can determine the weight coefficients of multiple parameters when calculating the temperature risk value through various methods. 、 、 、 。For example, the gas company management platform can determine the weight coefficients of multiple parameters when calculating the temperature risk value through the third preset regression algorithm.

[0095] In some embodiments, the first preset regression algorithm, the second preset regression algorithm, and the third preset regression algorithm can be the same or different. In some embodiments, the first preset regression algorithm, the second preset regression algorithm, and the third preset regression algorithm can include linear regression algorithms, polynomial regression algorithms, support vector regression algorithms, etc.

[0096] In some embodiments, the gas company management platform can obtain the temperature information and climate information around the gas pipeline during the corresponding durations of multiple first preset time periods in the historical data.

[0097] In some embodiments, the gas company management platform can determine the labels of multiple temperature information and climate information; based on the multiple temperature information and climate information and the corresponding labels, through the third preset regression algorithm, fit to obtain the weight coefficients of each parameter when calculating the temperature risk value.

[0098] For each temperature information and climate information, the gas company management platform can use the number of times the gas pipeline corresponding to this climate information fails due to high temperature or low temperature as the corresponding label.

[0099] Only as an example, assume that the labels corresponding to Information 1, Information 2, and Information 3 are y1, y2, and y3 respectively. Then the various parameters included in the three pieces of information are: Information 1: average temperature 、highest temperature 、lowest temperature , average humidity , weather risk coefficient ; Information 2: average temperature 、highest temperature 、lowest temperature , average humidity , weather risk coefficient ; Information 3: average temperature 、highest temperature , Minimum temperature , Average humidity , Weather risk coefficient . Based on formula (3), the following three equations can be obtained. According to these three equations, the gas company management platform can execute a regression algorithm to obtain the coefficients , , , .

[0100]

[0101]

[0102]

[0103] The process of calculating the weight coefficients corresponding to each parameter when calculating the corrosion risk value and the vibration risk value can be similar to that of the temperature risk value.

[0104] In some embodiments of the present invention, by using the first / second / third preset regression algorithm to determine the weight coefficients of multiple parameters when calculating the vibration risk value / corrosion risk value / temperature risk value, the accuracy of the weight coefficients can be improved, thereby improving the reliability of the calculated vibration risk value, corrosion risk value, and temperature risk value.

[0105] In some embodiments, the gas company management platform can also determine the corrosion risk value based on the temperature risk value, geological information, biological information, and facility information.

[0106] In some embodiments, the gas company management platform can also consider the temperature risk value and the corresponding weight coefficient. For example, a weighted term corresponding to the temperature risk value is added to the basis of formula (2) to obtain formula (4): (4) Wherein, is the temperature risk value at the first preset time period for this collection point, is the weight coefficient corresponding to this temperature risk value.

[0107] In some embodiments, the gas company management platform can determine the weight coefficient corresponding to the temperature risk value in various ways . For example, the gas company management platform can determine the proportion of the temperature risk value in the risk values of the three risk types (for example, vibration risk value, corrosion risk value, and temperature risk value) as the weight coefficient corresponding to the temperature risk value .

[0108] In some embodiments, the gas company management platform can also determine the vibration risk value based on the corrosion risk value, geological information, facility information, and vibration information.

[0109] In some embodiments, the gas company management platform may also consider the corrosion risk value and the corresponding weight coefficient. For example, on the basis of formula (1), a weighted term corresponding to the corrosion risk value is added to obtain formula (5): (5) Wherein, is the corrosion risk value of the collection point in the first preset time period, is the weight coefficient corresponding to the corrosion risk value.

[0110] In some embodiments, the gas company management platform may determine the weight coefficient corresponding to the corrosion risk value in various ways . For example, the gas company management platform may determine the proportion of the corrosion risk value in the risk values of three risk types (for example, vibration risk value, corrosion risk value, and temperature risk value) as the weight coefficient corresponding to the corrosion risk value .

[0111] In some embodiments, the gas company management platform may determine the weight coefficient of the temperature risk value when calculating the corrosion risk value through a second preset regression algorithm, and / or determine the weight coefficient of the corrosion risk value when calculating the vibration risk value through a first preset regression algorithm.

[0112] That is to say, when determining the weight coefficients of multiple parameters when calculating the corrosion risk value through the second preset regression algorithm, the gas company management platform may also determine the label corresponding to the temperature risk value, and fit to obtain the weight coefficients of each parameter (including the temperature risk value) when calculating the corrosion risk value 、 、 . The same applies when calculating the vibration risk value.

[0113] In some embodiments of the present invention, determining the corrosion risk value based on the temperature risk value, geological information, biological information, and facility information, and determining the vibration risk value based on the corrosion risk value, geological information, facility information, and vibration information can ensure that the corrosion risk value and the vibration risk value are more reliable, thereby ensuring the reliability of the target risk distribution.

[0114] Step 260, determine the target risk distribution of the target area based on the vibration risk value, corrosion risk value, and temperature risk value of each of the multiple first preset time periods.

[0115] The target risk distribution refers to the various risk types (e.g., vibration risk type, corrosion risk type, temperature risk type) and corresponding risk characteristics at each collection point within the target area. The vibration risk type refers to the risk caused by vibration, the corrosion risk type refers to the risk caused by corrosion, and the temperature risk type refers to the risk caused by high or low temperature. Among them, the risk characteristics may include the mean, minimum, and maximum values of the risk values corresponding to the various risk types at each collection point over multiple first preset time periods. For example, the risk characteristics may include the vibration risk mean, vibration risk minimum, and vibration risk maximum corresponding to each collection point over multiple first preset time periods; the corrosion risk mean, corrosion risk minimum, and corrosion risk maximum corresponding to each collection point over multiple first preset time periods; the temperature risk mean, temperature risk minimum, and temperature risk maximum corresponding to each collection point over multiple first preset time periods, etc.

[0116] In some embodiments, the gas company management platform can determine the target risk distribution of the target area based on the vibration risk values, corrosion risk values, and temperature risk values at each collection point over multiple first preset time periods. For example, the gas company management platform can perform statistical processing on the risk values (e.g., including vibration risk values, corrosion risk values, and temperature risk values) at each collection point over multiple first preset time periods to obtain the target risk distribution.

[0117] In some embodiments, the gas company management platform can determine the self-risk values corresponding to the vibration risk value, corrosion risk value, and temperature risk value based on the pipeline operation characteristics and pipeline material characteristics.

[0118] The pipeline operation characteristics are the characteristics indicating the planned transportation situation of the pipeline. The pipeline planned transportation refers to the transportation plan based on the gas pipeline planning. In some embodiments, the pipeline operation characteristics may include the gas flow rate and gas pressure of the pipeline planned transportation.

[0119] The pipeline material characteristics are the situations related to the material of the pipeline. In some embodiments, the pipeline material characteristics may include the pipeline wall material and the pipeline wall thickness.

[0120] In some embodiments, the gas company management platform can obtain the pipeline operation characteristics and pipeline material characteristics based on the pipeline laying map.

[0121] The self-risk value is a numerical value used to measure the risk degree generated by the operation of the gas pipeline itself (e.g., continuously transporting high-pressure or high-flow gas, etc.). In some embodiments, the self-risk value may include at least one or any combination of the vibration self-risk value corresponding to the vibration risk value, the corrosion self-risk value corresponding to the corrosion risk value, and the temperature self-risk value corresponding to the temperature risk value.

[0122] In some embodiments, the gas company management platform may construct a first feature vector based on pipeline operation characteristics and pipeline material characteristics, and match the vibration self-risk value, corrosion self-risk value, and temperature self-risk value in the first vector database based on the first feature vector.

[0123] The first vector database includes multiple first reference vectors and corresponding first vector labels. The gas company management platform may construct the first reference vectors based on the pipeline operation characteristics and pipeline material characteristics in the historical data. The first vector labels include at least one of the vibration self-risk value, corrosion self-risk value, and temperature self-risk value corresponding to the first reference vector.

[0124] In some embodiments, the gas company management platform may construct multiple first reference vectors based on historical data, cluster the multiple first reference vectors; count the number of gas pipelines that have failed (such as leaks, ruptures, etc.) due to each risk type (such as vibration, corrosion, temperature) in each category of the clustering results; and use the ratio of the number of gas pipelines that have failed under each risk type to the total number of gas pipelines in that category as the first vector label corresponding to the first reference vector of that category. Since the causes of gas pipeline failures will be traced after pipeline maintenance, the causes of gas pipeline failures in the historical data can be known.

[0125] In some embodiments, the gas company management platform may match the first reference vector with the highest vector similarity to the first feature vector in the first vector database, and determine the first vector label corresponding to the first reference vector with the highest vector similarity to the first feature vector as the self-risk value for each risk type, that is, the vibration self-risk value, the corrosion self-risk value, and the temperature self-risk value. Among them, the vector similarity can be determined according to the vector distance. The vector similarity is negatively correlated with the vector distance.

[0126] In some embodiments, the gas company management platform may determine the target risk distribution through various methods based on the vibration risk value, corrosion risk value, temperature risk value, and their corresponding self-risk values in the target area within multiple first preset time periods.

[0127] In some embodiments, for each acquisition point, the gas company management platform may calculate the average value of the risk values of each risk type determined in steps 230 to 250 at the acquisition point during multiple first preset time periods, and then multiply it by the respective self-risk value, as the risk mean corresponding to each risk type in the risk characteristics. For example, the gas company management platform may calculate the average value of the vibration risk values corresponding to multiple first preset time periods and then multiply it by the vibration self-risk value as the vibration risk mean, calculate the average value of the corrosion risk values corresponding to multiple first preset time periods and then multiply it by the corrosion self-risk value as the corrosion risk mean, and calculate the average value of the temperature risk values corresponding to multiple first preset time periods and then multiply it by the temperature self-risk value as the temperature risk mean.

[0128] In some embodiments, for each acquisition point, the gas company management platform may directly use the highest and lowest values of the risk values of each risk type determined in steps 230 to 250 at the acquisition point during multiple first preset time periods as the highest risk value and the lowest risk value corresponding to each risk type in the risk characteristics. For example, the gas company management platform may use the highest and lowest values of the vibration risk values in multiple first preset time periods as the highest vibration risk value and the lowest vibration risk value, respectively.

[0129] In the embodiments of the present invention, by determining the target risk distribution through the self-risk value, the probability of the occurrence of the risk mean corresponding to the risk type can be estimated, making the obtained target risk distribution more accurate.

[0130] In some embodiments, the gas company management platform may determine the target risk distribution based on the vibration risk value, corrosion risk value, temperature risk value, their respective self-risk values, and their confidence levels in the target area.

[0131] The confidence level of the self-risk value refers to the degree of certainty of the self-risk value. For example, the higher the accuracy of the self-risk value, the higher its confidence level.

[0132] The self-risk value can be determined in various ways. In some embodiments, the gas company management platform may determine the vector similarity between the first feature vector and the first reference vector as the confidence level of the self-risk value. Among them, the methods for determining vector similarity include similarity calculation methods based on distance, similarity calculation methods based on cosine of the included angle, similarity calculation methods based on Pearson correlation coefficient, etc.

[0133] In some embodiments, the gas company management platform may calculate the average risk value of each risk type determined in steps 230 to 250 within multiple first preset time periods, multiply the average by the corresponding self-risk value, and then multiply by the confidence level corresponding to the self-risk value. The calculation result is used as the risk average corresponding to each risk type in the risk characteristics. For example, the gas company management platform may calculate the average vibration risk value of multiple first preset time periods, multiply it by the vibration self-risk value, and then multiply by the confidence level of the vibration self-risk value, and use the calculation result as the vibration risk average.

[0134] In the embodiments of the present invention, further determining the target risk distribution through the confidence level of the self-risk value can make the obtained target risk distribution more accurate.

[0135] Step 270: Based on the target risk distribution, determine the target protection component information of the acquisition points laid in the target area.

[0136] The target protection component information is the relevant information of the pipeline protection components arranged. In some embodiments, the target protection component information may include the target protection component type and the corresponding first target protection level.

[0137] The target protection component type refers to the type of the pipeline protection components laid in the target area. The target protection component type may include an anti-corrosion component, a heat-insulation component, and / or a shock-proof component, etc. In some embodiments, for different risk types, the anti-corrosion component may correspond to the corrosion risk type, the heat-insulation component may correspond to the temperature risk type, and the shock-proof component may correspond to the vibration risk type.

[0138] The first target protection level refers to the protection level of the pipeline protection components laid in the target area. The protection level refers to the level divided according to the protection effect that the protection components can provide.

[0139] The first target protection level is related to the target protection component type. The first target protection level may include the anti-corrosion component protection level, the heat-insulation component protection level, and the shock-proof component protection level, which respectively represent the level of the anti-corrosion protection effect that the protection components can provide, the level of the heat-insulation protection effect that the protection components can provide, and the level of the shock-proof protection effect that the protection components can provide. In some embodiments, the higher the first target protection level, the better the protection effect.

[0140] In some embodiments, the gas company management platform may determine the target protection component information of the acquisition points laid in the target area in various ways based on the target risk distribution.

[0141] In some embodiments, for each acquisition point, the gas company management platform can determine the target protection component type deployed at the acquisition point and the corresponding first target protection level through a second preset table. The second preset table includes multiple target risk distributions and the first target protection level corresponding to each target risk distribution (for example, including the protection level of the anti-corrosion component, the protection level of the heat-insulation component, and the protection level of the anti-vibration component). In some embodiments, the second preset table can be set by technicians based on experience. In some embodiments, the second preset table can be constructed according to prior knowledge or historical data (for example, the first target protection levels required for different historical target risk distributions).

[0142] For example, the gas company management platform can, based on the target risk distribution, look up the corresponding protection levels of the anti-corrosion component, the heat-insulation component, and the anti-vibration component in the second preset table.

[0143] For more information on determining the target protection component information, reference can be made to Figure 3 and its related descriptions.

[0144] Step 280: Based on the target risk distribution and the pipeline laying map of the target area, determine the laying density distribution of the target protection components.

[0145] The laying density distribution includes the number of protection components of the pipeline protection components in the gas pipeline sections where each acquisition point in the target area is located.

[0146] In some embodiments, the gas company management platform can, based on the target risk distribution and the pipeline laying map of the target area, determine the laying density distribution of the target protection components in various ways.

[0147] In some embodiments, for each acquisition point, the gas company management platform can cluster the acquisition points in multiple gas pipeline laying areas in the historical data based on the risk characteristics of the acquisition point, find the multiple acquisition points in the target area that belong to the same category as this acquisition point in the clustering result, and use the average value of the number of protection components of each component type actually adopted by these acquisition points as the number of pipeline protection components in this acquisition point.

[0148] Step 290: Before and / or during the execution of the protection component layout operation, based on the laying density distribution, generate a valve control instruction and send it to the gas equipment object platform to adjust the gas transmission pressure of at least one gas pipeline in the target area.

[0149] The valve control instruction is an instruction used to indicate the regulating valve in the gas pipeline to perform pressure regulation.

[0150] In some embodiments, the gas company management platform can generate valve control instructions in various ways based on the laying density distribution. For example, for a collection point with a relatively small number of protection components laid, the probability of failure is relatively high, and a valve control instruction to reduce the pressure in the gas pipeline can be generated.

[0151] In some embodiments, for a collection point where the number of protection components laid exceeds a preset density threshold, the gas company management platform can generate valve control instructions for regulation at preset time intervals to keep the pressure in the gas pipeline corresponding to the collection point continuously within the standard range.

[0152] In some embodiments, the preset density threshold and the preset time interval can be set according to historical construction experience. For example, the gas company management platform can select the density threshold corresponding to the situation where no failure occurred during the entire historical construction as the current preset density threshold.

[0153] The standard range refers to the standard range of the gas pipeline pressure. In some embodiments, the standard range can be set by technicians according to experience.

[0154] In some embodiments, the gas company management platform can send the generated valve control instructions to the gas equipment object platform through the gas company sensing network platform. The gas equipment object platform can adjust the gas transmission pressure of at least one gas pipeline in the target area according to the valve control instructions. Among them, the gas equipment object platform can control the regulating valve in the gas pipeline to adjust the pressure.

[0155] In some embodiments of the present invention, laying corresponding pipeline protection components in key protection areas according to actual conditions during pipeline laying can ensure the service life of the gas pipeline; adjusting the gas transmission pressure of at least one gas pipeline in the target area according to the valve control instructions can avoid failures, thereby ensuring the service life of the gas pipeline and reducing the pipeline replacement cost.

[0156] It should be noted that the above description of process 200 is only for illustration and example, and does not limit the scope of application of the present invention. For those skilled in the art, various modifications and changes can be made to process 200 under the guidance of the present invention. However, these modifications and changes are still within the scope of the present invention. For example, steps 230, 240, and 250 can be carried out synchronously.

[0157] Figure 3 It is an exemplary schematic diagram of determining target protection component information shown in some embodiments of the present invention.

[0158] In some embodiments, such as Figure 3As shown, the gas company management platform can determine the pipeline protection strength 320 of the acquisition point based on the pipeline material characteristics 310 of the acquisition point; determine the risk protection level 340 of the acquisition point based on the pipeline protection strength 320 of the acquisition point and the target risk distribution 330 of the target area; and determine the second target protection level 350 of the acquisition point based on the risk protection level 340 of the acquisition point.

[0159] For the relevant descriptions of the acquisition point, pipeline material characteristics, target area, and target risk distribution, reference can be made to Figure 2 .

[0160] The pipeline protection strength refers to the protection level of the gas pipeline itself against various risk types. In some embodiments, the pipeline protection strength may include the anti-vibration protection level, the anti-corrosion protection level, and the high-temperature protection level.

[0161] In some embodiments, the gas company management platform can determine the pipeline protection strength through a third preset table. The third preset table includes multiple pipeline wall materials, multiple pipeline wall thicknesses, and the corresponding pipeline protection strengths. Among them, the pipeline wall thickness can be a range value.

[0162] In some embodiments, the third preset table can be set by technicians based on experience. In some embodiments, the third preset table can be constructed according to prior knowledge or historical data (for example, the pipeline protection strengths corresponding to different historical pipeline wall materials and pipeline wall thicknesses). The gas company management platform can search for the corresponding pipeline protection strength in the third preset table according to the pipeline wall material and pipeline wall thickness in the pipeline material characteristics.

[0163] The risk protection level refers to the degree of protection required for the acquisition point.

[0164] The risk protection level can be determined in various ways. In some embodiments, for each acquisition point, the gas company management platform can subtract the corresponding protection level in the pipeline protection strength of the acquisition point from the first target protection level determined according to the second preset table in step 270 to obtain the risk protection level of the acquisition point.

[0165] The second target protection level is the protection level of each target protection component type obtained by modifying the first target protection level based on the pipeline protection strength. In some embodiments, if the second target protection level is determined based on the first target protection level, the target protection component information includes the target protection component type and the corresponding second target protection level.

[0166] The second target protection level can be determined in various ways. In some embodiments, for each acquisition point, the gas company management platform can determine the risk protection level of the acquisition point as the second target protection level of the acquisition point.

[0167] In the embodiment of the present invention, the second target protection level is determined by determining the pipeline protection strength and the risk protection level. The target protection levels of different risk types can be specifically determined according to the pipeline material characteristics.

[0168] In some embodiments, as Figure 3 As shown, the gas company management platform can determine a second target protection level 350 of the collection point based on the risk protection level 340 and the component joint protection information 360 of multiple target protection components.

[0169] The component joint protection information includes the protection level of the protection component for each risk type. For example, the component joint protection information may include the vibration protection level, corrosion protection level, and thermal insulation protection level corresponding to the protection component.

[0170] Different types of protective components, while fulfilling their respective protective functions, can also achieve certain other protective functions due to their inherent structure. As an example, level 3 insulation material can achieve level 3 insulation, and due to its thickness, it also provides certain vibration and corrosion protection. For example, it can achieve level 1 vibration protection and level 2 corrosion protection. If level 1 vibration protection and level 3 insulation are actually required, then level 3 insulation material can be used directly to achieve both vibration and insulation requirements.

[0171] In some embodiments, the gas company management platform can obtain the risk type with the largest risk value in the target risk distribution, and obtain the protection level of the protection component corresponding to the risk type for each risk type; according to the protection level of the protection component for each risk type, adjust the protection level of the protection component in other risk types.

[0172] In an embodiment of the present invention, the second target protection level of the collection point is further determined by combining component protection information, so that the protection component can protect other smaller risk types while protecting the risk type with the highest risk value, thereby saving protection costs.

[0173] In some embodiments, as Figure 3As shown in the figure, the gas company management platform can construct the first risk feature map 332 of the target area based on the target risk distribution 330 and the pipeline laying map 331 of the target area; generate multiple candidate protection component combinations 370 corresponding to the acquisition points based on the first risk feature map 332, the risk protection level 340, and the component combined protection information 360; for each candidate protection component combination 370, update the first risk feature map 332 based on the candidate protection component combination 370 to obtain the second risk feature map 333 corresponding to the candidate protection component combination; determine the protection effect 335 corresponding to the candidate protection component combination 370 through the prediction model 334 based on the second risk feature map 333; determine the target protection component combination 380 based on the protection effects 335 corresponding to the multiple candidate protection component combinations 370; and determine the target protection component information 390 of the acquisition point based on the target protection component combination 380.

[0174] For more content about the pipeline laying map, please refer to Figure 2 and related descriptions.

[0175] The first risk feature map is a directed graph structure constructed based on the connection relationships of each gas pipeline in the pipeline laying map. The first risk feature map can be represented by nodes and edges connecting the nodes. The nodes in the graph are the acquisition points. The node features represent the risk type and the corresponding risk characteristics at this point, that is, the mean, minimum, and maximum values of the risk values corresponding to multiple first preset time periods. The edges in the graph represent the connection relationships of each node. If the gas pipelines where two nodes are located are directly connected or two nodes are within the same gas pipeline, there is an edge pointing from the upstream pipeline to the downstream pipeline between the two nodes.

[0176] The candidate protection component combination is a combination of protection components of three risk types with different grades and different materials.

[0177] In some embodiments, for each node in the first risk feature map, the gas company management platform can randomly generate a large number of candidate protection component combinations that meet the risk protection level of the acquisition point corresponding to this node. Meeting the risk protection level means that the protection level of a certain protection component type plus the protection levels of other protection component types for this risk type can meet the risk protection level.

[0178] The second risk feature map is a risk feature map updated on the first risk feature map according to the candidate protection component combination. Each candidate protection component combination can correspond to a second risk feature map.

[0179] The differences between the second risk feature map and the first risk feature map include different node features. In some embodiments, compared with the first risk feature map, the node features of the second risk feature map may further include the type of candidate protection components used by the node and the component combined protection information of the candidate protection components used by the node. The type of candidate protection components used by the node can be obtained according to the component combined protection information of the candidate protection component combination corresponding to the second risk feature map.

[0180] In some embodiments, compared with the first risk feature map, the node features of the second risk feature map may further include the future self-risk value of the acquisition point corresponding to the node.

[0181] The future self-risk value refers to the self-risk value within a preset future period. The preset future period refers to a subsequent period of time after the gas pipeline is built. For example, it can be one day or one week after the gas pipeline is built.

[0182] In some embodiments, the gas company management platform can obtain the change value of the pipeline material characteristics in the preset future period through the second vector database according to the environmental information and pipeline material characteristics; determine the future self-risk value through the second vector database according to the change value of the pipeline material characteristics in the preset future period.

[0183] The change value of the pipeline material characteristics refers to the change value of the pipeline wall thickness.

[0184] In some embodiments, the gas company management platform can construct a second feature vector based on the environmental information and pipeline material characteristics, and match the change value of the pipeline material characteristics in the preset future period in the second vector database based on the second feature vector.

[0185] The second vector database includes a plurality of second reference vectors and corresponding second vector labels. The gas company management platform can construct second reference vectors based on the environmental information and pipeline material characteristics around the gas pipeline in the historical data. The second vector labels include the change values of the pipeline material characteristics corresponding to the second reference vectors. In some embodiments, the gas company management platform can use the actual change value of the gas pipeline wall after a subsequent preset future period of time for the gas pipeline corresponding to the second reference vector in the historical data as the second vector label corresponding to the reference second feature vector.

[0186] In some embodiments, the gas company management platform can match the second reference vector with the highest vector similarity with the second feature vector in the second vector database, and use the second vector label corresponding to the second reference vector with the highest vector similarity with the second feature vector as the change value of the pipeline material characteristics in the preset future period.

[0187] In the embodiments of the present invention, since gas pipelines usually age over time, when predicting the protection effect of the protection components, it is necessary to consider the self-risk value of the gas pipeline after aging to ensure the accuracy of model prediction.

[0188] The protection effect of the candidate protection component combination can be measured by the predicted service life of the gas pipeline and the predicted number of failures during use. For example, the longer the predicted service life of the gas pipeline and the fewer the predicted number of failures during use, the better the protection effect; conversely, the worse the protection effect.

[0189] In some embodiments, for each candidate protection component combination, the gas company management platform can, based on the second risk feature map, predict the protection effect of each node in the second risk feature map through a prediction model, so as to obtain the protection effect of the candidate protection component combination.

[0190] In some embodiments, the prediction model is a machine learning model. For example, a neural network model, a graph neural network model (Graph Neural Networks, GNN), etc.

[0191] In some embodiments, as Figure 3 shown, the input of the prediction model 334 may include the second risk feature map 333, and the output of the prediction model 334 may include the protection effect 335 of each node in the second risk feature map 333.

[0192] In some embodiments, the prediction model can be obtained by training based on training data.

[0193] In some embodiments, the gas company management platform can obtain a training sample set composed of multiple labeled training samples and perform multiple rounds of iteration based on the training sample set.

[0194] The training samples may include sample second risk feature maps. The training samples may be sample second risk feature maps corresponding to multiple gas pipeline laying areas in historical data. The gas company management platform can first obtain a sample first risk feature map according to the historical risk situation corresponding to the historical data and the pipeline laying map; then update the node features according to the protection component combination actually selected in the historical data to obtain a sample second risk feature map.

[0195] The label may be the actual protection effect corresponding to the sample second risk feature map in the training sample. The label is the actual service life and the number of failures during use of each gas pipeline in the gas pipeline laying area corresponding to the training sample in the historical data for a period of time in the future at this historical moment. The period of time in the future at this historical moment is still the time that has occurred.

[0196] In some embodiments, the gas company management platform may input a training sample set into an initial prediction model to perform multiple rounds of iteration. At least one round of iteration includes: selecting one or more training samples from the training sample set, inputting the one or more training samples into the initial prediction model to obtain model prediction outputs corresponding to the one or more training samples; substituting the model prediction outputs corresponding to the one or more training samples and the labels of the one or more training samples into the formula of a predefined loss function to calculate the value of the loss function; and iteratively updating the model parameters in the initial prediction model according to the value of the loss function until the iteration end condition is met, at which point the iteration ends and a trained prediction model is obtained. Among them, the iteration end condition may include the convergence of the loss function, the number of iterations reaching a threshold, etc.

[0197] In the embodiments of the present invention, by determining the protection effect through a prediction model, the self-learning ability of the machine learning model can be utilized to find patterns from a large amount of historical data, obtain the relationship between the second risk feature map and the protection effect of the corresponding candidate protection component combination, improve the accuracy and efficiency of determining the protection effect, and further determine a protection component combination with better protection effect.

[0198] In some embodiments, in the training sample set of the prediction model, the number of training samples of any one of the multiple environmental types needs to be greater than the preset quantity threshold corresponding to that environmental type.

[0199] The environmental type is the environmental type where the acquisition point corresponding to the node is located. The environmental type may include residential areas, industrial areas, commercial areas, etc. The complexity of the environmental information in each environmental type refers to the sum of the microbial density, termite density, rodent quantity, number of factory types, and number of facility types included in the environmental information.

[0200] In some embodiments, the preset quantity threshold is positively correlated with the complexity of the environmental information in the environmental type.

[0201] In the embodiments of the present invention, since the environment directly affects the protection effect of the protection components, the greater the complexity of the environmental information, the greater the amount of information contained in the environmental information. At this time, for this type of environmental type, a larger number of training samples are required to ensure that the model is fully trained, thereby improving the accuracy of model prediction.

[0202] In some embodiments, the gas company management platform may determine the protection effect of the candidate protection component combination corresponding to the second risk feature map according to the protection effect of each node output by the prediction model. For example, the gas company management platform may calculate the mean value of the protection effects of each node to obtain the protection effect corresponding to the candidate protection component combination.

[0203] In some embodiments, the gas company management platform may select the candidate protection component combination with the best protection effect as the target protection component combination.

[0204] In some embodiments, the gas company management platform may determine the target protection component type and the corresponding second target protection level for each point based on the relevant information of the protection components recommended for use at each point included in the target protection component combination (including the target protection component type and the protection level), so as to determine the target protection component information of the acquisition points.

[0205] In the embodiments of the present invention, by constructing a risk feature map to predict the protection effect of the candidate protection component combination, and thus determining the target protection component combination with the best protection effect, more appropriate target protection component information can be determined, ensuring the service life of the gas pipeline.

[0206] One or more embodiments of the present invention provide a pipeline protection component layout system based on the intelligent gas Internet of Things. The system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, and a gas equipment object platform. The government safety supervision object platform includes a gas company management platform. The gas company management platform includes at least one processor and at least one memory. The at least one memory is used to store computer instructions. The at least one processor is used to execute at least part of the computer instructions to implement a pipeline protection component layout method according to any one of the above embodiments. The pipeline protection component layout system based on the intelligent gas Internet of Things may be a part of the intelligent gas Internet of Things.

[0207] One or more embodiments of the present invention provide a computer-readable storage medium. The computer-readable storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a pipeline protection component layout method according to any one of the above embodiments.

[0208] 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 the present invention. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to the present invention. Such modifications, improvements, and corrections are proposed in the present invention, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of the present invention.

[0209] In the meantime, the present invention uses specific terms to describe embodiments of the present invention. For example, "an embodiment", "one embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of the present invention. 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 the present invention does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the present invention can be appropriately combined.

[0210] 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 in the present invention are not used to limit the order of the processes and methods of the present invention. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration, and 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 the present invention. 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.

[0211] Similarly, it should be noted that, in order to simplify the expression of the disclosure of the present invention and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, sometimes multiple features are 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 the present invention are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiments disclosed above.

[0212] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used for the description of embodiments are modified by the modifiers "about", "approximate", or "substantially" in some examples. Unless otherwise stated, "about", "approximate", or "substantially" indicate that the said numbers allow a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values can change 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 general digits. Although the numerical ranges and parameters used to confirm the breadth of the scope in some embodiments of the present invention are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0213] For each patent, patent application, patent application publication, and other materials cited in this invention, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated by reference into this invention. This excludes application history files that are inconsistent with or conflict with the content of this invention, as well as files that limit the broadest scope of the claims of this invention (currently or subsequently appended to this invention). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this invention and the content described in this invention, the descriptions, definitions, and / or uses of terms in this invention shall prevail.

[0214] Finally, it should be understood that the embodiments described in this invention are only used to illustrate the principles of the embodiments of this invention. Other variations may also fall within the scope of this invention. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this invention may be considered consistent with the teachings of this invention. Accordingly, the embodiments of this invention are not limited to the embodiments explicitly presented and described in this invention.

Claims

1. A method for arranging pipeline protection components based on the intelligent gas Internet of Things, characterized in that The method is executed by the management platform of a gas company of a pipeline protection component layout system, and the method includes: Obtaining, through the sensing network platform of the gas company, environmental information of a target area in a plurality of first preset time periods from sensing devices set in a gas equipment object platform, where the environmental information includes temperature information, humidity information, geological information, and vibration information; Obtaining, through the government safety supervision sensing network platform, biological information, climate information, and facility information of the target area in the plurality of first preset time periods from the government safety supervision management platform; Determining, based on the geological information, the facility information, and the vibration information in the plurality of first preset time periods, the vibration risk value of the target area in each of the plurality of first preset time periods; Determining, based on the geological information, the biological information, and the facility information in the plurality of first preset time periods, the corrosion risk value of the target area in each of the plurality of first preset time periods; Determining, based on the climate information in the plurality of first preset time periods, the temperature risk value of the target area in each of the plurality of first preset time periods; Determining the target risk distribution of the target area based on the vibration risk value, the corrosion risk value, and the temperature risk value in each of the plurality of first preset time periods; Determining, based on the target risk distribution, target protection component information of a collection point laid in the target area, where the target protection component information includes a target protection component type and a corresponding first target protection level; Determining the laying density distribution of the target protection component based on the target risk distribution and the pipeline laying map of the target area; and, Before performing the protection component layout operation and / or during the execution of the protection component layout operation, generating a valve control instruction based on the laying density distribution and sending it to the gas equipment object platform to adjust the gas transmission pressure of at least one gas pipeline in the target area.

2. The method for arranging pipeline protection components based on the intelligent gas Internet of Things according to claim 1, characterized in that, The method further includes: Determining respective self-risk values corresponding to the vibration risk value, the corrosion risk value, and the temperature risk value based on the pipeline operation characteristics and the pipeline material characteristics, where the self-risk value is used to measure the risk degree generated by the operation of the gas pipeline itself; The determining the target risk distribution of the target area based on the vibration risk value, the corrosion risk value, and the temperature risk value in each of the plurality of first preset time periods includes: Determining the target risk distribution based on the vibration risk value, the corrosion risk value, the temperature risk value, and their respective corresponding self-risk values of the target area in each of the plurality of first preset time periods.

3. The method for arranging pipeline protection components based on the intelligent gas Internet of Things according to claim 1, characterized in that The determining the target protection component information of the collection point laid in the target area based on the target risk distribution includes: Determining the pipeline protection strength of the collection point based on the pipeline material characteristics of the collection point; Determining the risk protection level of the collection point based on the pipeline protection strength of the collection point and the target risk distribution of the target area; Determining the second target protection level of the collection point based on the risk protection level of the collection point.

4. The method for arranging pipeline protection components based on the intelligent gas Internet of Things according to claim 3, characterized in that, Determining the second target protection level of the acquisition point based on the risk protection level of the acquisition point includes: Determining the second target protection level of the acquisition point based on the risk protection level and the combined protection information of multiple target protection components.

5. The method for arranging pipeline protection components based on the intelligent gas Internet of Things according to claim 4, characterized in that, The method further includes: Constructing a first risk feature map of the target area based on the target risk distribution of the target area and the pipeline laying map; Generating multiple candidate protection component combinations corresponding to the acquisition point based on the first risk feature map, the risk protection level, and the combined protection information of the components; For each candidate protection component combination, Updating the first risk feature map based on the candidate protection component combination to obtain a second risk feature map corresponding to the candidate protection component combination; Determining the protection effect corresponding to the candidate protection component combination through a prediction model based on the second risk feature map, where the prediction model is a machine learning model; Determining a target protection component combination based on the protection effects corresponding to the multiple candidate protection component combinations; Determining the target protection component information of the acquisition point based on the target protection component combination.

6. A pipeline protection component layout system based on the intelligent gas Internet of Things, characterized in that, The pipeline protection component layout system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, and a gas equipment object platform. The government safety supervision object platform includes a gas company management platform; the gas company management platform includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least some of the computer instructions to implement: Obtaining environmental information of the target area in multiple first preset time periods from the sensing devices set in the gas equipment object platform through the gas company sensor network platform, where the environmental information includes temperature information, humidity information, geological information, and vibration information; Obtaining biological information, climate information, and facility information of the target area in the multiple first preset time periods from the government safety supervision management platform through the government safety supervision sensor network platform; Determining the vibration risk value of the target area in each of the multiple first preset time periods based on the geological information, the facility information, and the vibration information in the multiple first preset time periods; Determining the corrosion risk value of the target area in each of the multiple first preset time periods based on the geological information, the biological information, and the facility information in the multiple first preset time periods; Determining the temperature risk value of the target area in each of the multiple first preset time periods based on the climate information in the multiple first preset time periods; Determining the target risk distribution of the target area based on the vibration risk value, the corrosion risk value, and the temperature risk value in each of the multiple first preset time periods; Determining the target protection component information of the acquisition point laid in the target area based on the target risk distribution, where the target protection component information includes the target protection component type and the corresponding first target protection level; Determine the laying density distribution of the target protection components based on the pipeline laying atlas of the target risk distribution and the target area; and, Before and / or during the execution of the protection component layout operation, generate a valve control instruction based on the laying density distribution and send it to the gas equipment object platform to adjust the gas transmission pressure of at least one gas pipeline in the target area.

7. The pipeline protection component layout system based on the intelligent gas Internet of Things according to claim 6, characterized in that, The at least one processor is further configured to implement: Based on the pipeline operation characteristics and pipeline material characteristics, determine the self-risk values corresponding to the vibration risk value, the corrosion risk value, and the temperature risk value respectively, where the self-risk value is used to measure the risk degree generated by the operation of the gas pipeline itself; The determination of the target risk distribution of the target area based on the vibration risk value, the corrosion risk value, and the temperature risk value of each of the multiple first preset time periods includes: Determine the target risk distribution based on the vibration risk value, the corrosion risk value, the temperature risk value, and their respective corresponding self-risk values of the target area in each of the multiple first preset time periods.

8. The pipeline protection component layout system based on the intelligent gas Internet of Things according to claim 6, characterized in that, The at least one processor is further configured to implement: Determine the pipeline protection intensity of the collection point based on the pipeline material characteristics of the collection point; Determine the risk protection level of the collection point based on the pipeline protection intensity of the collection point and the target risk distribution of the target area; Determine the second target protection level of the collection point based on the risk protection level of the collection point.

9. The pipeline protection component layout system based on the intelligent gas Internet of Things according to claim 8, characterized in that The at least one processor is further configured to implement: Determine the second target protection level of the collection point based on the risk protection level and the component combined protection information of multiple target protection components.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which when executed by the processor implement the pipeline protection component layout method based on the intelligent gas Internet of Things as described in claim 1.

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