A method, device and system for monitoring gas leakage in a gas station

Abstract

The present application relates to the field of gas leakage monitoring technology, and specifically to a gas leakage monitoring method, device and system for a gas station, the method comprising: collecting burial depth, pressure data, gas flow data; obtaining gas monitoring data; obtaining a gas monitoring data sequence; calculating the abnormal gas change degree and gas downstream correlation of each monitoring point of each gas pipeline at each sampling time; and then obtaining the gas abnormal operation index of each monitoring point of each gas pipeline at each sampling time; obtaining the gas leakage abnormal coefficient of each monitoring point of each gas pipeline at each sampling time; using an abnormal detection algorithm to monitor the abnormal leakage position of the gas station. The present application improves the effectiveness and accuracy of gas leakage phenomena in gas stations.

Description

Technical Field

[0001] The present application relates to the technical field of gas leakage monitoring, and in particular to a method, device and system for monitoring gas leakage in a gas station. Background Art

[0002] As one of the indispensable energy sources in modern life, gas is widely used in households, industries and commerce. As an important part of the urban gas supply system, gas stations undertake key tasks such as gas storage, pressurization, allocation and transportation. However, gas leakage has always been a major hidden danger to the safe operation of gas stations. With the continuous development of the gas market, gas stations and gas pipelines have leaked from time to time, causing more and more serious environmental pollution, economic losses and harm to the lives and property of urban residents. Therefore, it is particularly important to monitor and deal with gas leakage in gas stations in a timely manner.

[0003] Early gas leak monitoring technology mainly relied on olfactory monitoring, which is not only inefficient but also inaccurate, making it difficult to detect gas leaks in a timely manner. With the advancement of gas leak technology, advanced gas leak monitoring technologies such as the pressure gradient method and the negative pressure wave method have emerged. However, since the negative pressure wave method has high requirements for data accuracy and real-time performance, and the layout of gas pipelines in gas stations is relatively complex, the negative wave pressure method is difficult to detect leaks in a timely manner when the gas pipeline leaks are small or in the early stages of a leak, resulting in delayed response and a high false alarm rate. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a gas leakage monitoring method, device and system for a gas station. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for monitoring gas leakage in a gas station, the method comprising the following steps:

[0006] Collect the buried depth of each monitoring point in the gas pipeline of the gas station, the pressure data of each monitoring point at each sampling time and the gas flow data;

[0007] Based on the pressure data and the gas flow data, the gas monitoring data of each monitoring point of each gas pipeline at each sampling time is obtained;

[0008] Based on the gas monitoring data, a gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time is obtained;

[0009] Based on the differences between each element in the gas monitoring data sequence and the standard gas monitoring data and the value of the gas monitoring data, the abnormal change degree of gas at each monitoring point of each gas pipeline at each sampling time is obtained;

[0010] Based on the difference between the gas monitoring data of the monitoring point and the gas monitoring data of the monitoring point after the gas pipeline, the gas downstream correlation of each monitoring point of each gas pipeline at each sampling time is obtained;

[0011] Obtain the abnormal gas operation index of each monitoring point of each gas pipeline at each sampling time based on the abnormal gas change degree and the downstream gas correlation;

[0012] Based on the burial depth, the gas abnormal operation index and the correlation between the gas monitoring data series of adjacent monitoring points, the gas leakage abnormality coefficient of each monitoring point of each gas pipeline at each sampling time is obtained;

[0013] Anomaly detection algorithms are used for the gas leakage anomaly coefficients of all monitoring points on the gas pipeline to monitor the abnormal leakage locations of gas stations.

[0014] Furthermore, the method for acquiring the gas monitoring data is:

[0015] For each monitoring point at each sampling time, the sum of the gas flow data and the pressure data is calculated, and the calculation result of an exponential function with a natural constant as the base and the sum as the exponent is calculated as the gas monitoring data of each monitoring point at each sampling time.

[0016] Furthermore, the method for acquiring the gas monitoring data sequence is:

[0017] For each monitoring point at each sampling moment, a sequence consisting of gas monitoring data at all sampling moments within a preset time period before the sampling moment is used as a sequence of gas monitoring data of each monitoring point at each sampling moment.

[0018] Furthermore, the method for obtaining the abnormal change degree of the gas is:

[0019] Calculate the sum of the preset standard pipeline pressure data and the preset standard gas flow data as the standard data sum value, and calculate the result of an exponential function with a natural constant as the base and the standard data sum value as the exponent as the standard gas monitoring data;

[0020] The calculation formula of the abnormal change degree of gas is: ; In the formula, For the Gas pipeline No. The monitoring point is Abnormal change degree of gas at the sampling time; For the Gas pipeline No. The monitoring point is The first Gas monitoring data, is the standard gas monitoring data. For the Gas pipeline No. The monitoring point is Gas monitoring data at the sampling time, For the Gas pipeline No. The monitoring point is The number of gas monitoring data in the gas monitoring data sequence at the sampling time.

[0021] Furthermore, the method for obtaining the downstream correlation of the gas is:

[0022] For each monitoring point of each gas pipeline at each sampling moment, a sequence consisting of the gas monitoring data of a preset number of monitoring points after the monitoring point on the gas pipeline is used as the downstream gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling moment; the absolute value of the difference between each gas monitoring data in the downstream gas monitoring data sequence of the monitoring point and the gas monitoring data of the monitoring point is calculated, and the sum of the reciprocals of all the absolute values ​​of the difference of the monitoring points is calculated as the gas downstream correlation of each monitoring point.

[0023] Furthermore, the method for obtaining the gas abnormal operation index is:

[0024] For each monitoring point of each gas pipeline at each sampling time, the product of the gas abnormality change degree and the gas downstream correlation degree is calculated as the gas abnormality operation index of each monitoring point of each gas pipeline at each sampling time.

[0025] Furthermore, the formula of the gas leakage anomaly coefficient is: ; In the formula, Indicates Gas pipeline No. The monitoring point is Gas leakage anomaly coefficient at the sampling time; Indicates Gas pipeline No. The monitoring point is Gas abnormal operation index at the sampling time; Indicates Gas pipeline No. Monitoring points and Monitoring points in Pearson correlation coefficient between gas monitoring data sequences monitored at the sampling time; Indicates Gas pipeline No. The burial depth of each monitoring point; It is the preset parameter factor.

[0026] Furthermore, the use of an abnormal data monitoring algorithm in combination with a gas leakage abnormality coefficient to monitor the abnormal leakage location of a gas station includes:

[0027] For each gas pipeline at each sampling time, the gas leakage anomaly coefficients of all monitoring points on the gas pipeline are taken as each set, and the abnormal data in each set is obtained using the abnormal data monitoring algorithm. The monitoring point where the abnormal data is located is the abnormal gas leakage location.

[0028] In a second aspect, an embodiment of the present application provides a gas leakage monitoring device for a gas station, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned gas leakage monitoring methods for a gas station when executing the computer program.

[0029] In a third aspect, an embodiment of the present application provides a gas leakage monitoring system for a gas station, the system comprising:

[0030] The data acquisition module is used to collect the buried depth of each monitoring point in the gas pipeline of the gas station, the pressure data of each monitoring point at each sampling time, and the gas flow data;

[0031] A gas leakage anomaly coefficient acquisition module is used to acquire the gas monitoring data of each monitoring point of each gas pipeline at each sampling time based on pressure data and gas flow data; acquire the gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time based on the gas monitoring data; acquire the gas anomaly variation degree of each monitoring point of each gas pipeline at each sampling time based on the difference between each element in the gas monitoring data sequence and the standard gas monitoring data and the size of the gas monitoring data value; acquire the gas downstream correlation degree of each monitoring point of each gas pipeline at each sampling time based on the difference between the gas monitoring data of the monitoring point and the gas monitoring data of the monitoring point after the gas pipeline; acquire the gas abnormal operation index of each monitoring point of each gas pipeline at each sampling time based on the gas abnormal variation degree and the gas downstream correlation; acquire the gas leakage anomaly coefficient of each monitoring point of each gas pipeline at each sampling time based on the burial depth, the gas abnormal operation index and the correlation between the gas monitoring data sequences of adjacent monitoring points;

[0032] The gas leakage monitoring module is used to use an anomaly detection algorithm for the gas leakage anomaly coefficients of all monitoring points on the gas pipeline to monitor the abnormal leakage locations of gas stations.

[0033] This application has at least the following beneficial effects:

[0034] The present application collects the gas flow and pipe pressure data inside the gas pipeline in the city gas gate station, and constructs the gas monitoring data based on the gas flow and pipeline pressure data, and then analyzes the changes of the gas flow rate and pipe pressure data over time in the same monitoring point, and constructs the gas abnormal operation index according to the changes of the gas flow and pipe pressure data. The beneficial effect is that the gas abnormal operation index takes into account the changes of the gas flow and pipe pressure over time during gas leakage and the influence of the gas leakage point on the gas flow and pipeline pressure of the downstream monitoring point. The gas abnormal operation index can more accurately reflect the severity of gas leakage in the city gas gate station; secondly, based on the gas leakage When a gas leak occurs, the different change characteristics of the gas flow rate and the pressure in the pipe upstream and downstream of the leakage point are taken into account and combined with the gas abnormal operation index to construct a gas leakage anomaly coefficient. The beneficial effect is that the gas leakage anomaly coefficient takes into account the change of the gas abnormal operation index when a gas leakage occurs and the significant difference between the gas flow rate and pipeline pressure data when there is a gas leakage point between adjacent monitoring points, as well as the influence of the buried depth of the gas pipeline on the gas leakage phenomenon. According to the gas leakage anomaly coefficient, it is possible to more accurately judge whether a gas leakage has occurred in a city gas gate station and the specific location of the gas leakage point, thereby improving the effectiveness and accuracy of gas leakage in gas stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 A flowchart of a method for monitoring gas leakage at a gas station provided in accordance with an embodiment of the present application;

[0037] Figure 2 A flow chart for obtaining a gas leakage anomaly coefficient is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the gas leakage monitoring method, device and system for a gas station proposed in the present application, its specific implementation method, structure, features and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0039] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0040] The following is a detailed description of a method, device and system for monitoring gas leakage at a gas station provided by the present application in conjunction with the accompanying drawings.

[0041] See also Figure 1 , which shows a flow chart of a method for monitoring gas leakage in a gas station provided by an embodiment of the present application, the method comprising the following steps:

[0042] Step S001, collecting the buried depth of each monitoring point in the gas pipeline of the gas station, the pressure data of each monitoring point at each sampling time and the gas flow data.

[0043] Gas stations have different functions due to their construction locations. They can be divided into city gate stations, gas transmission stations, storage and distribution stations, and automobile gas stations. The structure of a city gas gate station is usually composed of two gas manifolds, several gas pipelines, and filtering, odorizing, and distribution devices.

[0044] This application analyzes the gas leakage problem of city gate stations. In order to accurately monitor the gas leakage problem of city gas gate stations, monitoring points are evenly arranged at equal distances inside all gas pipelines of gas stations. Pressure sensors and flow sensors are installed at each monitoring point to collect the pressure data and gas flow data of the gas pipeline, and at the same time obtain the preset standard pipeline pressure data and standard gas flow data of the gas pipeline. This application arranges adjacent monitoring points in the gas pipeline at a distance of 1m, and arranges N monitoring points in total. The interval between the sampling time of each sensor sampling data is 1s. At the same time, the burial depth of each monitoring point is obtained.

[0045] In order to prevent data loss from occurring in the collected data, this application uses the mean filling method to pre-process the collected data. The mean filling method is a known technology, and this application will not describe its implementation process in detail. At the same time, in order to facilitate analysis, the obtained pressure and flow data are normalized. The normalized data is a known technology, and this application will not describe its specific implementation process in detail.

[0046] At this point, the pre-processed data of the internal pressure and gas flow of the city gas gate station gas pipeline are obtained.

[0047] Step S002, based on the pressure data and the gas flow data, obtain the gas monitoring data of each monitoring point of each gas pipeline at each sampling time; based on the gas monitoring data, obtain the gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time; based on the difference between each element in the gas monitoring data sequence and the standard gas monitoring data, and the size of the gas monitoring data value, obtain the gas abnormality change degree of each monitoring point of each gas pipeline at each sampling time; based on the difference between the gas monitoring data of the monitoring point and the gas monitoring data of the monitoring point after the gas pipeline, obtain the gas downstream correlation of each monitoring point of each gas pipeline at each sampling time; based on the gas abnormality change degree and the gas downstream correlation, obtain the gas abnormal operation index of each monitoring point of each gas pipeline at each sampling time.

[0048] This application is aimed at monitoring the gas leakage problem of the gas pipeline of the city gas gate station. Take one of the gas pipelines as an example.

[0049] For each gas pipeline, under normal circumstances, the internal pressure and flow of the gas pipeline of the city gas gate station are maintained in a relatively stable equilibrium state. However, when a gas leak occurs in the gas station, this relatively stable equilibrium state will be broken. Specifically, when a gas leak occurs, a portion of the upstream gas in the gas pipeline will quickly flow out from the leak point, resulting in a decrease in the flow of the downstream gas in the gas pipeline. At the same time, due to the reduction in gas flow, the gas density in the downstream of the gas pipeline decreases, which in turn leads to a decrease in the pressure in the downstream of the gas pipeline. That is, in the gas pipeline, the changes in gas flow data are synchronized with the changes in the pressure data in the pipe.

[0050] According to the above analysis, in order to comprehensively reflect the changes in the internal pressure and gas flow of the gas pipeline, a gas monitoring data is constructed based on the gas flow data and the internal pressure data of the pipe. , the calculation formula is: ; In the formula, Indicates Gas pipeline No. The monitoring point is Gas monitoring data at the sampling time; , Separate Gas pipeline No. The monitoring point is The gas flow data and pressure data at each sampling moment; e is a natural constant.

[0051] When the gas flow in the gas pipeline decreases, the gas density inside the gas pipeline decreases accordingly, which in turn causes the internal pressure of the gas pipeline to decrease. At this time, the gas monitoring data obtained decreases; conversely, the gas monitoring data obtained increases. The gas monitoring data can comprehensively reflect the changes in the internal pressure and gas flow of the gas pipeline, and amplify the changing trends of the internal pressure and gas flow of the gas pipeline, which is convenient for subsequent analysis.

[0052] Furthermore, the leakage of gas pipelines at city gas gate stations is analyzed based on the abnormal changes in gas monitoring data. For each monitoring point of each gas pipeline, when a gas pipeline leaks, the gas monitoring data monitored at adjacent sampling times will change accordingly.

[0053] Specifically, the present application takes into account that even if there is no gas leakage, there is still data fluctuation due to the influence of sensor accuracy or the internal structure of the pipeline. Therefore, for each monitoring point, a sequence consisting of gas monitoring data at all sampling times within a preset time period before the current sampling time is used as the gas monitoring data sequence of each monitoring point at each sampling time. In this embodiment, the preset time period is set to 1 minute, and the implementer may select other values ​​according to the actual situation. The data before the sampling time that is less than 1 minute will be repeatedly filled with the gas monitoring data at the last sampling time.

[0054] Furthermore, considering that gas leakage is not an instantaneous behavior, when gas leakage occurs, as time goes by, the gas monitoring data detected by the monitoring point downstream of the gas leakage point will decay and then tend to be stable. Therefore, for the gas monitoring data to form a data sequence , the real-time collected gas monitoring data is compared with the standard gas monitoring data to measure the severity of the gas pipeline leakage.

[0055] Further, the sum of the standard pipeline pressure data and the standard gas flow data is calculated as the standard data sum value, and the calculation result of the exponential function with the natural constant as the base and the standard data sum value as the exponent is calculated as the standard gas monitoring data. .

[0056] Secondly, it takes time for the leakage to affect the downstream distant monitoring points, that is, the farther the downstream monitoring point is from the leakage point, the later its gas monitoring data changes. In this application, the abnormal changes in the gas monitoring data of each monitoring point are , implementers can select other values ​​according to actual conditions.

[0057] Furthermore, for each monitoring point of each gas pipeline at each sampling time, the monitoring point after the gas pipeline The sequence of gas monitoring data of each monitoring point is used as the downstream gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time. , downstream gas monitoring data sequence The data in are sorted in order from upstream to downstream of the monitoring points in the gas pipeline. For each monitoring point, other monitoring points that the monitoring point passes through along the direction of gas transmission in the gas pipeline are regarded as monitoring points after each monitoring point.

[0058] Specifically, when gas leaks inside the gas pipeline, the gas monitoring data of the downstream monitoring points should respond in sequence. The more serious the gas leak, the farther the impact of the gas leak will spread in the same time, and the more data from the downstream monitoring points will give abnormal responses. Therefore, this application further calculates the downstream gas leak. Abnormal changes in gas monitoring data at each monitoring point.

[0059] Based on the above analysis, in order to reflect the degree of abnormal change in gas monitoring data, the degree of abnormal change in gas at each monitoring point on each gas pipeline at each sampling time is calculated. The calculation formula is: ; In the formula, For the Gas pipeline No. The monitoring point is The degree of abnormal change of gas monitored at the sampling time; For the Gas pipeline No. The monitoring point is The first Gas monitoring data, is the standard gas monitoring data. For the Gas pipeline No. The monitoring point is Gas monitoring data at the sampling time, For the Gas pipeline No. The monitoring point is The number of gas monitoring data in the gas monitoring data sequence at the sampling time.

[0060] It should be noted that when the first Gas pipeline When a gas leak occurs in the gas pipeline area in front of a monitoring point, The pressure and gas flow rate in the gas pipeline monitored at the sampling time decrease rapidly, that is, the gas monitoring data reduce, thereby In addition, the longer the gas leak lasts, the larger the calculated The larger the value of The larger the value of .

[0061] Furthermore, in order to reflect the changes in the gas monitoring data between each monitoring point on each gas pipeline and each monitoring point downstream of each gas pipeline, the gas downstream correlation of each monitoring point of each gas pipeline at each sampling time is calculated, and the calculation formula is: ; In the formula, For the Gas pipeline No. The monitoring point is The downstream correlation of the gas monitored at the sampling time; For the Gas pipeline No. Downstream gas monitoring data sequence of monitoring points The Gas monitoring data, For the Gas pipeline No. The monitoring point is Gas monitoring data monitored at the sampling time, For downstream gas monitoring data series The total number of gas monitoring data.

[0062] It should be noted that the more serious the gas leakage, the greater the difference between the gas monitoring data between the current monitoring point and the downstream monitoring point. The smaller, the The value of increases; on the contrary, The value of decreases.

[0063] Furthermore, in order to reflect the abnormal operation of the gas pipeline in the process of transporting gas, the abnormal gas operation index of each monitoring point of each gas pipeline at each sampling time is obtained, and the calculation formula is: ; In the formula, For the city gas gate station Gas pipeline No. The monitoring point is Gas abnormal operation index at the sampling time; For the city gas gate station Gas pipeline No. The monitoring point is The abnormal change degree of gas monitored at the sampling time, For the city gas gate station Gas pipeline No. The monitoring point is The downstream correlation of the gas monitored at the sampling time.

[0064] It should be noted that the final calculated city gas gate station Gas pipeline No. The monitoring point is Gas abnormal operation index at the sampling time The larger the value, the more serious the gas leakage of the city gas gate station; conversely, the smaller the value, the lighter the gas leakage of the city gas gate station.

[0065] Step S003, based on the burial depth, the gas abnormal operation index and the correlation between the gas monitoring data series of adjacent monitoring points, the gas leakage abnormality coefficient of each monitoring point of each gas pipeline at each sampling time is obtained.

[0066] Furthermore, when a gas leak occurs at a city gas gate station, the gas in the gas pipeline quickly flows out from the leakage point, causing the gas flow and pipeline pressure monitored by the downstream monitoring point inside the gas pipeline to decrease rapidly. At the same time, the upstream gas supply system cannot close the gas supply valve in time, causing the monitoring point upstream of the leakage point to still maintain a high gas flow and pipeline pressure. That is, the gas monitoring data downstream of the leakage point is large, and the gas monitoring data upstream of the leakage point still maintains a large value.

[0067] Based on the above analysis, this application evaluates the differences between the gas monitoring data between adjacent monitoring points on the same gas pipeline and then determines the specific location of the gas leakage point.

[0068] Specifically, when the monitoring point And monitoring points When there is no gas leakage point between the monitoring points, no matter whether there is gas leakage at other locations of the gas pipeline And monitoring points The gas monitoring data between the monitoring points are synchronized over time; With monitoring point When there is a gas leak, the upstream gas supply system cannot close the gas supply valve in time, which will cause the monitoring point to And monitoring points There will be significant differences between the gas monitoring data.

[0069] Therefore, for two adjacent monitoring points at each sampling time And monitoring points , calculate the adjacent monitoring points as well as Gas monitoring data series Pearson correlation coefficient between , where monitoring point Greater than or equal to 2.

[0070] The Pearson correlation coefficient between the gas monitoring data sequences of adjacent monitoring points is used to determine the adjacent monitoring points. as well as Whether there is a gas leak. Pearson correlation is a well-known technology, and the specific calculation process is not described in detail in this embodiment.

[0071] In addition, most of the gas pipelines in urban gas gate stations are buried underground. When the underground gas pipeline leaks, there is a close relationship between the gas leakage rate and the buried depth of the pipeline. Specifically, the shallower the buried depth of the pipeline, the greater the diffusion rate of the gas in the soil, and the faster the gas leakage rate. Therefore, the pipeline depth and soil porosity at each monitoring point are further obtained.

[0072] Based on the above analysis and combined with the gas abnormal operation index , construct the gas leakage anomaly coefficient , the calculation formula is: ; In the formula, Indicates Gas pipeline No. The monitoring point is Gas leakage anomaly coefficient at the sampling time; Indicates Gas pipeline No. The monitoring point is Gas abnormal operation index at the sampling time; Indicates Gas pipeline No. Monitoring points and Monitoring points in Pearson correlation coefficient between gas monitoring data sequences monitored at the sampling time; Indicates Gas pipeline No. The burial depth of each monitoring point; is the preset parameter adjustment factor. In order to prevent the denominator from being 0, the value of 𝛽 in this embodiment is 0.01. Among them, the gas leakage abnormality coefficient acquisition flow chart is as follows Figure 2 shown.

[0073] It should be noted that when the city gas gate station Gas pipeline Monitoring point and When a gas leak occurs in the pipeline between the monitoring points, Monitoring point and The gas monitoring data monitored by each monitoring point is no longer synchronized in the past period of time, that is, the calculated The value will decrease, and due to gas pipeline leakage, The value of increases; at the same time, the pipeline burial depth The shallower the gas is, the more serious the leakage of fuel blood will be. Gas pipeline No. The monitoring point is Gas leakage anomaly coefficient at the sampling time The value of increases. This indicates that the city gas gate station Gas pipeline in Monitoring points and The greater the possibility of gas leakage between monitoring points.

[0074] Step S004, using an anomaly detection algorithm for the gas leakage anomaly coefficients of all monitoring points on the gas pipeline to monitor the abnormal leakage locations of the gas stations.

[0075] At this point, according to the above steps, the gas leakage anomaly coefficient of each monitoring point of each gas pipeline of the city gas gate station at each sampling time is obtained , gas leakage anomaly coefficient By analyzing the changes in gas flow and pipeline pressure at the same monitoring point and the changes in gas flow and pipeline pressure between adjacent monitoring points when gas leakage occurs in the gas pipeline, the gas leakage phenomenon in the gas pipeline of the city gas gate station can be reflected more accurately.

[0076] Therefore, for each gas pipeline at each sampling time, the gas leakage anomaly coefficient of all monitoring points on the gas pipeline is calculated. As a set, denoted as set , using HBOS algorithm to The data in the collection is used for abnormal monitoring. The data in the set is arranged from large to small, and the set The data in is divided into the number of intervals Set as , Representing a collection The number of elements in , Indicates that the calculation result is rounded off.

[0077] According to the divided interval, the collection As input, count the number of elements in each interval and generate a histogram, and calculate the set based on the histogram The anomaly score of each data in . For the specific calculation process, please refer to The specific calculation process of the algorithm is not described here. Setting the abnormal threshold Finally, we can get the set Abnormal data in the set The monitoring point corresponding to the abnormal data in indicates the location of the gas leakage point of the city gas gate station.

[0078] At this point, based on the above analysis, it is possible to quickly determine whether a gas leak has occurred at the city gas gate station and the specific location of the gas leak.

[0079] Based on the same inventive concept as the above method, an embodiment of the present application also provides a gas leakage monitoring device for a gas station, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned gas leakage monitoring methods for a gas station are implemented.

[0080] Similarly, based on the same inventive concept as the above method, an embodiment of the present application provides a gas leakage monitoring system for a gas station, the system comprising:

[0081] The data acquisition module is used to collect the buried depth of each monitoring point in the gas pipeline of the gas station, the pressure data of each monitoring point at each sampling time, and the gas flow data;

[0082] A gas leakage anomaly coefficient acquisition module is used to acquire the gas monitoring data of each monitoring point of each gas pipeline at each sampling time based on pressure data and gas flow data; acquire the gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time based on the gas monitoring data; acquire the gas anomaly variation degree of each monitoring point of each gas pipeline at each sampling time based on the difference between each element in the gas monitoring data sequence and the standard gas monitoring data and the size of the gas monitoring data value; acquire the gas downstream correlation degree of each monitoring point of each gas pipeline at each sampling time based on the difference between the gas monitoring data of the monitoring point and the gas monitoring data of the monitoring point after the gas pipeline; acquire the gas abnormal operation index of each monitoring point of each gas pipeline at each sampling time based on the gas abnormal variation degree and the gas downstream correlation; acquire the gas leakage anomaly coefficient of each monitoring point of each gas pipeline at each sampling time based on the burial depth, the gas abnormal operation index and the correlation between the gas monitoring data sequences of adjacent monitoring points;

[0083] The gas leakage monitoring module is used to use an anomaly detection algorithm for the gas leakage anomaly coefficients of all monitoring points on the gas pipeline to monitor the abnormal leakage locations of gas stations.

[0084] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0086] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A gas leakage monitoring method for a gas station, characterized in that: The method comprises the following steps: Collect the buried depth of each monitoring point in the gas pipeline of the gas station, the pressure data of each monitoring point at each sampling time and the gas flow data; Based on the pressure data and the gas flow data, the gas monitoring data of each monitoring point of each gas pipeline at each sampling time is obtained; Based on the gas monitoring data, a gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time is obtained; Based on the differences between each element in the gas monitoring data sequence and the standard gas monitoring data and the value of the gas monitoring data, the abnormal change degree of gas at each monitoring point of each gas pipeline at each sampling time is obtained; Based on the difference between the gas monitoring data of the monitoring point and the gas monitoring data of the monitoring point after the gas pipeline, the gas downstream correlation of each monitoring point of each gas pipeline at each sampling time is obtained; Obtain the abnormal gas operation index of each monitoring point of each gas pipeline at each sampling time based on the abnormal gas change degree and the downstream gas correlation; Based on the burial depth, the gas abnormal operation index and the correlation between the gas monitoring data series of adjacent monitoring points, the gas leakage abnormality coefficient of each monitoring point of each gas pipeline at each sampling time is obtained; Use anomaly detection algorithms to detect abnormal gas leakage coefficients at all monitoring points on the gas pipeline and monitor the abnormal leakage locations at gas stations; The method for obtaining the abnormal change degree of gas is as follows: Calculate the sum of the preset standard pipeline pressure data and the preset standard gas flow data as the standard data sum value, and calculate the result of an exponential function with a natural constant as the base and the standard data sum value as the exponent as the standard gas monitoring data; The calculation formula of the abnormal change degree of gas is: ; In the formula, For the Gas pipeline No. The monitoring point is Abnormal variation of gas at the sampling time; For the Gas pipeline No. The monitoring point is The first Gas monitoring data, It is the standard gas monitoring data. For the Gas pipeline No. The monitoring point is Gas monitoring data at the sampling time, For the Gas pipeline No. The monitoring point is The number of gas monitoring data in the gas monitoring data sequence at the sampling time; The method for obtaining the downstream correlation of the gas is: For each monitoring point of each gas pipeline at each sampling time, a sequence consisting of gas monitoring data of a preset number of monitoring points after the monitoring point on the gas pipeline is used as a downstream gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time; the absolute value of the difference between each gas monitoring data in the downstream gas monitoring data sequence of the monitoring point and the gas monitoring data of the monitoring point is calculated, and the sum of the reciprocals of all the absolute values ​​of the difference of the monitoring point is calculated as the gas downstream correlation of each monitoring point; The method for obtaining the gas abnormal operation index is: For each monitoring point of each gas pipeline at each sampling time, the product of the gas abnormal change degree and the gas downstream correlation degree is calculated as the gas abnormal operation index of each monitoring point of each gas pipeline at each sampling time; The formula of the gas leakage abnormality coefficient is: ; In the formula, Indicates Gas pipeline No. The monitoring point is The gas leakage anomaly coefficient at the sampling time; Indicates Gas pipeline No. The monitoring point is Gas abnormal operation index at the sampling time; Indicates Gas pipeline No. Monitoring points and Monitoring points in Pearson correlation coefficient between gas monitoring data sequences monitored at the sampling time; Indicates Gas pipeline No. The burial depth of each monitoring point; It is the preset parameter factor.

2. A gas leakage monitoring method for a gas station according to claim 1, characterized in that: The method for obtaining the gas monitoring data is: For each monitoring point at each sampling time, the sum of the gas flow data and the pressure data is calculated, and the calculation result of an exponential function with a natural constant as the base and the sum as the exponent is calculated as the gas monitoring data of each monitoring point at each sampling time.

3. A gas leakage monitoring method for a gas station as claimed in claim 1, characterized in that: The method for obtaining the gas monitoring data sequence is: For each monitoring point at each sampling moment, a sequence consisting of gas monitoring data at all sampling moments within a preset time period before the sampling moment is used as a sequence of gas monitoring data of each monitoring point at each sampling moment.

4. A gas leakage monitoring method for a gas station as claimed in claim 1, characterized in that: The abnormal data monitoring algorithm is used in combination with the gas leakage abnormality coefficient to monitor the abnormal leakage location of the gas station, including: For each gas pipeline at each sampling time, the gas leakage anomaly coefficients of all monitoring points on the gas pipeline are taken as each set, and the abnormal data in each set is obtained using the abnormal data monitoring algorithm. The monitoring point where the abnormal data is located is the abnormal gas leakage location.

5. A gas leakage monitoring device for a gas station, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the gas leakage monitoring method for a gas station as described in any one of claims 1-4 are implemented.

6. A gas leakage monitoring system for a gas station, implementing a gas leakage monitoring method for a gas station as claimed in claim 1, characterized in that: The monitoring system comprises: The data acquisition module is used to collect the buried depth of each monitoring point in the gas pipeline of the gas station, the pressure data of each monitoring point at each sampling time, and the gas flow data; A gas leakage anomaly coefficient acquisition module is used to acquire the gas monitoring data of each monitoring point of each gas pipeline at each sampling time based on pressure data and gas flow data; acquire the gas monitoring data sequence of each monitoring point of each gas pipeline at each sampling time based on the gas monitoring data; acquire the gas anomaly variation degree of each monitoring point of each gas pipeline at each sampling time based on the difference between each element in the gas monitoring data sequence and the standard gas monitoring data and the size of the gas monitoring data value; acquire the gas downstream correlation degree of each monitoring point of each gas pipeline at each sampling time based on the difference between the gas monitoring data of the monitoring point and the gas monitoring data of the monitoring point after the gas pipeline; acquire the gas abnormal operation index of each monitoring point of each gas pipeline at each sampling time based on the gas abnormal variation degree and the gas downstream correlation; acquire the gas leakage anomaly coefficient of each monitoring point of each gas pipeline at each sampling time based on the burial depth, the gas abnormal operation index and the correlation between the gas monitoring data sequences of adjacent monitoring points; The gas leakage monitoring module is used to use an anomaly detection algorithm for the gas leakage anomaly coefficients of all monitoring points on the gas pipeline to monitor the abnormal leakage locations of gas stations.

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