A pipeline anomaly monitoring method and device, electronic equipment and medium

Abstract

The application relates to the technical field of data monitoring, in particular to a pipeline abnormality monitoring method and device, electronic equipment and a medium. The method comprises the following steps: comparing pressure values of each substation with corresponding preset standard pressure values of each station to determine whether an initial abnormal substation exists; if yes, determining a monitoring range corresponding to the initial abnormal substation according to an initial abnormal position; determining corresponding noise frequency and noise characteristics based on sound monitoring data corresponding to the monitoring range, and determining a first weight value based on the same; determining a predicted influence value according to the noise frequency and the noise characteristics; determining a pressure difference value by comparing the pressure value corresponding to the initial abnormal substation with the corresponding preset standard pressure value, and determining a second weight value according to the predicted influence value and the pressure difference value; determining whether the monitoring range is abnormal according to the first weight value and the second weight value, and if yes, generating warning information based on the monitoring range. The application can discover the existing abnormality of a transportation pipeline in time.

Description

Technical Field

[0001] This application relates to the field of data monitoring technology, and in particular to a pipeline anomaly monitoring method, device, electronic equipment, and medium. Background Technology

[0002] Natural gas is a highly efficient energy source with high calorific value and low carbon emissions. By constructing a natural gas pipeline network, centralized transportation and distributed supply of natural gas can be achieved, ensuring supply stability and meeting the needs of various sectors such as residential, industrial, and power plant users. Compared to other transportation methods, such as liquefied natural gas (LNG) transportation, natural gas pipeline transportation is more economical. Pipelines enable long-distance, large-scale transportation, improving natural gas utilization efficiency. However, because natural gas pipelines are typically buried underground or underwater, and natural gas leaks usually do not leave obvious pollutants or traces like liquid leaks, especially when the leakage volume in the pipeline is small, it is difficult to detect by observation alone.

[0003] Since natural gas is a flammable gas, when a pipeline leaks and the anomaly is not detected in time, the natural gas may accumulate underground or underwater. The accumulated natural gas may form a flammable mixture. Once the mixture comes into contact with a source of ignition or an electrical spark, it is highly likely to cause an explosion and fire, which greatly threatens the safety of people's property and lives in the surrounding environment. Therefore, there is an urgent need for a method to monitor anomalies in pipelines. Summary of the Invention

[0004] In order to detect anomalies in transportation pipelines in a timely manner, this application provides a pipeline anomaly monitoring method, device, electronic equipment, and medium.

[0005] Firstly, this application provides a pipeline anomaly monitoring method, which adopts the following technical solution:

[0006] A pipeline anomaly monitoring method, comprising:

[0007] Obtain the pressure value of each sub-site and compare the pressure value of each sub-site with the preset standard pressure value corresponding to each site to determine whether there are any initial abnormal sub-sites;

[0008] If so, then obtain the initial abnormal location of the initial abnormal sub-site, and determine the monitoring range corresponding to the initial abnormal sub-site based on the initial abnormal location;

[0009] Acquire sound monitoring data corresponding to the monitoring range, determine the corresponding noise frequency and noise characteristics based on the sound monitoring data, and determine a first weight value based on the noise frequency and noise characteristics;

[0010] The predicted impact value is determined based on the noise frequency and the noise characteristics;

[0011] The pressure difference value is determined by comparing the pressure value corresponding to the initial abnormal sub-site with the corresponding preset standard pressure value, and a second weight value is determined based on the predicted impact value and the pressure difference value.

[0012] Based on the first weight value and the second weight value, it is determined whether there is an anomaly in the monitoring range. If so, an alert message is generated based on the monitoring range and the alert message is fed back to the terminal device of the relevant staff.

[0013] By adopting the above technical solution, the pressure values ​​of different sub-stations are compared with the corresponding preset standard pressure values ​​to determine whether there are initial abnormal sub-stations. Then, based on the initial abnormal sub-stations, the scope that needs to be focused on is determined, rather than starting monitoring arbitrarily from a certain area. By focusing on monitoring the determined monitoring scope, it is possible to promptly detect anomalies that may have occurred or have occurred within the monitoring scope. Since leakage in the transportation pipeline may generate related noise, sound monitoring data is used to monitor whether there is a gas leak in the transportation pipeline within the monitoring scope, which helps to improve the accuracy of anomaly detection. In addition, the predicted impact value can be compared with the actual impact pressure value, that is, a reverse verification method can be used to determine whether a natural gas leak has occurred within the monitoring scope, which further improves the accuracy of anomaly detection. This application can quickly and accurately identify anomalies by using sound monitoring data and reverse verification impact pressure values.

[0014] In one possible implementation, determining the corresponding noise frequency and noise characteristics based on the sound monitoring data includes:

[0015] Identify the preset monitoring location information contained within the monitoring range, and determine the target monitoring location corresponding to the initial abnormal sub-site based on the initial abnormal location and the preset monitoring location information. The preset monitoring location information is the setting location of all preset sound monitoring devices contained within the monitoring range.

[0016] Acquire sound monitoring data corresponding to the target monitoring location and acquire ambient sound data corresponding to the target monitoring location. Perform noise reduction processing on the sound monitoring data based on the ambient sound data to obtain processed sound data.

[0017] The processed sound data is identified to obtain the corresponding noise frequency and noise characteristics.

[0018] By adopting the above technical solution, since transportation pipelines are generally long, sound monitoring devices are usually set up at intervals, that is, there may be a certain gap between two adjacent sound monitoring devices. Therefore, the location of the sound monitoring device may be far away from the actual location of the leak. In this case, when identifying the noise frequency and noise characteristics in the sound monitoring data, it may be affected by the ambient sound. Therefore, this application cleans the sound monitoring data in advance based on the ambient sound data. By identifying the noise frequency and noise characteristics contained in the cleaned and processed sound data, the accuracy of the identification results can be improved.

[0019] In one possible implementation, determining the target monitoring location corresponding to the initial anomaly substation based on the initial anomaly location and the preset monitoring location information includes:

[0020] The monitoring range is determined based on the initial anomaly location and the preset distance;

[0021] The initial monitoring location is determined based on the preset monitoring location information and the monitoring interval;

[0022] When there are multiple initial monitoring locations, the monitoring distance between each initial monitoring location and the initial anomaly location is determined based on each initial monitoring location and the initial anomaly location.

[0023] The initial monitoring location corresponding to the shortest monitoring distance is determined as the target monitoring location.

[0024] By adopting the above technical solution, in the process of determining the target monitoring location, the initial monitoring location corresponding to the shortest distance is selected as the target monitoring location by comparing the monitoring distance between each initial monitoring location and the initial anomaly location. The effect of this solution is that it can find the monitoring location closest to the initial anomaly location, thereby improving the accuracy of leak detection.

[0025] In one possible implementation, determining the preset standard pressure value corresponding to the substation includes:

[0026] Obtain the energy demand data of the sub-site, which includes the number of users with demand and the demand of each user;

[0027] The importance level of the substation is determined based on the energy demand data.

[0028] Based on the importance level and the preset correspondence, the preset judgment criteria corresponding to the sub-site are determined, and the preset judgment criteria are identified to obtain the corresponding preset standard pressure value. The preset correspondence is the correspondence between the importance level and the judgment criteria.

[0029] By adopting the above technical solution, corresponding judgment criteria are determined according to the importance level of the sub-sites, so as to separate the management of important sub-sites from less important sub-sites. By setting a lower warning threshold for important sub-sites, abnormal situations can be detected and measures can be taken in a timely manner; for less important sub-sites, a higher warning threshold is set to reduce false alarms and improve the fault tolerance of the system.

[0030] In one possible implementation, the method further includes:

[0031] Obtain energy usage information for each sub-site and the storage location of the energy usage information. The energy usage information includes the total number of users in the sub-site and the energy consumption of each user.

[0032] Access permissions for each sub-site are determined based on its energy usage information.

[0033] A site blockchain is generated based on the storage location and access permissions corresponding to each sub-site. The site blockchain contains at least one block, and each block corresponds to each sub-site.

[0034] When an access request is detected, the block to be accessed contained in the access request is identified, and the access user is authenticated based on the access request and the access permissions corresponding to the block to be accessed.

[0035] If authentication fails, an alert message will be generated.

[0036] By adopting the above technical solution, the energy consumption information of each sub-site is stored in the site blockchain for preservation. When a visitor needs to access the content in the access block, the visitor is authenticated. Authentication helps to protect the content in the site blockchain.

[0037] In one possible implementation, after generating the warning information based on the monitoring range, the method further includes:

[0038] Obtain the energy consumption information of the initial abnormal substation during a first preset time period, the energy consumption information including energy consumption rate and remaining energy;

[0039] The predicted energy consumption of the initial abnormal substation during the second preset time period is determined based on the energy consumption rate.

[0040] When the remaining energy is lower than the predicted energy consumption, obtain the abnormal access permissions of the initial abnormal sub-site, and generate an energy scheduling request based on the abnormal access permissions.

[0041] By adopting the above technical solution, the predicted energy consumption of the initial abnormal substation in the future can be determined by the energy consumption rate. The prediction can help relevant personnel understand the energy use trend of the initial abnormal substation, make preparations for energy scheduling and allocation in advance, and avoid energy shortages.

[0042] In one possible implementation, generating the energy dispatch request based on the abnormal access permissions includes:

[0043] Based on the abnormal access permissions, determine the scheduled sub-site corresponding to the initial abnormal sub-site;

[0044] Obtain the remaining energy amount corresponding to each scheduled sub-site, and the predicted energy consumption of each scheduled sub-site during the second preset time period;

[0045] Based on the remaining energy and predicted energy consumption of each dispatch substation, target dispatch substations are determined, and energy dispatch requests are generated based on the target dispatch substations.

[0046] By adopting the above technical solution, the energy reserves of each dispatchable substation can be clearly understood through the remaining energy, providing basic data for subsequent energy dispatch. Furthermore, by determining the predicted energy consumption of each dispatchable substation in the second preset time period, it is helpful to understand the energy demand of each substation in the future, thus providing an important basis for energy dispatch. Finally, based on the remaining energy and predicted energy consumption of each dispatchable substation, the target dispatchable substation can be determined, effectively finding the most suitable substation for energy dispatch to achieve optimal energy allocation.

[0047] Secondly, this application provides a pipeline anomaly monitoring device, which adopts the following technical solution:

[0048] A pipeline anomaly monitoring device, comprising:

[0049] The anomaly detection module is used to obtain the pressure value of each sub-site and compare the pressure value of each sub-site with the preset standard pressure value corresponding to each site to determine whether there is an initial abnormal sub-site.

[0050] The monitoring range determination module is used to obtain the initial abnormal location of the initial abnormal sub-site if the condition is met, and determine the monitoring range corresponding to the initial abnormal sub-site based on the initial abnormal location.

[0051] A first weighting module is used to acquire sound monitoring data corresponding to the monitoring range, determine the corresponding noise frequency and noise characteristics based on the sound monitoring data, and determine a first weight value based on the noise frequency and noise characteristics.

[0052] A module for determining predicted impact values ​​is used to determine predicted impact values ​​based on the noise frequency and the noise characteristics.

[0053] The second weighting module is used to compare the pressure value corresponding to the initial abnormal sub-site with the corresponding preset standard pressure value to determine the pressure difference value, and to determine the second weight value based on the predicted impact value and the pressure difference value.

[0054] The warning information generation module is used to determine whether there is an anomaly in the monitoring range based on the first weight value and the second weight value. If so, it generates a warning information based on the monitoring range and feeds the warning information back to the terminal device of the relevant staff.

[0055] By adopting the above technical solution, the pressure values ​​of different sub-stations are compared with the corresponding preset standard pressure values ​​to determine whether there are initial abnormal sub-stations. Then, based on the initial abnormal sub-stations, the scope that needs to be focused on is determined, rather than starting monitoring arbitrarily from a certain area. By focusing on monitoring the determined monitoring scope, it is possible to promptly detect anomalies that may have occurred or have occurred within the monitoring scope. Since leakage in the transportation pipeline may generate related noise, sound monitoring data is used to monitor whether there is a gas leak in the transportation pipeline within the monitoring scope, which helps to improve the accuracy of anomaly detection. In addition, the predicted impact value can be compared with the actual impact pressure value, that is, a reverse verification method can be used to determine whether a natural gas leak has occurred within the monitoring scope, which further improves the accuracy of anomaly detection. This application can quickly and accurately identify anomalies by using sound monitoring data and reverse verification impact pressure values.

[0056] In one possible implementation, when determining the corresponding noise frequency and noise characteristics based on the sound monitoring data, the first weighting module is specifically used for:

[0057] Identify the preset monitoring location information contained within the monitoring range, and determine the target monitoring location corresponding to the initial abnormal sub-site based on the initial abnormal location and the preset monitoring location information. The preset monitoring location information is the setting location of all preset sound monitoring devices contained within the monitoring range.

[0058] Acquire sound monitoring data corresponding to the target monitoring location and acquire ambient sound data corresponding to the target monitoring location. Perform noise reduction processing on the sound monitoring data based on the ambient sound data to obtain processed sound data.

[0059] The processed sound data is identified to obtain the corresponding noise frequency and noise characteristics.

[0060] In one possible implementation, when determining the target monitoring location corresponding to the initial anomaly sub-site based on the initial anomaly location and the preset monitoring location information, the first weighting module is specifically used for:

[0061] The monitoring range is determined based on the initial anomaly location and the preset distance;

[0062] The initial monitoring location is determined based on the preset monitoring location information and the monitoring interval;

[0063] When there are multiple initial monitoring locations, the monitoring distance between each initial monitoring location and the initial anomaly location is determined based on each initial monitoring location and the initial anomaly location.

[0064] The initial monitoring location corresponding to the shortest monitoring distance is determined as the target monitoring location.

[0065] In one possible implementation, the device further includes:

[0066] The demand data acquisition module is used to acquire the energy demand data of the sub-site. The energy demand data includes the number of demanding users and the demand amount of each demanding user.

[0067] The importance level determination module is used to determine the importance level of the sub-site based on the energy demand data.

[0068] The judgment criteria determination module is used to determine the preset judgment criteria corresponding to the sub-site based on the importance level and the preset correspondence, and to identify the preset judgment criteria to obtain the corresponding preset standard pressure value. The preset correspondence is the correspondence between the importance level and the judgment criteria.

[0069] In one possible implementation, the device further includes:

[0070] The energy information acquisition module is used to acquire energy usage information for each sub-site and the storage location of the energy usage information. The energy usage information includes the number of all users in the sub-site and the energy consumption of each user.

[0071] The access permission determination module is used to determine the access permissions of each sub-site based on the energy usage information of each sub-site.

[0072] A site blockchain generation module is used to generate a site blockchain based on the storage location and access permissions corresponding to each sub-site. The site blockchain contains at least one block, and each block corresponds to each sub-site.

[0073] An authentication module is used to identify the block to be accessed contained in the access request when an access request is detected, and to authenticate the visitor based on the access request and the access permissions corresponding to the block to be accessed.

[0074] The alarm module generates an alert message if authentication fails.

[0075] In one possible implementation, the device further includes:

[0076] The energy consumption information acquisition module is used to acquire the energy consumption information of the initial abnormal substation within a first preset time period. The energy consumption information includes the energy consumption rate and the remaining energy.

[0077] The prediction module is used to determine the predicted energy consumption of the initial abnormal substation within a second preset time period based on the energy consumption rate.

[0078] The scheduling module is used to obtain the abnormal access permissions of the initial abnormal sub-site when the remaining energy is lower than the predicted energy consumption, and to generate an energy scheduling request based on the abnormal access permissions.

[0079] In one possible implementation, when the scheduling module generates an energy scheduling request based on the abnormal access permissions, it specifically performs the following:

[0080] Based on the abnormal access permissions, determine the scheduled sub-site corresponding to the initial abnormal sub-site;

[0081] Obtain the remaining energy amount corresponding to each scheduled sub-site, and the predicted energy consumption of each scheduled sub-site during the second preset time period;

[0082] Based on the remaining energy and predicted energy consumption of each dispatch substation, target dispatch substations are determined, and energy dispatch requests are generated based on the target dispatch substations.

[0083] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0084] An electronic device comprising:

[0085] At least one processor;

[0086] Memory;

[0087] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described pipeline anomaly monitoring method.

[0088] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0089] A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described pipeline anomaly monitoring method.

[0090] In summary, this application includes at least one of the following beneficial technical effects:

[0091] By comparing the pressure values ​​of different sub-stations with corresponding preset standard pressure values, the existence of initial abnormal sub-stations can be determined. Based on these initial abnormal sub-stations, the scope requiring focused monitoring is determined, rather than arbitrarily starting monitoring from a certain area. By focusing monitoring on the determined monitoring scope, potential or actual anomalies within the monitoring area can be detected promptly. Since leaks in transportation pipelines may generate related noise, sound monitoring data is used to monitor whether gas leaks occur in the transportation pipelines within the monitoring scope, improving the accuracy of anomaly detection. Furthermore, by comparing the predicted impact value with the actual impact pressure value, i.e., through reverse verification, it can be determined whether a natural gas leak has occurred within the monitoring scope, further improving the accuracy of anomaly detection. This application, through sound monitoring data and reverse verification of impact pressure values, can quickly and accurately identify anomalies.

[0092] By determining the predicted energy consumption of the initial abnormal substation over a period of time through energy consumption rate, relevant personnel can understand the energy usage trend of the initial abnormal substation, make preparations for energy scheduling and allocation in advance, and avoid energy shortages. Attached Figure Description

[0093] Figure 1 This is a flowchart illustrating a pipeline anomaly monitoring method according to an embodiment of this application;

[0094] Figure 2 This is a schematic diagram of a monitoring range in one embodiment of this application;

[0095] Figure 3 This is a schematic diagram of a process for generating an energy dispatch request in an embodiment of this application;

[0096] Figure 4 This is a schematic diagram of the structure of a pipeline anomaly monitoring device according to an embodiment of this application;

[0097] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0098] The following is in conjunction with the appendix Figure 1-5 This application will be described in further detail.

[0099] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0100] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0101] Specifically, this application provides a pipeline anomaly monitoring method executed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this connection.

[0102] refer to Figure 1 , Figure 1 This is a flowchart illustrating a pipeline anomaly monitoring method according to an embodiment of this application. The method includes steps S110-S160, wherein:

[0103] Step S110: Obtain the pressure value of each sub-site, and compare the pressure value of each sub-site with the preset standard pressure value corresponding to each site to determine whether there is an initial abnormal sub-site.

[0104] Specifically, branch stations typically refer to separation stations or distribution stations within a natural gas pipeline system. To improve the security and reliability of natural gas supply, multiple branch stations are usually set up in the natural gas pipeline system to achieve multi-level supply of natural gas. Different branch stations can be distributed in different geographical locations and environments to cope with emergencies or pipeline failures, ensuring natural gas supply to users in different areas. For example, some branch stations may be located in city centers to provide natural gas to residential and commercial users, while others may be located in industrial areas or suburbs to provide natural gas to industrial or large users. Different users have different pressure and temperature requirements when using natural gas. Branch stations can regulate the pressure and temperature of natural gas in the pipeline to meet user requirements. In addition, branch stations can also meter the natural gas used by users.

[0105] The pressure values ​​corresponding to each substation can be measured by pressure sensors installed at each substation and transmitted to electronic equipment. These pressure sensors facilitate real-time monitoring of the pressure conditions at each substation. Since different substations serve different users, the preset standard pressures for each substation differ when determining if an initial abnormal substation exists among multiple substations.

[0106] When determining the preset standard pressure value corresponding to the substation, the following is included:

[0107] Obtain energy demand data for each substation, including the number of users and the demand of each user; determine the importance level of each substation based on the energy demand data; determine the preset judgment criteria for each substation based on the importance level and the preset correspondence; identify the preset judgment criteria to obtain the corresponding preset standard pressure value; the preset correspondence is the correspondence between the importance level and the judgment criteria.

[0108] Specifically, the electronic device stores energy demand data corresponding to different substations. The energy demand data of different substations can be collected by relevant staff at the substations and uploaded to the electronic device, or it can be obtained by organizing the uploaded data of different substations. The uploaded data is the demand data uploaded by users at different substations. The method of obtaining energy demand data is not specifically limited in this embodiment of the application. Different numbers of users with different needs and different demand quantities for each user correspond to different importance levels for each sub-site. To determine the importance level of a sub-site, the total demand for each sub-site can be determined first based on the demand quantity of each user within that sub-site. The initial importance level for each sub-site is then determined based on the total demand and a first mapping relationship. This first mapping relationship includes initial importance levels corresponding to different total demand quantities. The specific content of the first mapping relationship is not specifically limited in this embodiment and can be set by relevant technical personnel. Next, the number of users with different needs for each sub-site is compared to a preset demand quantity threshold. If the number of users with different needs for a sub-site is higher than the preset demand quantity threshold, the level adjustment amount is determined based on the difference between the number of users with different needs and the second mapping relationship. Finally, the initial importance level of each sub-site is adjusted based on the level adjustment amount to obtain the importance level for each sub-site. The second mapping relationship includes level adjustment amounts corresponding to different quantity differences. The specific content of the second mapping relationship is not specifically limited in this embodiment.

[0109] Different importance levels correspond to different preset judgment criteria. Since the higher the importance level, the greater the impact of an anomaly at the sub-site, the higher the importance level, the higher the preset judgment criteria. Each preset judgment criterion contains a corresponding preset standard pressure value. That is, after determining the preset judgment criterion for each sub-site, the preset standard pressure value for each sub-site can be determined.

[0110] Step S120: If yes, obtain the initial abnormal location of the initial abnormal sub-site and determine the monitoring range corresponding to the initial abnormal sub-site based on the initial abnormal location.

[0111] Specifically, when an initial abnormal substation exists, the initial abnormal location of that substation is obtained from the electronic device. The electronic device stores the location of each substation, and the initial abnormal location corresponding to the initial abnormal substation can be determined by traversing the electronic device according to the substation's station number. The monitoring range is the area centered on the abnormal substation with a preset distance as the radius, such as... Figure 2 As shown, the preset distance can be 50 meters or 100 meters. The specific distance is not specifically limited in this embodiment of the application and can be set by relevant technical personnel according to actual needs.

[0112] Step S130: Obtain sound monitoring data corresponding to the monitoring range, determine the corresponding noise frequency and noise characteristics based on the sound monitoring data, and determine the first weight value according to the noise frequency and noise characteristics.

[0113] Specifically, when natural gas leaks from a pipeline, the leaking natural gas rubs against the air around the pipeline, thus generating a unique sound wave. Compared to other sounds, such as the sound of flowing water, electric current, or mechanical operation, the sound wave frequency generated by a natural gas pipeline leak is different from the natural gas vibration. Therefore, by identifying the noise frequency and noise characteristics in the sound monitoring data, it is possible to determine whether a natural gas leak has occurred.

[0114] To improve the accuracy of the recognition results, when determining the corresponding noise frequency and noise characteristics based on sound monitoring data, the specific steps include:

[0115] Identify the preset monitoring location information included within the monitoring range, and determine the target monitoring location corresponding to the initial abnormal sub-site based on the initial abnormal location and the preset monitoring location information. The preset monitoring location information is the setting location of all preset sound monitoring devices included within the monitoring range. Obtain the sound monitoring data corresponding to the target monitoring location, and obtain the environmental sound data corresponding to the target monitoring location. Perform noise reduction processing on the sound monitoring data based on the environmental sound data to obtain processed sound data. Identify the processed sound data to obtain the corresponding noise frequency and noise characteristics.

[0116] Specifically, when identifying a preset monitoring location from the monitoring range, a preset device identifier can be identified from an image containing the monitoring range. Since the preset monitoring location is the location of a preset sound monitoring device, all preset monitoring devices contained within the monitoring range can be identified by identifying whether the image contains a preset device identifier. The device identifier can be the model, name, etc. of the monitoring device. In this embodiment of the application, no specific limitation is made, as long as the monitoring devices contained within the monitoring range can be identified based on the device identifier.

[0117] In this application, the location located on the same natural gas pipeline as the initial abnormal sub-station, and at a distance less than a preset interval, is defined as the target monitoring location. The corresponding sound monitoring device is designated as the target sound monitoring device. The sound monitoring data for the target monitoring location is collected by the target sound monitoring device. The environmental sound data for the target monitoring location is collected by a sound sensor installed on the ground corresponding to the target monitoring location. This environmental sound data characterizes the environmental sounds on the ground corresponding to the target monitoring location, such as rain, ocean waves, birdsong, and crowd noise. Noise reduction processing of the sound monitoring data based on the environmental sound data can be performed using signal processing or physical models. The specific noise reduction method is not specifically limited in this embodiment. When performing noise reduction based on signal processing, it can be achieved by changing the amplitude, frequency, or phase of the signal. Common signal processing methods include spectral subtraction, variational mode decomposition, and wavelet transform. These methods can be used to reduce noise in the sound monitoring data. The method for noise reduction of the sound monitoring data is not specifically limited in this embodiment and can be set by relevant personnel.

[0118] The processed audio data is noise-reduced audio data, free from noise such as rain, waves, and birdsong. To determine the noise frequencies contained in the processed audio data, a Fast Fourier Transform (FFT) method can be used to transform the processed audio data from the time domain to the frequency domain, thereby obtaining the frequency distribution of the processed audio data. To determine the noise characteristics in the processed audio data, audio data analysis and processing algorithms can be used to extract noise-related features from the processed audio data, such as amplitude and waveform. The audio data analysis and processing algorithms can be waveform analysis algorithms; the specific algorithm is not specifically limited in this embodiment, as long as it can identify the noise characteristics contained in the processed audio data.

[0119] Different noise frequencies correspond to different weight values, and different noise features correspond to different weight values. When determining the first weight value, the first sub-weight value can be determined first based on the noise frequency and frequency correspondence, the second sub-weight value can be determined based on the noise feature and feature correspondence, and then the first weight value can be determined based on the first sub-weight value and the second sub-weight value. The frequency correspondence contains sub-weight values ​​corresponding to different frequencies, and the feature correspondence contains sub-weight values ​​corresponding to different features. The specific content of the frequency correspondence and feature correspondence is not specifically limited in this embodiment of the application, and can be set by relevant personnel.

[0120] Furthermore, to improve the accuracy of leak detection, the target monitoring location corresponding to the initial anomaly sub-site is determined based on the initial anomaly location and preset monitoring location information. Specifically, this includes:

[0121] Based on the initial anomaly location and the preset distance, a monitoring interval is determined; based on the preset monitoring location information and the monitoring interval, an initial monitoring location is determined; when there are multiple initial monitoring locations, the monitoring distance between each initial monitoring location and the initial anomaly location is determined based on each initial monitoring location and the initial anomaly location; the initial monitoring location corresponding to the shortest monitoring distance is determined as the target monitoring location.

[0122] Specifically, the preset distance can be 5 meters or 10 meters. The specific distance length is not specifically limited in this embodiment and can be set by relevant technical personnel. The monitoring interval is formed with the initial abnormal sub-site as the center and the preset distance as the interval length. The monitoring interval may or may not include the initial monitoring position. When the monitoring interval does not include the initial monitoring position, the monitoring interval can be increased by expanding the preset distance. If the monitoring interval is increased to the preset interval threshold, the expansion of the preset distance will stop, that is, the expansion of the monitoring space will stop. At this time, an instruction reminder message can be generated and fed back to the terminal device of relevant personnel to remind them to collect the monitoring sound data at the initial abnormal sub-site by manually collecting noise.

[0123] When a monitoring interval contains multiple initial monitoring locations, the distance between each initial monitoring location and the initial anomaly substation is determined. These distances are then sorted, and the shortest distance is identified. The initial monitoring location corresponding to the shortest distance is then designated as the target monitoring location. The sorting of distances can be done using bubble sort or quicksort; the specific sorting method is not limited in this embodiment, as long as the shortest distance can be determined from the multiple distances. By comparing multiple distances, the monitoring location closest to the initial anomaly location can be found, thereby improving the accuracy of leak detection.

[0124] Step S140: Determine the predicted impact value based on the noise frequency and noise characteristics.

[0125] Specifically, the leakage rate of a natural gas pipeline can be predicted based on noise frequency and characteristics. This involves acquiring a large amount of historical sound data, which includes the noise frequency and characteristics corresponding to leaks in historical periods, as well as the corresponding leakage rates. Based on this extensive historical sound data, a mapping relationship between noise frequency and characteristics and pipeline leakage rates is established to obtain a trained model. This trained model is then used to predict new noise frequencies and characteristics, yielding the predicted leakage rate. The predicted impact value is the effect of the predicted result on the pressure value corresponding to the initial abnormal sub-site.

[0126] Step S150: Compare the pressure value corresponding to the initial abnormal sub-site with the corresponding preset standard pressure value to determine the pressure difference value, and determine the second weight value based on the predicted impact value and the pressure difference value.

[0127] Specifically, different sub-sites correspond to different preset standard pressure values. These preset standard pressure values ​​are not specifically limited in this embodiment and can be set by relevant technical personnel. Different pressure difference values ​​correspond to different weight values. When determining the second weight value based on the predicted impact value and the pressure difference value, the predicted impact value can be compared with the pressure difference value to obtain a pressure ratio. Then, the second weight value is determined based on the correspondence between the pressure ratio and the weight value. This correspondence includes different pressure ratios and their corresponding weight values; the closer the pressure ratio is to 1, the smaller the corresponding weight value. The specific correspondence between the pressure ratio and the weight value is not specifically limited in this embodiment and can be set by relevant technical personnel according to actual needs.

[0128] Step S160: Determine whether there is an anomaly in the monitoring range based on the first weight value and the second weight value. If so, generate an alert message based on the monitoring range and send the alert message to the terminal device of the relevant staff.

[0129] Specifically, a total weight value is generated based on the first and second weight values. When the total weight value is higher than the preset alarm weight value, an anomaly is determined to exist within the monitoring range. The preset alarm weight value can be 85% or 90%, and the specific value can be set by relevant technical personnel. This embodiment does not impose specific limitations on this value. The generated warning information includes a monitoring range identifier, which can be a building identifier or a pipeline identifier corresponding to the monitoring range. The specific identifier is not specifically limited in this embodiment, as long as relevant personnel can quickly identify the anomaly through the warning information.

[0130] In this application embodiment, the pressure values ​​of different sub-stations are compared with the corresponding preset standard pressure values ​​to determine whether there are initial abnormal sub-stations. Then, based on the initial abnormal sub-stations, the scope that needs to be focused on is determined, rather than starting monitoring arbitrarily from a certain area. By focusing on monitoring the determined monitoring scope, it is possible to promptly detect anomalies that may have occurred or have already occurred within the monitoring scope. Since leakage in the transportation pipeline may generate related noise, sound monitoring data is used to monitor whether there is a gas leak in the transportation pipeline within the monitoring scope, which helps to improve the accuracy of anomaly detection. In addition, the predicted impact value can be compared with the actual impact pressure value, that is, a reverse verification method can be used to determine whether a natural gas leak has occurred within the monitoring scope, which further improves the accuracy of anomaly detection. This application can quickly and accurately identify anomalies by using sound monitoring data and reverse verification impact pressure values.

[0131] Furthermore, the method provided in this application embodiment also includes:

[0132] The system acquires energy usage information and its storage location for each sub-site. Energy usage information includes the number of all users within the sub-site and the energy consumption of each user. Based on this information, the system determines the access permissions for each sub-site. A site blockchain is generated based on the storage location and access permissions for each sub-site. This blockchain contains at least one block, with each block corresponding to a specific sub-site. When an access request is detected, the system identifies the block to be accessed within the request and verifies the access user's identity based on the access request and the corresponding access permissions. If authentication fails, a warning message is generated.

[0133] Specifically, the energy usage information for each substation includes the actual number of users at that substation and the actual energy consumption of each user. When obtaining the energy usage information for each substation, it can be retrieved from the electronic device based on the substation's corresponding site identifier. The site identifier can be a substation number or a substation name; the specific site identifier is not specifically limited in this embodiment, as long as it allows identification of the energy usage information for each substation. The energy usage information is stored at the local storage address. Since different substations are located in different locations and are responsible for different areas, their importance levels also differ. When determining the access permissions for each substation based on its energy usage information, the embodiment described above for determining the importance level of a substation based on energy demand data can be referenced, and will not be elaborated upon here.

[0134] When generating a site blockchain based on the storage location corresponding to each sub-site (i.e., the storage location of energy usage information for each sub-site) and the access permissions for each sub-site, the storage location of each sub-site can first be converted into an address identifier based on the address identifier mapping relationship. Then, the address identifier and the corresponding access permission are bound together. The bound address identifier and the corresponding access permission are written into the block to obtain the site blockchain. The address identifier mapping relationship contains different storage locations and corresponding address identifiers. The access permission of each sub-site is the condition that needs to be met to access the block corresponding to that sub-site. Different sub-sites have different access permissions. For example, accessing block A may require facial recognition, but accessing block B may require both facial recognition and iris recognition. The specific access permissions can be set by relevant technical personnel.

[0135] The access request is uploaded by the user from the user terminal to the electronic device. The access request includes the user's identity information and the block to be accessed. The user's identity information facilitates the determination of the user's access permissions. This identity information can be the user's shopping account or mobile phone number; the specific type of identity information is not specifically limited in this embodiment, as long as it allows for the determination of the user's corresponding access permissions. By storing the energy consumption information of each sub-site in the site's blockchain, and verifying the visitor's identity when accessing content in the access block, authentication helps to securely protect the content in the site's blockchain.

[0136] Furthermore, in order to help relevant personnel understand the energy usage trends of initial abnormal sub-sites, the method provided in this application embodiment also includes steps Sa-Sc, such as... Figure 3 As shown, where:

[0137] Step Sa: Obtain energy consumption information of the initial abnormal sub-site within the first preset time period. The energy consumption information includes energy consumption rate and remaining energy.

[0138] Specifically, the first preset time period is a historical period prior to the current moment, which can be the previous 5 days or the previous 7 days. The specific time period is not specifically limited in this embodiment and can be set by relevant technical personnel. The energy consumption rate is the average consumption rate of the initial abnormal substation within the first preset time period, and the remaining energy is the remaining amount of the initial abnormal substation after the first preset time period, i.e., the remaining amount at the current moment.

[0139] Step Sb: Determine the predicted energy consumption of the initial abnormal sub-sites within the second preset time period based on the energy consumption rate.

[0140] Specifically, the second preset time period is a period of time after the current moment, which can be 5 days or 7 days after the current moment. The specific time period is not specifically limited in this embodiment of the application and can be set by relevant technical personnel. When determining the predicted energy consumption, the number of consumption days can be determined first according to the second preset time period, and then the energy consumption rate and the number of consumption days can be imported into the energy consumption calculation formula to obtain the predicted energy consumption. The energy consumption calculation formula is X=V*T, where X is used to represent the predicted energy consumption, V is used to represent the energy consumption rate, and T is used to represent the number of consumption days.

[0141] Step Sc: When the remaining energy is lower than the predicted energy consumption, obtain the abnormal access permissions for the initial abnormal sub-sites and generate an energy dispatch request based on the abnormal access permissions.

[0142] Specifically, the remaining energy is compared with the predicted energy consumption. When the remaining energy is lower than the predicted energy consumption, it indicates that the remaining energy is insufficient to sustain the consumption of the initially abnormal branch station in the second preset time period. In this case, it is necessary to dispatch from other branch stations to meet the natural gas demand of the users of the initially abnormal branch station in the second preset time period.

[0143] When dispatching natural gas from other substations to the initial abnormal substation, access permissions must be considered. Not every substation can dispatch gas to the initial abnormal substation. The process involves effectively identifying the most suitable substation for energy dispatch and generating an energy dispatch request based on the abnormal access permissions. This includes:

[0144] Based on the abnormal access permissions, determine the scheduled sub-sites corresponding to the initial abnormal sub-sites; obtain the remaining energy of each scheduled sub-site and the predicted energy consumption of each scheduled sub-site in the second preset time period; determine the target scheduled sub-sites based on the remaining energy and predicted energy consumption of each scheduled sub-site, and generate an energy scheduling request based on the target scheduled sub-sites.

[0145] Specifically, the scheduled substations corresponding to the initial abnormal substation are those that the initial abnormal substation can schedule, generally the upstream substations of the initial abnormal substation. The method for calculating the predicted energy consumption of each scheduled substation within the second preset time period can refer to the method for calculating the predicted energy consumption of the initial abnormal substation in the above embodiment, and will not be repeated here. Based on the remaining energy of each scheduled substation and its corresponding predicted energy consumption, the additional remaining energy for each scheduled substation is determined. The scheduled substation with the largest additional remaining energy is selected as the target scheduled substation. When there are multiple scheduled substations with the largest additional remaining energy, the distance between each scheduled substation and the initial abnormal substation can be calculated, and the scheduled substation with the shortest distance from the initial abnormal substation among the multiple scheduled substations with the largest additional remaining energy is selected as the target scheduled substation.

[0146] The energy dispatch request generated based on the target dispatchable sub-site contains the site identifier corresponding to the target dispatchable sub-site, which enables relevant personnel to respond quickly when they receive the energy dispatch request, make preparations for energy dispatch and allocation in advance, and avoid energy shortages.

[0147] The above embodiments describe a pipeline anomaly monitoring method from the perspective of process flow. The following embodiments describe a pipeline anomaly monitoring device from the perspective of virtual module or virtual unit. For details, please refer to the following embodiments.

[0148] This application provides a pipeline anomaly monitoring device, such as... Figure 4 As shown, the device may specifically include an anomaly detection module 410, a monitoring range determination module 420, a first weight determination module 430, a predicted impact value determination module 440, a second weight determination module 450, and a warning information generation module 460, wherein:

[0149] The anomaly detection module 410 is used to obtain the pressure value of each sub-site and compare the pressure value of each sub-site with the preset standard pressure value corresponding to each site to determine whether there is an initial abnormal sub-site.

[0150] The monitoring range determination module 420 is used to obtain the initial abnormal location of the initial abnormal sub-site if the condition is met, and determine the monitoring range corresponding to the initial abnormal sub-site based on the initial abnormal location.

[0151] The first weight module 430 is used to acquire sound monitoring data corresponding to the monitoring range, determine the corresponding noise frequency and noise characteristics based on the sound monitoring data, and determine the first weight value based on the noise frequency and noise characteristics.

[0152] The module 440 for determining the predicted impact value is used to determine the predicted impact value based on the noise frequency and noise characteristics.

[0153] The second weight module 450 is used to compare the pressure value corresponding to the initial abnormal sub-site with the corresponding preset standard pressure value to determine the pressure difference value, and to determine the second weight value based on the predicted impact value and the pressure difference value.

[0154] The warning information generation module 460 is used to determine whether there is an anomaly in the monitoring range based on the first weight value and the second weight value. If so, it generates warning information based on the monitoring range and feeds the warning information back to the terminal device of the relevant staff.

[0155] In one possible implementation, when determining the corresponding noise frequency and noise characteristics based on the sound monitoring data, the first weighting module 430 is specifically used for:

[0156] Identify the preset monitoring location information included within the monitoring range, and determine the target monitoring location corresponding to the initial abnormal sub-site based on the initial abnormal location and the preset monitoring location information. The preset monitoring location information is the setting location of all preset sound monitoring devices included within the monitoring range.

[0157] Acquire sound monitoring data corresponding to the target monitoring location and acquire ambient sound data corresponding to the target monitoring location. Perform noise reduction processing on the sound monitoring data based on the ambient sound data to obtain processed sound data.

[0158] The sound data is identified and processed to obtain the corresponding noise frequency and noise characteristics.

[0159] In one possible implementation, when determining the target monitoring location corresponding to the initial anomaly sub-site based on the initial anomaly location and preset monitoring location information, the first weighting module 430 is specifically used for:

[0160] The monitoring range is determined based on the initial anomaly location and the preset distance;

[0161] The initial monitoring location is determined based on the preset monitoring location information and monitoring range;

[0162] When there are multiple initial monitoring locations, the monitoring distance between each initial monitoring location and the initial anomaly location is determined based on each initial monitoring location and the initial anomaly location.

[0163] The initial monitoring location corresponding to the shortest monitoring distance is determined as the target monitoring location.

[0164] In one possible implementation, the device further includes:

[0165] The demand data acquisition module is used to acquire energy demand data for sub-sites. The energy demand data includes the number of demanding users and the demand amount of each demanding user.

[0166] The importance level determination module is used to determine the importance level of sub-sites based on energy demand data;

[0167] The judgment criteria determination module is used to determine the preset judgment criteria corresponding to the sub-site based on the importance level and the preset correspondence, identify the preset judgment criteria to obtain the corresponding preset standard pressure value, and the preset correspondence is the correspondence between the importance level and the judgment criteria.

[0168] In one possible implementation, the device further includes:

[0169] The energy information acquisition module is used to acquire energy usage information for each sub-site and the storage location of the energy usage information. The energy usage information includes the number of all users in the sub-site and the energy consumption of each user.

[0170] The access permission determination module is used to determine the access permissions of each sub-site based on the energy usage information of each sub-site.

[0171] The site blockchain generation module is used to generate a site blockchain based on the storage location and access permissions corresponding to each sub-site. The site blockchain contains at least one block, and each block corresponds to each sub-site.

[0172] The authentication module is used to identify the block to be accessed contained in the access request when an access request is detected, and to authenticate the visitor based on the access request and the access permissions corresponding to the block to be accessed.

[0173] The alarm module generates an alert message if authentication fails.

[0174] In one possible implementation, the device further includes:

[0175] The energy consumption information acquisition module is used to acquire the energy consumption information of the initial abnormal sub-site within the first preset time period. The energy consumption information includes the energy consumption rate and the remaining energy.

[0176] The prediction module is used to determine the predicted energy consumption of the initial abnormal sub-sites within a second preset time period based on the energy consumption rate.

[0177] The scheduling module is used to obtain the abnormal access permissions of the initial abnormal sub-sites when the remaining energy is lower than the predicted energy consumption, and to generate an energy scheduling request based on the abnormal access permissions.

[0178] In one possible implementation, when the scheduling module generates an energy scheduling request based on abnormal access permissions, it specifically performs the following:

[0179] Based on the abnormal access permissions, determine the scheduled sub-site corresponding to the initial abnormal sub-site;

[0180] Obtain the remaining energy for each scheduled substation and the predicted energy consumption for each scheduled substation during the second preset time period;

[0181] Based on the remaining energy and predicted energy consumption of each dispatch substation, the target dispatch substation is determined, and an energy dispatch request is generated based on the target dispatch substation.

[0182] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the pipeline anomaly monitoring device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0183] This application provides an electronic device, such as... Figure 5 As shown, Figure 5 The illustrated electronic device 500 includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502. Optionally, the electronic device 500 may also include a transceiver 504. It should be noted that in practical applications, the transceiver 504 is not limited to one type, and the structure of this electronic device 500 does not constitute a limitation on the embodiments of this application.

[0184] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 501 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0185] Bus 502 may include a pathway for transmitting information between the aforementioned components. Bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 502 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0186] The memory 503 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0187] The memory 503 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 501. The processor 501 is used to execute the application code stored in the memory 503 to implement the content shown in the foregoing method embodiments.

[0188] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0189] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0190] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0191] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of monitoring a pipeline for anomalies, the method comprising: The method comprises the following steps: acquiring pressure values of each sub-site, and comparing the pressure values of each sub-site with preset standard pressure values corresponding to each site to determine whether there is an initial abnormal sub-site; if so, acquiring an initial abnormal position of the initial abnormal sub-site, and determining a monitoring range corresponding to the initial abnormal sub-site according to the initial abnormal position; acquiring sound monitoring data corresponding to the monitoring range, determining a noise frequency and a noise feature corresponding to the sound monitoring data based on the sound monitoring data, and determining a first weight value according to the noise frequency and the noise feature, wherein when processing the noise frequency in the processed sound data, the noise frequency corresponding to the processed sound data is converted from time domain to frequency domain by using a fast Fourier transform method; when processing the noise feature in the processed sound data, the noise feature related to the noise is extracted from the processed sound data by using a waveform analysis algorithm, and the noise feature includes amplitude and waveform; determining a predicted influence value according to the noise frequency and the noise feature, wherein the predicted influence value is an influence of a prediction result on the pressure value corresponding to the initial abnormal sub-site, and the prediction result is a leakage speed when the natural gas pipeline leaks; and predicting the leakage speed of the natural gas pipeline according to the noise frequency and the noise feature; determining a pressure gap value by comparing the pressure value corresponding to the initial abnormal sub-site with the corresponding preset standard pressure value, and determining a second weight value according to the predicted influence value and the pressure gap value; determining whether there is an abnormality in the monitoring range according to the first weight value and the second weight value, and if so, generating warning information based on the monitoring range and feeding back the warning information to a terminal device of a relevant worker.

2. The method of claim 1, wherein, The method of determining the noise frequency and the noise feature corresponding to the sound monitoring data comprises the following steps: identifying preset monitoring position information contained in the monitoring range, and determining a target monitoring position corresponding to the initial abnormal sub-site according to the initial abnormal position and the preset monitoring position information, wherein the preset monitoring position information is a setting position of all preset sound monitoring devices contained in the monitoring range; acquiring sound monitoring data corresponding to the target monitoring position, and acquiring environmental sound data corresponding to the target monitoring position, performing noise reduction processing on the sound monitoring data according to the environmental sound data to obtain processed sound data; identifying the processed sound data to obtain the corresponding noise frequency and noise feature.

3. The method of claim 2, wherein, The method of determining the target monitoring position corresponding to the initial abnormal sub-site according to the initial abnormal position and the preset monitoring position information comprises the following steps: determining a monitoring interval according to the initial abnormal position and a preset distance; determining an initial monitoring position according to the preset monitoring position information and the monitoring interval; when there are multiple initial monitoring positions, determining a monitoring distance between each initial monitoring position and the initial abnormal position according to each initial monitoring position and the initial abnormal position; determining the initial monitoring position corresponding to the shortest monitoring distance as the target monitoring position.

4. The method of claim 1, wherein, The method of determining the preset standard pressure value corresponding to each sub-site comprises the following steps: Obtaining the energy demand data of the sub-station, the energy demand data containing the number of demand users and the demand of each demand user; Determining the importance level of the sub-station according to the energy demand data; According to the importance level and the preset corresponding relationship, the preset judgment standard corresponding to the sub-station is determined, and the corresponding preset standard pressure value is obtained by identifying the preset judgment standard. The preset corresponding relationship is the corresponding relationship between the importance level and the judgment standard.

5. The method of claim 1, wherein, Also includes: Obtaining the energy use information of each sub-station and the storage location of the energy use information, the energy use information including the number of all users in the sub-station and the energy consumption of each user; According to the energy use information of each sub-station, the access permission of each sub-station is determined; According to the storage location and access permission corresponding to each sub-station, a site blockchain is generated, the site blockchain containing at least one block, each block corresponding to each sub-station; When the access request is monitored, the to-be-accessed block contained in the access request is identified, and the identity of the visitor is verified based on the access request and the access permission corresponding to the to-be-accessed block; If the identity verification fails, an alert information is generated.

6. The method of claim 1, wherein, After the alert information is generated based on the monitoring range, it further includes: Obtaining the energy consumption information of the initial abnormal sub-station in a first preset time period, the energy consumption information containing the energy consumption rate and the energy remaining amount; According to the energy consumption rate, the predicted energy consumption amount of the initial abnormal sub-station in a second preset time period is determined; When the energy remaining amount is lower than the predicted energy consumption amount, the abnormal access permission of the initial abnormal sub-station is obtained, and an energy scheduling request is generated based on the abnormal access permission.

7. The method of claim 6, wherein, The energy scheduling request is generated based on the abnormal access permission, including: According to the abnormal access permission, the to-be-scheduled sub-station corresponding to the initial abnormal sub-station is determined; Obtaining the energy remaining amount of each to-be-scheduled sub-station and the predicted energy consumption amount of each to-be-scheduled sub-station in the second preset time period; According to the energy remaining amount and the predicted energy consumption amount of each to-be-scheduled sub-station, a target to-be-scheduled sub-station is determined, and an energy scheduling request is generated based on the target to-be-scheduled sub-station.

8. A pipeline anomaly monitoring apparatus, characterized by, It includes: Anomaly judgment module, for obtaining the pressure value of each sub-station, and comparing the pressure value of each sub-station with the preset standard pressure value corresponding to each sub-station to determine whether there is an initial abnormal sub-station; The determination monitoring range module is used for obtaining the initial abnormal position of the initial abnormal sub-station if yes, and determining the monitoring range corresponding to the initial abnormal sub-station according to the initial abnormal position; The first weight determining module is configured to acquire sound monitoring data corresponding to the monitoring range, determine a noise frequency and a noise feature corresponding to the sound monitoring data, and determine a first weight value according to the noise frequency and the noise feature. When determining the noise frequency in the processed sound data, the first weight determining module is configured to convert the processed sound data from a time domain to a frequency domain to obtain a noise frequency corresponding to the processed sound data according to a fast Fourier transform method. When determining the noise feature in the processed sound data, the first weight determining module is configured to extract a noise feature related to noise from the processed sound data by using a waveform analysis algorithm, and the noise feature includes an amplitude and a waveform. The predicted influence value determining module is configured to determine a predicted influence value according to the noise frequency and the noise feature, the predicted influence value being an influence of a prediction result on a pressure value corresponding to the initial abnormal substation, the prediction result being a leakage speed when the natural gas pipeline leaks, and the predicted influence value being the leakage speed of the natural gas pipeline according to the noise frequency and the noise feature. The second weight determining module is configured to determine a pressure gap value by comparing the pressure value corresponding to the initial abnormal substation with a corresponding preset standard pressure value, and determine a second weight value according to the predicted influence value and the pressure gap value. The warning information generating module is configured to determine whether there is an abnormality in the monitoring range according to the first weight value and the second weight value, generate warning information based on the monitoring range if there is an abnormality, and feed back the warning information to a terminal device of a relevant worker.

9. An electronic device, comprising: The electronic device includes: at least one processor; a memory; at least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute a pipeline anomaly monitoring method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, including: a computer program stored in the memory and capable of being loaded and executed by the processor to execute a pipeline anomaly monitoring method according to any one of claims 1-7.

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