Gas emergency treatment method and system

Abstract

The embodiment of the present invention discloses a gas emergency processing method and system, which includes: obtaining gas monitoring data of each network node among m network nodes to obtain m gas monitoring data; determining abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m; obtaining network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes; determining network attribute parameters of each network node among the n network nodes to obtain n network attribute parameters; obtaining video encoding parameters of each network node among the n network nodes to obtain n video encoding parameters; adjusting corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters; transmitting current gas monitoring data of the n network nodes to a control center through the n target video encoding parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of gas pipelines or network technologies, and in particular to a gas emergency treatment method and system. Background Art

[0002] At present, gas emergency dispatch faces many challenges when responding to emergencies. For example, the communication environment is unstable, the network signal is weak, and the environmental conditions are complex during the rescue process. In addition, when closing the valve to cut off the gas supply, it is impossible to accurately determine the specific residents who will be affected by closing the valve, which may cause waste of resources and delay rescue. Therefore, the problem of how to improve the efficiency of gas rescue needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present invention provide a gas emergency processing method and system, which are helpful to improve the efficiency of gas emergency.

[0004] In a first aspect, an embodiment of the present invention provides a gas emergency processing method, which is applied to a control center in a gas network system, wherein the gas network system further comprises m network nodes, wherein the control center is communicatively connected to the m network nodes, and each network node is used to monitor the gas situation of a location or an area, wherein m is an integer greater than 1, and the method comprises:

[0005] Obtaining gas monitoring data of each of the m network nodes to obtain m gas monitoring data;

[0006] Determine abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m;

[0007] Obtaining the network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes;

[0008] Determine a network attribute parameter of each of the n network nodes to obtain n network attribute parameters;

[0009] Obtaining a video encoding parameter of each of the n network nodes to obtain n video encoding parameters;

[0010] Adjusting corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters;

[0011] The current gas monitoring data of the n network nodes are transmitted to the control center through the n target video encoding parameters.

[0012] In a second aspect, an embodiment of the present invention provides a gas emergency processing system, wherein the gas network system includes a control center and m network nodes, wherein the control center is communicatively connected to the m network nodes, and each network node is used to monitor the gas situation at a location or an area, and m is an integer greater than 1, wherein:

[0013] The m network nodes are used to obtain gas monitoring data of each of the m network nodes to obtain m gas monitoring data; determine abnormal gas monitoring data in the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m; obtain network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes; determine network attribute parameters of each of the n network nodes to obtain n network attribute parameters; obtain video encoding parameters of each of the n network nodes to obtain n video encoding parameters;

[0014] The control center is used to adjust corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters;

[0015] The m network nodes are used to transmit the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters.

[0016] The implementation of the embodiments of the present invention has the following beneficial effects:

[0017] It can be seen that the gas emergency processing method and system described in the embodiment of the present invention are applied to the control center in the gas network system. The gas network system also includes m network nodes. The control center is communicated with the m network nodes. Each network node is used to monitor the gas situation of a location or an area. m is an integer greater than 1. The gas monitoring data of each network node in the m network nodes is obtained to obtain the m gas monitoring data. The abnormal gas monitoring data in the m gas monitoring data is determined to obtain n abnormal gas monitoring data. n is natural data less than or equal to m. The network nodes corresponding to the n abnormal gas monitoring data are obtained to obtain the n network nodes. The network attributes of each network node in the n network nodes are determined. Parameters are obtained, n network attribute parameters are obtained, video encoding parameters of each of the n network nodes are obtained, n video encoding parameters are obtained, corresponding video encoding parameters in the n video encoding parameters are adjusted according to the n network attribute parameters, n target video encoding parameters are obtained, and current gas monitoring data of the n network nodes are transmitted to the control center through the n target video encoding parameters. In this way, on the one hand, the abnormal gas monitoring data can be used to further locate the abnormal position or abnormal area, which can improve the efficiency of gas repair and ensure safety. On the other hand, the video stream transmission depth can be made to conform to the actual network situation, which is helpful to improve the efficiency of gas-related data transmission, ensure the efficiency and accuracy of gas rescue, and help improve the efficiency of gas rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a structural schematic diagram of a gas network system for implementing a gas emergency processing method provided by an embodiment of the present invention;

[0020] Figure 2 It is a flow chart of a gas emergency treatment method provided by an embodiment of the present invention;

[0021] Figure 3 It is a structural schematic diagram of a control center provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0024] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] The embodiments of the present invention are described in detail below.

[0026] See also Figure 1 , Figure 1 1 is a schematic diagram of the system architecture of a gas network system for implementing a gas emergency handling method provided in an embodiment of the present invention, wherein the gas network system includes a control center and m network nodes, the control center is communicatively connected to the m network nodes, each network node is used to monitor the gas situation at a location or an area, and m is an integer greater than 1.

[0027] Among them, the network node may include at least one gas device or network device, and the gas device may include at least one of the following: monitoring and metering workstation, compression relay station, gas pipeline monitoring equipment, gas valve, gas meter, etc., which are not limited here. The network device may include at least one of the following: edge server, gateway, router, repeater, communication base station, etc., which are not limited here. The control center may also include at least one of the following: server, control platform device, etc., which are not limited here.

[0028] Among them, the control center is responsible for communicating and controlling with m network nodes, and each network node is used to monitor the gas situation of a location or an area. The gas situation may include at least one of the following: gas usage, node network status, etc., which are not limited here.

[0029] In specific implementation, the gas network system can be realized based on spatial data analysis and geographic information system (GIS), combining spatial topology algorithm and shortest path algorithm to realize accurate analysis of gas pipeline network data and accurate positioning of affected residents. The gas network system not only reflects the network data transmission of gas conditions, but also reflects the spatial topology and path planning of the entire gas pipeline network.

[0030] To illustrate with an example, in an embodiment of the present application, for spatial data processing, spatial database technology is used to spatially process the pipeline network data, a pipeline network topology model is constructed, and the shortest path algorithm is used to calculate the shortest path between the affected residents and the scene of the incident; for GIS applications, a spatial data analysis module is developed based on GIS software such as ArcGIS, which may specifically include spatial buffer analysis, spatial connection analysis, etc., thereby achieving a visual display of the positioning and spatial relationship of the affected residents, that is, the specific gas situation can be visualized and displayed in the form of a video stream.

[0031] In the embodiments of the present application, network communication fusion and QoS algorithm technology can also be implemented. The core of this technology is based on multi-channel transmission and adaptive quality of service (QoS) management, combined with flow control algorithm and dynamic scheduling algorithm to achieve efficient management and optimization of data transmission at the rescue site.

[0032] To illustrate, in the embodiment of the present application, multi-channel transmission can be realized, that is, the unified management and control of multiple channels can be realized by using software defined network (SDN) technology, and the bandwidth utilization and stability of data transmission can be improved through link aggregation and load balancing. In addition, for the adaptive QoS algorithm, an adaptive QoS scheduling module is developed based on the priority queue and bandwidth dynamic allocation algorithm to adjust the data transmission priority and bandwidth allocation in real time according to the data type and network quality, so as to ensure the timely transmission of key data and the real-time response of emergency command.

[0033] In an embodiment of the present application, low latency and dynamic bit rate control can also be achieved, specifically based on the Real-Time Transport Protocol (RTP) and an adaptive bit rate adjustment algorithm, that is, it can include a network transmission optimization algorithm and a video encoding tuning algorithm to achieve low latency and efficient control of video data transmission.

[0034] For example, for network transmission optimization, based on congestion control and flow control algorithms, real-time monitoring and adjustment of data transmission are achieved, and transmission delay and packet loss rate are reduced through packet retransmission mechanism and routing optimization technology. For example, for video encoding optimization, based on the H.264 / H.265 video encoding standard, variable-length encoding and adaptive bit rate adjustment algorithms are combined to adjust video encoding parameters in real time according to network bandwidth and quality to ensure picture quality while minimizing bandwidth usage.

[0035] Based on this gas network system, the following functions can be realized:

[0036] The m network nodes are used to obtain gas monitoring data of each of the m network nodes to obtain m gas monitoring data; determine abnormal gas monitoring data in the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m; obtain network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes; determine network attribute parameters of each of the n network nodes to obtain n network attribute parameters; obtain video encoding parameters of each of the n network nodes to obtain n video encoding parameters;

[0037] The control center is used to adjust corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters;

[0038] The m network nodes are used to transmit the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters.

[0039] Optionally, the current gas monitoring data includes visualized gas monitoring data, and the system is further specifically used for:

[0040] Determine current gas monitoring data of a network node i, where the network node i is any network node among the n network nodes;

[0041] Determine the target abnormal change trend of the network node i according to the gas monitoring data of the two frames before and after in the current gas monitoring data; the time interval between the two frames before and after is greater than the set time length;

[0042] Determine a target early warning strategy corresponding to the abnormal change trend of the target;

[0043] The target early warning strategy is executed for the network node i.

[0044] Optionally, in determining the target abnormal change trend of the network node i according to the current gas monitoring data, the system is specifically configured to:

[0045] Obtain the first abnormal area and the number of first abnormal features of the front frame, and the second abnormal area and the number of second abnormal features of the rear frame through the gas monitoring data of the front and rear two frames;

[0046] determining a first variation according to the second abnormal region and the first abnormal region;

[0047] Determine a second change amount according to the second abnormal feature quantity and the first abnormal feature quantity;

[0048] The target abnormal change trend is determined according to the first change amount and the second change amount.

[0049] Optionally, in determining the abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, the m network nodes are specifically used to:

[0050] Acquire node attribute information of each of the m network nodes to obtain m node attribute information, where each node attribute information includes: node location, event type, and geographic location;

[0051] Determine historical gas monitoring data of each of the m network nodes according to the m node attribute information and the current time, and obtain m historical gas monitoring data;

[0052] Determine the deviation between the m gas monitoring data and corresponding historical gas monitoring data in the m historical gas monitoring data to obtain m deviations;

[0053] Obtaining an environmental parameter of each of the m network nodes to obtain m environmental parameters;

[0054] Determine the optimization coefficients corresponding to the m environmental parameters to obtain m optimization coefficients;

[0055] Optimizing corresponding deviations among the m deviations using the m optimization coefficients to obtain m target deviations;

[0056] Determine the abnormality evaluation values ​​corresponding to the m target deviations to obtain m abnormality evaluation values;

[0057] Selecting an abnormality degree evaluation value greater than a preset threshold value from the m abnormality degree evaluation values ​​to obtain n abnormality degree evaluation values;

[0058] The gas monitoring data corresponding to the n abnormality degree assessment values ​​are acquired to obtain the n abnormal gas monitoring data.

[0059] See also Figure 2 , Figure 2 : is a flow chart of a gas emergency processing method provided by an embodiment of the present invention, which is applied to a control center in a gas network system, wherein the gas network system further comprises m network nodes, wherein the control center is communicatively connected to the m network nodes, and each network node is used to monitor the gas situation at a location or an area, wherein m is an integer greater than 1, and may comprise the following steps:

[0060] 201. Obtain gas monitoring data of each of the m network nodes to obtain m gas monitoring data.

[0061] In an embodiment of the present application, the gas monitoring data may include at least one of the following: gas pipeline pressure, gas pipeline temperature, gas pipeline humidity, gas usage metering, gas usage rate, gas usage time, etc., which are not limited here.

[0062] The m network nodes may be part or all of the network nodes in the gas network system, and the m network nodes may all be network nodes that are not in a gas outage event or a maintenance event.

[0063] 202. Determine abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m.

[0064] In the specific implementation, since the gas rescue is mainly aimed at the location or area where the abnormal situation occurs, the abnormal gas monitoring data among the m gas monitoring data can be determined, and n abnormal gas monitoring data can be obtained. The abnormal gas monitoring data can then be used to further locate the abnormal position or abnormal area, which can improve the efficiency of gas emergency repair and ensure safety.

[0065] Optionally, the above step 202, determining abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, may include the following steps:

[0066] 21. Obtain node attribute information of each of the m network nodes to obtain m node attribute information, where each node attribute information includes: node location, event type, and geographic location;

[0067] 22. Determine historical gas monitoring data of each of the m network nodes according to the m node attribute information and the current time, and obtain m historical gas monitoring data;

[0068] 23. Determine the deviation between the m gas monitoring data and the corresponding historical gas monitoring data in the m historical gas monitoring data to obtain m deviations;

[0069] 24. Obtaining the environmental parameters of each of the m network nodes to obtain m environmental parameters;

[0070] 25. Determine the optimization coefficients corresponding to the m environmental parameters to obtain m optimization coefficients;

[0071] 26. Optimizing corresponding deviations among the m deviations using the m optimization coefficients to obtain m target deviations;

[0072] 27. Determine the abnormality evaluation values ​​corresponding to the m target deviations to obtain m abnormality evaluation values;

[0073] 28. Selecting the abnormality evaluation value greater than the preset threshold value from the m abnormality evaluation values ​​to obtain n abnormality evaluation values;

[0074] 29. Obtain the gas monitoring data corresponding to the n abnormality degree assessment values ​​to obtain the n abnormal gas monitoring data.

[0075] Among them, the node attribute information includes: node location, event type, geographic location, etc., which are not limited here. The event type can include at least one of the following: normal, gas outage event, maintenance event, etc., which are not limited here. For example, for a normal gas outage event, it is not necessary to obtain its gas monitoring data, or it can be considered that the gas monitoring data of the relevant network node is normal. For a maintenance event, it is not necessary to obtain its gas monitoring data, or it can be considered that the gas monitoring data of the relevant network node itself is abnormal and is under maintenance and has undergone emergency repairs.

[0076] In the specific implementation, the node attribute information of each of the m network nodes can be obtained to obtain m node attribute information. The historical gas monitoring data of each of the m network nodes can also be determined based on the m node attribute information and the current time to obtain m historical gas monitoring data. The historical gas monitoring data can be considered as gas monitoring data under normal circumstances.

[0077] Further, the deviation between m gas monitoring data and the corresponding historical gas monitoring data in the m historical gas monitoring data can be determined to obtain m deviations, that is, the absolute value of the difference between each gas monitoring data and the corresponding historical gas monitoring data can be determined, and then the ratio between the absolute value of the difference and the historical gas monitoring data can be determined, and the ratio is the deviation. Of course, the first visualized video image corresponding to the m gas monitoring data and the second visualized video image corresponding to the m historical gas monitoring data can also be determined, and the similarity value between the first visualized video image and the corresponding second visualized video image can be determined. When the similarity value is within the set range, the deviation is 0. The set range can be preset or the system defaults. The set range can include an upper threshold and a lower threshold, and the upper threshold and the lower threshold can be preset or the system defaults. When the similarity value is not within the set range, the mean of the upper threshold and the lower threshold is determined, the absolute value of the difference between the similarity value and the mean is determined, and the ratio between the absolute value and the mean is determined, and the ratio is the deviation.

[0078] The environmental parameters may include at least one of the following: ambient temperature, ambient humidity, weather, etc., which are not limited here. Considering the influence of factors such as thermal expansion and contraction, the environmental parameters may be used to dynamically optimize the deviation so that the degree of deviation is consistent with the actual situation. The preset threshold may also be pre-set or system default.

[0079] Next, the environmental parameters of each of the m network nodes can be obtained to obtain m environmental parameters. The mapping relationship between the preset environmental parameters and the optimization coefficient can be pre-stored, and the value of the optimization coefficient can be -0.1 to 0.1. The optimization coefficient corresponding to the m environmental parameters is determined based on the mapping relationship to obtain m optimization coefficients, and the corresponding deviations in the m deviations are optimized using the m optimization coefficients to obtain m target deviations, that is, target deviation = deviation * (1 + optimization coefficient). The mapping relationship between the preset deviation and the abnormality evaluation value can be pre-stored, and then the abnormality evaluation value corresponding to the m target deviations is determined based on the mapping relationship to obtain m abnormality evaluation values. The larger the abnormality evaluation value, the more serious the abnormality. Conversely, the smaller the abnormality evaluation value, the smaller the abnormality. In this way, the abnormality evaluation value greater than the preset threshold value can be selected from the m abnormality evaluation values ​​to obtain n abnormality evaluation values, and then the gas monitoring data corresponding to the n abnormality evaluation values ​​are obtained to obtain n abnormal gas monitoring data.

[0080] In this way, on the one hand, the deviation of the abnormal situation can be preliminarily determined based on the gas monitoring data and the historical gas monitoring data under normal conditions, and the deviation can be dynamically optimized using environmental parameters to make the deviation conform to the actual situation. On the other hand, the corresponding abnormality assessment value is determined based on the deviation, and the outstanding abnormality assessment value is selected. Thus, the abnormal network node can be located and the corresponding abnormal gas monitoring data can be obtained.

[0081] 203. Obtain the network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes.

[0082] In the embodiment of the present application, the network nodes corresponding to n abnormal gas monitoring data can be obtained to obtain n network nodes, and then the abnormal network nodes can be located to further accurately locate the gas rescue location.

[0083] 204. Determine a network attribute parameter of each of the n network nodes to obtain n network attribute parameters.

[0084] The network attribute parameters may include at least one of the following: network data type, node location, node number, network bandwidth, network quality, network throughput, etc., which are not limited here.

[0085] In a specific implementation, the network attribute parameters of each of the n network nodes can be determined to obtain n network attribute parameters, which is helpful to configure corresponding video encoding parameters for different network attributes, thereby improving the efficiency of gas visualization display.

[0086] 205. Obtain a video encoding parameter of each of the n network nodes to obtain n video encoding parameters.

[0087] In the embodiment of the present application, different network nodes may correspond to different video encoding parameters, and thus, the video encoding parameters of each of the n network nodes may be obtained to obtain n video encoding parameters.

[0088] 206. Adjust corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters.

[0089] In the specific implementation, the corresponding video encoding parameters among the n video encoding parameters can be adjusted according to the n network attribute parameters to obtain n target video encoding parameters. The n video encoding parameters can be partially or fully adjusted so that the video stream transmission depth conforms to the actual network conditions, which helps to improve the gas-related data transmission efficiency and ensure the efficiency and accuracy of gas rescue.

[0090] Optionally, the network attribute parameters include at least network bandwidth, network quality, and network throughput. The above step 206, adjusting corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters, may include the following steps:

[0091] 61. Obtain a target network bandwidth, a target network quality, and a target network throughput of a node corresponding to a network attribute parameter a; the network attribute parameter a is any one of the n network attribute parameters;

[0092] 62. Determine a target video encoding algorithm corresponding to the target network throughput;

[0093] 63. Determine x first algorithm control parameters corresponding to the target network bandwidth, where x is a natural number;

[0094] 64. Determine y second algorithm control parameters corresponding to the target network quality, where y is a natural number;

[0095] 65. Construct a first algorithm control parameter set according to the x first algorithm control parameters and the y second algorithm control parameters;

[0096] 66. Obtain a reference algorithm control parameter set corresponding to the x first algorithm control parameters, the reference algorithm control parameter set including the x first algorithm control parameters and y reference second algorithm control parameters;

[0097] 67. Determine a first algorithm evaluation parameter corresponding to the first algorithm control parameter set;

[0098] 68. Determine a second algorithm evaluation parameter corresponding to the reference algorithm control parameter set;

[0099] 69. Determine a target deviation effect value between the first algorithm evaluation parameter and the second algorithm evaluation parameter;

[0100] 700. Adjust the y second algorithm control parameters according to the target deviation effect value to obtain y target second algorithm control parameters;

[0101] 71. Update the first algorithm control parameter set using the y target second algorithm control parameters to obtain a target first algorithm control parameter set;

[0102] 72. Determine corresponding target video encoding parameters according to the target video encoding algorithm and the target first algorithm control parameter set.

[0103] Among them, the network attribute parameters at least include network bandwidth, network quality and network throughput. Taking network attribute parameter a as an example, network attribute parameter a is any network attribute parameter among n network attribute parameters, then the target network bandwidth, target network quality and target network throughput of the node corresponding to network attribute parameter a can be obtained, and the mapping relationship between the preset network throughput and the video encoding algorithm can be pre-stored. Then, the target video encoding algorithm corresponding to the target network throughput can be determined based on the mapping relationship. In this way, the corresponding video encoding algorithm can be configured for different network throughputs to ensure the actual video stream transmission requirements.

[0104] Among them, different video encoding algorithms correspond to different algorithm control parameters, and the algorithm control parameters can be used to control the encoding length, bit rate, encoding format, etc.

[0105] Next, a mapping relationship between a preset network bandwidth and an algorithm control parameter of a target video encoding algorithm may be pre-stored, and then, x first algorithm control parameters corresponding to the target network bandwidth may be determined based on the mapping relationship, where x is a natural number, and a mapping relationship between a preset network quality and an algorithm control parameter of a target video encoding algorithm may be pre-stored, that is, y second algorithm control parameters corresponding to the target network quality may be determined based on the mapping relationship, where y is a natural number, wherein there is no intersection between the x first algorithm control parameters and the y second algorithm control parameters, that is, the x first algorithm control parameters and the y second algorithm control parameters may be determined based on the mapping relationship. The method control parameters constitute the first algorithm control parameter set, that is, the target video encoding algorithm has x+y algorithm control parameters, among which x algorithm control parameters are related to the network bandwidth, and y algorithm control parameters are related to the network quality. In practical applications, the network bandwidth can be given priority, and then the network quality can be considered, that is, the x first algorithm control parameters can be fixed and kept unchanged, and then the reference algorithm control parameter set corresponding to the x first algorithm control parameters is obtained, the reference algorithm control parameter set includes x first algorithm control parameters and y reference second algorithm control parameters, that is, the reference algorithm control parameter set is the ideal algorithm control parameters under the target network bandwidth.

[0106] Furthermore, the first algorithm evaluation parameter corresponding to the first algorithm control parameter set can be determined. Specifically, the first algorithm control parameter set can be simulated or directly run to obtain the corresponding running time and video compression effect evaluation value. A weighted operation can be performed based on the running time and the video compression effect evaluation value to obtain the first algorithm evaluation parameter; the second algorithm evaluation parameter corresponding to the reference algorithm control parameter set can also be determined. Specifically, the second algorithm control parameter set can be simulated or directly run to obtain the corresponding running time and video compression effect evaluation value. A weighted operation can be performed based on the running time and the video compression effect evaluation value to obtain the second algorithm evaluation parameter. In this way, the target deviation effect value between the first algorithm evaluation parameter and the second algorithm evaluation parameter can be determined, that is, the target deviation effect value = (first algorithm evaluation parameter-second algorithm evaluation parameter) / second algorithm evaluation parameter. Since the first algorithm evaluation parameter represents the actual situation and the second algorithm evaluation parameter represents the ideal situation, the target deviation effect value can represent the gap between the actual situation and the ideal situation. The target deviation effect value can be positive or negative. Therefore, the target deviation effect value can be used to provide the adjustment direction and adjustment range of the second algorithm control parameter, so that the algorithm control parameter depth conforms to the actual network situation.

[0107] The simulation operation may be to perform video encoding for the target video encoding algorithm and the test data (gas monitoring data) under the first algorithm control parameter set to test the corresponding operation time and video compression effect evaluation value.

[0108] Next, y second algorithm control parameters can be adjusted according to the target deviation effect value to obtain y target second algorithm control parameters. Specifically, the mapping relationship between the preset deviation effect value and the adjustment parameter can be pre-stored, and then the target adjustment parameter corresponding to the target deviation effect value can be determined based on the mapping relationship. The y second algorithm control parameters are adjusted based on the target adjustment parameter to obtain y target second algorithm control parameters, the target second algorithm control parameter = (1 + target adjustment parameter) * second algorithm control parameter, and then the y target second algorithm control parameters are used to update the first algorithm control parameter set to obtain the target first algorithm control parameter set. Finally, the target video encoding algorithm and the target first algorithm control parameter set can be determined as the target video encoding parameters.

[0109] In this way, not only can the corresponding video encoding algorithm be configured for different network throughputs to ensure the actual video stream transmission needs, but the network bandwidth can also be considered first, and some algorithm control parameters can be fixed first, and then the network quality can be used to determine another part of the algorithm control parameters, as well as the ideal algorithm control parameters based on the actual network bandwidth and network quality. The difference in algorithm effect between the ideal algorithm control parameters and the actual algorithm control parameters can be used to dynamically optimize the other part of the algorithm control parameters, so that the optimized algorithm control parameters are deeply in line with the actual situation, which helps to ensure video transmission efficiency and video transmission quality, and helps to improve gas rescue efficiency and accuracy.

[0110] In the embodiment of the present application, dynamic calculation of network throughput can be considered to dynamically control the sending and receiving of streaming media data to achieve smooth rendering and playback without lags, thereby helping to ensure video transmission efficiency and quality, and helping to improve gas rescue efficiency and accuracy.

[0111] 207. Transmit the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters.

[0112] In an embodiment of the present application, the current gas monitoring data of n network nodes can be transmitted to the control center through n target video encoding parameters. Since the target video encoding parameter depth conforms to the actual network situation, the data transmission efficiency can be improved.

[0113] Optionally, transmitting the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters includes:

[0114] 71. Obtaining abnormality evaluation values ​​of the n network nodes to obtain n abnormality evaluation values;

[0115] 72. Obtain the importance levels of the n network nodes to obtain n importance levels;

[0116] 73. Determine adjustment parameters corresponding to the n importance levels to obtain n first adjustment parameters;

[0117] 74. Determine adjustment parameters corresponding to the n network attribute parameters to obtain n second adjustment parameters;

[0118] 75. Adjust the n abnormality evaluation values ​​according to the n first adjustment parameters and the n second adjustment parameters to obtain n actual abnormality evaluation values;

[0119] 76. Determine the data transmission priority order of the n network nodes according to the n actual abnormality degree evaluation values;

[0120] 77. According to the data sending priority order, the current gas monitoring data of the n network nodes are transmitted to the control center through the n target video encoding parameters.

[0121] In the embodiment of the present application, the abnormality evaluation values ​​of n network nodes can be obtained to obtain n abnormality evaluation values, and the importance levels of n network nodes can also be obtained to obtain n importance levels, and the mapping relationship between the preset importance levels and the adjustment parameters can be pre-stored, and then, the adjustment parameters corresponding to the n importance levels can be determined based on the mapping relationship to obtain n first adjustment parameters, and the value range of the first adjustment parameters can be 0 to 0.2. The mapping relationship between the preset network attribute parameters and the adjustment parameters can also be pre-stored, and the adjustment parameters corresponding to the n network attribute parameters can be determined based on the mapping relationship to obtain n second adjustment parameters, and the value range of the second adjustment parameters can be 0 to 0.1. Then, the n abnormality evaluation values ​​are adjusted according to the n first adjustment parameters and the n second adjustment parameters to obtain n actual abnormality evaluation values, that is, the actual Abnormality level assessment value = (1 + first adjustment parameter) * (1 + second adjustment parameter) * abnormality level assessment value. In this way, the abnormality level assessment can deeply consider the importance of the network node itself and the actual situation of the network, so that the abnormality level assessment is in line with both subjective intentions and the actual observable environment. Then, the data sending priority order of the n network nodes is determined according to the n actual abnormality level assessment values. The larger the actual abnormality level assessment value, the higher the priority. Conversely, the smaller the actual abnormality level assessment value, the lower the priority. Then, according to the data sending priority order, the current gas monitoring data of the n network nodes are transmitted to the control center through n target video encoding parameters. In this way, the gas monitoring data of the network nodes with obvious abnormality levels can be sent first, ensuring the timeliness of data sending of key network nodes, which helps to improve gas rescue efficiency and ensure safety.

[0122] Optionally, the current gas monitoring data includes visualized gas monitoring data, and may further include the following steps:

[0123] A1. Determine the current gas monitoring data of the network node i, where the network node i is any network node among the n network nodes;

[0124] A2. Determine the target abnormal change trend of the network node i according to the gas monitoring data of the two frames before and after the current gas monitoring data; the time interval between the two frames before and after is greater than the set time length;

[0125] A3. Determine a target early warning strategy corresponding to the abnormal change trend of the target;

[0126] A4. Execute the target early warning strategy for the network node i.

[0127] The current gas monitoring data includes visualized gas monitoring data. The visualized gas monitoring data can display gas flow rate, gas flow direction, gas flow rate, etc. The visualized gas monitoring data can also be a video frame formed by performing data visualization processing on the current gas monitoring data to obtain equivalent visualized data.

[0128] The set duration may be preset or set by system default, and the set duration may be related to factors such as time point, geographical location, season, and weather.

[0129] Among them, the target warning strategy may include at least one of the following: emergency rescue priority, warning priority, valve closing, reducing gas supply, alarm, dispatching gas combustion resources, etc., which are not limited here. The dispatching gas combustion resources may include at least one of the following: the number of gas rescue personnel, the ability level of gas rescue personnel, gas rescue equipment, etc., which are not limited here.

[0130] In the specific implementation, network node i can be taken as an example. Network node i is any network node among n network nodes. The current gas monitoring data of network node i can be determined, and the target abnormal change trend of network node i can be determined according to the gas monitoring data of the two frames before and after the current gas monitoring data. The time interval between the two frames is greater than the set duration. Furthermore, the mapping relationship between the preset abnormal change trend and the early warning strategy can be pre-stored. Based on the mapping relationship, the target early warning strategy corresponding to the target abnormal change trend can be determined, and the target early warning strategy is executed for network node i. In this way, not only can corresponding early warning strategies be provided based on different trend evolutions, but also dynamic early warnings can be provided based on the real-time situation of gas monitoring, which is helpful to improve the efficiency of gas rescue and ensure safety.

[0131] Optionally, the above step A2, determining the target abnormal change trend of the network node i according to the current gas monitoring data, may include the following steps:

[0132] A21, obtaining the first abnormal area and the number of first abnormal features of the front frame, and the second abnormal area and the number of second abnormal features of the rear frame through the gas monitoring data of the front and rear two frames;

[0133] A22. determining a first change amount according to the second abnormal area and the first abnormal area;

[0134] A23, determining a second change amount according to the second abnormal feature quantity and the first abnormal feature quantity;

[0135] A24. Determine the abnormal change trend of the target according to the first change amount and the second change amount.

[0136] The first abnormal region and the second abnormal region may both refer to the area of ​​the abnormal region.

[0137] In an embodiment of the present application, the first abnormal area and the number of first abnormal features in the front frame, as well as the second abnormal area and the number of second abnormal features in the rear frame can be obtained through the gas monitoring data of the two frames before and after. The first change amount can also be determined based on the second abnormal area and the first abnormal area, that is, the first change amount = |second abnormal area-first abnormal area| / first abnormal area.

[0138] Next, the second change amount can also be determined according to the second abnormal feature quantity and the first abnormal feature quantity, the second change amount = (the second abnormal feature quantity - the first abnormal feature quantity) / the first abnormal feature quantity, and the weight pair corresponding to the first change amount and the second change amount can also be obtained. The weight pair can be related to the environmental parameters inside the pipeline. The environmental parameters inside the pipeline can include at least one of the following: the temperature inside the pipeline, the humidity inside the pipeline, the pressure inside the pipeline, the gas flow inside the pipeline, etc., which are not limited here. The weight pair can include the first weight and the second weight. The first weight + the second weight = 1, then the target abnormal change trend can be determined according to the first change amount, the second change amount and the weight pair, the target abnormal change trend = the first change amount * the first weight + the second change amount * the second weight. The larger the target abnormal change trend, the more obvious the abnormal change. Conversely, the smaller the target abnormal change trend, the smaller the abnormal change. In this way, based on the abnormal area and feature conditions in the visualized gas monitoring data, its abnormal evolution can be analyzed, which is helpful to improve the accuracy of gas rescue decision-making and the efficiency of gas rescue.

[0139] It can be seen that the gas emergency processing method described in the embodiment of the present invention is applied to the control center in the gas network system. The gas network system also includes m network nodes. The control center is communicated with the m network nodes. Each network node is used to monitor the gas situation of a location or an area. m is an integer greater than 1. The gas monitoring data of each network node in the m network nodes is obtained to obtain the m gas monitoring data. The abnormal gas monitoring data in the m gas monitoring data is determined to obtain n abnormal gas monitoring data. n is natural data less than or equal to m. The network nodes corresponding to the n abnormal gas monitoring data are obtained to obtain the n network nodes. The network attribute parameters of each network node in the n network nodes are determined. number, obtain n network attribute parameters, obtain video encoding parameters of each of the n network nodes, obtain n video encoding parameters, adjust corresponding video encoding parameters in the n video encoding parameters according to the n network attribute parameters, obtain n target video encoding parameters, and transmit current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters. In this way, on the one hand, the abnormal gas monitoring data can be used to further locate the abnormal position or abnormal area, which can improve the efficiency of gas repair and ensure safety. On the other hand, the video stream transmission depth can be made to conform to the actual network situation, which is helpful to improve the efficiency of gas-related data transmission, ensure the efficiency and accuracy of gas rescue, and help improve the efficiency of gas rescue.

[0140] In accordance with the above embodiment, please refer to Figure 3 , Figure 3 : is a structural diagram of a control center provided by an embodiment of the present invention. As shown in the figure, the control center includes a processor, a memory, a communication interface and one or more programs. The one or more programs are stored in the memory and are configured to be executed by the processor. The gas network system also includes m network nodes. The control center is connected to the m network nodes in communication. Each network node is used to monitor the gas situation at a location or an area. m is an integer greater than 1. In the embodiment of the present invention, the program includes instructions for executing the following steps:

[0141] Obtaining gas monitoring data of each of the m network nodes to obtain m gas monitoring data;

[0142] Determine abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m;

[0143] Obtaining the network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes;

[0144] Determine a network attribute parameter of each of the n network nodes to obtain n network attribute parameters;

[0145] Obtaining a video encoding parameter of each of the n network nodes to obtain n video encoding parameters;

[0146] Adjusting corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters;

[0147] The current gas monitoring data of the n network nodes are transmitted to the control center through the n target video encoding parameters.

[0148] Optionally, the current gas monitoring data includes visualized gas monitoring data, and the program further includes instructions for executing the following steps:

[0149] Determine current gas monitoring data of a network node i, where the network node i is any network node among the n network nodes;

[0150] Determine the target abnormal change trend of the network node i according to the gas monitoring data of the two frames before and after in the current gas monitoring data; the time interval between the two frames before and after is greater than the set time length;

[0151] Determine a target early warning strategy corresponding to the abnormal change trend of the target;

[0152] The target early warning strategy is executed for the network node i.

[0153] Optionally, in determining the target abnormal change trend of the network node i according to the current gas monitoring data, the program includes instructions for executing the following steps:

[0154] Obtain the first abnormal area and the number of first abnormal features of the front frame, and the second abnormal area and the number of second abnormal features of the rear frame through the gas monitoring data of the front and rear two frames;

[0155] determining a first variation according to the second abnormal region and the first abnormal region;

[0156] Determine a second change amount according to the second abnormal feature quantity and the first abnormal feature quantity;

[0157] The target abnormal change trend is determined according to the first change amount and the second change amount.

[0158] Optionally, in the aspect of determining the abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, the above program includes instructions for executing the following steps:

[0159] Acquire node attribute information of each of the m network nodes to obtain m node attribute information, where each node attribute information includes: node location, event type, and geographic location;

[0160] Determine historical gas monitoring data of each of the m network nodes according to the m node attribute information and the current time, and obtain m historical gas monitoring data;

[0161] Determine the deviation between the m gas monitoring data and corresponding historical gas monitoring data in the m historical gas monitoring data to obtain m deviations;

[0162] Obtaining an environmental parameter of each of the m network nodes to obtain m environmental parameters;

[0163] Determine the optimization coefficients corresponding to the m environmental parameters to obtain m optimization coefficients;

[0164] Optimizing corresponding deviations among the m deviations using the m optimization coefficients to obtain m target deviations;

[0165] Determine the abnormality evaluation values ​​corresponding to the m target deviations to obtain m abnormality evaluation values;

[0166] Selecting an abnormality degree evaluation value greater than a preset threshold value from the m abnormality degree evaluation values ​​to obtain n abnormality degree evaluation values;

[0167] The gas monitoring data corresponding to the n abnormality degree assessment values ​​are acquired to obtain the n abnormal gas monitoring data.

[0168] Optionally, in the aspect of transmitting the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters, the program includes instructions for executing the following steps:

[0169] Obtaining abnormality evaluation values ​​of the n network nodes to obtain n abnormality evaluation values;

[0170] Obtaining the importance levels of the n network nodes to obtain n importance levels;

[0171] Determine adjustment parameters corresponding to the n importance levels to obtain n first adjustment parameters;

[0172] Determine the adjustment parameters corresponding to the n network attribute parameters to obtain n second adjustment parameters;

[0173] Adjusting the n abnormality evaluation values ​​according to the n first adjustment parameters and the n second adjustment parameters to obtain n actual abnormality evaluation values;

[0174] Determining a data transmission priority order of the n network nodes according to the n actual abnormality degree evaluation values;

[0175] According to the data transmission priority order, the current gas monitoring data of the n network nodes are transmitted to the control center through the n target video encoding parameters.

[0176] Optionally, the network attribute parameters include at least network bandwidth, network quality, and network throughput. In the aspect of adjusting corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters, the program includes instructions for performing the following steps:

[0177] Obtaining a target network bandwidth, a target network quality, and a target network throughput of a node corresponding to a network attribute parameter a; the network attribute parameter a is any one of the n network attribute parameters;

[0178] Determining a target video encoding algorithm corresponding to the target network throughput;

[0179] Determine x first algorithm control parameters corresponding to the target network bandwidth, where x is a natural number;

[0180] Determine y second algorithm control parameters corresponding to the target network quality, where y is a natural number;

[0181] Forming a first algorithm control parameter set according to the x first algorithm control parameters and the y second algorithm control parameters;

[0182] Acquire a reference algorithm control parameter set corresponding to the x first algorithm control parameters, the reference algorithm control parameter set including the x first algorithm control parameters and y reference second algorithm control parameters;

[0183] Determining a first algorithm evaluation parameter corresponding to the first algorithm control parameter set;

[0184] Determining a second algorithm evaluation parameter corresponding to the reference algorithm control parameter set;

[0185] determining a target deviation effect value between the first algorithm evaluation parameter and the second algorithm evaluation parameter;

[0186] Adjust the y second algorithm control parameters according to the target deviation effect value to obtain y target second algorithm control parameters;

[0187] Using the y target second algorithm control parameters, the first algorithm control parameter set is updated to obtain a target first algorithm control parameter set;

[0188] Corresponding target video encoding parameters are determined according to the target video encoding algorithm and the target first algorithm control parameter set.

[0189] It can be seen that in the control center of the gas network system in the embodiment of the present invention, the gas network system also includes m network nodes, the control center is communicated with the m network nodes, each network node is used to monitor the gas situation of a location or an area, m is an integer greater than 1, the gas monitoring data of each network node in the m network nodes is obtained, the m gas monitoring data are obtained, the abnormal gas monitoring data in the m gas monitoring data is determined, and n abnormal gas monitoring data are obtained, n is a natural data less than or equal to m, the network nodes corresponding to the n abnormal gas monitoring data are obtained, and n network nodes are obtained, the network attribute parameters of each network node in the n network nodes are determined, and n network nodes are obtained. Attribute parameters, obtain the video encoding parameters of each of the n network nodes, obtain n video encoding parameters, adjust the corresponding video encoding parameters in the n video encoding parameters according to the n network attribute parameters, obtain n target video encoding parameters, and transmit the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters. In this way, on the one hand, the abnormal gas monitoring data can be used to further locate the abnormal position or abnormal area, which can improve the efficiency of gas repair and ensure safety. On the other hand, the video stream transmission depth can be made to meet the actual network situation, which is helpful to improve the efficiency of gas-related data transmission, ensure the efficiency and accuracy of gas rescue, and help improve the efficiency of gas rescue.

[0190] An embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments.

[0191] The embodiment of the present invention further provides a computer program product, the computer program product comprising a non-transitory computer-readable storage medium storing a computer program, the computer program being operable to cause a computer to execute some or all of the steps of any method described in the above method embodiment. The computer program product may be a software installation package.

[0192] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0193] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0194] In the several embodiments provided by the present invention, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0195] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0196] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0197] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present invention. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0198] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0199] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A gas emergency treatment method, characterized in that: A control center applied to a gas network system, the gas network system further comprising m network nodes, the control center being communicatively connected to the m network nodes, each network node being used to monitor the gas situation at a location or an area, m being an integer greater than 1, the method comprising: Obtaining gas monitoring data of each of the m network nodes to obtain m gas monitoring data; Determine abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m; Obtaining the network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes; Determine a network attribute parameter of each of the n network nodes to obtain n network attribute parameters; Obtaining a video encoding parameter of each of the n network nodes to obtain n video encoding parameters; Adjusting corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters; Transmitting the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters; Wherein, the current gas monitoring data includes visualized gas monitoring data, and the method further includes: Determine current gas monitoring data of a network node i, where the network node i is any network node among the n network nodes; Determine the target abnormal change trend of the network node i according to the gas monitoring data of the two frames before and after in the current gas monitoring data; the time interval between the two frames before and after is greater than the set time length; Determine a target early warning strategy corresponding to the abnormal change trend of the target; Executing the target early warning strategy for the network node i; Wherein, determining the target abnormal change trend of the network node i according to the current gas monitoring data includes: The first abnormal area and the number of first abnormal features in the previous frame, and the second abnormal area and the number of second abnormal features in the next frame are obtained through the gas monitoring data of the previous and next two frames; the first abnormal area and the second abnormal area both refer to the area of ​​the abnormal area; Determine a first change amount according to the second abnormal area and the first abnormal area, specifically: first change amount = | second abnormal area - first abnormal area | / first abnormal area; Determine a second change amount according to the second abnormal feature quantity and the first abnormal feature quantity; The target abnormal change trend is determined according to the first change amount and the second change amount.

2. The method according to claim 1, characterized in that The determining of abnormal gas monitoring data among the m gas monitoring data to obtain n abnormal gas monitoring data includes: Acquire node attribute information of each of the m network nodes to obtain m node attribute information, where each node attribute information includes: node location, event type, and geographic location; Determine historical gas monitoring data of each of the m network nodes according to the m node attribute information and the current time, and obtain m historical gas monitoring data; Determine the deviation between the m gas monitoring data and corresponding historical gas monitoring data in the m historical gas monitoring data to obtain m deviations; Obtaining an environmental parameter of each of the m network nodes to obtain m environmental parameters; Determine the optimization coefficients corresponding to the m environmental parameters to obtain m optimization coefficients; Optimizing corresponding deviations among the m deviations using the m optimization coefficients to obtain m target deviations; Determine the abnormality evaluation values ​​corresponding to the m target deviations to obtain m abnormality evaluation values; Selecting an abnormality degree evaluation value greater than a preset threshold value from the m abnormality degree evaluation values ​​to obtain n abnormality degree evaluation values; The gas monitoring data corresponding to the n abnormality degree assessment values ​​are acquired to obtain the n abnormal gas monitoring data.

3. The method according to claim 2, characterized in that The transmitting the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters includes: Obtaining abnormality evaluation values ​​of the n network nodes to obtain n abnormality evaluation values; Obtaining the importance levels of the n network nodes to obtain n importance levels; Determine adjustment parameters corresponding to the n importance levels to obtain n first adjustment parameters; Determine the adjustment parameters corresponding to the n network attribute parameters to obtain n second adjustment parameters; Adjusting the n abnormality evaluation values ​​according to the n first adjustment parameters and the n second adjustment parameters to obtain n actual abnormality evaluation values; Determining a data transmission priority order of the n network nodes according to the n actual abnormality degree evaluation values; According to the data transmission priority order, the current gas monitoring data of the n network nodes are transmitted to the control center through the n target video encoding parameters.

4. The method according to claim 3, characterized in that The network attribute parameters at least include network bandwidth, network quality, and network throughput, and adjusting corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters includes: Obtaining a target network bandwidth, a target network quality, and a target network throughput of a node corresponding to a network attribute parameter a; the network attribute parameter a is any one of the n network attribute parameters; Determining a target video encoding algorithm corresponding to the target network throughput; Determine x first algorithm control parameters corresponding to the target network bandwidth, where x is a natural number; Determine y second algorithm control parameters corresponding to the target network quality, where y is a natural number; Forming a first algorithm control parameter set according to the x first algorithm control parameters and the y second algorithm control parameters; Acquire a reference algorithm control parameter set corresponding to the x first algorithm control parameters, the reference algorithm control parameter set including the x first algorithm control parameters and y reference second algorithm control parameters; Determining a first algorithm evaluation parameter corresponding to the first algorithm control parameter set; Determining a second algorithm evaluation parameter corresponding to the reference algorithm control parameter set; determining a target deviation effect value between the first algorithm evaluation parameter and the second algorithm evaluation parameter; Adjust the y second algorithm control parameters according to the target deviation effect value to obtain y target second algorithm control parameters; Using the y target second algorithm control parameters, the first algorithm control parameter set is updated to obtain a target first algorithm control parameter set; Corresponding target video encoding parameters are determined according to the target video encoding algorithm and the target first algorithm control parameter set.

5. A gas network system, characterized in that: The gas network system includes a control center and m network nodes, the control center is connected to the m network nodes in communication, each network node is used to monitor the gas situation of a location or an area, m is an integer greater than 1, wherein, The m network nodes are used to obtain gas monitoring data of each of the m network nodes to obtain m gas monitoring data; determine abnormal gas monitoring data in the m gas monitoring data to obtain n abnormal gas monitoring data, where n is natural data less than or equal to m; obtain network nodes corresponding to the n abnormal gas monitoring data to obtain n network nodes; determine network attribute parameters of each of the n network nodes to obtain n network attribute parameters; obtain video encoding parameters of each of the n network nodes to obtain n video encoding parameters; The control center is used to adjust corresponding video encoding parameters among the n video encoding parameters according to the n network attribute parameters to obtain n target video encoding parameters; The m network nodes are used to transmit the current gas monitoring data of the n network nodes to the control center through the n target video encoding parameters; Wherein, the current gas monitoring data includes visualized gas monitoring data, and the system is further specifically used for: Determine current gas monitoring data of a network node i, where the network node i is any network node among the n network nodes; Determine the target abnormal change trend of the network node i according to the gas monitoring data of the two frames before and after in the current gas monitoring data; the time interval between the two frames before and after is greater than the set time length; Determine a target early warning strategy corresponding to the abnormal change trend of the target; Executing the target early warning strategy for the network node i; Wherein, in determining the target abnormal change trend of the network node i according to the current gas monitoring data, the system is specifically used for: The first abnormal area and the number of first abnormal features in the previous frame, and the second abnormal area and the number of second abnormal features in the next frame are obtained through the gas monitoring data of the previous and next two frames; the first abnormal area and the second abnormal area both refer to the area of ​​the abnormal area; Determine a first change amount according to the second abnormal area and the first abnormal area, specifically: first change amount = | second abnormal area - first abnormal area | / first abnormal area; Determine a second change amount according to the second abnormal feature quantity and the first abnormal feature quantity; The target abnormal change trend is determined according to the first change amount and the second change amount.

6. The system according to claim 5, characterized in that In the aspect of determining the abnormal gas monitoring data among the m gas monitoring data to obtain the n abnormal gas monitoring data, the m network nodes are specifically used for: Acquire node attribute information of each of the m network nodes to obtain m node attribute information, where each node attribute information includes: node location, event type, and geographic location; Determine historical gas monitoring data of each of the m network nodes according to the m node attribute information and the current time, and obtain m historical gas monitoring data; Determine the deviation between the m gas monitoring data and corresponding historical gas monitoring data in the m historical gas monitoring data to obtain m deviations; Obtaining an environmental parameter of each of the m network nodes to obtain m environmental parameters; Determine the optimization coefficients corresponding to the m environmental parameters to obtain m optimization coefficients; Optimizing corresponding deviations among the m deviations using the m optimization coefficients to obtain m target deviations; Determine the abnormality evaluation values ​​corresponding to the m target deviations to obtain m abnormality evaluation values; Selecting an abnormality degree evaluation value greater than a preset threshold value from the m abnormality degree evaluation values ​​to obtain n abnormality degree evaluation values; The gas monitoring data corresponding to the n abnormality degree assessment values ​​are acquired to obtain the n abnormal gas monitoring data.

Images