A monitoring type selection method based on the amount of information determined by gas pipeline network nodes

Abstract

The present invention relates to a method for selecting a monitoring type based on the information content of gas pipeline network nodes. The method comprises: calculating the time-averaged information content, the spatial-averaged information content, and the regional-averaged information content; calculating the comprehensive information content, and selecting one or more monitoring types for monitoring based on the comprehensive information content. The present invention proposes incorporating information content analysis into the analysis of monitoring nodes, eliminating the need to reduce the scope of monitoring nodes or the type of analysis data, and scientifically guiding the efficient application of monitoring nodes in big data-based monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban gas pipeline network monitoring, and in particular relates to a monitoring type selection method and system based on the amount of information determined by gas pipeline network nodes. Background Art

[0002] Urban gas transmission pipelines are widely distributed, overlapping with power and water lines. These pipelines are also subject to various environmental factors, including traffic and construction. A gas leak can pose a serious threat to life and property. To ensure the safe operation of these pipelines, 24 / 7 monitoring of key operating parameters in key locations is often necessary. This allows for comprehensive monitoring of the entire system's safe operation and rapid location of fault points to ensure safe operation.

[0003] On the one hand, to ensure safety, major companies have been developing and researching gas pipeline network monitoring system software. This is a key product for urban environmental safety monitoring. It collects information such as combustible gas, pressure, flow, temperature, and valve status within the gas pipeline network. It also displays, analyzes, and issues warnings in real time, allows for rapid query of alarm data, and enables rapid location and resolution of alarms. On the other hand, big data is a series of processing methods for storing, computing, analyzing, and analyzing massive amounts of data. With the rapid development of big data technology in my country, gas pipeline network monitoring system software is also embracing this hot topic. However, big data technology often processes data volumes in the terabyte, even petabyte, or exabyte range, making it impossible for traditional data processing methods to handle these volumes. However, processing such large amounts of data requires prior knowledge of the affected areas, pre-planned warning zones, and the determination of the secondary and derivative levels of the disaster corresponding to the current warning information, which presents significant challenges in practical implementation. The amount of big data used for deep processing can be reduced by selecting some monitoring nodes, but arbitrary reduction will further aggravate the problem of decreased accuracy caused by the reduction of monitoring nodes. In the process of reducing monitoring nodes, it is necessary to consider whether the amount of information of the monitoring nodes is sufficient. Generally speaking, when the probability of a piece of information appearing is higher, it indicates that it is spread more widely, or in other words, it is cited to a higher degree. In the prior art, the pressure of big data analysis that may occur is often reduced by narrowing the scope of monitoring nodes or reducing the type of analysis data. The present invention proposes to introduce information volume analysis into the analysis process of monitoring nodes, so that there is no need to reduce the scope of monitoring nodes or reduce the type of analysis data, and scientifically guide the efficient application of monitoring nodes in big data-based monitoring. Summary of the Invention

[0004] In order to solve the above problems in the prior art, the present invention proposes a monitoring type selection method based on the amount of information determined by the gas pipeline network node, the method comprising:

[0005] Step S1: obtaining an unprocessed monitoring type of a gas pipe network monitoring node as a monitoring type to be identified;

[0006] Step S2: Acquire monitoring data of the monitoring type to be identified;

[0007] Step S3: Calculate the time-averaged information content, the spatial-averaged information content, and the regional-averaged information content; specifically, calculate the average information content contained in the monitoring data within a unit time range as the time-averaged information content, calculate the average information content contained in the monitoring data within the storage space range as the spatial-averaged information content, and calculate the average information content contained in the monitoring data within the spatial range of the monitoring node area as the regional-average information content;

[0008] Step S4: Calculate the comprehensive information amount of the monitoring type, and store the comprehensive information amount in association with the monitoring type;

[0009] The weighted comprehensive information is calculated using the following formula:

[0010]

[0011] in: is the weighted value;

[0012] Step S5: Determine whether all monitoring types have been processed. If so, proceed to the next step; otherwise, return to step S1;

[0013] Step S6: Select one or more monitoring types for monitoring based on the comprehensive information volume; specifically: select k monitoring types from all monitoring types so that the sum of the comprehensive information volumes corresponding to the k monitoring types exceeds and is closest to the information volume threshold.

[0014] Furthermore, various monitoring types are processed in turn according to the difficulty of obtaining monitoring data.

[0015] Furthermore, the monitoring types are pressure, flow, temperature, sound, and valve status.

[0016] Furthermore, monitoring data is obtained through sensors installed on the monitoring nodes.

[0017] Furthermore, in step S6, k monitoring types are selected in sequence according to the difficulty of obtaining the monitoring data.

[0018] A monitoring type selection system based on the amount of information determined by gas network nodes, the system comprising: an acquisition node, a cloud computing node, and a mobile terminal;

[0019] The acquisition node is used to acquire an unprocessed monitoring type of a gas pipe network monitoring node as the monitoring type to be identified; and acquire monitoring data of the monitoring type to be identified;

[0020] The cloud computing node is used to calculate the time-averaged information amount, the space-averaged information amount and the regional-averaged information amount; specifically: the average information amount contained in the monitoring data within a unit time range is calculated as the time-averaged information amount, the average information amount contained in the monitoring data within the storage space range is calculated as the space-averaged information amount, and the average information amount contained in the monitoring data within the spatial range of the monitoring node area is calculated as the regional-average information amount;

[0021] The cloud computing node is further used to calculate the comprehensive information amount of the monitoring type and store the comprehensive information amount in association with the monitoring type; the weighted comprehensive information amount is calculated using the following formula:

[0022]

[0023] in: is the weighted value;

[0024] The cloud computing node is further configured to determine whether all monitoring types have been processed. If so, one or more monitoring types are selected for monitoring based on the comprehensive information volume; k monitoring types are selected from all monitoring types so that the sum of the comprehensive information volumes corresponding to the k monitoring types exceeds and is closest to the information volume threshold; otherwise, the acquisition node is notified to continue acquiring monitoring data corresponding to the monitoring type of the next unprocessed gas pipeline network monitoring node;

[0025] The cloud computing node is further configured to feed back the obtained k monitoring types to the mobile terminal.

[0026] A big data computing node is used to execute the monitoring type selection method determined based on the amount of information of gas pipeline network nodes.

[0027] A processor is used to run a program, wherein when the program is running, the monitoring type selection method determined based on the amount of information of the gas pipeline network nodes is executed.

[0028] A computer-readable storage medium includes a program, which, when running on a computer, enables the computer to execute the monitoring type selection method determined based on the amount of information of gas pipeline network nodes.

[0029] An execution device includes a processor, the processor is coupled to a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the monitoring type selection method based on the amount of information determined by the gas pipeline network node is implemented.

[0030] The beneficial effects of the present invention include:

[0031] (1) The amount of information is introduced into the selection of gas pipeline network monitoring types. The information amount is used to perform deterministic quantitative measurement of randomly occurring events to be monitored. The minimum set of necessary monitoring types required for effective monitoring is determined from multiple dimensions, and the reduction of monitoring software and hardware overhead is scientifically guided. (2) The carrying and calculation methods of monitoring data are diversified and comprehensively measured by comprehensive information amount, which can effectively eliminate the differences in the carrying and calculation of monitoring data. The storage and acquisition methods of monitoring data are also set accordingly, reducing the subsequent acquisition and utilization overhead of monitoring data. (3) The weighted comprehensive information amount is set so that the setting of weights is related to the storage overhead, thereby improving the monitoring efficiency that can be brought by monitoring data per unit storage area and improving the availability of monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application, but do not constitute an improper limitation of the present invention. In the drawings:

[0033] Figure 1 Schematic diagram of the monitoring type selection method based on the amount of gas network node information of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments, wherein the exemplary embodiments and descriptions are only used to explain the present invention, but not as a limitation of the present invention;

[0035] For gas pipeline network monitoring, the occurrence of monitored events has complex characteristics such as abstraction, discreteness, and nonlinearity, and high randomness is the most important characteristic of monitored events. For gas pipeline networks, monitored events are often low-probability events with a low probability of occurrence, which means there is a large degree of uncertainty. This invention uses the information volume to quantitatively measure the deterministic nature of randomly occurring monitored events, and measures the minimum set of monitoring types required for effective monitoring from multiple dimensions, greatly reducing the monitoring software and hardware overhead.

[0036] As attached Figure 1 As shown, the present invention proposes a monitoring type selection method based on the amount of information determined by the gas pipeline network node, the method comprising the following steps:

[0037] Step S1: obtaining an unprocessed monitoring type of a gas pipe network monitoring node as a monitoring type to be identified;

[0038] Preferably: the monitoring type is pressure, flow, temperature, sound, valve status, etc.;

[0039] Preferably, the monitoring data is obtained by sensors installed on the monitoring nodes. Of course, the difficulty of installing sensors varies, and in practice, due to various limitations, even if sensors are installed, the corresponding content of the monitoring data may be empty or invalid data.

[0040] Step S2: Acquire monitoring data of the monitoring type to be identified; specifically, classify and store various types of monitoring data, and set a time index for each piece of monitoring data; obtain a monitoring node range, determine an access path to a storage node based on the monitoring node range, and access the storage node based on the access path to obtain monitoring data within the monitoring node range;

[0041] Preferably: pre-processing the acquired monitoring data, for example: filling missing data based on adjacent data; filling it with default values;

[0042] Preferably, before executing step S2, monitoring data is read from the sensor of each monitoring node in sequence, and the monitoring data is stored in one or more storage nodes according to the area to which the monitoring node belongs; the monitoring data on the same storage node is stored by type according to the monitoring type; that is, the monitoring data of the same monitoring type is placed in the same monitoring data file set;

[0043] The monitoring data is stored on one or more storage nodes according to the monitoring node's belonging area, specifically: the monitoring data is stored according to the proximity of the monitoring node's belonging area, so that the closer the monitoring node is, the closer the storage node corresponding to the monitoring data is; in this case, storing the monitoring data on the same storage node is the closest situation; the proximity of the storage node refers to the proximity of the communication distance, and the closer the storage node is, the lower the storage overhead;

[0044] Preferably, the monitoring data corresponding to the same monitoring node or the monitoring nodes belonging to the same attribution area are stored on the same storage node. Of course, when one storage node cannot store all the data, the data can be split and stored on multiple nearby storage nodes. This storage method allows the security management policy acting on the monitoring node to be easily transplanted to the storage node, so that the protection of the monitoring data can also be carried out through the node. Of course, this monitoring data storage method also facilitates the subsequent acquisition and calculation processing of the monitoring data.

[0045] Preferably: the classified and stored monitoring data is stored in blocks according to a preset block data volume; the size of each block is a unit monitoring data block size; the unit monitoring data block size is used to calculate the spatial average information content;

[0046] Step S3: Calculate the time-averaged information content, the spatial-averaged information content, and the regional-averaged information content; specifically, calculate the average information content contained in the monitoring data within a unit time range as the time-averaged information content, calculate the average information content contained in the monitoring data within the storage space range as the spatial-averaged information content, and calculate the average information content contained in the monitoring data within the spatial range of the monitoring node area as the regional-average information content;

[0047] The calculation of the average amount of information contained in the monitoring data within the unit time range as the time average information amount is specifically as follows: the monitoring data is divided into N1 first monitoring data according to the unit time interval; the time average information amount is calculated based on the following formula;

[0048] ;

[0049] in: is the probability of the occurrence of the monitoring event and the change of the monitoring data in the i-th first monitoring data corresponding to each other; the occurrence of the monitoring event and the change of the monitoring data correspond to each other, specifically: when the monitoring event occurs, the monitoring data within the corresponding time range undergoes a non-negligible fluctuation, for example: the difference between the monitoring data and the adjacent monitoring data exceeds the fluctuation limit, or the monitoring data does not conform to the predicted change of the monitoring data prediction function, or the difference between the monitoring data and the average value of the monitoring data exceeds the fluctuation limit, etc., one or more combinations thereof; that is, there is no need to perform alignment based on time points, but rather to determine the change based on the time range; provide tolerance for the offset of the monitoring data change, so as to accommodate more monitoring types and the actual monitoring capabilities of their corresponding physical sensors;

[0050] The calculation of the average information content contained in the monitoring data within the storage space as the spatial average information content is specifically as follows: sequentially reading the i-th unit monitoring data block stored in blocks; calculating the spatial average information content based on the following formula;

[0051] ;

[0052] in: is the probability of the occurrence of the monitoring event and the corresponding change of the monitoring data in the i-th unit monitoring data block; N2 is the number of blocks;

[0053] The calculation of the average information content contained in the monitoring data within the regional spatial range as the regional average information content is specifically as follows: the monitoring data is divided into N3 third monitoring data according to the belonging area; the regional average information content is calculated based on the following formula;

[0054] ;

[0055] in: is the probability corresponding to the occurrence of the monitoring event and the change of the monitoring data in the i-th third monitoring data;

[0056] Step S4: Calculate the comprehensive information amount of the monitoring type, and store the comprehensive information amount in association with the monitoring type;

[0057] The calculation of the comprehensive information amount of the monitoring data of the type is specifically as follows: the comprehensive information amount is calculated using the following formula;

[0058]

[0059] In actual scenarios, if the probability of an abnormal event that needs to be monitored is fixed, the greater the amount of information, the easier it is to monitor, and the lower the computing and storage resources required for monitoring. This makes monitoring simulation software and systems more efficient.

[0060] Alternatively: the weighted comprehensive information is calculated using the following formula;

[0061]

[0062] in: is a weighted value, which is closely related to the storage overhead or data size of each first monitoring data, unit monitoring data block, and third monitoring data. When the required storage overhead is greater, the weighted value is smaller, and vice versa;

[0063] Step S5: Determine whether all monitoring types have been processed. If so, proceed to the next step; otherwise, return to step S1;

[0064] Preferred: Process various monitoring types in order according to the difficulty of obtaining monitoring data;

[0065] Preferably, the difficulty of obtaining the sensor is the difficulty and cost of setting up the corresponding sensor;

[0066] Step S6: selecting one or more monitoring types for monitoring based on the comprehensive information volume; specifically, selecting k monitoring types from all monitoring types so that the sum of the comprehensive information volumes corresponding to the k monitoring types exceeds and is closest to the information volume threshold;

[0067] In other words, the above method can scientifically select the most appropriate k monitoring types from multiple monitoring types. During the selection process, the existing sensor installation situation can be taken into account, so that in actual scenarios, the selection can be made based on historical conditions or possible additional budgets.

[0068] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of programming language, including assembly or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple collaborative files (e.g., files storing one or more modules, subroutines, or code portions). A computer program can be deployed to execute on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A monitoring type selection method based on the amount of information of gas pipeline network nodes, characterized in that: include: Step S1: obtaining a monitoring type of an unprocessed gas pipe network monitoring node as a monitoring type to be identified; the monitoring type is pressure, flow, temperature, sound, or valve status; Step S2: Acquire monitoring data of the monitoring type to be identified; Step S3: Calculate the time-averaged information content, the spatial-averaged information content, and the regional-averaged information content; specifically, calculate the average information content contained in the monitoring data within a unit time range as the time-averaged information content, calculate the average information content contained in the monitoring data within the storage space range as the spatial-averaged information content, and calculate the average information content contained in the monitoring data within the spatial range of the monitoring node area as the regional-average information content; The monitoring data is divided into N1 first monitoring data according to the unit time interval; the time average information content is calculated based on the following formula; ; in: is the probability corresponding to the occurrence of the monitoring event and the change of the monitoring data in the i-th first monitoring data; Sequentially read the i-th unit monitoring data block stored in blocks; calculate the spatial average information content based on the following formula; ; in: is the probability of the occurrence of the monitoring event and the corresponding change of the monitoring data in the i-th unit monitoring data block; N2 is the number of blocks; The monitoring data is divided into N3 third monitoring data according to the belonging area; the regional average information content is calculated based on the following formula; ; in: is the probability corresponding to the occurrence of the monitoring event and the change of the monitoring data in the i-th third monitoring data; Step S4: Calculate the comprehensive information amount of the monitoring type, and store the comprehensive information amount in association with the monitoring type; The weighted comprehensive information is calculated using the following formula: ； in: is the weighted value; Step S5: Determine whether all monitoring types have been processed. If so, proceed to the next step; otherwise, return to step S1; Step S6: Select one or more monitoring types for monitoring based on the comprehensive information volume; specifically: select k monitoring types from all monitoring types so that the sum of the comprehensive information volumes corresponding to the k monitoring types exceeds and is closest to the information volume threshold.

2. The monitoring type selection method based on the amount of gas network node information according to claim 1 is characterized in that: Various monitoring types are handled in turn according to the difficulty of obtaining monitoring data.

3. The monitoring type selection method based on the amount of gas network node information according to claim 2 is characterized in that: The monitoring data is obtained through sensors installed on the monitoring nodes.

4. The method for selecting a monitoring type based on the amount of information of a gas network node according to claim 3, characterized in that: In step S6, k monitoring types are selected in sequence according to the difficulty of obtaining the monitoring data.

5. A monitoring type selection system based on the amount of information determined by gas network nodes, characterized in that: The system is used to implement the monitoring type selection method based on the amount of gas pipeline network node information as described in any one of claims 1 to 4 above; the system includes: an acquisition node, a cloud computing node and a mobile terminal.

6. A big data computing node, characterized in that: The big data computing node is used to execute the monitoring type selection method based on the amount of gas pipeline network node information as described in any one of claims 1 to 4 above.

7. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the monitoring type selection method based on the amount of gas network node information as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that The invention comprises a program which, when running on a computer, enables the computer to execute the monitoring type selection method based on the amount of gas network node information as described in any one of claims 1 to 4.

9. An execution device, characterized in that: It includes a processor, which is coupled to a memory, and the memory stores program instructions. When the program instructions stored in the memory are executed by the processor, the monitoring type selection method based on the amount of gas pipeline network node information as described in any one of claims 1 to 4 is implemented.

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