Pipe network damage point data merging processing method and device and storage medium

Abstract

The invention discloses a pipe network damage point data merging processing method. The method comprises the following steps: S1, acquiring at least two groups of previous detection data containing position information to form a data set; s2, selecting a group of damaged suspected point data in the data set as an optimal reference point according to a point location discretization condition in the data set; s3, determining a standard comparison range, and performing feature comparison on the technical parameters of the damage point in the standard comparison range of the optimal reference point; and S4, according to a feature comparison result, selecting technical parameter entries, which need to be combined, of the damaged points, and obtaining final display and combination information. According to the method, spatial position calculation and judgment of the reference point location and any detection point location are utilized, preliminary screening and evaluation are carried out in cooperation with a spatial analysis model and a discrete data analysis technology, and the recognition accuracy can be improved while the recognition efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, equipment, and storage medium for merging and processing data on pipeline damage points. Background Technology

[0002] In the process of monitoring damage points in gas pipelines, Geographic Information System (GIS), Global Positioning System (GPS), and remote sensing technology are routinely used for monitoring damage points in modern pipelines. Among them, GIS provides strong support for accurate location positioning and in-depth analysis by integrating information resources such as spatial data, maps, and statistical data; GPS achieves high-precision positioning of ground location using satellite signals; and remote sensing technology uses sensors on satellites or aircraft to obtain detailed information about the earth's surface, further enriching the content of geographic data.

[0003] Based on the above technologies, a series of specific positioning algorithms (such as triangulation, Doppler effect, beacon positioning, etc.), sensor technologies (such as accelerometers, gyroscopes, etc.), and data processing technologies (including data cleaning, data fusion, data mining, etc.) are usually used to achieve accurate monitoring of pipeline damage points. However, in practical applications, although these technologies can provide detailed location information, they often cannot form a comprehensive and continuous data stream for subsequent analysis and decision support when processing location information from different times.

[0004] Specifically, in the process of merging and processing pipeline damage point data, existing technologies have failed to effectively merge location information from previous times, making it difficult to construct a complete time-space dimension map of damage point changes. This limitation restricts the prediction of damage trends and maintenance planning operations, thereby affecting the overall safety and efficiency of the pipeline network.

[0005] Therefore, this application proposes a method for merging and processing pipeline damage point data to solve the above-mentioned technical problems. Summary of the Invention

[0006] The main objective of this invention is to provide a method for merging and processing pipeline damage point data, so as to solve the technical problems mentioned in the background art.

[0007] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: A method for merging and processing pipeline damage point data includes the following steps: S1. Obtain at least two sets of historical detection data containing location information to form a dataset; S2. Based on the dispersion of points in the dataset, select a set of suspected damaged points in the dataset as the optimal reference points; S3. Determine the standard comparison range, and perform feature comparison of the technical parameters of the damaged points within the standard comparison range of the optimal reference point; S4. Based on the feature comparison results, select the technical parameter items that need to be merged for the damaged points, and obtain the final display and merging information.

[0008] Preferably, in step S1, the location information is converted into coordinates in a specified coordinate system within the dataset. Preferably, the specific selection process for the reference point in step S2 includes: S21. Obtain the geographic coordinate data of the discretely distributed damaged points and suspected damaged points in the target area from the dataset; S22. Construct a circular detection area with a preset radius for the damaged points and suspected damaged points, and determine the detection area where the number of damaged reference points in the area reaches a preset density threshold by density screening; S23. For the screened detection area, calculate the spatial centroid coordinates of all damaged reference points within the detection area and generate the regional feature center point; S24. Execute the nearest neighbor matching algorithm to select the signal point with the smallest Euclidean distance to the feature center point of the region from the candidate signal point subset as the optimal reference point.

[0009] Preferably, the method for determining the standard comparison range in step S3 includes: S31. Filter and retain location data within a specified time period based on time characteristics; S32. Construct a circular region with a variable radius, centered on the optimal reference point; S33. Determine the minimum radius of the circular region based on the preset region density. The circular region with the minimum radius is the standard comparison range.

[0010] Preferably, the comparison logic in step S3 is as follows: First, compare the ranking of the optimal reference point with that of other points; If the data points are of the same level, then compare the maximum gradient value of the optimal reference point with that of other data points. If the maximum gradient values ​​of the point data are the same, then compare the burial depth of the optimal reference point with that of other point data.

[0011] Preferably, the hierarchy classification logic is as follows: If the maximum gradient value is ≥70, then the point is classified as level one. If the maximum gradient value is less than or equal to 50 and the level is less than 70, then the level of this point is determined to be level two. If 40 ≤ maximum gradient value < 50, then the level of this point is determined to be level three.

[0012] Preferably, during the comparison process, priority is given to comparing point data where the difference in the maximum gradient value is within 10 and the difference in burial depth is within 0.1m.

[0013] Preferably, the merging logic for the technical parameter entries includes: Based on the feature comparison results, if the location level data are different, the highest level data is retained as the highest level data after merging. If the maximum gradient values ​​of the points are different, the highest maximum gradient value will be retained as the merged maximum gradient value. If the burial depth data for different locations are different, the maximum burial depth data will be retained as the merged burial depth data.

[0014] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0015] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0016] As can be seen from the above technical solution, the present invention provides a method for merging and processing pipeline rupture point data. Compared with the prior art, the present invention has the following advantages: 1. This invention improves the accuracy of data comparison and merging by using a unified location information benchmark, which facilitates the accurate identification of the actual location and changing trend of pipeline damage points. Furthermore, by employing density screening and spatial centroid calculation in the selection process of the optimal reference point, the selection of reference points can be optimized, reducing sources of error and thereby improving the accuracy of damage point identification and the reliability of subsequent analysis. This enables precise monitoring and assessment of gas network damage.

[0017] 2. By introducing time feature screening and constructing a variable radius circular region based on preset regional density in the determination of the standard comparison range, the present invention can dynamically adjust the comparison range to adapt to different density distributions, thereby enhancing the flexibility and targeting of feature comparison and improving the effectiveness and scientific nature of the combined processing of technical parameters of damaged points.

[0018] 3. By comparing the hierarchical classification and priority settings in the logic, this invention can refine the criteria for judging the degree of damage and accurately select key comparison parameters according to specific conditions. This makes the data merging process of pipeline damage points consider both the overall damage level and local minor differences. At the same time, it also stipulates the principle of retaining the maximum value in the merging logic of subsequent technical parameter items, which can ensure that the merged data reflects the most serious damage situation. Even in the case of data fluctuations, it can ensure the accuracy of risk warning, further enhancing the safety of gas network operation and maintenance efficiency.

[0019] 4. This invention utilizes the spatial position calculation and judgment of reference points and any detection point, combined with spatial analysis models and discrete data analysis techniques for preliminary screening and evaluation, which can improve recognition efficiency while also enhancing recognition accuracy.

[0020] It should be understood that the descriptions in this section are not intended to identify key or essential features of embodiments of the invention, nor are they intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Of course, implementing any product of the invention does not necessarily require achieving all of the advantages described above simultaneously. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic diagram illustrating the standard comparison range determination and interface construction of the present invention; Figure 3 This is a schematic diagram of the operation interface for merging the technical parameter items of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] For details in the embodiments, please refer to Figures 1 to 3 .

[0024] like Figure 1 As shown in the figure. The method for merging and processing pipeline damage point data proposed in this embodiment of the invention includes the following steps: S1. Obtain at least two sets of historical detection data containing location information to form a dataset. The location information of the detection data can be coordinates, stakes + offsets, etc.

[0025] It is important to note that the location information in the dataset needs to be converted to coordinates in a specified coordinate system. By using a unified location information benchmark, the accuracy of data comparison and merging can be improved, making it easier to accurately identify the actual location and trend of pipeline damage points.

[0026] S2. Based on the dispersion of points in the dataset, select a set of suspected damaged points in the dataset as the optimal reference points.

[0027] The specific procedures for selecting reference points include: S21. Obtain the geographic coordinate data of the discretely distributed damaged points and suspected damaged points in the target area from the dataset; S22. Construct a circular detection area with a preset radius for the damaged points and suspected damaged points, and determine the detection area where the number of damaged reference points in the area reaches a preset density threshold through density screening; S23. For the screened detection area, calculate the spatial centroid coordinates of all damaged reference points within the detection area and generate the regional feature center point; S24. Execute the nearest neighbor matching algorithm to select the signal point with the smallest Euclidean distance to the regional feature center point from the candidate signal point subset as the optimal reference point.

[0028] For example, refer to Figure 2 Within the data within the marked box, the center point of the circular detection area is selected as the optimal reference point from among the 16 damaged points. By employing density filtering and spatial centroid calculation in the selection process of the optimal reference point, the selection of the reference point can be optimized, reducing sources of error and thereby improving the accuracy of damaged point identification and the reliability of subsequent analysis, thus enabling precise monitoring and assessment of the damage to the gas grid.

[0029] S3. Determine the standard comparison range and perform feature comparison of the technical parameters of the damage point within the standard comparison range of the optimal reference point.

[0030] The methods for determining the standard comparison range include: S31. Filter and retain location data within a specified time period based on time characteristics; S32. Construct a circular region with a variable radius, centered on the optimal reference point; S33. Based on the preset area density, determine the minimum radius of the circular area. The circular area with the minimum radius is the standard comparison range. (Refer to...) Figure 2The data with the highest degree of dispersion was determined to be 100 meters. The historical data was compared with the data from the past three years. By introducing time feature filtering and constructing a circular region with a variable radius based on the preset regional density in the determination of the standard comparison range, the comparison range can be dynamically adjusted to adapt to different density distributions. This enhances the flexibility and targeting of feature comparison and improves the effectiveness and scientific nature of the combined processing of technical parameters of the damaged points.

[0031] The comparison logic at this point is: First, compare the ranking of the optimal reference point with that of other points; If the data points are of the same level, then compare the maximum gradient value of the optimal reference point with that of other data points. If the maximum gradient values ​​of the point data are the same, then compare the burial depth of the optimal reference point with that of other point data.

[0032] The logic for classifying levels is as follows: If the maximum gradient value is ≥70, then the point is classified as level one. If the maximum gradient value is less than or equal to 50 and the level is less than 70, then the level of this point is determined to be level two. If 40 ≤ maximum gradient value < 50, then the level of this point is determined to be level three.

[0033] In summary, by comparing the hierarchical classification and priority settings in the logic, we can refine the criteria for judging the degree of damage and accurately select key comparison parameters according to specific conditions. This allows the data merging and processing of pipeline damage points to consider both the overall damage level and local subtle differences.

[0034] In addition, it should be noted that during the comparison process, priority should be given to comparing data points with a maximum gradient value difference within 10 and a burial depth difference within 0.1m.

[0035] S4. Based on the feature comparison results, select the technical parameter items that need to be merged for the damaged points, and obtain the final display and merging information.

[0036] refer to Figure 3 The merging logic for technical parameter entries at this point includes: Based on the feature comparison results, if the location level data are different, the highest level data is retained as the highest level data after merging. If the maximum gradient values ​​of the points are different, the highest maximum gradient value will be retained as the merged maximum gradient value. If the burial depth data for different locations are different, the maximum burial depth data will be retained as the merged burial depth data.

[0037] In summary, by stipulating the principle of retaining the maximum value in the merging logic of technical parameter items, it can be ensured that the merged data reflects the most severe damage situation, and the accuracy of risk warning can be guaranteed even in the case of data fluctuations, thereby further enhancing the safety and maintenance efficiency of the gas network operation.

[0038] Therefore, the method of this application utilizes the spatial position calculation and judgment of the reference point and any detection point, combined with spatial analysis model and discrete data analysis technology for preliminary screening and evaluation. The number of damaged reference points in the area is determined by density screening, and the spatial centroid coordinates of these points are calculated to generate the regional feature center point. This method can improve the recognition efficiency and the recognition accuracy, which not only helps to accurately select the optimal reference point, but also provides a solid foundation for subsequent feature comparison.

[0039] In a further specific embodiment, the location data in the table below can be used, and any positioning point can be selected as a reference point for spatial and discrete analysis with other detection points.

[0040]

[0041] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0042] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0043] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform any of the methods for merging pipeline rupture point data in the above embodiments.

[0044] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0045] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus. Memory, used to store computer programs; The processor, when executing the program stored in memory, implements the above-mentioned method for merging and processing data of pipeline network damage points.

[0046] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect Standard (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0047] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0048] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0049] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0050] It should also be noted that electronic devices include terminal devices, which can also be called terminals, user equipment, mobile stations, mobile terminals, etc. Terminal devices can be mobile phones, smart TVs, wearable devices, tablets, computers with wireless transceiver capabilities, virtual reality terminal devices, augmented reality terminal devices, wireless terminals in industrial control, wireless terminals in autonomous driving, wireless terminals in remote surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, and so on. The embodiments of this application do not limit the specific technologies or device forms used in the terminal devices.

[0051] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium (SSD), etc.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0053] Furthermore, it should be noted that if any directional indication (such as up, down, left, right, front, back, etc.) is involved in the embodiments of the present invention, the directional indication is only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0054] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, in the embodiments of this invention, "multiple" refers to two or more. Moreover, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

Claims

1. A method for merging and processing pipeline network damage point data, characterized in that, Includes the following steps: S1. Obtain at least two sets of historical detection data containing location information to form a dataset; S2. Based on the dispersion of points in the dataset, select a set of suspected damaged points in the dataset as the optimal reference points; S3. Determine the standard comparison range, and perform feature comparison of the technical parameters of the damaged points within the standard comparison range of the optimal reference point; S4. Based on the feature comparison results, select the technical parameter items that need to be merged for the damaged points, and obtain the final display and merging information.

2. The method for merging and processing pipeline damage point data as described in claim 1, characterized in that, In step S1, the location information is converted into coordinates in a specified coordinate system within the dataset.

3. The method for merging and processing pipeline damage point data as described in claim 1, characterized in that, The specific selection process for the reference point in step S2 includes: S21. Obtain the geographic coordinate data of the discretely distributed damaged points and suspected damaged points in the target area from the dataset; S22. Construct a circular detection area with a preset radius for the damaged points and suspected damaged points, and determine the detection area where the number of damaged reference points in the area reaches a preset density threshold by density screening; S23. For the screened detection area, calculate the spatial centroid coordinates of all damaged reference points within the detection area and generate the regional feature center point; S24. Execute the nearest neighbor matching algorithm to select the signal point with the smallest Euclidean distance to the feature center point of the region from the candidate signal point subset as the optimal reference point.

4. The method for merging and processing pipeline damage point data as described in claim 1, characterized in that, The method for determining the standard comparison range in step S3 includes: S31. Filter and retain location data within a specified time period based on time characteristics; S32. Construct a circular region with a variable radius, centered on the optimal reference point; S33. Determine the minimum radius of the circular region based on the preset region density. The circular region with the minimum radius is the standard comparison range.

5. The method for merging and processing pipeline damage point data as described in claim 1, characterized in that, The comparison logic in step S3 is as follows: First, compare the ranking of the optimal reference point with that of other points; If the data points are of the same level, then compare the maximum gradient value of the optimal reference point with that of other data points. If the maximum gradient values ​​of the point data are the same, then compare the burial depth of the optimal reference point with that of other point data.

6. The method for merging and processing pipeline damage point data as described in claim 5, characterized in that, The logic for classifying levels is as follows: If the maximum gradient value is ≥70, then the point is classified as level one. If the maximum gradient value is less than or equal to 50 and the level is less than 70, then the level of this point is determined to be level two. If 40 ≤ maximum gradient value < 50, then the level of this point is determined to be level three.

7. The method for merging and processing pipeline damage point data as described in claim 5, characterized in that, During the comparison process, priority is given to comparing data points with a maximum gradient value difference within 10 and a burial depth difference within 0.1m.

8. The method for merging and processing pipeline damage point data as described in claim 5, characterized in that, The merging logic for the technical parameter entries includes: Based on the feature comparison results, if the location level data are different, the highest level data is retained as the highest level data after merging. If the maximum gradient values ​​of the points are different, the highest maximum gradient value will be retained as the merged maximum gradient value. If the burial depth data for different locations are different, the maximum burial depth data will be retained as the merged burial depth data.

9. A computer-readable storage medium, characterized in that, The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

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