An automatic inspection method and device for rail transit protection areas based on artificial intelligence

Abstract

The present invention discloses an automatic inspection method and device for a rail transit protection area based on artificial intelligence, which relates to the field of maintenance and management of rail transit protection areas. The key inspection sub-areas that generate negatively correlated tight signals are sorted according to the periodic blockage frequency to obtain a sequence of the same inspection sub-areas. Within the sequence of the same inspection sub-areas, for each key inspection sub-areas, the blockage time period with the least number of blockages in the historical period is found, and the inspection frequency corresponding to this time period is marked as the inspection basis value. The inspection basis values corresponding to all key inspection sub-areas are summed and averaged to obtain the final inspection frequency, providing a relatively reasonable frequency reference for the inspection work in the rail protection area, avoiding blind inspections, and solving the problem of how to reasonably allocate inspection resources according to the blockage and inspection correlation conditions of different key inspection sub-areas in the rail transit protection area.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance and management of rail transit protection areas, and specifically relates to an automatic inspection method and device for rail transit protection areas based on artificial intelligence. Background Art

[0002] During the operation of rail transit, the drainage pipes in the rail transit protection area are crucial for ensuring the normal operation of the tracks. However, the traditional inspection methods for rail transit protection areas often rely on manual experience and have many drawbacks, such as unreasonable allocation of inspection resources, inability to adjust the inspection frequency in a timely manner according to the actual situation, and difficulty in detecting potential blockage problems in advance.

[0003] In the prior art, in the case of the continuous expansion of the scope of the rail transit protection area, there is a lack of quantitative analysis of the correlation between the number of inspections and the number of blockages, and it is impossible to adjust the inspection strategy according to the actual situation of different regions, which is likely to cause waste of inspection resources or inadequate inspections. Therefore, in this application, key inspection sub-areas are identified, and the change in the number of blockages in the key inspection sub-areas within the historical period is analyzed to reflect the blockage change law in the key inspection sub-areas. The inspection work is reasonably arranged, the historical inspection times of the key inspection sub-areas in multiple blockage periods are extracted from the inspection logs, a change curve of the inspection times is constructed, and it is combined and compared with the change curve of the number of blockages in the existing key inspection sub-areas to reflect the degree of tightness of the correlation between the number of inspections and the number of blockages within the historical period. This is beneficial to optimizing the inspection strategy, adjusting the inspection frequency for the key inspection sub-areas in a targeted manner. For each key inspection sub-area, find the blockage period with the least number of blockages within the historical period, sum up and take the average value of the inspection basis values corresponding to all key inspection sub-areas to obtain the final inspection frequency, providing a relatively reasonable frequency reference for the inspection work in the rail transit protection area, avoiding blind inspections, and solving the problem of how to reasonably allocate inspection resources according to the blockage and inspection correlation conditions of different key inspection sub-areas in the rail transit protection area. Summary of the Invention

[0004] The purpose of the present invention is to provide an automatic inspection method and device for rail transit protection areas based on artificial intelligence to solve at least one of the above-mentioned prior art problems.

[0005] In a first aspect, an automatic inspection method for rail transit protection areas based on artificial intelligence includes:

[0006] Step 1: Extract the drawings of the drainage pipes along the line through a panoramic navigation system to generate an inspection pipe area, analyze the blockage data of the inspection pipe area within the historical period, and obtain key inspection sub-areas;

[0007] Step 2: Analyze the changes in the number of congestion times in multiple key inspection sub-areas during the historical period to obtain the number of change data, and quantify the number of change data to obtain a linear change signal;

[0008] Step 3: Based on the linear change signal, extract historical inspection data of multiple key inspection sub-areas from the inspection log, perform correlation analysis on the historical inspection data and the congestion data, and obtain a negative correlation signal;

[0009] Step 4: Summarize the key inspection sub-areas that generate negative correlation signals, obtain the same inspection sub-area sequence, obtain the inspection frequency, and complete the inspection work of the key inspection sub-areas in the same inspection sub-area sequence.

[0010] Beneficial effects of the present invention:

[0011] 1. The present invention obtains the frequency mean of pipeline sub-areas in historical periods to identify key inspection sub-areas, analyzes the change in the number of blockages in key inspection sub-areas in historical periods, obtains the number of change data, quantifies the number of change data, and obtains the linear value of blockage change. The linear value of blockage change reflects the change pattern of blockage in key inspection sub-areas, thereby rationally arranging inspection work;

[0012] 2. The present invention extracts the historical inspection times of key inspection sub-areas in multiple congestion periods from inspection logs, constructs an inspection times change curve, and compares and analyzes it with the congestion times change curve of existing key inspection sub-areas. The proportion of negatively correlated periods in all historical periods and the degree of dispersion of the slope ratio of the inspection sub-curve and the congestion sub-curve in the negatively correlated period reflect the close correlation between the inspection times and the congestion times in the historical period, which is conducive to optimizing inspection strategies and adjusting the inspection frequency of key inspection sub-areas in a targeted manner.

[0013] 3. The present invention sorts the key inspection sub-areas that generate negatively correlated signals according to the periodic congestion frequency to obtain a sequence of the same inspection sub-areas. Within the sequence of the same inspection sub-areas, for each key inspection sub-area, find the congestion period with the least number of congestion in the historical period, mark the number of inspections corresponding to the period as the inspection basis value, sum and average the inspection basis values corresponding to all key inspection sub-areas, and obtain the final inspection frequency. This provides a more reasonable frequency reference for the inspection work in the rail protection area, avoids blind inspections, and solves the problem of how to reasonably allocate inspection resources in the rail transit protection area according to the congestion and inspection correlation of different key inspection sub-areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 is a flowchart of an automatic inspection method for a rail transit protection area based on artificial intelligence according to the present invention;

[0016] Figure 2 is a schematic diagram of an automatic inspection system for a rail transit protection area based on artificial intelligence according to the present invention;

[0017] Figure 3 is a schematic diagram of an automatic inspection device for a rail transit protection area based on artificial intelligence according to the present invention. Detailed implementation manners

[0018] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1

[0020] Figure 1 is a flowchart of an automatic inspection method for a rail transit protection area based on artificial intelligence provided in Embodiment 1 of the present invention. The embodiments of the present invention are applicable to reflecting the blockage change law in the key inspection sub-area through the linear value of the blockage change. This automatic inspection method for a rail transit protection area based on artificial intelligence can be executed by an automatic inspection system for a rail transit protection area based on artificial intelligence. This automatic inspection system for a rail transit protection area based on artificial intelligence can be implemented by software and / or hardware, and this automatic inspection system for a rail transit protection area based on artificial intelligence can be configured in an automatic inspection device for a rail transit protection area based on artificial intelligence. Optionally, an automatic inspection device for a rail transit protection area based on artificial intelligence can be an electronic device, and this electronic device can be a notebook, a desktop computer, a smart tablet, etc. The embodiments of the present invention do not limit this.

[0021] As Figure 1 shown, an automatic inspection method for a rail transit protection area based on artificial intelligence provided in the embodiments of the present invention specifically includes the following steps:

[0022] Step 1: Import the design drawings of the drainage pipes along the line within the track protection area into the panoramic navigation system to generate the pipeline inspection area, obtain the blockage data of the pipeline inspection area within the historical period, where the blockage data includes the blockage frequency, and compare it with the blockage frequency threshold to obtain the key inspection sub-areas;

[0023] In some embodiments, the design drawings of the drainage pipes are converted through image conversion within the panoramic navigation system to obtain the pipeline inspection area;

[0024] Divide the pipeline inspection area into several pipeline sub-areas with equal areas;

[0025] Divide the historical period into several historical time periods with equal time intervals;

[0026] Obtain the number of blockages of the pipeline sub-area within the historical time period and calculate the sum mean to obtain the periodic blockage frequency;

[0027] Compare the periodic blockage frequency with the periodic blockage frequency threshold, and the process is as follows:

[0028] If the periodic blockage frequency is greater than or equal to the periodic blockage frequency threshold, it indicates that the analyzed pipeline sub-area has a high blockage frequency within the historical period, and it is marked as a key inspection sub-area;

[0029] If the periodic blockage frequency is less than the periodic blockage frequency threshold, it indicates that the analyzed pipeline sub-area has a low blockage frequency within the historical period, and it is marked as a non-key inspection sub-area;

[0030] Step 2: Analyze the change in the number of blockages of multiple key inspection sub-areas within the historical period to obtain the change data of the number of times, where the change data of the number of times includes the linear coincidence quantity and the linear proximity value, perform quantization processing on the change data of the number of times to obtain the linear value of the blockage change, and compare it with the linear threshold of the blockage change. If the linear value of the blockage change is greater than or equal to the linear threshold of the blockage change, a linear change signal is generated;

[0031] In some embodiments, randomly select a key inspection sub-area;

[0032] For the key inspection sub-area, mark the historical time periods with blockages as blockage time periods;

[0033] Mark the historical time periods without blockages as non-blockage time periods;

[0034] Establish a two-dimensional coordinate system, where the x-axis represents time and the y-axis represents the number of blockages. Substitute the number of blockages of the key inspection sub-area within all blockage time periods into the two-dimensional coordinate system to obtain the curve of the change in the number of blockages;

[0035] Connect the two endpoint coordinates of the clogging frequency change curve to obtain the fitting clogging reference line;

[0036] It should be noted that the two endpoint coordinates of the clogging frequency change curve are not on the same horizontal line;

[0037] Substitute the two endpoint coordinates of the clogging frequency change curve into the slope formula to obtain the slope corresponding to the fitting clogging reference line, and combine the two endpoint coordinates of the clogging frequency change curve to obtain the fitting clogging reference equation: , where represents the clogging reference slope, and b represents a constant;

[0038] Exclude the two endpoint coordinates of the clogging frequency change curve;

[0039] Substitute the remaining endpoint X coordinates on the clogging frequency change curve into the fitting clogging reference equation to obtain the fitting reference coordinates ( , ), where represents the Y coordinate on the fitting clogging reference line, x represents the X coordinate on the clogging frequency change curve, and e is the total number of remaining endpoint X coordinates on the aperture change curve;

[0040] Exemplarily, combine the endpoints with the same X coordinate to obtain multiple different Y combinations;

[0041] It should be noted that the endpoint X coordinates within different Y combinations are the same, but the Y coordinates are different;

[0042] Arbitrarily select one of the different Y combinations;

[0043] Take the difference between two different Y coordinates within the different Y combination, and take the absolute value to obtain the different Y combination difference;

[0044] Calculate the different Y combination difference through the Euclidean calculation formula to obtain the linear approximation value;

[0045] Divide the clogging frequency change curve into several clogging sub-curves of equal length, obtain the partial clogging sub-curves where the clogging sub-curves coincide with the fitting comparison line, count the number of partial clogging sub-curves, and calculate the ratio with the total number of clogging sub-curves divided within the clogging frequency change curve to obtain the linear coincidence quantity;

[0046] It should be noted that the clogging frequency change curve is divided according to each clogging period;

[0047] Calculate the ratio of the linear coincidence quantity to the linear approximation value to obtain the clogging change linear value;

[0048] It should be noted that the meaning represented by the linear value of clogging change is a quantification of the tightness of the linear relationship between the clogging frequency change curve and the fitted clogging reference line, comprehensively considering two factors: the degree of curve fluctuation and the local coincidence situation. The number of linear coincidences reflects the coincidence situation between the clogging frequency change curve and the fitted clogging reference line in the local interval, and the linear proximity value reflects the degree of deviation of the curve from the fitted clogging reference line. Combining the two to obtain the linear value of clogging change is conducive to identifying the clogging change law in the key inspection sub-region, so as to reasonably arrange the inspection work;

[0049] Compare the linear value of clogging change with the linear threshold of clogging change, and the process is as follows:

[0050] If the linear value of clogging change is greater than or equal to the linear threshold of clogging change, it indicates that the difference degree between the clogging frequency change curve and the fitted clogging reference line is large, and the local coincidence degree is high, generating a linear change signal;

[0051] If the linear value of clogging change is less than the linear threshold of clogging change, it indicates that the difference degree between the clogging frequency change curve and the fitted clogging reference line is small, and the local coincidence degree is low, generating a non-linear change signal;

[0052] Based on the non-linear change signal, obtain the number of clogs that occur in the key inspection area during the clogging period, and extract the number of clogs that occur the most during the clogging period as the inspection frequency of the key inspection area;

[0053] The specific implementation scheme of the embodiment of the present invention is: by obtaining the frequency average value of the pipeline sub-region in the historical period, identifying the key inspection sub-region, analyzing the change of the clogging frequency in the historical period of the key inspection sub-region, obtaining the change data, and performing quantization processing on the change data to obtain the linear value of clogging change, so as to reflect the clogging change law in the key inspection sub-region through the linear value of clogging change, and thus reasonably arrange the inspection work.

[0054] Embodiment Two

[0055] The embodiment of the present invention provides an automatic inspection method for a rail transit protection area based on artificial intelligence, which specifically includes the following steps:

[0056] Step Three: Based on the linear change signal, extract the historical inspection data of multiple key inspection sub-regions in multiple clogging periods through the inspection log. Among them, the historical inspection data includes the historical inspection frequency. Perform correlation analysis on the historical inspection data and the clogging data to obtain the correlation tightness value, and compare it with the correlation tightness threshold. If the correlation tightness value is greater than or equal to the correlation tightness threshold, generate a negative correlation tightness signal;

[0057] In some embodiments, randomly select the key inspection sub-region;

[0058] Taking the x-axis as time and the y-axis as the number of inspections, establish a two-dimensional coordinate system, obtain the historical number of inspections in the key inspection sub-areas during multiple congestion periods, and substitute them into the two-dimensional coordinate system to obtain the inspection number change curve;

[0059] Compare and analyze the inspection number change curve with the congestion number change curve. The process is as follows:

[0060] Divide the inspection number change curve in the same way as the congestion number change curve is divided to obtain several inspection sub-curves. Among them, the number of inspection sub-curves is the same as the number of congestion sub-curves;

[0061] Arbitrarily select an inspection sub-curve and obtain the slope of the inspection sub-curve;

[0062] At the same time, obtain a congestion sub-curve within the same congestion period as the inspection sub-curve and obtain the slope of the congestion sub-curve;

[0063] Compare the slope of the inspection sub-curve with the slope of the congestion sub-curve. The process is as follows:

[0064] If the positive and negative properties of the slope of the inspection sub-curve are the same as those of the slope of the congestion sub-curve, it indicates that the number of inspections during the analyzed congestion period needs to be increased;

[0065] If the positive and negative properties of the slope of the inspection sub-curve are different from those of the slope of the congestion sub-curve, it indicates that with the increase in the number of inspections during the analyzed congestion period, the number of congestions has decreased. Mark the analyzed congestion period as a negatively correlated period;

[0066] Count the number of negatively correlated periods and calculate the ratio with the total number of historical periods within the historical cycle to obtain the negatively correlated quantity;

[0067] Extract the slopes of the inspection sub-curve and the congestion sub-curve within the negatively correlated period and calculate the ratio to obtain the negatively correlated coefficient;

[0068] Calculate the variance of the negatively correlated coefficient to obtain the negatively correlated tightness value;

[0069] Calculate the ratio of the negatively correlated quantity to the negatively correlated tightness value to obtain the unit tightness value;

[0070] Calculate the sum and average of the unit tightness values corresponding to all key inspection sub-areas to obtain the correlation tightness value;

[0071] It can be understood that the meaning of the correlation closeness value is: it is used to measure the correlation closeness between the number of inspections and the number of congestion in the historical period, by combining the proportion of negative correlation periods in all historical periods (reflected by the number of negative correlations) and the dispersion of the slope ratio of the inspection sub-curve and the congestion sub-curve in the negative correlation period (reflected by the negative correlation closeness value). Specifically, a higher negative correlation number indicates that in more periods, increasing the number of inspections is effective in reducing the number of congestion. A smaller negative correlation closeness value indicates that in the negative correlation period, the ratio of the slope of the inspection sub-curve to the slope of the congestion sub-curve is relatively stable.

[0072] The correlation closeness value is compared with the correlation closeness threshold, and the process is as follows;

[0073] If the correlation closeness value is greater than or equal to the correlation closeness threshold, it means that the negative correlation period accounts for a high proportion of the total period, and the negative correlation between the number of inspections and the number of congestion is relatively close, generating a negative correlation closeness signal;

[0074] If the correlation closeness value is less than the correlation closeness threshold, it means that the negative correlation period accounts for a high proportion of the total period, and the negative correlation between the number of inspections and the number of congestion is not close, generating a non-negative correlation closeness signal;

[0075] The specific implementation plan of the embodiment of the present invention is: extract the historical inspection times of key inspection sub-areas in multiple congestion periods from the inspection log, construct an inspection times change curve, and combine it with the congestion times change curve of the existing key inspection sub-areas for comparison and analysis. The proportion of negatively correlated periods in all historical periods and the degree of discreteness of the slope ratio of the inspection sub-curve and the congestion sub-curve in the negatively correlated period reflect the close correlation between the inspection times and the congestion times in the historical period, which is conducive to optimizing the inspection strategy and adjusting the inspection frequency of key inspection sub-areas in a targeted manner.

[0076] Example 3

[0077] Step 4: Summarize the key inspection sub-areas that generate negative correlation signals to obtain a sequence of the same inspection sub-areas. Based on the sequence of the same inspection sub-areas, obtain the inspection frequency and complete the inspection work of the key inspection sub-areas within the sequence of the same inspection sub-areas.

[0078] In some embodiments, the key inspection sub-areas that generate negative correlation close signals are sorted according to the periodic congestion frequency to obtain a sequence of the same inspection sub-areas;

[0079] In the same inspection sub-area series, randomly select a key inspection sub-area;

[0080] Obtain the congestion period with the least congestion in the key inspection sub-area during the historical period and mark it as the least congested period;

[0081] Mark the number of inspections corresponding to the least congested period as the inspection basis value;

[0082] Sum up and average the inspection basis values corresponding to all key inspection sub - areas to obtain the inspection frequency;

[0083] Exemplarily, the inspection frequency obtained through acquisition provides a relatively reasonable frequency reference for the inspection work in the track protection area, avoiding blind inspections, and solving the problem of how to reasonably allocate inspection resources according to the congestion and inspection correlation conditions of different key inspection sub - areas in the rail transit protection area;

[0084] The specific implementation plan of the embodiment of the present invention is as follows: Sort the key inspection sub - areas that generate negatively - correlated tight signals according to the periodic congestion frequency to obtain the same inspection sub - area sequence. In the same inspection sub - area sequence, for each key inspection sub - area, find the congestion period with the least number of congestion occurrences in its historical period, mark the number of inspections corresponding to this period as the inspection basis value, sum up and average the inspection basis values corresponding to all key inspection sub - areas to obtain the final inspection frequency, which provides a relatively reasonable frequency reference for the inspection work in the track protection area, avoiding blind inspections, and solving the problem of how to reasonably allocate inspection resources according to the congestion and inspection correlation conditions of different key inspection sub - areas in the rail transit protection area.

[0085] Embodiment 4

[0086] As Figure 2 , the embodiment of the present invention provides an automatic inspection system for rail transit protection areas based on artificial intelligence, including:

[0087] Key inspection screening module: Extract the drawings of drainage pipelines along the line through the panoramic navigation system to generate the inspection pipeline area, analyze the congestion data of the inspection pipeline area in the historical period to obtain key inspection sub - areas;

[0088] Congestion linear analysis module: Analyze the change in the number of congestion occurrences in the key inspection sub - areas in the historical period to obtain the change data of the number of occurrences, and perform quantization processing on the change data of the number of occurrences to obtain a linear change signal;

[0089] Correlation tightness analysis module: Based on the linear change signal, extract the historical inspection data of multiple key inspection sub - areas from the inspection log, and perform correlation analysis on the historical inspection data and the congestion data to obtain a negatively - correlated tight signal;

[0090] Inspection strategy adjustment module: Summarize the key inspection sub - areas that generate negatively - correlated tight signals to obtain the same inspection sub - area sequence, obtain the inspection frequency, and complete the inspection work on the key inspection sub - areas within the same inspection sub - area sequence.

[0091] Example 5

[0092] Reference Figure 3 , the embodiment of the present invention further provides a computer device 3, including: a memory 302, a processor 301, and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements an automatic inspection method for a rail transit protection area based on artificial intelligence as described in any one of the above methods.

[0093] The computer device 3 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that

[0094] Figure 3 This is only an example of the computer device 3 and does not constitute a limitation on the computer device 3. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0095] The so-called processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, 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, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0096] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as the hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 3. Further, the memory 302 may also include both the internal storage unit and the external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0097] Embodiment Six

[0098] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements an automatic inspection method for a rail transit protection area based on artificial intelligence as described in any one of the above methods.

[0099] In this embodiment, if the 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 storage medium. Based on this understanding, to implement all or part of the processes in the above embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium may not be an electrical carrier signal and a telecommunication signal.

[0100] In the above embodiments, the descriptions of the various embodiments have their own focuses. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0102] In the embodiments disclosed in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0103] Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the device or unit can be in electrical, mechanical or other forms.

[0104] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation.

[0106] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. An automatic inspection method for rail transit protection areas based on artificial intelligence, characterized in that, It includes the following steps: Obtain the pipeline inspection area, analyze the blockage data of the pipeline inspection area within the historical period, and obtain the key inspection sub-areas; Analyze the changes in the number of blockages in multiple key inspection sub-areas within the historical period, obtain the linear approximation value and the number of linear coincidences, perform quantization processing, and generate a linear change signal; Extract the historical inspection data of multiple key inspection sub-areas, conduct a correlation analysis of the historical inspection data and the blockage data, obtain the unit tightness value, and compare it with the unit tightness threshold to generate a negative correlation tightness signal; The process of conducting a correlation analysis of the historical inspection data and the blockage data is as follows: Arbitrarily select a key inspection sub-area; Construct a curve of the change in the number of inspections and divide it to obtain several inspection sub-curves; Arbitrarily obtain the slope of an inspection sub-curve; Simultaneously obtain the slope of a blockage sub-curve during the same blockage period as the inspection sub-curve; If the signs of the slopes of the inspection sub-curve and the blockage sub-curve are different, record the analyzed blockage period as a negatively correlated period; The analysis process of the unit tightness value is as follows: Statistical the proportion of the number of negatively correlated periods; Extract the slopes of the inspection sub-curve and the blockage sub-curve during the negatively correlated period and calculate the ratio to obtain the negative correlation coefficient; Calculate the variance of the negative correlation coefficient to obtain the negative correlation tightness value; Calculate the ratio of the number of negatively correlated periods to the negative correlation tightness value to obtain the unit tightness value; Summarize the key inspection sub-areas with negative correlation tightness signals to obtain the same inspection sub-area sequence, obtain the inspection frequency, and complete the inspection work on the key inspection sub-areas within the same inspection sub-area sequence; The process of summarizing the same inspection sub-area sequence and obtaining the inspection frequency is as follows: Summarize the key inspection sub-areas with negative correlation tightness signals according to the periodic blockage frequency to obtain the same inspection sub-area sequence, and arbitrarily select a key inspection sub-area; Record the blockage period with the least number of blockages as the least blockage period, and use the corresponding number of inspections as the inspection basis value; Sum and average the inspection basis values corresponding to all key inspection sub-areas to obtain the inspection frequency.

2. The automatic inspection method for the rail transit protection area based on artificial intelligence according to claim 1, wherein, The method for obtaining the key inspection sub-areas is as follows: Divide the pipeline inspection area into several pipeline sub-areas with equal areas; Obtain the blockage frequency of the pipeline sub-areas within the historical period; mark the pipeline sub-areas whose blockage frequency exceeds the blockage frequency threshold as key inspection sub-areas.

3. The automatic inspection method for rail transit protection areas based on artificial intelligence according to claim 1, characterized in that, The analysis process of the linear approximation value is as follows: Arbitrarily select a key inspection sub-area; Mark the historical periods with blockages as blockage periods; Construct a curve of the change in the number of blockages over time; Connect the two endpoint coordinates of the curve of the change in the number of blockages to obtain a fitted blockage reference line, and obtain the slope to get the fitted blockage reference equation; Excluding the two endpoint coordinates of the curve of the change in the number of blockages, substitute the remaining endpoint X coordinates into the fitted blockage reference equation to obtain the fitted reference coordinates; Combine the two different Y coordinates corresponding to the same X coordinate of the endpoints to obtain multiple groups of different Y combinations; Arbitrarily select a different Y combination, subtract the two different Y coordinates, take the absolute value, and calculate the difference of the different Y combinations through the Euclidean calculation formula to obtain the linear approximation value.

4. The automatic inspection method for the rail transit protection area based on artificial intelligence according to claim 1, wherein, The analysis process of the linear coincidence quantity is as follows: Divide the curve of the change in the number of blockages into several blockage sub-curves of equal length, obtain the partial blockage sub-curves where the blockage sub-curves coincide with the fitting comparison line, count the proportion of the number of partial blockage sub-curves, and obtain the linear coincidence quantity.

5. The automatic inspection method for the rail transit protection area based on artificial intelligence according to claim 1, wherein, The process of generating the linear change signal is as follows: Calculate the ratio of the linear coincidence quantity to the linear proximity value to obtain the linear value of the blockage change; If the linear value of the blockage change is greater than or equal to the linear threshold of the blockage change, generate a linear change signal.

6. The automatic inspection method for the rail transit protection area based on artificial intelligence according to claim 1, wherein, The process of generating the negatively correlated tight signal is as follows: Perform a sum and average calculation on the unit tightness values corresponding to all key inspection sub-areas to obtain the correlation tightness value; If the correlation tightness value is greater than or equal to the correlation tightness threshold, generate a negatively correlated tight signal.

7. An automatic inspection device for a rail transit protection area based on artificial intelligence, characterized in that, The device includes: Key inspection screening module: Obtain the inspected pipeline area, analyze the blockage data in the inspected pipeline area within the historical period, and obtain the key inspection sub-areas; Blockage linear analysis module: Analyze the change in the number of blockages in multiple key inspection sub-areas within the historical period, obtain the linear proximity value and the linear coincidence quantity, perform quantization processing, and generate a linear change signal; Correlation tight analysis module: Extract the historical inspection data of multiple key inspection sub-areas, perform correlation analysis on the historical inspection data and the blockage data to obtain the unit tightness value, and compare it with the unit tightness threshold to generate a negatively correlated tight signal; The process of performing correlation analysis on the historical inspection data and the blockage data is as follows: Arbitrarily select a key inspection sub-area; Construct a curve of the change in the number of inspections and divide it to obtain several inspection sub-curves; Arbitrarily obtain the slope of an inspection sub-curve; At the same time, obtain the slope of a blockage sub-curve during the same blockage period as the inspection sub-curve; If the positive and negative of the slope of the inspection sub-curve are different from the slope of the blockage sub-curve, record the analyzed blockage period as a negatively correlated period; The analysis process of the unit tightness value is as follows: Statistical proportion of the number of negatively correlated periods; Extract the slopes of the inspection sub-curve and the blockage sub-curve during the negatively correlated period and perform a ratio calculation to obtain the negative correlation coefficient; Perform a variance calculation on the negative correlation coefficient to obtain the negative correlation tightness value; Perform a ratio calculation on the number of negatively correlated periods and the negative correlation tightness value to obtain the unit tightness value; Inspection strategy adjustment module: Summarize the key inspection sub-areas of the negatively correlated tight signal to obtain the same inspection sub-area sequence, obtain the inspection frequency, and complete the inspection work on the key inspection sub-areas within the same inspection sub-area sequence; The process of summarizing the same inspection sub-area sequence and obtaining the inspection frequency is as follows: Summarize the key inspection sub-areas of the negatively correlated tight signal according to the periodic blockage frequency to obtain the same inspection sub-area sequence, and arbitrarily select a key inspection sub-area; Record the blockage period with the least number of blockages as the least blockage period, and use the corresponding number of inspections as the inspection basis value; Perform a sum and average calculation on the inspection basis values corresponding to all key inspection sub-areas to obtain the inspection frequency.

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