Intelligent Detection and Analysis Device for Brake Shoe Cracks of Subway Vehicles and Its Control Method

Abstract

The present invention discloses an intelligent detection and analysis device for brake shoe cracks of subway vehicles and its control method, which relates to the technical fields of railways, subways and electromechanical equipment. The detection and analysis device includes a camera assembly, the camera assembly communicates with a chip assembly, the chip assembly communicates with a main chip, and the operation result of the main chip is displayed through a display; it also includes an audio sensor and an alarm; the information collected by the audio sensor is input into the main chip, and the main chip can drive the alarm to give an alarm. The control method applied to the above detection and analysis device can automatically detect the number and position information of cracks on each brake shoe, judge the state of the brake shoe and draw a conclusion on whether replacement is needed, and visually display it through the display; the audio sensor is used to assist in analyzing the brake shoe cracks, and when the vehicle noise is detected to be too loud, the alarm emits an alarm sound. The present invention solves the problems existing in the traditional detection methods, such as high consumption of manpower and material resources and low detection efficiency.

Description

Technical Field

[0001] The present invention relates to the technical fields of railways, subways and electromechanical equipment, and specifically relates to an intelligent detection and analysis device for brake shoe cracks of subway vehicles and a control method therefor. Background Technique

[0002] The brake shoe is an important device in the braking system of subway vehicles. During the braking process of the vehicle, braking force is generated through the friction between the brake shoe and the wheel set, thereby stopping the vehicle. In the braking system of subway vehicles, a certain number or length of cracks are allowed to exist in the brake shoe; that is, when the cracks are controlled within a certain range and do not cause serious consequences, replacement is not necessary; however, if there are too many or too long cracks, a new brake shoe must be replaced.

[0003] When judging whether a brake shoe needs to be replaced, it is first necessary to check whether the brake shoe is in a normal state. The traditional inspection method requires maintenance staff to get under the subway vehicle and manually observe the length and number of cracks in the brake shoe using a flashlight, and perform manual statistics and subsequent analysis and judgment. This method shows problems such as high labor intensity, inconvenient operation, high consumption of manpower and material resources, unstable detection accuracy, and low detection efficiency in actual use. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent detection and analysis device for brake shoe cracks of subway vehicles and a control method therefor, which can intelligently and quickly detect and count the number and position information of brake shoe cracks, thereby automatically judging and displaying the state of the brake shoe and whether replacement is needed, so as to solve the problems of high consumption of manpower and material resources and low detection efficiency existing in the traditional manual inspection method for brake shoe cracks.

[0005] The present invention is implemented by adopting the following technical solutions:

[0006] An intelligent detection and analysis device for brake shoe cracks of subway vehicles includes a camera assembly. The camera assembly communicates with a chip assembly, the chip assembly communicates with a main chip, and the operation result of the main chip is displayed through a display. All brake shoes in the subway vehicle are photographed by the camera assembly, and the obtained images are sent to the chip assembly for processing, so that the number and position information of the cracks of each brake shoe can be obtained. The main chip integrates the information corresponding to each brake shoe and drives the display to display, so that maintenance personnel can intuitively obtain the detection results.

[0007] Furthermore, it also includes an audio sensor and an alarm; the information collected by the audio sensor is input into the main chip, and the main chip drives the alarm to give an alarm. The audio sensor is used for auxiliary analysis of brake shoe cracks; the alarm can adopt one of the existing technologies, and its function is to remind the driver or maintenance personnel to check the brake shoe state information displayed on the display in time, that is, the number, position of the cracks of each brake shoe and whether replacement is needed, so as to effectively prevent serious consequences caused by untimely handling.

[0008] Further, the chip in the chip component is an FPGA chip, the main chip is an ARM chip, and the display is a touch screen. The FPGA chip is a further developed product based on programmable devices such as PAL, GAL, and CPLD. It not only solves the deficiencies of custom circuits but also overcomes the shortcomings of the limited number of gate circuits in the original programmable devices, and is suitable for the multi-channel brake shoe system to which the present invention is applied; the ARM chip has the advantages of high performance, low power consumption, and low cost, which helps to reduce the overall cost of this detection and analysis device while ensuring the working effect; the touch screen can visually display various information about the brake shoe cracks and can also achieve a certain man-machine interaction function.

[0009] Further, the camera component includes 1 - N cameras, and the chip component includes 1 - N chips, where N is the number of brake shoes; each brake shoe corresponds to one camera and one chip. In this detection and analysis device, the number of cameras and chips is the same as the number of brake shoes, the number of main chips, touch screens, and audio sensors is one, and the number of alarms can be one or more.

[0010] A control method for an intelligent detection and analysis device for brake shoe cracks of subway vehicles, which is applied to the above-mentioned intelligent detection and analysis device, specifically includes the following steps:

[0011] S1: Each camera collects image information of the corresponding brake shoe from a certain direction, communicates with the corresponding chip, and sends the image information to the chip;

[0012] S2: Each chip analyzes the received image information and makes a statistical judgment;

[0013] S3: Each chip communicates with the main chip and sends the result of the statistical judgment to the main chip;

[0014] S4: The main chip drives the display to display the results of the statistical judgments received from each chip.

[0015] Through the above steps, this detection and analysis device automatically detects the number and position information of cracks on each brake shoe, judges the state of the brake shoe, and draws a conclusion on whether replacement is needed; the above information and conclusion are visually displayed through the display. The acquisition direction of the brake shoe image by the camera is not limited, and generally, a front view is taken.

[0016] Further, it also includes the following steps:

[0017] S5: The audio sensor automatically collects audio signals and inputs them to the main chip;

[0018] S6: The main chip analyzes the received audio signals and drives the alarm to alarm when the signal is too large.

[0019] Through the above steps, the audio sensor realizes the auxiliary analysis of the brake shoe crack, that is, when the vehicle noise is detected to be too loud, the main chip drives the alarm to emit an alarm sound.

[0020] Further, in S2, the chip counts the number and location of cracks and judges whether replacement is needed according to the brake shoe state; in S4, the display shows the number and location of cracks in the corresponding brake shoe counted by each chip, as well as the result of whether replacement is needed. Among them, S2 is specifically implemented through the following sub-steps:

[0021] S21: After the chip receives the image of the corresponding brake shoe, it performs grayscale transformation on the image;

[0022] S22: The chip calculates the number of cracks in the brake shoe image and simultaneously performs region segmentation on the image;

[0023] S23: The chip identifies the cracks in the brake shoe image through image recognition technology. After recognition, it summarizes the number of cracks in each area of the brake shoe and counts the crack locations;

[0024] S24: The chip compares the summary result in S23 with the established upper limit value of each area to judge the final result of whether replacement is needed.

[0025] Through the above steps, the chip intelligently analyzes the image information of each brake shoe, can conveniently and quickly calculate information such as the number and location of cracks, and makes a comprehensive judgment to obtain a conclusion on whether the brake shoe needs to be replaced.

[0026] Further, the specific process of region segmentation in S22 includes the following sub-steps:

[0027] S22-1: Denote the width of the brake shoe shown in each image as M and the length as N; where the M value and N value of brake shoes of different vehicles are slightly different.

[0028] S22-2: Taking the center of the brake shoe shown in the image as the origin, establish an orthogonal rectangular coordinate system in the image;

[0029] S22-3: Then, taking the origin of the orthogonal rectangular coordinate system as the center, delimit a quasi-elliptical area, and the equation of the quasi-elliptical area is:

[0030] K 2 / y 2 +J 2 / x 2 =1, J = 0.698M, K = 0.763N;

[0031] S22-4: Based on S22-2 and S22-3, the chip divides the brake shoe image into 20 sub-regions, denoted as A1, A2, A3, A4, B1, B2, B3, B4, C1, C2, C3, C4, D1, D2, D3, D4, E1, E2, E3, and E4 respectively. Among them:

[0032] The domain of the A1 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 <1;

[0033] The domain of the A2 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 <1;

[0034] The domain of the A3 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 <1;

[0035] The domain of the A4 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 <1;

[0036] The domain of the B1 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 >1;

[0037] The domain of the B2 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 >1;

[0038] The domain of the B3 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 >1;

[0039] The domain of area B4 satisfies the restrictive conditions: x ∈ (0, J / 2), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 > 1;

[0040] The domain of area C1 satisfies the restrictive conditions: x ∈ (-J / 2, 0), y ∈ (K / 2, N / 2);

[0041] The domain of area C2 satisfies the restrictive conditions: x ∈ (0, J / 2), y ∈ (K / 2, N / 2);

[0042] The domain of area C3 satisfies the restrictive conditions: x ∈ (-J / 2, 0), y ∈ (-N / 2, -K / 2);

[0043] The domain of area C4 satisfies the restrictive conditions: x ∈ (0, J / 2), y ∈ (-N / 2, -K / 2);

[0044] The domain of area D1 satisfies the restrictive conditions: x ∈ (-M / 2, -J / 2), y ∈ (K / 2, N / 2);

[0045] The domain of area D2 satisfies the restrictive conditions: x ∈ (J / 2, M / 2), y ∈ (K / 2, N / 2);

[0046] The domain of area D3 satisfies the restrictive conditions: x ∈ (-M / 2, -J / 2), y ∈ (-N / 2, -K / 2);

[0047] The domain of area D4 satisfies the restrictive conditions: x ∈ (J / 2, M / 2), y ∈ (-N / 2, -K / 2);

[0048] The domain of area F1 satisfies the restrictive conditions: x ∈ (-M / 2, -J / 2), y ∈ (0, K / 2);

[0049] The domain of area E2 satisfies the restrictive conditions: x ∈ (J / 2, M / 2), y ∈ (0, K / 2);

[0050] The domain of area E3 satisfies the restrictive conditions: x ∈ (-M / 2, -J / 2), y ∈ (-K / 2, 0);

[0051] The domain of area E4 satisfies the restrictive conditions: x ∈ (J / 2, M / 2), y ∈ (-K / 2, 0).

[0052] Through the above steps, each brake shoe image can be divided into 20 small areas. Based on these subdivided areas, the chip can perform zonal statistics on cracks through image recognition technology, thereby reducing the difficulty of chip recognition and statistical calculation, and contributing to the simplicity and intuitiveness of subsequent display.

[0053] Further, the 20 small regions in S22-4 can be correspondingly merged into five large regions A, B, C, D, and E, where:

[0054] Let the number of cracks in regions A1, A2, A3, and A4 in the brake shoe image be recorded as a1, a2, a3, and a4 respectively. Then the total number of cracks a in region A (region A1+A2+A3+A4) is a = a1 + a2 + a3 + a4;

[0055] Let the number of cracks in regions B1, B2, B3, and B4 in the brake shoe image be recorded as b1, b2, b3, and b4 respectively. Then the total number of cracks b in region B (region B1+B2+B3+B4) is b = b1 + b2 + b3 + b4;

[0056] Let the number of cracks in regions C1, C2, C3, and C4 in the brake shoe image be recorded as c1, c2, c3, and c4 respectively. Then the total number of cracks c in region C (region C1+C2+C3+C4) is c = c1 + c2 + c3 + c4;

[0057] Let the number of cracks in regions D1, D2, D3, and D4 in the brake shoe image be recorded as d1, d2, d3, and d4 respectively. Then the total number of cracks d in region D (region D1+D2+D3+D4) is d = d1 + d2 + d3 + d4;

[0058] Let the number of cracks in regions E1, E2, E3, and E4 in the brake shoe image be recorded as e1, e2, e3, and e4 respectively. Then the total number of cracks e in region E (region E1+E2+E3+E4) is e = e1 + e2 + e3 + e4.

[0059] Through the above steps, the chip integrates and calculates the number and location information of the cracks in the five large regions of the brake shoe image, facilitating subsequent result judgment and display.

[0060] Further, if the following conditions are met:

[0061] a ≥ k1, the brake shoe must be replaced.

[0062] a < k1 but b ≥ k2, then the brake shoe must be replaced.

[0063] a < k1 and b < k2 but e ≥ k3, then the brake shoe must be replaced.

[0064] a < k1 and b < k2 and e < k3 but c ≥ k4, then the brake shoe must be replaced.

[0065] a < k1 and b < k2 and e < k3 and c < k4 but d ≥ k5, then the brake shoe must be replaced.

[0066] If a < k1, b < k2, e < k3, c < k4, and d < k5, then the brake shoe does not need to be replaced.

[0067] Wherein, when M > 80, k1 takes the value of 1, k2 takes the value of 3, k3 takes the value of 4, k4 takes the value of 11, and k5 takes the value of 14; when M < 80, k1 takes the value of 1, k2 takes the value of 4, k3 takes the value of 6, k4 takes the value of 15, and k5 takes the value of 16.

[0068] Through the above steps, the chip calculates the number and location information of the cracks on the brake shoe, and determines whether the brake shoe needs to be replaced; for other brake shoes on this vehicle, the same above steps are used for detection and analysis; the information of each brake shoe on the vehicle is sent to the main chip through the corresponding chip respectively, and the main chip drives the display to show the results such as the number and location of the cracks on each brake shoe and whether it needs to be replaced; at the same time, the main chip collects the audio signal output by the audio sensor to assist in detecting the state of the vehicle brake shoe.

[0069] The beneficial effects achieved by the present invention are:

[0070] (1) The camera component is used to collect image information of each brake shoe of the vehicle, and the chip component is used to perform regional information statistics on the image, obtaining the number and location information of the cracks in each region and determining whether replacement is needed; compared with the traditional manual inspection method for brake shoe cracks, the present invention can quickly and effectively judge whether the brake shoe needs to be replaced, and has high image detection accuracy, high automation degree, and more accurate judgment, which can greatly improve the detection efficiency of vehicle maintenance personnel for vehicles and reduce the labor intensity of maintenance personnel.

[0071] (2) The main chip summarizes the crack information in each region of each brake shoe and drives the display for intuitive display, eliminating the cumbersome manual recording process in the traditional method; further, through the audio sensor and the alarm, the auxiliary analysis of brake shoe cracks and automatic alarm prompts are realized. Compared with the prior art, the analysis and reminder functions of the present invention are more comprehensive, and have higher intelligence, more practicality and convenience. Description of the Drawings

[0072] Figure 1 is the flow chart of the detection and analysis principle in the embodiment of the present invention;

[0073] Figure 2 is the regional segmentation diagram of the brake shoe structure of the subway vehicle of the present invention. Detailed Embodiments

[0074] To clearly illustrate the solution in the present invention, the following further description is made with reference to the drawings:

[0075] Please refer to Figure 1 , Embodiment 1 of the present invention:

[0076] An intelligent detection and analysis device for brake shoe cracks of subway vehicles, including a camera assembly. The camera assembly communicates with an FPGA chip assembly, and the FPGA chip assembly communicates with an ARM chip. The operation result of the ARM chip is displayed through a touch screen. Further, the detection and analysis device also includes an audio sensor and a buzzer. The information collected by the audio sensor is input into the ARM chip, and the ARM chip can drive the buzzer to alarm. Among them:

[0077] The camera assembly includes 1 - N cameras, and the FPGA chip assembly includes 1 - N chips, where N is the number of brake shoes. Each brake shoe corresponds to a camera and an FPGA chip. In this embodiment, N takes the value of 8, that is, the vehicle to which the detection and analysis device acts in this embodiment is provided with 8 brake shoes, and each brake shoe corresponds to a camera and an FPGA chip. In addition, this device also includes an ARM chip, an audio sensor and a buzzer.

[0078] The working principle of this embodiment is as follows:

[0079] All brake shoes inside the subway vehicle are photographed through the camera assembly, and the obtained images are sent to the FPGA chip assembly for processing, and then the crack quantity and position information of each brake shoe can be obtained. The ARM chip integrates the information corresponding to each brake shoe and drives the touch screen to display, so that the maintenance personnel can intuitively obtain the detection results. The audio sensor is used for auxiliary analysis of brake shoe cracks; the buzzer is used to remind the driver or maintenance personnel to check the brake shoe status information displayed on the display in time, that is, the crack quantity, position of each brake shoe and whether it needs to be replaced, so as to effectively prevent serious consequences caused by untimely processing.

[0080] Please refer to Figures 1 to 2 , Embodiment 2 of the present invention:

[0081] A control method for an intelligent detection and analysis device for brake shoe cracks of subway vehicles, which is applied to the detection and analysis device described in Embodiment 1, and specifically includes the following steps:

[0082] S1: Eight cameras respectively collect the front views of the corresponding brake shoes, communicate with their respective corresponding FPGA chips, and send the image information to the FPGA chips;

[0083] S2: Each FPGA chip analyzes the received image information and makes statistical judgments;

[0084] S3: Each FPGA chip communicates with the ARM chip and sends the statistical judgment results to the ARM chip;

[0085] S4: The ARM chip drives the display to display the statistical judgment results received by each FPGA chip.

[0086] S5: The audio sensor automatically collects audio signals and inputs them into the ARM chip;

[0087] S6: The ARM chip analyzes the received audio signals and drives the buzzer to alarm when the signals are too large.

[0088] Through the above steps, the number and position information of cracks on each brake shoe can be automatically detected, the state of each brake shoe can be judged, and a conclusion on whether replacement is needed can be obtained. In addition, the audio sensor is used to assist in analyzing the cracks on the brake shoe. That is, when the vehicle noise is detected to be too large, the ARM chip will drive the buzzer to emit an alarm sound, thus reminding the maintenance personnel to pay attention in time and take measures.

[0089] Among them, in S2, each FPGA chip counts the number and position of cracks on the corresponding brake shoe and judges whether replacement is needed according to the brake shoe state; in S4, the touch screen displays the number and position of cracks in the corresponding brake shoe counted by each FPGA chip, as well as the result of the judgment on whether replacement is needed. In the present invention, the analysis and judgment principles of 8 FPGA chips are the same. Below, taking FPGA chip 1, camera 1, and brake shoe 1 as examples, the sub-steps of S2 are described in detail:

[0090] S21: FPGA chip 1 receives the front view of brake shoe 1 taken by camera 1, and then performs grayscale transformation on the image;

[0091] S22: FPGA chip 1 calculates the number of cracks in the front view of brake shoe 1 and simultaneously divides the image into regions;

[0092] S23: FPGA chip 1 identifies the cracks in the front view of brake shoe 1 through image recognition technology, summarizes the number of cracks in each region of brake shoe 1 after recognition, and counts the crack positions;

[0093] S24: FPGA chip 1 compares the summary result in S23 with the established upper limit value of each region, thereby judging the final result of whether brake shoe 1 needs to be replaced.

[0094] Among them, the specific process of region division in S22 includes the following sub-steps:

[0095] S22-1: Denote the width of brake shoe 1 shown in the front view as M and the length as N;

[0096] S22-2: Taking the center of brake shoe 1 shown in the front view as the origin, establish an orthogonal rectangular coordinate system in the front view;

[0097] S22-3: Then, taking the origin of the orthogonal rectangular coordinate system as the center, delimit a quasi-elliptical region. The equation of the quasi-elliptical region is:

[0098] K 2 / y 2 +J 2 / x 2 = 1, J = 0.698M, K = 0.763N;

[0099] S22-4: Based on S22-2 and S22-3, the FPGA chip 1 divides the front view of the brake shoe 1 into 20 sub-regions, denoted as A1, A2, A3, A4, B1, B2, B3, B4, C1, C2, C3, C4, D1, D2, D3, D4, E1, E2, E3, and E4. Among them:

[0100] The domain of the A1 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 < 1;

[0101] The domain of the A2 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 < 1;

[0102] The domain of the A3 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 < 1;

[0103] The domain of the A4 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 < 1;

[0104] The domain of the B1 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 > 1;

[0105] The domain of the B2 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (0, K / 2) and K 2 / y 2 +J 2 / x 2 > 1;

[0106] The domain of Region B3 satisfies the following constraints: x ∈ (-J / 2, 0), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 > 1;

[0107] The domain of Region B4 satisfies the following constraints: x ∈ (0, J / 2), y ∈ (-K / 2, 0) and K 2 / y 2 +J 2 / x 2 > 1;

[0108] The domain of Region C1 satisfies the following constraints: x ∈ (-J / 2, 0), y ∈ (K / 2, N / 2);

[0109] The domain of Region C2 satisfies the following constraints: x ∈ (0, J / 2), y ∈ (K / 2, N / 2);

[0110] The domain of Region C3 satisfies the following constraints: x ∈ (-J / 2, 0), y ∈ (-N / 2, -K / 2);

[0111] The domain of Region C4 satisfies the following constraints: x ∈ (0, J / 2), y ∈ (-N / 2, -K / 2);

[0112] The domain of Region D1 satisfies the following constraints: x ∈ (-M / 2, -J / 2), y ∈ (K / 2, N / 2);

[0113] The domain of Region D2 satisfies the following constraints: x ∈ (J / 2, M / 2), y ∈ (K / 2, N / 2);

[0114] The domain of Region D3 satisfies the following constraints: x ∈ (-M / 2, -J / 2), y ∈ (-N / 2, -K / 2);

[0115] The domain of Region D4 satisfies the following constraints: x ∈ (J / 2, M / 2), y ∈ (-N / 2, -K / 2);

[0116] The domain of Region F1 satisfies the following constraints: x ∈ (-M / 2, -J / 2), y ∈ (0, K / 2);

[0117] The domain of Region E2 satisfies the following constraints: x ∈ (J / 2, M / 2), y ∈ (0, K / 2);

[0118] The domain of Region E3 satisfies the following constraints: x ∈ (-M / 2, -J / 2), y ∈ (-K / 2, 0);

[0119] The domain of Region E4 satisfies the following constraints: x ∈ (J / 2, M / 2), y ∈ (-K / 2, 0).

[0120] Further, the 20 small regions in S22-4 can be correspondingly merged into five large regions A, B, C, D, and E, where:

[0121] Let the number of cracks in regions A1, A2, A3, and A4 in the front view of the brake shoe 1 be denoted as a1, a2, a3, and a4 respectively. Then the total number of cracks a in region A (region A1 + A2 + A3 + A4) is a = a1 + a2 + a3 + a4;

[0122] Let the number of cracks in regions B1, B2, B3, and B4 in the front view of the brake shoe 1 be denoted as b1, b2, b3, and b4 respectively. Then the total number of cracks b in region B (region B1 + B2 + B3 + B4) is b = b1 + b2 + b3 + b4;

[0123] Let the number of cracks in regions C1, C2, C3, and C4 in the front view of the brake shoe 1 be denoted as c1, c2, c3, and c4 respectively. Then the total number of cracks c in region C (region C1 + C2 + C3 + C4) is c = c1 + c2 + c3 + c4;

[0124] Let the number of cracks in regions D1, D2, D3, and D4 in the front view of the brake shoe 1 be denoted as d1, d2, d3, and d4 respectively. Then the total number of cracks d in region D (region D1 + D2 + D3 + D4) is d = d1 + d2 + d3 + d4;

[0125] Let the number of cracks in regions F1, E2, E3, and E4 in the front view of the brake shoe 1 be denoted as e1, e2, e3, and e4 respectively. Then the total number of cracks e in region E (region F1 + E2 + E3 + E4) is e = e1 + e2 + e3 + e4.

[0126] Further, if the following conditions are met:

[0127] a ≥ k1, then the brake shoe 1 must be replaced.

[0128] a < k1 but b ≥ k2, then the brake shoe 1 must be replaced.

[0129] a < k1 and b < k2 but e ≥ k3, then the brake shoe 1 must be replaced.

[0130] a < k1 and b < k2 and e < k3 but c ≥ k4, then the brake shoe 1 must be replaced.

[0131] a < k1 and b < k2 and e < k3 and c < k4 but d ≥ k5, then the brake shoe 1 must be replaced.

[0132] a < k1 and b < k2 and e < k3 and c < k4 and d < k5, then the brake shoe 1 does not need to be replaced.

[0133] Among them, when M > 80, k1 takes the value of 1, k2 takes the value of 3, k3 takes the value of 4, k4 takes the value of 11, and k5 takes the value of 14; when M < 80, k1 takes the value of 1, k2 takes the value of 4, k3 takes the value of 6, k4 takes the value of 15, and k5 takes the value of 16.

[0134] Through the above steps, the FPGA chip 1 calculates the number and position information of the cracks on the brake shoe 1, and determines whether the brake shoe needs to be replaced; for the other 7 brake shoes on this vehicle, the above same steps are used for detection and analysis; the information of each brake shoe on the vehicle is sent to the ARM chip through the corresponding FPGA chip, and the ARM chip drives the touch screen to display the results such as the number and position of the cracks on each brake shoe and whether it needs to be replaced; meanwhile, the ARM chip collects the audio signal output by the audio sensor to assist in detecting the state of the vehicle brake shoe.

[0135] Of course, the above content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of the embodiments of the present invention. The present invention is not limited to the above examples either. Equivalent changes and improvements made by those of ordinary skill in the art within the essence of the present invention shall fall within the scope covered by the patent of the present invention.

Claims

1. An intelligent detection and analysis device for brake shoe cracks of subway vehicles, characterized in that: it includes a camera component, the camera component communicates with a chip component, the chip component communicates with a main chip, and the operation result of the main chip is displayed through a display; it also includes an audio sensor and an alarm; the information collected by the audio sensor is input into the main chip, and the main chip drives the alarm to sound an alarm; the chips in the chip component adopt FPGA chips, the main chip adopts an ARM chip, and the display adopts a touch screen; the camera component includes 1-N cameras, the chip component includes 1-N chips, where N is the number of brake shoes; each brake shoe corresponds to a camera and a chip; An intelligent detection and analysis device control method for brake shoe cracks of subway vehicles using the above intelligent detection and analysis device for brake shoe cracks of subway vehicles specifically includes the following steps: S1: Each camera collects image information of the corresponding brake shoe from a certain direction, communicates with the corresponding chip, and sends the image information to the chip; S2: Each chip analyzes the received image information and makes a statistical judgment; S3: Each chip communicates with the main chip and sends the result of the statistical judgment to the main chip; S4: The main chip drives the display to display the results of the statistical judgments of each chip received; In S2, the chip counts the number and position of cracks and judges whether replacement is needed according to the brake shoe state; in S4, the display shows the number and position of cracks in the corresponding brake shoe counted by each chip, as well as the result of the judgment on whether replacement is needed; among them, S2 is specifically implemented through the following sub-steps: S21: After the chip receives the image of the corresponding brake shoe, it performs gray-scale transformation on the image; S22: The chip calculates the number of cracks in the brake shoe image and simultaneously performs region segmentation on the image; S23: The chip identifies the cracks in the brake shoe image through image recognition technology, summarizes the number of cracks in each region of the brake shoe after recognition, and counts the crack positions; S24: The chip compares the summary result in S23 with the established upper limit value of each region to judge and obtain the final result of whether replacement is needed.

2. The intelligent detection and analysis device for brake shoe cracks of subway vehicles according to claim 1, characterized in that: the intelligent detection and analysis device control method for brake shoe cracks of subway vehicles further includes the following steps: S5: The audio sensor automatically collects audio signals and inputs them into the main chip; S6: The main chip analyzes the received audio signals and drives the alarm to sound an alarm when the signals are too large.

3. The intelligent detection and analysis device for brake shoe cracks of subway vehicles according to claim 1, characterized in that: the specific process of region segmentation in S22 includes the following sub-steps: S22-1: Denote the width of the brake shoe shown in each image as M and the length as N; S22-2: Take the center of the brake shoe shown in the image as the origin and establish an orthogonal rectangular coordinate system in the image; S22-3: Then, with the origin of the orthogonal rectangular coordinate system as the center, delimit a quasi-elliptical region, and the equation of the quasi-elliptical region is: K^2 / y^2 + J^2 / x^2 = 1, J = 0.698M, K = 0.763N; S22-4: Based on S22-2 and S22-3, the chip divides the brake shoe image into 20 sub-regions, denoted as A1, A2, A3, A4, B1, B2, B3, B4, C1, C2, C3, C4, D1, D2, D3, D4, E1, E2, E3, and E4 respectively; where: The domain of the A1 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (0, K / 2) and K^2 / y^2 + J^2 / x^2 < 1; The domain of the A2 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (0, K / 2) and K^2 / y^2 + J^2 / x^2 < 1; The domain of the A3 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (-K / 2, 0) and K^2 / y^2 + J^2 / x^2 < 1; The domain of the A4 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (-K / 2, 0) and K^2 / y^2 + J^2 / x^2 < 1; The domain of the B1 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (0, K / 2) and K^2 / y^2 + J^2 / x^2 > 1; The domain of the B2 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (0, K / 2) and K^2 / y^2 + J^2 / x^2 > 1; The domain of the B3 region satisfies the restriction conditions: x ∈ (-J / 2, 0), y ∈ (-K / 2, 0) and K^2 / y^2 + J^2 / x^2 > 1; The domain of the B4 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (-K / 2, 0) and K^2 / y^2 + J^2 / x^2 > 1; The domain of the C1 region satisfies the restriction conditions: x ∈ ((-J) / 2, 0), y ∈ (K / (2, N / 2)); The domain of the C2 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ (K / (2, N / 2)); The domain of the C3 region satisfies the restriction conditions: x ∈ ((-J) / 2, 0), y ∈ ((-N) / 2, (-K) / 2); The domain of the C4 region satisfies the restriction conditions: x ∈ (0, J / 2), y ∈ ((-N) / 2, (-K) / 2); The domain of the D1 region satisfies the restriction conditions: x ∈ ((-M) / 2, (-J) / 2), y ∈ (K / (2, N / 2)); The domain of the D2 region satisfies the restriction conditions: x ∈ (J / 2, M / 2), y ∈ (K / (2, N / 2)); The domain of the D3 region satisfies the restriction conditions: x ∈ ((-M) / 2, (-J) / 2), y ∈ ((-N) / (2, (-K) / 2)); The domain of the D4 region satisfies the restriction conditions: x ∈ (J / 2, M / 2), y ∈ ((-N) / (2, (-K) / 2)); The domain of the E1 region satisfies the restriction conditions: x ∈ ((-M) / 2, (-J) / 2), y ∈ ((0, K) / 2); The domain of the E2 region satisfies the restriction conditions: x ∈ (J / 2, M / 2), y ∈ ((0, K) / 2); The domain of the E3 region satisfies the following constraints: x ∈ ((-M) / 2, (-J) / 2), y ∈ ((-K) / 2, 0); The domain of the E4 region satisfies the following constraints: x ∈ (J / 2, M / 2), y ∈ ((-K) / 2, 0).

4. The intelligent detection and analysis device for brake shoe cracks of subway vehicles according to claim 3, characterized in that: The 20 small regions in S22-4 are correspondingly merged into five large regions A, B, C, D, and E, where: Let the number of cracks in the A1, A2, A3, and A4 regions in the brake shoe image be denoted as a1, a2, a3, and a4 respectively. Then the total number of cracks a in the A region (A1 + A2 + A3 + A4 regions) is a = a1 + a2 + a3 + a4; Let the number of cracks in the B1, B2, B3, and B4 regions in the brake shoe image be denoted as b1, b2, b3, and b4 respectively. Then the total number of cracks b in the B region (B1 + B2 + B3 + B4 regions) is b = b1 + b2 + b3 + b4; Let the number of cracks in the C1, C2, C3, and C4 regions in the brake shoe image be denoted as c1, c2, c3, and c4 respectively. Then the total number of cracks c in the C region (C1 + C2 + C3 + C4 regions) is c = c1 + c2 + c3 + c4; Let the number of cracks in the D1, D2, D3, and D4 regions in the brake shoe image be denoted as d1, d2, d3, and d4 respectively. Then the total number of cracks d in the D region (D1 + D2 + D3 + D4 regions) is d = d1 + d2 + d3 + d4; Let the number of cracks in the E1, E2, E3, and E4 regions in the brake shoe image be denoted as e1, e2, e3, and e4 respectively. Then the total number of cracks e in the E region (E1 + E2 + E3 + E4 regions) is e = e1 + e2 + e3 + e4.

5. The intelligent detection and analysis device for brake shoe cracks of subway vehicles according to claim 4, characterized in that: If the following conditions are met: a ≥ k1, then the brake shoe must be replaced; a < k1 but b ≥ k2, then the brake shoe must be replaced; a < k1 and b < k2 but e ≥ k3, then the brake shoe must be replaced; a < k1 and b < k2 and e < k3 but c ≥ k4, then the brake shoe must be replaced; a < k1 and b < k2 and e < k3 and c < k4 but d ≥ k5, then the brake shoe must be replaced; a < k1 and b < k2 and e < k3 and c < k4 and d < k5, then the brake shoe does not need to be replaced; where, when M > 80, k1 takes the value of 1, k2 takes the value of 3, k3 takes the value of 4, k4 takes the value of 11, and k5 takes the value of 14; when M < 80, k1 takes the value of 1, k2 takes the value of 4, k3 takes the value of 6, k4 takes the value of 15, and k5 takes the value of 16.

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