A belt status detection method based on AI detection and recognition

Abstract

The present invention relates to the technical field of belt status detection, and specifically discloses a belt status detection method based on AI detection and recognition, the method comprising: belt deviation detection, deviation cause confirmation feedback, belt wear detection, wear degree confirmation feedback and belt tear detection warning; the present invention determines whether a target belt is deviating based on a running video of the target belt, and if deviating, confirms the cause of the deviation of the target belt, confirms the wear degree of each area of ​​the target belt by constructing a three-dimensional information model of each area, and simultaneously issues a tear severity warning by analyzing the tear severity coefficient of the target belt, thereby improving the efficiency of troubleshooting the cause of the belt deviation and the accuracy of judging the degree of belt wear, accurately evaluating the expansion direction and speed of cracks, providing effective data support for preventing and controlling tearing, and helping to timely discover potential tearing risks, thereby reducing the possibility of belt tearing accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of belt status detection, and in particular to a belt status detection method based on AI detection and recognition. Background Art

[0002] In industries like coal, mining, and power, conveyor belts are crucial material transport equipment, and their operating status is directly linked to production efficiency and safety. However, traditional belt status monitoring methods, which rely mostly on manual inspections and simple sensor monitoring, suffer from issues such as untimely monitoring, poor accuracy, and slow response. With the rapid development of artificial intelligence (AI) technology, AI-based belt status monitoring systems have become a research hotspot, offering broad application prospects.

[0003] The existing methods for monitoring energy consumption in engineering construction still have the following problems: 1. When detecting the belt deviation status, the current method only relies on manual observation to determine whether the belt is deviating, which makes the confirmation of the deviation status contain large errors. At the same time, when the belt deviates, manual investigation of the cause of the deviation is not only time-consuming, but also the investigation process is relatively cumbersome, and it is impossible to accurately locate the cause of the belt deviation, which reduces the efficiency of investigating the cause of the belt deviation.

[0004] 2. When detecting the wear status of the belt, the degree of wear of the belt is currently determined only by observing the overall pattern of the belt to see whether it is broken or flattened. The wear condition of each pattern of the belt in the vertical direction is not analyzed, which reduces the coverage of the belt wear analysis and the accuracy of the belt wear judgment, making it impossible to guarantee the good condition of the belt.

[0005] 3. When detecting the tearing status of the belt, the cracking posture of each crack in each area of ​​the belt is not deeply analyzed. Only the density of cracks in each area is considered, and the cracking angle of each crack is not considered. The analysis dimension is relatively single, and it is impossible to accurately evaluate the expansion direction and speed of the cracks, thereby failing to provide effective data support for the prevention and control of tearing. At the same time, potential tearing risks may not be discovered in time, thereby increasing the possibility of belt tearing accidents. Summary of the Invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a belt status detection method based on AI detection and recognition is proposed.

[0007] The objectives of the present invention can be achieved through the following technical solutions: The present invention provides a belt state detection method based on AI detection and recognition, comprising the following steps: S1, belt deviation detection: randomly select edge monitoring points on the edge of the target belt, use a high-definition camera to shoot the target belt in real time, obtain the running video of the target belt, and judge whether the target belt is deviated. If it is deviated, execute step S2, and if it is not deviated, execute step S3.

[0008] S2. Confirmation and feedback on the cause of deviation: Collect the tension of each monitoring point of the target belt during each operation, the speed of the two rollers and the vibration frequency of the motor, confirm the cause of the deviation of the target belt, and provide feedback.

[0009] S3. Belt wear detection: Randomly arrange detection points in each component pattern of each area of ​​the target belt, construct a three-dimensional information model of each area, and obtain the y-axis coordinate value of each detection point in each component pattern of each area to analyze the wear degree of each area of ​​the target belt.

[0010] S4. Wear degree confirmation and feedback: confirm the wear degree of each area of ​​the target belt and provide feedback on the wear degree of each area.

[0011] S5. Belt tear detection and warning: Collect images corresponding to each area of ​​the target belt, analyze the tear severity coefficient of the target belt, and compare it with the set value. If it is greater than or equal to the set value, a tear severity warning will be issued.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention determines whether the target belt is deviating based on the running video of the target belt. If it is deviating, the cause of the deviation of the target belt is confirmed based on the tension of each monitoring point of the target belt during each operation, the rotation speed of the two rollers and the vibration frequency of the motor, thereby reducing the error in manually observing whether the belt state is deviating, and avoiding the problems of long time-consuming cycle and cumbersome troubleshooting process in manually troubleshooting the cause of the deviation, thereby improving the efficiency of troubleshooting the cause of the belt deviation.

[0013] (2) The present invention constructs a three-dimensional information model of each area, and obtains the y-axis coordinate value of each detection point in each component pattern of each area, analyzes the wear degree of each area of ​​the target belt, improves the coverage of the belt wear analysis, and at the same time improves the accuracy of the belt wear degree judgment, thereby ensuring the good condition of the belt.

[0014] (3) The present invention calculates the crack density and crack angle hazard coefficient of the target belt based on the images corresponding to each area of ​​the target belt, thereby analyzing the tear severity coefficient of the target belt. It can accurately evaluate the expansion direction and speed of the crack, thereby providing effective data support for the prevention and control of tearing, and helping to timely discover potential tearing risks, thereby reducing the possibility of belt tearing accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 Schematic diagram of the process steps of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, the present invention provides a belt state detection method based on AI detection and recognition, including: S1, belt deviation detection: randomly selecting edge monitoring points on the edge of the target belt, using a high-definition camera to shoot the target belt in real time, obtaining the running video of the target belt, and judging whether the target belt is deviating. If it is deviating, execute step S2; if it is not deviating, execute step S3.

[0019] In a specific embodiment of the present invention, the target belt has two edges. This embodiment takes any one of the edges as an example for analysis. The analysis of the two edges is similar to the analysis of one edge, and will not be repeated here.

[0020] In a specific embodiment of the present invention, the specific process of determining whether the target belt is deviating is as follows: A1. Divide the running video of the target belt into running images in frames, and locate the position of each edge monitoring point from each running image of the target belt.

[0021] A2. Overlap and compare each running image of the target belt with the preset boundary image of the target belt stored in the database, so as to locate the distance from each edge monitoring point in each running image to the preset boundary line, and record it as Lij , where i represents the number of the running image, i=1,2,...,n, and j represents the number of the edge monitoring point, j=1,2,...,m.

[0022] A3. Calculate the target belt deviation β.

[0023] In a specific embodiment of the present invention, the specific process of calculating the deviation of the target belt is as follows: B1, subtracting the distance from each edge monitoring point in each running image of the target belt to the preset boundary line from the set reference distance to obtain the deviation distance of each edge monitoring point in each running image of the target belt, and performing mean calculation to obtain the average deviation distance of the edge monitoring point in each running image of the target belt, and recording it as

[0024] B2. Extract the maximum value of the deviation distance of each edge monitoring point in each running image of the target belt and record it as

[0025] B3. Calculate the target belt deviation β. Wherein, L′ represents the set reference distance, a1 and a2 represent the set average deviation distance deviation and maximum deviation distance deviation corresponding to the deviation degree assessment weights, a1+a2=1, e represents a natural constant, and n represents the number of running images.

[0026] In a specific embodiment of the present invention, a1 is set to 0.5, and a2 is set to 0.5. The average deviation distance reflects the average degree of belt deviation during operation, helping to understand the overall trend of belt deviation. The maximum deviation distance reveals the degree of belt deviation in extreme situations and is important for assessing the safety risks associated with belt deviation. It is generally necessary to consider both the average deviation distance and the maximum deviation distance in combination; they complement each other to provide more comprehensive information on belt deviation.

[0027] A4. Compare the target belt deviation with the set reference deviation. If the target belt deviation is greater than or equal to the set reference deviation, it indicates that the target belt is deviating. Otherwise, it indicates that the target belt is not deviating.

[0028] S2. Confirmation and feedback on the cause of deviation: Collect the tension of each monitoring point of the target belt during each operation, the speed of the two rollers and the vibration frequency of the motor, confirm the cause of the deviation of the target belt, and provide feedback.

[0029] It should be noted that the tension of each monitoring point of the target belt during each operation, the rotation speed of the two rollers and the vibration frequency of the motor are collected by the belt tension sensor, the roller rotation speed sensor and the vibration detector respectively.

[0030] In a specific embodiment of the present invention, the specific process of confirming the cause of the deviation of the target belt is: C1. Calculating the roller rotation synchronization degree ω of the target belt based on the rotation speed of the two rollers during each operation of the target belt.

[0031] In a specific embodiment of the present invention, the specific process of calculating the target belt roller rotation synchronization is as follows: D1, the speed of the two rollers of the target belt during each operation is subtracted from each other to obtain the speed deviation of the two rollers of the target belt during each operation, and recorded as Δv d , where d represents the number of each run, d = 1, 2, ..., k.

[0032] D2. Calculate the target belt's roller rotation synchronization ω, Wherein, Δv′ represents the speed deviation of the set reference, and k represents the number of operations.

[0033] C2. Calculate the tension uniformity δ of the target belt during operation based on the tension of each monitoring point during each operation of the target belt.

[0034] It should be noted that the specific process of calculating the tension uniformity of the target belt during operation is as follows: the tension of each monitoring point of the target belt during each operation is recorded as τ dt , where t represents the number of the monitoring point, t = 1, 2, ..., h.

[0035] Calculate the tension uniformity δ of the target belt during operation, Among them, τ d ( t+1 ) represents the tension of the target belt at the t+1th monitoring point during the dth operation, and Δτ represents the tension deviation of the set reference.

[0036] C3. Calculate the vibration abnormality index ξ of the target belt during operation based on the vibration frequency of the motor during each operation of the target belt.

[0037] It should be noted that the specific process of calculating the vibration abnormality index of the target belt during operation is as follows: the vibration frequency of the motor of the target belt during each operation is recorded as μ d .

[0038] Calculate the vibration abnormality index ξ of the target belt during operation, Here, μ′ and Δμ represent the reference vibration frequency and the vibration frequency deviation, respectively.

[0039] C4. When δ<δ′, the cause of the target belt deviation is recorded as uneven tension; when ω<ω′, the cause of the target belt deviation is recorded as asynchronous roller rotation; when ξ≥ξ′, the cause of the target belt deviation is recorded as abnormal motor vibration, where δ′, ω′ and ξ′ represent the set reference tension uniformity, roller rotation synchronization and vibration abnormality index, respectively.

[0040] The embodiment of the present invention determines whether the target belt is deviating based on the running video of the target belt. If it is deviating, the cause of the deviation of the target belt is confirmed according to the tension of each monitoring point of the target belt during each operation, the rotation speed of the two rollers and the vibration frequency of the motor, thereby reducing the error existing in manual observation of whether the belt state is deviating, and avoiding the problems of long time-consuming cycle and cumbersome troubleshooting process in manual investigation of the cause of deviation, thereby improving the efficiency of troubleshooting the cause of belt deviation.

[0041] S3. Belt wear detection: Randomly arrange detection points in each component pattern of each area of ​​the target belt, construct a three-dimensional information model of each area, and obtain the y-axis coordinate value of each detection point in each component pattern of each area to analyze the wear degree of each area of ​​the target belt.

[0042] It should be noted that the specific method of constructing the three-dimensional information model of each area is: by collecting images of each area of ​​the target belt, importing the images of each area into the three-dimensional modeling software, and using the three-dimensional modeling software to identify and construct the three-dimensional information model of each area.

[0043] In a specific embodiment of the present invention, the specific process of analyzing the wear degree of each area of ​​the target belt is as follows: E1, based on the y-axis coordinate value of each detection point in each component line of each area, calculate the wear degree of each component line of each area of ​​the target belt Where r represents the number of the region, r = 1, 2, ..., z, and g represents the number of the component texture, g = 1, 2, ..., p.

[0044] In a specific embodiment of the present invention, the specific process of calculating the wear degree of each component line of each area of ​​the target belt is as follows: F1, record the y-axis coordinate value of each detection point in each component line of each area as y rgf , where f represents the number of the detection point, f = 1, 2, ..., q.

[0045] F2. Extract the initial position of each detection point in each component pattern of each area of ​​the target belt from the database, and bring it into the three-dimensional information model of the corresponding area to obtain the initial y-axis coordinate value of each detection point in each component pattern of each area, which is recorded as

[0046] F3. Calculate the wear of each component pattern in each area of ​​the target belt Wherein, Δy′ represents the allowable deviation value of the y-axis setting, and q represents the number of detection points.

[0047] E2. Compare the wear degree of each component line in each area of ​​the target belt with the wear degree of the set reference. If the wear degree of a component line is greater than the wear degree of the set reference, then record the component line as a wear line. Count the number of wear lines in each area of ​​the target belt and record it as ε r .

[0048] E3. Calculate the wear of each area of ​​the target belt Among them, σ represents the proportion of the number of wear lines of the set reference, and p represents the number of component lines.

[0049] S4. Wear degree confirmation and feedback: confirm the wear degree of each area of ​​the target belt and provide feedback on the wear degree of each area.

[0050] In a specific embodiment of the present invention, the method for confirming the degree of wear of each area of ​​the target belt is: comparing the degree of wear of each area of ​​the target belt with the wear degree set corresponding to each degree of wear stored in the database; if the degree of wear of a certain area is within the wear degree set corresponding to a certain degree of wear, then the degree of wear is used as the degree of wear of the area, thereby obtaining the degree of wear of each area of ​​the target belt.

[0051] The embodiment of the present invention constructs a three-dimensional information model of each area, and obtains the y-axis coordinate value of each detection point in each component pattern of each area, analyzes the wear of each area of ​​the target belt, improves the coverage of the belt wear analysis, and at the same time improves the accuracy of the belt wear degree judgment, thereby ensuring the good condition of the belt.

[0052] S5. Belt tear detection and warning: Collect images corresponding to each area of ​​the target belt, analyze the tear severity coefficient of the target belt, and compare it with the set value. If it is greater than or equal to the set value, a tear severity warning will be issued.

[0053] It should be noted that the images corresponding to each area of ​​the target belt are acquired through a high-definition camera.

[0054] In a specific embodiment of the present invention, the specific process of analyzing the tear severity coefficient of the target belt is as follows: G1, locating the number of cracks, the length of each crack, the length of the region and the area of ​​the region from the image corresponding to each region of the target belt, and calculating the crack density of the target belt

[0055] It should be noted that the specific process of calculating the crack density of the target belt is as follows: the number of cracks, the length of the region and the area of ​​the region corresponding to each region of the target belt are respectively recorded as M r 、l r and S r .

[0056] The maximum value of each crack length corresponding to each area of ​​the target belt is extracted and recorded as

[0057] Calculate the crack density of the target belt Among them, σ1 and σ2 represent the percentage of crack number and crack length per unit area of ​​the set reference, respectively, and z represents the number of regions.

[0058] G2. Calculate the crack angle hazard coefficient ψ of the target belt.

[0059] In a specific embodiment of the present invention, the specific process of calculating the crack opening angle hazard coefficient of the target belt is: H1. Establish a rectangular coordinate system with the center point of one side edge of the target belt as the origin, the direction of the belt conveying goods as the y-axis, and the direction perpendicular to the belt conveying goods as the x-axis.

[0060] H2. Locate the two end point coordinates of each crack in each region in the rectangular coordinate system and record them as and Wherein, w represents the crack number, w = 1, 2, ..., u.

[0061] H3. Calculate the angle θ of each crack in each area rw ,

[0062] H4, the angle of each crack in each area is 0 Make a difference and get the difference between each crack in each area and 0 0 The deviation angle between them is recorded as the first deviation angle. If the first deviation angle of a crack in a certain area is less than the set first deviation angle, the crack is determined to be a first deviation crack. The number of first deviation cracks in each area is counted and recorded as

[0063] H5, the angle of each crack in each area is 90 0 The difference between each crack in each area and 90 0 The deviation angle between each crack in each area and the 90 0 The absolute value of the deviation angle between the two is recorded as the second deviation angle. If the second deviation angle of a crack in a certain area is less than the set second deviation angle, the crack is determined to be a second deviation crack. The number of second deviation cracks in each area is counted and recorded as

[0064] H6. Calculate the crack angle risk factor ψ of the target belt. Where ζ′ represents the number of deviation cracks used as a reference.

[0065] It should be noted that the crack angles of the target belt in the direction of the goods conveyed by the belt and perpendicular to the direction of the goods conveyed by the belt are more likely to lead to rapid crack expansion and cause more serious structural damage to the target belt. Therefore, when calculating the crack angle hazard factor of the target belt, the crack angles in the direction of the goods conveyed by the belt and perpendicular to the direction of the goods conveyed by the belt are mainly considered.

[0066] G3. Calculate the tear severity coefficient θ of the target belt. Among them, b1 and b2 represent the weights of the tear severity assessment corresponding to the set reference crack density and crack angle hazard factors, respectively, and b1+b2=1.

[0067] In a specific embodiment of the present invention, b1 is set to a value of 0.5, and b2 is set to a value of 0.5. The crack density reflects the number and distribution of cracks on the belt surface. A high crack density means that the structural integrity of the belt is more severely damaged, which may cause the belt to lose its load-bearing capacity more quickly or break suddenly. Therefore, crack density is an important indicator for measuring the risk of belt tearing. The crack angle can affect the crack propagation speed and direction, thereby affecting the overall strength and stability of the belt. Therefore, when calculating the tear severity coefficient of the target belt, the crack density and crack angle hazard factors should be comprehensively considered.

[0068] The embodiment of the present invention calculates the crack density and crack opening angle hazard coefficient of the target belt based on the images corresponding to each area of ​​the target belt, thereby analyzing the tear severity coefficient of the target belt. The crack expansion direction and speed can be accurately evaluated, thereby providing effective data support for preventing and controlling tearing, and helping to timely discover potential tearing risks, thereby reducing the possibility of belt tearing accidents.

[0069] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A belt status detection method based on AI detection and recognition, characterized in that: The steps include: S1. Belt deviation detection: Randomly select edge monitoring points on the edge of the target belt, use a high-definition camera to shoot the target belt in real time, obtain the target belt operation video, and determine whether the target belt is deviating. If it is deviating, execute step S2; if not, execute step S3; S2. Confirmation and feedback on the cause of deviation: The tension of the target belt at each monitoring point, the speed of the two rollers, and the vibration frequency of the motor are collected during each operation to confirm the cause of the deviation of the target belt and provide feedback; S3. Belt wear detection: Randomly arrange the detection points in each component pattern of each area of ​​the target belt, build a three-dimensional information model of each area, and obtain the information of each detection point in each component pattern of each area. Axis coordinate values, analyze the wear of each area of ​​the target belt; S4. Wear degree confirmation and feedback: confirm the wear degree of each area of ​​the target belt and provide feedback on the wear degree of each area; S5. Belt tear detection and warning: Collect images corresponding to each area of ​​the target belt, analyze the tear severity coefficient of the target belt, and compare it with the set value. If the coefficient is greater than or equal to the set value, a tear severity warning is issued; The specific process of determining whether the target belt is deviated is as follows: A1. Divide the running video of the target belt into running images in frames, and locate the position of each edge monitoring point from each running image of the target belt; A2. Overlap and compare each running image of the target belt with the preset boundary image of the target belt stored in the database, so as to locate the distance from each edge monitoring point in each running image to the preset boundary line, and record it as ,in, Indicates the number of the running image, , Indicates the number of the edge monitoring point, ; A3. Calculate the target belt deviation ; A4. Compare the target belt deviation with the set reference deviation. If the target belt deviation is greater than or equal to the set reference deviation, it indicates that the target belt is deviating; otherwise, it indicates that the target belt is not deviating. The specific process of calculating the target belt deviation is as follows: B1. Subtract the distance from each edge monitoring point to the preset boundary line in each running image of the target belt from the distance of the set reference to obtain the deviation distance of each edge monitoring point in each running image of the target belt, and calculate the average of the deviation distances to obtain the average deviation distance of the edge monitoring points in each running image of the target belt, and record it as ; B2. Extract the maximum value of the deviation distance of each edge monitoring point in each running image of the target belt and record it as ; B3. Calculate the target belt deviation , ,in, Indicates the distance to set the reference. and They represent the weights of the set average deviation distance and maximum deviation distance corresponding to the deviation degree assessment. , represents a natural constant, Indicates the number of running images.

2. The belt status detection method based on AI detection and recognition according to claim 1, characterized in that: The specific process of confirming the cause of the deviation of the target belt is as follows: C1. Calculate the target belt's roller rotation synchronization based on the target belt's two roller speeds during each run. ; C2. Calculate the tension uniformity of the target belt during operation based on the tension of each monitoring point during each operation of the target belt. ; C3. Calculate the vibration abnormality index of the target belt during operation based on the vibration frequency of the motor during each operation of the target belt. ; C4. When When the target belt is running off the track, the cause is recorded as uneven tension. When the target belt is running off the track, the cause is recorded as the roller rotation is not synchronized. When the target belt deviation is recorded as abnormal motor vibration, 、 and They respectively represent the tension uniformity, roller rotation synchronization and vibration abnormality index of the set reference.

3. The belt status detection method based on AI detection and recognition according to claim 2 is characterized in that: The specific process of calculating the target belt roller rotation synchronization is as follows: D1. Subtract the speed of the two rollers of the target belt during each operation to obtain the speed deviation of the two rollers of the target belt during each operation, and record it as ,in, Indicates the number of each run, ; D2. Calculate the target belt roller rotation synchronization , ,in, Indicates the speed deviation of the set reference. Indicates the number of runs.

4. The belt status detection method based on AI detection and recognition according to claim 1, characterized in that: The specific process of analyzing the wear degree of each area of ​​the target belt is as follows: E1, based on each detection point in each component texture of each area Axis coordinate values, calculate the wear of each component pattern in each area of ​​the target belt ,in, Indicates the area number, , Indicates the number of the texture. ; E2. Compare the wear degree of each component line in each area of ​​the target belt with the wear degree of the set reference. If the wear degree of a component line is greater than the wear degree of the set reference, then record the component line as the wear line. Count the number of wear lines in each area of ​​the target belt and record it as ; E3. Calculate the wear of each area of ​​the target belt , ,in, Indicates the ratio of the number of wear lines of the set reference, Indicates the number of textures.

5. The belt status detection method based on AI detection and recognition according to claim 4 is characterized in that: The specific process of calculating the wear degree of each component pattern in each area of ​​the target belt is as follows: F1. The detection points of each component texture in each area The axis coordinate value is recorded as ,in, Indicates the number of the detection point, ; F2. Extract the initial position of each detection point in each component pattern of each area of ​​the target belt from the database, and bring it into the three-dimensional information model of the corresponding area to obtain the initial position of each detection point in each component pattern of each area. Axis coordinate value, recorded as ; F3. Calculate the wear of each component pattern in each area of ​​the target belt , ,in, express The axis setting allows deviation values, Indicates the number of detection points.

6. The belt status detection method based on AI detection and recognition according to claim 4, characterized in that: The method for confirming the degree of wear of each area of ​​the target belt is: comparing the degree of wear of each area of ​​the target belt with the wear degree set corresponding to each degree of wear stored in the database; if the wear degree of a certain area is within the wear degree set corresponding to a certain degree of wear, then the wear degree is used as the wear degree of the area, thereby obtaining the wear degree of each area of ​​the target belt.

7. The belt status detection method based on AI detection and recognition according to claim 4, characterized in that: The specific process of analyzing the tear severity coefficient of the target belt is as follows: G1. Locate the number of cracks, length of each crack, length of the region and area of ​​the region from the image corresponding to each region of the target belt, and calculate the crack density of the target belt ; G2. Calculate the crack angle risk factor of the target belt ; G3. Calculate the tear severity coefficient of the target belt , ,in, and They represent the weights of the tear severity assessment corresponding to the reference crack density and crack angle hazard coefficient, .

8. The belt status detection method based on AI detection and recognition according to claim 7, characterized in that: The specific process of calculating the crack angle hazard coefficient of the target belt is as follows: H1. Establish a rectangular coordinate system with the center point of one side edge of the target belt as the origin, the direction of the belt conveying goods as the y-axis, and the direction perpendicular to the belt conveying goods as the x-axis; H2. Locate the two end point coordinates of each crack in each region in the rectangular coordinate system and record them as and ,in, Indicates the crack number, ; H3. Calculate the angle of each crack in each area , ; H4, the angle of each crack in each area and The difference is made to obtain the cracks in each area and The deviation angle between them is recorded as the first deviation angle. If the first deviation angle of a crack in a certain area is less than the set first deviation angle, the crack is determined to be a first deviation crack. The number of first deviation cracks in each area is counted and recorded as ; H5, the angle of each crack in each area and The difference is made to obtain the cracks in each area and The deviation angle between each crack in each area and The absolute value of the deviation angle between the two is recorded as the second deviation angle. If the second deviation angle of a crack in a certain area is less than the set second deviation angle, the crack is determined to be a second deviation crack. The number of second deviation cracks in each area is counted and recorded as ; H6. Calculate the crack angle risk factor of the target belt , ,in, Indicates the number of deviation cracks for the set reference.

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