Intelligent form recognition and precise reset control system for large skip bucket of vertical shaft

Abstract

The invention relates to the technical field of skip recognition control, in particular to an intelligent form recognition and precise reset control system for a large skip of a vertical shaft. The invention discloses an intelligent form identification and precise reset control system for a large skip bucket of a vertical shaft. The intelligent form identification and precise reset control system comprises a fatigue area acquisition module, a fatigue area acquisition sub-module, an area division module, an average height change acquisition module, a loading area adjustment module, a cargo height adjustment sub-module, a loading area adjustment sub-module, an area monitoring adjustment module and a frequency adjustment module. The loading area is adjusted by combining the real-time cargo height and historical height change data of each area, so that damage is reduced, and the service life of equipment is prolonged; the tolerance times of all the areas are adjusted through a frequency adjusting formula, it is ensured that equipment is kept stable in different operation states, and resetting control over the skip bucket is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of skip identification and control, and in particular to an intelligent form identification and precise resetting control system for a large-scale skip in a vertical shaft. Background Art

[0002] As the core equipment in the mine hoisting system, the vertical shaft skip is responsible for the efficient transportation of materials such as ore and coal. During long-term use, the internal structure of the skip will be affected by load impact and fatigue damage, resulting in a safety hazard of reduced local structural strength or even deformation. Especially during the hoisting process of the skip, the reset control becomes more complicated and challenging due to the deformation of the skip. At present, the fatigue damage of mine skips mainly relies on regular maintenance and manual monitoring, but this method has the problems of long monitoring cycle, poor real-time performance and high maintenance cost; in addition, the cargo loading method of the skip usually adopts traditional uniform loading or empirical loading, without considering the dynamic changes of the fatigue area, which is easy to aggravate fatigue damage and shorten the life of the equipment. Summary of the invention

[0003] In order to overcome the defects of cargo loading damage and reset control rigidity, the present invention provides an intelligent shape recognition and precise reset control system for large-scale skips in vertical shafts.

[0004] The technical implementation scheme of the present invention is: a control system for intelligent shape recognition and precise resetting of a large vertical shaft bucket, comprising:

[0005] A fatigue area acquisition module, wherein the fatigue area acquisition module is used to obtain the fatigue area of ​​the skip using the SN curve formula and the Miner criterion according to the historical use data of the skip;

[0006] A fatigue area acquisition submodule, wherein the fatigue area acquisition submodule obtains the cumulative fatigue damage degree of the bucket according to the fatigue life of the bucket using the Miner criterion, and obtains the fatigue area of ​​the bucket according to the cumulative fatigue damage degree;

[0007] A region division module, wherein the region division module is used to divide the region in the bucket;

[0008] An average height change acquisition module, the average height change acquisition module is used to acquire historical cargo height change data, and acquire average height change data based on the historical cargo height change data;

[0009] A loading area adjustment module, the loading area adjustment module is used to obtain the real-time cargo height of each area in the skip when the cargo is loaded, and adjust the cargo loading area according to the fatigue area of ​​the skip;

[0010] A cargo height adjustment submodule, which adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area;

[0011] A loading area adjustment submodule, which adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area;

[0012] A regional monitoring and adjustment module, the regional monitoring and adjustment module is used to obtain monitoring data of the skip during operation and adjust the tolerance times of each region according to the monitoring data;

[0013] A times adjustment module is used to adjust the number of times that each area of ​​the skip exceeds a second preset range during operation according to the monitoring data using a frequency adjustment formula.

[0014] Preferably, the fatigue area acquisition module is used to obtain the fatigue area of ​​the bucket according to the historical use data of the bucket using the SN curve formula and the Miner criterion, including: obtaining the historical use data of the bucket, the historical use data of the bucket including the actually measured stress amplitude, the fatigue strength coefficient and fatigue index of the bucket material, obtaining the fatigue life of the target area of ​​the bucket according to the historical use data of the bucket using the SN curve formula, obtaining the cumulative fatigue damage degree of the bucket according to the fatigue life of the bucket using the Miner criterion, and obtaining the fatigue area of ​​the bucket according to the cumulative fatigue damage degree, wherein the SN curve formula is:

[0015]

[0016] Where N f is the fatigue life of the target area of ​​the bucket; σ a is the actual measured stress amplitude; σ 0 is the fatigue strength coefficient of the bucket material; b is the fatigue index of the bucket material.

[0017] Preferably, the fatigue area acquisition submodule obtains the cumulative fatigue damage degree of the bucket according to the fatigue life of the bucket using the Miner criterion, and obtains the fatigue area of ​​the bucket according to the cumulative fatigue damage degree, including: obtaining the number of load cycles actually experienced by the bucket target area and the corresponding fatigue life under different stress states, using the Miner criterion to obtain the cumulative fatigue damage degree of the bucket target area, when the cumulative fatigue damage degree is greater than the first preset threshold and less than 1, the bucket target area is used as a fatigue area; when the cumulative fatigue damage degree is greater than or equal to 1, the bucket target area is used as a damaged area, and the damaged area is identified and repaired using a deep learning model; wherein the Miner criterion is:

[0018]

[0019] Where D is the cumulative fatigue damage degree of the bucket target area; n i is the number of cycles under the i-th level stress; N fi is the fatigue life under the first stress level; i is the load stress level.

[0020] Preferably, the area division module is used to divide the area within the bucket, including: dividing the area within the bucket to obtain a first area, a second area and a third area, wherein the first area is the left area within the bucket; the second area is the middle area within the bucket; and the third area is the right area within the bucket.

[0021] Preferably, the average height change acquisition module is used to obtain historical cargo height change data, and obtain average height change data based on the historical cargo height change data, including: obtaining historical cargo height change data of each area when the bucket is running on the guide rail, averaging the historical cargo height change data of each area within a preset time period based on the historical cargo height change data, and obtaining average height change data of each area.

[0022] Preferably, the loading area adjustment module is used to obtain the real-time cargo height of each area in the bucket when the cargo is loaded, and adjust the cargo loading area according to the fatigue area of ​​the bucket, including: when the fatigue area of ​​the bucket is in the first area, adjusting the cargo loading area to the third area, obtaining the real-time cargo height of each area, and adjusting the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area; when the fatigue area of ​​the bucket is in the third area, adjusting the cargo loading area to the first area, obtaining the real-time cargo height of each area, and adjusting the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area; when the fatigue area of ​​the bucket is in the first area and the third area at the same time or there is no fatigue area of ​​the bucket, adjusting the cargo loading area to the second area, obtaining the real-time cargo height of each area, and adjusting the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area.

[0023] Preferably, the cargo height adjustment submodule adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area, including: using an initial adjustment formula according to the real-time cargo height of the first area and the third area and the average height change data of each area, obtaining the adjusted initial cargo height of each area, and adjusting the cargo height of each area according to the initial cargo height of each area, wherein the initial adjustment formula is:

[0024] H=min(h0 -θ*h y ,h max );

[0025] Where H is the initial cargo height after adjustment in the target area; h 0 is the standard cargo height of the target area; h y is the average height change data of the target area; h max is the maximum cargo height in the target area; θ is the adjustment coefficient.

[0026] Preferably, the loading area adjustment submodule adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area, including: when the fatigue area of ​​the bucket is in the first area, adjusting the loading area of ​​the cargo to the third area, and when the real-time cargo height in the third area reaches the maximum cargo height of the third area or the real-time cargo height in the first area is higher than the highest height of the fatigue area in the first area, adjusting the loading area of ​​the cargo to other areas so that the cargo height of each area is increased to the adjusted initial cargo height; when the fatigue area of ​​the bucket is in the third area, adjusting the loading area of ​​the cargo to the first area, and when the real-time cargo height in the first area reaches the maximum cargo height of the first area or the real-time cargo height in the third area is higher than the highest height of the fatigue area in the third area, adjusting the loading area of ​​the cargo to other areas so that the cargo height of each area is increased to the adjusted initial cargo height.

[0027] Preferably, the area monitoring and adjustment module is used to obtain monitoring data of the bucket during operation, and adjust the tolerance times of each area according to the monitoring data, including: obtaining monitoring data of the bucket during operation, the monitoring data including the number of times the bucket shakes beyond a first preset range during operation, the height of the cargo in the bucket in each area, and the number of fatigue areas in each area of ​​the bucket, and adjusting the tolerance times of each area of ​​the bucket exceeding a second preset range during operation using a frequency adjustment formula according to the monitoring data.

[0028] Preferably, the frequency adjustment module uses a frequency adjustment formula according to the monitoring data to adjust the number of times that each area of ​​the skip exceeds the second preset range during operation, including: wherein the frequency adjustment formula is:

[0029]

[0030] Where N α N is the number of times the jth area of ​​the adjusted skip exceeds the second preset range during operation; 0 is the number of times the jth area of ​​the bucket exceeds the second preset range during operation; H is the initial cargo height after adjustment in the target area; hmax is the maximum cargo height in the target area; N 1j F is the number of times the bucket shakes beyond the first preset range in the jth area during operation; j is the number of fatigue areas in the jth area of ​​the bucket; β, γ, and δ are adjustment parameters.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention adopts the SN curve formula and the Miner criterion, combines the historical usage data of the bucket, calculates its cumulative fatigue damage degree and dynamically identifies the fatigue area, avoids irreversible damage caused by long-term stress accumulation, and improves the reliability of the equipment;

[0033] 2. The present invention dynamically adjusts the loading area by dividing the internal area of ​​the bucket and combining the real-time cargo height and historical height change data of each area. When the fatigue level of the target area is high, the system can automatically optimize the loading strategy, thereby reducing damage and extending the life of the equipment;

[0034] 3. The present invention obtains monitoring data in real time, including the number of times the bucket shakes, the height of the goods in each area of ​​the bucket and the number of fatigue areas in each area of ​​the bucket, and dynamically adjusts the tolerance number of times each area exceeds the second preset range based on the frequency adjustment formula, to ensure that the equipment remains stable under different operating conditions and reduce abnormal vibration or unbalanced loading. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0036] Figure 2 It is a block diagram of the loading area adjustment module of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0038] Intelligent shape recognition and precise resetting control system for large vertical shaft skips, such as Figure 1 and Figure 2 As shown, including:

[0039] A fatigue area acquisition module is used to obtain the fatigue area of ​​the skip using the SN curve formula and the Miner criterion according to the historical use data of the skip;

[0040] The historical usage data of the bucket is obtained. The historical usage data of the bucket includes the actually measured stress amplitude, fatigue strength coefficient and fatigue index of the bucket material. The fatigue life of the target area of ​​the bucket is obtained using the SN curve formula according to the historical usage data of the bucket. The cumulative fatigue damage degree of the bucket is obtained using the Miner criterion according to the fatigue life of the bucket, and the fatigue area of ​​the bucket is obtained according to the cumulative fatigue damage degree. The SN curve formula is:

[0041]

[0042] Where N f is the fatigue life of the target area of ​​the bucket; σ a is the actual measured stress amplitude; σ 0 is the fatigue strength coefficient of the bucket material; b is the fatigue index of the bucket material.

[0043] It should be noted that σ a is the actual measured stress amplitude, and is the range of periodic stress variation that the bucket bears under different load conditions; σ 0 is the fatigue strength coefficient of the skip material, which is determined by the characteristics of the material itself and is usually obtained through experiments or material manuals; b is the fatigue index of the skip material, which indicates the sensitivity of the material to fatigue damage and is also provided by experiments or material databases. This calculation is used to determine the theoretical maximum number of cycles that each area of ​​the skip can withstand under different stress levels.

[0044] The fatigue area acquisition submodule obtains the cumulative fatigue damage of the bucket using the Miner criterion according to the fatigue life of the bucket, and obtains the fatigue area of ​​the bucket according to the cumulative fatigue damage;

[0045] The number of load cycles actually experienced by the bucket target area and the corresponding fatigue life under different stress states are obtained, and the cumulative fatigue damage degree of the bucket target area is obtained using the Miner criterion. When the cumulative fatigue damage degree is greater than the first preset threshold and less than 1, the bucket target area is regarded as a fatigue area; when the cumulative fatigue damage degree is greater than or equal to 1, the bucket target area is regarded as a damaged area, and the deep learning model is used to identify and repair the damaged area; wherein the Miner criterion is:

[0046]

[0047] Where D is the cumulative fatigue damage degree of the bucket target area; n i is the number of cycles under the i-th level stress; N fi is the fatigue life under the first stress level; i is the load stress level.

[0048] It should be noted that, through sensor monitoring and operation data analysis, the number of load cycles actually experienced by the target area of ​​the bucket under different stress levels and the fatigue life corresponding to different stresses are statistically analyzed. When the cumulative fatigue damage degree of the target area of ​​the bucket is between the first preset threshold and 1, the target area is marked as a fatigue area, indicating that the area is in a high fatigue damage and the load distribution needs to be optimized to extend its service life; when the cumulative fatigue damage degree of the target area of ​​the bucket is greater than or equal to 1, the system marks the target area as a damaged area, and uses a deep learning model to intelligently identify the damaged area and generate a repair plan, which includes structural reinforcement, material replacement or operation mode adjustment.

[0049] A region division module is used to divide the region in the bucket;

[0050] The area in the bucket is divided into a first area, a second area and a third area, wherein the first area is a left area in the bucket; the second area is a middle area in the bucket; and the third area is a right area in the bucket.

[0051] It should be noted that the target area of ​​the skip is a part of the area planned in advance within the first area, the second area and the third area.

[0052] An average height change acquisition module, which is used to acquire historical cargo height change data, and acquire average height change data based on the historical cargo height change data;

[0053] Obtain the historical cargo height change data of each area when the skip is running on the guide rail, take the average of the historical cargo height change data of each area within a preset time period according to the historical cargo height change data, and obtain the average height change data of each area.

[0054] It should be noted that, based on the historical cargo height change data, the historical cargo height change data of each area within the preset time period are averaged to obtain the average height change data of each area, and the average height change data of each area is dynamically adjusted over time, and the average of all historical cargo height change data within the preset time period separated from the current time is averaged to obtain the average height change data of each area, wherein the historical cargo height change data is the data on the change in cargo height in the first area, second area and third area of ​​the skip due to the influence of the vibration and tilt of the skip guide rail when the cargo relies on the skip to rise from the bottom to the top.

[0055] The loading area adjustment module is used to obtain the real-time cargo height of each area in the bucket when loading cargo, and adjust the cargo loading area according to the fatigue area of ​​the bucket;

[0056] When the fatigue zone of the bucket is in the first zone, the loading zone of the cargo is adjusted to the third zone, the real-time cargo height of each zone is obtained, and the cargo height of each zone is adjusted according to the real-time cargo height of each zone and the average height change data of each zone; when the fatigue zone of the bucket is in the third zone, the loading zone of the cargo is adjusted to the first zone, the real-time cargo height of each zone is obtained, and the cargo height of each zone is adjusted according to the real-time cargo height of each zone and the average height change data of each zone; when the fatigue zone of the bucket is in the first zone and the third zone at the same time or there is no fatigue zone of the bucket, the loading zone of the cargo is adjusted to the second zone, the real-time cargo height of each zone is obtained, and the cargo height of each zone is adjusted according to the real-time cargo height of each zone and the average height change data of each zone.

[0057] It should be noted that during the cargo loading process, the system detects the cargo height in each area of ​​the skip in real time through sensors or machine vision and stores it in the data center. When the fatigue area of ​​the skip is in the first area, the loading area of ​​the cargo is adjusted to the third area, that is, the center of gravity of the load is shifted to the area with less force, so as to reduce the additional load in the fatigue area and prevent the cargo from continuously scouring the fatigue area, thereby increasing the damage to the fatigue area. Then, through the cargo height adjustment submodule, the degree of inclination of the skip caused by the influencing factors of the vibration and inclination of the skip guide rail is reduced; when the fatigue area of ​​the skip is in the third area, the loading area of ​​the cargo is adjusted to the first area; when the fatigue area of ​​the skip is in the first area and the third area at the same time or there is no fatigue area, the loading area of ​​the cargo is adjusted to the second area, that is, the central area of ​​the skip or other optimal force-bearing area, so as to improve the overall load balance and reduce the occurrence of stress concentration points.

[0058] The cargo height adjustment submodule adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area;

[0059] The initial adjustment formula is used according to the real-time cargo height of the first area and the third area and the average height change data of each area to obtain the adjusted initial cargo height of each area, and the cargo height of each area is adjusted according to the initial cargo height of each area, wherein the initial adjustment formula is:

[0060] H=min(h 0 -θ*h y ,h max );

[0061] Where H is the initial cargo height after adjustment in the target area; h 0 is the standard cargo height of the target area; h y is the average height change data of the target area; h maxis the maximum cargo height of the target area; θ is the adjustment coefficient.

[0062] It should be noted that the initial cargo height of each area is adjusted according to the average height change data of each area, reducing the degree of skip skew caused by influencing factors such as skip guide vibration and inclination, h 0 is the standard cargo height of the target area, that is, the set value before adjustment; h y is the average height change data of the target area, reflecting the height change trend of this area after the skip is lifted; h max is the maximum cargo height of the target area, that is, the safety loading limit value.

[0063] Loading area adjustment sub-module, which adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area;

[0064] When the fatigue area of the skip is in the first area, the loading area of the cargo is adjusted to the third area, and when the real-time cargo height of the third area reaches the maximum cargo height of the third area or the real-time cargo height of the first area is higher than the highest height of the fatigue area in the first area, the loading area of the cargo is adjusted to other areas, so that the cargo height of each area increases to the adjusted initial cargo height; when the fatigue area of the skip is in the third area, the loading area of the cargo is adjusted to the first area, and when the real-time cargo height of the first area reaches the maximum cargo height of the first area or the real-time cargo height of the third area is higher than the highest height of the fatigue area in the third area, the loading area of the cargo is adjusted to other areas, so that the cargo height of each area increases to the adjusted initial cargo height.

[0065] It should be noted that the real-time cargo height is the current cargo height of each area, which is the data obtained by real-time monitoring through sensors or machine vision systems; when the real-time cargo height of the third area reaches the maximum cargo height of the third area or the real-time cargo height of the first area is higher than the highest height of the fatigue area in the first area, the system will stop loading goods in the third area and transfer the loading area to other areas, such as the second area or other areas not affected by fatigue, so that the goods will not directly contact the fatigue area in the first area during loading, thereby extending the overall service life of the skip; similarly, when the real-time cargo height of the first area reaches the maximum cargo height of the first area or the real-time cargo height of the third area is higher than the highest height of the fatigue area in the third area, the system will stop loading goods in the first area and adjust the loading area to other areas.

[0066] Area monitoring and adjustment module, which is used to obtain the monitoring data of the skip during operation and adjust the tolerance times of each area according to the monitoring data;

[0067] Acquire monitoring data of the skip during operation, the monitoring data including the number of times the skip shakes beyond a first preset range during operation, the height of cargo in each area of ​​the skip and the number of fatigue areas in each area of ​​the skip. Adjust the tolerance number of times each area of ​​the skip exceeds a second preset range during operation using a frequency adjustment formula based on the monitoring data.

[0068] It should be noted that the number of shakes exceeding the first preset range is the number of times the bucket shakes during the operation of the bucket. When the amplitude of the shake exceeds the predetermined first preset range, it reflects that acceptable abnormal vibrations have occurred in the operation of the bucket. The number of fatigue areas in each area of ​​the bucket, for example, three fatigue areas appear in the first area. The increase in fatigue areas means that the tolerance number of times the area exceeds the second preset range needs to be reduced, and the area should be better monitored to predict abnormal phenomena in the area in advance, and better reset control should be performed according to the tolerance number of times the area exceeds the second preset range.

[0069] The number adjustment module uses the frequency adjustment formula based on the monitoring data to adjust the tolerance number of times that each area of ​​the skip exceeds the second preset range during operation.

[0070] The frequency adjustment formula is:

[0071]

[0072] Where N α N is the number of times the jth area of ​​the adjusted skip exceeds the second preset range during operation; 0 is the number of times the jth area of ​​the bucket exceeds the second preset range during operation; H is the initial cargo height after adjustment in the target area; h max is the maximum cargo height in the target area; N 1j F is the number of times the bucket shakes beyond the first preset range in the jth area during operation; j is the number of fatigue areas in the jth area of ​​the bucket; β, γ, and δ are adjustment parameters.

[0073] It should be noted that the more fatigue areas there are, the more serious the depression or bulge in the bucket area will be. When it exceeds the second preset range, it is more likely to collide with the surrounding walls, causing increased damage to the bucket. Therefore, the tolerance times are adjusted by region to more flexibly adapt to work requirements in different environments and improve the adaptability and long-term stability of the equipment.

[0074] While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments.The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. Intelligent shape recognition and precise resetting control system for large vertical shaft skip, characterized in that: include: A fatigue area acquisition module, wherein the fatigue area acquisition module is used to obtain the fatigue area of ​​the skip using the SN curve formula and the Miner criterion according to the historical use data of the skip; A fatigue area acquisition submodule, wherein the fatigue area acquisition submodule obtains the cumulative fatigue damage degree of the bucket according to the fatigue life of the bucket using the Miner criterion, and obtains the fatigue area of ​​the bucket according to the cumulative fatigue damage degree; A region division module, wherein the region division module is used to divide the region in the bucket; An average height change acquisition module, the average height change acquisition module is used to acquire historical cargo height change data, and acquire average height change data based on the historical cargo height change data; A loading area adjustment module, the loading area adjustment module is used to obtain the real-time cargo height of each area in the skip when the cargo is loaded, and adjust the cargo loading area according to the fatigue area of ​​the skip; A cargo height adjustment submodule, which adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area; A loading area adjustment submodule, which adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area; A regional monitoring and adjustment module, the regional monitoring and adjustment module is used to obtain monitoring data of the skip during operation and adjust the tolerance times of each region according to the monitoring data; A times adjustment module is used to adjust the number of times that each area of ​​the skip exceeds a second preset range during operation according to the monitoring data using a frequency adjustment formula.

2. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 1 is characterized in that: The fatigue region acquisition module is used to obtain the fatigue region of the bucket according to the historical use data of the bucket using the SN curve formula and the Miner criterion, including: obtaining the historical use data of the bucket, the historical use data of the bucket including the actually measured stress amplitude, the fatigue strength coefficient and fatigue index of the bucket material, obtaining the fatigue life of the target area of ​​the bucket according to the historical use data of the bucket using the SN curve formula, obtaining the cumulative fatigue damage degree of the bucket according to the fatigue life of the bucket using the Miner criterion, and obtaining the fatigue region of the bucket according to the cumulative fatigue damage degree, wherein the SN curve formula is: Where N f is the fatigue life of the target area of ​​the bucket; σ a is the actual measured stress amplitude; σ0 is the fatigue strength coefficient of the skip material; b is the fatigue index of the skip material.

3. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 2 is characterized in that: The fatigue area acquisition submodule obtains the cumulative fatigue damage of the bucket according to the fatigue life of the bucket using the Miner criterion, and obtains the fatigue area of ​​the bucket according to the cumulative fatigue damage, including: obtaining the number of load cycles actually experienced by the bucket target area and the corresponding fatigue life under different stress states, using the Miner criterion to obtain the cumulative fatigue damage of the bucket target area, when the cumulative fatigue damage is greater than the first preset threshold and less than 1, the bucket target area is used as the fatigue area; when the cumulative fatigue damage is greater than or equal to 1, the bucket target area is used as the damaged area, and the damaged area is identified and repaired using a deep learning model; wherein the Miner criterion is: Where D is the cumulative fatigue damage degree of the bucket target area; n i is the number of cycles under the i-th level stress; N fi is the fatigue life under the first stress level; i is the load stress level.

4. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 1 is characterized in that: The area division module is used to divide the area in the bucket, including: dividing the area in the bucket to obtain a first area, a second area and a third area, wherein the first area is the left area in the bucket; the second area is the middle area in the bucket; and the third area is the right area in the bucket.

5. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 4 is characterized in that: The average height change acquisition module is used to obtain historical cargo height change data, and obtain average height change data based on the historical cargo height change data, including: obtaining historical cargo height change data of each area when the bucket is running on the guide rail, taking the average value of the historical cargo height change data of each area within a preset time period according to the historical cargo height change data, and obtaining the average height change data of each area.

6. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 5 is characterized in that: The loading area adjustment module is used to obtain the real-time cargo height of each area in the bucket when the cargo is loaded, and adjust the cargo loading area according to the fatigue area of ​​the bucket, including: when the fatigue area of ​​the bucket is in the first area, the cargo loading area is adjusted to the third area, the real-time cargo height of each area is obtained, and the cargo height of each area is adjusted according to the real-time cargo height of each area and the average height change data of each area; when the fatigue area of ​​the bucket is in the third area, the cargo loading area is adjusted to the first area, the real-time cargo height of each area is obtained, and the cargo height of each area is adjusted according to the real-time cargo height of each area and the average height change data of each area; when the fatigue area of ​​the bucket is in the first area and the third area at the same time or there is no fatigue area of ​​the bucket, the cargo loading area is adjusted to the second area, the real-time cargo height of each area is obtained, and the cargo height of each area is adjusted according to the real-time cargo height of each area and the average height change data of each area.

7. The intelligent shape recognition and precise resetting control system for large vertical shaft buckets according to claim 6 is characterized in that: The cargo height adjustment submodule adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area, including: using an initial adjustment formula according to the real-time cargo height of the first area and the third area and the average height change data of each area, obtaining the adjusted initial cargo height of each area, and adjusting the cargo height of each area according to the initial cargo height of each area, wherein the initial adjustment formula is: H=min(h0-θ*h y ,h max ); Where H is the initial cargo height after adjustment in the target area; h0 is the standard cargo height in the target area; h y is the average height change data of the target area; h max is the maximum cargo height in the target area; θ is the adjustment coefficient.

8. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 7 is characterized in that: The loading area adjustment submodule adjusts the cargo height of each area according to the real-time cargo height of each area and the average height change data of each area, including: when the fatigue area of ​​the bucket is in the first area, adjusting the loading area of ​​the cargo to the third area, and when the real-time cargo height of the third area reaches the maximum cargo height of the third area or the real-time cargo height of the first area is higher than the highest height of the fatigue area in the first area, adjusting the loading area of ​​the cargo to other areas so that the cargo height of each area increases to the adjusted initial cargo height; when the fatigue area of ​​the bucket is in the third area, adjusting the loading area of ​​the cargo to the first area, and when the real-time cargo height of the first area reaches the maximum cargo height of the first area or the real-time cargo height of the third area is higher than the highest height of the fatigue area in the third area, adjusting the loading area of ​​the cargo to other areas so that the cargo height of each area increases to the adjusted initial cargo height.

9. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 1 is characterized in that: The regional monitoring and adjustment module is used to obtain monitoring data of the skip during operation, and adjust the tolerance times of each area according to the monitoring data, including: obtaining monitoring data of the skip during operation, the monitoring data including the number of times the skip shakes beyond a first preset range during operation, the height of cargo in each area of ​​the skip and the number of fatigue areas in each area of ​​the skip, and adjusting the tolerance times of each area of ​​the skip exceeding a second preset range during operation using a frequency adjustment formula according to the monitoring data.

10. The intelligent shape recognition and precise resetting control system for large vertical shaft skips according to claim 9 is characterized in that: The frequency adjustment module uses a frequency adjustment formula according to the monitoring data to adjust the tolerance times of each area of ​​the skip exceeding the second preset range during operation, including: wherein the frequency adjustment formula is: Where N α The number of times the jth area of ​​the adjusted skip exceeds the second preset range during operation; N0 is the number of times the bucket exceeds the second preset range in the jth area during operation; H is the initial cargo height after adjustment in the target area; h max is the maximum cargo height in the target area; N 1j F is the number of times the bucket shakes beyond the first preset range in the jth area during operation; j is the number of fatigue areas in the jth area of ​​the bucket; β, γ, and δ are adjustment parameters.

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