A method for creating an intelligent scheduling platform for mine auxiliary transportation

Abstract

The present invention relates to the technical field of Internet of Things, and in particular to a method for creating an intelligent scheduling platform for auxiliary transportation of mines, the method comprising the following steps: obtaining UWB tag data of a mine car in a secondary inclined shaft, and performing signal time difference analysis according to the UWB tag data of the mine car in the secondary inclined shaft, thereby obtaining signal time difference data; constructing three-dimensional coordinates of the mine car according to the signal time difference data, thereby obtaining three-dimensional coordinate data of the mine car; obtaining data of a mine car blocker in the secondary inclined shaft, and extracting brake structure features of the mine car according to the data of the mine car blocker in the secondary inclined shaft, thereby obtaining brake structure data of the mine car; performing mine car simulation according to the brake structure data of the mine car and the three-dimensional coordinate data of the mine car, thereby obtaining mine car simulation data; performing high-risk mine car number setting according to the mine car simulation data, thereby obtaining high-risk mine car number data. The present invention realizes efficient mine car material transportation scheduling based on Internet of Things technology and optimizes mine production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a method for creating an intelligent scheduling platform for mine auxiliary transportation. Background Art

[0002] Traditional methods for creating intelligent scheduling platforms for mine auxiliary transportation usually rely on static data models and preset rules, and fail to fully consider dynamically changing factors in the mine environment, such as real-time mine car positions, load changes, and switch status. This method is often based on past experience and fixed parameter settings, and lacks the ability to flexibly adjust to specific mine conditions and task requirements. This incomplete scheduling strategy leads to reduced operating efficiency of mine cars in emergencies, causing transportation delays and waste of resources. The application of scheduling algorithms is often limited to simple linear programming, and cannot handle multi-objective optimization and real-time dynamic adjustment problems. This makes the scheduling system slow to respond to emergencies, unable to achieve rapid decision-making and efficient scheduling, and thus affects the overall production efficiency of the mine. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for creating an intelligent scheduling platform for mine auxiliary transportation to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a method for creating an intelligent scheduling platform for mine auxiliary transportation includes the following steps:

[0005] Step S1: Acquire the UWB tag data of the auxiliary inclined shaft mine car, and perform signal time difference analysis based on the UWB tag data of the auxiliary inclined shaft mine car to obtain signal time difference data; construct the three-dimensional coordinates of the mine car based on the signal time difference data to obtain the three-dimensional coordinate data of the mine car;

[0006] Step S2: obtaining the data of the auxiliary inclined shaft mine car blocker, and extracting the brake structure characteristics of the mine car according to the data of the auxiliary inclined shaft mine car blocker, thereby obtaining the brake structure data of the mine car; performing a mine car simulation according to the brake structure data of the mine car and the three-dimensional coordinate data of the mine car, thereby obtaining the mine car simulation data; setting the high-risk mine car number according to the mine car simulation data, thereby obtaining the high-risk mine car number data;

[0007] Step S3: Acquire mine production plan data and mine car operation status data; perform material transportation task allocation according to the mine production plan data and the mine car operation status data, thereby obtaining mine car material transportation task data; perform intelligent transportation scheduling according to high-risk mine car number data and mine car material transportation task data, thereby obtaining mine car material transportation intelligent scheduling data;

[0008] Step S4: Design an intelligent scheduling platform architecture based on the intelligent scheduling data of mine car material transportation, so as to obtain the intelligent scheduling platform architecture data; create an intelligent scheduling platform based on the intelligent scheduling platform architecture data and the intelligent scheduling data of mine car material transportation, so as to obtain the intelligent scheduling platform data.

[0009] The present invention can accurately locate the position of the mine car in three-dimensional space by acquiring the UWB tag data of the auxiliary inclined shaft mine car and performing signal time difference analysis. This real-time positioning information provides basic data for subsequent scheduling and monitoring, which can effectively avoid collisions and misoperations between mine cars and improve safety. Analyzing the data of the mine car's car blocker and extracting the braking structure characteristics can help evaluate the braking performance and reliability of the mine car, identify potential braking failures or abnormal wear, take maintenance measures in time, reduce runaway accidents caused by brake failure, and ensure the safety of the transportation process. Integrating the mine production plan and the mine car operation status data to intelligently allocate material transportation tasks can improve transportation efficiency. Based on the understanding of the actual needs of the mine, the transportation tasks are reasonably allocated to ensure that each mine car performs the task at the right time and location, reducing resource waste and transportation delays. The platform architecture design based on the intelligent transportation scheduling data of the mine car can ensure that the scheduling platform has flexibility and scalability, and design an architecture that adapts to different mine conditions, so that the system can be functionally expanded and optimized as needed in the future, so as to cope with the ever-changing mine environment and operational needs.

[0010] Optionally, step S1 specifically includes:

[0011] Step S11: acquiring the UWB tag data of the auxiliary inclined shaft mine car, and performing short pulse signal feature extraction according to the UWB tag data of the auxiliary inclined shaft mine car, thereby obtaining short pulse signal data;

[0012] Step S12: setting the pulse signal receiving node reference time according to the short pulse signal data, thereby obtaining the pulse signal receiving node reference time data;

[0013] Step S13: Calculate the time difference according to the pulse signal receiving node reference time data and the short pulse signal data, so as to obtain the pulse signal time difference data;

[0014] Step S14: Acquire short pulse signal propagation speed data;

[0015] Step S15: Calculate the three-dimensional coordinates according to the pulse signal time difference data and the short pulse signal propagation speed data, so as to obtain the three-dimensional coordinate data of the mine car.

[0016] The present invention obtains the UWB tag data of the mine car and extracts the characteristics of the short pulse signal to provide high-precision basic data for subsequent positioning. The short pulse signal has a strong anti-interference ability and can provide stable positioning information in a complex mining environment to ensure the accuracy of the real-time position of the mine car, thereby providing a reliable basis for intelligent scheduling. By setting the reference time of the receiving node, the clocks between the nodes can be effectively synchronized, and the subsequent time difference calculation will be more accurate, avoiding the positioning error caused by time asynchrony, directly affecting the reliability of subsequent calculations, and ensuring that the system can maintain efficient operation in a dynamic environment. By calculating the time difference data of the pulse signal, the distance between the mine car and each receiving node can be accurately estimated, which is crucial for three-dimensional positioning and provides the necessary conditions for the accurate positioning of the mine car in space, thereby supporting the scheduling system to track the position of the mine car in real time and improve the scheduling efficiency. Accurately obtaining the propagation speed of the short pulse signal is the basis for three-dimensional coordinate calculation. Through this data, the error caused by environmental changes can be eliminated in the subsequent coordinate calculation, thereby ensuring the accuracy of the positioning result and enhancing the reliability of mine transportation scheduling. Combining the pulse signal time difference data and signal propagation speed data to perform three-dimensional coordinate calculations can achieve accurate positioning of the mine car in the secondary inclined shaft. This positioning information enables the dispatching platform to grasp the position of the mine car in real time, and perform intelligent dispatching accordingly, optimize transportation routes, reduce transportation time, and improve overall transportation efficiency.

[0017] Optionally, step S2 specifically includes:

[0018] Step S21: acquiring data of the auxiliary inclined shaft mine car arrester, and extracting the brake structure characteristics of the mine car according to the data of the auxiliary inclined shaft mine car arrester, thereby obtaining the brake structure data of the mine car;

[0019] Step S22: performing braking abnormality analysis on the braking structure data of the mine car, thereby obtaining braking abnormality data;

[0020] Step S23: performing driving track abnormality analysis on the three-dimensional coordinate data of the mine car, thereby obtaining driving track abnormality data;

[0021] Step S24: performing a mine sports car simulation according to the abnormal braking data and the abnormal driving track data, thereby obtaining the mine sports car simulation data;

[0022] Step S25: setting the high-risk mine car number according to the mine car simulation data, thereby obtaining the high-risk mine car number data.

[0023] By acquiring the data of the mine car blocker and extracting the braking structure characteristics, the present invention can fully understand the braking system status of the mine car, identify the key performance indicators of the braking system, including the wear state of the brake pad and the transmission efficiency of the braking force, thereby providing a solid foundation for subsequent abnormal analysis, which helps to timely discover potential problems of the braking system and ensure the safety and reliability of the mine car during transportation. Abnormal analysis of the braking structure data can reveal the performance deviation of the braking system in actual operation, and can detect potential risks such as braking delay and braking failure, thereby helping maintenance personnel to perform maintenance and adjustments in time to avoid accidents caused by braking failures, greatly improving the safety of the mine car in complex environments, and providing protection for the production safety of the mine. Using the three-dimensional coordinate data of the mine car for driving trajectory abnormal analysis, it is possible to identify the abnormal behavior of the mine car during driving, such as speeding, deviation from the route, etc., which can help managers monitor the operating status of the mine car in real time, adjust the driving strategy of the mine car in time, ensure the safe driving of the mine car in the inclined shaft, and avoid safety accidents caused by abnormal trajectory. Combining abnormal braking data with abnormal driving trajectory data for mine car simulation can simulate possible out-of-control scenarios and assess risks, providing reliable data support for the formulation of corresponding preventive and emergency measures. The results of the mine car simulation help to understand the behavioral responses of mine cars in different situations in advance, and then optimize the operation safety strategy of mine cars. Determining the number of high-risk mine cars based on the simulation results will help implement accurate monitoring and maintenance, and ensure timely inspection and repair in high-risk situations. This numbering setting can focus resources on high-risk vehicles, improve the efficiency of preventive and response measures, reduce the chance of mine car loss of control or accidents, and improve the operation safety of the entire mine.

[0024] Optionally, step S22 is specifically:

[0025] Step S221: extracting brake pad features according to the brake structure data of the mine car, thereby obtaining brake pad data;

[0026] Step S222: performing wear thickness statistics on the brake pad data to obtain wear thickness data;

[0027] Step S223: obtaining brake pad initial thickness data and maximum brake pad wear rate data;

[0028] Step S224: Calculating the wear rate according to the wear thickness data and the brake pad initial thickness data, thereby obtaining the brake pad wear rate data;

[0029] Step S225: classifying the brake pad wear rate data into wear abnormality according to the highest brake pad wear rate data, and obtaining wear rate abnormality data if the brake pad wear rate data is greater than the highest brake pad wear rate data;

[0030] Step S226: performing crack detection on the wear thickness data to obtain wear crack data;

[0031] Step S227: Integrate the braking abnormality data according to the wear crack data and the wear rate abnormality data, so as to obtain the braking abnormality data.

[0032] The present invention can obtain detailed information about the performance and state of the brake pad by extracting the brake pad features from the brake structure data of the mine car, which is helpful to identify the material properties, design parameters and use conditions of the brake pad, thereby laying the foundation for subsequent wear analysis. Accurate brake pad data can help the operation team understand the effectiveness and safety of the brake system. Wear thickness statistics of the brake pad data can clearly evaluate the current state of each brake pad, reveal the wear mode of different mine cars under different working conditions, and provide important maintenance data. This statistical analysis helps to formulate a suitable maintenance plan and reduce the risk of sudden brake failure. By obtaining the initial thickness and maximum wear rate data of the brake pad, a comparative benchmark is established, which provides a reference for subsequent wear calculations, helps to accurately evaluate the wear of the brake pad, and understanding the initial state and wear limit helps the maintenance team to effectively monitor and predict, avoiding premature or late replacement of the brake pad. Wear rate calculation based on wear thickness data and initial thickness data can quantify the degree of wear of the brake pad, provide key data for maintenance decisions of the brake system, and help operators determine whether the brake pad needs to be replaced. The transparency of the wear rate improves the predictability of the system and reduces the risk of unexpected failures. By comparing the wear rate data with the maximum wear rate data, the brake pads with abnormal wear can be identified. Timely detection of excessively worn brake pads can effectively prevent brake failures, providing clear guidance for maintenance personnel to help them replace them at the right time to ensure the safe operation of the mine car. Crack detection of wear thickness data can identify potential crack problems on the surface of the brake pads, which is the key to ensuring the safety of the brake system. Early detection of cracks can prevent serious safety hazards. By adopting advanced detection methods, the integrity of the brake pads is ensured, further improving the safety of mine transportation. According to the integration of wear crack data and wear rate abnormality data, comprehensive brake abnormality data can be formed, which can comprehensively evaluate the status of the brake system and provide a basis for subsequent scheduling decisions. Through real-time monitoring and analysis, managers can quickly take measures to deal with potential safety risks and ensure the stability and reliability of the mine car during operation.

[0033] Optionally, step S226 is specifically:

[0034] Perform wear uniformity calculation on the wear thickness data to obtain wear uniformity data;

[0035] Uniformity division is performed according to the wear uniformity data, thereby obtaining uneven wear thickness data;

[0036] Performing magnetization processing on the worn area according to the uneven wear thickness data, thereby obtaining magnetization data of the worn area;

[0037] Conduct magnetic powder spraying simulation on the magnetization data of the wear area to obtain magnetic powder spraying simulation data;

[0038] Conduct magnetic powder accumulation detection on magnetic powder spraying simulation data to obtain magnetic powder accumulation data;

[0039] The wear crack area is located according to the magnetic powder accumulation data to obtain the wear crack data.

[0040] The present invention can reveal the wear condition of brake pads or other wear parts by calculating the wear uniformity of wear thickness data. By determining the uniformity data, the operation team can identify abnormal wear patterns, provide a scientific basis for subsequent maintenance and replacement decisions, help to discover potential failure risks in advance, and ensure that the equipment operates in the best state. Uniformity division is performed according to the wear uniformity data to obtain uneven wear thickness data, which can analyze the wear condition more carefully. By identifying the uneven wear area, the team can conduct targeted inspections and maintenance to ensure that the wear does not affect the operating performance of the mine car. It can also provide important references for optimizing scheduling and operation, and help the scheduling system better adapt to the wear condition. Magnetizing the wear area of ​​the uneven wear thickness data can make the subsequent detection process more effective. By magnetizing the specific wear area, the detection ability of cracks and defects can be enhanced, providing a reliable basis for subsequent fault diagnosis. This process improves the accuracy of detection and helps to extend the service life of the equipment. Conducting magnetic powder spraying simulation can provide important reference data for actual detection. Through simulation, the team can evaluate the effect and accuracy of spraying and optimize the use and detection process of magnetic powder. This simulation process lays the foundation for subsequent actual operations and ensures the effectiveness and reliability of the detection. Magnetic powder accumulation detection of magnetic powder spraying simulation data can identify wear cracks and their characteristics. By detecting the accumulation, the operation team can intuitively understand the shape, location and severity of the cracks. This information is crucial for timely maintenance and safety decisions, and can effectively prevent safety hazards caused by equipment failure. Locating the wear crack area based on magnetic powder accumulation data can provide precise guidance for subsequent repairs and replacements. By accurately locating the cracks, the team can quickly develop a corresponding maintenance plan, reduce downtime, and improve the operating efficiency of the mine car. This positioning process not only improves the targeted maintenance, but also reduces resource waste, ensuring the safety and efficiency of mine transportation.

[0041] Optionally, step S23 is specifically:

[0042] Step S231: Draw a three-dimensional trajectory map according to the three-dimensional coordinate data of the mine car, thereby obtaining three-dimensional trajectory map data;

[0043] Step S232: extracting the features of the mine car curve trajectory according to the three-dimensional trajectory map data, thereby obtaining the mine car curve trajectory data;

[0044] Step S233: performing a mine car cornering simulation according to the mine car cornering trajectory data, thereby obtaining the mine car cornering simulation data;

[0045] Step S234: Calculate the centrifugal force of the mine car cornering simulation data, so as to obtain the centrifugal force data of the mine car cornering;

[0046] Step S235: Calculate the lateral force of the mine car cornering simulation data, so as to obtain the lateral force data of the mine car cornering;

[0047] Step S236: performing overturning risk assessment according to the centrifugal force data of the mine car traveling on the curve and the lateral force data of the mine car traveling on the curve, thereby obtaining the overturning risk data of the mine car traveling on the curve;

[0048] Step S237: performing overturning angle analysis according to the mine car turning simulation data, thereby obtaining the mine car turning overturning angle data;

[0049] Step S238: performing statistics on the turning time of the mine car according to the overturning risk data of the mine car when traveling on a curved road and the overturning angle data of the mine car when passing a curve, thereby obtaining the turning time data of the mine car;

[0050] Step S239: obtaining normal turning time data of the mine car, and comparing the turning time data of the mine car with the abnormal driving trajectory time according to the normal turning time data of the mine car, so as to obtain abnormal driving trajectory data.

[0051] The present invention can intuitively display the driving path of the mine car in the mine by drawing a three-dimensional trajectory diagram according to the three-dimensional coordinate data of the mine car. This graphical data helps the operation team quickly understand the movement mode of the mine car, identify potential driving obstacles and safety hazards, and provide a clear visual basis for subsequent analysis. By extracting the characteristics of the mine car curve trajectory from the three-dimensional trajectory diagram data, the specific behavior of the mine car in the curve can be identified and analyzed, which is helpful to understand the dynamic response of the mine car under different curve conditions, identify key parameters such as curve radius and inclination, and provide necessary information for safety assessment. By performing a mine car cornering simulation, the performance of the mine car can be predicted in a virtual environment. This simulation can not only save time and cost for field testing, but also help engineers optimize the cornering ability of the mine car in the design stage and improve operational safety and stability. Centrifugal force calculation of the mine car cornering simulation data can quantify the centrifugal force to which the mine car is subjected when cornering, which helps to determine whether the mine car is at risk of losing control, supports the formulation of more rigorous safety standards, and ensures that the mine car can operate safely under specific conditions. Through lateral force calculation, the lateral force on the mine car during the cornering process can be evaluated, and the stability of the mine car under specific turning conditions can be revealed, providing necessary data support for subsequent risk assessment to ensure the safety of the mine car. Combining the centrifugal force and lateral force data of the mine car's curve driving to conduct overturning risk assessment can comprehensively judge the possibility of overturning of the mine car during driving, provide the operation team with important information about the safety of the mine car, and help them formulate corresponding safety measures and prevention strategies. The overturning angle analysis of the mine car's cornering simulation data can quantify the degree of inclination of the mine car during the cornering process, and provide intuitive data support for judging whether the mine car has a risk of overturning, so that the team can take timely intervention measures to reduce safety hazards. The cornering time statistics based on the overturning risk data and overturning angle data of the mine car's curve driving are helpful to evaluate the cornering efficiency of the mine car under different conditions. It can not only be used for optimizing scheduling, but also as a basis for formulating safe driving standards. By comparing the data with the normal cornering time of the mine car, the time period of abnormal driving trajectory can be identified, providing a monitoring and early warning mechanism for the operation team, helping to quickly discover potential safety problems, so as to adjust the scheduling strategy in time to ensure the efficiency and safety of the mine car.

[0052] Optionally, step S237 is specifically:

[0053] According to the mine car cornering simulation data, the mine car center of gravity height feature extraction and the mine car width feature extraction are performed to obtain the mine car center of gravity height data and the mine car width data;

[0054] The overturning moment is calculated according to the height data of the center of gravity of the mine car and the centrifugal force data of the mine car traveling on a curve, so as to obtain the overturning moment data;

[0055] Get the mine car load data;

[0056] The anti-overturning moment is calculated according to the width data and the load data of the mine car, so as to obtain the anti-overturning moment data;

[0057] The overturning angle is determined according to the overturning moment data and the anti-overturning moment data. If the overturning moment is greater than the anti-overturning moment, the overturning angle determination standard data is obtained.

[0058] The overturning angle of the mine car is determined according to the overturning angle determination standard data, so as to obtain the overturning angle data of the mine car when turning.

[0059] The present invention can effectively evaluate the stability of the mine car when driving on a curve by extracting the center of gravity height and width characteristics of the mine car. These data provide the necessary basis for the subsequent calculation of the overturning moment and ensure the accuracy of the analysis. The calculation of the overturning moment combined with the centrifugal force data of the curve driving can quantitatively analyze the safety of the mine car during the turning process and provide a scientific basis for judging the potential overturning risk. The acquired load data further enhances the understanding of the behavior of the mine car, so that the anti-overturning ability under different load conditions can be accurately evaluated. Through the calculation of the anti-overturning moment, the safety boundary of the mine car during driving can be identified to ensure the effective control of risks in actual operation. When the overturning moment is greater than the anti-overturning moment, the determined overturning angle standard data can be timely warned, reduce the probability of accidents, and improve the driving safety of the mine car. Finally, through the overturning angle determination, the overturning angle data of the mine car when turning can be obtained, which provides real-time feedback for the intelligent scheduling system, enhances its dynamic adjustment ability, ensures the safe operation of the mine car in various complex environments, and thus improves the efficiency and safety of the entire mine transportation system.

[0060] Optionally, step S3 specifically includes:

[0061] Step S31: obtaining mine production plan data and mine car operation status data;

[0062] Step S32: extracting the characteristics of the mine car to be dispatched state according to the mine car running state data, thereby obtaining the mine car to be dispatched state data;

[0063] Step S33: allocating material transportation tasks to the mine car scheduling status data according to the mine production plan data, thereby obtaining the mine car material transportation task data;

[0064] Step S34: performing completion statistics according to the mine car material transportation task data, thereby obtaining the mine car material transportation task completion data, and performing idle mine car identification according to the mine car material transportation task completion data, thereby obtaining the idle mine car data;

[0065] Step S35: obtaining the distribution data of the auxiliary inclined shaft turnout;

[0066] Step S36: performing mine car dispatching path planning on the mine car data according to the auxiliary inclined shaft switch distribution data and the idle mine car data, thereby obtaining the mine car dispatching path data;

[0067] Step S37: extracting the mine sports car transportation task features according to the high-risk mine car number data, thereby obtaining the mine sports car transportation task data;

[0068] Step S38: Intelligently dispatch material transportation for the mine car transportation task data according to the idle mine car data and the mine car dispatching path data, thereby obtaining the mine car material transportation intelligent dispatching data.

[0069] The present invention extracts the characteristics of the mine car running status data and identifies the mine cars in the state to be scheduled, so that the available transportation resources can be understood in real time. Task allocation is carried out in combination with the production plan to ensure that the mine car resources are reasonably distributed according to priority and demand, thereby improving the scheduling efficiency. By counting the completion of the transportation tasks, the idle mine cars can be identified and marked, which helps to optimize the utilization rate of the mine cars and avoid waste of resources. After obtaining the distribution data of the auxiliary inclined shaft turnout, the best path can be planned in the scheduling to reduce the transportation time and path conflict. Combined with this information, an efficient transportation path can be formulated to ensure the timely and safe transportation of materials. Extracting relevant task features based on the high-risk mine car number data helps to pay special attention to these high-risk vehicles during scheduling to reduce the risk of accidents. By combining idle mine cars with scheduling paths, an intelligent scheduling strategy for material transportation can be formulated to achieve flexible deployment and task allocation, and improve the response speed and safety of the overall mine car scheduling system.

[0070] Optionally, step S36 is specifically:

[0071] Step S361: extracting turnout features and mine track features according to the turnout distribution data of the auxiliary inclined shaft, thereby obtaining turnout data and mine track data;

[0072] Step S362: constructing a mine road network model according to the turnout data and the mine road data, thereby obtaining a mine road network model;

[0073] Step S363: performing shortest path planning for mine car scheduling on the mine road network model according to the idle mine car data, thereby obtaining the shortest path data for mine car scheduling;

[0074] Step S364: performing intelligent turnout path management on the shortest path data for mine car scheduling according to the idle mine car data, thereby obtaining intelligent turnout path data;

[0075] Step S365: The mine car dispatching path is integrated according to the intelligent turnout path data and the mine car dispatching shortest path data, so as to obtain the mine car dispatching path data.

[0076] The present invention can fully understand the infrastructure status of the mine through the feature extraction of turnouts and mine roads, and provide key data support for the subsequent construction of road network models. This process can reveal the traffic capacity of the mine road and the operating characteristics of the turnout, which is helpful to optimize the traffic layout of the mine. The construction of the mine road network model can intuitively present the traffic system of the mine, and provide a powerful tool for analyzing and optimizing the scheduling of mine cars. This model not only improves the understanding of the mobility of mine traffic, but also provides a scientific basis for subsequent path planning. Scheduling the shortest path planning for idle mine cars can ensure the effective use of resources, reduce the empty load situation through reasonable path selection, improve transportation efficiency, and reduce operating costs. At the same time, the intelligent turnout path management combines the actual operation data, can dynamically adjust the scheduling path, improve the efficiency of the use of the turnout, ensure the rapid passage of the mine car, and further enhance the flexibility of scheduling. Finally, through path fusion, the intelligent turnout path is combined with the shortest path, and a more efficient scheduling strategy can be achieved to ensure that the mine car completes the transportation task in the most optimal way in a complex mine environment, improve the overall operation efficiency, and reduce potential risks. Such a system design not only improves the scientificity and accuracy of scheduling, but also lays the foundation for intelligent management of mines.

[0077] Optionally, step S4 is specifically:

[0078] Step S41: Designing a database architecture according to the mine car material transportation intelligent scheduling data, thereby obtaining database architecture data;

[0079] Step S42: dividing the system modules according to the mine car material transportation intelligent scheduling data, thereby obtaining system module data;

[0080] Step S43: Designing the intelligent scheduling platform architecture according to the system module data and the database architecture data, thereby obtaining the intelligent scheduling platform architecture data;

[0081] Step S44: Designing a user interface according to the mine car material transportation intelligent scheduling data, thereby obtaining user interface data;

[0082] Step S45: Create an intelligent scheduling platform according to the intelligent scheduling platform architecture data and the user interface data, thereby obtaining the intelligent scheduling platform data.

[0083] The present invention can establish an efficient and reliable data storage and management system by designing the database architecture of the intelligent scheduling data of mine car material transportation, ensure the structure and consistency of the data, and provide a basis for subsequent data analysis and application. The division of system modules can make the overall system more modular and flexible, facilitate the independent development and maintenance of each functional module, and promote the scalability and reusability of the system, and improve development efficiency. The intelligent scheduling platform architecture design combines the database architecture and system module data, which can achieve more efficient information flow and resource allocation, and ensure the rapid response and efficient operation of the scheduling process. The design of the user interface focuses on the user experience, and improves the user's operating efficiency and satisfaction through an intuitive interface and easy-to-use operation process, thereby promoting the wide application of the platform. Finally, the intelligent scheduling platform architecture is integrated with the user interface data to create an intelligent scheduling platform, which can form a comprehensive and easy-to-operate system, making the mine car material transportation scheduling process more intelligent and efficient, and ultimately optimizing the overall efficiency and safety of mine production operations. This series of steps together provide an integrated solution for mine auxiliary transportation and promote the process of intelligent management of mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0085] Figure 1 A schematic diagram of the steps of the method for creating an intelligent scheduling platform for auxiliary mine transportation according to the present invention;

[0086] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0087] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0088] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0089] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0090] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0091] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0092] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for creating an intelligent scheduling platform for mine auxiliary transportation, the method comprising the following steps:

[0093] Step S1: Acquire the UWB tag data of the auxiliary inclined shaft mine car, and perform signal time difference analysis based on the UWB tag data of the auxiliary inclined shaft mine car to obtain signal time difference data; construct the three-dimensional coordinates of the mine car based on the signal time difference data to obtain the three-dimensional coordinate data of the mine car;

[0094] In this embodiment, the UWB (ultra-wideband) tag data of the auxiliary inclined shaft mine car is collected. The UWB tag installed on the mine car is used to capture the signal using the receiver and record the timestamp of the received signal; next, the signal propagation time difference is calculated by performing time difference analysis on the signals received by different receivers. Specifically, the calculation method is: for each pair of receivers, the time difference is calculated using the formula Δt=t1-t2, where t1 and t2 are the times when the signal is received, and the time difference data is applied to the three-dimensional positioning algorithm, and the three-dimensional coordinate data of the mine car is obtained using triangulation. This process involves the known spatial position of the receiver and the signal propagation speed. Through further processing of the three-dimensional coordinate data, accurate mine car position data is generated.

[0095] Step S2: obtaining the data of the auxiliary inclined shaft mine car blocker, and extracting the brake structure characteristics of the mine car according to the data of the auxiliary inclined shaft mine car blocker, thereby obtaining the brake structure data of the mine car; performing a mine car simulation according to the brake structure data of the mine car and the three-dimensional coordinate data of the mine car, thereby obtaining the mine car simulation data; setting the high-risk mine car number according to the mine car simulation data, thereby obtaining the high-risk mine car number data;

[0096] In this embodiment, the data of the car blocker of the auxiliary inclined shaft mine car is obtained through mechanical sensors and acceleration sensors installed on the track. These sensors collect dynamic data when the mine car approaches, contacts and leaves the car blocker, including information such as the braking force, mine car speed, and trigger displacement of the car blocker; the braking structure feature extraction process uses an algorithm based on signal processing to decompose the collected raw data through wavelet transform, and extract key features such as the instantaneous change value of the braking force and the average braking force under stable conditions; combined with the three-dimensional coordinate data of the mine car, the behavior of the mine car in actual operation is simulated on the mine environment simulation platform, and the distribution of stress and displacement during the braking process of the mine car is dynamically simulated using the finite element analysis method. The generated mine car simulation data will show the braking performance and operating status of the mine car. According to the simulation data, the expert system algorithm is used to screen out the mine cars whose braking performance does not meet the safety standards, and their numbers are set as high-risk mine cars and recorded in the system's risk database.

[0097] Step S3: Acquire mine production plan data and mine car operation status data; perform material transportation task allocation according to the mine production plan data and the mine car operation status data, thereby obtaining mine car material transportation task data; perform intelligent transportation scheduling according to high-risk mine car number data and mine car material transportation task data, thereby obtaining mine car material transportation intelligent scheduling data;

[0098] In this embodiment, the mine production plan data is obtained through the mine information management system (such as the ERP system), including daily material transportation requirements, mine car scheduling schedule, etc. The mine car operation status data is transmitted to the central control system in real time by the sensors installed on the mine car, covering detailed information such as the speed, load, and current operation status of the mine car. Based on the production plan and operation status data, the linear programming method is used to allocate material transportation tasks, match the tasks with the idle time and carrying capacity of the mine car, and output the mine car material transportation task data. The high-risk mine car number data is incorporated into the transportation task allocation logic, and the decision tree algorithm is used to preferentially allocate tasks to low-risk mine cars to ensure the safety of the scheduling plan.

[0099] Step S4: Design an intelligent scheduling platform architecture based on the intelligent scheduling data of mine car material transportation, so as to obtain the intelligent scheduling platform architecture data; create an intelligent scheduling platform based on the intelligent scheduling platform architecture data and the intelligent scheduling data of mine car material transportation, so as to obtain the intelligent scheduling platform data.

[0100] In this embodiment, the architecture design of the intelligent scheduling platform is carried out based on the intelligent scheduling data of mine car material transportation. During the architecture design process, the system functional requirements are clarified, including real-time monitoring, task allocation, and fault warning. The system modeling tool is used to create a platform architecture diagram to indicate the relationship between each functional module and the data flow. Then, according to the architecture design data, the specific implementation of the intelligent scheduling platform is carried out, and the database technology is applied to establish a data storage system, and the data analysis tool is used to write the scheduling algorithm to ensure the real-time scheduling capability of mine car material transportation. By testing and optimizing the scheduling platform, the stability and reliability of the system are ensured, so as to obtain complete intelligent scheduling platform data.

[0101] Optionally, step S1 specifically includes:

[0102] Step S11: acquiring the UWB tag data of the auxiliary inclined shaft mine car, and performing short pulse signal feature extraction according to the UWB tag data of the auxiliary inclined shaft mine car, thereby obtaining short pulse signal data;

[0103] In this embodiment, short pulse signal data emitted by UWB (ultra-wideband) tags on the mine cars in the secondary inclined shaft are collected. By installing UWB tags on the mine cars, the system periodically sends short pulse signals, which are received by multiple receiving nodes arranged around the mine. Data collection uses signal processing equipment to capture the amplitude, duration, waveform and other characteristics of the short pulse signals. The captured signals are spectrally analyzed through the fast Fourier transform (FFT) method to extract the key features of the short pulse signals, such as signal strength, rising and falling edge times, and pulse width. After these processes, a complete short pulse signal data set is formed, providing a basis for subsequent analysis.

[0104] Step S12: setting the pulse signal receiving node reference time according to the short pulse signal data, thereby obtaining the pulse signal receiving node reference time data;

[0105] In this embodiment, a reference time of the pulse signal receiving node is set. When each receiving node receives a signal, it records the timestamp of the signal's arrival. To ensure the consistency of the time reference, a global positioning system (GPS) or a high-precision clock module is used to synchronize the time for each receiving node. At this time, the collected time data will be used as the reference time data of the pulse signal receiving node. The data should include the timestamp and its corresponding receiving node number for accurate use in subsequent calculations and data analysis.

[0106] Step S13: Calculate the time difference according to the pulse signal receiving node reference time data and the short pulse signal data, so as to obtain the pulse signal time difference data;

[0107] In this embodiment, the reference time data of the pulse signal receiving node and the short pulse signal data are used to calculate the time difference. The time difference of signal propagation is calculated by comparing the timestamps of the same pulse signal received at different receiving nodes. Specifically, a reference receiving node is set, and other nodes perform subtraction operations relative to the timestamp of the node to obtain the time difference data. The formula used is:

[0108] Δt=t_receiving-t_reference,

[0109] Among them, Δt is the time difference, t_receiving is the time when the current receiving node receives the signal, and t_reference is the time of the reference node. In this way, by sorting out the time difference data of all nodes, a pulse signal time difference data set is formed, laying the foundation for subsequent coordinate calculation.

[0110] Step S14: Acquire short pulse signal propagation speed data;

[0111] In this embodiment, the propagation speed data of the short pulse signal is obtained. This data is usually based on an existing signal propagation model and adjusted using known environmental parameters such as temperature and humidity. The propagation speed of the short pulse signal in the air is about 99% of the speed of light (about 299,792,458 m / s), while the actual propagation speed in the mine is affected by environmental factors and can be obtained through experimental measurement. By sending a short pulse signal in a controlled environment and recording the propagation time of the signal at a known distance, the propagation speed data of the short pulse signal is calculated using the formula v=d / t, where v is the propagation speed, d is the known distance, and t is the propagation time.

[0112] Step S15: Calculate the three-dimensional coordinates according to the pulse signal time difference data and the short pulse signal propagation speed data, so as to obtain the three-dimensional coordinate data of the mine car.

[0113] In this embodiment, the three-dimensional coordinates are calculated using the pulse signal time difference data and the short pulse signal propagation speed data. First, a three-dimensional coordinate system is set, and the coordinate information of the receiving node is used as a known parameter. According to the time difference of different receiving nodes and the propagation speed, the three-dimensional coordinates of the mine car are calculated using the triangulation positioning algorithm. The distance between each receiving node and the mine car is calculated using the formula:

[0114] d=v Δt,

[0115] Where v is the propagation speed, d is the known distance, and Δt is the time difference. The relative distance between the mine car and each receiving node is obtained. Then, these distances and the known coordinates of the receiving nodes are used to locate the mine car through geometric methods, and finally the three-dimensional coordinate data of the mine car is determined. In this way, the obtained three-dimensional coordinate data will be used for subsequent mine car positioning and scheduling management.

[0116] Optionally, step S2 specifically includes:

[0117] Step S21: acquiring data of the auxiliary inclined shaft mine car arrester, and extracting the brake structure characteristics of the mine car according to the data of the auxiliary inclined shaft mine car arrester, thereby obtaining the brake structure data of the mine car;

[0118] In this embodiment, the data of the car blocker of the auxiliary inclined shaft mine car is obtained. Through the sensor installed on the car blocker, the key parameters such as the force applied during the mine car braking, the braking time and the position are monitored in real time. The collected data include the maximum braking force applied by the car blocker, the duration of the application time and the braking reaction time. To ensure the accuracy of the data, high-frequency sampling technology is used to collect data hundreds of times per second to form a continuous braking force time series. The collected raw data is feature extracted, and the signal processing technology is used to analyze the changing trend of the braking force, and the key characteristic values ​​of the braking structure, such as the average braking force, the maximum braking force and its change rate, are calculated. After analysis, the braking structure data of the mine car is formed, which provides a basis for the subsequent braking abnormality analysis.

[0119] Step S22: performing braking abnormality analysis on the braking structure data of the mine car, thereby obtaining braking abnormality data;

[0120] In this embodiment, braking abnormality analysis is performed based on the braking structure data of the mine car. A threshold range of normal braking characteristics is set to ensure comparison with the normal operating state. A normal distribution model of braking characteristics is established using statistical analysis methods, such as mean and standard deviation. The actual measured braking characteristics are compared with the model to determine whether there are abnormal conditions that exceed the set threshold. The degree of braking abnormality is determined by calculating an abnormality index (e.g., Z-score). If a parameter exceeds the normal range, it is recorded as braking abnormality data. Braking abnormality data includes information such as abnormality type, duration, and frequency of occurrence, which provide support for subsequent safety analysis and risk assessment.

[0121] Step S23: performing driving track abnormality analysis on the three-dimensional coordinate data of the mine car, thereby obtaining driving track abnormality data;

[0122] In this embodiment, the three-dimensional coordinate data of the mine car is used to analyze the driving trajectory abnormality. The standard of the normal driving trajectory is determined, including the reasonable range of the trajectory and the predetermined driving speed. The three-dimensional coordinate data is processed using a moving average filter to smooth the trajectory to reduce the impact of noise. Then, by setting thresholds, such as the maximum deviation distance and the maximum speed change rate, the difference between the actual driving trajectory and the normal trajectory is analyzed. If a point on the actual trajectory deviates from the normal trajectory by more than the set maximum deviation distance or the speed change exceeds the maximum rate, the point is recorded as an abnormal point and the driving trajectory abnormal data is generated. The driving trajectory abnormal data includes the abnormal position, deviation degree and deviation duration, which provides a basis for subsequent data integration.

[0123] Step S24: performing a mine sports car simulation according to the abnormal braking data and the abnormal driving track data, thereby obtaining the mine sports car simulation data;

[0124] In this embodiment, the braking abnormality data and the driving trajectory abnormality data are integrated and input into the mine car simulation module. This module is based on physical simulation software such as ANSYS or ADAMS for modeling and dynamic simulation, taking into account the mass, speed, braking force, trajectory and other parameters of the mine car. During the simulation process, these input data are combined with the mine operating environment, and the behavior of the mine car under various working conditions is simulated through stress analysis and dynamic calculation to predict possible instability or running away situations. The output data is the mine car simulation data, including risk time points and risk paths.

[0125] Step S25: setting the high-risk mine car number according to the mine car simulation data, thereby obtaining the high-risk mine car number data.

[0126] In this embodiment, according to the high-risk areas and potential out-of-control points in the mine car simulation data, statistical analysis software (such as R language or Matlab) is used to compare the numbers of all mine cars and screen out high-risk mine cars. The number of high-risk mine cars is set based on the abnormal conditions, braking abnormalities and cumulative risk values ​​of trajectory deviations that appear multiple times in the simulation. For example, when a mine car is marked as high-risk more than 5 times in the simulation, its number is set as a high-risk mine car. This information is stored in the mine central database for subsequent risk management and scheduling strategy formulation.

[0127] Optionally, step S22 is specifically:

[0128] Step S221: extracting brake pad features according to the brake structure data of the mine car, thereby obtaining brake pad data;

[0129] In this embodiment, brake pad feature extraction is performed through the brake structure data of the mine car. The brake pad in the brake structure is analyzed in detail, and the dynamic feature data of the brake pad under working condition is collected, including temperature, braking frequency, and applied pressure. The physical properties of the brake pad are monitored in real time using a sensor array, with special attention paid to the friction coefficient, thermal deformation, and its performance under different working conditions. The signal processing algorithm is used to perform Fourier transform on the collected raw data to extract the frequency domain features and time domain features of the brake pad. Finally, brake pad data containing brake pad performance parameters and features is generated, providing a basis for subsequent wear thickness statistics.

[0130] Step S222: performing wear thickness statistics on the brake pad data to obtain wear thickness data;

[0131] In this embodiment, the wear thickness statistics are performed using brake pad data. The thickness of the brake pad is measured regularly by precision measuring instruments to ensure the accuracy of the data. The collected data includes thickness values ​​at multiple time points, and the average thickness, minimum thickness, and maximum thickness of the brake pad are calculated by statistical analysis methods. The standard deviation analysis method is used to determine the stability of the thickness distribution, and the wear degree is classified by the set threshold. For example, the wear threshold is set to 80% of the new thickness to monitor the wear condition. Finally, the wear thickness data is formed, which lays the foundation for wear rate calculation and abnormal analysis.

[0132] Step S223: obtaining brake pad initial thickness data and maximum brake pad wear rate data;

[0133] In this embodiment, the initial thickness data and the maximum brake pad wear rate data of the brake pad are obtained. The initial thickness data is obtained through the technical specifications of the new brake pad, and the manufacturing thickness of each brake pad is recorded for subsequent comparison. The maximum brake pad wear rate data can be obtained through historical data analysis and field testing, usually including the statistical results of wear rate under different working conditions. These data are obtained by combining long-term monitoring and recording, and a reasonable upper limit of wear rate is determined by regression analysis of multiple running test data. These data will serve as important reference values ​​in the subsequent wear rate calculation.

[0134] Step S224: Calculating the wear rate according to the wear thickness data and the brake pad initial thickness data, thereby obtaining the brake pad wear rate data;

[0135] In this embodiment, the wear rate is calculated based on the wear thickness data and the initial thickness data of the brake pad. The wear rate is calculated using the formula, which is: wear rate = (initial thickness - current thickness) / initial thickness × 100%. The wear thickness data and initial thickness data are extracted to ensure the accuracy and consistency of the data. During the calculation process, programming languages ​​such as Python or MATLAB are used for data processing to ensure efficiency and accuracy. After the calculation is completed, the wear rate data of all brake pads are integrated into a data set to provide a basis for subsequent abnormal classification.

[0136] Step S225: classifying the brake pad wear rate data into wear abnormality according to the highest brake pad wear rate data, and obtaining wear rate abnormality data if the brake pad wear rate data is greater than the highest brake pad wear rate data;

[0137] In this embodiment, the brake pad wear rate data is classified as wear abnormality based on the highest brake pad wear rate data. A standard threshold is set, usually at 95% of the highest wear rate. The wear rate data is compared using a data analysis tool to identify all wear rates above the set threshold. By calculating the difference between each wear rate value and the highest wear rate, the data that exceeds the difference range is marked as wear rate abnormality data. The generated wear rate abnormality data includes abnormal values, occurrence time, and duration, providing data support for further analysis of braking performance.

[0138] Step S226: performing crack detection on the wear thickness data to obtain wear crack data;

[0139] In this embodiment, the wear uniformity calculation is performed on the wear thickness data to evaluate the wear state of the brake pad. The wear thickness data of each brake pad at different positions are collected to form a complete set of wear data sets. By calculating the difference between each wear thickness value and the average thickness of the brake pad, the wear uniformity data is obtained by variance analysis. In the specific operation, the wear uniformity is calculated using the formula: uniformity = 1-(standard deviation / average thickness), where the standard deviation reflects the discrete degree of wear. A uniformity value close to 1 indicates uniform wear, while a uniformity value close to 0 indicates uneven wear. The wear uniformity data finally formed provides a basis for subsequent uniformity division. In the uniformity division process, a reasonable uniformity threshold, such as 0.8, is set as a standard for judging the wear state. According to the calculated uniformity data, all brake pads that do not meet the uniformity standard are identified, and their wear thickness information is recorded and classified as uneven wear thickness data. These uneven wear thickness data provide target areas for subsequent magnetization processing. Wear area magnetization processing is performed on the uneven wear thickness data. By using electromagnetic equipment, an appropriate magnetic field is applied to the uneven wear area to highlight the characteristics of the area. After magnetization, magnetic powder spraying simulation is used. During the simulation, fine magnetic powder is evenly sprayed on the wear area to observe its reaction. After magnetic powder spraying, visual inspection and data recording are carried out to obtain magnetic powder spraying simulation data to form a preliminary assessment of the state of the wear area.

[0140] The magnetic powder spraying simulation data is tested for magnetic powder accumulation, and the image processing algorithm is used to analyze the accumulation of magnetic powder in different wear areas. By setting thresholds and detection parameters, the areas with more accumulation are identified and the relevant data are recorded. Finally, the wear crack area is located in combination with the magnetic powder accumulation data to form a wear crack data set. This data set includes information such as crack location, depth and severity, providing a basis for subsequent maintenance and safety decisions.

[0141] Step S227: Integrate the braking abnormality data according to the wear crack data and the wear rate abnormality data, so as to obtain the braking abnormality data.

[0142] In this embodiment, the braking abnormality data is integrated based on the wear crack data and the wear rate abnormality data. The two types of data are cross-analyzed to find the correlation between the occurrence of cracks and the wear rate abnormality. By setting the correlation analysis model, the crack risk caused by the wear rate abnormality is identified. If the crack and the wear rate abnormality exist at the same time, it is recorded as severe braking abnormality data. The final braking abnormality data set includes the abnormality type, occurrence time, impact degree and recommended treatment measures, which provides an important decision-making basis for the safe operation of the mine car.

[0143] Optionally, step S226 is specifically:

[0144] Perform wear uniformity calculation on the wear thickness data to obtain wear uniformity data;

[0145] In this embodiment, the wear thickness data of each brake pad at multiple measurement points are collected to form an array containing the wear value of each measurement point. The average value and standard deviation of the array are calculated, and the uniformity value is obtained by the formula: wear uniformity = 1 - (standard deviation / average thickness). A uniformity value close to 1 indicates uniform wear, and a uniformity value close to 0 indicates uneven wear. By setting a threshold (for example, 0.2), it is determined whether the wear meets the uniformity standard, and a wear uniformity data set is formed to provide a basis for subsequent analysis.

[0146] Uniformity division is performed according to the wear uniformity data, thereby obtaining uneven wear thickness data;

[0147] In this embodiment, uniformity classification is performed based on the obtained wear uniformity data, with the goal of identifying brake pads with uneven wear. By setting a specific uniformity threshold (such as 0.8), the uniformity of each brake pad is classified. Brake pads with uniformity lower than this threshold are marked as unevenly worn, and their corresponding wear thickness data are recorded. The resulting uneven wear thickness data set will include specific locations and their degree of wear. This data provides a target area for subsequent magnetization processing, ensuring the pertinence and effectiveness of the processing.

[0148] Performing magnetization processing on the worn area according to the uneven wear thickness data, thereby obtaining magnetization data of the worn area;

[0149] In this embodiment, electromagnetic equipment is used to apply a specific magnetic field to the area where uneven wear is identified. A DC power supply is usually selected to generate a stable magnetic field. During the magnetization process, a suitable magnetic field strength (such as 200-400 mT) is set, and the processing time is ensured to be long enough so that the magnetic field evenly covers the target area. By measuring and adjusting the applied current and magnetic field strength, the magnetization data of the worn area generated will lay the foundation for subsequent magnetic particle testing.

[0150] Conduct magnetic powder spraying simulation on the magnetization data of the wear area to obtain magnetic powder spraying simulation data;

[0151] In this embodiment, fine ferromagnetic powder is selected and a pneumatic spraying device is used to evenly spray the magnetic powder on the magnetized wear area. By adjusting the spraying pressure and distance, it is ensured that the magnetic powder can fully cover the target area and effectively adhere. After spraying, by observing the distribution of magnetic powder in different wear areas, the generated magnetic powder spraying simulation data will record the spraying effect of each area to ensure that the magnetic powder can form agglomeration in the potential crack area.

[0152] Conduct magnetic powder accumulation detection on magnetic powder spraying simulation data to obtain magnetic powder accumulation data;

[0153] In this embodiment, through image processing technology, high-definition camera equipment is used to capture images of the area after spraying, and computer vision algorithms are used to analyze the distribution of magnetic powder in the image. A threshold value (such as a stacking ratio greater than a certain value) is set to distinguish between normal and abnormal areas, and binarization processing is performed to identify the accumulation of magnetic powder. Ultimately, the magnetic powder accumulation data formed includes the location of the accumulation area and its accumulation density, providing support for subsequent crack area positioning.

[0154] The wear crack area is located according to the magnetic powder accumulation data to obtain the wear crack data.

[0155] In this embodiment, based on the accumulation detection results, image analysis algorithms (such as edge detection and region growth) are used to locate the area with severe accumulation, and combined with the known wear area data to confirm the potential crack location. Effective positioning standards, such as accumulation area and accumulation density, are set to form wear crack data. The final data includes the specific location, shape and size of the crack, providing important information for further maintenance and safety management.

[0156] Optionally, step S23 is specifically:

[0157] Step S231: Draw a three-dimensional trajectory map according to the three-dimensional coordinate data of the mine car, thereby obtaining three-dimensional trajectory map data;

[0158] In this embodiment, a three-dimensional trajectory diagram is drawn based on the three-dimensional coordinate data of the mine car. The three-dimensional coordinate data of the mine car at different time points are collected, including the position information of the X, Y, and Z axes. Computer graphics technology is used to draw the motion trajectory of the mine car using three-dimensional visualization software (such as Matplotlib or Plotly in MATLAB, Python, etc.). By drawing path lines, continuous coordinate points are connected to form a three-dimensional trajectory diagram, which intuitively displays the motion path of the mine car. In this process, the range of the coordinate axis needs to be set to ensure the integrity and clarity of the trajectory diagram, and the three-dimensional trajectory diagram data finally generated provides a basis for subsequent analysis.

[0159] Step S232: extracting the features of the mine car curve trajectory according to the three-dimensional trajectory map data, thereby obtaining the mine car curve trajectory data;

[0160] In this embodiment, the mine car curve trajectory feature extraction is performed based on the three-dimensional trajectory map data. The generated three-dimensional trajectory map is analyzed to identify the curve part in the trajectory, which is usually achieved by calculating the curvature of the trajectory. The curvature threshold is set to determine what kind of curvature value can be regarded as a curve. By calculating the curvature value of each point, the trajectory segment that meets the conditions is extracted as the mine car curve trajectory data. These data include the starting point, end point, curvature radius and angle of the curve, etc. The curve features are recorded to provide data support for subsequent cornering simulation.

[0161] Step S233: performing a mine car cornering simulation according to the mine car cornering trajectory data, thereby obtaining the mine car cornering simulation data;

[0162] In this embodiment, the mine car cornering simulation is performed according to the mine car curve trajectory data, and the motion parameters of the mine car, including speed, steering angle and center of mass position, are set based on the characteristic data of the curve. A physical simulation software (such as MATLAB Simulink or a custom simulation model) is used to construct a mathematical model of the mine car moving on the curve. The mine car cornering simulation data is obtained by simulating and calculating the motion trajectory of the mine car under different speeds and steering conditions. The simulation results contain information such as the position change, speed change and acceleration of the mine car during the cornering process, which provides the necessary data basis for the subsequent centrifugal force and lateral force calculation.

[0163] Step S234: Calculate the centrifugal force of the mine car cornering simulation data, so as to obtain the centrifugal force data of the mine car cornering;

[0164] In this embodiment, centrifugal force calculation is performed on the mining car cornering simulation data. The calculation formula of centrifugal force is:

[0165] F_c=m v² / r,

[0166] Where F_c is the centrifugal force, m is the mass of the mine car, v is the speed of the mine car, and r is the radius of the curve. According to the curve simulation data, the speed at each moment and its corresponding curve radius are extracted, and the centrifugal force data is calculated point by point using the above formula. Through programming tools such as Python, a cyclic calculation process is created to finally generate the centrifugal force data of the mine car traveling on the curve. These data are helpful for analyzing the stability of the mine car when turning, and provide a basis for subsequent risk assessment.

[0167] Step S235: Calculate the lateral force of the mine car cornering simulation data, so as to obtain the lateral force data of the mine car cornering;

[0168] In this embodiment, the lateral force is calculated for the mine car cornering simulation data. The lateral force is an important factor affecting the stability of the mine car cornering, and its calculation formula is:

[0169] F_s=m a_s ,

[0170] Where F_s is the lateral force, m is the mass of the mine car, and a_s is the lateral acceleration. The acceleration information of the mine car in the curve, especially the lateral acceleration, is obtained through the cornering simulation data. On this basis, the lateral force is calculated point by point using the above formula, and the lateral force value at each moment is recorded. The final lateral force data of the mine car in the curve will be used for further overturning risk assessment and analysis.

[0171] Step S236: performing overturning risk assessment according to the centrifugal force data of the mine car traveling on the curve and the lateral force data of the mine car traveling on the curve, thereby obtaining the overturning risk data of the mine car traveling on the curve;

[0172] In this embodiment, the overturning risk is assessed based on the centrifugal force data and lateral force data of the mine car traveling on the curve. The assessment criteria for the overturning risk are set, including the safety thresholds of the centrifugal force and the lateral force. By comparing the centrifugal force and lateral force actually calculated with the set thresholds, the stability of the mine car when turning is judged. If the calculation results show that the centrifugal force or lateral force exceeds the set safety threshold, it is recorded as overturning risk data. Combining these data, the degree of overturning risk of the mine car under specific curve and speed conditions is assessed to provide a basis for safety management.

[0173] Step S237: performing overturning angle analysis according to the mine car turning simulation data, thereby obtaining the mine car turning overturning angle data;

[0174] In this embodiment, the overturning angle analysis is performed based on the mine car cornering simulation data. The calculation of the overturning angle is based on the relationship between the center of gravity height of the mine car and the lateral force and centrifugal force, and the specific formula is:

[0175] tan(θ)=F_s / (m g),

[0176] Where θ is the rollover angle, F_s is the lateral force, and g is the gravitational acceleration. By analyzing the cornering simulation data, the lateral force value at each moment is extracted, and the rollover angle is calculated point by point based on the mass and center of gravity height of the mine car. The resulting mine car cornering rollover angle data will help further understand the driving stability under specific circumstances.

[0177] Step S238: performing statistics on the turning time of the mine car according to the overturning risk data of the mine car when traveling on a curved road and the overturning angle data of the mine car when passing a curve, thereby obtaining the turning time data of the mine car;

[0178] In this embodiment, the turning time of the mine car is counted based on the overturning risk data of the mine car when traveling on the curve and the overturning angle data of the mine car when turning. The time information in the turning simulation data is extracted, and the total time of the mine car when passing each curve is recorded. By comprehensively considering the overturning risk data and the overturning angle, the safe turning time of the mine car under specific conditions is determined. The calculation process includes comparing the turning time under different conditions and recording all situations that exceed the safe time to form the turning time data of the mine car to ensure safety and reliability.

[0179] Step S239: obtaining normal turning time data of the mine car, and comparing the turning time data of the mine car with the abnormal driving trajectory time according to the normal turning time data of the mine car, so as to obtain abnormal driving trajectory data.

[0180] In this embodiment, the normal turning time data of the mine car is obtained, and the abnormal driving trajectory time is compared with the normal turning time data of the mine car. The normal turning time data can be obtained through previous tests, including the standard time under different environmental and speed conditions. The normal turning time is compared with the actual turning time of the mine car, and the abnormal situation exceeding the normal time is determined by statistical analysis method. By calculating the time difference exceeding the threshold, the abnormal driving trajectory data is formed. Finally, these data provide a basis for further safety assessment and improvement.

[0181] Optionally, step S237 is specifically:

[0182] According to the mine car cornering simulation data, the mine car center of gravity height feature extraction and the mine car width feature extraction are performed to obtain the mine car center of gravity height data and the mine car width data;

[0183] In this embodiment, the mine car cornering simulation data is analyzed to extract the center of gravity height and width characteristics of the mine car. The extraction of the center of gravity height depends on the geometric model of the mine car. By calculating the mass distribution of each component of the model, the formula is used:

[0184] ,

[0185] Calculate the center of gravity height, where Indicates the quality of each component. Indicates the corresponding height. The width characteristics of the mine car are obtained by measuring the width of the car body and the outermost edge of the wheel position. After measurement and calculation, the center of gravity height and width data of the mine car are finally obtained, providing basic data for subsequent overturning analysis.

[0186] The overturning moment is calculated according to the height data of the center of gravity of the mine car and the centrifugal force data of the mine car traveling on a curve, so as to obtain the overturning moment data;

[0187] In this embodiment, the overturning moment is calculated using the mine car center of gravity height data and the mine car curve driving centrifugal force data. The overturning moment calculation formula is:

[0188] ,

[0189] Where m is the mass of the minecart, g is the acceleration due to gravity, is the center of gravity height. By combining the driving state of the mine car with the measurement data of the centrifugal force, the overturning moment data under specific cornering conditions can be obtained. Ensure that the influence of dynamic factors on the overturning moment is taken into account during the calculation process to ensure that the data is accurate and reliable.

[0190] Get the mine car load data;

[0191] In this embodiment, the load data is usually obtained through a weighing sensor, which is installed at the bottom of the mine car to monitor the weight of the material carried by the mine car in real time. By combining with the mine car operation monitoring system, the load data is recorded and updated in real time to ensure the accuracy and timeliness of the data. This data will provide the necessary input for the subsequent anti-overturning moment calculation.

[0192] The anti-overturning moment is calculated according to the width data and the load data of the mine car, so as to obtain the anti-overturning moment data;

[0193] In this embodiment, the anti-overturning moment is calculated based on the width data of the mine car and the load data of the mine car. The anti-overturning moment can be calculated by the formula:

[0194] Ma=W L ,

[0195] Where W is the load of the mine car, and L is the horizontal distance from the support point to the center of gravity. The width data provides the basis for the stability of the mine car when turning. By combining the load data, the anti-overturning moment can be calculated more accurately. Make sure that the width and load parameters are fully combined during the calculation to ensure the reliability of the anti-overturning moment data.

[0196] The overturning angle is determined according to the overturning moment data and the anti-overturning moment data. If the overturning moment is greater than the anti-overturning moment, the overturning angle determination standard data is obtained.

[0197] In this embodiment, the overturning angle is determined based on the overturning moment data and the anti-overturning moment data. A threshold of the overturning angle under standard conditions is set to determine whether overturning occurs. In the calculation process, the formula

[0198] ,

[0199] Calculate the overturning angle. Greater than the anti-overturning moment The risk of overturning is determined and relevant data is recorded when the vehicle is in motion. This standard provides an important basis for safety monitoring of mine vehicles during driving, ensuring that preventive measures are taken in a timely manner.

[0200] The overturning angle of the mine car is determined according to the overturning angle determination standard data, so as to obtain the overturning angle data of the mine car when turning.

[0201] In this embodiment, the actual measured rollover angle is compared with the set judgment criteria to analyze the performance of the mine car under different cornering conditions. If the actual rollover angle exceeds the set threshold, it is recorded as a rollover risk state, and the mine car cornering rollover angle data is formed. This data will be used for subsequent mine car design improvements and safety assessments to ensure the stability and safety of the mine car during driving.

[0202] Optionally, step S3 specifically includes:

[0203] Step S31: obtaining mine production plan data and mine car operation status data;

[0204] In this embodiment, the mine production plan data is extracted from the mine management system. The production plan data includes information such as material requirements, transportation schedules, and mine car allocation in each mining area. These data are accessed through a database query tool to ensure the real-time and accuracy of the data. At the same time, the operating status data is obtained from the mine car monitoring system, which includes the current position, speed, load status, and fault information of the mine car. The operating status data is collected through real-time sensors and monitoring systems to ensure the timely update of monitoring information. The two parts of data are combined to form a basic data set for subsequent scheduling and task allocation.

[0205] Step S32: extracting the characteristics of the mine car to be dispatched state according to the mine car running state data, thereby obtaining the mine car to be dispatched state data;

[0206] In this embodiment, data analysis technology is used to classify the running status of the mine cars to identify which mine cars are in the waiting state. The criteria for determining the waiting state include whether the mine car is unloaded, has not been assigned a task, and is relatively stationary. The mine cars to be scheduled are identified by setting a stationary time threshold (such as 10 minutes). The data analysis tool processes the acquired running status data to extract the waiting state data to form the basic information for mine car scheduling.

[0207] Step S33: allocating material transportation tasks to the mine car scheduling status data according to the mine production plan data, thereby obtaining the mine car material transportation task data;

[0208] In this embodiment, the specific requirements for material transportation in the mine production plan are analyzed, including the demand for each material and the transportation priority. By comparing the available status of the mine cars to be scheduled, the most suitable mine car is determined for task allocation. Using the priority algorithm, the task is assigned to the mine car whose load capacity meets the requirements, and the mine car material transportation task data is generated. This process ensures the efficient execution of transportation tasks and maximizes the use of existing resources.

[0209] Step S34: performing completion statistics according to the mine car material transportation task data, thereby obtaining the mine car material transportation task completion data, and performing idle mine car identification according to the mine car material transportation task completion data, thereby obtaining the idle mine car data;

[0210] In this embodiment, completion statistics are performed based on the mine car material transportation task data. The statistical indicators include the task completion status of each mine car, the transportation time and its deviation from the scheduled plan. Whether the task is completed is determined by setting a completion threshold (for example, 90%). After the completion statistics are completed, idle mine cars are identified based on this data. Idle mine cars are defined as mine cars that have not completed the transportation task within the scheduled time or have not participated in transportation for a long time. The system will automatically identify these idle mine cars and generate idle mine car data to provide information support for subsequent scheduling.

[0211] Step S35: obtaining the distribution data of the auxiliary inclined shaft turnout;

[0212] In this embodiment, the distribution data of the auxiliary inclined shaft turnouts are obtained, including the specific location and connection relationship of each turnout inside the mine. These data are sorted and displayed through geographic information system (GIS) technology to ensure the accuracy of the passage path inside the mine. The turnout data will include information such as the turnout number, geographic coordinates, and turnout status (such as open or closed), providing a reference for mine car dispatching path planning.

[0213] Step S36: performing mine car dispatching path planning on the mine car data according to the auxiliary inclined shaft switch distribution data and the idle mine car data, thereby obtaining the mine car dispatching path data;

[0214] In this embodiment, the mine car dispatching path planning is performed on the mine car data according to the auxiliary inclined shaft turnout distribution data and the idle mine car data. A path planning algorithm (such as A* algorithm or Dijkstra algorithm) is used to calculate the optimal dispatching path in real time considering the actual situation of the mine. During the planning process, factors such as the current state of the mine car, turnout distribution, and driving efficiency need to be considered to ensure that the planned path is both efficient and safe. The mine car dispatching path data is generated for subsequent execution.

[0215] Step S37: extracting the mine sports car transportation task features according to the high-risk mine car number data, thereby obtaining the mine sports car transportation task data;

[0216] In this embodiment, according to the high-risk mine car number data, the corresponding mine car's sports car transportation task characteristics are extracted through data query and screening functions. This operation uses historical sports car data and real-time data for comparison to extract key characteristics such as task frequency, running time, and task load of high-risk mine cars. This operation involves multi-table association query of the SQL database to output a high-risk mine car transportation task data file containing task details and operation statistics.

[0217] Step S38: Intelligently dispatch material transportation for the mine car transportation task data according to the idle mine car data and the mine car dispatching path data, thereby obtaining the mine car material transportation intelligent dispatching data.

[0218] In this embodiment, intelligent material transportation scheduling is performed on the mine car transportation task data according to the idle mine car data and the mine car scheduling path data. The intelligent scheduling system is used to automatically optimize the scheduling plan through the set algorithm (such as genetic algorithm or ant colony algorithm). The scheduling system comprehensively considers the current idle mine car status, the urgency of the transportation task and the path planning data to achieve the best transportation efficiency. Intelligent scheduling data for mine car material transportation is generated to guide the actual transportation operation and ensure that the material transportation process is efficient and orderly.

[0219] Optionally, step S36 is specifically:

[0220] Step S361: extracting turnout features and mine track features according to the turnout distribution data of the auxiliary inclined shaft, thereby obtaining turnout data and mine track data;

[0221] In this embodiment, turnout feature extraction is performed based on the turnout distribution data of the auxiliary inclined shaft. Turnout feature extraction includes information such as turnout type, location, size and status. Geographic Information System (GIS) is used to perform spatial analysis on the turnout to form a complete turnout data set. Mine road feature extraction focuses on the shape, length, width of the mine road and its connection with the turnout. The accuracy and completeness of the data are ensured through three-dimensional scanning and measurement of the mine road. The extracted turnout data and mine road data will constitute the basic information of the mine transportation network and provide data support for subsequent steps.

[0222] Step S362: constructing a mine road network model according to the turnout data and the mine road data, thereby obtaining a mine road network model;

[0223] In this embodiment, the extracted turnout data and mine road data are used to construct a mine road network model. Using graph theory methods, turnouts and mine roads are regarded as nodes and edges of the graph, and a complete mine road network is formed by establishing connection relationships between nodes. In specific operations, it is necessary to define the weight of the node (such as traffic flow, traffic capacity) and the length of the edge (such as transportation time, distance) to ensure that the model can truly reflect the traffic conditions of the mine. Finally, the constructed mine road network model will be used for path planning and scheduling analysis.

[0224] Step S363: performing shortest path planning for mine car scheduling on the mine road network model according to the idle mine car data, thereby obtaining the shortest path data for mine car scheduling;

[0225] In this embodiment, the shortest path planning for mine car dispatching is performed based on the idle mine car data. The Dijkstra algorithm or A* algorithm is used in combination with the mine road network model to calculate the shortest path from the current position of the idle mine car to the target location. During the calculation process, the weight of the node (such as the current traffic conditions, the state of the turnout) and the length of the edge are considered to ensure that the planned path is both safe and efficient. The shortest path data for mine car dispatching generated in the end will provide accurate route guidance for actual dispatching.

[0226] Step S364: performing intelligent turnout path management on the shortest path data for mine car scheduling according to the idle mine car data, thereby obtaining intelligent turnout path data;

[0227] In this embodiment, intelligent turnout path management is performed on the shortest path data for mine car scheduling based on idle mine car data. The state information of the turnout is analyzed and the available turnouts are identified to optimize the scheduling path. Dynamic programming technology is used to manage the turnouts in the path in real time to meet the scheduling needs of the mine car. For example, when a turnout on a path fails, the system automatically adjusts the path and selects a spare turnout. Through this process, the generated intelligent turnout path data ensures the flexibility and reliability of mine car scheduling.

[0228] Step S365: The mine car dispatching path is integrated according to the intelligent turnout path data and the mine car dispatching shortest path data, so as to obtain the mine car dispatching path data.

[0229] In this embodiment, the two paths are merged by an algorithm to form a comprehensive dispatching path. During the path fusion process, the switching point of the turnout and the turning radius of the mine car need to be considered to ensure the feasibility and safety of the final path. At the same time, the priority of the fused path is set, for example, the path with short transportation time or high transportation safety factor is given priority. Finally, the output mine car dispatching path data will be used for actual transportation scheduling to ensure the efficient operation of the mine car.

[0230] Optionally, step S4 is specifically:

[0231] Step S41: Designing a database architecture according to the mine car material transportation intelligent scheduling data, thereby obtaining database architecture data;

[0232] In this embodiment, the data types that need to be stored are determined, including mine car status, scheduling tasks, path information, and user information. Adopting the relational database design principles, data tables are constructed, and the fields and data types of each data table are defined. For example, the mine car status table may include fields such as mine car ID, current position, load status, and running status. Next, the relationship between data tables is designed to ensure the integrity and consistency of the data, and the ER diagram (entity-relationship diagram) is used to visualize the database architecture. Finally, the generated database architecture data will serve as the basis for subsequent system development.

[0233] Step S42: dividing the system modules according to the mine car material transportation intelligent scheduling data, thereby obtaining system module data;

[0234] In this embodiment, the functional requirements of the intelligent scheduling system are analyzed to determine the core modules, such as the data acquisition module, the scheduling decision module, the user management module, and the monitoring module. Each module must have clear functions and interfaces. For example, the data acquisition module is responsible for obtaining real-time data from sensors and databases, and the scheduling decision module generates a scheduling plan based on the intelligent scheduling algorithm. Through module division, the maintainability and scalability of the system are ensured, so as to obtain system module data and provide a basis for subsequent architecture design.

[0235] Step S43: Designing the intelligent scheduling platform architecture according to the system module data and the database architecture data, thereby obtaining the intelligent scheduling platform architecture data;

[0236] In this embodiment, the layered architecture concept is adopted to divide the system into data layer, business logic layer and presentation layer. The data layer is responsible for interacting with the database, the business logic layer processes the scheduling algorithm and decision logic, and the presentation layer is responsible for the display of the user interface. Combined with the existing module division, the specific functions and interaction methods of each layer are defined to ensure clear interfaces and smooth data between layers. Ultimately, the intelligent scheduling platform architecture data formed will provide a clear framework for the implementation and integration of the system.

[0237] Step S44: Designing a user interface according to the mine car material transportation intelligent scheduling data, thereby obtaining user interface data;

[0238] In this embodiment, the user's needs and operation process are determined, and the functional modules of the interface are defined, including real-time monitoring, task management, data analysis, and system settings. Use prototyping tools (such as Axure or Sketch) to draw interface prototypes, design intuitive and easy-to-operate interface layouts, and ensure the clarity of information display. Each interface element needs to define its function and interaction method, such as buttons, charts, data input boxes, etc., to ensure that users can easily obtain the required information. Finally, the generated user interface data will serve as an important reference for system development.

[0239] Step S45: Create an intelligent scheduling platform according to the intelligent scheduling platform architecture data and the user interface data, thereby obtaining the intelligent scheduling platform data.

[0240] In this embodiment, modern development frameworks (such as Spring Boot, Django, etc.) are used to implement the actual coding of the system based on the designed architecture and user interface. In this process, the modular design needs to be converted into specific functional modules to realize the interface and data interaction between the modules. The database architecture needs to be effectively connected to the system to ensure real-time update and query of data. After testing and debugging, the system is ensured to run stably, and the final intelligent scheduling platform data will support the efficient scheduling and management of mine cars.

[0241] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0242] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for creating an intelligent scheduling platform for mine auxiliary transportation, characterized in that: The following steps are involved: Step S1: Acquire the UWB tag data of the auxiliary inclined shaft mine car, and perform signal time difference analysis based on the UWB tag data of the auxiliary inclined shaft mine car to obtain signal time difference data; construct the three-dimensional coordinates of the mine car based on the signal time difference data to obtain the three-dimensional coordinate data of the mine car; Step S2: obtaining the data of the auxiliary inclined shaft mine car arrester, and extracting the brake structure characteristics of the mine car according to the data of the auxiliary inclined shaft mine car arrester, so as to obtain the brake structure data of the mine car; The mine car simulation is performed according to the brake structure data of the mine car and the three-dimensional coordinate data of the mine car, so as to obtain the mine car simulation data; According to the mine car simulation data, high-risk mine car numbers are set to obtain high-risk mine car number data; wherein step S2 includes: Step S21: acquiring data of the auxiliary inclined shaft mine car arrester, and extracting the brake structure characteristics of the mine car according to the data of the auxiliary inclined shaft mine car arrester, thereby obtaining the brake structure data of the mine car; Step S22: performing braking abnormality analysis on the braking structure data of the mine car to obtain braking abnormality data; wherein step S22 includes: Step S221: extracting brake pad features according to the brake structure data of the mine car, thereby obtaining brake pad data; Step S222: performing wear thickness statistics on the brake pad data to obtain wear thickness data; Step S223: obtaining brake pad initial thickness data and maximum brake pad wear rate data; Step S224: Calculating the wear rate according to the wear thickness data and the brake pad initial thickness data, thereby obtaining the brake pad wear rate data; Step S225: classifying the brake pad wear rate data into wear abnormality according to the highest brake pad wear rate data, and obtaining wear rate abnormality data if the brake pad wear rate data is greater than the highest brake pad wear rate data; Step S226: performing crack detection on the wear thickness data to obtain wear crack data; wherein step S226 includes: Perform wear uniformity calculation on the wear thickness data to obtain wear uniformity data; Uniformity division is performed according to the wear uniformity data, thereby obtaining uneven wear thickness data; Perform magnetization processing on the worn area according to the uneven wear thickness data, so as to obtain magnetization data of the worn area; Conduct magnetic powder spraying simulation on the magnetization data of the wear area to obtain magnetic powder spraying simulation data; Conduct magnetic powder accumulation detection on magnetic powder spraying simulation data to obtain magnetic powder accumulation data; The wear crack area is located according to the magnetic powder accumulation data to obtain the wear crack data; Step S227: integrating the braking abnormality data according to the wear crack data and the wear rate abnormality data, thereby obtaining the braking abnormality data; Step S23: performing driving track abnormality analysis on the three-dimensional coordinate data of the mine car, thereby obtaining driving track abnormality data; wherein step S23 includes: Step S231: Draw a three-dimensional trajectory map according to the three-dimensional coordinate data of the mine car, thereby obtaining three-dimensional trajectory map data; Step S232: extracting the features of the mine car curve trajectory according to the three-dimensional trajectory map data, thereby obtaining the mine car curve trajectory data; Step S233: performing a mine car cornering simulation according to the mine car cornering trajectory data, thereby obtaining the mine car cornering simulation data; Step S234: Calculate the centrifugal force of the mine car cornering simulation data, so as to obtain the centrifugal force data of the mine car cornering; Step S235: Calculate the lateral force of the mine car cornering simulation data, so as to obtain the lateral force data of the mine car cornering; Step S236: performing overturning risk assessment according to the centrifugal force data of the mine car traveling on the curve and the lateral force data of the mine car traveling on the curve, thereby obtaining the overturning risk data of the mine car traveling on the curve; Step S237: performing overturning angle analysis according to the mine car cornering simulation data, thereby obtaining the mine car cornering overturning angle data; wherein step S237 includes: According to the mine car cornering simulation data, the mine car center of gravity height feature extraction and the mine car width feature extraction are performed to obtain the mine car center of gravity height data and the mine car width data; The overturning moment is calculated according to the height data of the center of gravity of the mine car and the centrifugal force data of the mine car traveling on a curve, so as to obtain the overturning moment data; Get the mine car load data; The anti-overturning moment is calculated according to the width data and the load data of the mine car, so as to obtain the anti-overturning moment data; The overturning angle is determined according to the overturning moment data and the anti-overturning moment data. If the overturning moment is greater than the anti-overturning moment, the overturning angle determination standard data is obtained. The overturning angle of the mine car is determined according to the overturning angle determination standard data, so as to obtain the overturning angle data of the mine car when turning; Step S238: performing statistics on the turning time of the mine car according to the overturning risk data of the mine car when traveling on a curved road and the overturning angle data of the mine car when turning, so as to obtain the turning time data of the mine car; Step S239: obtaining normal turning time data of the mine car, and performing driving track abnormality time comparison on the turning time data of the mine car according to the normal turning time data of the mine car, thereby obtaining driving track abnormality data; Step S24: performing a mine sports car simulation according to the abnormal braking data and the abnormal driving track data, thereby obtaining the mine sports car simulation data; Step S25: setting the high-risk mine car number according to the mine car simulation data, thereby obtaining the high-risk mine car number data; Step S3: Acquire mine production plan data and mine car operation status data; perform material transportation task allocation according to the mine production plan data and the mine car operation status data, thereby obtaining mine car material transportation task data; perform intelligent transportation scheduling according to high-risk mine car number data and mine car material transportation task data, thereby obtaining mine car material transportation intelligent scheduling data; Step S4: Design an intelligent scheduling platform architecture based on the intelligent scheduling data of mine car material transportation, so as to obtain the intelligent scheduling platform architecture data; create an intelligent scheduling platform based on the intelligent scheduling platform architecture data and the intelligent scheduling data of mine car material transportation, so as to obtain the intelligent scheduling platform data.

2. The method for creating an intelligent scheduling platform for mine auxiliary transportation according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring the UWB tag data of the auxiliary inclined shaft mine car, and performing short pulse signal feature extraction according to the UWB tag data of the auxiliary inclined shaft mine car, thereby obtaining short pulse signal data; Step S12: setting the pulse signal receiving node reference time according to the short pulse signal data, thereby obtaining the pulse signal receiving node reference time data; Step S13: Calculate the time difference according to the pulse signal receiving node reference time data and the short pulse signal data, so as to obtain the pulse signal time difference data; Step S14: Acquire short pulse signal propagation speed data; Step S15: Calculate the three-dimensional coordinates according to the pulse signal time difference data and the short pulse signal propagation speed data, so as to obtain the three-dimensional coordinate data of the mine car.

3. The method for creating an intelligent scheduling platform for mine auxiliary transportation according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: obtaining mine production plan data and mine car operation status data; Step S32: extracting the characteristics of the mine car to be dispatched state according to the mine car running state data, thereby obtaining the mine car to be dispatched state data; Step S33: allocating material transportation tasks to the mine car scheduling status data according to the mine production plan data, thereby obtaining the mine car material transportation task data; Step S34: performing completion statistics according to the mine car material transportation task data, thereby obtaining the mine car material transportation task completion data, and performing idle mine car identification according to the mine car material transportation task completion data, thereby obtaining the idle mine car data; Step S35: obtaining the distribution data of the auxiliary inclined shaft turnout; Step S36: planning the mine car dispatching path for the mine car data according to the auxiliary inclined shaft switch distribution data and the idle mine car data, so as to obtain the mine car dispatching path data; Step S37: extracting the mine sports car transportation task features according to the high-risk mine car number data, thereby obtaining the mine sports car transportation task data; Step S38: Intelligently dispatch material transportation for the mine car transportation task data according to the idle mine car data and the mine car dispatching path data, thereby obtaining the mine car material transportation intelligent dispatching data.

4. The method for creating an intelligent scheduling platform for mine auxiliary transportation according to claim 3 is characterized in that: Step S36 is specifically as follows: Step S361: extracting turnout features and mine track features according to the turnout distribution data of the auxiliary inclined shaft, thereby obtaining turnout data and mine track data; Step S362: constructing a mine road network model according to the turnout data and the mine road data, thereby obtaining a mine road network model; Step S363: performing shortest path planning for mine car scheduling on the mine road network model according to the idle mine car data, thereby obtaining the shortest path data for mine car scheduling; Step S364: performing intelligent turnout path management on the shortest path data for the mine car dispatching according to the idle mine car data, thereby obtaining intelligent turnout path data; Step S365: The mine car dispatching path is integrated according to the intelligent turnout path and the mine car dispatching shortest path data, so as to obtain the mine car dispatching path data.

5. The method for creating an intelligent scheduling platform for mine auxiliary transportation according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: Designing a database architecture according to the mine car material transportation intelligent scheduling data, thereby obtaining database architecture data; Step S42: dividing the system modules according to the mine car material transportation intelligent scheduling data, thereby obtaining system module data; Step S43: Designing the intelligent scheduling platform architecture according to the system module data and the database architecture data, thereby obtaining the intelligent scheduling platform architecture data; Step S44: Designing a user interface according to the mine car material transportation intelligent scheduling data, thereby obtaining user interface data; Step S45: Create an intelligent scheduling platform according to the intelligent scheduling platform architecture data and the user interface data, thereby obtaining the intelligent scheduling platform data.

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