Downhole monorail crane predictive control method and system based on digital road spectrum

Abstract

The invention is suitable for the technical field of automatic control of mining auxiliary transportation equipment, and provides an underground monorail crane predictive control method and system based on a digital road spectrum, and the method comprises the following steps: obtaining a position and a road spectrum; a parameter online identification step; a target speed dynamic correction step; a composite torque instruction generation step; a driving execution and braking cooperation step; the method has the beneficial effects that speed self-adaptive correction is realized through road spectrum pre-reading and parameter online identification, and the smooth torque is output in combination with composite predictive control, so that pure-electric-drive stable speed regulation of the monorail crane is realized. According to the scheme, instability slipping under complex working conditions is effectively overcome, the safety risk that mechanical braking intervention is triggered due to the fact that the speed is out of control is avoided, and the response speed, the operation stability and the intrinsic safety level of a whole machine system are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of automated control technology applicable to mining auxiliary transportation equipment, and particularly relates to a predictive control method and system for underground monorail cranes based on digital road spectrum. Background Technology

[0002] As a key piece of equipment in coal mine auxiliary transportation systems, underground monorails play a crucial role in transporting materials, equipment, and personnel along roadways. With the advancement of intelligent coal mine construction, unmanned driving and adaptive control technologies for monorails have become a research hotspot in the industry. However, the underground roadway environment is extremely complex, featuring various harsh conditions such as long-distance undulating slopes, small-radius curves, and abrupt changes in track surface conditions. This poses a severe challenge to the autonomous, safe, and stable operation of monorails.

[0003] Existing monorail control systems generally lack the ability to anticipate road conditions ahead, and cannot adaptively adjust target speed and drive commands based on dynamically changing vehicle loads and real-time rail adhesion. When vehicles enter long downhill sections or low-adhesion sections, they are highly susceptible to speed loss, exceeding safety thresholds and ultimately triggering the emergency braking system. This passive "loss of control - emergency stop" protection mode not only leads to low transportation efficiency but also causes cumulative damage to critical transmission components due to frequent emergency braking impacts, fundamentally restricting the smoothness, safety, reliability, and service life of the entire system. Summary of the Invention

[0004] The purpose of this invention is to provide a predictive control method and system for downhole monorail cranes based on digital road spectrum, aiming to solve the problems mentioned in the background art.

[0005] The present invention is implemented as follows: On the one hand, a predictive control method for underground monorail cranes based on digital road spectrum is provided. The method includes the following steps: obtaining the position and road spectrum: obtaining the absolute position of the locomotive, querying the speed map to obtain the reference speed and slip coefficient of the current section, and reading the slope sequence of each section within a set distance ahead.

[0006] Online parameter identification steps: Under traction conditions and when the acceleration is greater than the set threshold, identify the total mass of the locomotive and the rail surface adhesion coefficient online.

[0007] Target speed dynamic correction steps: Based on the total mass of the locomotive, the rail surface adhesion coefficient, and the radius of curvature of the current section, calculate the correction coefficient and correct the reference speed to obtain the target speed.

[0008] The compound torque command generation steps are as follows: The torque command is calculated by combining super-helical sliding mode feedback control with feedforward control based on the pre-aiming slope.

[0009] Drive execution and braking coordination steps: The torque command is sent to the traction drive unit for execution, and the hydraulic braking unit is fully released during normal operation. The hydraulic braking unit is only controlled to intervene in limited emergency or parking conditions.

[0010] As a further aspect of the present invention, the step of obtaining location and road spectrum specifically includes: collecting the pulse signal output by the rotary encoder installed on the shaft end of the traction motor to accumulate the relative mileage.

[0011] The absolute mileage coordinates are read from the RFID tags embedded in the sidewall of the tunnel by a passive RFID reader installed at the bottom of the vehicle body.

[0012] The relative mileage calculated from the pulse signal is fused with the absolute mileage coordinate value to correct and calculate the locomotive's real-time absolute position.

[0013] Using the real-time absolute position as an index, the reference speed and slip coefficient of the current feature section are queried and extracted from the speed map built into the vehicle controller.

[0014] Starting from the current position, the slope sequence of each section within a set distance ahead is read in advance and stored in the cache for later use.

[0015] As a further embodiment of the present invention, the online parameter identification step specifically includes: real-time acquisition of the rotational speed of the traction motor and performing differential calculation to obtain the acceleration of the locomotive.

[0016] The longitudinal tilt angle of the current track is obtained by the tilt sensor on the vehicle body to determine the current gradient.

[0017] Under traction conditions and when the acceleration exceeds a set threshold, the original estimate of the locomotive's total mass is calculated based on the following dynamic equations. : In the formula, The total mass of the locomotive, To accelerate the locomotive, For traction force, As the basic operating resistance, It is the acceleration due to gravity. The longitudinal tilt angle of the current track; the original estimated value The final locomotive mass is output after smoothing by a moving average filter.

[0018] The available adhesion coefficient of the current rail surface is estimated in real time and compared with a preset typical threshold to determine whether the current rail surface has entered a slippery state.

[0019] As a further aspect of the present invention, the target speed dynamic correction step specifically includes: calculating a load correction coefficient limited to a preset range based on the identified total locomotive mass. : In the formula, The rated full load weight of the locomotive, This refers to the total mass of the locomotive.

[0020] Based on the real-time estimated rail surface adhesion coefficient, calculate an adhesion correction coefficient no greater than 1. : In the formula, The rail surface adhesion coefficient is estimated in real time. This represents the ideal adhesion coefficient in the velocity map.

[0021] Extract the radius of curvature of the current segment Combined with the preset locomotive overturning stability safety factor With gravitational acceleration Calculate the maximum safe passage speed .

[0022] Therefore, based on the maximum safe passage speed Compared with the reference speed of the current section Calculate the curvature correction factor : .

[0023] The reference speed is multiplied sequentially by the load correction factor. Adhesion correction factor and curvature correction factor Generate the target velocity after comprehensive dynamic correction. : .

[0024] The target speed is updated once every fixed period and sent to the traction drive unit as the speed closed-loop setpoint.

[0025] As a further aspect of the present invention, the composite torque command generation step specifically includes: calculating the feedback torque component of the speed loop based on the super-helical sliding mode control algorithm according to the speed tracking error between the target speed and the actual speed.

[0026] The total lag time of the system was determined by a step response test, and the aiming time was set to 1.5 to 2.5 times the total lag time.

[0027] The aiming distance is calculated based on the current vehicle speed and the aiming time, and the corresponding slope value at the aiming distance ahead is extracted from the cache.

[0028] The feedforward torque required to overcome the slope resistance is calculated based on the corresponding slope value, and the feedback torque component is superimposed with the feedforward torque to generate the final torque command.

[0029] As a further aspect of the present invention, another aspect is a predictive control system for underground monorail cranes based on digital road spectrum. The system includes: an on-board controller module, which has a built-in speed map indexed by roadway mileage, used to execute control algorithms, coordinate data from various modules, and calculate target speed and torque commands.

[0030] The positioning unit module, connected to the vehicle controller module, includes a rotary encoder and a passive RFID reader, used to obtain the absolute position of the locomotive in the roadway in real time.

[0031] The status perception unit module includes a current sensor and a tilt sensor, which are used to collect locomotive operating status and track environment parameters, and transmit the data to the on-board controller module for online identification.

[0032] As a further aspect of the present invention, it also includes: a traction drive unit module, comprising an explosion-proof frequency converter and a permanent magnet synchronous traction motor, for receiving torque commands issued by the on-board controller module, and independently completing the power drive and speed regulation of the monorail under normal operating conditions.

[0033] The hydraulic braking unit module, controlled by the vehicle controller module, is used to intervene in braking when the emergency stop button is pressed, a serious malfunction occurs, or the vehicle is in a parked state, and to maintain full release during normal speed regulation.

[0034] This invention provides a predictive control method and system for underground monorail cranes based on digital road spectrum. By pre-reading the road spectrum and identifying parameters online, adaptive speed correction is achieved. Combined with composite predictive control to output smooth torque, the monorail crane achieves precise and stable pure electric drive speed regulation, effectively avoiding emergency braking intervention due to speed runaway. This solution effectively overcomes instability and slippage under complex working conditions, eliminates the wear hazards caused by emergency braking impact, and significantly improves the response speed, operational stability, and intrinsic safety level of the entire system. Attached Figure Description

[0035] Figure 1 This is the main flowchart of a predictive control method for downhole monorail cranes based on digital road spectrum.

[0036] Figure 2 This is a structural block diagram of a predictive control system for a downhole monorail crane based on digital road spectrum. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0038] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0039] This invention provides a predictive control method and system for underground monorail cranes based on digital road spectrum, which solves the technical problems in the background art.

[0040] like Figure 1 The diagram shows the main flowchart of a predictive control method for underground monorail cranes based on digital road spectrum, provided by an embodiment of the present invention. The predictive control method for underground monorail cranes based on digital road spectrum includes: Step S1: Obtaining the absolute position of the locomotive, querying the speed map to obtain the reference speed and slip coefficient of the current section, and reading the slope sequence of each section within a set distance ahead.

[0041] Parameter online identification step S2: Under traction conditions and when the acceleration is greater than the set threshold, identify the total mass of the locomotive and the rail surface adhesion coefficient online.

[0042] Target speed dynamic correction step S3: Calculate the correction coefficient based on the total mass of the locomotive, the rail surface adhesion coefficient, and the radius of curvature of the current section, and correct the reference speed to obtain the target speed.

[0043] Step S4 for generating composite torque command: Calculate torque command by combining super-helical sliding mode feedback control with feedforward control based on pre-aiming slope.

[0044] Drive execution and braking coordination step S5: Send the torque command to the traction drive unit for execution, and fully release the hydraulic braking unit during normal operation, and control the hydraulic braking unit to intervene only in limited emergency or parking conditions.

[0045] In this embodiment, after the locomotive starts, it first enters the position and road spectrum acquisition step. Through multi-source sensor fusion positioning, the precise coordinates of the locomotive in the 3D digital map are locked in real time, and the feature parameters of the current and forward roads are extracted. Then, the online parameter identification step proceeds. The locomotive uses the dynamic changes in its own operating state to estimate the overall vehicle mass and track surface condition, which are difficult to measure directly. Based on this, the target speed dynamic correction step integrates the above environmental and load information, and uses multiple maintenance positive coefficients to safely reduce the basic speed limit, generating the desired speed that conforms to the current physical limits. Next, the composite torque command generation step combines nonlinear robust control and look-ahead information to calculate a smooth and hysteresis-free driving torque. Finally, in the drive execution and braking coordination step, the four-quadrant operation capability of the motor takes over the acceleration and deceleration control across the entire speed range.

[0046] In a preferred embodiment of the present invention, the step of obtaining the location and road spectrum specifically includes: collecting the pulse signal output by the rotary encoder installed on the shaft end of the traction motor to accumulate the relative mileage.

[0047] The absolute mileage coordinates are read from the RFID tags embedded in the sidewall of the tunnel by a passive RFID reader installed at the bottom of the vehicle body.

[0048] The relative mileage calculated from the pulse signal is fused with the absolute mileage coordinate value to correct and calculate the locomotive's real-time absolute position.

[0049] Using the real-time absolute position as an index, the reference speed and slip coefficient of the current feature section are queried and extracted from the speed map built into the vehicle controller.

[0050] Starting from the current position, the slope sequence of each section within a set distance ahead is read in advance and stored in the cache for later use.

[0051] In this embodiment, an incremental rotary encoder installed on the non-output shaft end of the traction motor collects high-frequency pulse signals and obtains the locomotive's relative displacement through integration. To eliminate accumulated errors, an explosion-proof passive RFID reader is installed at the bottom of the vehicle body. When the locomotive passes an RFID tag pre-embedded in the sidewall of the tunnel, the absolute mileage coordinates burned into the tag are read. The onboard controller uses a Kalman filter algorithm to calibrate the encoder mileage using these coordinates as the observation value. After the position calculation is completed, the controller uses the absolute position as a pointer to access the internally stored speed map and extract the reference speed and slip coefficient of the current road segment. At the same time, a first-in-first-out data buffer is established, using the current mileage plus a set 50-meter advance aiming distance as an index to pre-read the discrete sequence of the gradient of the longitudinal profile of the track ahead, providing environmental perception data support for feedforward control.

[0052] In a preferred embodiment of the present invention, the online parameter identification step specifically includes: real-time acquisition of the rotational speed of the traction motor and differential calculation to obtain the acceleration of the locomotive.

[0053] The longitudinal tilt angle of the current track is obtained by the tilt sensor on the vehicle body to determine the current gradient.

[0054] Under traction conditions and when the acceleration exceeds a set threshold, the original estimate of the locomotive's total mass is calculated based on the following dynamic equations. : In the formula, The total mass of the locomotive, To accelerate the locomotive, For traction force, As the basic operating resistance, It is the acceleration due to gravity. This represents the longitudinal tilt angle of the current track.

[0055] The original estimated value The final locomotive mass is output after smoothing by a moving average filter.

[0056] The available adhesion coefficient of the current rail surface is estimated in real time and compared with a preset typical threshold to determine whether the current rail surface has entered a slippery state.

[0057] In this embodiment, the traction motor speed signal undergoes first-order differential and low-pass filtering to obtain the actual locomotive acceleration after noise reduction. Simultaneously, a high-precision tilt sensor is used to acquire the actual gradient angle of the current track in real time. To avoid identification divergence caused by insufficient excitation during uniform speed operation, identification is only initiated when the motor is in traction output mode and the absolute value of the acceleration is greater than 0.2 m / s². At this time, the controller substitutes various real-time parameters and solves the dynamic equations to obtain the original estimate of the locomotive's total mass. .

[0058] In a preferred embodiment of the present invention, the target speed dynamic correction step specifically includes: calculating a load correction coefficient limited to a preset range based on the identified locomotive total mass. : In the formula, The rated full load weight of the locomotive, This refers to the total mass of the locomotive.

[0059] Based on the real-time estimated rail surface adhesion coefficient, calculate an adhesion correction coefficient no greater than 1. : In the formula, The rail surface adhesion coefficient is estimated in real time. This represents the ideal adhesion coefficient in the velocity map.

[0060] Extract the radius of curvature of the current segment Combined with the preset locomotive overturning stability safety factor With gravitational acceleration Calculate the maximum safe passage speed .

[0061] Therefore, based on the maximum safe passage speed Compared with the reference speed of the current section Calculate the curvature correction factor : .

[0062] The reference speed is multiplied sequentially by the load correction factor. Adhesion correction factor and curvature correction factor Generate the target velocity after comprehensive dynamic correction. : .

[0063] The target speed is updated once every fixed period and sent to the traction drive unit as the speed closed-loop setpoint.

[0064] It should be understood that, firstly, a load derating assessment is performed. The onboard controller divides the pre-calibrated rated full-load mass of the locomotive by the total locomotive mass obtained through online identification to obtain an initial load ratio. To ensure braking safety redundancy under heavy-load downhill conditions, this ratio is strictly clamped within the range of 0.8 to 1.0 by a software limiting module, serving as the final load correction factor. Secondly, an anti-skid derating assessment is performed. The real-time estimated track adhesion coefficient is read and divided by the ideal adhesion coefficient for the current section, pre-stored in the speed map. The resulting quotient is limited to no more than 1, and to prevent complete loss of traction under extremely slippery road conditions, the overall output range of this quotient is lowered to 0.5, thus obtaining the adhesion correction coefficient. Next, an anti-tipping reduction assessment is performed. The controller extracts the radius of curvature of the current section, combines it with the gravitational acceleration and a pre-set locomotive overturning stability safety factor of 1.5, and calculates the maximum safe passing speed of the current curve by taking the square root of the centripetal force physical model. Subsequently, this maximum safe passing speed is divided by the reference speed of the section, and the result is set to an upper limit of 1.0 and a lower limit of 0.4, thus outputting the curvature correction factor. Finally, the target velocity after comprehensive dynamic correction is generated. This refers to the target speed that balances dynamic boundaries and environmental constraints. This target speed is updated in real time to the underlying traction drive unit at a fixed interval of 50 milliseconds, serving as the given reference for the speed closed loop.

[0065] In a preferred embodiment of the present invention, the composite torque command generation step specifically includes: calculating the feedback torque component of the speed loop based on the super-helical sliding mode control algorithm according to the speed tracking error between the target speed and the actual speed.

[0066] The total lag time of the system was determined by a step response test, and the aiming time was set to 1.5 to 2.5 times the total lag time.

[0067] The aiming distance is calculated based on the current vehicle speed and the aiming time, and the corresponding slope value at the aiming distance ahead is extracted from the cache.

[0068] The feedforward torque required to overcome the slope resistance is calculated based on the corresponding slope value, and the feedback torque component is superimposed with the feedforward torque to generate the final torque command.

[0069] In this embodiment, a super-spiral sliding mode control algorithm is employed in the feedback control loop. Based on the speed tracking error and its integral surface, a sliding mode surface is constructed to calculate a continuous and smooth feedback torque component, effectively reducing the impact of high-frequency chattering on mechanical transmission components. In the look-ahead feedforward loop, the total lag time from command issuance to actual vehicle speed response is determined through a step response test, and the look-ahead time is set to twice this time. The look-ahead distance is obtained by multiplying the current vehicle speed by the look-ahead time, and the slope value at this look-ahead position is extracted from the buffer. Subsequently, the feedforward torque required to overcome the tangential component of gravity along the track is calculated based on the look-ahead slope. Finally, the feedforward torque used to offset known slope interference is superimposed with the feedback torque used to eliminate model errors, and this superposition is sent to the drive unit as the final torque command.

[0070] like Figure 2 As shown, in another preferred embodiment of the present invention, a predictive control system for underground monorail cranes based on digital road spectrum is provided. The system includes: an on-board controller module 100, which has a built-in speed map indexed by roadway mileage, used to execute control algorithms, coordinate data from various modules, and calculate target speed and torque commands.

[0071] The positioning unit module 200, connected to the vehicle controller module 100, includes a rotary encoder and a passive RFID reader, used to obtain the absolute position of the locomotive in the roadway in real time.

[0072] The status perception unit module 300 includes a current sensor and a tilt sensor, which are used to collect locomotive operating status and track environment parameters, and transmit the data to the on-board controller module for online identification.

[0073] In this embodiment, the main component of the system is the explosion-proof vehicle-mounted controller module 100, which employs an industrial-grade controller with an integrated dual-core DSP and FPGA architecture. Its internal non-volatile memory contains a digital speed map indexed by the mileage of the underground tunnel. This controller is responsible for executing all sliding mode control and online identification calculations. The positioning unit module 200 is connected to the controller via a CAN bus and consists of passive RFID readers installed at both ends of the vehicle and explosion-proof rotary encoders fixed to the motor shafts, ensuring decimeter-level positioning accuracy even in harsh environments. The state perception unit module 300 includes a high-frequency Hall current sensor and a high-vibration-resistant tilt sensor, acquiring underlying electrical and spatial pose data at an extremely high sampling rate. After low-pass filtering, the data is sent to the controller, providing a high-fidelity data source for online identification.

[0074] As another preferred embodiment of the present invention, it further includes: a traction drive unit module 400, including an explosion-proof frequency converter and a permanent magnet synchronous traction motor, for receiving torque commands issued by the vehicle controller module 100, and independently completing the power drive and speed regulation of the monorail under normal operating conditions.

[0075] The hydraulic braking unit module 500, controlled by the vehicle controller module 100, is used to intervene in braking when the emergency stop button is pressed, a serious malfunction occurs, or the vehicle is in a parking state, and to maintain full release during normal speed regulation.

[0076] In this embodiment, the traction drive unit module 400 uses an explosion-proof frequency converter to drive a permanent magnet synchronous traction motor. Leveraging the high torque density of the permanent magnet motor and the four-quadrant operation capability of the frequency converter, this module can seamlessly switch between electric and regenerative braking states independently based on torque commands during normal operation, achieving flexible drive and speed regulation across the entire speed range. The hydraulic braking unit module 500 employs a fail-safe structure with spring braking and hydraulic release. During normal locomotive operation, the onboard controller continuously outputs signals to maintain hydraulic station pressure, ensuring complete release of the friction brake shoes and avoiding the potential for frequent friction and overheating of the brake shoes during downhill speed regulation. At the software level, this hydraulic braking unit is strictly configured to immediately release pressure and mechanically lock up only when the emergency stop button is pressed, the frequency converter experiences a serious malfunction, or the locomotive needs to be parked, maximizing system efficiency and inherent safety.

[0077] The above embodiments of the present invention provide a predictive control method for underground monorail cranes based on digital road spectrum, and a predictive control system for underground monorail cranes based on digital road spectrum. This method achieves adaptive speed correction through road spectrum pre-reading and online parameter identification, and combines composite predictive control to output smooth torque, allowing the monorail crane to completely eliminate its dependence on mechanical braking speed regulation. This solution effectively overcomes instability and slippage under complex working conditions, eliminates the wear hazards caused by frequent intervention of braking components, and significantly improves the response speed, operational stability, and intrinsic safety level of the entire system.

[0078] In order for the above methods and systems to operate smoothly, the system may include more or fewer components than those described above, or combine certain components, or different components, in addition to the various modules mentioned above. For example, it may include input / output devices, network access devices, buses, processors, and memory.

[0079] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the system, connecting various parts via various interfaces and lines.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A predictive control method for a downhole monorail crane based on digital path spectrum, characterized in that, The method includes: Steps to obtain location and road map: Obtain the absolute position of the locomotive, query the speed map to obtain the base speed and slip coefficient of the current section, and read the gradient sequence of each section within the set distance ahead; Online parameter identification steps: Under traction conditions and when the acceleration is greater than the set threshold, identify the total mass of the locomotive and the rail surface adhesion coefficient online; Target speed dynamic correction steps: Based on the total mass of the locomotive, the rail surface adhesion coefficient, and the radius of curvature of the current section, calculate the correction coefficient and correct the reference speed to obtain the target speed; The composite torque command generation steps are as follows: The torque command is calculated by combining super-helical sliding mode feedback control with feedforward control based on the pre-aiming slope; Drive execution and braking coordination steps: The torque command is sent to the traction drive unit for execution, and the hydraulic braking unit is fully released during normal operation. The hydraulic braking unit is only controlled to intervene in limited emergency or parking conditions.

2. The predictive control method for downhole monorail cranes based on digital path spectrum according to claim 1, characterized in that, The steps for obtaining location and road spectrum specifically include: The pulse signal output by the rotary encoder installed on the shaft end of the traction motor is collected to accumulate the relative mileage; The absolute mileage coordinates of the RFID tags embedded in the sidewalls of the tunnel are read by a passive RFID reader installed at the bottom of the vehicle body. The relative mileage calculated from the pulse signal is fused with the absolute mileage coordinate value to correct and calculate the locomotive's real-time absolute position. Using the real-time absolute position as an index, the reference speed and slip coefficient of the current feature section are queried and extracted from the speed map built into the vehicle controller; Starting from the current position, the slope sequence of each section within a set distance ahead is read in advance and stored in the cache for later use.

3. The predictive control method for downhole monorail cranes based on digital path spectrum according to claim 1, characterized in that, The online parameter identification steps specifically include: The speed of the traction motor is collected in real time and differential calculation is performed to obtain the acceleration of the locomotive; The longitudinal tilt angle of the current track is obtained by the tilt sensor on the vehicle body to determine the current gradient; Under traction conditions and when the acceleration exceeds a set threshold, the original estimate of the locomotive's total mass is calculated based on the following dynamic equations. : ； In the formula, The total mass of the locomotive, To accelerate the locomotive, For traction force, As the basic operating resistance, It is the acceleration due to gravity. This represents the longitudinal tilt angle of the current track. The original estimated value The final locomotive mass is output by smoothing the data using a moving average filter. The available adhesion coefficient of the current rail surface is estimated in real time and compared with a preset typical threshold to determine whether the current rail surface has entered a slippery state.

4. The predictive control method for downhole monorail cranes based on digital path spectrum according to claim 1, characterized in that, The target velocity dynamic correction step specifically includes: Based on the identified total locomotive mass, calculate the load correction coefficient within a preset range. : ； In the formula, The rated full load weight of the locomotive, The total mass of the locomotive; Based on the real-time estimated rail surface adhesion coefficient, calculate an adhesion correction coefficient no greater than 1. : ； In the formula, The rail surface adhesion coefficient is estimated in real time. The ideal adhesion coefficient in the velocity map; Extract the radius of curvature of the current segment Combined with the preset locomotive overturning stability safety factor With gravitational acceleration Calculate the maximum safe passage speed ; Therefore, based on the maximum safe passage speed Compared with the reference speed of the current section Calculate the curvature correction factor : ； The reference speed is multiplied sequentially by the load correction factor. Adhesion correction factor and curvature correction factor Generate the target velocity after comprehensive dynamic correction. : ； The target speed is updated once every fixed period and sent to the traction drive unit as the speed closed-loop setpoint.

5. The predictive control method for downhole monorail cranes based on digital path spectrum according to claim 1, characterized in that, The composite torque command generation step specifically includes: Based on the speed tracking error between the target speed and the actual speed, the feedback torque component of the speed loop is calculated using the super-helical sliding mode control algorithm; The total lag time of the system was determined by a step response test, and the preview time was set to 1.5 to 2.5 times the total lag time. The aiming distance is calculated based on the current vehicle speed and the aiming time, and the corresponding slope value at the aiming distance ahead is extracted from the cache. The feedforward torque required to overcome the slope resistance is calculated based on the corresponding slope value, and the feedback torque component is superimposed with the feedforward torque to generate the final torque command.

6. A predictive control system for a downhole monorail crane based on digital path spectrum, characterized in that, The system employs the predictive control method for downhole monorail cranes based on digital path spectrum as described in any one of claims 1-5, wherein the system comprises: The vehicle-mounted controller module has a built-in speed map indexed by lane mileage, which is used to execute control algorithms, coordinate data from various modules, and calculate target speed and torque commands. The positioning unit module, connected to the vehicle controller module, includes a rotary encoder and a passive RFID reader, used to obtain the absolute position of the locomotive in the tunnel in real time. The status perception unit module includes a current sensor and a tilt sensor, which are used to collect locomotive operating status and track environment parameters, and transmit the data to the on-board controller module for online identification.

7. The predictive control system for a downhole monorail crane based on digital path spectrum according to claim 6, characterized in that, Also includes: The traction drive unit module includes an explosion-proof frequency converter and a permanent magnet synchronous traction motor, which is used to receive torque commands issued by the on-board controller module and independently complete the power drive and speed regulation of the monorail under normal operating conditions. The hydraulic braking unit module, controlled by the vehicle controller module, is used to intervene in braking when the emergency stop button is pressed, a serious malfunction occurs, or the vehicle is in a parked state, and to maintain full release during normal speed regulation.

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