A Health Status-Based Road-Cloud Cooperative Scheduling and Obstacle Avoidance Method and System for Monorail Cranes

Abstract

This invention relates to a road-cloud collaborative scheduling and obstacle avoidance method and system for monorail cranes based on health status, belonging to the field of industrial automation. It solves the problem in existing technologies where the underlying obstacle avoidance control module and the predictive maintenance results in the cloud are disconnected, preventing the overall system from achieving adaptive reverse compensation of obstacle avoidance parameters. The method involves collecting status data from each monorail crane, aggregating it through roadside edge nodes, and uploading it to the cloud. The cloud calculates the brake health, motor health, and load-gradient influence factor for each monorail crane based on this data; it then calculates the overall health of each monorail crane; this overall health is converted into control parameters; and the monorail cranes are controlled according to these parameters to achieve collaborative scheduling and obstacle avoidance. This invention enables adaptive adjustment of obstacle avoidance parameters, improving vehicle safety.

Description

Technical Field

[0001] This invention relates to the field of industrial automation, and in particular to a method and system for road-cloud collaborative scheduling and obstacle avoidance of monorail cranes based on health status. Background Technology

[0002] Heavy-duty monorail systems in underground coal mines primarily handle the long-distance, steep-slope transportation of high-value materials such as hydraulic supports, serving as a crucial link in intelligent mining auxiliary transportation. With the introduction of autonomous driving and vehicle-road-cloud collaborative technologies, environmental perception based on multi-sensor fusion, high-precision positioning, and cloud-based equipment status monitoring technologies can significantly improve mine transportation efficiency and safety. When the system faces harsh operating conditions such as heavy loads and frequent start-stop operations, an ideal collaborative control system should be able to form a deep closed loop between the equipment's physical health status, the underlying autonomous driving obstacle avoidance, and the top-level multi-vehicle scheduling, ensuring ultimate safety in complex environments.

[0003] Existing unmanned monorail systems typically use fixed physical distance thresholds to trigger deceleration and emergency braking control, while relying on independent cloud-based operation and maintenance platforms to monitor the status of components such as motors and brakes and issue over-limit alarms. However, under conditions where the monorail is operating with defects, brake shoe wear or motor performance degradation can lead to a significant increase in actual braking response time and braking distance. If the underlying obstacle avoidance control module and the predictive operation and maintenance results in the cloud are disconnected, the system cannot achieve adaptive reverse compensation of obstacle avoidance parameters. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a health status-based road-cloud collaborative scheduling and obstacle avoidance method for monorail cranes, in order to solve the problem that the existing underlying obstacle avoidance control module and the predictive operation and maintenance results in the cloud are disconnected, which makes it impossible for the overall system to achieve adaptive reverse compensation of obstacle avoidance parameters.

[0005] On one hand, embodiments of the present invention provide a road-cloud collaborative scheduling and obstacle avoidance method and system for monorail cranes based on health status. The system collects status data from each monorail crane, which is then aggregated by roadside edge nodes and uploaded to the cloud. The cloud calculates the brake health, motor health, and load-gradient influence factor for each monorail crane based on its status data. Based on these parameters, a comprehensive health score for each monorail crane is obtained. The cloud then converts this comprehensive health score into control parameters. Finally, based on these control parameters, collaborative scheduling and obstacle avoidance based on spatiotemporal conflicts are performed on each monorail crane.

[0006] Furthermore, based on the brake health, motor health, and load-gradient influence factor of each monorail, the comprehensive health of each monorail is obtained, including: the comprehensive health is... ,in, For brake health, For the health of the motor, The load-slope influencing factor. For slope, This represents the current load.

[0007] Furthermore, the brake health of each monorail crane includes: obtaining the theoretical shortest braking distance based on the current vehicle speed, gradient, and adhesion coefficient; obtaining the actual braking distance based on the braking distance measured during the most recent effective online braking process, the reference braking distance in the actual vehicle calibration table, the fusion weight of the online measurement values, the new brake shoe thickness, the minimum allowable brake shoe thickness, the brake shoe wear correction coefficient, the braking response delay correction coefficient, and the reference braking response time under healthy conditions; and obtaining the brake health based on the theoretical shortest braking distance and the actual braking distance using the following formula: ;in, For the theoretical shortest braking distance, This is the actual braking distance.

[0008] Furthermore, based on the reference normal temperature, motor temperature, maximum allowable temperature, maximum allowable motor vibration value, and motor vibration limit, the motor health of each monorail is calculated using the following formula, including: Motor health is... ;in This is the normal reference temperature for the motor. For the stator temperature of the motor, To the maximum permissible temperature, This refers to the vibration amplitude of the motor bearing. For vibration limits, This is the weighting coefficient for stator temperature. Here is the weighting coefficient for bearing vibration, and .

[0009] Furthermore, based on the comprehensive health score, the status of each monorail is divided into different state intervals—healthy, sub-healthy, and unhealthy—according to a first health threshold and a second health threshold; the cloud platform converts the comprehensive health score of each monorail into control parameters based on the state interval of each monorail.

[0010] Furthermore, the control parameters include dynamic increments of the safety distance. Maximum permissible speed The cloud platform converts the overall health status of each monorail gantry into control parameters based on its status range. This includes: The task permission flag is a flag assigned by the cloud platform to the vehicle based on the overall health status of each monorail gantry. Specifically, when a monorail gantry is in a healthy range, the task permission flag indicates full permission; when it is in a sub-healthy range, the task permission flag indicates limited permission; and when it is in an unhealthy range, the task permission flag indicates locked permission. The dynamic increment of the safety distance is... ;in, The time required for the maximum communication latency in the vehicle-road-cloud closed loop. This is the health decay compensation coefficient. This is the load compensation coefficient. This is the slope compensation coefficient. For rated load, For the current load, For the overall health of the monorail, This is the absolute value of the slope. To collect vehicle speed; Maximum permissible speed of monorail The overall health status of the monorail is continuously reduced within the healthy range; when the overall health status of the monorail is in the sub-health range, the maximum permissible speed is... ,in This refers to the rated maximum operating speed of the monorail under healthy conditions. The basic speed limit coefficient at the entrance to the sub-health zone The sensitivity coefficient for the rate of depreciation corresponding to the decline in health. The first health threshold is set; when the overall health of each monorail is in the unhealthy range, the speed limit coefficient is suddenly reduced to a preset constant, and each monorail crawls at a low speed at the preset constant.

[0011] Furthermore, during the meeting of trains, the control of each monorail based on control parameters to achieve coordinated scheduling and obstacle avoidance includes: the cloud calculating the meeting time of the two trains based on the current time; the cloud calculating the shortest complete braking time of the two trains and the difference in health between the two trains based on the load, gradient, and health status of the two trains; and performing meeting scheduling and obstacle avoidance based on the meeting time, shortest complete braking time, health status difference, and task priority of the two trains.

[0012] Furthermore, the control of each monorail based on control parameters to achieve coordinated scheduling and obstacle avoidance also includes: when communication is interrupted and the monorail enters an islanded state, the following actions are taken: freezing the last obtained comprehensive health status, dynamic increment of safe distance, maximum allowable speed, and task permission flag from the cloud, and caching the obtained status data; superimposing an independent safe distance on the last obtained dynamic increment of safe distance as the current dynamic increment of safe distance; setting a preset extremely low crawling speed as the current maximum allowable speed; rejecting new task instructions; when communication is restored, the monorail uploads the cached status data, the cloud recalculates the comprehensive health status, and obtains new control parameters; the current control parameters in the islanded state smoothly transition to the new control parameters.

[0013] Furthermore, an independent safety distance is superimposed on the last obtained dynamic increment of the safety distance to form the current dynamic increment of the safety distance, including: the current dynamic increment of the safety distance is... ,in The last obtained dynamic increment of the safe distance, For independent safety distances; among which, Meets the ultimate physical requirements for slope erosion control on downhill slopes: ;in, This is the minimum allowable wheel-rail adhesion coefficient in the mine. This represents the maximum downhill gradient that the route may encounter ahead. The last obtained speed of the monorail. For edge safety distance, This is the acceleration due to gravity.

[0014] On the other hand, embodiments of the present invention provide a road-cloud collaborative scheduling and obstacle avoidance system for monorail cranes based on health status. The monorail crane road-cloud collaborative scheduling and obstacle avoidance system includes an onboard control terminal, roadside edge nodes, and a cloud-based operation and maintenance and scheduling platform. The onboard control terminal is located on each monorail crane and is used to collect the status data of the corresponding monorail crane. The roadside edge nodes are used to summarize the status data of each monorail crane and upload it to the cloud. The cloud-based operation and maintenance and scheduling platform is used to perform the following: calculate the brake health, motor health, and load-gradient influence factor of each monorail crane based on the status data of each monorail crane; obtain the comprehensive health of each monorail crane based on the brake health, motor health, and load-gradient influence factor of each monorail crane; and convert the comprehensive health of each monorail crane into control parameters. The onboard control terminal is also used to perform collaborative scheduling and obstacle avoidance of the monorail cranes based on spatiotemporal conflicts based on the control parameters.

[0015] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. By coordinating vehicle, road, and cloud, the health status of the monorail is calculated. The health status in the cloud is then combined with the control of the monorail, enabling adaptive adjustment of obstacle avoidance parameters and improving vehicle safety.

[0016] 2. A dynamic mapping between health status and obstacle avoidance distance is introduced, and the physical components of load and slope are also included in the calculation, which physically eliminates the hidden danger of insufficient braking distance caused by "sickness + heavy load downhill"; 3. By deeply linking health status with task scheduling and combining it with spatiotemporal conflict prediction for scheduling, sub-healthy locomotives are automatically avoided in high-load or high-risk sections, which not only ensures overall transportation efficiency but also avoids the risk of scrapping inferior equipment. 4. When the network is lost, a safety margin is added based on the worst physical limits; limited slope and hysteresis anti-jitter mechanisms are set during state switching and network recovery to meet the high reliability and control stability requirements of mining industry.

[0017] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0018] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 A schematic diagram of the road-cloud collaborative scheduling and obstacle avoidance method for monorail cranes based on health status; Figure 2 This is a schematic diagram of the overall architecture of a health-state-based monorail crane road-cloud collaborative scheduling and obstacle avoidance system; Figure 3 This is a schematic diagram illustrating the calculation and grading process for the comprehensive health score H; Figure 4 A schematic diagram of the collaborative obstacle avoidance control plane in scenarios involving multiple vehicles meeting and rear-end collisions. Figure 5 A flowchart illustrating the degraded operation of a monorail gantry crane in a scenario where communication between the vehicle and the ground is interrupted due to network outage. Detailed Implementation

[0019] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0020] A specific embodiment of the present invention discloses a method for road-cloud cooperative scheduling and obstacle avoidance of monorail cranes based on health status, such as... Figure 1 As shown.

[0021] include: Step S1: Collect the status data of each monorail crane, summarize it through the roadside edge nodes, and upload it to the cloud. Step S2: The cloud calculates the brake health, motor health, and load-gradient influence factor of each monorail based on the status data of each monorail. Based on the brake health, motor health, and load-gradient influence factor of each monorail, the comprehensive health of each monorail is obtained. The cloud platform converts the overall health status of each monorail gantry into control parameters; Step S3: Control each monorail crane according to the control parameters to achieve coordinated scheduling and obstacle avoidance.

[0022] By using vehicle-road-cloud collaboration to calculate the health status of the monorail, and combining the health status in the cloud with the control of the monorail, adaptive adjustment of obstacle avoidance parameters is achieved, thereby improving vehicle safety.

[0023] Specifically, in step S1, combined with Figure 1 and Figure 2 As shown, during the transportation task, the data acquisition module in the vehicle control terminal collects the status data of each monorail crane.

[0024] The vehicle-mounted data acquisition module collects operational and health-related status data at preset intervals and reports them to the roadside edge node via a wireless link.

[0025] Specifically, the status data includes rapidly changing parameters and slowly changing parameters, which are acquired through multiple corresponding sensors.

[0026] To ensure the system's robustness in harsh mining environments, the following preprocessing and fault-tolerance mechanisms are implemented: For slowly varying parameters such as brake shoe thickness, temperature, and vibration, sliding window filtering or median filtering is used for smoothing, eliminating obviously abrupt changes in values; multi-sensor fusion and cross-validation: If a critical sensor fails for an extended period, the system can be downgraded to use a backup estimation model for cross-validation. If effective physical data cannot be obtained after fusion validation, the health status of the corresponding component is forcibly reduced to a conservative value, and a cloud-based maintenance alarm is triggered to avoid misjudgments due to a single sensor failure.

[0027] The backup estimation models include: vehicle speed estimation model, load estimation model, and gradient estimation model. The vehicle speed estimation model calculates the differences between each pair of wheel speed sensor speed, UWB positioning differential speed, and IMU acceleration integral speed. If the difference is less than or equal to a preset threshold, it is considered consistent. If at least two data sources are consistent, their weighted average is taken as the current valid vehicle speed; if only one data source is valid, a safety margin is added to the calculated value as the output, with an additional 10% speed redundancy; if all three data sources fail, a local emergency stop or network outage / islanding degradation mode is directly triggered. The load estimation model collects the stator drive current of the motor in real time. Current acceleration and slope angle ,according to Online reverse engineering to solve for the current equivalent mass of the whole vehicle As an estimated load, among which The resistance encountered during load estimation; The slope estimation model compares the slope calculated by pitch angle and roadside elevation change rate with the calibrated slope obtained by matching the current positioning with a high-precision map database. A Kalman filter is used for data fusion and error smoothing to output a comprehensive estimated slope.

[0028] Specifically, step S2 is executed by the cloud. This includes steps S21-S23.

[0029] Step S21: The cloud calculates the brake health, motor health, and load-gradient influence factor of each monorail based on the status data of each monorail; including steps S211-S213.

[0030] Step S211: Calculate the brake health of each monorail crane, including: Step S2111: Based on the current vehicle speed, gradient, and adhesion coefficient, obtain the theoretical shortest braking distance; the theoretical shortest braking distance can be expressed as: ;in, , , The viscosity coefficient is 1. For gravitational acceleration, the denominator is taken as "when the vehicle brakes downhill". " " is used when braking uphill." "Number.

[0031] Step S2112: Based on the braking distance measured in the most recent effective online braking process, the reference braking distance in the actual vehicle calibration table, the fusion weight of the online measurement values, the new brake shoe thickness, the minimum allowable brake shoe thickness, the brake shoe wear correction coefficient, the braking response delay correction coefficient, and the reference braking response time under healthy conditions, the actual braking distance is obtained. Actual braking distance is expressed as ;in, The braking distance is the distance measured during the most recent effective online braking process; This is the reference braking distance from the actual vehicle calibration table; The fusion weights for online measurements; The thickness of the new brake shoe; Minimum allowable brake shoe thickness; This is the brake shoe wear correction factor; This is the braking response delay correction factor; This is the reference braking response time under healthy conditions.

[0032] Step S2113: Based on the theoretical shortest braking distance and the actual braking distance, obtain the brake health status using the following formula: ;in, For the theoretical shortest braking distance, This is the actual braking distance.

[0033] Step S212: Based on the reference normal temperature, motor temperature, maximum allowable temperature, maximum allowable motor vibration value, and motor vibration limit, calculate the motor health of each monorail crane using the following formula, including: Motor health is... ;in This is the normal reference temperature for the motor. For the stator temperature of the motor, To the maximum permissible temperature, This refers to the vibration amplitude of the motor bearing. For vibration limits, This is the weighting coefficient for stator temperature. Here is the weighting coefficient for bearing vibration, and .

[0034] Step S213: Calculate the load-slope influence factor.

[0035] The load-slope influence factor is expressed as: ,in The load-bearing influence calibration factor, The slope influence calibration coefficient. This represents the influence of the downhill slope. For the current load, This is the rated load capacity.

[0036] It should be noted that steps S211-S213 above are not sequential and can be executed in parallel.

[0037] Step S22: Based on the brake health, motor health, and load-gradient influence factor of each monorail, obtain the comprehensive health of each monorail.

[0038] Specifically, based on the brake health, motor health, and load-gradient influence factor of each monorail, the comprehensive health of each monorail is obtained, including: the comprehensive health is... ,in, For brake health, For the health of the motor, This is the load-slope influence factor.

[0039] Step S23: The cloud platform converts the overall health status of each monorail crane into control parameters.

[0040] Specifically, based on the overall health status, the status of each monorail is divided into different state intervals: healthy interval, sub-healthy interval, and unhealthy interval according to the first health threshold and the second health threshold; the cloud platform converts the overall health status of each monorail into control parameters according to the state interval of each monorail.

[0041] The first health threshold is a preset value used to distinguish between healthy and sub-healthy monorails, and can be set to 0.8.

[0042] The second health threshold is a preset value used to distinguish between sub-healthy and unhealthy monorail cranes, and can be set to 0.6.

[0043] The control parameters include the dynamic increment of the safety distance. Maximum permissible speed And task permission flags.

[0044] The task permission flag is a corresponding permission code flag assigned to the vehicle by the cloud based on the overall health status. When the vehicle is in a healthy range, the task permission flag is full permission; when it is in a sub-healthy range, the task permission flag is limited permission; and when it is in an unhealthy range, the task permission flag is locked permission.

[0045] The dynamic increment of the safe distance is ; in, The time required for the maximum communication latency in the vehicle-road-cloud closed loop. This is the health decay compensation coefficient. This is the load compensation coefficient. This is the slope compensation coefficient. For rated load, For the current load, For the overall health of the monorail, This is the absolute value of the slope. To collect vehicle speed; Maximum permissible speed of monorail The overall health status of the monorail gantry crane continuously decreases within the health range; the cloud-based assessment module detects its... The value shows a slow downward trend, when Exceeding the threshold Once the system enters the sub-healthy zone and remains stable within the anti-shock time window, the cloud platform gradually increases the obstacle avoidance distance at the set maximum rate of change, based on a continuous mapping curve or interpolation LUT table. Speed ​​limiter Simultaneously, the locomotive's task priority is lowered in the multi-car dispatch queue, restricting it from receiving heavy-load tasks on long downhill slopes. Once the locomotive has completed brake shoe replacement and cooling at the maintenance depot, a new round of data collection will calculate... The system has significantly recovered to a healthy level. After verifying that the data is stable, it automatically relaxes the underlying security limits and reopens full-speed overload permissions, achieving a fully automated closed loop of "assessment-reduction-repair-recovery". When the overall health of a monorail crane is in the sub-healthy range, the maximum permissible speed... ,in This refers to the rated maximum operating speed of the monorail under healthy conditions. The basic speed limit coefficient at the entrance to the sub-health zone The sensitivity coefficient for the rate of depreciation corresponding to the decline in health. The first health threshold; When the overall health of each monorail is in the unhealthy range, the speed limit coefficient is suddenly reduced to a preset constant, and each monorail crawls at a low speed at the preset constant.

[0046] Step S3 is executed by the onboard control unit. This includes controlling each monorail according to control parameters to achieve coordinated scheduling and obstacle avoidance.

[0047] Step S3 includes collaborative scheduling and obstacle avoidance control in multi-vehicle meeting scenarios. For example... Figure 4 As shown, this includes: the cloud calculating the meeting time of the two vehicles based on the current time; the cloud calculating the shortest complete braking time of the two vehicles and the difference in their health based on their load, gradient, and health status; and performing meeting scheduling and obstacle avoidance based on the meeting time, shortest complete braking time, health difference, and task priority of the two vehicles.

[0048] When two vehicles are meeting oncoming traffic and one vehicle is in poor health, such as Figure 4 As shown, at the "Y"-shaped junction in the transport lane, vehicle A and vehicle B approach each other from opposite directions. The roadside or cloud-based dispatch module obtains the speeds of the two vehicles. Current spacing And calculate the meeting time. ; Based on the load, gradient, and health status of both vehicles, their shortest complete braking time was calculated. and ; For any vehicle ,in Option A or B can be chosen, depending on the current load. Obtain the equivalent mass of the whole vehicle ;in, For the empty weight of the monorail crane, Given the current load, the equivalent deceleration provided by the brakes is expressed as... The maximum allowable deceleration on the adhesive side is expressed as: ;in, Customize the power for the vehicle; For vehicles Brake health; For vehicles The slope is defined as downhill. This is the rolling resistance coefficient; For vehicles The wheel-rail adhesion coefficient of the section in which it is located. Taking into account both brake capacity and wheel-rail adhesion limitations, the effective braking deceleration is expressed as: ;when Less than the preset minimum safe deceleration If the system determines that the vehicle does not have the safe braking capability at the current speed, it will directly trigger an extremely low speed operation or parking strategy. Vehicles in effect The shortest complete braking time is expressed as The corresponding full braking distance is .

[0049] Comprehensive comparison of scheduling optimization modules Introducing a health difference value in conjunction with braking time. If vehicle A is in a healthy range and performing a heavy-load task, while vehicle B is in a sub-healthy or unhealthy range, the system will make a safe distance decision before the meeting point. The system issues a mandatory deceleration and lateral movement maneuver instruction to vehicle B in advance. This distance meets the physical constraints of the worst-case scenario, and is represented as follows: ,in The safe distance is set at the edge; vehicle B's operating permissions are restored only after vehicle A has safely driven away.

[0050] By combining the overall health status of the monorail with spatiotemporal conflict prediction, mandatory yielding and rear-end collision risk control were achieved, eliminating the hidden danger of collisions induced by sub-healthy vehicles and improving the safety of monorail operation.

[0051] Step S4 also includes control of the monorail entering an islanded state when communication is interrupted.

[0052] like Figure 5 As shown. When communication is interrupted, after the monorail enters an islanded state, the following actions are performed: freeze the last comprehensive health status, dynamic increment of safe distance, maximum permissible speed, and task permission flag obtained from the cloud, and cache the obtained status data; The independent safety distance is superimposed on the last obtained dynamic increment of the safety distance to serve as the current dynamic increment of the safety distance; the preset extremely low crawling speed is used as the current maximum allowable speed; new task instructions are rejected; when communication is restored, the monorail gantry uploads the cached status data, and the cloud recalculates the comprehensive health status to obtain new control parameters; the current control parameters of the island state are smoothly transitioned to the new control parameters.

[0053] Specifically, the vehicle control unit monitors the heartbeat messages from roadside base stations, and when continuous... If no valid heartbeat is received within a preset cycle, communication is considered interrupted. The vehicle control terminal immediately freezes the last set of parameters obtained from the cloud, including the last overall health status. and its corresponding , and the maximum slope perceived at that time .

[0054] The current dynamic increment of safe distance is obtained by superimposing an independent safe distance on the last obtained dynamic increment of safe distance, including: the current dynamic increment of safe distance is... ,in The last obtained dynamic increment of the safe distance, For independent safety distances; among which, Meets the ultimate physical requirements for slope erosion control on downhill slopes: ;in, This is the minimum allowable wheel-rail adhesion coefficient in the mine. This represents the maximum downhill gradient that the route may encounter ahead. The last obtained speed of the monorail. For edge safety distance, This is the acceleration due to gravity.

[0055] Once the vehicle-mounted device receives a set number of valid heartbeats and completes authentication, the network is considered to have recovered. The vehicle-mounted device then packages and uploads the cached blind spot data, and the cloud recalculates the new health status. The system uses a slope limit function to smoothly transition the vehicle's obstacle avoidance distance and speed limit from the isolated state to the new cloud state, avoiding step disturbances.

[0056] Another embodiment also discloses A health-state-based monorail crane road-cloud collaborative scheduling and obstacle avoidance system, such as Figure 2As shown. The monorail system includes an onboard control terminal, roadside edge nodes, and a cloud-based operation and dispatch platform. The onboard control terminal is located on each monorail and is used to collect the status data of the corresponding monorail. The roadside edge nodes are used to aggregate the status data of each monorail and upload it to the cloud. The cloud-based operation and dispatch platform is used to perform the following: calculate the brake health, motor health, and load-gradient influence factor of each monorail based on the status data of each monorail; obtain the comprehensive health of each monorail based on the brake health, motor health, and load-gradient influence factor of each monorail; and convert the comprehensive health of each monorail into control parameters. The onboard control terminal is also used to perform coordinated scheduling and obstacle avoidance of the monorail based on spatiotemporal conflicts based on the control parameters.

[0057] It is understood that this system is used to implement the methods of any of the above embodiments.

[0058] Specifically, the vehicle control unit includes a data acquisition module, a network outage detection and degradation module, and a local control state machine module. The data acquisition module collects vehicle speed data. acceleration Current load ,slope and the remaining thickness of the brake shoe Braking response time Motor stator temperature Motor bearing vibration amplitude Information on the status of key components; if necessary, the wheel-rail adhesion coefficient can also be estimated. The network outage detection and degradation module is used to monitor the status of the communication link; the local control state machine is used to execute the underlying control logic.

[0059] The roadside edge node includes a WIFI6+UWB fusion base station and an edge computing unit, used for vehicle-to-road wireless communication, high-precision positioning, local aggregation of multi-vehicle operation data, and basic collision detection.

[0060] The cloud-based operations and scheduling platform includes a data storage module, a health assessment module, a policy generation module, and a scheduling optimization module, responsible for data storage and overall health throughout the entire lifecycle. Evaluation, dynamic control parameter generation, and multi-vehicle collaborative scheduling.

[0061] Compared with existing technologies, this embodiment provides a road-cloud collaborative scheduling and obstacle avoidance method and system for monorail cranes based on health status. It adopts dynamic obstacle avoidance distances according to different vehicle and road conditions, introducing a dynamic mapping between health status and obstacle avoidance distance. It incorporates load, gradient physical force components, and communication latency into the calculation, eliminating the hidden danger of insufficient braking distance caused by "defective vehicles + heavy-load downhill." It deeply binds health status with task scheduling, combining spatiotemporal conflict prediction for scheduling, automatically avoiding sub-healthy locomotives in high-load or high-risk sections, ensuring overall transportation efficiency while avoiding the risk of scrapping inferior equipment. It possesses multi-sensor cross-verification fault tolerance capabilities to prevent single-point failures. In the event of network outage, it uses a rigorous worst-case physical limit with added safety margins. It incorporates limited slope and hysteresis anti-jitter mechanisms during state switching and network recovery, meeting the high reliability and control stability requirements of the mining industry.

[0062] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0063] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for road-cloud collaborative scheduling and obstacle avoidance of monorail cranes based on health status, characterized in that, The status data of each monorail gantry is collected, aggregated by roadside edge nodes, and then uploaded to the cloud. The cloud-based system calculates the brake health, motor health, and load-gradient influence factor of each monorail based on the status data of each monorail. Based on the brake health, motor health, and load-gradient influence factor of each monorail, the comprehensive health of each monorail is obtained. The cloud platform converts the overall health status of each monorail gantry into control parameters; Each monorail is controlled according to control parameters to achieve coordinated scheduling and obstacle avoidance.

2. The method according to claim 1, characterized in that, The comprehensive health of each monorail gantry is obtained based on the brake health, motor health, and load-gradient influence factor of each monorail gantry, including: Overall health level is ,in, For brake health, For the health of the motor, The load-slope influencing factor. For slope, This represents the current load.

3. The method according to claim 2, characterized in that, Calculate the brake health of each monorail crane, including: The theoretical shortest braking distance for the monorail is obtained based on the current vehicle speed, gradient, and adhesion coefficient. The actual braking distance of the monorail is obtained based on the braking distance, reference braking distance, new brake shoe thickness, minimum allowable brake shoe thickness, brake shoe wear correction factor, braking response delay correction factor, and reference braking response time under healthy conditions measured during the most recent effective online braking process of the monorail. Based on the theoretical shortest braking distance and the actual braking distance of the monorail, the brake health of the monorail is obtained using the following formula: ； in, For the theoretical shortest braking distance, This is the actual braking distance.

4. The method according to claim 3, characterized in that, Based on the reference normal temperature, motor temperature, maximum allowable temperature, maximum allowable motor vibration value, and motor vibration limit, the motor health of each monorail crane is calculated using the following formula, including: Motor health status is ; in This is the normal reference temperature for the motor. For the stator temperature of the motor, To the maximum permissible temperature, This refers to the vibration amplitude of the motor bearing. For vibration limits, This is the weighting coefficient for stator temperature. Here is the weighting coefficient for bearing vibration, and .

5. The method according to claim 1, characterized in that, It also includes dividing the status of each monorail crane into different status intervals—healthy, sub-healthy, and unhealthy—based on the comprehensive health status and according to a first health threshold and a second health threshold; the cloud platform converts the comprehensive health status of each monorail crane into control parameters based on the status interval of each monorail crane.

6. The method according to claim 5, characterized in that, The control parameters include the dynamic increment of the safety distance. Maximum permissible speed and task permission flags; The cloud platform converts the overall health status of each monorail gantry into control parameters based on the status range of each monorail gantry, including: The task permission flag is a corresponding permission code flag assigned to the vehicle by the cloud based on the overall health of each monorail. When each monorail is in the healthy range, the task permission flag is full permission; when it is in the sub-healthy range, the task permission flag is limited permission; and when it is in the unhealthy range, the task permission flag is locked permission. The dynamic increment of the safe distance is ; in, The time required for the maximum communication latency in the vehicle-road-cloud closed loop. This is the health decay compensation coefficient. This is the load compensation coefficient. This is the slope compensation coefficient. For rated load, For the current load, For the overall health of the monorail, This is the absolute value of the slope. To collect vehicle speed; Maximum permissible speed of monorail The overall health status of the monorail is continuously reduced within the healthy range; when the overall health status of the monorail is in the sub-health range, the maximum permissible speed is... ,in This refers to the rated maximum operating speed of the monorail under healthy conditions. The basic speed limit coefficient at the entrance to the sub-health zone The sensitivity coefficient for the rate of depreciation corresponding to the decline in health. The first health threshold is set; when the overall health of each monorail is in the unhealthy range, the monorail crawls at a low speed less than a preset constant.

7. The method according to claim 1, characterized in that, During the meeting of trains, the control of each monorail gantry according to control parameters is used to achieve coordinated scheduling and obstacle avoidance, including: The cloud calculates the meeting time between the two vehicles based on the current time; The cloud-based system calculates the shortest complete braking time for both vehicles and the difference in their health status based on their load, gradient, and health status. Based on the meeting point, shortest complete braking time, health difference, and task priority of the two vehicles, the meeting scheduling and obstacle avoidance are performed.

8. The method according to claim 1, characterized in that, The method of controlling each monorail according to control parameters to achieve coordinated scheduling and obstacle avoidance also includes: When communication is interrupted, after the monorail enters islanded mode, the following actions are executed: Freeze the last overall health status, dynamic increment of safe distance, maximum allowed speed, and task permission flag obtained from the cloud, and cache the obtained status data; The independent safety distance is superimposed on the last obtained dynamic increment of the safety distance to serve as the current dynamic increment of the safety distance. Set the preset extremely low crawling speed as the current maximum allowed speed; Refuse to add new task instructions; Once communication is restored, the monorail will upload the cached status data, and the cloud will recalculate the overall health status to obtain new control parameters; the current control parameters in the isolated state will smoothly transition to the new control parameters.

9. The method according to claim 8, characterized in that, The current dynamic increment of safe distance is obtained by superimposing an independent safe distance on the last obtained dynamic increment of safe distance, including: The current dynamic increment of the safe distance is ,in The last obtained dynamic increment of the safe distance, To maintain an independent safety distance; in, Meets the ultimate physical requirements for slope erosion control on downhill slopes: in, This is the minimum allowable wheel-rail adhesion coefficient in the mine. This represents the maximum downhill gradient that the route may encounter ahead. The last obtained speed of the monorail. For edge safety distance, This is the acceleration due to gravity.

10. A health-state-based monorail crane road-cloud collaborative scheduling and obstacle avoidance system, characterized in that, The monorail crane road-cloud collaborative scheduling and obstacle avoidance system includes an onboard control terminal, roadside edge nodes, and a cloud-based operation and scheduling platform; among which... The vehicle-mounted control terminal is located on each monorail and is used to collect the status data of the corresponding monorail. The roadside edge nodes are used to collect the status data of each monorail and upload it to the cloud. The cloud-based operation and maintenance and scheduling platform is used to perform the following: calculate the brake health, motor health, and load-gradient influence factor of each monorail based on the status data of each monorail; obtain the comprehensive health of each monorail based on the brake health, motor health, and load-gradient influence factor of each monorail; and convert the comprehensive health of each monorail into control parameters. The on-board control terminal is also used to perform coordinated scheduling and obstacle avoidance of the monorail based on spatiotemporal conflicts, according to control parameters.

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