Elevator control method and system based on Internet of Things

Abstract

The invention relates to the technical field of elevator control, in particular to an elevator control method and system based on the Internet of Things. The method comprises the following steps: acquiring operation condition data of the elevator at the current moment in real time; based on the operation condition data, a dynamic boundary model is constructed, and the model defines a feasible solution space of control parameters under the current condition in real time according to load constraints and heat dissipation constraints; in the feasible solution space, executing an improved tabu search algorithm, and searching and determining an optimal control parameter for the current lifting task by taking the minimization of a comprehensive cost function as a target; and finally, the optimal control parameters are output to drive the elevator to complete the lifting task. The control performance, the safety, the energy efficiency and the environment adaptability of the elevator in a complex and changeable environment are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of hoist control technology. More specifically, this invention relates to a control method and system for a hoist based on the Internet of Things (IoT). Background Technology

[0002] As a core vertical transportation device in construction sites, mines, ports, and other locations, the control precision and operating efficiency of hoists directly affect production progress and safety. Modern hoists mainly adopt a conveyor belt structure, using a motor to drive the conveyor belt to achieve vertical or inclined material transport.

[0003] Existing hoist control technology primarily relies on PLC-based programmed control. This control method drives the hoist's operation through pre-set fixed parameters, such as a constant operating speed and acceleration. During initial equipment startup, technicians, based on theoretical calculations and experience, set a relatively conservative set of operating parameters to ensure safe operation under various possible load conditions. This approach is mature and reliable at the hardware level, meeting basic material hoisting needs. However, as modern industrial production places higher demands on efficiency, energy consumption, and safety, the drawbacks of this traditional control method are becoming increasingly apparent.

[0004] Its core flaw lies in the contradiction between the rigidity of its control logic and the dynamic nature of the operating environment. First, control strategies based on fixed parameters cannot adapt to real-time changes in actual working conditions. Key parameters such as material flow rate, humidity, conveyor belt tension, and motor temperature fluctuate continuously during lifting operations. Traditional PLC systems lack the ability to sense this dynamic data and can only operate according to parameters designed for the worst-case scenario. This results in operating efficiency far below the equipment's potential under light loads or good operating conditions, causing serious energy waste and time cost losses. On the other hand, under heavy loads or poor equipment conditions, the parameters may be too aggressive, approaching or even exceeding safety thresholds, leading to safety hazards. Summary of the Invention

[0005] The purpose of this invention is to propose a control method and system for hoists based on the Internet of Things, in order to solve the problems of poor optimization effect of hoist control parameters, insufficient environmental adaptability, and difficulty in balancing safety and efficiency in the prior art.

[0006] This invention provides a control method for a hoist based on the Internet of Things (IoT). The control method includes: acquiring the hoist's operating condition data at the current moment; constructing a dynamic boundary model based on the operating condition data, the model being used to limit the feasible solution space of control parameters under the current operating condition; executing an improved tabu search algorithm within the feasible solution space, with the goal of minimizing the comprehensive cost function, to search for and determine the optimal control parameters for the current hoisting task; and driving the hoist to complete the hoisting task according to the optimal control parameters. The improved tabu search algorithm includes employing an adaptive tabu list, the tabu step size of which is dynamically adjusted according to the comprehensive volatility index of the operating condition data, the comprehensive volatility index being used to characterize the recent volatility level. The improved tabu search algorithm also includes employing an opportunistic amnesty criterion, whereby when the comprehensive volatility index exceeds a preset high volatility threshold, the taboo solutions in the tabu list are re-evaluated for cost under the current operating condition; if the cost evaluation is optimal, the taboo solutions are amended.

[0007] Optionally, the operating condition data includes at least one of the following: current material flow rate, conveyor belt tension, motor temperature, ambient temperature, ambient humidity, conveyor belt speed, drive wheel torque, and material moisture content.

[0008] Optionally, the process of constructing the dynamic boundary model includes: calculating a first speed limit and an acceleration limit based on the current material flow rate and conveyor belt tension; calculating a second speed limit under heat dissipation constraints based on motor temperature and ambient temperature; determining the speed feasible region and the acceleration feasible region, wherein the maximum value of the speed feasible region is the minimum value of the first speed limit and the second speed limit, and the minimum value of the speed feasible region is 0; the maximum value of the acceleration feasible region is the acceleration limit, and the minimum value is 0.

[0009] The aforementioned dynamic boundary model can adjust the feasible range of control parameters in real time based on IoT data, maximizing efficiency and energy efficiency while ensuring that the motor does not overheat and the equipment is not overloaded, thus achieving a dynamic balance between safety and economy.

[0010] Optionally, the method for constructing the adaptive tabu list includes: collecting key operating condition parameters within the most recent N time windows, the key operating condition parameters including material flow rate, motor temperature, and conveyor belt tension; calculating the standard deviation of each key operating condition parameter and defining a comprehensive volatility index as the weighted sum of the standard deviations of each parameter; determining the operating condition volatility level based on the magnitude of the comprehensive volatility index, the operating condition volatility level including low volatility, medium volatility, and high volatility; using the minimum tabu step size when the operating condition volatility level is low volatility, using the medium tabu step size when the operating condition volatility level is medium volatility, and using the maximum tabu step size when the operating condition volatility level is high volatility.

[0011] The aforementioned adaptive adjustment strategy enables the tabu list to dynamically adjust its memory depth based on environmental stability. When the environment changes drastically, the tabu period is extended to avoid frequent backtracking, while when the environment is stable, the tabu period is shortened to accelerate convergence.

[0012] Optionally, the opportunistic amnesty criterion includes: comparing a comprehensive volatility index calculated in real time based on the operating condition data with a preset high volatility threshold; if the comprehensive volatility index exceeds the high volatility threshold, then traversing all forbidden solutions in the taboo list; for each forbidden solution, re-evaluating its cost function value under the dynamic boundary model of the current operating condition; if its cost function value is better than the cost function value of the current solution and satisfies the feasibility constraint, then removing it from the taboo list to allow it to be selected.

[0013] The aforementioned opportunistic amnesty mechanism can proactively capture optimization opportunities brought about by environmental improvements, avoid missing efficient control strategies due to taboo constraints, and significantly improve the algorithm's response speed and optimization effect to environmental changes.

[0014] Optionally, the comprehensive cost function is a weighted sum of operating time cost, energy consumption cost, and safety risk cost, wherein the weight coefficient of each cost component is adjusted according to the operating mode.

[0015] The comprehensive cost function improves the direct impact of the three core indicators—efficiency, energy consumption, and safety—on the operating speed control parameters to be optimized.

[0016] Optionally, the control parameters include maximum acceleration and steady-state operating speed.

[0017] Optionally, it also includes continuously monitoring operating condition data during the execution of the lifting task. When the comprehensive fluctuation index is detected to be greater than the preset high fluctuation threshold, the dynamic boundary model is recalculated. If the difference between the recalculated feasible solution space and the original feasible solution space exceeds the preset threshold, the execution of the current control parameters is interrupted and the tabu search algorithm is restarted for online optimization. The hoist is then driven to complete the lifting task based on the optimized new control parameters.

[0018] Optionally, a control system for an IoT-based hoist further includes: a processor for running a computer program; and a memory storing the computer program.

[0019] The beneficial effects of this invention are as follows: The solution of this invention can deeply integrate real-time environmental condition perception with tabu search algorithm, realize real-time adaptive adjustment of feasible solution space through dynamic boundary model, dynamically adjust search memory depth according to the fluctuation of operating conditions through environmental adaptive tabu table, and actively capture optimization opportunities brought about by environmental improvement through opportunistic amnesty criterion. Thus, it realizes environmentally-aware intelligent optimization of hoist control parameters, solves the problems of safety and efficiency contradiction caused by fixed constraints, low search efficiency caused by fixed tabu step size, and optimization lag caused by lack of opportunity recognition in traditional methods, and significantly improves the control performance, safety, energy efficiency and environmental adaptability of hoist in complex and variable environments. Attached Figure Description

[0020] Figure 1 A flowchart of an IoT-based hoist control method according to an embodiment of this application. Detailed Implementation

[0021] This invention discloses a control method and system for a hoist based on the Internet of Things (IoT), comprising the following steps: S1: Obtain the operating condition data of the hoist at the current moment.

[0022] When the elevator starts, an IoT sensor network deployed at key locations on the elevator collects real-time operating data. In this embodiment, the elevator is a material hoist. The operating data includes, but is not limited to: current material flow rate, conveyor belt tension, motor temperature, ambient temperature, conveyor belt speed, and material moisture content.

[0023] Specifically, material flow rate is acquired via a laser scanning sensor installed at the feed inlet, measured in tons per hour; conveyor belt tension is monitored in real time by a tension sensor, measured in kilonewtons; motor temperature is collected by a thermocouple temperature sensor, measured in degrees Celsius; ambient temperature and humidity are acquired through an environmental monitoring station; conveyor belt speed is measured by an encoder, measured in meters per second; drive wheel torque is acquired by a torque sensor, measured in Newton-meters; and material moisture content is detected by a microwave humidity sensor, measured as a percentage. All sensor data is aggregated through an industrial IoT gateway and uploaded to an edge computing node for preprocessing and storage. The data acquisition frequency is set to 1 Hz to ensure the control system can promptly detect changes in operating conditions. A sliding data window containing the most recent 60 data points is maintained for subsequent analysis of operating condition fluctuations.

[0024] S2: Based on the operating condition data, construct a dynamic boundary model, which is used to limit the feasible solution space of the control parameters under the current operating condition.

[0025] First, calculate the upper limit of the first speed and the upper limit of acceleration under load constraints based on the current material flow rate and conveyor belt tension. Specifically, the upper limit of the first speed... and acceleration limit The calculation expression is:

[0026] In the formula, in the formula, This is the upper limit of the first speed, expressed in meters per second. This represents the maximum permissible tension of the conveyor belt, measured in kilonewtons (kN), and is a preset safety threshold based on the equipment specifications. It should be noted that the coefficient... , , These are the inherent physical characteristic parameters of the system, which can be predetermined through preset calibration or theoretical modeling and stored in the controller. The basic tension of the hoist, measured in Newtons. The effect of material flow rate on tension is expressed in force / (mass / time). The formula represents the effect of operating speed on tension, expressed in kilograms per second. Its physical meaning lies in: from the maximum safe tension... Subtract the tension determined by the base load and the current material load from the remaining tension, and then divide the remaining tension by the speed influence coefficient. This gives the highest safe speed that can be achieved under the current operating conditions.

[0027]

[0028] in, This is the upper limit of acceleration, expressed in meters per second². This is the maximum permissible torque of the drive motor, measured in Newton-meters, and is a preset performance threshold based on the motor's performance. The current material flow rate is measured in real time by the sensor, in tons per hour; The speed of the conveyor belt is measured in real time by the sensor at the current moment, in meters per second; , , is the torque characteristic coefficient of the system. This represents the system's equivalent inertia coefficient, with units of kilogram-meter. Similarly, the coefficient... , , , These are also inherent physical characteristic parameters of the system, which can be predetermined through preset calibration or theoretical modeling. The physical meaning of this formula is: subtracting the base torque required to maintain uniform speed operation under the current conditions from the maximum output torque of the motor yields the remaining torque available for acceleration; then dividing this remaining torque by the system's equivalent inertia coefficient... This allows us to obtain the maximum achievable linear acceleration. This calculation ensures that the conveyor belt tension will not exceed a safety threshold under the current material load.

[0029] Secondly, a second speed limit under heat dissipation constraints is calculated based on the motor temperature and ambient temperature. When the motor temperature is below the warning temperature, the second speed limit is set to the rated speed of the equipment; when the motor temperature is between the warning temperature and the critical temperature, the second speed limit is limited to 30% of the rated speed; when the motor temperature reaches or exceeds the critical temperature, the second speed limit is forcibly limited to the minimum safe speed. For example, the warning temperature is set to 75 degrees Celsius, the critical temperature is set to 85 degrees Celsius, and the minimum safe speed is set to 0. This mechanism ensures that the motor increases its heat dissipation time by reducing its speed under high-temperature operating conditions, achieving temperature self-regulation and protection.

[0030] Finally, the feasible regions for speed and acceleration are determined by combining the above two constraints. The lower limit of the feasible region for speed is 0, and the upper limit is the minimum of the first and second speed upper limits. The lower limit of the feasible region for acceleration is 0, and the upper limit is the upper limit of acceleration under load constraints. Through the above dynamic boundary model, the feasible range of control parameters can be adaptively adjusted according to real-time operating conditions, maximizing operating efficiency and energy efficiency while ensuring that the motor does not overheat and the equipment is not overloaded, thus achieving a dynamic balance between safety and economy.

[0031] S3: Calculate the recent volatility of operating condition data and determine the level of operating condition volatility.

[0032] To enable the tabu search algorithm to adapt to drastic environmental changes, it is necessary to quantify the recent volatility of operating conditions. First, key operating parameters, including material flow rate, motor temperature, and conveyor belt tension, are extracted from the sliding data window over the most recent 60 time steps. Then, the standard deviation of each key operating parameter is calculated; the standard deviation reflects the dispersion of the parameter within the observation time window. Next, the standard deviations of each parameter are normalized and then weighted and summed to obtain a comprehensive volatility index. The normalization process divides the standard deviation of each parameter by its normal fluctuation range, making parameters with different dimensions comparable. The expression for the comprehensive volatility index is:

[0033] In the formula, This represents a comprehensive volatility indicator; , , These represent the standard deviations of material flow rate, motor temperature, and conveyor belt tension, respectively. , , These represent the normal fluctuation range of material flow rate, motor temperature, and conveyor belt tension, respectively. The normal fluctuation range is the difference between the historical maximum and historical minimum values ​​of the corresponding operating parameters when the elevator is in a stable operating state within a preset historical statistical period. , , The weighting coefficients for each parameter are 0.4, 0.3, and 0.3, respectively, with a sum of 1. Finally, based on the magnitude of the comprehensive volatility index, the operating condition volatility level is determined using a trisection method: low volatility, medium volatility, and high volatility. The operating condition volatility level directly affects the tabu step size setting in the tabu search algorithm, enabling the algorithm to dynamically adjust its search strategy according to environmental stability.

[0034] S4: Within the feasible solution space, execute an improved tabu search algorithm to search for and determine the optimal control parameters for the current boosting task, with the goal of minimizing the overall cost function.

[0035] First, a comprehensive cost function is constructed as the optimization objective. The comprehensive cost function consists of three components: operating time cost, energy consumption cost, and safety risk cost. The expression for the comprehensive cost function is:

[0036] In the formula, Indicates total cost; Indicates runtime cost; Indicates energy consumption cost; Indicates the cost of security risks; , , These are weighting coefficients, whose values ​​are adjusted according to the operating mode. For example, in high-efficiency mode, the weights are set to 0.6, 0.2, and 0.2; in energy-saving mode, they are set to 0.2, 0.6, and 0.2; and in safety mode, they are set to 0.2, 0.2, and 0.6. The operating time cost is calculated by dividing the height required for one lift by the stable operating speed to be optimized. The energy consumption cost is obtained by multiplying the time cost required for one lift by the rated power. The safety risk cost is obtained by weighted summation of the ratio of the current acceleration to the acceleration limit and the ratio of the current speed to the speed limit. The comprehensive cost function clarifies the direct impact of the operating speed control parameters to be optimized on the three core indicators of efficiency, energy consumption, and safety.

[0037] First, a random initial running velocity that satisfies the current dynamic boundary constraints is generated, with the maximum acceleration set to 0. The initial running velocity is then verified to meet the constraints of the feasible solution space constructed in step S2. If it does, it serves as the starting point for the search; otherwise, fine-tuning is performed until feasibility is achieved.

[0038] Next, the adaptive tabu table is initialized. The tabu table is initialized as an empty set to record recently accessed control parameter adjustments, preventing the algorithm from getting stuck in local loops. The tabu table uses a first-in, first-out (FIFO) queue structure, and its capacity, i.e., the tabu step size, is set according to the operating condition fluctuation level determined in step S3. When the operating condition fluctuation level is low, the tabu step size is set to 5; when the operating condition fluctuation level is medium, the tabu step size is set to 10; and when the operating condition fluctuation level is high, the tabu step size is set to 15. This adaptive strategy allows the tabu table to dynamically adjust its memory depth based on environmental stability. When the environment changes drastically, the tabu period is extended to avoid the algorithm frequently backtracking to previously explored but unsatisfactory solutions; when the environment is stable, the tabu period is shortened to accelerate convergence.

[0039] Finally, the algorithm termination conditions are set. The maximum number of iterations is set to 100, and the threshold for consecutive generations without improvement is set to 20. If no better solution is found for 20 consecutive generations, the algorithm is considered to have converged.

[0040] Based on the current solution, by slightly increasing or decreasing the running speed and maximum acceleration (for example, increasing or decreasing the speed by 0.1 m / s and the acceleration by 0.05 m / s²), a set of 20 to 30 candidate solutions is generated. Then, the candidate solution set is traversed, and a feasibility check is performed on each candidate solution, eliminating solutions that do not meet the constraints of the dynamic boundary model constructed in step S2.

[0041] Next, tabu checks and exceptions are performed on feasible candidate solutions. First, for each feasible candidate solution, it is checked whether the parameter adjustment action from the current solution to that candidate solution is in the current tabu list. If it is in the tabu list, it is marked as a tabu solution; otherwise, it is marked as a non-tabuation solution. At the same time, the comprehensive volatility index calculated in real time in step S3 is compared with a preset high volatility threshold. For example, the high volatility threshold can be set to 0.8.

[0042] If the overall volatility index exceeds the preset high volatility threshold, indicating that the operating condition has entered an unstable state, the amnesty criterion is triggered, and all forbidden actions in the taboo list are iterated. For each forbidden action and the corresponding combination of control parameters, the overall cost function value is re-evaluated under the dynamic boundary model of the current new operating condition.

[0043] After tabu checks and amnesty decisions, the candidate solution with the smallest comprehensive cost function value is selected as the new current solution from the set of candidate solutions containing both non-tabuated and amnestized solutions. Parameter adjustment actions from this iteration are added to the end of the tabu list; if the tabu list length exceeds the current tabu step size, the earliest added action is removed.

[0044] Check if any of the following termination conditions are met: reaching the maximum number of iterations (100) or having no improvement for more than 20 consecutive iterations. If either termination condition is met, output the global optimal solution; otherwise, increment the iteration counter and return to the candidate solution generation step to continue the search.

[0045] S5: Drive the hoist to complete the hoisting task according to the optimal control parameters.

[0046] Once the tabu search algorithm meets the termination condition, it outputs the globally optimal solution as the optimal running speed for the current lifting task. Control commands are sent to actuators such as frequency converters and brakes via the industrial fieldbus, driving the hoist to complete the lifting task according to the optimized scheme.

[0047] Through the above method, the present invention realizes intelligent optimization control of the hoist based on IoT data fusion and environmental adaptive tabu search, which significantly improves the control performance, safety, energy efficiency and environmental adaptability of the hoist in complex and ever-changing environments.

[0048] According to a second aspect of this application, this application also provides a control system for an IoT-based hoist. The system includes a processor and a memory, the memory storing computer program instructions. When the computer program instructions are executed by the processor, they implement the IoT-based hoist control method described in the first aspect of this application. The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface; their configuration and functions are known in the art and will not be described further here.

[0049] 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 equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be within the scope of protection of the present invention.

Claims

1. A control method for a hoist based on the Internet of Things, characterized in that, The control method includes: acquiring the operating condition data of the hoist at the current moment; Based on the operating condition data, a dynamic boundary model is constructed, which is used to limit the feasible solution space of the control parameters under the current operating condition; Within the feasible solution space, an improved tabu search algorithm is executed to search for and determine the optimal control parameters for the current boosting task with the goal of minimizing the overall cost function. The hoist is driven to complete the hoisting task based on the optimal control parameters. The improved tabu search algorithm includes the use of an adaptive tabu table, wherein the tabu step size of the adaptive tabu table is dynamically adjusted according to the comprehensive volatility index of the operating condition data, and the comprehensive volatility index is used to characterize the degree of recent volatility. The improved tabu search algorithm also includes an opportunistic amnesty criterion. When the comprehensive volatility index exceeds a preset high volatility threshold, the tabu solutions in the tabu list are re-evaluated for cost under the current operating conditions. If the cost evaluation is good, the tabu solutions are amended.

2. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The operating condition data includes at least one of the following: current material flow rate, conveyor belt tension, motor temperature, ambient temperature, ambient humidity, conveyor belt speed, drive wheel torque, and material moisture content.

3. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The construction process of the dynamic boundary model includes: Calculate the first speed limit and acceleration limit based on the current material flow rate and conveyor belt tension; calculate the second speed limit under heat dissipation constraints based on the motor temperature and ambient temperature. Determine the feasible region of velocity and the feasible region of acceleration. The maximum value of the feasible region of velocity is the minimum value of the first upper limit of velocity and the second upper limit of velocity, and the minimum value of the feasible region of velocity is 0. The maximum value of the feasible region of acceleration is the upper limit of acceleration, and the minimum value of acceleration is 0.

4. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The method for constructing the adaptive tabu list includes: Collect key operating parameters within the most recent N time windows, including material flow rate, motor temperature, and conveyor belt tension. Calculate the standard deviation of each key operating condition parameter and define the comprehensive volatility index as the weighted sum of the standard deviations of each parameter; The operating condition volatility level is determined based on the magnitude of the comprehensive volatility index, and the operating condition volatility level includes low volatility, medium volatility, and high volatility. The minimum forbidden step size is used when the operating condition fluctuation level is low, the medium forbidden step size is used when the operating condition fluctuation level is medium, and the maximum forbidden step size is used when the operating condition fluctuation level is high.

5. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The criteria for the opportunistic pardon include: The comprehensive volatility index, calculated in real time based on the operating condition data, is compared with a preset high volatility threshold. If the overall volatility index exceeds the high volatility threshold, then all forbidden solutions in the tabu list are traversed. For each taboo solution, its cost function value is re-evaluated under the dynamic boundary model of the current working condition. If its cost function value is better than the cost function value of the current solution and satisfies the feasibility constraint, it is removed from the taboo list to be allowed to be selected.

6. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The comprehensive cost function is a weighted sum of operating time cost, energy consumption cost, and safety risk cost, wherein the weight coefficient of each cost component is adjusted according to a preset operating mode.

7. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The control parameters include stable operating speed and maximum acceleration.

8. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The control method further includes: During the execution of the task, the operating condition data is continuously monitored. When the comprehensive fluctuation index is detected to be greater than the preset high fluctuation threshold, the dynamic boundary model is recalculated. If the difference between the recalculated feasible solution space and the original feasible solution space exceeds a preset threshold, the execution of the current control parameters is interrupted and the tabu search algorithm is restarted for online optimization. The hoist is then driven to complete the hoisting task based on the optimized new control parameters.

9. The control method for a hoist based on the Internet of Things according to claim 1, characterized in that, The calculation expression for the comprehensive volatility index is as follows: ； In the formula, This represents a comprehensive volatility indicator; , , These represent the standard deviations of material flow rate, motor temperature, and conveyor belt tension in the operating condition data, respectively. , , These represent the normal fluctuation range of material flow rate, motor temperature, and conveyor belt tension, respectively. , , These are the weighting coefficients for each parameter.

10. A control system for a hoist based on the Internet of Things, characterized in that, include: processor; A memory, wherein a computer program is stored; Wherein, when the processor is configured to execute the computer program, it implements a control method for an IoT-based hoist as described in any one of claims 1 to 9.

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