Intelligent control method and system for elevator car door motor control box

Abstract

The embodiment of the invention provides an intelligent control method and system for an elevator car door motor control box. The method comprises the steps that sensor data of various sensors are collected; an operation sign oscillogram is updated based on the sensor data, and a parameterized portal crane digital model is dynamically calibrated according to the operation sign oscillogram; after a door opening and closing instruction is received, simulation running operation of a target motion curve is conducted in the door machine digital model, a simulation result is obtained, the needed feed-forward compensation amount is calculated according to the simulation result and the current parameters of the door machine digital model, the feed-forward compensation amount and the closed-loop control output parameters are fused, and the door opening and closing instruction is obtained. An actuator control instruction is generated; abstracting a task corresponding to an actuator control instruction and a control box task into a heterogeneous computing task, and abstracting a control box computing unit into a heterogeneous computing resource; scene self-adaptive intelligent allocation of computing resources is realized, and scene self-adaptive intelligent scheduling is realized through a multi-objective optimization engine.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an intelligent control method for an elevator car door operator control box, an intelligent control system for an elevator car door operator control box, a computer device, and a computer-readable storage medium. Background Technology

[0002] Modern elevators are complex mechatronic systems, mainly divided into multiple mechanical and electrical systems, such as the traction system, guiding system, car system, door operator control system, weight balancing system, and safety protection system. Traditional elevator door operator control systems typically operate based on fixed control parameters, such as PID parameters or parameters corresponding to motion curves. Their control logic is fixed after installation and commissioning. This static control mode has significant drawbacks: First, the system cannot detect and compensate for dynamic disturbances caused by mechanical wear, track deviation, load changes, etc., in real time. This leads to problems such as incomplete door closing, increased operating noise, and frequent false triggering of torque protection after long-term operation. Control accuracy also degrades over time and with changing operating conditions. Summary of the Invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide an intelligent control method for an elevator car door operator control box, an intelligent control system for an elevator car door operator control box, a computer device, and a computer-readable storage medium to overcome or at least partially solve the above problems.

[0004] To address the aforementioned problems, this invention discloses an intelligent control method for elevator car door operator control boxes, applied to an elevator car door operator control system. The elevator car door operator control system is connected to multiple elevator door operators corresponding to various elevator car door operator control boxes. Each elevator door operator is equipped with multiple sensors. The method includes:

[0005] Collect sensor data from multiple sensors;

[0006] The operating condition waveform is updated based on the sensor data, and the parameterized gantry crane digital model is dynamically calibrated based on the operating condition waveform.

[0007] After receiving the door opening / closing command, the target motion curve is simulated in the calibrated door operator digital model to obtain the simulation results. Based on the simulation results and the current parameters of the door operator digital model, the required feedforward compensation amount is calculated. The feedforward compensation amount is then fused with the closed-loop control output parameters to generate actuator control commands.

[0008] The tasks corresponding to the actuator control commands and the control box tasks are abstracted into heterogeneous computing tasks, and the control box computing units are abstracted into heterogeneous computing resources.

[0009] Based on the preset current operating mode and the real-time system load, a multi-objective optimization scheduling engine dynamically allocates computing resources to each heterogeneous computing task. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The engine solves for the current optimal task scheme and adjusts the task scheduling strategy accordingly.

[0010] Preferably, the multiple sensors include an encoder, a current sensor, a frame vibration sensor, a micro-sensor of the contact force between the door knife and the door panel, a drive motor winding temperature sensor, and an ambient noise microphone; the acquisition of sensor data from the multiple sensors includes:

[0011] It collects position signals, current signals, vibration spectrum signals, contact force signals, and audio waveform signals from various sensors.

[0012] Preferably, the step of updating the operational vital sign waveform based on the sensor data, and dynamically calibrating the parameterized gantry crane digital model based on the operational vital sign waveform, includes:

[0013] The collected synchronization signals are fused according to the time sequence to form a single-run characteristic waveform diagram containing multi-dimensional correlation information; the multi-dimensional correlation information includes correlation information of torque, position, vibration, and audio.

[0014] By dynamically adjusting the operating characteristic parameters in the door operator digital model through reverse system identification, the error between the output response of the door operator digital model and the actual response of the elevator door operator is minimized, and the parameterized door operator digital model is dynamically calibrated.

[0015] Preferably, the method further includes:

[0016] Based on the long-term trend parameters derived from the digital model of the elevator door operator, prediction and early warning are performed to obtain local optimization strategies. These local optimization strategies are then uploaded to the cloud knowledge base, where optimization strategies for similar working conditions are retrieved and integrated, and then output to another elevator door operator.

[0017] Preferably, the prediction and early warning based on long-term trend parameters derived from the digital model of the elevator door operator, to obtain a local optimization strategy, the local optimization strategy is uploaded to a cloud knowledge base, optimization strategies under similar working conditions are retrieved and integrated from the cloud knowledge base, and then output to another elevator door operator, including:

[0018] Obtain the mean value of the operating data derived from the digital model of the gantry crane to form a time series of operating data;

[0019] The time series data of the running data is trained using a lightweight LSTM model to obtain the prediction model parameters. When the predicted value exceeds the maintenance threshold, the corresponding early warning information is generated.

[0020] The combination of feedforward compensation amounts under different local loads, as well as the prediction model parameters, are uploaded to the cloud-based group knowledge base.

[0021] When an elevator door operator sends the current environment and operating condition characteristics to the cloud knowledge base;

[0022] Similarity matching is performed in the cloud knowledge base, and the historical optimization strategy set or model parameters with the highest matching degree are sent to the elevator door operator to control the elevator door operator to run the operation using the historical optimization strategy set or model parameters.

[0023] Preferably, after receiving the door opening / closing command, the target motion curve is simulated in the door operator digital model to obtain simulation results. Based on the simulation results and the current parameters of the door operator digital model, the required feedforward compensation is calculated. The feedforward compensation is then fused with the closed-loop control output parameters to generate actuator control commands, including:

[0024] Obtain the preset target motion curve data;

[0025] Before each door opening and closing operation, the target motion curve data is input into the calibrated door operator digital model to obtain the simulation operation of the door opening and closing process and the simulation results.

[0026] The difference between the motor output torque curve in the simulation results and the theoretical torque curve under no disturbance is calculated to obtain the feedforward compensation torque curve; wherein, the feedforward compensation torque curve includes the feedforward compensation amount;

[0027] The feedforward compensation torque curve is superimposed with the torque command of the closed-loop PID control output parameters to obtain the final torque setpoint, and the actuator control command is generated.

[0028] Preferably, based on the preset current operating mode and the real-time system load, a multi-objective optimization scheduling engine dynamically allocates computing resources to each heterogeneous computing task; the optimization objectives of the multi-objective optimization scheduling engine include minimizing the gate opening / closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing control tracking accuracy; solving for the current optimal task scheme and adjusting the task scheduling strategy includes:

[0029] The tasks corresponding to actuator control instructions, safety monitoring tasks, fault diagnosis tasks, and communication tasks are abstracted into heterogeneous computing tasks with different requirements.

[0030] The processor's multiple performance cores and energy efficiency cores, as well as the FPGA units used for signal processing, are abstracted into heterogeneous computing resources with different characteristics;

[0031] The multi-objective optimization scheduling engine receives working condition instructions and monitors the real-time load rate of each computing core. The multi-objective optimization scheduling engine aims to minimize the gate opening and closing cycle time, minimize the peak processor load, minimize the total system energy consumption, and maximize the control tracking accuracy. It uses a lightweight multi-objective evolutionary algorithm to solve for the current optimal task core allocation scheme and execution time slice.

[0032] Based on the optimal task core allocation scheme and execution time slice, the task scheduling strategy of the operating system or underlying driver is adjusted in real time to bind high-priority core control tasks to high-performance cores and migrate low-priority core control tasks to energy-efficient cores.

[0033] This invention discloses an intelligent control system for an elevator car door operator control box, applied to an elevator car door operator control system. The elevator car door operator control system is connected to multiple elevator door operators corresponding to various elevator car door operator control boxes. Each elevator door operator is equipped with multiple sensors. The system includes:

[0034] The data acquisition module is used to acquire sensor data from various sensors.

[0035] The calibration module is used to update the operating characteristic waveform diagram based on the sensor data, and dynamically calibrate the parameterized gantry crane digital model according to the operating characteristic waveform diagram.

[0036] The feedforward compensation module is used to perform a simulation operation on the target motion curve in the calibrated door operator digital model after receiving the door opening and closing command, obtain the simulation results, calculate the required feedforward compensation amount based on the simulation results and the current parameters of the door operator digital model, and fuse the feedforward compensation amount with the closed-loop control output parameters to generate actuator control commands.

[0037] Heterogeneous modules are used to abstract the tasks corresponding to actuator control commands and control box tasks into heterogeneous computing tasks, and to abstract the control box computing units into heterogeneous computing resources.

[0038] The task scheduling strategy module is used to dynamically allocate computing resources to each heterogeneous computing task based on the preset current working mode and the real-time system load through a multi-objective optimization scheduling engine. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the peak processor load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The engine solves for the current optimal task scheme and adjusts the task scheduling strategy accordingly.

[0039] This invention discloses a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the intelligent control method for the elevator car door operator control box described above.

[0040] This invention discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the intelligent control method for the elevator car door operator control box described above.

[0041] The embodiments of the present invention have the following advantages:

[0042] In this embodiment of the invention, the intelligent control method of the elevator car door operator control box includes: collecting sensor data from multiple sensors; updating the operating characteristic waveform based on the sensor data; dynamically calibrating the parameterized door operator digital model according to the operating characteristic waveform; after receiving a door opening / closing command, performing a simulation operation of the target motion curve in the door operator digital model to obtain simulation results; calculating the required feedforward compensation amount based on the simulation results and the current parameters of the door operator digital model; fusing the feedforward compensation amount with the closed-loop control output parameters to generate actuator control commands; abstracting the task corresponding to the actuator control commands and the control box task into heterogeneous computing tasks; and abstracting the control box computing unit into... Heterogeneous computing resources; based on the preset current operating mode and the real-time system load, a multi-objective optimization scheduling engine dynamically allocates computing resources to each heterogeneous computing task; the optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The optimal task scheme is obtained by solving the problem, and the task scheduling strategy is adjusted. This achieves feedforward compensation based on real-time system state prediction, fundamentally transforming control from passive response to active intervention, significantly improving anti-disturbance capability and trajectory tracking accuracy, and realizing scene-adaptive intelligent allocation of computing resources. The multi-objective optimization engine achieves scene-adaptive intelligent scheduling. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of an embodiment of an intelligent control method for an elevator car door operator control box according to an embodiment of the present invention;

[0045] Figure 2 This is a structural block diagram of an embodiment of an intelligent control system for an elevator car door operator control box according to an embodiment of the present invention;

[0046] Figure 3 This is an internal structural diagram of a computer device according to one embodiment. Detailed Implementation

[0047] To make the technical problems, technical solutions, and beneficial effects solved by the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0048] In one core concept of this invention, a modern gantry crane controller integrates multiple computational tasks such as position loop, speed loop, torque loop, and diagnostic algorithms, and can be considered as a set of heterogeneous control algorithm models. Existing systems employ static scheduling strategies with fixed priorities or simple polling, which cannot dynamically allocate computing resources such as CPU / GPU cores and memory bandwidth according to real-time scenarios, such as prioritizing efficiency during peak hours or prioritizing quiet operation at night. This makes it difficult to achieve a dynamic optimal balance among multiple objectives such as efficiency, energy consumption, noise, and reliability. In the embodiment of this invention, firstly, multiple time curves for the entire door opening and closing process are formed based on data from multi-dimensional sensors. These curves are then used to calibrate the digital model of the door operator, further obtaining feedforward disturbance compensation. After generating corresponding instructions, the task scheduling strategy is obtained through a multi-objective intelligent scheduling engine for heterogeneous control tasks. This achieves feedforward compensation based on real-time system state prediction, fundamentally transforming control from passive response to active intervention, significantly improving anti-disturbance capability and trajectory tracking accuracy. It also enables scene-adaptive intelligent allocation of computing resources. For example, under unchanged hardware conditions, the system can run faster during peak hours and run more quietly and save more power at night. Scene-adaptive intelligent scheduling is achieved through a multi-objective optimization engine.

[0049] Reference Figure 1 This diagram illustrates an embodiment of an intelligent control method for an elevator car door operator control box according to the present invention. The method is applied to an elevator car door operator control system, which is connected to multiple elevator door operators corresponding to multiple elevator car door operator control boxes. Each elevator door operator is equipped with various sensors, and the method may specifically include the following steps:

[0050] Step S101: Collect sensor data from multiple sensors;

[0051] The elevator in this embodiment of the invention includes an elevator car and guide rails. In addition, it may also include various types of control devices such as a controller. The control device can be connected to various systems of the elevator, including a traction system, a guide system, a door system, a weight balancing system, and an electric drive system.

[0052] The guiding system may include guide rails, which restrict the freedom of movement of the car and counterweight, allowing them to move vertically only along the guide rails. The guiding system may also include components such as guide shoes and guide rail frames. Additionally, the traction system may include traction wire ropes, guide pulleys, and deflector pulleys, which are primarily responsible for outputting and transmitting power to enable the elevator to move up and down.

[0053] The door system includes a car door, landing door, elevator door operator and door lock device, and an elevator car door operator control box, used to close or open the car entrance; the elevator car door operator control box operates an elevator car door operator control system; the elevator car door operator control system is connected to multiple elevator car door operator control boxes corresponding to elevator door operators, that is, the elevator car door operator control system can control the operation of multiple elevator door operators.

[0054] The weight balancing system can include a counterweight and a weight compensation device. The weight balancing system can balance the weight of the car and ensure the normal operation of the elevator's traction drive. The electric drive system is a system that works in conjunction with the traction system. It mainly includes a traction motor, a power supply system, a speed feedback device, and a motor speed control device. The traction motor is connected to the traction steel wire rope of the traction system to provide power for the elevator's up and down movement.

[0055] On the other hand, the control device can also be connected to devices such as a position display device, control panel, and floor selector to control the displayed information; in addition, the home elevator may also include other safety protection devices, such as speed governor, safety brake, buffer, and terminal station protection device, etc., and the embodiments of the present invention do not impose too many restrictions on this.

[0056] In this embodiment of the invention, the elevator door operator is equipped with multiple sensors, including an encoder, a current sensor, a frame vibration sensor, a micro-sensor of the contact force between the door blade and the door panel, a drive motor winding temperature sensor, and an ambient noise microphone. This embodiment of the invention does not impose excessive limitations on these sensors. Furthermore, the collection of sensor data from these multiple sensors includes:

[0057] It collects position signals, current signals, vibration spectrum signals, contact force signals, and audio waveform signals from various sensors.

[0058] Step S102: Update the operating vital signs waveform diagram based on the sensor data, and dynamically calibrate the parameterized gantry crane digital model according to the operating vital signs waveform diagram.

[0059] In this embodiment of the invention, the position-time curve, torque-time curve, vibration spectrum, and audio waveform of the door operator's operating characteristics waveform can be constructed in real time to record the entire process of each door opening and closing, forming a comprehensive operating characteristics diagram.

[0060] Specifically, in this embodiment of the invention, the step of updating the operational vital sign waveform based on the sensor data, and dynamically calibrating the parameterized gantry crane digital model based on the operational vital sign waveform, includes:

[0061] The collected synchronization signals are fused according to the time sequence to form a single-run characteristic waveform diagram containing multi-dimensional correlation information; the multi-dimensional correlation information includes correlation information of torque, position, vibration, and audio.

[0062] By dynamically adjusting the operational characteristic parameters in the door operator digital model using reverse system identification, the error between the output response of the door operator digital model and the actual response of the elevator door operator is minimized, thus dynamically calibrating the parameterized door operator digital model. Reverse system identification refers to the process of estimating system model parameters using the input and output data of the actual system.

[0063] In this embodiment of the invention, the single-run vital sign waveform refers to a multi-dimensional time-series data set formed by synchronously collecting and fusing multiple sensors during a single complete door opening and closing cycle of the elevator door operator. It not only includes the traditional motion trajectory but also integrates multi-dimensional information reflecting mechanical state, environmental interaction, and acoustic characteristics, serving as the data foundation for subsequent state identification, fault diagnosis, and intelligent compensation.

[0064] First, synchronous triggering and high-speed data acquisition: when the elevator door operator begins a door opening / closing action, the control system sends a synchronous trigger signal to all sensors to ensure that the start times of all data acquisitions are aligned. Each sensor acquires data at its highest sampling frequency.

[0065] Among them, the encoder can collect high-precision data on the change of the gate operator's position (angle or displacement) over time, forming a position-time curve;

[0066] A current sensor can collect the motor drive current, indirectly reflecting the output torque and forming a torque-time curve.

[0067] Vibration sensors can collect the vibration acceleration of the frame in three axes and obtain the vibration spectrum over time through Fast Fourier Transform (FFT).

[0068] The contact force microsensor can collect minute force changes when the door knife contacts the door panel, forming a contact force-time curve. The ambient microphone can collect sound signals during operation, and after noise reduction processing, obtain the audio waveform-time curve.

[0069] Since different sensors may have different sampling frequencies, interpolation algorithms are needed to unify all data onto the same time axis. For example, using the highest sampling rate as a reference (such as 10kHz for a vibration sensor), other signals are aligned to the same time point using linear or spline interpolation.

[0070] The aligned data is organized into a multidimensional vector according to time points. At any given time... The data vector can be represented as:

[0071] ;

[0072] Arranging the vectors of all time points in chronological order creates a time-series matrix containing multi-dimensional correlation information of torque, position, vibration, and audio, yielding a waveform diagram of the vital signs for a single operation. These dimensions are physically correlated; for example, the phase difference between position and torque reflects load inertia. The location and timing of vibration spectrum peaks can indicate track joints or wear points. The correlation between audio energy and vibration energy at specific frequencies can pinpoint the source of abnormal noise. This correlation information is crucial for subsequent condition diagnosis and model calibration, implicitly revealing the system's current health status and disturbance characteristics.

[0073] The door operator digital model in this embodiment of the invention is a mathematical model built within the elevator car door operator control system that can simulate the physical behavior of a real elevator door operator. Parametric dynamic equations are typically used to describe the motion of the door operator system.

[0074] Operating characteristic parameters refer to those parameters in the gantry crane's digital model that change with the crane's operating status and environment. For example, the coefficient of friction of the guide rails varies with lubrication conditions and wear; the stiffness of the drive belt changes with temperature and usage time; and the moment of inertia of the gantry crane may vary with the load, which can include wind pressure. These parameters are not fixed but need to be estimated and adjusted based on real-time data.

[0075] The digital model of the gantry crane in this embodiment of the invention can be represented as follows:

[0076] ;

[0077] in: Indicates the position of the door operator (e.g., motor rotation angle); It is the system's equivalent moment of inertia; It is the viscous damping coefficient; It is a nonlinear frictional torque, a function of velocity; It is the external load torque, such as wind pressure and track resistance; It is the motor torque constant; It is the motor current (i.e., the control input).

[0078] Specifically, the operating characteristic parameters may include the equivalent moment of inertia. Included The coefficient of friction and the damping coefficient in Motor efficiency parameters ;

[0079] The reverse system identification in this embodiment of the invention is a process of inferring input from results: by observing the system's output response (i.e., the waveform of a single run), one can infer which model parameters caused such a response. The goal is to adjust the model parameters so that the model's predicted output is infinitely close to the actual measurement data.

[0080] Specifically, the input and output data are as follows: Input During this door opening and closing process, the actual sequence of current commands sent by the controller to the motor Output True position response sequence extracted from vital sign waveforms .

[0081] The same input Input the current parameters into the gantry crane digital model, run the simulation, and obtain the model's predicted output. .

[0082] The root mean square error (RMSE) is commonly used as a metric to calculate the error between the predicted output and the actual output.

[0083] ;

[0084] Where i = 1, 2, 3, ..., N, i refers to the number of time points; optimization algorithms such as gradient descent, particle swarm optimization, or extended Kalman filtering are used to automatically adjust the running characteristic parameters in the model (e.g., ...). (Parameters).

[0085] The core of this invention is to find a set of parameters that minimize the error. Minimize. This is equivalent to solving an optimization problem:

[0086] When the error When the error falls below a preset threshold (e.g., position error less than 0.1 mm), the iteration stops. The optimized parameter set obtained at this point is then updated into the gantry crane's digital model. At this point, the model is considered the most accurate digital representation of the physical gantry crane at the current moment, thus completing the dynamic calibration.

[0087] By using reverse system identification and multiple real operating data to reverse-engineer and correct the physical parameters inside the model, such as the friction coefficient B and the moment of inertia J, the door operator digital model is transformed from a fixed ideal model into a digital model that can be continuously updated and closely approximates the current health status of the real elevator door operator.

[0088] Step S103: After receiving the door opening / closing command, perform a simulation operation on the target motion curve in the calibrated door operator digital model to obtain the simulation results. Based on the simulation results and the current parameters of the door operator digital model, calculate the required feedforward compensation amount, and fuse the feedforward compensation amount with the closed-loop control output parameters to generate actuator control commands.

[0089] In a specific example of this invention, after receiving the door opening / closing command, a simulation operation of the target motion curve is performed in the door operator digital model to obtain simulation results. Based on the simulation results and the current parameters of the door operator digital model, the required feedforward compensation amount is calculated. The feedforward compensation amount is then fused with the closed-loop control output parameters to generate actuator control commands, including:

[0090] Obtain the preset target motion curve data;

[0091] Before each door opening and closing operation, the target motion curve data is input into the calibrated door operator digital model to obtain the simulation operation of the door opening and closing process and the simulation results.

[0092] The difference between the motor output torque curve in the simulation results and the theoretical torque curve under no disturbance is calculated to obtain the feedforward compensation torque curve; wherein, the feedforward compensation torque curve includes the feedforward compensation amount;

[0093] The feedforward compensation torque curve is superimposed with the torque command of the closed-loop PID control output parameters to obtain the final torque setpoint, and the actuator control command is generated.

[0094] The target motion curve data in this embodiment of the invention refers to a pre-set, ideal gantry crane motion trajectory plan without any disturbance. Specifically, the target motion curve data typically includes time-series data in the following three core dimensions: position-time curve. This describes the ideal displacement change of the door leaf from its initial position (0%) to its target position (100%); it is typically designed as a smooth S-curve to ensure no impact during start-up and stopping. Speed-Time Curve Obtained by differentiating the position curve, it exhibits a trapezoidal or parabolic shape of "acceleration-uniform speed-deceleration". The maximum speed value is set according to safety standards and usage scenarios. (Acceleration-time curve) The acceleration is obtained by differentiating the velocity curve, ensuring continuous acceleration without abrupt changes. These three curves together constitute the complete target motion curve data, representing the ideal motion reference pursued by the control system. This can be expressed as time. Function vectors:

[0095] ;

[0096] in It is the preset total time to complete the entire door opening and closing action.

[0097] When the elevator door operator control system receives the door opening / closing command, it performs the following steps:

[0098] Step 1: Obtain the target motion curve; retrieve the preset target motion curve data corresponding to the current command (open or close the door) from the memory. .

[0099] Step 2: Input the position command sequence from the target motion curve into the calibrated gantry crane digital model. As expected input, it is provided to the gantry crane digital model that has already undergone dynamic calibration through reverse system identification. At this point, the gantry crane digital model contains the most accurate actual physical parameters at the current moment, such as the coefficient of friction and moment of inertia.

[0100] Step 3: Model simulation calculation. The gantry crane digital model undergoes forward simulation calculation based on its dynamic equations. As the desired position, the model needs to deduce the torque (or current) that the motor should output to track this ideal trajectory. The simulation can be viewed as solving the following problem: Given... ,beg Make the model output position as close as possible .

[0101] Step 4: Obtain simulation results. The simulation calculation outputs a complete set of time series data, i.e., the simulation results, which mainly include: the motor output torque curve. The torque output of the motor, calculated by the model, required to track the ideal trajectory. Predicted position response curve. :exist Driven by the engine, the model predicts the actual trajectory of the gantry crane. Other state variables include predicted vibrations and currents. These results together constitute a high-precision prediction of the actual physical motion that is about to occur.

[0102] Motor output torque curve This is the torque time series calculated through simulation in the calibrated digital model of the gantry crane. It represents the torque time series under the current equipment conditions, such as current friction and current inertia, in order to ensure the gantry crane strictly follows the target motion curve. Motion refers to the torque that the motor theoretically needs to output.

[0103] Theoretical torque curve Under conditions of no disturbance, ideal new installation, and parameters at nominal values, the elevator door operator is driven to follow the same target motion curve. The torque curve required for motion.

[0104] The core idea of ​​feedforward compensation in this invention is to subtract the required torque under the ideal state from the required torque under the current actual state, and the difference is the torque that needs to be compensated.

[0105] The calculation steps are as follows:

[0106] 1. Data alignment: Ensure and They have the same timeline and sampling points.

[0107] 2. Point-by-point subtraction: at each time point Calculate the difference:

[0108] ;

[0109] 3. Generate the feedforward compensation torque curve: the difference at all time points. Arranged in chronological order, this forms the feedforward compensation torque curve. .

[0110] 4. Feedforward compensation amount: Each value on This refers to the feedforward compensation amount at the corresponding moment. The feedforward compensation amount is a torque value, representing the torque that needs to be applied to the motor in advance to counteract the additional resistance caused by the current state deviating from the ideal state.

[0111] The output parameter of closed-loop PID control refers to the torque command calculated from the real-time measured position error in a traditional feedback control loop (usually a PID controller). .

[0112] Position error ,in It is the actual position measured by the encoder in real time.

[0113] PID controller based on error Calculate using its integrals and differentials;

[0114] The superposition process and the final torque setpoint are as follows: In each control cycle (e.g., every 1 millisecond), the control system performs the following composite calculation:

[0115] ;

[0116] in, It is a pre-calculated feedforward compensation torque. It is the feedback torque calculated based on the current instantaneous error.

[0117] Final torque setpoint This refers to the combined total torque command described above. It represents the target total torque that the motor should output at the current moment.

[0118] Generating actuator control commands: Motor drivers typically receive current commands. Because the output torque of a motor is directly proportional to the current: Therefore, the final torque setpoint It needs to be converted to a current command:

[0119] ;

[0120] in, This refers to the actuator control command that is ultimately sent to the motor driver; the driver adjusts its power output according to the command to make the motor generate the corresponding torque.

[0121] In this embodiment of the invention, a high-fidelity digital model is used to predict in advance the additional torque demand caused by the current equipment state. A feedforward compensation quantity is generated, and the compensation force is actively applied before the error occurs, solving the inherent delay problem of traditional PID control. This achieves model-based predictive feedforward compensation control, significantly improving the system's control accuracy and adaptability. Feedforward control enables the system to have a strong ability to suppress modelable, slowly varying disturbances, such as wear and temperature changes, allowing the elevator door operator to operate with high precision, smoothness, and energy efficiency under all operating conditions.

[0122] Step S104: Abstract the tasks corresponding to the actuator control commands and the control box tasks into heterogeneous computing tasks, and abstract the control box computing units into heterogeneous computing resources.

[0123] Step S105: Based on the preset current working mode and the real-time system load, the multi-objective optimization scheduling engine dynamically allocates computing resources to each heterogeneous computing task. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The optimal task scheme is obtained by solving the problem, and the task scheduling strategy is adjusted.

[0124] In this embodiment of the invention, based on a preset current operating mode and real-time system load, a multi-objective optimization scheduling engine dynamically allocates computing resources to each heterogeneous computing task. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the gate opening / closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing control tracking accuracy. Solving for the current optimal task scheme and adjusting the task scheduling strategy includes:

[0125] The tasks corresponding to actuator control instructions, safety monitoring tasks, fault diagnosis tasks, and communication tasks are abstracted into heterogeneous computing tasks with different requirements.

[0126] The processor's multiple performance cores and energy efficiency cores, as well as the FPGA units used for signal processing, are abstracted into heterogeneous computing resources with different characteristics;

[0127] The multi-objective optimization scheduling engine receives working condition instructions and monitors the real-time load rate of each computing core. The multi-objective optimization scheduling engine aims to minimize the gate opening and closing cycle time, minimize the peak processor load, minimize the total system energy consumption, and maximize the control tracking accuracy. It uses a lightweight multi-objective evolutionary algorithm to solve for the current optimal task core allocation scheme and execution time slice.

[0128] Based on the optimal task core allocation scheme and execution time slice, the task scheduling strategy of the operating system or underlying driver is adjusted in real time to bind high-priority core control tasks to high-performance cores and migrate low-priority core control tasks to energy-efficient cores.

[0129] The multi-objective optimization scheduling engine is a dedicated software decision-making process embedded within the gantry crane controller. Its core is to make real-time trade-offs among multiple conflicting performance objectives (such as speed, energy consumption, computational load, and control accuracy), and to dynamically and automatically allocate limited computing resources (CPU cores, FPGAs, etc.) to multiple tasks containing actuator control commands based on the current operating scenario (operating mode) and the system's real-time load, in order to achieve the optimal balance of overall system performance.

[0130] Heterogeneous computing tasks refer to computing jobs with different requirements for computing resources (such as computing power, response speed, and runtime). In this embodiment of the invention, the key tasks within the controller are abstracted as follows:

[0131] The task corresponding to the actuator control command is the core motion control loop task. It is responsible for calculating the accurate motor current command in real time according to the feedforward compensation algorithm as described above. Its characteristics are computationally intensive, extremely short cycle (e.g., 1 millisecond), and stringent latency requirements. It is a hard real-time task with priority. There are various types of tasks corresponding to the actuator control command, and this embodiment of the invention does not impose too many restrictions on them.

[0132] Safety monitoring tasks can be responsible for real-time monitoring of the gantry crane's status, such as detecting obstacles, overcurrent, and overspeed. It requires high reliability and a deterministic response time, but the computational load is usually less than that of motion control, and the cycle may be slightly longer (e.g., 5-10 milliseconds). It is also a "hard real-time" task, but its priority is slightly lower than that of core control.

[0133] Fault diagnosis tasks can be responsible for analyzing historical data (such as vibration spectra and trend parameters) and running lightweight AI models (such as LSTM) to predict potential faults. They are characterized by relatively complex computations and long processing times, but do not have high real-time requirements, falling under the category of soft real-time or non-real-time tasks, allowing for a certain degree of latency.

[0134] The communication task can be responsible for exchanging data with the elevator main controller, other subsystems, or the cloud. Its characteristics include low load but periodic execution, moderate real-time requirements, but the need to ensure timely and stable communication.

[0135] On the other hand, heterogeneous computing resources refer to hardware processing units with different computing capabilities and power consumption characteristics.

[0136] A high-performance core refers to the most powerful core in a processor. It has a high clock speed, enabling it to quickly complete complex calculations, but its power consumption is also relatively high. Here, it is abstracted as a resource with high computing power and high power consumption.

[0137] An energy-efficient core refers to a core within the same processor designed for energy efficiency. It has moderate computing power but extremely low power consumption. Here, it is abstracted as a resource with moderate computing power and extremely low power consumption.

[0138] An FPGA (Field-Programmable Gate Array) is a type of programmable hardware that excels at parallel processing of specific tasks, such as digital signal processing and filtering. In this embodiment of the invention, it is abstracted as a dedicated acceleration resource that is extremely efficient and has extremely low latency for specific computational tasks (such as FFT).

[0139] The operation process of the multi-objective optimization scheduling engine in this embodiment of the invention is as follows, realizing intelligent and adaptive management of complex computational tasks within the elevator door operator controller:

[0140] 1. Receive instructions and monitor load;

[0141] The scheduling engine continuously listens for "operational condition commands" from upper layers (such as elevator management systems) or preset schedules, such as "peak efficiency mode," "nighttime silent mode," or "energy-saving mode." Simultaneously, through the operating system interface, it reads the CPU utilization percentage of each computing core (performance core, energy efficiency core) in real time as a quantitative indicator of the system's real-time load. The FPGA's load is monitored through its internal status registers or task queue length.

[0142] 2. Solving for the optimal allocation scheme based on a multi-objective evolutionary algorithm;

[0143] The scheduling engine's built-in lightweight multi-objective evolutionary algorithm solves its problem through a dynamic optimization loop:

[0144] Initialization: The algorithm generates a set of random "task-resource" allocation schemes as initial candidate solutions. For example, a scheme might be "binding motion control to performance core A, placing fault diagnosis in energy efficiency core B, and offloading FFT calculation to the FPGA".

[0145] Evaluation: For each candidate solution, the engine simulates execution and evaluates its performance on four preset elevator operation targets: estimating the total door opening and closing time (efficiency), predicting the peak processor load (balance), estimating the total energy consumption (energy saving), and predicting the degree of control accuracy degradation based on the model (quality).

[0146] Iterative optimization: The algorithm simulates the process of "natural selection," retaining solutions that perform well across multiple objectives (i.e., non-dominated solutions), and generating new solutions through "crossover" and "mutation" operations. After multiple iterations, the algorithm converges to a set of "Pareto optimal" solutions, which are characterized by the inability to improve any one objective without harming at least one other objective.

[0147] Final Decision: The scheduling engine selects a final solution from the Pareto optimal set based on the current "working condition instructions". For example, in "peak efficiency mode", it will choose the solution with the shortest door opening and closing time; in "nighttime silent mode", it will choose the solution with the most stable control, high precision, and low energy consumption. This final solution is the current optimal task core allocation scheme and execution time slice.

[0148] In a preferred embodiment, the lightweight multi-objective evolutionary algorithm can also employ a hybrid initialization strategy based on task profiling and resource models. Specifically,

[0149] First, heuristic rule injection is performed. Specifically, a set of high-quality seed solutions is pre-generated. For example: Rule 1: The task corresponding to the actuator control instruction, i.e., the core motion control loop task, must be bound to an idle high-performance core. Rule 2: Tasks with hardware acceleration mapping (such as FFT) are preferentially assigned to the FPGA. Rule 3: Periodically synchronized tasks are assigned to the same core as much as possible to reduce synchronization overhead.

[0150] Then, chaotic mapping is used for filling. Specifically, after injecting the seed solution, the remaining individuals in the population are generated using chaotic mapping. This chaotic mapping can include Logistic mapping, rather than purely random generation, based on the current optimal task core allocation scheme and execution time slice. This ensures population diversity while making individuals more evenly distributed in the solution space, improving exploration efficiency. Hybrid initialization and history reuse increase convergence speed several times, meeting real-time response requirements. The initial population quality is significantly improved, searching in high-performance regions from the first generation, greatly shortening the number of iterations required to reach a satisfactory solution.

[0151] 3. Adjust task scheduling strategies in real time;

[0152] After obtaining the optimal allocation scheme, the scheduling engine implements the strategy by calling the operating system's real-time scheduling interface or by directly configuring the underlying driver. Specific operations include:

[0153] High-priority real-time tasks (such as motion control) are bound to designated high-performance cores to ensure they have exclusive access to core resources and are not interfered with by other tasks, thereby guaranteeing extremely low latency and jitter.

[0154] Secondary priority tasks (such as fault diagnosis and some communication tasks) are migrated to energy-efficient cores. This ensures that these tasks are executed while allowing high-performance cores to focus on critical tasks, thereby reducing overall system power consumption.

[0155] Set appropriate execution priorities and CPU time slice quotas for each task to ensure that, even on shared cores, such as multiple background tasks on the same energy efficiency core, they can be executed in an orderly manner according to the established strategy.

[0156] In this embodiment of the invention, the entire process of the multi-objective optimization scheduling engine is as follows: perception (operating conditions and load), decision-making (solving the optimal allocation of multiple objectives through evolutionary algorithms), and execution (adjusting the scheduling strategy through system calls). This embodiment transforms the elevator door operator controller from a static task executor into an autonomous intelligent system that dynamically optimizes resources and intelligently balances various performance aspects based on the scenario and its own state. Thus, without changing the hardware, it achieves an adaptive optimal balance between operating efficiency, energy consumption, noise, and reliability.

[0157] In this embodiment of the invention, the method further includes: predicting and warning based on long-term trend parameters derived from the digital model of the elevator door operator, obtaining a local optimization strategy, uploading the local optimization strategy to a cloud knowledge base, retrieving and integrating optimization strategies under similar working conditions from the cloud knowledge base, and outputting them to another elevator door operator.

[0158] In one specific example, the prediction and early warning based on long-term trend parameters derived from the digital model of the elevator door operator are used to obtain a local optimization strategy. This local optimization strategy is then uploaded to a cloud knowledge base. Optimization strategies for similar operating conditions are retrieved and integrated from the cloud knowledge base and output to another elevator door operator. This includes:

[0159] Obtain the mean value of the operating data derived from the digital model of the gantry crane to form a time series of operating data;

[0160] The time series data of the running data is trained using a lightweight LSTM model to obtain the prediction model parameters. When the predicted value exceeds the maintenance threshold, the corresponding early warning information is generated.

[0161] The combination of feedforward compensation amounts under different local loads, as well as the prediction model parameters, are uploaded to the cloud-based group knowledge base.

[0162] When an elevator door operator sends the current environment and operating condition characteristics to the cloud knowledge base;

[0163] Similarity matching is performed in the cloud knowledge base, and the historical optimization strategy set or model parameters with the highest matching degree are sent to the elevator door operator to control the elevator door operator to run the operation using the historical optimization strategy set or model parameters.

[0164] In this embodiment of the invention, the intelligent control method of the elevator car door operator control box includes: collecting sensor data from multiple sensors; updating the operating characteristic waveform based on the sensor data; dynamically calibrating the parameterized door operator digital model according to the operating characteristic waveform; after receiving a door opening / closing command, performing a simulation operation of the target motion curve in the door operator digital model to obtain simulation results; calculating the required feedforward compensation amount based on the simulation results and the current parameters of the door operator digital model; fusing the feedforward compensation amount with the closed-loop control output parameters to generate actuator control commands; abstracting the task corresponding to the actuator control commands and the control box task into heterogeneous computing tasks; and abstracting the control box computing unit into... Heterogeneous computing resources; based on the preset current operating mode and the real-time system load, a multi-objective optimization scheduling engine dynamically allocates computing resources to each heterogeneous computing task; the optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The optimal task scheme is obtained by solving the problem, and the task scheduling strategy is adjusted. This achieves feedforward compensation based on real-time system state prediction, fundamentally transforming control from passive response to active intervention, significantly improving anti-disturbance capability and trajectory tracking accuracy, and realizing scene-adaptive intelligent allocation of computing resources. The multi-objective optimization engine achieves scene-adaptive intelligent scheduling.

[0165] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this embodiment is not limited to the described order of actions, because according to this embodiment, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this embodiment.

[0166] Reference Figure 2This diagram illustrates a structural block diagram of an intelligent control system embodiment for an elevator car door operator control box, applied to an elevator car door operator control system. The elevator car door operator control system is connected to multiple elevator door operators corresponding to various elevator car door operator control boxes. Each elevator door operator is equipped with various sensors and may specifically include the following modules:

[0167] The data acquisition module 301 is used to acquire sensor data from multiple sensors;

[0168] Calibration module 302 is used to update the operating characteristic waveform diagram based on the sensor data, and dynamically calibrate the parameterized gantry crane digital model according to the operating characteristic waveform diagram;

[0169] The feedforward compensation module 303 is used to perform a simulation operation on the target motion curve in the calibrated door operator digital model after receiving the door opening and closing command, obtain the simulation results, calculate the required feedforward compensation amount based on the simulation results and the current parameters of the door operator digital model, and fuse the feedforward compensation amount with the closed-loop control output parameters to generate actuator control commands.

[0170] Heterogeneous module 304 is used to abstract the tasks corresponding to the actuator control instructions and the control box tasks into heterogeneous computing tasks, and to abstract the control box computing units into heterogeneous computing resources.

[0171] The task scheduling strategy module 305 is used to dynamically allocate computing resources to each heterogeneous computing task according to the preset current working mode and the real-time system load through a multi-objective optimization scheduling engine. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The engine solves for the current optimal task scheme and adjusts the task scheduling strategy.

[0172] Preferably, the multiple sensors include an encoder, a current sensor, a frame vibration sensor, a micro-sensor of the contact force between the door knife and the door panel, a drive motor winding temperature sensor, and an ambient noise microphone; the acquisition module includes:

[0173] The acquisition submodule is used to acquire position signals, current signals, vibration spectrum signals, contact force signals, and audio waveform signals from various sensors.

[0174] Preferably, the calibration module includes:

[0175] A submodule is formed to fuse the collected synchronization signals according to the time sequence to form a single-run characteristic waveform diagram containing multi-dimensional correlation information; the multi-dimensional correlation information includes correlation information of torque, position, vibration, and audio.

[0176] The operation feature parameter submodule is used to dynamically adjust the operation feature parameters in the door operator digital model through reverse system identification, so as to minimize the error between the output response of the door operator digital model and the actual response of the elevator door operator, and dynamically calibrate the parameterized door operator digital model.

[0177] Preferably, the system further includes:

[0178] The local optimization strategy module is used to predict and warn based on long-term trend parameters derived from the digital model of the elevator door operator, obtain local optimization strategies, upload the local optimization strategies to the cloud knowledge base, retrieve and integrate optimization strategies under similar working conditions from the cloud knowledge base, and output them to another elevator door operator.

[0179] Preferably, the local optimization strategy module includes:

[0180] The acquisition submodule is used to acquire the mean value of the operating data derived from the digital model of the gantry crane, and form a time series of operating data.

[0181] The generation submodule is used to train the running data time series using a lightweight LSTM model to obtain prediction model parameters, and generate corresponding early warning information when the predicted value exceeds the maintenance threshold.

[0182] The upload submodule is used to upload the combination of feedforward compensation amounts under different loads and the prediction model parameters from the local system to the cloud-based group knowledge base.

[0183] The sending submodule is used when an elevator door operator sends the current environment and operating condition characteristics to the cloud knowledge base;

[0184] The control submodule is used to perform similarity matching in the cloud knowledge base, send the historical optimization strategy set or model parameters with the highest matching degree to the elevator door operator, and control the elevator door operator to run the operation using the historical optimization strategy set or model parameters.

[0185] Preferably, the feedforward compensation module includes:

[0186] The acquisition submodule is used to acquire preset target motion curve data;

[0187] The simulation results submodule is used to input the target motion curve data into the calibrated door operator digital model before each door opening and closing operation to obtain the simulation operation of the door opening and closing process and obtain the simulation results.

[0188] The analysis submodule is used to analyze the difference between the motor output torque curve in the simulation results and the theoretical torque curve under no disturbance, and calculate the feedforward compensation torque curve; wherein, the feedforward compensation torque curve includes the feedforward compensation amount;

[0189] The instruction generation submodule is used to superimpose the feedforward compensation torque curve with the torque instruction of the closed-loop PID control output parameter to obtain the final torque setpoint and generate the actuator control instruction.

[0190] Preferably, the task scheduling strategy module includes:

[0191] The heterogeneous computing task submodule is used to abstract the tasks corresponding to actuator control instructions, safety monitoring tasks, fault diagnosis tasks, and communication tasks into heterogeneous computing tasks with different requirements.

[0192] The heterogeneous computing resource service submodule is used to abstract multiple performance cores and energy efficiency cores of the processor, as well as FPGA units for signal processing, into heterogeneous computing resources with different characteristics.

[0193] The working condition instruction submodule is used to control the multi-objective optimization scheduling engine to receive working condition instructions and monitor the real-time load rate of each computing core; the minimization submodule is used to control the multi-objective optimization scheduling engine to minimize the gate opening and closing cycle time, minimize the peak processor load, minimize the total system energy consumption, and maximize the control tracking accuracy as optimization objectives. It uses a lightweight multi-objective evolutionary algorithm to solve for the current optimal task core allocation scheme and execution time slice.

[0194] The adjustment submodule is used to adjust the task scheduling strategy of the operating system or underlying driver in real time according to the optimal task core allocation scheme and execution time slice, binding high-priority core control tasks to high-performance cores and migrating low-priority core control tasks to energy-efficient cores.

[0195] The various modules in the intelligent control system of the elevator car door operator control box described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0196] The intelligent control system of the elevator car door operator control box provided above can be used to execute the intelligent control method of the elevator car door operator control box provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0197] In one embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent control system method for an elevator car door operator control box. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0198] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0199] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above embodiments.

[0200] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above embodiments.

[0201] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0202] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0203] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0204] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0205] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0206] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0207] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0208] The present invention has provided a detailed description of an intelligent control method for an elevator car door operator control box, an intelligent control system for an elevator car door operator control box, a computer device, and a computer-readable storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An intelligent control method for an elevator car door operator control box, characterized in that, An elevator car door operator control system is applied to an elevator car door operator control system, wherein the elevator car door operator control system is connected to multiple elevator car door operator control boxes corresponding to elevator door operators, and each elevator door operator is equipped with various sensors. The method includes: Collect sensor data from multiple sensors; The operating condition waveform is updated based on the sensor data, and the parameterized gantry crane digital model is dynamically calibrated based on the operating condition waveform. After receiving the door opening / closing command, the target motion curve is simulated in the calibrated door operator digital model to obtain the simulation results. Based on the simulation results and the current parameters of the door operator digital model, the required feedforward compensation amount is calculated. The feedforward compensation amount is then fused with the closed-loop control output parameters to generate actuator control commands. The tasks corresponding to the actuator control commands and the control box tasks are abstracted into heterogeneous computing tasks, and the control box computing units are abstracted into heterogeneous computing resources. Based on the preset current operating mode and the real-time system load, a multi-objective optimization scheduling engine dynamically allocates computing resources to each heterogeneous computing task. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the processor peak load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The engine solves for the current optimal task scheme and adjusts the task scheduling strategy accordingly.

2. The intelligent control method for the elevator car door operator control box according to claim 1, characterized in that, The various sensors include an encoder, a current sensor, a frame vibration sensor, a micro-sensor of the contact force between the door knife and the door panel, a drive motor winding temperature sensor, and an ambient noise microphone; the sensor data collected from these various sensors includes: It collects position signals, current signals, vibration spectrum signals, contact force signals, and audio waveform signals from various sensors.

3. The intelligent control method for the elevator car door operator control box according to claim 1, characterized in that, The step of updating the operational vital signs waveform based on the sensor data, and dynamically calibrating the parameterized gantry crane digital model based on the operational vital signs waveform, includes: The collected synchronization signals are fused according to the time sequence to form a single-run characteristic waveform diagram containing multi-dimensional correlation information; the multi-dimensional correlation information includes correlation information of torque, position, vibration, and audio. By dynamically adjusting the operating characteristic parameters in the door operator digital model through reverse system identification, the error between the output response of the door operator digital model and the actual response of the elevator door operator is minimized, and the parameterized door operator digital model is dynamically calibrated.

4. The intelligent control method for the elevator car door operator control box according to claim 1, characterized in that, The method further includes: Based on the long-term trend parameters derived from the digital model of the elevator door operator, prediction and early warning are performed to obtain local optimization strategies. These local optimization strategies are then uploaded to the cloud knowledge base, where optimization strategies for similar working conditions are retrieved and integrated, and then output to another elevator door operator.

5. The intelligent control method for the elevator car door operator control box according to claim 4, characterized in that, The prediction and early warning are based on long-term trend parameters derived from the digital model of the elevator operator, resulting in a local optimization strategy. This local optimization strategy is then uploaded to a cloud knowledge base. The cloud knowledge base is used to retrieve and integrate optimization strategies for similar operating conditions, which are then output to another elevator operator. This process includes: Obtain the mean value of the operating data derived from the digital model of the gantry crane to form a time series of operating data; The time series data of the running data is trained using a lightweight LSTM model to obtain the prediction model parameters. When the predicted value exceeds the maintenance threshold, the corresponding early warning information is generated. The combination of feedforward compensation amounts under different local loads, as well as the prediction model parameters, are uploaded to the cloud-based group knowledge base. When an elevator door operator sends the current environment and operating condition characteristics to the cloud knowledge base; Similarity matching is performed in the cloud knowledge base, and the historical optimization strategy set or model parameters with the highest matching degree are sent to the elevator door operator to control the elevator door operator to run the operation using the historical optimization strategy set or model parameters.

6. The intelligent control method for the elevator car door operator control box according to claim 1, characterized in that, Upon receiving the door opening / closing command, the system performs a simulation operation on the target motion curve in the door operator digital model to obtain simulation results. Based on the simulation results and the current parameters of the door operator digital model, the required feedforward compensation is calculated. The feedforward compensation is then fused with the closed-loop control output parameters to generate actuator control commands, including: Obtain the preset target motion curve data; Before each door opening and closing operation, the target motion curve data is input into the calibrated door operator digital model to obtain the simulation operation of the door opening and closing process and the simulation results. The difference between the motor output torque curve in the simulation results and the theoretical torque curve under no disturbance is calculated to obtain the feedforward compensation torque curve; wherein, the feedforward compensation torque curve includes the feedforward compensation amount; The feedforward compensation torque curve is superimposed with the torque command of the closed-loop PID control output parameters to obtain the final torque setpoint, and the actuator control command is generated.

7. The intelligent control method for the elevator car door operator control box according to claim 1, characterized in that, The process dynamically allocates computing resources to each heterogeneous computing task based on a preset current operating mode and real-time system load using a multi-objective optimization scheduling engine. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the gate opening / closing cycle time, minimizing peak processor load, minimizing total system energy consumption, and maximizing control tracking accuracy. The optimal task solution is obtained by solving for the current optimal task plan, and the task scheduling strategy is adjusted, including: The tasks corresponding to actuator control instructions, safety monitoring tasks, fault diagnosis tasks, and communication tasks are abstracted into heterogeneous computing tasks with different requirements. The processor's multiple performance cores and energy efficiency cores, as well as the FPGA units used for signal processing, are abstracted into heterogeneous computing resources with different characteristics; The multi-objective optimization scheduling engine receives working condition instructions and monitors the real-time load rate of each computing core. The multi-objective optimization scheduling engine aims to minimize the gate opening and closing cycle time, minimize the peak processor load, minimize the total system energy consumption, and maximize the control tracking accuracy. It uses a lightweight multi-objective evolutionary algorithm to solve for the current optimal task core allocation scheme and execution time slice. Based on the optimal task core allocation scheme and execution time slice, the task scheduling strategy of the operating system or underlying driver is adjusted in real time to bind high-priority core control tasks to high-performance cores and migrate low-priority core control tasks to energy-efficient cores.

8. An intelligent control system for an elevator car door operator control box, characterized in that, An elevator car door operator control system is applied to an elevator car door operator control system, which is connected to multiple elevator car door operator control boxes corresponding to elevator door operators. Each elevator door operator is equipped with various sensors. The system includes: The data acquisition module is used to acquire sensor data from various sensors. The calibration module is used to update the operating characteristic waveform diagram based on the sensor data, and dynamically calibrate the parameterized gantry crane digital model according to the operating characteristic waveform diagram. The feedforward compensation module is used to perform a simulation operation on the target motion curve in the calibrated door operator digital model after receiving the door opening and closing command, obtain the simulation results, calculate the required feedforward compensation amount based on the simulation results and the current parameters of the door operator digital model, and fuse the feedforward compensation amount with the closed-loop control output parameters to generate actuator control commands. Heterogeneous modules are used to abstract the tasks corresponding to actuator control commands and control box tasks into heterogeneous computing tasks, and to abstract the control box computing units into heterogeneous computing resources. The task scheduling strategy module is used to dynamically allocate computing resources to each heterogeneous computing task based on the preset current working mode and the real-time system load through a multi-objective optimization scheduling engine. The optimization objectives of the multi-objective optimization scheduling engine include minimizing the door opening and closing cycle time, minimizing the peak processor load, minimizing the total system energy consumption, and maximizing the control tracking accuracy. The engine solves for the current optimal task scheme and adjusts the task scheduling strategy accordingly.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent control method for the elevator car door operator control box as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent control method for the elevator car door operator control box as described in any one of claims 1 to 6.

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