Limiting recognition and control system for elevator

Abstract

The invention discloses an elevator limiting recognition and control system. A core control unit of the control system is used for coordinating and processing signals of all components and comprehensively controlling operation of the elevator. The up-down first-stage speed reduction switch is used for detecting the speed change of the elevator in the running process, and when the elevator approaches the limiting position, the speed reduction action is triggered; whether the elevator reaches the leveling position is detected, and a signal is provided for elevator stopping. The up-and-down releveling sensor is used for assisting in adjusting the leveling precision of the elevator when the releveling function is achieved; safety guarantee is set, when the elevator exceeds a normal limiting range, the limit switch acts, and operation of the elevator is forcibly stopped; and running position information and speed information of the elevator are monitored in real time. Through accurate set value calculation and various condition judgment, the accuracy of elevator limiting recognition is improved, whether the elevator is close to the limiting position or not can be detected timely and accurately, and safety accidents caused by limiting judgment errors are effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator control, and in particular to an elevator limit identification and control system. Background Art

[0002] There are many drawbacks in the methods currently adopted by various elevator manufacturers when dealing with elevator limit problems. Among them, the independent limit switch method not only requires the additional installation of special limit switch equipment, which increases the hardware cost, but also its corresponding control circuit and detection mechanism also make the software design more complicated, increasing the development and maintenance costs. Although the method of using a deceleration switch + upper and lower leveling signals can realize the limit function to a certain extent, it is necessary to process the logical relationship of multiple signals at the same time, the difficulty of software programming is relatively large, and higher requirements are also placed on the stability of the system. As for the solution of using special-shaped leveling signals, on the one hand, the special-shaped leveling signal device itself has a high cost, and on the other hand, its special signal processing method also increases the complexity of the software, which brings inconvenience to later debugging and maintenance. The present invention provides a limit identification and control system using the original deceleration switch + a leveling signal of the elevator to solve the above problems. Summary of the invention

[0003] The purpose of the present invention is to solve the above problems and to design an elevator limit identification and control system.

[0004] To achieve the above object, the technical solution of the present invention is that, further, in the above-mentioned elevator limit identification and control system, the elevator limit identification and control system includes the following modules:

[0005] The elevator main control board is used as the core control unit of the control system, responsible for coordinating and processing the signals of various components and comprehensively controlling the operation of the elevator;

[0006] The up and down 1-stage deceleration switch is used to detect the speed change of the elevator during operation. When the elevator approaches the limit position, the deceleration action is triggered;

[0007] The leveling sensor is used to detect whether the elevator has reached the leveling position and provide a signal for the elevator to stop.

[0008] Up and down re-leveling sensors are used to assist in adjusting the leveling accuracy of the elevator when the re-leveling function is available;

[0009] The limit switch is used for safety protection. When the elevator exceeds the normal limit range, the limit switch will be activated to force the elevator to stop running;

[0010] The elevator drive motor encoder is used to monitor the elevator's operating position and speed information in real time.

[0011] Furthermore, in the above-mentioned elevator limit identification and control system, the elevator main control board includes the following submodules:

[0012] The first judgment submodule is used to judge if the elevator is in a normal high-speed operation mode and the following conditions are met to identify the elevator limit;

[0013] The second judgment submodule is used for the elevator to go down. When the corresponding lower 1-stage deceleration switch is actuated, the elevator running distance is greater than the set value A, and the corresponding lower elevator limit is identified;

[0014] The third judgment submodule is used for the elevator to go down. When the corresponding next-level deceleration switch is actuated and the leveling sensor is actuated, the elevator running time exceeds the set value T1, and the corresponding next elevator limit is identified;

[0015] The fourth judgment submodule is used for the elevator to go down, corresponding to the action of the next level deceleration switch, and the elevator running time exceeds the set value T2, then the corresponding lower elevator limit is identified;

[0016] The identification submodule is used to identify the corresponding lower elevator limit when the elevator is in a stopped state after the lower 1-stage deceleration switch is actuated and the leveling sensor is actuated;

[0017] The configuration submodule is used to identify the corresponding lower elevator limit when the lower 1-level deceleration switch and the leveling sensor are activated and the upper re-leveling sensor is invalid when the re-leveling function is configured.

[0018] Furthermore, in the above-mentioned elevator limit identification and control system, it is characterized in that the elevator limit identification and control system includes the following submodules:

[0019] The distance judgment submodule is used in the process of elevator going down. When the corresponding next-level deceleration switch is actuated, if the elevator running distance is greater than the set value A, the corresponding next elevator limit is identified;

[0020] The identification submodule is used in the process of elevator going down. When the corresponding next-level deceleration switch and leveling sensor are actuated, the corresponding lower elevator limit is identified.

[0021] Furthermore, in the above-mentioned elevator limit identification and control system, the elevator limit identification and control system includes the following submodules:

[0022] An acquisition submodule, used to acquire the power during the operation of the elevator and the operation status data of the elevator, wherein the status data at least includes position, speed, and acceleration;

[0023] A processing submodule is used to normalize the voltage and current data in the running status data to the interval [0,1], and to perform standard processing on the position, speed, and acceleration to obtain standard running status data;

[0024] Establishing a submodule for adopting an MLP multilayer perceptron, setting the number of hidden layers and the number of neurons of the MLP multilayer perceptron, and obtaining an MLP neural network model;

[0025] Define submodules for defining loss functions using mean square error functions and optimizing neural network parameters using gradient descent algorithms;

[0026] The adjustment submodule is used to input the standard operating status data into the MLP neural network model for training. When the MLP neural network model predicts that a power outage is about to occur, the power-off holding operation of the elevator limit signal is triggered, and the self-locking setting and reverse operation parameters are adjusted according to the control strategy parameters output by the model.

[0027] Furthermore, in the above-mentioned elevator limit identification and control system, the elevator limit identification and control system includes the following submodules:

[0028] The extraction submodule is used to extract independent limit signal identification and analyze the vibration characteristics of the elevator during operation and the current fluctuation characteristics of the motor;

[0029] The screening submodule is used to screen the extracted features using the PCA principal component analysis algorithm, and select features with high correlation and strong independence with limit recognition as the input of the neural network;

[0030] Construct a submodule for constructing an independent limit signal recognition neural network model using CNN convolutional neural network and RNN recurrent neural network;

[0031] The acquisition submodule is used to obtain elevator operation data samples, including normal operation data and abnormal situation data, mark the corresponding limit status, and use the labeled data to train the neural network model;

[0032] An input submodule is used to input the feature data extracted in real time into the trained independent limit signal recognition neural network model when the elevator is running;

[0033] The judgment submodule is used for the model to judge the limit status of the elevator according to the input features and send the results to the elevator main control board. If the model judges that the elevator is close to the limit or is in an abnormal state, the main control board will immediately take control measures.

[0034] Further, in the above-mentioned elevator limit identification and control system, the elevator limit identification and control system includes:

[0035] A typical calculation formula for the set value A is as follows:

[0036] A=L(1st level strong reduction distance to terminal station leveling position) / 3.14 / rope wheel diameter*reduction ratio*hanging ratio*encoder pulse number;

[0037] A typical calculation formula for the set value T1 is as follows:

[0038] T1 = constant t1 + zero speed holding time + brake release time + error time, where constant t1 is generally 1.2s and error time is generally 500ms;

[0039] A typical calculation formula for the set value T2 is as follows:

[0040]

[0041] Among them, a is the deceleration. The use of T1 and T2 parameters can prevent the elevator encoder from being abnormal and unable to detect the distance. The limit signal is identified before the system triggers other faults to stop the operation of the elevator.

[0042] Further, in the above-mentioned elevator limit identification and control system, a method for implementing the elevator limit identification and control system is implemented, and the method comprises the following steps:

[0043] If the elevator is in normal high-speed operation mode, the elevator limit is identified if the following conditions are met;

[0044] During the elevator's descent, after the corresponding next-level deceleration switch is actuated, if the elevator's running distance is greater than the set value A, the corresponding lower elevator limit is identified;

[0045] During the elevator descent, if the corresponding next-level deceleration switch is actuated and the leveling sensor is actuated, and the elevator running time exceeds the set value T1, the corresponding lower elevator limit is identified;

[0046] During the elevator's descent, the corresponding next-level deceleration switch is actuated, and if the elevator running time exceeds the set value T2, the corresponding lower elevator limit is identified;

[0047] After the corresponding down-level deceleration switch is actuated and the leveling sensor is actuated, the elevator is in a stopped state, and the corresponding down-level elevator limit is identified;

[0048] When the re-leveling function is configured, after the corresponding lower 1-level deceleration switch is actuated and the leveling sensor is actuated, the upper re-leveling sensor is invalid, and the corresponding lower elevator limit is identified.

[0049] Further, in the above-mentioned elevator limit identification and control system, a method for implementing the elevator limit identification and control system is implemented, and the method comprises the following steps:

[0050] Acquire the power and operation status data of the elevator during the operation of the elevator, wherein the status data includes at least position, speed, and acceleration;

[0051] Normalizing the voltage and current data in the operating status data to the interval [0,1], and performing standard processing on the position, speed, and acceleration to obtain standard operating status data;

[0052] Using an MLP multilayer perceptron, setting the number of hidden layers and the number of neurons in the MLP multilayer perceptron, and obtaining an MLP neural network model;

[0053] The loss function is defined using the mean square error function, and the parameters of the neural network are optimized using the gradient descent algorithm;

[0054] The standard operating status data is input into the MLP neural network model for training. When the MLP neural network model predicts an imminent power outage, the power-off holding operation of the elevator limit signal is triggered, and the self-locking setting and reverse operation parameters are adjusted according to the control strategy parameters output by the model.

[0055] Further, in the above-mentioned elevator limit identification and control system, a method for implementing the elevator limit identification and control system is implemented, and the method comprises the following steps:

[0056] After the elevator limit signal is established, it will self-lock and the elevator needs to run in the reverse direction until the corresponding up / down 1-level deceleration switch is restored.

[0057] Extract independent limit signal identification, and analyze the vibration characteristics of the elevator during operation and the current fluctuation characteristics of the motor;

[0058] The PCA principal component analysis algorithm is used to screen the extracted features, and the features with high correlation and strong independence with limit recognition are selected as the input of the neural network;

[0059] Use CNN convolutional neural network and RNN recurrent neural network to build an independent limit signal recognition neural network model;

[0060] Obtain elevator operation data samples, including normal operation data and abnormal situation data, mark the corresponding limit status, and use the labeled data to train the neural network model;

[0061] When the elevator is running, the feature data extracted in real time is input into the trained independent limit signal recognition neural network model;

[0062] The model determines the elevator's limit status based on the input features and sends the results to the elevator main control board. If the model determines that the elevator is close to the limit or is in an abnormal state, the main control board immediately takes control measures.

[0063] Its beneficial effect is that the elevator main control board is used to set as the core control unit of the control system, responsible for coordinating and processing the signals of various components, and comprehensively controlling the operation of the elevator; the upper and lower 1-level deceleration switch is used to detect the speed change of the elevator during operation, and trigger the deceleration action when the elevator approaches the limit position; the leveling sensor is used to detect whether the elevator has reached the leveling position and provide a signal for the elevator to stop. The upper and lower re-leveling sensors are used to assist in adjusting the leveling accuracy of the elevator when there is a re-leveling function; the limit switch is used to set as a safety guarantee. When the elevator exceeds the normal limit range, the limit switch is activated to force the elevator to stop running; the elevator drive motor encoder is used to monitor the running position information and speed information of the elevator in real time. Through precise set value calculation and multiple condition judgments, the accuracy of the elevator limit recognition is improved, and it can detect whether the elevator is close to the limit position in a timely and accurate manner, effectively avoiding safety accidents caused by limit judgment errors. The power-off retention and self-locking functions enhance the stability and safety of the system, ensure the safety state of the elevator in emergencies, and reduce potential safety risks. The method of independently identifying limit signals enables the system to work reliably even in complex situations such as abnormal floor information, thereby improving the reliability and adaptability of the system and providing more comprehensive protection for the safe operation of the elevator. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiment.The drawings are only for the purpose of illustrating the preferred embodiments and are not to be construed as limiting the invention.

[0065] Figure 1 This is a schematic diagram of a first embodiment of an elevator limit identification and control system in an embodiment of the present invention;

[0066] Figure 2 Schematic diagram of a second embodiment of an elevator limit identification and control system according to an embodiment of the present invention;

[0067] Figure 3 The third embodiment schematic diagram of an elevator limit identification and control system in the embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0070] The present invention will be described in detail below in conjunction with the accompanying drawings. Figure 1 As shown, an elevator limit identification and control system, the elevator limit identification and control system includes the following modules:

[0071] The elevator main control board is used as the core control unit of the control system, responsible for coordinating and processing the signals of various components and comprehensively controlling the operation of the elevator;

[0072] The up and down 1-stage deceleration switch is used to detect the speed change of the elevator during operation. When the elevator approaches the limit position, the deceleration action is triggered;

[0073] The leveling sensor is used to detect whether the elevator has reached the leveling position and provide a signal for the elevator to stop.

[0074] Up and down re-leveling sensors are used to assist in adjusting the leveling accuracy of the elevator when the re-leveling function is available;

[0075] The limit switch is used for safety protection. When the elevator exceeds the normal limit range, the limit switch will be activated to force the elevator to stop running;

[0076] The elevator drive motor encoder is used to monitor the elevator's operating position and speed information in real time.

[0077] Specifically, in this embodiment, the elevator main control board includes the following submodules:

[0078] The first judgment submodule is used to judge if the elevator is in a normal high-speed operation mode and the following conditions are met to identify the elevator limit;

[0079] The second judgment submodule is used for the elevator to go down. When the corresponding lower 1-stage deceleration switch is actuated, the elevator running distance is greater than the set value A, and the corresponding lower elevator limit is identified;

[0080] The third judgment submodule is used for the elevator to go down. When the corresponding next-level deceleration switch is actuated and the leveling sensor is actuated, the elevator running time exceeds the set value T1, and the corresponding next elevator limit is identified;

[0081] The fourth judgment submodule is used for the elevator to go down, corresponding to the action of the next level deceleration switch, and the elevator running time exceeds the set value T2, then the corresponding lower elevator limit is identified;

[0082] The identification submodule is used to identify the corresponding lower elevator limit when the elevator is in a stopped state after the lower 1-stage deceleration switch is actuated and the leveling sensor is actuated;

[0083] The configuration submodule is used to identify the corresponding lower elevator limit when the lower 1-level deceleration switch and the leveling sensor are activated and the upper re-leveling sensor is invalid when the re-leveling function is configured.

[0084] Specifically, in this embodiment, the elevator limit identification and control system includes the following submodules:

[0085] The distance judgment submodule is used in the process of elevator going down. When the corresponding next-level deceleration switch is actuated, if the elevator running distance is greater than the set value A, the corresponding next elevator limit is identified;

[0086] The identification submodule is used in the process of elevator going down. When the corresponding next-level deceleration switch and leveling sensor are actuated, the corresponding lower elevator limit is identified.

[0087] Specifically, in this embodiment, the elevator limit identification and control system includes the following submodules:

[0088] An acquisition submodule, used to acquire the power during the operation of the elevator and the operation status data of the elevator, wherein the status data at least includes position, speed, and acceleration;

[0089] A processing submodule is used to normalize the voltage and current data in the running status data to the interval [0,1], and to perform standard processing on the position, speed, and acceleration to obtain standard running status data;

[0090] Establishing a submodule for adopting an MLP multilayer perceptron, setting the number of hidden layers and the number of neurons of the MLP multilayer perceptron, and obtaining an MLP neural network model;

[0091] Define submodules for defining loss functions using mean square error functions and optimizing neural network parameters using gradient descent algorithms;

[0092] The adjustment submodule is used to input the standard operating status data into the MLP neural network model for training. When the MLP neural network model predicts that a power outage is about to occur, the power-off holding operation of the elevator limit signal is triggered, and the self-locking setting and reverse operation parameters are adjusted according to the control strategy parameters output by the model.

[0093] Specifically, in this embodiment, the elevator limit identification and control system includes the following submodules:

[0094] The extraction submodule is used to extract independent limit signal identification and analyze the vibration characteristics of the elevator during operation and the current fluctuation characteristics of the motor;

[0095] The screening submodule is used to screen the extracted features using the PCA principal component analysis algorithm, and select features with high correlation and strong independence with limit recognition as the input of the neural network;

[0096] Construct a submodule for constructing an independent limit signal recognition neural network model using CNN convolutional neural network and RNN recurrent neural network;

[0097] The acquisition submodule is used to obtain elevator operation data samples, including normal operation data and abnormal situation data, mark the corresponding limit status, and use the labeled data to train the neural network model;

[0098] An input submodule is used to input the feature data extracted in real time into the trained independent limit signal recognition neural network model when the elevator is running;

[0099] The judgment submodule is used for the model to judge the limit status of the elevator according to the input features and send the results to the elevator main control board. If the model judges that the elevator is close to the limit or is in an abnormal state, the main control board will immediately take control measures.

[0100] Specifically, the elevator limit identification and control system in this embodiment includes:

[0101] A typical calculation formula for the set value A is as follows:

[0102] A=L(1st level strong reduction distance to terminal station leveling position) / 3.14 / rope wheel diameter*reduction ratio*hanging ratio*encoder pulse number;

[0103] A typical calculation formula for the set value T1 is as follows:

[0104] T1 = constant t1 + zero speed holding time + brake release time + error time, where constant t1 is generally 1.2s and error time is generally 500ms;

[0105] A typical calculation formula for the set value T2 is as follows:

[0106]

[0107] Among them, a is the deceleration. The use of T1 and T2 parameters can prevent the elevator encoder from being abnormal and unable to detect the distance. The limit signal is identified before the system triggers other faults to stop the operation of the elevator.

[0108] Its beneficial effect is that the elevator main control board is used to set as the core control unit of the control system, responsible for coordinating and processing the signals of various components, and comprehensively controlling the operation of the elevator; the upper and lower 1-level deceleration switch is used to detect the speed change of the elevator during operation, and trigger the deceleration action when the elevator approaches the limit position; the leveling sensor is used to detect whether the elevator has reached the leveling position and provide a signal for the elevator to stop. The upper and lower re-leveling sensors are used to assist in adjusting the leveling accuracy of the elevator when there is a re-leveling function; the limit switch is used to set as a safety guarantee. When the elevator exceeds the normal limit range, the limit switch is activated to force the elevator to stop running; the elevator drive motor encoder is used to monitor the running position information and speed information of the elevator in real time. Through precise set value calculation and multiple condition judgments, the accuracy of the elevator limit recognition is improved, and it can detect whether the elevator is close to the limit position in a timely and accurate manner, effectively avoiding safety accidents caused by limit judgment errors. The power-off retention and self-locking functions enhance the stability and safety of the system, ensure the safety state of the elevator in emergencies, and reduce potential safety risks. The method of independently identifying limit signals enables the system to work reliably even in complex situations such as abnormal floor information, thereby improving the reliability and adaptability of the system and providing more comprehensive protection for the safe operation of the elevator.

[0109] In this embodiment, please refer to Figure 2 In a second embodiment of an elevator limit identification and control system according to an embodiment of the present invention, the elevator limit identification and control system includes the following submodules:

[0110] An acquisition submodule, used to acquire the power during the operation of the elevator and the operation status data of the elevator, wherein the status data at least includes position, speed, and acceleration;

[0111] A processing submodule is used to normalize the voltage and current data in the running status data to the interval [0,1], and to perform standard processing on the position, speed, and acceleration to obtain standard running status data;

[0112] Establishing a submodule for adopting an MLP multilayer perceptron, setting the number of hidden layers and the number of neurons of the MLP multilayer perceptron, and obtaining an MLP neural network model;

[0113] Define submodules for defining loss functions using mean square error functions and optimizing neural network parameters using gradient descent algorithms;

[0114] The adjustment submodule is used to input the standard operating status data into the MLP neural network model for training. When the MLP neural network model predicts that a power outage is about to occur, the power-off holding operation of the elevator limit signal is triggered, and the self-locking setting and reverse operation parameters are adjusted according to the control strategy parameters output by the model.

[0115] In this embodiment, please refer to Figure 3 In a third embodiment of an elevator position limit identification and control system according to an embodiment of the present invention, the elevator position limit identification and control system further comprises:

[0116] An elevator limit identification and control system comprises an elevator main control board, an up and down 1-stage deceleration switch, a leveling sensor, an up and down re-leveling sensor, a limit switch, and an encoder installed on an elevator drive motor.

[0117] An elevator limit identification and control system, its working process is as follows, a typical application is as follows Figure 1 As shown:

[0118] If the elevator is in normal high-speed operation mode / re-leveling operation mode / maintenance operation mode / normal self-rescue mode / power failure self-rescue operation mode, the elevator limit is identified if the following conditions are met.

[0119] During the elevator's upward / downward movement, after the corresponding up / down 1-stage deceleration switch is actuated, if the elevator's running distance is greater than the set value A, the corresponding up / down elevator limit is identified.

[0120] During the elevator's upward / downward movement, if the corresponding up / down 1-stage deceleration switch is actuated and the leveling sensor is actuated, and the elevator running time exceeds the set value T1, the corresponding up / down elevator limit is identified.

[0121] During the elevator's upward / downward movement, the corresponding up / down 1-stage deceleration switch is actuated. If the elevator running time exceeds the set value T2, the corresponding up / down elevator limit is identified.

[0122] After the corresponding up / down 1-stage deceleration switch is actuated and the leveling sensor is actuated, the elevator is in the stopped state, and the corresponding up / down elevator limit is identified.

[0123] When the re-leveling function is configured, after the corresponding up / down 1-stage deceleration switch is actuated and the leveling sensor is actuated, when the upper re-leveling sensor is invalid (at the bottom floor) or the lower re-leveling sensor is invalid (at the top floor), the corresponding up / down elevator limit is identified.

[0124] When the elevator is in the other states described in (1), the elevator limit is identified if the following conditions are met.

[0125] During the elevator's upward / downward movement, after the corresponding up / down 1-stage deceleration switch is actuated, if the elevator's running distance is greater than the set value A, the corresponding up / down elevator limit is identified.

[0126] During the elevator's upward / downward movement, the corresponding up / down 1-stage deceleration switch and leveling sensor are activated, and the corresponding up / down elevator limit is identified.

[0127] To prevent the position from being unrecognizable after a power outage, the elevator limit signal is kept on during a power outage.

[0128] After the elevator limit signal is established, it will self-lock and the elevator needs to run in the reverse direction until the corresponding up / down 1-level deceleration switch is restored.

[0129] The elevator's current floor information is not used, and the limit signal is independently identified. This can ensure that the elevator can be stopped reliably when the elevator's current floor information is abnormal, avoiding the elevator from rushing to the top or bottom.

[0130] A typical calculation formula for the set value A is as follows:

[0131] A=L(1st level strong reduction distance to terminal station leveling position) / 3.14 / pulley diameter*reduction ratio*hanging ratio*encoder pulse number

[0132] A typical calculation formula for the set value T1 is as follows:

[0133] T1 = constant t1 + zero speed holding time + brake release time + error time, where constant t1 is generally 1.2s and error time is generally 500ms.

[0134] A typical calculation formula for the set value T2 is as follows:

[0135]

[0136] Among them, a is the deceleration. The use of T1 and T2 parameters can prevent the elevator encoder from being abnormal and unable to detect the distance. The limit signal is identified before the system triggers other faults to stop the operation of the elevator.

[0137] For ease of use, the elevator is also equipped with a limit signal shielding function so that maintenance and operation can continue after the limit signal is established.

[0138] In order to avoid the situation where the elevator accidentally hits the limit switch and stops in the non-door area when the elevator limit signal recognition is abnormal, causing people to be trapped; this solution installs the limit switch in the terminal station leveling door opening area. When the elevator hits the limit switch and stops running, if it is still in the door area, the elevator will open the door to let people in and keep the door open to avoid people being trapped.

[0139] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An elevator limit identification and control system, characterized in that: The elevator limit identification and control system comprises: The elevator main control board is used as the core control unit of the control system, responsible for coordinating and processing the signals of various components and comprehensively controlling the operation of the elevator; The up and down 1-stage deceleration switch is used to detect the speed change of the elevator during operation. When the elevator approaches the limit position, the deceleration action is triggered; The leveling sensor is used to detect whether the elevator has reached the leveling position and provide a signal for the elevator to stop. Up and down re-leveling sensors are used to assist in adjusting the leveling accuracy of the elevator when the re-leveling function is available; The limit switch is used for safety protection. When the elevator exceeds the normal limit range, the limit switch will be activated to force the elevator to stop running; The elevator drive motor encoder is used to monitor the elevator's operating position and speed information in real time.

2. An elevator limit identification and control system according to claim 1, characterized in that: The elevator main control board includes the following submodules: The first judgment submodule is used to judge if the elevator is in a normal high-speed operation mode and the following conditions are met to identify the elevator limit; The second judgment submodule is used for the elevator to go down. When the corresponding lower 1-stage deceleration switch is actuated, the elevator running distance is greater than the set value A, and the corresponding lower elevator limit is identified; The third judgment submodule is used for the elevator to go down. When the corresponding next-level deceleration switch is actuated and the leveling sensor is actuated, the elevator running time exceeds the set value T1, and the corresponding next elevator limit is identified; The fourth judgment submodule is used for the elevator to go down, corresponding to the action of the next level deceleration switch, and the elevator running time exceeds the set value T2, then the corresponding lower elevator limit is identified; The identification submodule is used to identify the corresponding lower elevator limit when the elevator is in a stopped state after the lower 1-stage deceleration switch is actuated and the leveling sensor is actuated; The configuration submodule is used to identify the corresponding lower elevator limit when the lower 1-level deceleration switch and the leveling sensor are activated and the upper re-leveling sensor is invalid when the re-leveling function is configured.

3. An elevator limit identification and control system according to claim 1, characterized in that: The elevator limit identification and control system includes the following submodules: The distance judgment submodule is used in the process of elevator going down. When the corresponding next-level deceleration switch is actuated, if the elevator running distance is greater than the set value A, the corresponding next elevator limit is identified; The identification submodule is used in the process of elevator going down. When the corresponding next-level deceleration switch and leveling sensor are actuated, the corresponding lower elevator limit is identified.

4. An elevator limit identification and control system according to claim 1, characterized in that: The elevator limit identification and control system includes the following submodules: An acquisition submodule, used to acquire the power during the operation of the elevator and the operation status data of the elevator, wherein the status data at least includes position, speed, and acceleration; A processing submodule is used to normalize the voltage and current data in the running status data to the interval [0,1], and to perform standard processing on the position, speed, and acceleration to obtain standard running status data; Establishing a submodule for adopting an MLP multilayer perceptron, setting the number of hidden layers and the number of neurons of the MLP multilayer perceptron, and obtaining an MLP neural network model; Define submodules for defining loss functions using mean square error functions and optimizing neural network parameters using gradient descent algorithms; The adjustment submodule is used to input the standard operating status data into the MLP neural network model for training. When the MLP neural network model predicts that a power outage is about to occur, the power-off holding operation of the elevator limit signal is triggered, and the self-locking setting and reverse operation parameters are adjusted according to the control strategy parameters output by the model.

5. An elevator limit identification and control system according to claim 1, characterized in that: The elevator limit identification and control system includes the following submodules: The extraction submodule is used to extract independent limit signal identification and analyze the vibration characteristics of the elevator during operation and the current fluctuation characteristics of the motor; The screening submodule is used to screen the extracted features using the PCA principal component analysis algorithm, and select features with high correlation and strong independence with limit recognition as the input of the neural network; Construct a submodule for constructing an independent limit signal recognition neural network model using CNN convolutional neural network and RNN recurrent neural network; The acquisition submodule is used to obtain elevator operation data samples, including normal operation data and abnormal situation data, mark the corresponding limit status, and use the labeled data to train the neural network model; An input submodule is used to input the feature data extracted in real time into the trained independent limit signal recognition neural network model when the elevator is running; The judgment submodule is used for the model to judge the limit status of the elevator according to the input features and send the results to the elevator main control board. If the model judges that the elevator is close to the limit or is in an abnormal state, the main control board will immediately take control measures.

6. An elevator limit identification and control system according to claim 1, characterized in that: The elevator limit identification and control system comprises: A typical calculation formula for the set value A is as follows: A=L(1st level strong reduction distance to terminal station leveling position) / 3.14 / rope wheel diameter*reduction ratio*hanging ratio*encoder pulse number; A typical calculation formula for the set value T1 is as follows: T1 = constant t1 + zero speed holding time + brake release time + error time, where constant t1 is generally 1.2s and error time is generally 500ms; A typical calculation formula for the set value T2 is as follows: Among them, a is the deceleration. The use of T1 and T2 parameters can prevent the elevator encoder from being abnormal and unable to detect the distance. The limit signal is identified before the system triggers other faults to stop the operation of the elevator.

7. A method for implementing an elevator limit identification and control system as claimed in claim 2, characterized in that: The method comprises the following sub-steps: If the elevator is in normal high-speed operation mode, the elevator limit is identified if the following conditions are met; During the elevator's descent, after the corresponding next-level deceleration switch is actuated, if the elevator's running distance is greater than the set value A, the corresponding lower elevator limit is identified; During the elevator descent, if the corresponding next-level deceleration switch is actuated and the leveling sensor is actuated, and the elevator running time exceeds the set value T1, the corresponding lower elevator limit is identified; During the elevator's descent, the corresponding next-level deceleration switch is actuated, and if the elevator running time exceeds the set value T2, the corresponding lower elevator limit is identified; After the corresponding down-level deceleration switch is actuated and the leveling sensor is actuated, the elevator is in a stopped state, and the corresponding down-level elevator limit is identified; When the re-leveling function is configured, after the corresponding lower 1-level deceleration switch is actuated and the leveling sensor is actuated, the upper re-leveling sensor is invalid, and the corresponding lower elevator limit is identified.

8. A method for implementing an elevator limit identification and control system as claimed in claim 4, characterized in that: The method comprises the following sub-steps: Acquire the power and operation status data of the elevator during the operation of the elevator, wherein the status data includes at least position, speed, and acceleration; Normalizing the voltage and current data in the operating status data to the interval [0,1], and performing standard processing on the position, speed, and acceleration to obtain standard operating status data; Using an MLP multilayer perceptron, setting the number of hidden layers and the number of neurons in the MLP multilayer perceptron, and obtaining an MLP neural network model; The loss function is defined using the mean square error function, and the parameters of the neural network are optimized using the gradient descent algorithm; The standard operating status data is input into the MLP neural network model for training. When the MLP neural network model predicts an imminent power outage, the power-off holding operation of the elevator limit signal is triggered, and the self-locking setting and reverse operation parameters are adjusted according to the control strategy parameters output by the model.

9. A method for implementing an elevator limit identification and control system as claimed in claim 5, characterized in that: The method comprises the following sub-steps: After the elevator limit signal is established, it will self-lock and the elevator needs to run in the reverse direction until the corresponding up / down 1-level deceleration switch is restored. Extract independent limit signal identification, and analyze the vibration characteristics of the elevator during operation and the current fluctuation characteristics of the motor; The PCA principal component analysis algorithm is used to screen the extracted features, and the features with high correlation and strong independence with limit recognition are selected as the input of the neural network; Use CNN convolutional neural network and RNN recurrent neural network to build an independent limit signal recognition neural network model; Obtain elevator operation data samples, including normal operation data and abnormal situation data, mark the corresponding limit status, and use the labeled data to train the neural network model; When the elevator is running, the feature data extracted in real time is input into the trained independent limit signal recognition neural network model; The model determines the elevator's limit status based on the input features and sends the results to the elevator main control board. If the model determines that the elevator is close to the limit or is in an abnormal state, the main control board immediately takes control measures.

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