Elevator limit recognition and control system

Abstract

The application discloses an elevator limiting position recognition and control system. A core control unit is arranged as a control system, responsible for coordinating and processing signals of various components, and comprehensively controlling operation of the elevator; up and down first-stage deceleration switches are used for detecting speed changes of the elevator during operation, triggering deceleration actions when the elevator approaches a limiting position, and providing signals for stopping the elevator to a landing position; up and down re-landing sensors are used for assisting in adjusting landing accuracy of the elevator when there is a re-landing function; limit switches are arranged as safety guarantees, and the limit switches act to forcibly stop operation of the elevator when the elevator exceeds a normal limiting range; and real-time monitoring of position information and speed information of the elevator is realized. Through accurate setting value calculation and multiple condition judgment, accuracy of elevator limiting position recognition is improved, the elevator can timely and accurately detect whether the elevator approaches a limiting position, and safety accidents caused by limiting position 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 issues. Among them, the independent limit switch method not only requires the additional installation of special limit switch equipment, which increases hardware costs, but its corresponding control circuit and detection mechanism also make the software design more complicated, increasing development and maintenance costs. The method of adopting a deceleration switch + upper and lower leveling signals, although it 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 adopting 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 subsequent debugging and maintenance. The present invention provides a method for realizing limit identification and control system by utilizing 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] The technical solution of the present invention to achieve the above object 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 signals from 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] Upper and lower re-leveling sensors are used to assist in adjusting the leveling accuracy of the elevator when the re-leveling function is enabled;

[0009] The limit switch is used for safety protection. When the elevator exceeds the normal limit range, the limit switch will be activated and the elevator will be forced 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 determine if the elevator is in normal high-speed operation mode and meets the following conditions to identify the elevator limit;

[0013] The second judgment submodule is used to identify the corresponding lower elevator limit when the elevator travels a distance greater than the set value A after the corresponding lower deceleration switch is actuated during the elevator's descent process;

[0014] The third judgment submodule is used to identify the corresponding lower elevator limit when the elevator is descending and the corresponding lower deceleration switch is actuated and the leveling sensor is actuated, and the elevator running time exceeds the set value T1;

[0015] The fourth judgment submodule is used to identify the corresponding lower elevator limit when the elevator is in the downward process and the corresponding lower deceleration switch is actuated and the elevator running time exceeds the set value T2;

[0016] The identification submodule is used to identify the corresponding lower elevator limit when the elevator is in a stopped state after the next level deceleration switch is activated and the leveling sensor is activated;

[0017] Configuration submodule, used for configuring the re-leveling function. When the corresponding down 1-level deceleration switch is actuated and the leveling sensor is actuated, the up re-leveling sensor is invalid, and the corresponding down elevator limit is identified.

[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 to identify the corresponding lower elevator limit when the elevator travels a distance greater than the set value A after the corresponding lower deceleration switch is actuated during the elevator's descent process.

[0020] The identification submodule is used to identify the corresponding lower elevator limit when the next deceleration switch and the leveling sensor are activated during the elevator's downward movement.

[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, configured to acquire power during elevator operation and operation status data of the elevator, wherein the status data includes at least position, speed, and acceleration;

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

[0024] Establishing a submodule for adopting 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;

[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 hold 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 and motor current fluctuation characteristics during elevator operation;

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

[0030] Construct a submodule for building 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] The input submodule is used to input the real-time extracted feature data into the trained independent limit signal recognition neural network model when the elevator is running;

[0033] The judgment submodule is used by the model to judge the limit status of the elevator based on the input features and send 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 will immediately take control measures.

[0034] Furthermore, 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(Level 1 strong reduction distance to terminal leveling position) / 3.14 / pulley 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, and the elevator is stopped.

[0042] Furthermore, 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 includes the following steps:

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

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

[0045] During the elevator's descent, if the corresponding deceleration switch for the next level is actuated and the leveling sensor is actuated, and the elevator's 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 activated. If the elevator's running time exceeds the set value T2, the corresponding next elevator limit is identified.

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

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

[0049] Furthermore, 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 includes the following steps:

[0050] Acquiring power and elevator operation status data during elevator operation, 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 to obtain 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 hold 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] Furthermore, 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 includes 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 and motor current fluctuation characteristics during elevator operation;

[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] Utilize 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] The system's beneficial effects include the use of an elevator main control board, serving as the core control unit of the control system, responsible for coordinating and processing signals from various components to provide comprehensive control of the elevator's operation. Up and down deceleration switches detect speed changes during operation, triggering deceleration when the elevator approaches a limit position. Leveling sensors detect whether the elevator has reached the leveling position and provide a signal for the elevator to stop. Up and down re-leveling sensors assist in adjusting the elevator's leveling accuracy when the re-leveling function is enabled. Limit switches are configured for safety, triggering a forced stop when the elevator exceeds its normal limit range. The elevator drive motor encoder monitors the elevator's operating position and speed in real time. Through precise setpoint calculation and multiple conditional judgments, the system improves the accuracy of limit recognition, enabling timely and accurate detection of the elevator's approach to a limit position, effectively avoiding accidents caused by misjudgment of limit positions. Power-off hold and self-locking functions enhance system stability and safety, ensuring the elevator's safe state in emergencies and reducing 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 skilled in the art by reading the following detailed description of the preferred embodiment.The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0065] Figure 1 Schematic diagram of a first embodiment of an elevator limit identification and control system according to 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 Schematic diagram of a third embodiment of an elevator limit identification and control system according to an embodiment of the present invention. DETAILED DESCRIPTION

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

[0069] Those skilled in the art will understand that, unless otherwise stated, the singular forms "a", "an", and "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 stated features, integers, steps, operations, elements and / or components, but does not preclude 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 with reference to the accompanying drawings. Figure 1 As shown, an 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] Upper and lower re-leveling sensors are used to assist in adjusting the leveling accuracy of the elevator when the re-leveling function is enabled;

[0075] The limit switch is used for safety protection. When the elevator exceeds the normal limit range, the limit switch will be activated and the elevator will be forced 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, the elevator main control board in this embodiment includes the following submodules:

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

[0079] The second judgment submodule is used to identify the corresponding lower elevator limit when the elevator travels a distance greater than the set value A after the corresponding lower deceleration switch is actuated during the elevator's descent process;

[0080] The third judgment submodule is used to identify the corresponding lower elevator limit when the elevator is descending and the corresponding lower deceleration switch is actuated and the leveling sensor is actuated, and the elevator running time exceeds the set value T1;

[0081] The fourth judgment submodule is used to identify the corresponding lower elevator limit when the elevator is in the downward process and the corresponding lower deceleration switch is actuated and the elevator running time exceeds the set value T2;

[0082] The identification submodule is used to identify the corresponding lower elevator limit when the elevator is in a stopped state after the next level deceleration switch is activated and the leveling sensor is activated;

[0083] Configuration submodule, used for configuring the re-leveling function. When the corresponding down 1-level deceleration switch is actuated and the leveling sensor is actuated, the up re-leveling sensor is invalid, and the corresponding down elevator limit is identified.

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

[0085] The distance judgment submodule is used to identify the corresponding lower elevator limit when the elevator travels a distance greater than the set value A after the corresponding lower deceleration switch is actuated during the elevator's descent process.

[0086] The identification submodule is used to identify the corresponding lower elevator limit when the next deceleration switch and the leveling sensor are activated during the elevator's downward movement.

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

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

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

[0090] Establishing a submodule for adopting 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;

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

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

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

[0096] Construct a submodule for building 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] The input submodule is used to input the real-time extracted feature data into the trained independent limit signal recognition neural network model when the elevator is running;

[0099] The judgment submodule is used by the model to judge the limit status of the elevator based on the input features and send 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 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(Level 1 strong reduction distance to terminal leveling position) / 3.14 / pulley 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, and the elevator is stopped.

[0108] The system's beneficial effects include the use of an elevator main control board, serving as the core control unit of the control system, responsible for coordinating and processing signals from various components to provide comprehensive control of the elevator's operation. Up and down deceleration switches detect speed changes during operation, triggering deceleration when the elevator approaches a limit position. Leveling sensors detect whether the elevator has reached the leveling position and provide a signal for the elevator to stop. Up and down re-leveling sensors assist in adjusting the elevator's leveling accuracy when the re-leveling function is enabled. Limit switches are configured for safety, triggering a forced stop when the elevator exceeds its normal limit range. The elevator drive motor encoder monitors the elevator's operating position and speed in real time. Through precise setpoint calculation and multiple conditional judgments, the system improves the accuracy of limit recognition, enabling timely and accurate detection of the elevator's approach to a limit position, effectively avoiding accidents caused by misjudgment of limit positions. Power-off hold and self-locking functions enhance system stability and safety, ensuring the elevator's safe state in emergencies and reducing 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 position limit identification and control system according to an embodiment of the present invention, the elevator position limit identification and control system includes the following submodules:

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

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

[0112] Establishing a submodule for adopting 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;

[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 hold 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 includes:

[0116] An elevator limit identification and control system includes an elevator main control board, an upper and lower level deceleration switch, a leveling sensor, an upper and lower re-leveling sensor, a limit switch, and an encoder installed on the elevator drive motor.

[0117] An elevator limit identification and control system has the following working process. 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 up / down process, after the corresponding up / down 1-stage deceleration switch is actuated, if the elevator 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 up / down process, 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 a 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, if the up re-leveling sensor is invalid (at the bottom floor) or the down 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 up / down process, after the corresponding up / down 1-stage deceleration switch is actuated, if the elevator 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 maintained during 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 system does not use the elevator's current floor information and independently identifies the limit signal. This ensures that even when the elevator's current floor information is abnormal, the elevator can be reliably stopped to avoid hitting the top or bottom.

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

[0131] A=L(Level 1 forced reduction distance to terminal 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, and the elevator is stopped.

[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 is recognized abnormally, causing people to be trapped; this solution installs the limit switch in the door opening area of ​​the terminal leveling. When the elevator hits the limit switch and stops running, if it is still in the door area, the elevator door will open 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 to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An elevator limit identification and control system, characterized in that: The elevator limit identification and control system includes: The elevator main control board is used to be 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 elevator main control board includes the following submodules: a first judgment submodule is used to judge whether the elevator is in normal high-speed operation mode and meets the following conditions to identify the elevator limit; a second judgment submodule is used to identify the corresponding lower elevator limit when the corresponding lower deceleration switch is actuated during the elevator descent process and the elevator running distance is greater than the set value A; a third judgment submodule is used to identify the corresponding lower elevator limit when the corresponding lower deceleration switch is actuated during the elevator descent process and the leveling sensor is After the action, if the elevator running time exceeds the set value T1, the corresponding lower elevator limit is identified; the fourth judgment submodule is used for when the elevator is in the downward process, the corresponding lower deceleration switch is actuated, and the elevator running time exceeds the set value T2, then the corresponding lower elevator limit is identified; the identification submodule is used for when the elevator is in the stopped state after the corresponding lower deceleration switch is actuated and the leveling sensor is actuated, then the corresponding lower elevator limit is identified; the configuration submodule is used for when the re-leveling function is configured, when the upper re-leveling sensor is invalid after the corresponding lower deceleration switch is actuated and the leveling sensor is actuated, then the corresponding lower elevator limit is identified; 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; the 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 and the elevator will be forced 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 limit identification and control system includes the following submodules: The distance judgment submodule is used to identify the corresponding lower elevator limit when the elevator travels a distance greater than the set value A after the corresponding lower deceleration switch is actuated during the elevator's descent process. The identification submodule is used to identify the corresponding lower elevator limit when the next deceleration switch and the leveling sensor are activated during the elevator's downward movement.

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: An acquisition submodule, configured to acquire power and operation status data of the elevator during operation, wherein the status data includes position, speed, and acceleration; A processing submodule is used to normalize the voltage and current data in the operating status data to the interval [0, 1], and to perform standard processing on the position, speed, and acceleration to obtain standard operating status data; Establishing a submodule for adopting 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; 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 hold 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.

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: The extraction submodule is used to extract independent limit signal identification and analyze the vibration characteristics and motor current fluctuation characteristics during elevator operation; The screening submodule is used to screen the extracted features using the PCA principal component analysis algorithm and select features that are highly correlated with limit recognition and have strong independence as the input of the neural network; Construct a submodule for building 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; The input submodule is used to input the real-time extracted feature data into the trained independent limit signal recognition neural network model when the elevator is running; The judgment submodule is used by the model to judge the limit status of the elevator based on the input features and send 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 will immediately take control measures.

5. The elevator limit identification and control system according to claim 1, characterized in that: The elevator limit identification and control system includes: A typical calculation formula for the set value A is as follows: A = L (1st level strong reduction distance to terminal leveling position) / 3.14 / sheave 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: in, For 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, and the elevator is stopped.

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

7. A method for implementing an elevator limit identification and control system according to claim 4, characterized in that: The method comprises the following sub-steps: Acquire power and elevator operating status data during elevator operation, the status data including 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 to obtain 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 hold 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.

8. A method for implementing an elevator limit identification and control system according to 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 and motor current fluctuation characteristics during elevator operation; 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; Utilize 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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