Elevator safe operation control system and method based on multivariate data and intelligent elevator

Abstract

The invention discloses an elevator safe operation control system and method based on multivariate data and an intelligent elevator. The elevator safe operation control system comprises a sensor control module, an access control module and an early warning prompt module. The sensor control module is used for collecting elevator operation data; the access control module comprises a stable stopping analysis unit used for receiving elevator operation data, judging whether an elevator reaches a target stable stopping state or not, and generating a stable stopping signal when the elevator reaches the target stable stopping state; the unlocking control unit is used for generating an unlocking control instruction according to the stable stopping signal; the delay control unit is used for responding to the unlocking control instruction to control the elevator door to be opened and closed in a delayed mode according to preset duration. The early warning prompt module comprises a behavior recognition unit used for detecting the behavior mode of a passenger and recognizing the dangerous elevator taking behavior of the passenger; and the early warning prompt unit is used for carrying out early warning prompt on the passengers through voice or an indicator light when the dangerous elevator taking behavior exists. Stable analysis is conducted on elevator stopping, and the safety of elevator running is improved by controlling the door opening and closing in a delayed mode.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator intelligent control, and in particular to an elevator safe operation control system and method based on multivariate data, and an intelligent elevator. Background Art

[0002] With the acceleration of urbanization, high-rise buildings are increasing day by day. As an indispensable part of high-rise buildings, the safety of elevators is increasingly valued by people. In the process of using elevators, there will be a more dangerous situation, that is, although the elevator stops at the designated call floor, it does not really stop. At this time, if the door is opened and closed at will, and the passengers enter and exit the elevator unconsciously, it is easy to be accompanied by the risk of rapid descent or upward movement of the elevator, causing serious accidents. Traditional systems are also relatively weak in monitoring passenger behavior and lack effective means to identify and prevent passengers' dangerous behaviors. Although some more advanced elevator control systems have introduced sensor technology to enhance the perception of elevator status, there are still problems with single data processing and inability to fully reflect the operating status of the elevator. Summary of the invention

[0003] In order to solve at least one of the technical problems mentioned above, the present invention provides an elevator safety operation control system, method and intelligent elevator based on multivariate data.

[0004] In a first aspect, the present invention provides an elevator safety operation control system based on multivariate data, the system comprising:

[0005] Sensor control module, access control module and early warning prompt module;

[0006] The sensor control module is used to collect elevator operation data;

[0007] The access control module includes a stop stability analysis unit, an unlocking control unit and a delay control unit;

[0008] The stop analysis unit is used to receive the elevator operation data, analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stop state, and generate a stop signal when the elevator reaches the target stop state; the unlock control unit is used to generate an unlock control instruction according to the stop signal; the delay control unit is used to respond to the unlock control instruction and control the elevator door to open and close with a preset delay time;

[0009] The early warning prompt module includes a behavior recognition unit and an early warning prompt unit;

[0010] The behavior recognition unit is used to detect the passenger's behavior pattern and identify the passenger's dangerous elevator-riding behavior; the early warning prompt unit is used to warn the passenger through voice or indicator light when there is dangerous elevator-riding behavior.

[0011] Preferably, the sensor control module includes an inertial measurement unit and a pressure sensor;

[0012] The inertial measurement unit is used to monitor the running speed and acceleration of the elevator in real time, and the pressure sensor is used to detect the impact force when the elevator enters the landing state.

[0013] Preferably, the stable stop analysis unit is used to analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stable stop state, including:

[0014] Calculate the distance the elevator moves when it enters the landing state:

[0015]

[0016] In the formula, S represents the moving distance of the elevator when it enters the landing state, L represents the length of the elevator rope, and v, v max are the running speed and maximum allowable speed of the elevator, respectively; a is the acceleration of the elevator; t is the running time of the elevator when it enters the landing state; k1 and k2 are adjustment factors, and their value range is (0.001, 0.01);

[0017] Judge SH≤∈1 and FF b Whether ≤∈2 holds true at the same time;

[0018] Where H is the standard distance when the elevator enters the landing state, F, F b are the impact force when the elevator enters the landing state and the corresponding preset impact force value, respectively, ∈1 and ∈2 are constants greater than 0;

[0019] If SH≤∈1 and FF b ≤∈2 are both true, and it is determined that the elevator has reached the target stable stop state.

[0020] Preferably, the behavior recognition unit is used to detect the behavior pattern of passengers and identify the dangerous elevator riding behavior of passengers, including:

[0021] When it is determined that the passenger is a child and is riding the elevator alone, it is marked as dangerous elevator behavior;

[0022] When it is determined that the passenger is carrying a pet and the pet and the passenger do not enter the elevator car together, it is marked as dangerous elevator behavior;

[0023] When it is determined that a passenger's carry-on items are caught by the elevator door, or separated from the elevator car by the elevator door, it is marked as dangerous elevator behavior;

[0024] When it is determined that a passenger is carrying flammable or explosive items, it will be marked as dangerous elevator behavior;

[0025] When it is determined that a passenger is leaning on the elevator door or pulling the elevator door by hand, it is marked as dangerous elevator riding behavior.

[0026] Preferably, the early warning prompt module further includes a load distribution evaluation unit, which is used to:

[0027] Collect images of the interior of the elevator car, identify the location information of each passenger in the image, and determine the coordinate frame of each passenger in the image;

[0028] Extracting features from the coordinate frame, and predicting the weight of each passenger based on the extracted features and a preset weight prediction model;

[0029] Based on the location information and weight of each passenger, the load distribution in the elevator car is evaluated; when it is determined that the load distribution needs to be optimized, optimization instructions and corresponding operation instructions are generated and sent to the early warning prompt unit.

[0030] In a second aspect, the present invention further provides an intelligent elevator, comprising an elevator safety operation control system based on multivariate data as described in any one of the first aspects.

[0031] In a third aspect, the present invention further provides a method for controlling safe operation of an elevator based on multivariate data, which is applied to a safe operation control system for an elevator based on multivariate data as described in any one of the first aspects, the method comprising:

[0032] Collect elevator operation data, analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stop state, and generate a stop signal when the elevator reaches the target stop state;

[0033] Generate an unlocking control command according to the stop signal, and control the elevator door to open and close with a preset delay according to the unlocking control command;

[0034] Detect passengers' behavior patterns and identify passengers' dangerous elevator behaviors; when dangerous elevator behaviors occur, passengers will be warned through voice or indicator lights.

[0035] Preferably, analyzing the elevator operation data by a preset algorithm to determine whether the elevator has reached the target stable stop state includes:

[0036] Calculate the distance the elevator moves when it enters the landing state:

[0037]

[0038] In the formula, S represents the moving distance of the elevator when it enters the landing state, L represents the length of the elevator rope, and v, v maxare the running speed and maximum allowable speed of the elevator, respectively; a is the acceleration of the elevator; t is the running time of the elevator when it enters the landing state; k1 and k2 are adjustment factors, and their value range is (0.001, 0.01);

[0039] Judge SH≤∈1 and FF b Whether ≤∈2 holds true at the same time;

[0040] Where H is the standard distance when the elevator enters the landing state, F, F b are the impact force when the elevator enters the landing state and the corresponding preset impact force value, respectively, ∈1 and ∈2 are constants greater than 0;

[0041] If SH≤∈1 and FF b ≤∈2 are both true, and it is determined that the elevator has reached the target stable stop state.

[0042] Preferably, the method further comprises:

[0043] Collect images of the interior of the elevator car, identify the location information of each passenger in the image, and determine the coordinate frame of each passenger in the image;

[0044] Extracting features from the coordinate frame, and predicting the weight of each passenger based on the extracted features and a preset weight prediction model;

[0045] The load distribution in the elevator car is evaluated based on the position information and weight of each passenger; when it is determined that the load distribution needs to be optimized, optimization instructions and corresponding operation instructions are generated to prompt passengers to change their position distribution in the elevator car.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention provides an elevator safety operation control system based on multivariate data, the system comprising: a sensor control module, an access control module and an early warning prompt module; the sensor control module is used to collect elevator operation data; the access control module comprises a stop analysis unit, an unlocking control unit and a delay control unit; the stop analysis unit is used to receive the elevator operation data, analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached a target stop state, and generate a stop signal when the elevator reaches the target stop state; the unlocking control unit is used to generate an unlocking control instruction according to the stop signal; the delay control unit is used to respond to the unlocking control instruction and control the elevator door to delay opening and closing according to a preset time length; the early warning prompt module comprises a behavior recognition unit and an early warning prompt unit; the behavior recognition unit is used to detect the behavior pattern of passengers and identify the dangerous elevator behavior of passengers; the early warning prompt unit is used to warn the passengers through voice or indicator lights when there is dangerous elevator behavior.

[0048] The present invention realizes the refined management of the elevator operation status and the intelligent monitoring of passenger behavior by introducing advanced data analysis algorithms and behavior recognition technologies. By calculating the moving distance of the elevator when it enters the landing state, and combining factors such as impact force to comprehensively judge whether the elevator is truly stopped, and using a delay mechanism to control the opening and closing of the door, the safety of the elevator door control operation is improved. At the same time, the abnormal behavior of passengers can be identified and responded to in a timely manner, and early warnings can be given through voice or indicator lights, thereby effectively preventing safety hazards caused by human factors. Finally, by monitoring the distribution of passengers in the elevator, the load distribution of the car is optimized, which further improves the safety of elevator operation and improves the passengers' elevator experience.

[0049] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0051] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0052] Figure 1 A schematic diagram of the structure of an elevator safety operation control system based on multivariate data provided by an embodiment of the present invention;

[0053] Figure 2 for Figure 1 A schematic diagram of the structure of the subunits of the sensor control module 10;

[0054] Figure 3 A schematic diagram of the structure of an elevator safety operation control system based on multivariate data provided by another embodiment of the present invention;

[0055] Figure 4 A flowchart of a method for controlling safe operation of an elevator based on multivariate data provided by an embodiment of the present invention;

[0056] Figure 5 A flowchart of a method for controlling safe operation of an elevator based on multivariate data is provided as another embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] At present, when passengers use elevators, they are prone to enter and exit the elevator at will before the elevator stops steadily. Due to the lack of precise operation control and monitoring and early warning mechanisms, serious accidents may occur once the elevator moves suddenly, resulting in irreversible consequences. To this end, the present invention provides an elevator safety operation control system based on multivariate data, which can accurately judge the stop signal, combine with an effective delayed door opening and closing control mechanism and early warning prompts, ensure that passengers enter and exit the elevator after the elevator stops steadily, and greatly improve the safety of elevator use.

[0061] Sensor control module 10, access control module 20 and early warning prompt module 30;

[0062] The sensor control module 10 is used to collect elevator operation data;

[0063] The access control module 20 includes a stop stability analysis unit 201, an unlocking control unit 202, and a delay control unit 203;

[0064] The stop analysis unit 201 is used to receive the elevator operation data, analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stop state, and generate a stop signal when the elevator reaches the target stop state; the unlock control unit 202 is used to generate an unlock control instruction according to the stop signal; the delay control unit 203 is used to respond to the unlock control instruction and control the elevator door to open and close with a preset delay time;

[0065] The early warning prompt module 30 includes a behavior recognition unit 301 and an early warning prompt unit 302;

[0066] The behavior recognition unit 301 is used to detect the behavior pattern of passengers and identify the dangerous elevator-riding behaviors of passengers; the early warning prompt unit 302 is used to warn passengers through voice or indicator lights when there is dangerous elevator-riding behavior.

[0067] In this embodiment, the sensors of the elevator control system may generally include the following:

[0068] Speed ​​sensor: used to measure the instantaneous speed of the elevator to ensure that the elevator runs within the specified speed range. The speed sensor can be a magnetic sensor based on the Hall effect or an optical encoder, which calculates the speed by detecting the encoder disk on the rotating shaft.

[0069] Position sensor: used to determine the location of the elevator floor. Common position sensors include but are not limited to grating rulers, photoelectric encoders or linear resistors, which can accurately indicate the specific location of the elevator in the shaft.

[0070] Weight sensors: Installed at the bottom or ceiling of the elevator, they measure the weight of the elevator to prevent overloading. These are usually strain gauge sensors or piezoelectric sensors that can detect the weight change as soon as the passenger enters the elevator.

[0071] Vibration sensor: monitors the vibration of the elevator during operation and promptly detects problems such as wear or looseness of mechanical parts. This type of sensor can be an accelerometer, which can detect abnormal vibration during the operation of the elevator.

[0072] Temperature and humidity sensors: monitor environmental conditions in the elevator machine room and shaft to ensure that electrical equipment is in optimal working condition. If environmental conditions exceed the set range, it may cause damage to equipment such as motors.

[0073] Proximity sensors: Installed near the elevator doors, they detect if there is an object blocking the normal opening and closing of the elevator doors. These sensors can be infrared or ultrasonic types and are able to detect obstacles before the elevator doors close.

[0074] Communication module: responsible for aggregating the data of the above-mentioned sensors and transmitting them to the central control system via wireless or wired means. The communication module may include RFID, Bluetooth, Wi-Fi or a dedicated industrial bus protocol (such as CAN bus).

[0075] See also Figure 2 In a preferred embodiment, the sensor control module 10 specifically includes an inertial measurement unit 101 and a pressure sensor 102; the inertial measurement unit 101 is used to monitor the running speed and acceleration of the elevator in real time, and the pressure sensor 102 is used to detect the impact force when the elevator enters the landing state. That is, the inertial measurement unit 101 can use the aforementioned speed sensor and acceleration sensor to realize the monitoring of the running speed and acceleration, wherein the acceleration sensor can use a MEMS acceleration sensor. These sensors are designed based on micromechanical structures. When the external acceleration changes, the internal tiny mass block will be displaced, and this displacement is converted into an electrical signal output through a conversion circuit. The acceleration sensor can be installed at different positions inside the elevator car, such as the bottom, top or side wall, or it can be installed on the elevator traction machine, guide rail or other key moving parts. The choice of installation position depends on the specific parameters that need to be monitored, such as vertical acceleration, lateral vibration, etc.

[0076] Therefore, by collecting diversified elevator operation data, we can respond quickly to abnormal situations and take corrective measures in a timely manner. Collecting accurate data can support more accurate decision-making, help improve elevator safety performance and reduce accident risks.

[0077] Furthermore, the access control module 20 is intended to analyze the elevator from the landing state to the stable parking state when the elevator is about to reach the target floor, so as to determine whether the elevator has stopped smoothly. For ease of understanding, the "landing" and "parking" states are first explained as follows:

[0078] Landing: usually refers to the process of the elevator car gradually decelerating from the moving state until its bottom touches the bottom buffer device or the ground after reaching the designated floor. During this process, the elevator is still completing the final deceleration action until it stops completely, so the focus is on how the elevator car can smoothly touch the landing position.

[0079] Stopping: It refers to the state after the elevator stops completely and before the door opens. At this time, the elevator is no longer moving and is ready to let passengers in and out. Stopping may also involve the operation of the elevator door and level adjustment (if any) to ensure that the gap between the elevator floor and the floor plane is within a safe range.

[0080] Specifically, in this embodiment, the access control module 20 includes a stop analysis unit 201, an unlock control unit 202 and a delay control unit 203; the stop analysis unit 201 is used to receive the elevator operation data, analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stop state, and generate a stop signal when the elevator reaches the target stop state; the unlock control unit 202 is used to generate an unlock control instruction according to the stop signal; the delay control unit 203 is used to respond to the unlock control instruction and control the elevator door to open and close with a preset delay time.

[0081] In one embodiment, the stop-steady state analysis unit 201 is used to analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stop-steady state, including:

[0082] Calculate the distance the elevator moves when it enters the landing state:

[0083]

[0084] In the formula, S represents the moving distance of the elevator when it enters the landing state, L represents the length of the elevator rope, and v, v max are the running speed and maximum allowable speed of the elevator, respectively; a is the acceleration of the elevator; t is the running time of the elevator when it enters the landing state; k1 and k2 are adjustment factors, and their value range is (0.001, 0.01);

[0085] Judge SH≤∈1 and FF b Whether ≤∈2 holds true at the same time;

[0086] Where H is the standard distance when the elevator enters the landing state, F, F b are the impact force when the elevator enters the landing state and the corresponding preset impact force value, respectively, ∈1 and ∈2 are constants greater than 0;

[0087] If SH≤∈1 and FF b ≤∈2 are both true, and it is determined that the elevator has reached the target stable stop state.

[0088] In this embodiment, The movement of the elevator after entering the landing state is similar to free fall. In this scenario, the acceleration a of the elevator replaces the gravity acceleration g. Here, an adjustment factor k1 is used to adjust the actual moving distance during deceleration. This part calculates the theoretical moving distance based on the elevator deceleration acceleration and time.

[0089] It should be noted that the elevator usually rebounds when it is about to stop. The "rebound distance" of the elevator usually refers to the short-term rebound phenomenon caused by the elastic deformation of the mechanical structure (such as springs, buffers, etc.) or other factors when the elevator stops or lands. This phenomenon may cause the elevator to rise a distance after reaching the target floor, and then fall back to the correct position. Generally, the smaller the rebound distance, the more it can ensure the safety and comfort of passengers. Therefore, Taking into account the impact of rebound distance, It represents the relationship between the elevator speed and the maximum allowable speed, and combines the length L of the elevator steel rope and the adjustment factor k2 to calculate the size of the elevator rebound distance. Among them, when the elevator speed is lower than the maximum speed, the ratio is less than 1, and vice versa. The role of this part may be to compensate for the extra moving distance caused by the speed difference, and adjust the compensation amount through the adjustment factor k2.

[0090] Therefore, the above formula takes into account factors such as the elevator's acceleration, speed, running time and rope length to predict the actual distance the elevator moves when it decelerates to enter the landing state. Through such calculations, it is possible to more accurately evaluate whether the elevator can land smoothly according to the expected distance.

[0091] Furthermore, two specific judgment conditions are given, namely, judging SH≤∈1 and FF b ≤∈2. Where H is the standard distance when the elevator enters the landing state, F, F b are the impact force when the elevator enters the landing state and the corresponding impact force preset value, ∈1 and ∈2 are constants greater than 0, usually a small value. If SH≤∈1 and FF b ≤∈2 are both true, and it is determined that the elevator has reached the target stable stop state.

[0092] In summary, this embodiment uses a stable stop analysis, taking into account the theoretical moving distance and rebound distance, and determines whether the elevator has reached the target stable stop state from the two aspects of displacement and impact force, so that the landing process of the elevator can be accurately controlled, the impact force can be reduced, and passengers can experience a smoother riding experience. At the same time, it ensures that the elevator lands smoothly within a predetermined distance to avoid mechanical damage or accident risks that may be caused by excessive impact force.

[0093] Furthermore, the main responsibility of the unlocking control unit 202 is to generate an unlocking control instruction after the elevator stops. This process depends on the stop signal. Only when the stop analysis unit 201 confirms that the elevator has reached the target stop state, will it send a stop signal to the unlocking control unit 202. The unlocking control instruction usually triggers a series of actions, including but not limited to releasing the locked state of the elevator door to prepare for the subsequent door opening action. However, when it is detected that the elevator has not stopped, no unlocking instruction will be generated. The delay control unit 203 is a part that responds to the unlocking control instruction. Its main task is to control the elevator door to perform the switching action according to the preset time delay according to the unlocking control instruction. For example, a short delay time such as 1-2 seconds can be set to ensure that the door is opened after the elevator is truly stable. This can provide an additional layer of safety.

[0094] Therefore, by delaying the opening and closing of the elevator door, we can ensure that the elevator is indeed in a stable state, avoid abnormal door opening and closing due to incomplete stopping, and increase safety. Appropriate delay can provide passengers with sufficient time to prepare for entering and exiting the elevator, which is especially important for passengers with limited mobility. In addition, delay control can help coordinate the work of other systems inside the elevator, such as lighting and ventilation, so that they can operate synchronously with the door control system and improve efficiency, thereby significantly improving the safety and reliability of elevator operation, while also providing a better user experience.

[0095] The early warning prompt module 30 includes a behavior recognition unit 301 and an early warning prompt unit 302, which are mainly used to recognize some dangerous elevator behaviors and issue early warning prompts.

[0096] In one embodiment, the behavior recognition unit 301 is used to detect the behavior pattern of passengers and identify dangerous elevator behaviors of passengers, including:

[0097] When it is determined that the passenger is a child and is riding the elevator alone, it is marked as dangerous elevator behavior;

[0098] When it is determined that the passenger is carrying a pet and the pet and the passenger do not enter the elevator car together, it is marked as dangerous elevator behavior;

[0099] When it is determined that a passenger's carry-on items are caught by the elevator door, or separated from the elevator car by the elevator door, it is marked as dangerous elevator behavior;

[0100] When it is determined that a passenger is carrying flammable or explosive items, it will be marked as dangerous elevator behavior;

[0101] When it is determined that a passenger is leaning on the elevator door or pulling the elevator door by hand, it is marked as dangerous elevator riding behavior.

[0102] In this embodiment, the identification behavior pattern can be specifically collected by the camera in the elevator video data, so as to analyze it. Once the above behaviors occur, they will be marked as dangerous elevator behaviors by the system and an early warning prompt will be immediately triggered. When the behavior recognition unit 301 detects that the passenger has dangerous elevator behaviors, the early warning prompt unit 302 can start the preset voice prompt system. The voice prompt content can be a clear warning statement, such as "Please note that your items are caught by the door, please take them out as soon as possible", or a general prompt to remind passengers to pay attention to safety, such as "Do not lean against the elevator door". In addition to the voice prompt, the early warning prompt unit 302 can also warn passengers through indicator lights set inside and outside the elevator. For example, when a dangerous behavior is detected, the warning light inside or outside the elevator will flash or light up to attract the attention of passengers and remind them to take necessary measures. In addition, some elevator systems may also be equipped with a multimedia display screen for displaying text warning information or animation demonstrations to further enhance the effect of the early warning. In some cases, the early warning prompt unit 302 can also be linked with the emergency stop mechanism of the elevator. When extremely dangerous behavior is detected, the emergency stop button is automatically activated to prevent the elevator from continuing to run.

[0103] In this way, through timely warning prompts, passengers can quickly realize potential risks and take appropriate measures to avoid danger, which can reduce elevator failures or accidents caused by improper behavior of passengers, thereby reducing the accident rate.

[0104] See also Figure 3 In one embodiment, the early warning module 30 further includes a load distribution evaluation unit 303, which is used to:

[0105] Collect images of the interior of the elevator car, identify the location information of each passenger in the image, and determine the coordinate frame of each passenger in the image;

[0106] Extracting features from the coordinate frame, and predicting the weight of each passenger based on the extracted features and a preset weight prediction model;

[0107] The load distribution in the elevator car is evaluated based on the location information and weight of each passenger; when it is determined that the load distribution needs to be optimized, an optimization instruction and corresponding operation instructions are generated and sent to the early warning prompt unit 302.

[0108] In this embodiment, the load distribution evaluation unit 303 first needs to install one or more cameras inside the elevator car to capture the situation inside the elevator in real time. Through image processing technology, the position information of each passenger in the image is identified, and the coordinate frame of each passenger in the image is determined. Specifically,

[0109] Camera installation: Choose a suitable angle and position to install the camera, ensuring that it covers most areas inside the elevator car.

[0110] Image processing: Use computer vision techniques, such as object detection algorithms (YOLO, SSD, etc.), to identify passengers in the image and mark the location (coordinate box) of each passenger.

[0111] Furthermore, feature extraction is performed on the identified coordinate frame, and the extracted information may include but is not limited to features such as the size, shape, and height of the passenger. These features are then input into a preset weight prediction model to predict the approximate weight of each passenger. A deep learning model, such as a convolutional neural network, may be used to extract features of passengers in the image. The preset weight prediction model may be a trained machine learning model that is trained with a large amount of sample data and is able to estimate the volume based on the features of the passenger, and then estimate the passenger's weight by combining the volume multiplied by the density formula.

[0112] Finally, the load distribution in the elevator car is evaluated based on the position information and predicted weight of each passenger. If the evaluation result shows that the load distribution is uneven or exceeds the safety threshold, an optimization instruction is generated, and the corresponding operation instructions are attached, and sent to the early warning prompt unit 302 to prompt the passengers to reposition to optimize the load distribution, such as "please move a few steps to the right, passenger on the left".

[0113] Therefore, by evaluating the load distribution, this embodiment can effectively prevent elevator failures or accidents caused by unbalanced loading and ensure the safety of passengers. By reasonably distributing the load, the uneven force during elevator operation can be reduced, which helps to extend the service life of the elevator system. Through timely prompts and guidance, passengers can be helped to adjust their standing positions, thereby improving the stability of elevator operation and riding comfort.

[0114] In summary, the control system provided by the present invention realizes the refined management of the elevator operation status and the intelligent monitoring of passenger behavior by introducing advanced data analysis algorithms and behavior recognition technologies. By calculating the moving distance of the elevator when it enters the landing state, and combining factors such as impact force to comprehensively judge whether the elevator is truly stopped, and using a delay mechanism to control the opening and closing of the door, the safety of the elevator door control operation is improved. At the same time, the abnormal behavior of passengers can be identified and responded to in a timely manner, and early warnings can be given through voice or indicator lights, thereby effectively preventing safety hazards caused by human factors. Finally, by monitoring the distribution of passengers in the elevator, the load distribution of the car is optimized, which further improves the safety of elevator operation and improves the passengers' elevator experience.

[0115] In one embodiment, the present invention further provides an intelligent elevator, comprising an elevator safety operation control system based on multivariate data as described in any one of the above embodiments.

[0116] See also Figure 4In one embodiment, the present invention further provides a method for controlling safe operation of an elevator based on multivariate data, the method comprising:

[0117] S10, collecting elevator operation data, analyzing the elevator operation data through a preset algorithm to determine whether the elevator has reached a target stop state, and generating a stop signal when the elevator reaches the target stop state;

[0118] S20, generating an unlocking control instruction according to the stop signal, and controlling the elevator door to open and close with a preset delay time according to the unlocking control instruction;

[0119] S30, detecting the behavior pattern of passengers and identifying the dangerous elevator-riding behaviors of passengers; when there is dangerous elevator-riding behaviors, the passengers are warned through voice or indicator lights.

[0120] In one embodiment, analyzing the elevator operation data by a preset algorithm to determine whether the elevator has reached the target stable stop state includes:

[0121] Calculate the distance the elevator moves when it enters the landing state:

[0122]

[0123] In the formula, S represents the moving distance of the elevator when it enters the landing state, L represents the length of the elevator rope, and v, v max are the running speed and maximum allowable speed of the elevator, respectively; a is the acceleration of the elevator; t is the running time of the elevator when it enters the landing state; k1 and k2 are adjustment factors, and their value range is (0.001, 0.01);

[0124] Judge SH≤∈1 and FF b Whether ≤∈2 holds true at the same time;

[0125] Where H is the standard distance when the elevator enters the landing state, F, F b are the impact force when the elevator enters the landing state and the corresponding preset impact force value, respectively, ∈1 and ∈2 are constants greater than 0;

[0126] If SH≤∈1 and FF b ≤∈2 are both true, and it is determined that the elevator has reached the target stable stop state.

[0127] See also Figure 5 In a preferred embodiment, the method further comprises:

[0128] S60, collecting an image of the interior of the elevator car, identifying the position information of each passenger in the image, and determining a coordinate frame of each passenger in the image;

[0129] S50, extracting features from the coordinate frame, and predicting the weight of each passenger based on the extracted features and a preset weight prediction model;

[0130] S60. Evaluate the load distribution in the elevator car based on the position information and weight of each passenger; when it is determined that the load distribution needs to be optimized, generate optimization instructions and corresponding operation instructions to prompt the passengers to change their position distribution in the elevator car.

[0131] It can be understood that the method provided in this embodiment is applied to the elevator safety operation control system based on multivariate data as provided in any of the aforementioned embodiments. Its specific implementation and corresponding effects can be detailed with reference to the description of the above embodiments. For the sake of brevity, they will not be repeated here.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. An elevator safety operation control system based on multivariate data, characterized in that: The system comprises: Sensor control module, access control module and early warning prompt module; The sensor control module is used to collect elevator operation data; The access control module includes a stop stability analysis unit, an unlocking control unit and a delay control unit; The stop analysis unit is used to receive the elevator operation data, analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stop state, and generate a stop signal when the elevator reaches the target stop state; the unlock control unit is used to generate an unlock control instruction according to the stop signal; the delay control unit is used to respond to the unlock control instruction and control the elevator door to open and close with a preset delay time; The early warning prompt module includes a behavior recognition unit and an early warning prompt unit; The behavior recognition unit is used to detect the passenger's behavior pattern and identify the passenger's dangerous elevator-riding behavior; the early warning prompt unit is used to warn the passenger through voice or indicator light when there is dangerous elevator-riding behavior.

2. The elevator safe operation control system based on multivariate data according to claim 1, characterized in that: The sensor control module includes an inertial measurement unit and a pressure sensor; The inertial measurement unit is used to monitor the running speed and acceleration of the elevator in real time, and the pressure sensor is used to detect the impact force when the elevator enters the landing state.

3. The elevator safe operation control system based on multivariate data according to claim 2, characterized in that: The stable stop analysis unit is used to analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stable stop state, including: Calculate the distance the elevator moves when it enters the landing state: In the formula, S represents the moving distance of the elevator when it enters the landing state, L represents the length of the elevator rope, and v, v max are the running speed and maximum allowable speed of the elevator, respectively; a is the acceleration of the elevator; t is the running time of the elevator when it enters the landing state; k1 and k2 are adjustment factors, and their value range is (0.001, 0.01); Judge SH≤∈1 and FF b Whether ≤∈2 holds true at the same time; Where H is the standard distance when the elevator enters the landing state, F, F b are the impact force when the elevator enters the landing state and the corresponding preset impact force value, respectively, ∈1 and ∈2 are constants greater than 0; If SH≤∈1 and FF b ≤∈2 are both true, and it is determined that the elevator has reached the target stable stop state.

4. The elevator safe operation control system based on multivariate data according to claim 1, characterized in that: The behavior recognition unit is used to detect the behavior pattern of passengers and identify the dangerous elevator riding behavior of passengers, including: When it is determined that the passenger is a child and is riding the elevator alone, it is marked as dangerous elevator behavior; When it is determined that the passenger is carrying a pet and the pet and the passenger do not enter the elevator car together, it is marked as dangerous elevator behavior; When it is determined that a passenger's carry-on items are caught by the elevator door, or separated from the elevator car by the elevator door, it is marked as dangerous elevator behavior; When it is determined that a passenger is carrying flammable or explosive items, it will be marked as dangerous elevator behavior; When it is determined that a passenger is leaning on the elevator door or pulling the elevator door by hand, it is marked as dangerous elevator riding behavior.

5. The elevator safe operation control system based on multivariate data according to claim 1, characterized in that: The early warning prompt module also includes a load distribution evaluation unit, which is used to: Collect images of the interior of the elevator car, identify the location information of each passenger in the image, and determine the coordinate frame of each passenger in the image; Extracting features from the coordinate frame, and predicting the weight of each passenger based on the extracted features and a preset weight prediction model; Based on the location information and weight of each passenger, the load distribution in the elevator car is evaluated; when it is determined that the load distribution needs to be optimized, optimization instructions and corresponding operation instructions are generated and sent to the early warning prompt unit.

6. An intelligent elevator, characterized in that: It comprises an elevator safe operation control system based on multivariate data as described in any one of claims 1-5.

7. A method for controlling safe operation of an elevator based on multivariate data, applied to the control system for safe operation of an elevator based on multivariate data as claimed in any one of claims 1 to 5, characterized in that: The method comprises: Collect elevator operation data, analyze the elevator operation data through a preset algorithm to determine whether the elevator has reached the target stop state, and generate a stop signal when the elevator reaches the target stop state; Generate an unlocking control command according to the stop signal, and control the elevator door to open and close with a preset delay according to the unlocking control command; Detect passengers' behavior patterns and identify passengers' dangerous elevator behaviors; when dangerous elevator behaviors occur, passengers will be warned through voice or indicator lights.

8. The elevator safe operation control method based on multivariate data according to claim 7, characterized in that: The analyzing the elevator operation data by a preset algorithm to determine whether the elevator has reached the target stable stop state includes: Calculate the distance the elevator moves when it enters the landing state: In the formula, S represents the moving distance of the elevator when it enters the landing state, L represents the length of the elevator rope, and v, v max are the running speed and maximum allowable speed of the elevator, respectively; a is the acceleration of the elevator; t is the running time of the elevator when it enters the landing state; k1 and k2 are adjustment factors, and their value range is (0.001, 0.01); Judge SH≤∈1 and FF b Whether ≤∈2 holds true at the same time; Where H is the standard distance when the elevator enters the landing state, F, F b are the impact force when the elevator enters the landing state and the corresponding preset impact force value, respectively, ∈1 and ∈2 are constants greater than 0; If SH≤∈1 and FF b ≤∈2 are both true, and it is determined that the elevator has reached the target stable stop state.

9. The elevator safe operation control method based on multivariate data according to claim 7, characterized in that: The method further comprises: Collect images of the interior of the elevator car, identify the location information of each passenger in the image, and determine the coordinate frame of each passenger in the image; Extracting features from the coordinate frame, and predicting the weight of each passenger based on the extracted features and a preset weight prediction model; The load distribution in the elevator car is evaluated based on the position information and weight of each passenger; when it is determined that the load distribution needs to be optimized, optimization instructions and corresponding operation instructions are generated to prompt passengers to change their position distribution in the elevator car.

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