Integrated intelligent fault early warning system for elevator door knife

Abstract

The invention discloses an integrated intelligent fault early warning system for an elevator door knife, and relates to the technical field of elevator safety detection, the integrated intelligent fault early warning system comprises a sensor monitoring module, an analysis processing module, an alarm module, a wireless communication module and an intelligent prediction and maintenance module, the dynamic threshold value adjusting unit dynamically adjusts an early warning threshold value according to the running state of the elevator door knife and external environment factors. The early warning threshold value is dynamically adjusted through the dynamic threshold value adjusting unit according to the running state of the elevator door knife and the external environment factors so as to reduce false alarm and missing alarm, the reasons of false alarm and missing alarm are analyzed, the threshold value setting of the system is further optimized so as to improve the accuracy and reliability of the system, and the early warning threshold value is automatically adjusted through the reinforcement learning algorithm. The system can adapt to different operation environments and fault modes, and the decision strategy is learned and optimized, so that the early warning threshold value is automatically adjusted, and the system can adapt to different operation environments and fault modes.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator safety detection, and in particular to an integrated intelligent fault warning system for elevator door blades. Background Art

[0002] The elevator door knife integrated intelligent fault warning system is designed to improve the stability and safety of the elevator door system. The elevator door knife integrated intelligent fault warning system combines the elevator door knife with the intelligent fault warning technology to realize real-time monitoring of the elevator door system status and fault warning. The system collects the operation data of the elevator door system in real time through integrated sensors, controllers, communication modules and other components. The elevator door knife integrated intelligent fault warning system is suitable for all types of elevators, especially elevators in high-rise residential buildings, commercial buildings, hospitals and other places.

[0003] The defects of the elevator door knife fault warning system in the prior art are:

[0004] 1. Patent document CN118405556A discloses an elevator door knife control system and application that integrates a high-performance digital accompanying cable. The control system cannot dynamically adjust the warning threshold according to the operating status of the elevator door knife and external environmental factors, which can easily cause false alarms and missed alarms in the system, increase potential safety hazards, and cannot guarantee the accuracy and reliability of the system.

[0005] 2. Patent document CN221165491U discloses an elevator door fault monitoring system, which cannot dynamically adjust the warning threshold according to the operating status of the elevator door knife and external environmental factors, which may easily cause false alarms and missed alarms of the system, increase potential safety hazards, and fail to guarantee the accuracy and reliability of the system.

[0006] 3. Patent document CN118289600A discloses an elevator door fault monitoring system and method. The fault monitoring system cannot predict the future operating status and fault trend of the elevator, cannot predict potential fault risks and formulate maintenance plans in advance, and is prone to emergency repairs due to faults. The high fault rate reduces the reliability and safety of the elevator.

[0007] 4. Patent document CN116331989A discloses a universal elevator fault warning system, which cannot predict the future operating status and fault trend of the elevator, cannot predict potential fault risks and formulate maintenance plans in advance, and is prone to emergency repairs due to faults. The high fault rate reduces the reliability and safety of the elevator. Summary of the invention

[0008] The object of the present invention is to provide an integrated intelligent fault warning system for elevator door blades to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above-mentioned object, the present invention provides the following technical solutions: an integrated intelligent fault warning system for elevator door blades, comprising a sensor monitoring module, an analysis and processing module, an alarm module, a wireless communication module and an intelligent prediction and maintenance module;

[0010] The analysis and processing module receives the signal from the sensor monitoring module, and filters, denoises and analyzes the received signal to extract characteristic values ​​and trend information. The analysis and processing module includes a data storage unit and a dynamic threshold adjustment unit. The dynamic threshold adjustment unit dynamically adjusts the warning threshold according to the operating status of the elevator door knife and external environmental factors.

[0011] Preferably, the sensor monitoring module monitors the operating status of the elevator door knife in real time, and the sensor monitoring module includes a position sensor, a speed sensor, a temperature sensor and an infrared detection sensor. The position sensor, speed sensor and temperature sensor detect the position, speed and temperature of the elevator door knife respectively, and the infrared detection sensor detects the presence of foreign objects in the elevator door knife.

[0012] Preferably, the data storage unit is used to store and manage data.

[0013] Preferably, the warning threshold is automatically adjusted using a reinforcement learning algorithm, and the Q-learning algorithm formula is as follows:

[0014] Q(s,a)←Q(s,a)+a[r+γmaxa'Q(s',a')-Q(s,a)]

[0015] Among them, Q(s,a) represents the Q value when action a is selected in state s, r is the current reward, γ is the discount factor, which represents the decay rate of future rewards, s' is the state of the next step, a' is the action selected in the next step, and α is the learning rate, which represents the sensitivity of the agent to environmental feedback;

[0016] Define state s as the operating data of the door knife, action a as adjusting the warning threshold, and reward r as a certain measure based on the accuracy of the warning. By continuously interacting with the environment, the agent learns how to adjust the warning threshold in different states to maximize the cumulative reward.

[0017] Preferably, the alarm module is used to send out an audible and visual alarm signal through an alarm device when the analysis and processing module detects that there is a fault in the elevator door knife. The alarm device includes an audible and visual alarm and a display screen. The audible and visual alarm sends out an audible and visual alarm signal to remind passengers and elevator maintenance personnel to pay attention to safety, and the display screen displays fault information to help passengers and maintenance personnel quickly understand the fault situation.

[0018] Preferably, the wireless communication module sends the fault information to the elevator maintenance personnel, and the timely transmission of the fault information enables the maintenance personnel to quickly rush to the scene to troubleshoot and repair the fault.

[0019] Preferably, the intelligent prediction and maintenance module performs in-depth analysis on the operation data of the elevator door knife based on the prediction model of big data analysis and statistical learning algorithm, predicts potential failure risks and formulates maintenance plans in advance.

[0020] Preferably, Bayesian theorem is used for probabilistic reasoning to predict the probability of elevator door blade failure based on historical data and current observations. The algorithm formula is as follows:

[0021]

[0022] Where P is the probability of an event occurring, x i is the independent variable, β i is the regression coefficient.

[0023] Perform time series modeling on the operation data of elevator door blades to predict future operation status and failure trends.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. The present invention dynamically adjusts the warning threshold value according to the operating state of the elevator door knife and external environmental factors through a dynamic threshold adjustment unit to reduce false alarms and missed alarms, prevent false alarms and missed alarms from causing unnecessary troubles to maintenance personnel, reduce potential safety hazards caused by false alarms or missed alarms, analyze the causes of false alarms and missed alarms, further optimize the threshold setting of the system to improve the accuracy and reliability of the system, regularly perform system calibration and verification to ensure the stability and accuracy of the system, and use a reinforcement learning algorithm to automatically adjust the warning threshold value so that the system can adapt to different operating environments and failure modes. The reinforcement learning algorithm interacts with the environment, learns and optimizes the decision-making strategy to automatically adjust the warning threshold value so that the system can adapt to different operating environments and failure modes.

[0026] 2. The present invention uses Bayesian theorem for probabilistic reasoning, predicts the failure probability of the elevator door knife according to historical data and current observation values, performs time series modeling on the operation data of the elevator door knife, predicts future operation status and failure trends, conducts in-depth analysis on the operation data of the elevator door knife, predicts potential failure risks and formulates maintenance plans in advance. Intelligent early warning and in-depth analysis enable the system to reduce unnecessary maintenance and inspection times, thereby improving maintenance efficiency. Formulating maintenance plans in advance can avoid emergency repairs and downtime caused by failures. The real-time monitoring and early warning functions of the system can ensure that the elevator door knife is repaired in time before a failure occurs, which helps to reduce the occurrence of failures and improve the reliability and safety of elevators. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the working process of the fault warning system of the present invention;

[0028] Figure 2 It is a schematic diagram of the analysis and processing module of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0030] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0031] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection or a movable connection, or a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0032] Embodiment 1, an embodiment provided by the present invention: an integrated intelligent fault warning system for elevator door blades, comprising a sensor monitoring module;

[0033] The sensor monitoring module monitors the operating status of the elevator door knife in real time. The sensor monitoring module includes a position sensor, a speed sensor, a temperature sensor and an infrared detection sensor. The position sensor, the speed sensor and the temperature sensor detect the position, speed and temperature of the elevator door knife respectively, and the infrared detection sensor detects the presence of foreign objects in the elevator door knife.

[0034] Embodiment 2, based on the above embodiment, the present invention provides an embodiment: an integrated intelligent fault warning system for elevator door blades, comprising a sensor monitoring module, an analysis and processing module and a wireless communication module;

[0035] The sensor monitoring module monitors the operating status of the elevator door knife in real time. The sensor monitoring module includes a position sensor, a speed sensor, a temperature sensor and an infrared detection sensor. The position sensor, the speed sensor and the temperature sensor detect the position, speed and temperature of the elevator door knife respectively, and the infrared detection sensor detects the presence of foreign matter in the elevator door knife;

[0036] The analysis and processing module receives the signal from the sensor monitoring module, and performs filtering, denoising and analysis on the received signal to extract characteristic values ​​and trend information. The analysis and processing module includes a data storage unit and a dynamic threshold adjustment unit. The dynamic threshold adjustment unit dynamically adjusts the warning threshold according to the operating state of the elevator door knife and external environmental factors.

[0037] The wireless communication module sends the fault information to the elevator maintenance personnel. The timely transmission of the fault information enables the maintenance personnel to rush to the scene to troubleshoot and repair the fault.

[0038] Embodiment 3, based on the above embodiment, the present invention provides an embodiment: an integrated intelligent fault warning system for elevator door blades, comprising a sensor monitoring module, an analysis and processing module, an alarm module and a wireless communication module;

[0039] The sensor monitoring module monitors the operating status of the elevator door knife in real time. The sensor monitoring module includes a position sensor, a speed sensor, a temperature sensor and an infrared detection sensor. The position sensor, the speed sensor and the temperature sensor detect the position, speed and temperature of the elevator door knife respectively, and the infrared detection sensor detects the presence of foreign matter in the elevator door knife;

[0040] The analysis and processing module receives the signal from the sensor monitoring module, and filters, denoises and analyzes the received signal to extract characteristic values ​​and trend information. The analysis and processing module includes a data storage unit and a dynamic threshold adjustment unit. The dynamic threshold adjustment unit dynamically adjusts the warning threshold according to the operating status of the elevator door knife and external environmental factors. The data storage unit is used to store and manage data.

[0041] The reinforcement learning algorithm is used to automatically adjust the warning threshold. The Q-learning algorithm formula is as follows:

[0042] Q(s,a)←Q(s,a)+α[r+γmaxa'Q(s',a')-Q(s,a)]

[0043] Among them, Q(s,a) represents the Q value when action a is selected in state s, r is the current reward, γ is the discount factor, which represents the decay rate of future rewards, s' is the state of the next step, a' is the action selected in the next step, and α is the learning rate, which represents the sensitivity of the agent to environmental feedback;

[0044] Define the state s as the operating data of the door knife, the action a as adjusting the warning threshold, and the reward r as a measure based on the accuracy of the warning. By continuously interacting with the environment, the agent learns how to adjust the warning threshold in different states to maximize the cumulative reward.

[0045] The alarm module is used to send out an audible and visual alarm signal through an alarm device when the analysis and processing module detects that there is a fault in the elevator door knife. The alarm device includes an audible and visual alarm and a display screen. The audible and visual alarm sends out an audible and visual alarm signal to remind passengers and elevator maintenance personnel to pay attention to safety, and the display screen displays fault information to help passengers and maintenance personnel quickly understand the fault situation;

[0046] The wireless communication module sends the fault information to the elevator maintenance personnel. The timely transmission of the fault information enables the maintenance personnel to rush to the scene to troubleshoot and repair the fault.

[0047] Embodiment 4, based on the above embodiment, the present invention provides an embodiment: an integrated intelligent fault warning system for elevator door blades, comprising a sensor monitoring module, an analysis and processing module, an alarm module, a wireless communication module and an intelligent prediction and maintenance module;

[0048] The sensor monitoring module monitors the operating status of the elevator door knife in real time. The sensor monitoring module includes a position sensor, a speed sensor, a temperature sensor and an infrared detection sensor. The position sensor, the speed sensor and the temperature sensor detect the position, speed and temperature of the elevator door knife respectively, and the infrared detection sensor detects the presence of foreign matter in the elevator door knife;

[0049] The analysis and processing module receives the signal from the sensor monitoring module, and performs filtering, denoising and analysis on the received signal to extract characteristic values ​​and trend information. The analysis and processing module includes a data storage unit and a dynamic threshold adjustment unit. The dynamic threshold adjustment unit dynamically adjusts the warning threshold according to the operating state of the elevator door knife and external environmental factors. The data storage unit is used to store and manage data;

[0050] The reinforcement learning algorithm is used to automatically adjust the warning threshold. The Q-learning algorithm formula is as follows:

[0051] Q(s,a)←Q(s,a)+α[r+γmaxa'Q(s',a')-Q(s,a)]

[0052] Among them, Q(s,a) represents the Q value when action a is selected in state s, r is the current reward, γ is the discount factor, which represents the decay rate of future rewards, s' is the state of the next step, a' is the action selected in the next step, and α is the learning rate, which represents the sensitivity of the agent to environmental feedback;

[0053] Define the state s as the operating data of the door knife, the action a as adjusting the warning threshold, and the reward r as a measure based on the accuracy of the warning. By continuously interacting with the environment, the agent learns how to adjust the warning threshold in different states to maximize the cumulative reward.

[0054] The alarm module is used to send out an audible and visual alarm signal through an alarm device when the analysis and processing module detects that there is a fault in the elevator door knife. The alarm device includes an audible and visual alarm and a display screen. The audible and visual alarm sends out an audible and visual alarm signal to remind passengers and elevator maintenance personnel to pay attention to safety, and the display screen displays fault information to help passengers and maintenance personnel quickly understand the fault situation;

[0055] The wireless communication module sends fault information to the elevator maintenance personnel. The timely transmission of fault information enables the maintenance personnel to rush to the scene to conduct fault investigation and repair;

[0056] The intelligent prediction and maintenance module conducts in-depth analysis of the elevator door knife operation data based on the prediction model of big data analysis and statistical learning algorithms.

[0057] Embodiment 5, based on the above embodiment, the present invention provides an embodiment: an integrated intelligent fault warning system for elevator door blades, comprising a sensor monitoring module, an analysis and processing module, an alarm module, a wireless communication module and an intelligent prediction and maintenance module;

[0058] The sensor monitoring module monitors the operating status of the elevator door knife in real time. The sensor monitoring module includes a position sensor, a speed sensor, a temperature sensor and an infrared detection sensor. The position sensor, the speed sensor and the temperature sensor detect the position, speed and temperature of the elevator door knife respectively, and the infrared detection sensor detects the presence of foreign matter in the elevator door knife;

[0059] The analysis and processing module receives the signal from the sensor monitoring module, and performs filtering, denoising and analysis on the received signal to extract characteristic values ​​and trend information. The analysis and processing module includes a data storage unit and a dynamic threshold adjustment unit. The dynamic threshold adjustment unit dynamically adjusts the warning threshold according to the operating state of the elevator door knife and external environmental factors. The data storage unit is used to store and manage data;

[0060] The reinforcement learning algorithm is used to automatically adjust the warning threshold. The Q-learning algorithm formula is as follows:

[0061] Q(s,a)←Q(s,a)+α[r+γmaxa'Q(s',a')-Q(s,a)]

[0062] Among them, Q(s,a) represents the Q value when action a is selected in state s, r is the current reward, γ is the discount factor, which represents the decay rate of future rewards, s' is the state of the next step, a' is the action selected in the next step, and α is the learning rate, which represents the sensitivity of the agent to environmental feedback;

[0063] Define the state s as the operating data of the door knife, the action a as adjusting the warning threshold, and the reward r as a measure based on the accuracy of the warning. By continuously interacting with the environment, the agent learns how to adjust the warning threshold in different states to maximize the cumulative reward.

[0064] The alarm module is used to send out an audible and visual alarm signal through an alarm device when the analysis and processing module detects that there is a fault in the elevator door knife. The alarm device includes an audible and visual alarm and a display screen. The audible and visual alarm sends out an audible and visual alarm signal to remind passengers and elevator maintenance personnel to pay attention to safety, and the display screen displays fault information to help passengers and maintenance personnel quickly understand the fault situation;

[0065] The wireless communication module sends fault information to the elevator maintenance personnel. The timely transmission of fault information enables the maintenance personnel to rush to the scene to conduct fault investigation and repair;

[0066] The intelligent prediction and maintenance module uses a prediction model based on big data analysis and statistical learning algorithms to conduct in-depth analysis of the elevator door blade operation data, predict potential failure risks, and formulate maintenance plans in advance;

[0067] Bayesian theorem is used for probabilistic reasoning to predict the probability of elevator door blade failure based on historical data and current observations. The algorithm formula is as follows:

[0068]

[0069] Where P is the probability of an event occurring, x i is the independent variable, β i is the regression coefficient.

[0070] Perform time series modeling on the operation data of elevator door blades to predict future operation status and failure trends.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An integrated intelligent fault warning system for elevator door blades, characterized in that: It includes sensor monitoring module, analysis and processing module, alarm module, wireless communication module and intelligent prediction and maintenance module; The analysis and processing module receives the signal from the sensor monitoring module, and filters, denoises and analyzes the received signal to extract characteristic values ​​and trend information. The analysis and processing module includes a data storage unit and a dynamic threshold adjustment unit. The dynamic threshold adjustment unit dynamically adjusts the warning threshold according to the operating status of the elevator door knife and external environmental factors.

2. According to claim 1, an integrated intelligent fault warning system for elevator door blades is characterized by: The sensor monitoring module monitors the operating status of the elevator door knife in real time. The sensor monitoring module includes a position sensor, a speed sensor, a temperature sensor and an infrared detection sensor. The position sensor, speed sensor and temperature sensor detect the position, speed and temperature of the elevator door knife respectively. The infrared detection sensor detects the presence of foreign matter in the elevator door knife.

3. The integrated intelligent fault warning system for elevator door blades according to claim 1 is characterized by: The data storage unit is used to store and manage data.

4. The integrated intelligent fault warning system for elevator door blades according to claim 1 is characterized by: The warning threshold is automatically adjusted using a reinforcement learning algorithm. The Q-learning algorithm formula is as follows: Q(s,a)←Q(s,a)+α[r+γmaxa'Q(s',a')-Q(s,a)] Among them, Q(s,a) represents the Q value when action a is selected in state s, r is the current reward, γ is the discount factor, which represents the decay rate of future rewards, s' is the state of the next step, a' is the action selected in the next step, and α is the learning rate, which represents the sensitivity of the agent to environmental feedback; Define state s as the operating data of the door knife, action a as adjusting the warning threshold, and reward r as a certain measure based on the accuracy of the warning. By continuously interacting with the environment, the agent learns how to adjust the warning threshold in different states to maximize the cumulative reward.

5. The integrated intelligent fault warning system for elevator door blades according to claim 1 is characterized by: The alarm module is used to send out an audible and visual alarm signal through an alarm device when the analysis and processing module detects that there is a fault in the elevator door knife. The alarm device includes an audible and visual alarm and a display screen. The audible and visual alarm sends out an audible and visual alarm signal to remind passengers and elevator maintenance personnel to pay attention to safety, and the display screen displays fault information to help passengers and maintenance personnel quickly understand the fault situation.

6. The integrated intelligent fault warning system for elevator door blades according to claim 1 is characterized by: The wireless communication module sends the fault information to the elevator maintenance personnel, and the timely transmission of the fault information enables the maintenance personnel to quickly rush to the scene to troubleshoot and repair the fault.

7. The integrated intelligent fault warning system for elevator door blades according to claim 1 is characterized by: The intelligent prediction and maintenance module conducts in-depth analysis of the operation data of the elevator door knife based on the prediction model of big data analysis and statistical learning algorithm, predicts potential failure risks and formulates maintenance plans in advance.

8. The integrated intelligent fault warning system for elevator door blades according to claim 7 is characterized by: Bayesian theorem is used for probabilistic reasoning to predict the probability of elevator door blade failure based on historical data and current observations. The algorithm formula is as follows: Where P is the probability of an event occurring, x i is the independent variable, β i is the regression coefficient. Perform time series modeling on the operation data of elevator door blades to predict future operation status and failure trends.

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