Elevator travel limiting monitoring management system

Abstract

The invention discloses an elevator travel limiting monitoring management system, which relates to the technical field of travel limiting management monitoring and comprises a travel detection module, an intelligent monitoring device, a limiting judgment module, an execution control module and a remote monitoring platform. According to the method, the laser measurement and standard gauge composite calibration technology is adopted, the stroke detection precision is improved through the optical fiber synchronization and dynamic error compensation unit, the fault pre-judgment accuracy is further improved by combining the abnormal mode recognition model constructed by the convolutional neural network, and the fault prediction accuracy is further improved through the three-level risk instruction grading response strategy. Under electromagnetic braking, hydraulic buffering and mechanical band-type braking triple-combination braking, the accident response time is shortened, three-dimensional visual reconstruction of the elevator running state is achieved by means of a digital twin platform constructed through a 5G network, full-life-cycle data of equipment are completely traced in cooperation with a block chain evidence storage device, the preventive maintenance efficiency is improved, and the safety and reliability of the equipment are improved. And therefore, the elevator operation safety and the intelligent monitoring accuracy are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of travel limit management and monitoring, and specifically provides an elevator travel limit monitoring and management system. Background Art

[0002] In the field of elevator safety control, the travel limit monitoring system is the core protection device to prevent the car from overshooting or bottoming out. The traditional elevator limit system mainly relies on mechanical limit switches or photoelectric sensors to achieve emergency stop control by triggering physical switches at fixed positions.

[0003] The existing defects of the elevator travel limit monitoring and management system are as follows:

[0004] 1. The patent document US08602172B2 discloses an elevator group management system. However, the management system in the above document has the technical problems of relying on a single-wavelength laser or mechanical sensor, lacking a multi-wavelength complementary structure and calibration system, and having poor measurement accuracy;

[0005] 2. The patent document US08567569B2 discloses an elevator group management system. However, the management system in the above document has the technical problem of being difficult to distinguish real displacement and environmental disturbance noise, resulting in compensation lag;

[0006] 3. The patent document US09682843B2 discloses an elevator group management system. However, the management system in the above document has the technical problems of lacking a hierarchical response mechanism and being prone to mechanical shock damage and passenger discomfort during emergency braking;

[0007] 4. The patent document CN104176567A discloses an elevator monitoring and management system. However, the management system in the above document has the technical problems of lacking the integration of blockchain evidence storage and digital twin technology, being unable to predict the failure time of components, and having low security and maintenance costs. Summary of the Invention

[0008] The purpose of the present invention is to provide an elevator travel limit monitoring and management system to solve the technical problems raised in the above background art.

[0009] To achieve the above purpose, the present invention provides the following technical solution: An elevator travel limit monitoring and management system includes a travel detection module, an intelligent monitoring device, a limit determination module, an execution control module, and a remote monitoring platform. The travel detection module includes a laser measurement instrument and a calibration standard instrument. The laser measurement instrument includes a laser emission unit and a laser reception unit, which are respectively installed at the top of the elevator shaft and the top of the car, and measure data in real time through a fiber optic synchronization interface. The calibration standard instrument includes a standard length rail fixed at the bottom pit of the elevator and an angle calibrator installed on the side wall, and is connected to the laser measurement instrument through a calibration bus;

[0010] The intelligent monitoring device receives the raw data of the stroke detection module through the first high-speed data channel. The intelligent monitoring device includes an edge computing unit for performing spatio-temporal alignment of multi-source data, a dynamic error compensation unit for receiving the reference data of a calibration standard instrument and generating an error compensation coefficient, and an abnormal mode recognition unit for receiving the compensated data and outputting an operating state feature vector through a convolutional neural network;

[0011] The limit determination module is connected to the intelligent monitoring device through the second high-speed data channel. It includes a physical model platform for calculating the theoretical trajectory based on data, a prediction unit for receiving the feature vector of the intelligent monitoring device and predicting the actual trajectory deviation, and a fusion decision maker that integrates the physical model and the prediction result using the D-S evidence theory to generate a three-level risk instruction;

[0012] The execution control module receives the instruction of the limit determination module through the control bus. It includes an electromagnetic braking unit for starting a linearly increasing brake in response to a first-level instruction, a hydraulic buffer unit for adjusting the opening of a damping valve according to a second-level instruction, and an emergency braking mechanism for triggering mechanical locking under a third-level instruction;

[0013] The remote monitoring platform establishes two-way communication with each module through a 5G network. It includes a digital twin platform for real-time reconstruction of the three-dimensional operating state of the elevator, a blockchain evidence storage for encrypting and storing calibration records and abnormal events, and a maintenance decision unit for analyzing the component life based on historical data.

[0014] Preferably, the laser emission unit of the laser measuring instrument adopts a dual-wavelength alternating emission mode, where: the first working wavelength is 850 nm, and the measurement accuracy is ±0.5 mm / m;

[0015] The second working wavelength is 1550 nm, and the measurement accuracy is ±1.2 mm / m.

[0016] Preferably, the fiber optic synchronization interface adopts TDM time division multiplexing technology, including: the uplink channel transmits laser ranging data, with a bandwidth ≥2 Gbps, the downlink channel transmits calibration control signals, with a delay ≤1 ms, and the clock synchronization accuracy reaches the nanosecond level.

[0017] Preferably, the surface of the standard length rail of the calibration standard instrument is provided with magnetically encoded marks distributed at equal intervals. The angle calibrator includes a three-axis gyroscope and a laser reflection array. The calibration bus adopts a timestamp synchronization protocol to match the sampling period of the laser measuring instrument with the physical spacing of the magnetically encoded marks and generate a dynamic calibration curve.

[0018] Preferably, the dynamic error compensation unit adopts the Kalman filtering algorithm, uses the reference data of the standard length track as the observation value, and the real-time data of the laser measuring instrument as the state quantity to construct an error transfer function, and outputs a compensation coefficient to the data preprocessing layer of the abnormal mode recognition unit.

[0019] Preferably, the convolutional neural network includes parallel spatio-temporal convolution branches, where the spatial branch uses a 3x3 convolution kernel to extract the spatial features of the laser data, and the temporal branch uses causal convolution to extract the temporal features of the car speed. Finally, the operating state feature vector is output after weighted fusion through the attention mechanism.

[0020] Preferably, the physical model platform is built-in with the rigid-flexible coupling dynamics equation of the elevator. The input parameters include the radius of the traction wheel, the elastic modulus of the wire rope, and the mass of the car load. The theoretical trajectory is solved by the Runge-Kutta method, and the coherence analysis is performed in the frequency domain with the laser measurement data to generate the model confidence weight for the fusion decision maker to call.

[0021] Preferably, the linear increasing braking strategy of the electromagnetic braking unit satisfies the formula:

[0022] F(t) = k1 * v(t) + k2 * Δs

[0023] Where F(t) is the real-time braking force, v(t) is the instantaneous speed of the car, Δs is the distance difference from the target limit, k1 and k2 are adjustable gain coefficients, and the gain coefficients change stepwise with the three-level risk instruction level.

[0024] Preferably, the blockchain evidence storage device adopts a hierarchical Merkle tree structure, stores the calibration records and abnormal events in blocks according to the time stamp. Each block header contains the SHA-3 hash value of the previous block, and the operation records of the maintenance personnel are verifiably encrypted through zero-knowledge proof technology.

[0025] Preferably, the maintenance decision unit integrates the Weibull distribution life prediction model. The input parameters include the electromagnetic braking response delay time, the number of actions of the hydraulic buffer valve, and the laser light intensity attenuation rate. The probability distribution diagram of the remaining life of each component is output, and a spare part replacement priority list is automatically generated.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] 1. The present invention adopts a composite calibration technology of laser measurement and standard rail, improves the stroke detection accuracy through an optical fiber synchronization and dynamic error compensation unit, constructs an abnormal pattern recognition model by combining a convolutional neural network, thereby improving the accuracy of fault prediction. Then, through a three-level risk instruction hierarchical response strategy, under the triple composite braking of electromagnetic braking, hydraulic buffering, and mechanical braking, the accident response time is shortened. And relying on the digital twin platform constructed by the 5G network to realize the three-dimensional visual reconstruction of the elevator operation state, and cooperating with the blockchain evidence storage device to completely trace the data of the entire life cycle of the equipment, improving the efficiency of preventive maintenance, and thus significantly improving the elevator operation safety and the accuracy of intelligent monitoring;

[0028] 2. The present invention realizes real-time and accurate ranging in complex environments through a dual-wavelength alternating emission mode, combined with the 2Gbps high-speed data transmission and nanosecond-level synchronization accuracy of the TDM time-division multiplexing fiber optic interface. The magnetic coding mark of the standard length rail and the three-axis gyroscope of the angle calibrator cooperate, and together with the timestamp synchronization protocol, dynamically match the laser sampling period and the physical distance to generate a multi-dimensional calibration curve, thereby reducing the cumulative error caused by temperature deformation and mechanical vibration. The closed-loop feedback mechanism of the laser reflection array and the calibration bus enables the system to maintain millimeter-level absolute measurement accuracy even under dynamic conditions such as elevator acceleration and sway, and can provide a high-confidence data basis for limit determination;

[0029] 3. The present invention, through the dynamic error compensation unit, fuses the reference data of the standard length rail and the real-time state quantity of the laser measuring instrument based on the Kalman filter algorithm, dynamically corrects the non-linear deviation caused by environmental disturbances through the error transfer function, and accurately captures the abnormal distribution of laser data through the 3x3 convolutional kernel of the spatial branch. The causal convolution of the time branch deeply mines the sudden change mode of the car speed, and then through the attention mechanism, dynamically allocates feature weights to achieve improved recognition accuracy of floor leveling sway and guide rail wear, and realizes the closed-loop linkage of error compensation and feature extraction through the preprocessing layer, providing a high signal-to-noise ratio feature input for elevator safety state assessment;

[0030] 4. The present invention uses a hierarchical Merkle tree structure in the blockchain evidence storage device to realize the traceable storage of calibration records and abnormal events. Through the SHA-3 hash chain and zero-knowledge proof technology, while ensuring the immutability of operation records, privacy protection is achieved. The maintenance decision unit integrates the Weibull distribution model, based on multi-dimensional decay parameters such as electromagnetic braking delay, hydraulic valve action frequency, and laser attenuation rate, outputs a probabilistic life prediction curve, combines the historical fault data stored in the blockchain, improves the accuracy of component failure prediction, and generates a dynamically optimized spare part replacement list, thereby reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a schematic diagram of the system structure of the present invention;

[0032] Figure 2 Schematic structural diagram of the system main control flow of the present invention;

[0033] Figure 3 Schematic structural diagram of the laser measurement and calibration flow of the present invention;

[0034] Figure 4 Schematic structural diagram of the multi-source data fusion flow of the present invention;

[0035] Figure 5 Schematic structural diagram of the blockchain evidence storage flow of the present invention;

[0036] Figure 6 Schematic structural diagram of the predictive maintenance decision-making flow of the present invention.

[0037] In the figure: 1. Stroke detection module; 2. Intelligent monitoring device; 3. Limit determination module; 4. Execution control module; 5. Remote monitoring platform; 6. Laser measuring instrument; 7. Calibration standard instrument; 8. Laser emission unit; 9. Laser receiving unit; 10. Standard length rail; 11. Angle calibrator; 12. Edge computing unit; 13. Dynamic error compensation unit; 14. Abnormal mode recognition unit; 15. Physical model platform; 16. Prediction unit; 17. Fusion decision maker; 18. Electromagnetic braking unit; 19. Hydraulic buffer unit; 20. Emergency braking mechanism; 21. Digital twin platform; 22. Blockchain evidence storage device; 23. Maintenance decision-making unit. Specific implementation manners

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0039] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", "front end", "rear end", "both ends", "one end", "the other end", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0040] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, terms such as "installation", "equipped with", "connection", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0041] Embodiment 1: Please refer to Figure 1 and Figure 2 , an embodiment provided by the present invention: An elevator travel limit monitoring and management system includes a travel detection module 1, an intelligent monitoring device 2, a limit determination module 3, an execution control module 4, and a remote monitoring platform 5. The travel detection module 1 includes a laser measurement instrument 6 and a calibration standard instrument 7. The laser measurement instrument 6 includes a laser emission unit 8 and a laser reception unit 9, and they are respectively installed at the top of the elevator shaft and the top of the car, and measure data in real time through a fiber optic synchronization interface. The calibration standard instrument 7 includes a standard length rail 10 fixed at the bottom pit of the elevator and an angle calibrator 11 installed on the side wall, and is connected to the laser measurement instrument 6 through a calibration bus;

[0042] The intelligent monitoring device 2 receives the original data of the travel detection module 1 through a first high-speed data channel. The intelligent monitoring device 2 includes an edge computing unit 12 for performing spatio-temporal alignment of multi-source data, a dynamic error compensation unit 13 for receiving the reference data of the calibration standard instrument 7 and generating an error compensation coefficient, and an abnormal mode recognition unit 14 for receiving the compensated data and outputting an operating state feature vector through a convolutional neural network;

[0043] The limit determination module 3 is connected to the intelligent monitoring device 2 through a second high-speed data channel, and includes a physical model platform 15 for calculating the theoretical trajectory according to the data, a prediction unit 16 for receiving the feature vector of the intelligent monitoring device 2 and predicting the actual trajectory deviation, and a fusion decision maker 17 that integrates the physical model and the prediction result using the D-S evidence theory to generate a three-level risk instruction;

[0044] The execution control module 4 receives the instruction of the limit determination module 3 through a control bus, and includes an electromagnetic braking unit 18 for responding to a first-level instruction to start a linearly increasing brake, a hydraulic buffer unit 19 for adjusting the opening of the damping valve according to a second-level instruction, and an emergency braking mechanism 20 for triggering mechanical locking under a third-level instruction;

[0045] The remote monitoring platform 5 establishes two-way communication with each module through the 5G network, including a digital twin platform 21 for real-time reconstruction of the three-dimensional operation state of the elevator, a blockchain evidence storage device 22 for encrypting and storing calibration records and abnormal events, and a maintenance decision-making unit 23 for analyzing the component life according to historical data;

[0046] Furthermore, by adopting the laser measurement and standard rail composite calibration technology, the stroke detection accuracy is improved through the optical fiber synchronization and dynamic error compensation unit 13. Combining with the abnormal pattern recognition model constructed by the convolutional neural network, the accuracy of fault prediction is further improved. Then, through the three-level risk instruction hierarchical response strategy, under the triple composite braking of electromagnetic braking, hydraulic buffering, and mechanical braking, the accident response time is shortened. Relying on the digital twin platform 21 constructed by the 5G network, the three-dimensional visualization reconstruction of the elevator operation state is realized. Cooperating with the blockchain evidence storage device 22, the whole life cycle data of the equipment is completely traced, and the preventive maintenance efficiency is improved. An overall closed-loop management system of "precision perception-intelligent judgment-hierarchical braking-cloud traceability" is formed, thereby significantly improving the operation safety of the elevator and the accuracy of intelligent monitoring.

[0047] Example 2: Please refer to Figure 2 and Figure 3 , an embodiment provided by the present invention: The laser emission unit 8 of the laser measuring instrument 6 adopts a dual-wavelength alternating emission mode, where: the first working wavelength is 850nm, and the measurement accuracy is ±0.5mm / m;

[0048] The second working wavelength is 1550nm, and the measurement accuracy is ±1.2mm / m;

[0049] The optical fiber synchronization interface adopts the TDM time division multiplexing technology, including: the uplink channel transmits laser ranging data, the bandwidth ≥2Gbps, the downlink channel transmits calibration control signals, the delay ≤1ms, and the clock synchronization accuracy reaches the nanosecond level;

[0050] The surface of the standard length rail 10 of the calibration standard instrument 7 is provided with magnetically encoded marks evenly distributed at equal intervals. The angle calibrator 11 includes a three-axis gyroscope and a laser reflection array. The calibration bus adopts a timestamp synchronization protocol to match the sampling period of the laser measuring instrument 6 with the physical spacing of the magnetically encoded marks, and generates a dynamic calibration curve;

[0051] Furthermore, through the dual-wavelength alternating emission mode, combined with the 2Gbps high-speed data transmission of the TDM time-division multiplexing fiber optic interface and the nanosecond-level synchronization accuracy, real-time and precise ranging in complex environments is achieved. The magnetic coding marks on the standard length rail 10 cooperate with the three-axis gyroscope of the angle calibrator 11, and together with the timestamp synchronization protocol, dynamically match the laser sampling period and the physical distance to generate a multi-dimensional calibration curve. Furthermore, the cumulative error caused by temperature deformation and mechanical vibration is reduced. The closed-loop feedback mechanism of the laser reflection array and the calibration bus enables the system to maintain millimeter-level absolute measurement accuracy even under dynamic conditions such as elevator acceleration and swaying, forming a full-link calibration system of "high-frequency measurement - dynamic calibration - error suppression", which can provide a high-confidence data basis for limit determination.

[0052] Example 3: Please refer to Figure 2 and Figure 4 , an embodiment provided by the present invention: The dynamic error compensation unit 13 uses the Kalman filter algorithm, takes the reference data of the standard length rail 10 as the observation value, and the real-time data of the laser measuring instrument 6 as the state quantity, constructs an error transfer function, and outputs a compensation coefficient to the data preprocessing layer of the abnormal mode recognition unit 14;

[0053] The convolutional neural network includes parallel spatio-temporal convolutional branches. Among them, the spatial branch uses a 3x3 convolutional kernel to extract the spatial features of the laser data, and the temporal branch uses causal convolution to extract the temporal features of the car speed. Finally, the running state feature vector is output after weighted fusion through the attention mechanism;

[0054] Furthermore, through the dynamic error compensation unit 13, based on the Kalman filter algorithm, fuses the reference data of the standard length rail 10 and the real-time state quantity of the laser measuring instrument 6, dynamically corrects the non-linear deviation caused by environmental disturbances through the error transfer function, accurately captures the abnormal distribution of the laser data through the 3x3 convolutional kernel of the spatial branch, deeply mines the mutation mode of the car speed through the causal convolution of the temporal branch, and then dynamically allocates feature weights through the attention mechanism, so as to improve the recognition accuracy of floor shaking and guide rail wear, and realize the closed-loop linkage of error compensation and feature extraction through the preprocessing layer, forming an intelligent analysis chain of "dynamic deviation correction - spatio-temporal modeling - adaptive fusion", providing high-signal-to-noise ratio feature input for elevator safety status assessment.

[0055] Example 4: Please refer to Figure 1 , an embodiment provided by the present invention: The linear increasing braking strategy of the electromagnetic braking unit 18 satisfies the formula:

[0056] F(t) = k1 * v(t) + k2 * Δs

[0057] Where F(t) is the real-time braking force, v(t) is the instantaneous speed of the car, Δs is the distance difference from the target limit, k1 and k2 are adjustable gain coefficients, and the gain coefficients change step by step with the three-level risk instruction level;

[0058] Furthermore, through the dynamic gain coefficient, the traditional stepped braking is upgraded to continuous gradient control. While ensuring a 200ms ultra-fast response, the braking acceleration fluctuation is suppressed within the range of ±0.2g, which not only avoids component damage caused by mechanical impact but also eliminates secondary risks caused by car oscillation. Cooperating with the damping co-control of the hydraulic buffer unit 19, a composite braking system of "dynamic calculation - hierarchical force application - kinetic energy dissipation" is formed, thereby shortening the elevator overrun distance and significantly improving safety and smoothness under extreme conditions.

[0059] Example 5: Please refer to Figure 5 and Figure 6 , an embodiment provided by the present invention: The blockchain evidence depositor 22 adopts a hierarchical Merkle tree structure, stores calibration records and abnormal events in blocks according to timestamps, each block header contains the SHA-3 hash value of the previous block, and verifiably encrypts the maintenance personnel's operation records through zero-knowledge proof technology;

[0060] The maintenance decision-making unit 23 integrates a Weibull distribution life prediction model. The input parameters include the electromagnetic braking response delay time, the action times of the hydraulic buffer valve, and the laser light intensity attenuation rate, outputs the remaining life probability distribution diagrams of each component, and automatically generates a spare part replacement priority list;

[0061] Furthermore, through the hierarchical Merkle tree structure of the blockchain evidence depositor 22, traceable storage of calibration records and abnormal events is realized. Through the SHA-3 hash chain and zero-knowledge proof technology, while ensuring the immutability of operation records, privacy protection is achieved. The maintenance decision-making unit 23 integrates the Weibull distribution model, based on multi-dimensional degradation parameters such as electromagnetic braking delay, hydraulic valve action frequency, and laser attenuation rate, outputs a probabilistic life prediction curve, combines with the historical failure data stored in the blockchain, improves the accuracy of component failure prediction, and generates a dynamically optimized spare part replacement list, thereby reducing maintenance costs. The two form a full-life cycle data value chain through the "trusted evidence depositing - life modeling - intelligent decision-making" closed loop, which not only meets the compliance requirements of special equipment supervision but also realizes the coordinated optimization of spare part inventory turnover rate and equipment availability rate, promoting the transformation of the elevator operation and maintenance mode towards predictability and precision.

[0062] Example 6: Please refer to Figure 2 , an embodiment provided by the present invention: The working steps of the elevator travel limit monitoring and management system are as follows:

[0063] S1. The laser measurement instrument 6 activates the dual-wavelength alternating emission mode. The 850-nm wavelength is used for high-precision ranging, and the 1550-nm wavelength is used for anti-interference compensation. Through the TDM time-division multiplexing technology of the fiber optic synchronous interface, the ranging data is uploaded to the intelligent monitoring device 2 in real time with a bandwidth of ≥2 Gbps. At the same time, it receives the magnetic coding marker positioning signal and the three-axis gyroscope attitude data of the calibration standard instrument 7. The calibration bus dynamically matches the sampling period and the physical distance through the timestamp synchronization protocol, and generates a multi-dimensional calibration curve to eliminate mechanical vibration errors;

[0064] S2. The edge computing unit 12 performs spatio-temporal alignment on the laser ranging data, the reference value of the standard length rail 10, and the feedback of the angle calibrator 11. The dynamic error compensation unit 13 constructs an error transfer function based on the Kalman filter algorithm, corrects the laser measurement noise with the standard rail data as the observation value, and outputs the compensated data to the abnormal mode recognition unit 14. The spatio-temporal parallel convolutional neural network simultaneously extracts the spatial distribution characteristics of the laser data and the temporal pattern of the car speed, and generates an operating state feature vector through the attention mechanism fusion to realize the early identification of floor offset and guide rail wear;

[0065] S3. The physical model platform 15 calculates the operation trajectory based on the data and generates the model confidence weight; the prediction unit 16 combines the feature vector of the intelligent monitoring device 2, and the fusion decision maker 17 applies the D-S evidence theory to integrate the physical model confidence and the prediction deviation probability, and outputs a three-level risk instruction;

[0066] S4. The execution control module 4 dynamically adjusts the gain coefficient according to the risk level and calculates through the formula;

[0067] S5. The digital twin platform 21 receives the data streams of each module through the 5G network, reconstructs the three-dimensional operation state of the elevator and maps the physical parameters in real time. The blockchain depositor 22 adopts a hierarchical Merkle tree structure, encrypts and stores the calibration records and operation logs in blocks according to the timestamp, and the block headers are associated through the SHA-3 hash chain. The maintenance records are verified by zero-knowledge proof and then uploaded to the chain. The maintenance decision unit 23 based on the Weibull distribution model, inputs the parameters of electromagnetic braking delay, hydraulic valve action times, and laser attenuation rate, generates the probability distribution diagram of the remaining life of the components, and automatically optimizes the spare part replacement list;

[0068] S6. The system periodically calls the historical operation data, optimizes the Kalman filter parameters, the weights of the convolutional neural network, and the boundary conditions of the physical model through the reinforcement learning algorithm. The calibration standard instrument 7 automatically triggers the recalibration process according to the laser light intensity attenuation rate, updates the dynamic calibration curve, and the maintenance decision unit 23 combines the fault case library stored in the blockchain, and iteratively improves the shape parameter and scale parameter of the Weibull distribution model to continuously improve the accuracy of life prediction.

[0069] Working principle: by adopting laser measurement and standard rail composite calibration technology, the stroke detection accuracy is improved through optical fiber synchronization and dynamic error compensation unit 13, and the abnormal pattern recognition model constructed by convolutional neural network is combined to improve the accuracy of fault prediction. Then, through the three-level risk instruction hierarchical response strategy, under the triple combined braking of electromagnetic braking, hydraulic buffering and mechanical brake, the accident response time is shortened, and the digital twin platform 21 built on the 5G network is used to realize the three-dimensional visualization reconstruction of the elevator operation status, and cooperate with the blockchain evidence storage device 22 to fully trace the data of the entire life cycle of the equipment to improve the efficiency of preventive maintenance, and form a closed-loop management system of "precision perception-intelligent judgment-grading braking-cloud tracing" as a whole, thereby significantly improving the safety of elevator operation and the accuracy of intelligent monitoring. Through the dual-wavelength alternating emission mode, combined with the 2Gbps high-speed data transmission and nanosecond synchronization accuracy of the TDM time-division multiplexing optical fiber interface, real-time and accurate distance measurement in complex environments is achieved. The magnetic coding mark of the standard length rail 10 and the three-axis gyroscope of the angle calibrator 11 work together, and cooperate with the timestamp synchronization protocol to dynamically match the laser sampling period and the physical spacing to generate a multi-dimensional calibration curve, thereby reducing temperature deformation and mechanical The accumulated error caused by vibration, the closed-loop feedback mechanism of the laser reflection array and the calibration bus, enables the system to maintain millimeter-level absolute measurement accuracy under dynamic conditions of elevator acceleration and shaking, forming a full-link calibration system of "high-frequency measurement-dynamic calibration-error suppression", which can provide a high-confidence data basis for limit judgment, and integrate the benchmark data of the standard length rail 10 and the real-time state quantity of the laser measuring instrument 6 through the dynamic error compensation unit 13 based on the Kalman filter algorithm, and dynamically correct the nonlinear deviation caused by environmental disturbance through the error transfer function, and accurately use the 3x3 convolution kernel of the spatial branch. The laser data distribution anomaly is captured, and the causal convolution of the time branch deeply mines the car speed mutation pattern. The feature weights are then dynamically allocated through the attention mechanism to improve the recognition accuracy of leveling shaking and guide rail eccentric wear. The closed-loop linkage between error compensation and feature extraction is realized through the preprocessing layer, forming an intelligent analysis chain of "dynamic correction-time-space modeling-adaptive fusion", providing high signal-to-noise ratio feature input for elevator safety status assessment, and upgrading the traditional step-type braking to continuous gradual control through the dynamic gain coefficient. While ensuring the 200ms ultra-fast response, the braking acceleration fluctuation is suppressed to ±0.Within the range of 2g, it not only avoids component damage caused by mechanical shock but also eliminates the secondary risk caused by car oscillation. Cooperating with the damping co-control of the hydraulic buffer unit 19, it forms a composite braking system of "dynamic calculation - hierarchical force application - kinetic energy dissipation", thereby shortening the elevator over-travel distance and significantly improving safety and smoothness under extreme conditions. The calibration records and traceable storage of abnormal events are realized through the blockchain evidence depositor 22 using a hierarchical Merkle tree structure. Through the SHA-3 hash chain and zero-knowledge proof technology, while ensuring the immutability of operation records, privacy protection is achieved. The maintenance decision-making unit 23 integrates a Weibull distribution model. Based on multi-dimensional degradation parameters such as electromagnetic braking delay, hydraulic valve action frequency, and laser attenuation rate, it outputs a probabilistic life prediction curve. Combining with the historical failure data stored in the blockchain, it improves the accuracy of component failure prediction and generates a dynamically optimized spare part replacement list, thereby reducing maintenance costs. The two form a full-life cycle data value chain through the closed-loop of "trusted evidence depositing - life modeling - intelligent decision-making", which not only meets the regulatory compliance requirements for special equipment but also realizes the collaborative optimization of spare part inventory turnover rate and equipment availability rate, promoting the transformation of the elevator operation and maintenance mode towards predictability and precision.

[0070] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. An elevator travel limit monitoring and management system, comprising a travel detection module (1), an intelligent monitoring device (2), a limit determination module (3), an execution control module (4) and a remote monitoring platform (5), characterized in that: The travel detection module (1) includes a laser measuring instrument (6) and a calibration standard instrument (7). The laser measuring instrument (6) includes a laser emitting unit (8) and a laser receiving unit (9), which are respectively installed at the top of the elevator shaft and the top of the car, and measure data in real time through an optical fiber synchronization interface. The calibration standard instrument (7) includes a standard length rail (10) fixed at the bottom pit of the elevator and an angle calibrator (11) installed on the side wall, and is connected to the laser measuring instrument (6) through a calibration bus; The intelligent monitoring device (2) receives the original data of the travel detection module (1) through a first high-speed data channel. The intelligent monitoring device (2) includes an edge computing unit (12) for performing spatio-temporal alignment of multi-source data, a dynamic error compensation unit (13) for receiving the reference data of the calibration standard instrument (7) and generating an error compensation coefficient, and an abnormal mode recognition unit (14) for receiving the compensated data and outputting a running state feature vector through a convolutional neural network; The limit judgment module (3) is connected to the intelligent monitoring device (2) through a second high-speed data channel, and includes a physical model platform (15) for calculating the theoretical trajectory according to the data, a prediction unit (16) for receiving the feature vector of the intelligent monitoring device (2) and predicting the deviation of the actual trajectory, and a fusion decision maker (17) that integrates the physical model and the prediction result using the D-S evidence theory to generate a three-level risk instruction; The execution control module (4) receives the instruction of the limit judgment module (3) through a control bus, and includes an electromagnetic braking unit (18) for starting a linearly increasing brake in response to a first-level instruction, a hydraulic buffer unit (19) for adjusting the opening of the damping valve according to a second-level instruction, and an emergency braking mechanism (20) for triggering mechanical locking under a third-level instruction; The remote monitoring platform (5) establishes two-way communication with each module through a 5G network, and includes a digital twin platform (21) for real-time reconstruction of the three-dimensional running state of the elevator, a blockchain evidence storage device (22) for encrypting and storing calibration records and abnormal events, and a maintenance decision-making unit (23) for analyzing the component life according to historical data.

2. The elevator travel limit monitoring and management system according to claim 1, characterized in that: The laser emitting unit (8) of the laser measuring instrument (6) adopts a dual-wavelength alternating emission mode, where: the first working wavelength is 850 nm, and the measurement accuracy is ±0.5 mm / m; The second working wavelength is 1550 nm, and the measurement accuracy is ±1.2 mm / m.

3. The elevator travel limit monitoring and management system according to claim 1, characterized in that: The optical fiber synchronization interface adopts TDM time division multiplexing technology, including: the uplink channel transmits laser ranging data, the bandwidth ≥ 2 Gbps, the downlink channel transmits calibration control signals, the delay ≤ 1 ms, and the clock synchronization accuracy reaches the nanosecond level.

4. The elevator travel limit monitoring and management system according to claim 1, characterized in that: The surface of the standard length rail (10) of the calibration standard instrument (7) is provided with magnetically encoded marks evenly distributed at equal intervals. The angle calibrator (11) includes a three-axis gyroscope and a laser reflection array. The calibration bus adopts a timestamp synchronization protocol to match the sampling period of the laser measuring instrument (6) with the physical spacing of the magnetically encoded marks, and generates a dynamic calibration curve.

5. The elevator travel limit monitoring and management system according to claim 1, wherein: The dynamic error compensation unit (13) adopts the Kalman filtering algorithm. Taking the reference data of the standard length track (10) as the observation value and the real-time data of the laser measuring instrument (6) as the state quantity, it constructs an error transfer function and outputs a compensation coefficient to the data preprocessing layer of the abnormal mode recognition unit (14).

6. The elevator travel limit monitoring and management system according to claim 1, wherein: The convolutional neural network includes parallel spatio-temporal convolutional branches. Among them, the spatial branch uses a 3x3 convolutional kernel to extract the spatial features of the laser data, and the temporal branch uses causal convolution to extract the temporal features of the car speed. Finally, the operation state feature vector is output after weighted fusion by the attention mechanism.

7. The elevator travel limit monitoring and management system according to claim 1, wherein: The physical model platform (15) internally stores the rigid-flexible coupling dynamics equation of the elevator. The input parameters include the radius of the traction wheel, the elastic modulus of the wire rope, and the mass of the car load. The theoretical trajectory is solved by the Runge-Kutta method, and the coherence analysis is carried out in the frequency domain with the laser measurement data to generate the model confidence weight for the fusion decision maker (17) to call.

8. A lift travel limit monitoring and management system according to claim 1, characterized in that: The linear increasing braking strategy of the electromagnetic braking unit (18) satisfies the formula: F(t) = k1*v(t) + k2*Δs where F(t) is the real-time braking force, v(t) is the instantaneous speed of the car, Δs is the distance difference from the target limit, k1 and k2 are adjustable gain coefficients, and the gain coefficients change stepwise with the three-level risk instruction level.

9. The elevator travel limit monitoring and management system according to claim 1, characterized in that: The blockchain evidence storage device (22) adopts a hierarchical Merkle tree structure, stores the calibration records and abnormal events in blocks according to the time stamp. Each block header contains the SHA-3 hash value of the previous block, and the operation records of the maintenance personnel are verifiably encrypted by the zero-knowledge proof technology.

10. A lift travel limit monitoring and management system according to claim 1, characterized in that: The maintenance decision unit (23) integrates the Weibull distribution life prediction model. The input parameters include the electromagnetic braking response delay time, the number of actions of the hydraulic buffer valve, and the laser light intensity attenuation rate. It outputs the remaining life probability distribution diagram of each component and automatically generates a spare part replacement priority list.

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