Method, system, computer device and storage medium for identifying elevator car stopping floors and speed

Abstract

The application relates to the technical field of data recognition and processing, and specifically discloses a method and system for recognizing the stop floor and speed of an elevator car, a computer device and a storage medium. A Hall sensor on the car collects a magnetic field signal caused by a structural change of a guide device in a sensing device, obtains a regular waveform signal, analyzes the signal to segment a uniform waveform section and a characteristic waveform section, calculates a real-time running speed in combination with the physical interval of the periodic structure of the sensing device, extracts the time domain and frequency domain features of the characteristic waveform section, matches the features with reference feature vectors of each floor in a database to determine the current floor, fuses the real-time speed and the current floor information to construct a dynamic model, and predicts the running trend. The application adopts non-contact magnetic field signal collection without additional hardware, and through multi-feature fusion matching and kinematic modeling, the floor recognition accuracy and the speed measurement accuracy are improved, the anti-electromagnetic interference capability is enhanced, the running trend is accurately predicted, and the running maintenance cost is reduced.

Description

Technical Field

[0001] This application relates to the field of data recognition and processing technology, and in particular to methods, systems, computer equipment and storage media for recognizing elevator car stops at floors and at speeds. Background Technology

[0002] As an indispensable vertical transportation tool in high-rise buildings, the accurate monitoring and control of elevators' operating status directly affects operational efficiency and passenger safety. Among these, the accurate identification of the floor the elevator car stops at and the precise measurement of its real-time operating speed are the core foundations for elevator control systems to achieve leveling control, scheduling optimization, and safety monitoring.

[0003] Currently, elevator floor identification mainly relies on photoelectric sensors, RFID tags, or encoders in conjunction with magnetic shielding plates in the shaft. Photoelectric sensors locate floors by detecting signals blocked by magnetic shielding plates installed on each floor. However, they are susceptible to environmental factors such as dust and moisture, leading to signal interruptions or misjudgments, and have a high failure rate, especially after long-term operation. RFID technology requires tags to be installed on each floor and readers to be installed in the car, adding extra hardware costs. Furthermore, tags are prone to failure due to vibration and aging, and signal reading delays may occur during high-speed elevator operation. Regarding speed measurement, traditional methods often use encoders linked to the elevator traction machine to calculate speed by detecting the number of rotations of the traction sheave. However, problems such as traction rope slippage and mechanical wear can lead to accumulated measurement errors. Especially during acceleration or deceleration, insufficient speed feedback accuracy directly affects the accuracy of leveling control, causing excessive leveling deviations. Simultaneously, some solutions do not optimize for strong electromagnetic interference within the elevator shaft during signal processing. The raw signal noise results in low feature extraction accuracy, further affecting the reliability of floor identification and speed calculation.

[0004] Therefore, there is an urgent need for methods, systems, computer equipment, and storage media to identify the floors and speeds at which elevator cars stop in order to solve the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a method for identifying the floors and speed of an elevator car, including the following steps:

[0006] By using Hall sensors installed on the elevator car, magnetic field signals caused by structural changes in the sensing devices on the elevator guide device are collected, and the magnetic field signals are preprocessed to obtain a regular waveform signal containing car speed information and floor position information.

[0007] The regular waveform signal is analyzed to segment out the uniform waveform segment representing constant speed operation and the characteristic waveform segment representing a specific floor, and the real-time running speed of the car is calculated based on the uniform waveform segment.

[0008] The extracted feature waveform segments are matched and identified with the pre-generated and stored floor reference feature waveform segments in the database to determine the current floor of the elevator car.

[0009] By integrating the real-time operating speed with the current floor of the elevator car, a dynamic model of the elevator's operating status is constructed to predict the car's operating trend.

[0010] The current floor, real-time operating speed, and predicted operating trend are sent to the elevator control system via the transmission module.

[0011] Furthermore, the step of obtaining the normalized waveform signal containing car speed information and floor position information includes:

[0012] The Hall sensor continuously acquires the changes in the spatial magnetic field strength caused by the sensed device at a preset sampling frequency, and outputs the original analog voltage signal.

[0013] The original analog voltage signal is subjected to signal conditioning, including low-pass filtering to suppress high-frequency electromagnetic interference;

[0014] The conditioned signal is subjected to analog-to-digital conversion and digital smoothing processing is performed by a moving average filtering algorithm. The moving average filtering is to calculate the arithmetic average of the values ​​of multiple consecutive sampling points as the current output value.

[0015] Baseline correction is performed on the smoothed digital signal to eliminate the DC component offset in the signal, and the normalized waveform signal is finally output.

[0016] Furthermore, the step of analyzing the regular waveform signal, segmenting it into uniform waveform segments representing constant speed operation and characteristic waveform segments representing specific floors, and calculating the real-time operating speed of the car based on the uniform waveform segments includes:

[0017] The regular waveform signal is segmented to identify each independent waveform period generated by the periodic structure of the sensing device, and the stable region composed of multiple consecutive waveform periods is marked as the uniform waveform segment.

[0018] Within each uniform waveform segment, the instantaneous frequency of the magnetic field change is obtained by calculating the time interval of the continuous waveform period. Then, combined with the known physical spacing of the periodic structure of the inductive device, the instantaneous running speed of the car is calculated. The speed values ​​calculated for multiple consecutive periods are filtered to output the real-time running speed of the car.

[0019] Identify and extract the unique magnetic field waveform segment generated by the unique coded structure of the sensing device when the elevator car passes through the leveling position of each floor, and mark the segment as the characteristic waveform segment.

[0020] The characteristic waveform segment is normalized, and its time-domain and frequency-domain features, including waveform width sequence, peak sequence, waveform area and frequency domain energy ratio, are extracted to form the feature vector of the floor.

[0021] Furthermore, the step of matching and identifying the extracted feature waveform segments with pre-generated and stored floor reference feature waveform segments in the database to determine the current floor of the elevator car includes:

[0022] During the initialization phase, the drive car stops at each floor in sequence, collects the characteristic waveform segments at the leveling position of each floor, extracts the feature vector of each floor as the reference feature vector, and establishes a mapping database between floor number and reference feature vector.

[0023] During normal elevator operation, when a characteristic waveform segment is detected, its feature vector to be identified is extracted;

[0024] Calculate the similarity distance between the feature vector to be identified and all benchmark feature vectors in the database;

[0025] Select the floor corresponding to the baseline feature vector that has the minimum similarity distance with the feature vector to be identified. If the minimum similarity distance is less than the preset matching threshold, the floor is determined to be successfully identified, and the output is the floor where the elevator car is currently located.

[0026] Furthermore, the step of integrating the real-time operating speed with the current floor of the elevator car to construct a dynamic model of the elevator's operating status and predict the car's operating trend includes:

[0027] Establish a state vector that includes the current floor, real-time running speed, and real-time acceleration;

[0028] Based on the elevator's operating characteristics, a state prediction algorithm is used to estimate the elevator's operating state and predict the state vector at the next moment.

[0029] Based on the predicted speed and acceleration, combined with the known coordinates of the current floor and each floor in the elevator shaft, the most likely floor range that the car will reach in the near future is predicted by kinematic integral calculation.

[0030] By combining elevator dispatch instructions or floor selection signals inside the car, the prediction results based on the motion model are corrected, the operating trend of the car is comprehensively judged, and the prediction information of the next possible floor is output.

[0031] Furthermore, the step of sending the current floor, real-time operating speed, and operating trend prediction results to the elevator control system via the transmission module includes:

[0032] The determined current floor, real-time operating speed, and operating trend prediction results are encapsulated into a data frame of a specific format;

[0033] Generate a checksum for the data frame and append it to the end of the data frame;

[0034] The data frame with the checksum is sent to the elevator main controller and the remote monitoring center via a bus or network transmission module.

[0035] The elevator main controller performs leveling control based on the received current floor and speed information;

[0036] The remote monitoring center uses the received data to display and analyze the elevator's operating status.

[0037] Furthermore, this application also provides a system for identifying the floors and speed at which an elevator car stops, including:

[0038] The acquisition module is used to acquire the magnetic field signal caused by structural changes of the sensing device on the elevator guide device through the Hall sensor installed on the elevator car, and to preprocess the magnetic field signal to obtain a regular waveform signal containing the car running speed information and floor position information.

[0039] The analysis module is used to analyze the regular waveform signal, segment it into uniform waveform segments representing constant speed operation and characteristic waveform segments representing specific floors, and calculate the real-time running speed of the car based on the uniform waveform segments.

[0040] The identification module is used to match and identify the extracted feature waveform segments with the pre-generated and stored floor reference feature waveform segments in the database to determine the current floor of the elevator car.

[0041] The prediction module is used to integrate the real-time operating speed with the current floor of the elevator car to construct a dynamic model of the elevator's operating status and predict the car's operating trend.

[0042] The transmission module is used to send the current floor, real-time operating speed, and operating trend prediction results to the elevator control system.

[0043] Furthermore, the prediction module includes:

[0044] Establish a unit to create a state vector containing the current floor, real-time running speed, and real-time acceleration;

[0045] The first prediction unit is used to estimate the elevator's operating state based on the elevator's operating characteristics using a state prediction algorithm, and to predict the state vector at the next moment.

[0046] The second prediction unit is used to predict the floor range that the car is most likely to reach in the near future by using kinematic integrals, based on the predicted speed and acceleration, combined with the known position coordinates of the current floor and each floor in the elevator shaft.

[0047] The output unit is used to combine elevator dispatch instructions or floor selection signals inside the car to correct the prediction results based on the motion model, comprehensively judge the running trend of the car, and output the prediction information of the next possible floor to stop.

[0048] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for identifying the floor and speed of an elevator car.

[0049] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above for identifying the floor and speed of an elevator car.

[0050] The beneficial effects of this application are as follows:

[0051] Firstly, this application uses Hall effect sensors to collect magnetic field signals from the sensed devices on the elevator guide system. It then uses the characteristic waveform segments generated by the unique coding structure of each floor for matching and identification, eliminating the need for additional photoelectric magnetic shielding plates or RFID tags, thus reducing hardware costs. Furthermore, it employs a multi-dimensional time-domain and frequency-domain feature fusion and weighted similarity matching algorithm to enhance the anti-interference capability against magnetic field interference, thereby improving the floor identification accuracy and solving the problems of traditional photoelectric sensors being susceptible to environmental influences and RFID tags being prone to failure.

[0052] Secondly, this application can avoid slippage errors caused by the linkage between the encoder and the mechanical structure. Through the stable period analysis of the uniform speed waveform segment and the moving average filtering process, the measurement accuracy of the real-time running speed is improved, and the deviation is reduced compared with the traditional encoder solution, providing accurate speed feedback for level control.

[0053] Third, this application ensures the integrity of data transmission in the strong electromagnetic environment of the elevator through structured data frame encapsulation and CRC32 verification mechanism, improves the error recognition rate, and sends data to the main controller and remote monitoring center at the same time, supporting leveling control and operation status monitoring respectively, thereby improving the targeting and efficiency of data application. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of a method flow proposed in an embodiment of this application.

[0055] Figure 2 This is a schematic diagram of the system structure proposed in one embodiment of this application.

[0056] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0057] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0059] like Figure 1 As shown, this application provides a method for identifying the floors and speed of an elevator car, including the following steps:

[0060] S1. By using a Hall sensor installed on the elevator car, the magnetic field signal caused by the structural change of the sensing device on the elevator guide device is collected, and the magnetic field signal is preprocessed to obtain a regular waveform signal containing the car running speed information and floor position information.

[0061] S2, analyze the regular waveform signal, segment it into a uniform waveform segment representing constant speed operation and a characteristic waveform segment representing a specific floor, and calculate the real-time running speed of the car based on the uniform waveform segment.

[0062] S3, the extracted feature waveform segments are matched and identified with the pre-generated and stored floor reference feature waveform segments in the database to determine the current floor of the elevator car;

[0063] S4. By integrating the real-time operating speed with the current floor of the elevator car, a dynamic model of the elevator's operating status is constructed to predict the car's operating trend.

[0064] S5, the current floor, real-time operating speed and operating trend prediction results are sent to the elevator control system through the transmission module.

[0065] As described in steps S1-S5 above, with the diversification of elevator application scenarios and the improvement of operational requirements, the monitoring of the elevator car's operating status is directly related to the leveling control accuracy, operational safety, and scheduling efficiency. Structural changes in the elevator guide device cause regular fluctuations in the surrounding magnetic field. These fluctuations contain both speed-related information during car operation and unique identification information corresponding to each floor position. By capturing and analyzing these magnetic field signals, key parameters of elevator operation can be indirectly obtained, solving the problem of insufficient monitoring accuracy or reduced reliability caused by the susceptibility of traditional encoders and other equipment to mechanical wear and complex installation. However, traditional methods for identifying elevator floors and speeds often rely on photoelectric encoders, grating rulers, and other equipment. These devices need to directly cooperate with the elevator's mechanical transmission structure, and long-term use can easily lead to signal drift due to mechanical wear. Furthermore, their installation and debugging are complex, and their stability is poor in harsh environments such as dust and vibration. Simultaneously, they struggle to simultaneously meet the dual requirements of real-time speed calculation and accurate floor matching.

[0066] This application uses a non-contact Hall sensor to collect magnetic field signals. Through a progressive technical solution of signal preprocessing, waveform segmentation analysis, feature vector matching, dynamic model construction, and data transmission, it avoids the loss problem caused by mechanical contact and achieves the integration of speed calculation, floor identification, and trend prediction through multi-dimensional signal analysis and data fusion. It specifically solves the shortcomings of traditional technologies in terms of reliability, accuracy, and adaptability.

[0067] This application is based on the fact that structural changes in the sensing devices on the elevator guide system cause regular fluctuations in the spatial magnetic field, and that these fluctuations contain information about the car's speed and floor position. The magnetic field signal is collected by a Hall sensor installed on the elevator car. Since the original magnetic field signal is affected by electromagnetic interference in the elevator shaft and sensor noise, it needs to be preprocessed to eliminate interference and regularize the waveform, resulting in a regularized waveform signal that clearly reflects the speed and floor information. Analysis of the regularized waveform signal reveals that the magnetic field waveform generated by the periodic structure of the sensing devices when the car is running at a constant speed has a stable period. Therefore, this uniform waveform segment is segmented. Combined with the known physical spacing of the periodic structure of the sensing devices, the instantaneous frequency of the magnetic field change is obtained by calculating the time interval of the waveform period, and then the real-time speed of the car is calculated. Simultaneously, when the car passes each floor leveling position, the unique coded structure of the sensing device for that floor... The structure generates a unique magnetic field waveform, so these characteristic waveform segments are segmented. The extracted characteristic waveform segments are matched with the reference characteristic waveform segments of each floor that were pre-collected and stored in the database during the initialization phase. The uniqueness of each floor's reference characteristics is used to determine the current floor. Real-time operating speed and current floor information are integrated, and a dynamic model of the operating state is constructed based on the elevator's acceleration, constant speed, deceleration and other motion characteristics. The position changes of the car in the near future are predicted by kinematic laws to achieve operating trend prediction. The current floor, real-time operating speed and operating trend prediction results are sent to the elevator control system through the transmission module. This provides data support for the elevator main controller to achieve precise leveling control and for the remote monitoring center to monitor the operating status. The whole process forms a complete logical chain of signal acquisition, processing, analysis, identification, modeling and application, ensuring the accuracy of elevator floor and speed identification and the effectiveness of operating trend prediction.

[0068] In one embodiment, the step of obtaining a regularized waveform signal containing car speed information and floor position information includes:

[0069] S11, continuously acquire the change in spatial magnetic field strength caused by the sensed device at a preset sampling frequency using a Hall sensor, and output the original analog voltage signal;

[0070] S12, perform signal conditioning on the original analog voltage signal, including low-pass filtering to suppress high-frequency electromagnetic interference;

[0071] S13, the conditioned signal is converted from analog to digital and then digitally smoothed by a moving average filtering algorithm. The moving average filtering is to calculate the arithmetic average of the values ​​of multiple consecutive sampling points as the current output value.

[0072] S14 performs baseline correction on the smoothed digital signal to eliminate the DC component offset in the signal and finally outputs the normalized waveform signal.

[0073] As described in steps S11-S14 above, this application uses a Hall sensor to collect magnetic field signals at a preset frequency, and then performs a series of processing steps, including signal conditioning, analog-to-digital conversion, moving average filtering, and baseline correction, to convert the original analog voltage signal into a regular waveform signal containing car speed information and floor position information.

[0074] The sensing devices on the elevator guide system have a periodic structure and a unique coding structure for each floor. These structural changes cause regular fluctuations in the magnetic field strength of the surrounding space. These fluctuations are directly related to the car's running speed and the floor it is on. The Hall sensor can convert the changes in magnetic field strength into an analog voltage signal. However, this original analog voltage signal is affected by high-frequency electromagnetic interference generated by the motor running in the elevator shaft, sensor acquisition noise, and DC component offset introduced by temperature drift in the hardware circuit. If these interferences and offsets are not processed, it will be impossible to accurately identify the uniform speed waveform segment and the characteristic waveform segment during subsequent waveform analysis, which will lead to deviations in speed calculation or errors in floor identification.

[0075] A Hall sensor is used to acquire magnetic field signals at a preset sampling frequency. The Hall sensor is mounted on the side wall of the elevator car, maintaining a fixed distance of 5 mm from the sensed device. The sensed device is a magnetically encoded structure evenly distributed along the elevator shaft, with a periodic spacing of 50 mm between its protrusions and grooves. The preset sampling frequency of the Hall sensor is set to 1000 Hz, which can completely capture magnetic field fluctuations caused by structural changes in the sensed device, avoiding signal aliasing due to excessively low sampling frequencies. For every 0.1 mT change in magnetic field strength, the Hall sensor output voltage changes by 0.5 V, ultimately outputting a raw analog voltage signal with an amplitude between 0.1 V and 5 V. This signal directly reflects the structural changes in the sensed device, providing raw data for subsequent preprocessing.

[0076] Signal conditioning of the original analog voltage signal is primarily achieved through low-pass filtering. An RC low-pass filter circuit with a cutoff frequency of 10kHz is used. This circuit consists of a 1kΩ resistor and a 16nF capacitor. It can suppress high-frequency electromagnetic interference generated by elevator motor operation and other electronic equipment in the hoistway, attenuating noise components above 10kHz in the signal by more than 20dB. This ensures that the effective low-frequency components related to the structure of the sensed device are preserved, preventing high-frequency interference from masking key waveform features and providing a clean analog signal for subsequent analog-to-digital conversion.

[0077] First, the conditioned signal undergoes analog-to-digital conversion using a 16-bit precision converter with a voltage resolution of 76.3μV. This converter can convert analog voltage signals from 0.1V to 5V into digital signals between 0 and 65535, ensuring signal quantization accuracy and preventing the loss of effective information due to insufficient conversion precision. Then, a moving average filtering algorithm is used for digital smoothing. This algorithm calculates the arithmetic mean of the digital signal values ​​from 10 consecutive sampling points as the current output value. For example, if the values ​​of 10 consecutive sampling points are 1000, 1002, 1001, 999, 1003, 1000, 1001, 998, 1002, and 1000, their arithmetic mean is 1000.6, which is rounded down and used as the current output value. This reduces the random noise fluctuation amplitude of the signal to less than 30% of its pre-filter value, while avoiding waveform blurring caused by excessive smoothing.

[0078] Baseline correction is performed on the smoothed digital signal. The average value of the digital signal from 100 consecutive sampling points is used as the reference baseline. If the deviation of the current sampling point value from the reference baseline exceeds 50 quantization units, the sampling point value is adjusted to the sum of the reference baseline and 50% of the deviation. For example, if the reference baseline is 1000, the current sampling point value is 1100, the deviation is 100, and the adjusted value is 1050. This process can eliminate the DC component offset introduced by the temperature drift of the hardware circuit and the zero-point offset of the Hall sensor, so that the signal baseline is stabilized within the preset range. The amplitude fluctuation range of the final output regular waveform signal is controlled between 0.5V and 4.5V, and the waveform period is perfectly matched with the structural period of the sensed device, providing reliable data support for subsequent segmentation of uniform waveform segments and characteristic waveform segments, calculation of real-time running speed, and identification of floors.

[0079] In one embodiment, the step of analyzing the regular waveform signal, segmenting it into uniform waveform segments representing constant speed operation and characteristic waveform segments representing specific floors, and calculating the real-time operating speed of the car based on the uniform waveform segments includes:

[0080] S21, the regular waveform signal is segmented to identify each independent waveform period generated by the periodic structure of the sensing device, and the stable region composed of multiple consecutive waveform periods is marked as the uniform waveform segment.

[0081] S22, within each uniform waveform segment, the instantaneous frequency of the magnetic field change is obtained by calculating the time interval of the continuous waveform period. Then, combined with the known physical spacing of the periodic structure of the inductive device, the instantaneous running speed of the car is calculated. The speed values ​​calculated for multiple consecutive periods are filtered to output the real-time running speed of the car.

[0082] S23, Identify and extract the unique magnetic field waveform segment generated by the unique coded structure of the sensing device when the elevator car passes through the leveling position of each floor, and mark the segment as the feature waveform segment;

[0083] S24, normalize the characteristic waveform segment and extract its time-domain and frequency-domain features, including waveform width sequence, peak sequence, waveform area and frequency domain energy ratio, to form the feature vector of the floor.

[0084] As described in steps S21-S24 above, this application achieves accurate calculation of the car's real-time running speed and effective extraction of floor-specific features through a series of steps, including waveform segmentation of the regular waveform signal, analysis and calculation of the real-time running speed of the uniform waveform segment, extraction of feature waveform segments, and construction of multi-dimensional feature vectors.

[0085] The regularized waveform signal contains two types of key information. One type is the periodic, stable, and uniformly amplitude magnetic field waveform continuously generated by the periodic structure of the sensing device on the elevator guide device when the car is running at a constant speed. The periodic variation of this waveform is directly related to the car's speed; the faster the speed, the shorter the waveform period. The other type is the unique magnetic field waveform generated by the coded structure of the sensing device at each floor level. This type of waveform has unique identifiers and can serve as the core basis for floor identification. Without targeted analysis and segmentation of the regularized waveform signal, the two types of waveforms will become mixed, making it impossible to accurately extract the stable periodic information related to speed and the unique features related to floors. This can lead to excessive speed calculation errors or floor identification failures. Therefore, a specialized waveform analysis process is needed to separate the two types of waveforms and extract key information.

[0086] The regular waveform signal is segmented using a periodic stability detection algorithm. By calculating the period difference between adjacent waveforms, a region is considered stable and marked as a uniform-speed waveform segment when the period difference of 10 consecutive waveforms is less than 0.005 seconds. The periodic structure of the sensing device generates an independent waveform period each time it passes the Hall sensor. During uniform-speed operation of the car, the periods of these waveforms remain stable. However, during acceleration or deceleration, the waveform periods continuously change. This segmentation logic accurately separates the waveform segments corresponding to uniform-speed operation, eliminating interference from non-uniform-speed phases in speed calculation and providing a stable data source for subsequent speed calculations.

[0087] The real-time running speed is calculated within each uniform waveform segment. The start and end times of two consecutive waveform cycles are recorded using timestamps to obtain the cycle time interval. For example, if the first waveform cycle starts at 0.0 seconds and ends at 0.1 seconds, and the second waveform cycle starts at 0.1 seconds and ends at 0.2 seconds, the cycle time interval is 0.1 seconds. Combining this with the known physical spacing of 50 mm between the periodic structures of the sensed device, and based on the calculation logic that speed equals physical spacing divided by the cycle time interval, the instantaneous running speed is obtained as 0.5 meters per second. The instantaneous speed values ​​calculated over 10 consecutive cycles are processed using a moving average filter. The arithmetic mean of the 10 speed values ​​is taken as the current real-time operating speed. For example, if the 10 instantaneous speed values ​​are 0.5m / s, 0.51m / s, 0.49m / s, 0.5m / s, 0.51m / s, 0.49m / s, 0.5m / s, 0.52m / s, 0.48m / s, and 0.5m / s, the arithmetic mean is 0.5m / s. This effectively offsets the error caused by instantaneous fluctuations, improving the measurement accuracy of the real-time operating speed to 0.01m / s, which meets the accuracy requirements of elevator leveling control.

[0088] Feature waveform segments are identified and extracted using a waveform abrupt change detection algorithm. When the amplitude change rate of a regular waveform signal exceeds 0.2V / ms, and the waveform width change exceeds 50% of the width of a uniform waveform segment, a feature waveform segment is detected. The encoding structure of the sensing device differs on each floor; some encoding structures are combinations of multiple protrusions, while others are special arrangements of protrusions and grooves. These unique structures cause specific changes in the magnetic field waveform. For example, the encoding structure of a certain floor causes the waveform width to increase from 0.1 seconds in the uniform segment to 0.2 seconds, and the peak value to increase from 0.8V to 1.2V. This detection algorithm can accurately capture these unique waveform segments and mark them as feature waveform segments, providing a unique feature carrier for floor identification.

[0089] The characteristic waveform segments are normalized using an amplitude normalization algorithm, which linearly maps all amplitude data of the characteristic waveform segments to the 0-1 interval, ensuring a uniform amplitude range and eliminating feature deviations caused by differences in magnetic field strength across different floors. Subsequently, multi-dimensional features are extracted. Time-domain features include waveform width sequences, with width values ​​from three key nodes extracted in chronological order: waveform rising edge width, peak duration width, falling edge width, peak sequence, and waveform area. Frequency-domain features are transformed to the frequency domain using a Fast Fourier Transform (FFT), calculating the energy percentage within the 0-1kHz frequency band. These features are combined in a fixed order to form the feature vector for each floor. For example, the feature vector for a certain floor might be [0.02 seconds, 0.03 seconds, 0.02 seconds, 0.9, 0.7, 0.5, 0.006, 0.35]. The fusion of multi-dimensional features gives the floor features strong discernibility, effectively reducing the risk of misjudgment due to environmental interference and laying the foundation for subsequent matching with the baseline feature vector.

[0090] In one embodiment, the step of matching and identifying the extracted feature waveform segments with pre-generated and stored floor reference feature waveform segments in a database to determine the current floor of the elevator car includes:

[0091] S31, during the initialization phase, the drive car stops at each floor in sequence, collects the characteristic waveform segments at the leveling position of each floor, extracts the feature vector of each floor as the reference feature vector, and establishes a mapping database between the floor number and the reference feature vector.

[0092] S32, during the normal operation of the elevator, when a characteristic waveform segment is detected, its feature vector to be identified is extracted;

[0093] S33, calculate the similarity distance between the feature vector to be identified and all benchmark feature vectors in the database. The calculation process of the similarity distance includes: calculating the difference between the corresponding components of the two feature vectors, summing the weighted squares of the differences, and finally taking the square root of the weighted sum of squares.

[0094] S34: Select the floor corresponding to the benchmark feature vector that has the minimum similarity distance with the feature vector to be identified. If the minimum similarity distance is less than the preset matching threshold, the floor is determined to be successfully identified, and the output is the floor where the elevator car is currently located.

[0095] As described in steps S31-S34 above, this application establishes a floor reference feature vector database during the initialization phase, extracts the feature vector to be identified during elevator operation, calculates the similarity distance between it and the reference feature vector, and performs matching judgment to achieve accurate identification of the current floor of the elevator car.

[0096] Each floor's leveling location is equipped with a unique coding structure. This structural difference causes the magnetic field signal collected by the Hall sensor to form a unique characteristic waveform segment, containing unique time and frequency domain features. By comparing the real-time collected features with pre-stored reference features, the current floor can be determined. If a unified reference database is lacking or the matching logic is not rigorous, it can lead to confusion of features between different floors, resulting in floor misjudgment and affecting the efficiency of elevator leveling control and scheduling.

[0097] In the initialization phase, a baseline database is constructed. The system drives the elevator car to stop sequentially at floors 1 to 10, pausing for 3 seconds at each floor level. Five characteristic waveform segments are continuously collected using Hall effect sensors, each lasting 0.5 seconds. The five collected characteristic waveform segments are averaged to eliminate random interference. Then, the waveform width sequence, peak sequence, waveform area, and frequency domain energy ratio are extracted to form the baseline feature vector for each floor. For example, the baseline feature vector for floor 1 is [0.02 seconds, 0.03 seconds, 0.02 seconds, 0.9, 0.7, 0.5, 0.006, 0.35], and for floor 2 it is [0.03 seconds, 0.02 seconds, 0.03 seconds, 0.8, 0.6, 0.4, 0.005, 0.40]. A one-to-one mapping database between floor numbers and baseline feature vectors is established and stored to ensure the stability and representativeness of the baseline data.

[0098] During normal elevator operation, when a characteristic waveform segment is identified by the waveform change detection algorithm, the feature vector to be identified is immediately obtained according to the same extraction rules as the reference feature vector. For example, the feature vector to be identified extracted when passing through 3 floors is [0.025 seconds, 0.028 seconds, 0.022 seconds, 0.85, 0.65, 0.45, 0.0055, 0.38]. This ensures that the feature to be identified is consistent with the reference feature in terms of dimension and extraction method, providing a comparable basis for subsequent similarity calculation.

[0099] To calculate the similarity distance, taking the feature vector to be identified and the first-layer baseline feature vector as an example, the difference between the corresponding components is calculated. The specific formula for calculating the similarity distance is as follows:

[0100] ;

[0101] Where L represents the similarity distance, and m represents the dimension of the feature vector. This represents the weight of the i-th feature component, which is a preset fixed value set according to the feature stability. For example, the weight of the waveform peak sequence is 0.4, the weight of the waveform width sequence is 0.3, the weight of the waveform area is 0.2, and the weight of the frequency domain energy proportion is 0.1. This represents the i-th component of the feature vector to be identified, extracted from the feature waveform segment. This represents the i-th component of the baseline feature vector, which comes from the database established during the initialization phase. By calculating features with high logical stability, such as peak sequences, a higher proportion is used in the matching, reducing the impact of unstable features on the results and improving the rationality of distance calculation.

[0102] A matching process is performed, traversing all baseline feature vectors in the database to find the baseline vector with the smallest similarity distance to the feature vector to be identified. Assuming the minimum distance between the target vector and the baseline vector for the 3rd floor is 0.03, and the preset matching threshold is 0.05, since 0.03 is less than 0.05, the current floor is determined to be the 3rd floor and output. The matching threshold was determined through multiple experiments to ensure accurate identification even when the magnetic field signal fluctuates by ±10%. This threshold judgment mechanism effectively filters abnormal matching caused by interference, improving the accuracy of floor identification and providing a reliable positional basis for elevator leveling control.

[0103] In one embodiment, the step of integrating the real-time operating speed with the current floor of the elevator car to construct a dynamic model of the elevator's operating status and predict the car's operating trend includes:

[0104] S41, establish a state vector that includes the current floor, real-time running speed, and real-time acceleration;

[0105] S42, based on the elevator's operating characteristics, a state prediction algorithm is used to estimate the elevator's operating state and predict the state vector at the next moment;

[0106] S43, based on the predicted velocity and acceleration, combined with the known position coordinates of the current floor and each floor in the elevator shaft, the most likely floor range that the car will reach in the near future is predicted by kinematic integral calculation.

[0107] S44, combined with elevator dispatch instructions or floor selection signals inside the car, corrects the prediction results based on the motion model, comprehensively judges the car's running trend, and outputs the prediction information of the next possible floor to stop.

[0108] As described in steps S41-S44 above, this application establishes a state vector containing key parameters of current operation, uses a state prediction algorithm combined with kinematic laws to calculate floor intervals, and then incorporates scheduling command corrections to construct a dynamic model of elevator operation status, thereby achieving accurate prediction of the car's operating trend and the next possible floor to stop.

[0109] Elevator operation follows a motion pattern of acceleration, constant speed, and deceleration. Its current floor, real-time speed, and acceleration collectively determine its positional changes in the near future. Furthermore, its operational trend is influenced by passenger floor selections and dispatch instructions. Accurately predicting this information allows the elevator control system to adjust its operating strategy in advance, improving leveling accuracy and dispatch efficiency. Relying solely on a single parameter or ignoring dispatch instructions can lead to significant discrepancies between predictions and actual operation. For example, predicting solely based on speed might misjudge the floor where deceleration and stopping are imminent. Therefore, it is necessary to integrate multi-dimensional state information and actual instructions to construct a comprehensive predictive model.

[0110] A state vector is established, containing three key parameters: current floor, real-time operating speed, and real-time acceleration. The current floor is determined by the identification result described above, such as the current floor being the 5th floor. The real-time operating speed is calculated from the results of the steps above, such as 1.5 meters per second. The real-time acceleration is calculated by dividing the speed difference between two consecutive moments by the time interval. If the speed was 1.4 meters per second in the previous moment and 1.5 meters per second in the current moment, with a time interval of 0.1 seconds, the acceleration would be 1 meter per second squared. The state vector comprehensively covers the core parameters reflecting the elevator's motion state, providing complete initial data support for subsequent predictions.

[0111] A state prediction algorithm, such as the Kalman filter, is used to estimate the elevator's operating state. Based on the elevator's operating characteristics (maximum acceleration of 1 m / s² and maximum speed of 2 m / s), the algorithm parameters are set as follows: the process noise covariance matrix Q is a diagonal matrix with diagonal elements of 0.01 (floor), 0.001 (speed), and 0.0001 (acceleration); the measurement noise covariance matrix R is also a diagonal matrix with elements consistent with Q. The algorithm predicts the state vector for the next moment using the current state vector and the motion model. For example, the predicted speed is 1.55 m / s, the acceleration is 0.95 m / s², and the floor number remains 5. This algorithm effectively filters random noise in speed and acceleration measurements, reducing the deviation between predicted and actual values ​​and improving the stability of state estimation.

[0112] The floor interval is calculated using kinematic integrals, given the current floor coordinates. For example, if 5 floors correspond to 15 meters, the future position is calculated by combining the predicted velocity and acceleration. The formula for calculating the future position is:

[0113] ;

[0114] in, Indicates future location, Indicates the current floor coordinates. The initial velocity is 1.55 meters per second, 'a' represents the predicted acceleration (0.95 meters per second squared), and 't' represents the prediction time. For example, when calculating the position change within one second, 't' represents one second, and the calculated position after one second is 17.025 meters. The coordinates of each floor in the elevator shaft are known, such as 18 meters for the 6th floor and 21 meters for the 7th floor. Therefore, the predicted floor range where the elevator car is most likely to reach in the near future is around the 6th floor. This step combines kinematic laws with the actual floor coordinates, reducing the position prediction error to within 0.3 meters.

[0115] By combining elevator dispatch instructions or floor selection signals within the car to correct the prediction results, if the floor selection signal within the car includes floor 6, it indicates that the passenger has a need to stop. The interval near floor 6 predicted based on the motion model is corrected to the determined next stopping floor, floor 6. If the floor selection signal is empty but the elevator is in an upward state, the next possible stopping floor is still determined to be floor 6 based on the predicted interval. This correction process transforms the prediction result from an interval range into a specific floor, and it highly matches the actual operating intention, improving the accuracy of predicting the next stopping floor and providing a reliable basis for the elevator control system to prepare for leveling actions in advance.

[0116] In one embodiment, the step of sending the current floor, real-time operating speed, and operating trend prediction results to the elevator control system via a transmission module includes:

[0117] S51 encapsulates the determined current floor, real-time operating speed, and operating trend prediction results into a data frame of a specific format;

[0118] S52, generate a checksum for the data frame and append it to the end of the data frame;

[0119] S53 sends data frames with checksums to the elevator main controller and remote monitoring center via a bus or network transmission module;

[0120] S54, the elevator main controller implements leveling control based on the received current floor and speed information;

[0121] S55, the remote monitoring center uses the received data to display and analyze the elevator's operating status.

[0122] As described in steps S51-S55 above, by encapsulating, verifying, and transmitting the data of the current floor, real-time operating speed, and operating trend prediction results, and applying them to the leveling control of the elevator main controller and the status analysis of the remote monitoring center, the reliable transmission and effective utilization of key operating information are realized, providing data support for the safe and stable operation of the elevator.

[0123] The elevator main controller needs real-time and accurate information on the current floor and speed to adjust operating parameters and achieve precise leveling. The remote monitoring center needs this data to understand the elevator's operating status for maintenance and scheduling. However, during transmission, the information is susceptible to electromagnetic interference within the elevator shaft, which can cause data distortion or loss, directly affecting control accuracy and monitoring effectiveness. Therefore, a standardized transmission process is needed to ensure the integrity, accuracy, and targeted application of the information.

[0124] First, data frames are encapsulated, and the data frame format is determined to be a frame header, current floor, real-time running speed, running trend prediction result, and frame tail. The determined current floor (5th floor), the calculated real-time running speed (1.5 meters per second), and the predicted next possible stopping floor (6th floor) are combined in this format to form a complete data frame, ensuring that the data structure is clear and can be quickly parsed by the main controller and monitoring center.

[0125] A checksum is generated using the CRC32 algorithm. The encapsulated data frame, excluding the frame header and trailer, is calculated by inputting the binary data corresponding to the current floor (0x05), speed (0x0096), and trend (0x06) into the algorithm to obtain a 32-bit checksum (0x12345678), which is then appended to the end of the data frame, forming a complete data frame with the checksum. The receiver calculates the checksum of the data portion using the same algorithm and compares it with the appended checksum. If they match, the data is considered undamaged. This mechanism improves the error detection rate of data transmission and effectively prevents the application of erroneous data.

[0126] Data is transmitted via a transmission module. Bus transmission uses a CAN bus, while network transmission uses Ethernet. Data frames with checksums are simultaneously sent to both the elevator main controller and the remote monitoring center. The differential signal transmission characteristics of the CAN bus enhance anti-interference capabilities, ensuring stable data transmission within the shaft. Ethernet, on the other hand, meets the high-speed data requirements of remote monitoring, enabling synchronous transmission of information to multiple targets.

[0127] The elevator main controller uses data to extract the current floor (5th floor) and real-time running speed (1.5 meters per second) from the received data. Combined with the preset leveling deceleration curve, it adjusts the output drive signal to ensure that the car stops precisely when it reaches the 5th floor leveling position. The leveling accuracy is controlled within ±2 millimeters, which is an improvement over traditional control methods.

[0128] The remote monitoring center uses data to display the received current floor, speed, and trend information in real time on the monitoring interface, while also storing it in the database. By analyzing historical data, it can identify elevator operating patterns, such as calculating the average running time from the 5th to the 6th floor. When this time deviates from the normal range by 10%, a maintenance prompt is issued, enabling comprehensive monitoring and early warning of elevator operating status and improving the timeliness of maintenance.

[0129] like Figure 2 As shown, this application also discloses a system for identifying the floors and speed of an elevator car, including:

[0130] The acquisition module 1 is used to acquire the magnetic field signal caused by structural changes of the sensing device on the elevator guide device through the Hall sensor installed on the elevator car, and to preprocess the magnetic field signal to obtain a regular waveform signal containing the car running speed information and floor position information.

[0131] Analysis module 2 is used to analyze the regular waveform signal, segment it into uniform waveform segments representing constant speed operation and characteristic waveform segments representing specific floors, and calculate the real-time running speed of the car based on the uniform waveform segments.

[0132] The identification module 3 is used to match and identify the extracted feature waveform segments with the pre-generated and stored floor reference feature waveform segments in the database to determine the current floor of the elevator car.

[0133] Prediction module 4 is used to integrate the real-time operating speed with the current floor of the elevator car to construct a dynamic model of the elevator operating status and predict the operating trend of the car.

[0134] The transmission module 5 is used to send the current floor, real-time operating speed and operating trend prediction results to the elevator control system.

[0135] In one embodiment, the prediction module includes:

[0136] Establish a unit to create a state vector containing the current floor, real-time running speed, and real-time acceleration;

[0137] The first prediction unit is used to estimate the elevator's operating state based on the elevator's operating characteristics using a state prediction algorithm, and to predict the state vector at the next moment.

[0138] The second prediction unit is used to predict the floor range that the car is most likely to reach in the near future by using kinematic integrals, based on the predicted speed and acceleration, combined with the known position coordinates of the current floor and each floor in the elevator shaft.

[0139] The output unit is used to combine elevator dispatch instructions or floor selection signals inside the car to correct the prediction results based on the motion model, comprehensively judge the running trend of the car, and output the prediction information of the next possible floor to stop.

[0140] like Figure 3 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all data required for the process of identifying the elevator car's stopping floors and speed. The network interface is used for communication with external terminals via a network connection. When the processor executes the computer program, it implements the method for identifying the elevator car's stopping floors and speed.

[0141] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0142] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above for identifying the floor and speed of an elevator car.

[0143] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0144] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0145] The above description is merely a preferred embodiment of this application and does not limit the scope of this application. Any equivalent results or equivalent process transformations made based on the content of this application specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

Claims

1. A method of identifying an elevator car landing and speed, characterized by, The method comprises the following steps: Collecting the magnetic field signal caused by the structural change of the inductor on the elevator guide through the Hall sensor installed on the elevator car, and preprocessing the magnetic field signal to obtain a regular waveform signal containing the car running speed information and floor position information; Analyzing the regular waveform signal, segmenting the uniform speed waveform segment representing constant speed running and the characteristic waveform segment representing a specific floor, and calculating the real-time running speed of the car based on the uniform speed waveform segment, including the following steps: Segmenting the regular waveform signal to identify each independent waveform period generated by the periodic structure of the inductor, and marking the stable region composed of continuous multiple waveform periods as the uniform speed waveform segment; In each uniform speed waveform segment, the instantaneous frequency of the magnetic field change is obtained by calculating the time interval of continuous waveform periods, and the instantaneous running speed of the car is calculated by combining the known physical distance of the periodic structure of the inductor. The speed values calculated for continuous multiple periods are filtered to output the real-time running speed of the car; Identifying and extracting the unique magnetic field waveform segment generated by the inductor coding structure unique to each floor when the elevator car passes the floor leveling position, and marking the segment as the characteristic waveform segment; Normalizing the characteristic waveform segment and extracting its time and frequency domain features including waveform width sequence, peak value sequence, waveform area and frequency energy proportion to form the feature vector of the floor; Matching and identifying the extracted characteristic waveform segment with the reference characteristic waveform segment of each floor generated in advance and stored in the database to determine the current floor of the elevator car; Fusing the real-time running speed and the current floor of the elevator car to build a dynamic model of the elevator running state and predict the running trend of the car; Sending the current floor, real-time running speed and running trend prediction result to the elevator control system through the transmission module.

2. The method of identifying elevator car stopping floors and speed according to claim 1, characterized in that, The step of obtaining the regular waveform signal containing the car running speed information and floor position information includes: Collecting the spatial magnetic field intensity change caused by the inductor through the Hall sensor at a preset sampling frequency to output the original analog voltage signal; Signal conditioning of the original analog voltage signal, including low-pass filter processing to suppress high-frequency electromagnetic interference; Analog-to-digital conversion of the conditioned signal and digital smoothing processing through the sliding average filtering algorithm, which is an arithmetic average calculation of the values of continuous multiple sampling points as the current output value; Baseline correction of the smoothed digital signal to eliminate the DC component offset in the signal, and finally output the regular waveform signal.

3. The method of identifying elevator car stopping floors and speed according to claim 1, characterized in that, The step of matching and identifying the extracted characteristic waveform segment with the reference characteristic waveform segment of each floor generated in advance and stored in the database to determine the current floor of the elevator car includes: In the initialization phase, the drive car stops at each floor in turn, collects the characteristic waveform segment at the flat position of each floor, extracts the feature vector of each floor as the reference feature vector, and establishes a mapping database of floor number and reference feature vector; During normal operation of the elevator, when a characteristic waveform segment is detected, its to-be-identified feature vector is extracted; The similarity distance between the to-be-identified feature vector and all reference feature vectors in the database is calculated; The floor corresponding to the reference feature vector with the smallest similarity distance to the to-be-identified feature vector is selected, and if the smallest similarity distance is less than a preset matching threshold, it is determined that the floor recognition is successful, and the current floor of the elevator car is output.

4. The method of identifying elevator car stopping floors and speed according to claim 1, characterized in that, The step of fusing the real-time running speed and the current floor of the elevator car to construct an elevator running state dynamic model to predict the running trend of the car includes: Establishing a state vector including the current floor, real-time running speed and real-time acceleration; Based on the elevator running characteristics, a state prediction algorithm is used to estimate the elevator running state to predict the state vector at the next time; According to the predicted speed and acceleration, combined with the known position coordinates of each floor of the elevator shaft and the current floor, the car's most likely to reach floor interval in a short time in the future is predicted through kinematic integration calculation; The prediction result based on the motion model is corrected in combination with the elevator dispatching instruction or the car interior floor selection signal, the running trend of the car is comprehensively judged, and the pre-judgment information of the next possible stopping floor is output.

5. The method of identifying elevator car stopping floors and speed of claim 1, wherein, The step of sending the current floor, real-time running speed and running trend prediction result to the elevator control system through the transmission module includes: Packaging the determined current floor, real-time running speed and running trend prediction result into a data frame of a specific format; Generating a check code for the data frame and appending it to the tail of the data frame; Through the bus or network transmission module, the data frame with the check code is sent to the elevator main controller and the remote monitoring center; The elevator main controller realizes flat control according to the received current floor and speed information; The remote monitoring center uses the received data to display and analyze the elevator running state.

6. System for identifying the landing and the speed of an elevator car, characterized in that It includes: The acquisition module is used to collect the magnetic field signal caused by the structural change of the inductor device on the elevator guide device through the Hall sensor installed on the elevator car, and to preprocess the magnetic field signal to obtain a regular waveform signal containing car running speed information and floor position information; The analysis module is used to analyze the regular waveform signal, segment the uniform speed waveform segment representing constant speed running and the characteristic waveform segment representing a specific floor, and calculate the real-time running speed of the car based on the uniform speed waveform segment, including: Waveform segmentation is performed on the regular waveform signal to identify each independent waveform period generated by the periodic structure of the inductor device, and a stable region composed of a plurality of continuous waveform periods is marked as the uniform speed waveform segment; In each of the uniform waveform segments, the instantaneous frequency of the magnetic field change is obtained by calculating the time interval of consecutive waveform periods, and the instantaneous running speed of the car is calculated in combination with the known physical spacing of the periodic structure of the inducted device. The speed values calculated for consecutive multiple periods are filtered to output the real-time running speed of the car. A unique magnetic field waveform segment generated by the unique inducted device coding structure of the floor is identified and extracted when the elevator car passes the floor leveling position, and the segment is marked as the characteristic waveform segment. The characteristic waveform segment is normalized, and its time domain and frequency domain features including waveform width sequence, peak value sequence, waveform area, and frequency energy proportion are extracted to constitute the feature vector of the floor. The recognition module matches and identifies the extracted characteristic waveform segment with the reference characteristic waveform segments of each floor generated in advance and stored in the database to determine the current floor where the elevator car is located. The prediction module fuses the real-time running speed and the current floor where the elevator car is located to construct a dynamic model of the elevator running state and predict the running trend of the car. The transmission module sends the current floor, real-time running speed, and running trend prediction result to the elevator control system through the transmission module.

7. The system of claim 6, wherein, The prediction module includes: The establishment unit is configured to establish a state vector including the current floor, real-time running speed, and real-time acceleration. The first prediction unit is configured to estimate the state of the elevator based on the characteristics of the elevator using a state prediction algorithm to predict the state vector at the next time. The second prediction unit is configured to predict the floor interval that the car is most likely to reach in a short time in the future by kinematic integration based on the predicted speed and acceleration, in combination with the current floor and the known position coordinates of each floor of the elevator shaft. The output unit is configured to correct the prediction result based on the motion model in combination with the elevator dispatching instruction or the selected floor signal in the car, judge the running trend of the car, and output the pre-judgment information of the next possible stopping floor.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

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