Floor detection method and device

Abstract

The invention discloses a floor detection method and device, relates to the technical field of elevator operation floor positioning, and aims at correcting errors from a sensor data source, avoiding state misjudgment caused by acceleration data distortion, and synchronously collecting to guarantee data space-time matching so as to improve state recognition accuracy. The static elevation data screening eliminates the interference of elevation fluctuation when the elevator moves, the precision of a data source constructed by a floor elevation mapping table is ensured, and the constructed standard elevation reference provides a stable reference for elevator floor detection. Meanwhile, regular correction is carried out subsequently based on the floor elevation mapping table so as to resist elevation drift of the barometer affected by the environment, and errors are controlled layer by layer in the whole process from data processing, state recognition to table construction. The problems of insufficient precision and poor stability caused by sensor errors, data mismatching, state misjudgment and environment interference in elevator floor detection are effectively solved, and the precision and long-term stability of elevator floor detection are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of elevator floor positioning technology, and in particular to a floor detection method and device. Background Technology

[0002] With the acceleration of urbanization and the continuous expansion of high-rise buildings, elevators, as core equipment in vertical transportation, require precise and real-time monitoring of their operating height to ensure passenger safety and optimize scheduling efficiency. Under the development trend of smart buildings and intelligent elevator systems, the intelligent upgrading of elevator operation status monitoring further highlights the core requirements for high detection accuracy, reliability, and environmental adaptability.

[0003] Currently, elevator height detection methods mostly rely on single-type sensors, including accelerometers, photoelectric sensors, magnetic sensors, and barometric pressure sensors. However, due to limitations such as sensor characteristics, installation costs, and environmental interference, single-sensor solutions struggle to meet the multiple requirements of accuracy, stability, and deployment costs in real-world scenarios, becoming a technological bottleneck for the intelligent upgrading of elevators. Summary of the Invention

[0004] This invention provides a floor detection method and device. It uses dynamic acceleration data obtained by a triaxial accelerometer to reflect the motion state of the elevator, and combines it with relative height change information obtained by a barometer to form a complementary measurement mechanism. The accelerometer is used for sensitive detection of dynamic changes, and the barometer provides a low-frequency drift reference. This can effectively suppress the error accumulation and fluctuation problems of a single sensor, and solve the technical problem of how to improve the accuracy and stability of elevator floor detection.

[0005] The first aspect of this invention provides a floor detection method, comprising: Correct the three-axis accelerometer of the target building; Acceleration data from the triaxial accelerometer and elevation data from the barometer were collected synchronously within a preset time period after correction. The elevator's operating status is identified using the acceleration data and the elevation data to determine the static elevation data. A floor elevation mapping table for the target building is constructed based on the static elevation data.

[0006] Optionally, the triaxial accelerometer of the target building being corrected includes: Collect triaxial acceleration data of the target building during a continuous static state period using triaxial accelerometers, and perform mean calculation to obtain the static triaxial acceleration mean. The zero-point offset value is obtained by using the average static triaxial acceleration. The corrected zero-point offset value is summed with the triaxial acceleration data to obtain the corrected triaxial accelerometer acceleration data.

[0007] Optionally, it also includes: The difference between the triaxial acceleration data of the stationary state segment and the mean of the static triaxial acceleration is calculated to obtain the rejection deviation value associated with each of the triaxial acceleration data; When the rejection deviation value is greater than the preset deviation threshold, the associated triaxial acceleration data is rejected to obtain triaxial acceleration data for the continuous stationary state segment after rejection.

[0008] Optionally, the step of using the acceleration data and the elevation data to identify the elevator's operating status and determine the static elevation data includes: Determine whether the acceleration data is within the preset steady-state range of the gravity component; When the acceleration data is within the preset steady-state range of the gravity component, the vertical velocity value of the elevator is calculated using the elevation data of adjacent moments. When the speed value is less than the preset speed threshold, the current elevation data is used as the target elevation data. Collect a preset number of target elevation data to obtain static elevation data.

[0009] Optionally, constructing the floor elevation mapping table of the target building based on the static elevation data includes: The minimum value is selected from multiple target elevation data as the benchmark elevation, and an elevation distribution histogram is constructed using a fixed interval step size; Frequency statistics are performed on each of the target elevation data according to the elevation distribution histogram to obtain the interval frequency of each elevation interval; Based on the comparison results between the frequency of each interval and the preset interval filtering threshold, candidate floor intervals are selected from each of the elevation intervals. The average value of multiple target elevation data within each candidate floor interval is calculated, and the average target elevation is used as the standard floor elevation of the corresponding floor. Based on the standard elevation of each floor, calculate the standard elevation difference between adjacent candidate floor intervals; When the standard elevation difference is within the preset effective range of floor height, the floor associated with the elevation difference value is marked as a valid floor. Calculate the effective standard elevation difference between adjacent effective floors; When the effective standard elevation difference is greater than the preset intercalation spacing threshold, the preset intercalation elevation is used for intercalation to complete the floor and generate a floor elevation mapping table containing the floor elevation mapping relationship.

[0010] Optionally, it also includes: When the barometer exhibits elevation drift, the floor elevation mapping table is corrected using the reference elevation frequency offset or elevation scaling factor.

[0011] A second aspect of the present invention provides a floor detection device, comprising: Correction module, used to correct the triaxial accelerometer of the target building; The data acquisition module is used to synchronously acquire the acceleration data of the corrected triaxial accelerometer and the elevation data of the barometer within a preset time period; The status determination module is used to identify the elevator's operating status using the acceleration data and the elevation data, and to determine the static elevation data. The floor construction module is used to construct a floor elevation mapping table for the target building based on the static elevation data.

[0012] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the floor detection method as described in any of the preceding claims.

[0013] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the floor detection method as described in any of the preceding claims.

[0014] The fifth aspect of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the floor detection method as described in any of the preceding claims.

[0015] As can be seen from the above technical solutions, the present invention has the following advantages: This invention first corrects the zero-point offset of the triaxial accelerometers of the target building to eliminate inherent sensor errors and obtain accurate acceleration data. Then, it simultaneously collects the corrected acceleration data from the triaxial accelerometers and the elevation data from the barometers within a preset time period. By fusing the acceleration data, it initially determines the elevator's operating status and verifies the elevator's true static state by combining the speed calculated from the barometers based on elevation and time differences, thereby determining the static elevation data for that state. Finally, it constructs a floor elevation mapping table based on the static elevation data to obtain the standard elevation reference for each floor of the target building. This invention corrects errors at the sensor data source, avoiding misjudgments of the state caused by distorted acceleration data, and simultaneously collects… Ensuring the spatiotemporal matching of data improves the accuracy of status identification. The screening of static elevation data eliminates the interference of elevation fluctuations during elevator movement, ensuring the accuracy of the data source for the floor elevation mapping table. The constructed standard elevation reference provides a stable benchmark for elevator floor detection. Furthermore, subsequent periodic corrections can be made based on this floor elevation mapping table to resist elevation drift of the barometer due to environmental influences. By controlling errors at every stage of the entire process from data processing and status identification to table construction, the problems of insufficient accuracy and poor stability caused by sensor errors, data mismatch, status misjudgment, and environmental interference in elevator floor detection are effectively solved, significantly improving the accuracy and long-term stability of elevator floor detection. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the steps of a floor detection method provided in an embodiment of the present invention; Figure 2 The elevator elevation map obtained by the barometer provided in this embodiment of the invention; Figure 3 This is an elevator elevation map calculated by an accelerometer according to an embodiment of the present invention; Figure 4 The elevation distribution histogram provided in this embodiment of the invention; Figure 5 This is a structural block diagram of a floor detection device provided in an embodiment of the present invention; Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] This invention provides a floor detection method and device. By utilizing the dynamic response capability of an accelerometer and the absolute displacement trend sensing capability of a barometer, it achieves accurate identification of the vertical running state of an elevator car. This overcomes the problems of reducing the cumulative error caused by the second integral of acceleration, avoiding the high dependence of the optical system on structural modifications and environmental cleanliness, avoiding the defects of magnetic marking systems in terms of construction cost and electromagnetic interference, and compensating for the shortcomings of barometers when used alone, such as large accuracy fluctuations and poor real-time performance.

[0019] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] Currently, methods for detecting elevator running height mostly rely on single-type sensors such as accelerometers, photoelectric sensors, magnetic sensors, or barometric pressure sensors. However, these methods have the following drawbacks in practical applications: An accelerometer-based method for estimating elevator height involves performing a second integration of the acceleration signal output by the accelerometer in the vertical direction to calculate the car's displacement. However, accelerometers commonly suffer from zero-bias drift and the superposition of random noise, leading to a rapid accumulation of errors during integration. This accumulation, especially during long-term operation, results in significant errors, ultimately causing severe inaccuracies in displacement estimation and failing to meet the technical requirements for high-precision, long-term stable monitoring.

[0021] Measurement methods based on photoelectric encoders or grating rulers: These methods rely on installing optical scales, reflective surfaces, or readers on the elevator shaft or car for precise displacement measurement. However, these solutions require modifications to the elevator structure or the addition of optical components, resulting in complex system installation, high hardware costs, and long maintenance cycles. Furthermore, elevator shafts are commonly contaminated with dust, oil, and other pollutants, which can affect the performance of optical components, leading to signal misreading or measurement interruptions, and insufficient stability under complex operating conditions.

[0022] Methods for detecting magnetic markers in wellbore using magnetic sensors: These technologies typically require the placement of permanent magnets or magnetic encoder markings along the wellbore, with position detected by Hall effect sensors or magnetoresistive sensors. While the method has a relatively simple structure, it relies on manually and uniformly distributing the magnetic source, making construction difficult, time-consuming, and complex to debug, resulting in high system construction and maintenance costs. Furthermore, in environments with electromagnetic interference or high magnetic background noise, the sensors may experience false triggering or misidentification, making reliability difficult to guarantee.

[0023] Methods for measuring elevator height using barometers: Barometric height measurement is based on the physical law of atmospheric pressure changing with altitude, and theoretically can be used to determine vertical displacement. However, in practical applications, air pressure changes are significantly affected by factors such as ambient temperature variations, air disturbances in enclosed spaces, and airflow exchange within buildings, leading to large fluctuations and poor stability in measurement results. Furthermore, barometric height estimation has nonlinear characteristics, making it difficult to obtain linear and repeatable height change values ​​within a small range, thus unsuitable for real-time precise control and status identification requirements.

[0024] Triaxial accelerometers, currently the most widely used type, can simultaneously acquire acceleration data along three orthogonal directions (typically the X, Y, and Z axes), thus reflecting the motion trend of the measured object in three-dimensional space. A typical triaxial accelerometer is manufactured based on a microelectromechanical system (MEMS), and internally typically includes a mass, springs, dampers, and capacitive or piezoresistive sensing elements. By detecting the minute displacement of the mass under external acceleration, the system converts mechanical motion into an electrical signal, thereby obtaining the acceleration value.

[0025] Currently, triaxial accelerometers are widely used in mobile devices, smart wearables, automotive electronics, industrial monitoring, and elevator motion sensing. In elevator position determination scenarios, they can be used to calculate the elevator's vertical acceleration changes, thereby obtaining displacement or height information through integration. However, this technical solution suffers from problems such as error accumulation, dynamic drift, and difficulty in benchmark calibration in practical applications, making it difficult to meet the requirements for high-precision and high-stability position recognition. Therefore, it needs to be used in conjunction with other sensors to improve the overall accuracy of the determination.

[0026] A barometric pressure sensor is a sensor used to measure changes in ambient atmospheric pressure. It is widely used in altitude estimation, environmental monitoring, industrial control, meteorological equipment, and mobile terminals. Its basic working principle is as follows: when external air pressure acts on the sensor's pressure-sensitive diaphragm, the diaphragm undergoes a slight deformation, causing a change in its resistance, capacitance, or piezoelectric properties. This change is converted into a voltage or digital signal proportional to the air pressure by a dedicated detection circuit, ultimately outputting the absolute or relative air pressure value of the current environment.

[0027] In altitude detection applications, since atmospheric pressure changes exponentially with altitude, barometric pressure sensors can calculate the relative altitude of the device by measuring the difference between the current air pressure and a reference air pressure. Typically, a drop of approximately 8-12 Pa (depending on altitude, temperature, and other conditions) corresponds to a vertical height change of about 1 meter. Therefore, barometric pressure sensors are particularly suitable for sensitive monitoring of vertical displacement.

[0028] In industrial environments, barometric pressure sensors can replace traditional contact-based height detection elements, especially suitable for locations with limited installation space and where mechanical contact is not feasible. For example, in elevator height detection, drone altitude hold, and indoor navigation systems, the air pressure at the equipment's location finely adjusts as it moves up and down, and the sensor can output the real-time trend of altitude changes. Compared to photoelectric and magnetic position sensors, barometric pressure sensors do not rely on shaft structures, magnetic markers, or visual references, offering advantages such as flexible installation and strong environmental adaptability.

[0029] Due to its non-contact operation, the barometric pressure sensor is insensitive to common industrial interferences such as dust, oil, and obstructions, and can operate stably in confined spaces and harsh weather environments for extended periods. However, the barometric pressure sensor is also sensitive to factors such as ambient temperature and pressure fluctuations in sealed chambers, which may introduce drift errors. Therefore, this invention integrates it with an accelerometer to improve overall measurement accuracy and stability.

[0030] In summary, traditional methods often rely on a single sensor, such as an accelerometer or barometer, to infer altitude changes. However, these methods have significant problems in the following three aspects: 1. Sensor error accumulation: Using only acceleration timing and dual-integral height estimation can easily lead to rapid error accumulation, especially during elevator acceleration and deceleration, where the performance is unstable. 2. Environmental interference: Using a barometer alone to determine the floor level is sensitive to fluctuations in ambient air pressure (such as weather, air conditioning systems, etc.), which can cause errors in height determination. 3. Difficulty in state recognition: Existing solutions cannot effectively distinguish the elevator's start-up, running, and stop states, making it difficult to extract the accurate "elevator running segment," resulting in inaccurate floor change judgment.

[0031] This invention provides a floor detection method. The core technical idea is to extract the start and stop sections of elevator operation using acceleration signals, eliminate stationary and disturbed sections, and effectively locate the floor change sections. In the effective operating section, the height change is calculated using the air pressure difference measured by a barometer. By segmenting the analysis, the influence of air pressure drift is avoided. Finally, combined with the height change of the operating section, a quantitative judgment is made based on the height between floors, and the number of floors passed or reached is output. The technical idea of ​​this invention is to determine the operating section by using acceleration signals, which greatly reduces the interference of air pressure drift on the recognition results and significantly improves the recognition accuracy. At the same time, it integrates data from multiple sensors to achieve complementary redundancy, which effectively improves the robustness in complex environments. This gives the floor detection device the advantage of strong anti-interference ability and makes it applicable to various elevator operating scenarios, including complex conditions such as speed change, shaking, and slow stop, with better versatility and engineering adaptability.

[0032] Therefore, this invention not only has a breakthrough advantage in elevator floor recognition accuracy, but also demonstrates significant superiority in terms of stability and scalability in actual deployment.

[0033] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a floor detection method provided in an embodiment of the present invention.

[0034] The present invention provides a floor detection method, comprising: Step 101: Correct the three-axis accelerometer of the target building.

[0035] The target building refers to a multi-story building that requires the coordinated use of accelerometers and barometers to accurately detect elevator floors. Specifically, it refers to a building equipped with passenger / freight elevators whose elevator trajectory is vertical (including typical states such as ascending, descending, and stationary), such as residential buildings, office buildings, and commercial complexes. Its core characteristics are that the elevator has a clear floor stop requirement and the building's floor structure conforms to the conventional floor height range.

[0036] In this embodiment of the invention, the accelerometer needs to be pre-installed in the elevator car of the target building or on the detection equipment that accompanies the elevator to ensure that the accelerometer can synchronously collect the three-axis acceleration data during the elevator's movement. Correcting the three-axis accelerometer of the target building is essentially to eliminate the zero-point offset of the accelerometer in the elevator scenario of that building, especially the dynamic offset of the z-axis caused by gravity. This lays the data foundation for the subsequent synchronous collection of "corrected acceleration data", "barometer elevation data", and accurate determination of the elevator state (stationary / uniform speed / acceleration, etc.), and ultimately adapts to the elevator floor detection requirements of the target building.

[0037] It should be noted that research has found that the magnitude of the zero-point offset of the accelerometer changes over time. In order to obtain more accurate accelerometer data, it is necessary to continuously adjust the zero-point offset values ​​of the x, y, and z axes. Currently, the zero-point offset has little effect on the x and y axes, so only the zero-point offset of the z axis is corrected at present.

[0038] Further, step 101 may include the following sub-steps: S11. Collect triaxial acceleration data of the target building during the continuous static state segment of the triaxial accelerometer, and perform mean calculation to obtain the static triaxial acceleration mean. In this embodiment of the invention, when the accelerometer initially determines that the elevator is stationary, the determination condition is that the z-axis acceleration fluctuation is ≤ ±0.2 m / s². 2 The acceleration fluctuation along the x and y axes is ≤ ±0.1 m / s². 2The duration is ≥1 second. Collect raw z-axis acceleration data within a 3-second time window (1 second before, 1 second before, and 1 second after). Calculate the arithmetic mean of the z-axis acceleration within the window and store the static triaxial acceleration mean in the zero-point offset correction list.

[0039] S12. Use the average static triaxial acceleration to perform zero-point correction and obtain the corrected zero-point offset value; In this embodiment of the invention, the time coverage length of the zero-point offset correction list is monitored in real time. Based on the sampling frequency, the time span corresponding to the list data is ≥2 seconds. The following operations are performed: Extract the average z-axis acceleration from the list; The corrected zero-point offset value is obtained by summing the historical zero-point offset value of the triaxial accelerometer with the mean acceleration value. Clear the zero-point offset correction list and prepare for the next round of corrections.

[0040] S13. The corrected zero-point offset value and the triaxial acceleration data are summed to obtain the corrected triaxial accelerometer acceleration data.

[0041] In this embodiment of the invention, the correction is completed by subtracting the corrected zero-point offset value from the real-time acquired triaxial acceleration data.

[0042] Furthermore, step 101 may also include the following sub-steps to suppress the risk of overcorrection and ensure the stability of the zero-point offset update: S14. Perform a difference calculation between the triaxial acceleration data of the stationary state segment and the mean value of the static triaxial acceleration to obtain the rejection deviation value associated with each of the triaxial acceleration data.

[0043] In this embodiment of the invention, for the original triaxial acceleration data collected in the static state segment, the focus is on the z-axis data, because the z-axis is most affected by gravity and the zero-point offset has the most significant interference on state discrimination. The absolute difference between each point and the mean of static triaxial acceleration is calculated to obtain the removal deviation value associated with each data point. The removal deviation value is used to quantify the degree of deviation of a single data point from the mean.

[0044] S15. When the rejection deviation value is greater than the preset deviation threshold, the associated triaxial acceleration data is rejected to obtain the triaxial acceleration data of the continuous stationary state segment after rejection.

[0045] In this embodiment of the invention, if the set historical zero-point offset value differs significantly from the actual value, overcorrection may occur. Therefore, a preset deviation threshold is set to mitigate the oscillations caused by correction. The preset deviation threshold is preferably 0.05 m / s. 2If a certain z-axis acceleration data is greater than a preset deviation threshold, it is determined to be abnormal data. Abnormal data is removed from the triaxial acceleration data set of the stationary state segment, and data with a rejection deviation value less than or equal to the preset deviation threshold are retained to form triaxial acceleration data of the continuous stationary state segment after rejection.

[0046] Step 102: Synchronously collect the acceleration data of the corrected triaxial accelerometer and the elevation data of the barometer within a preset time period.

[0047] In this embodiment of the invention, based on the operational status detection requirements of the target building elevator, a collection cycle matching the sampling frequency of the corrected triaxial accelerometer is set (e.g., triggering collection once every 0.5 seconds). Through a hardware clock synchronization mechanism, the x, y, and z axis acceleration data output by the corrected triaxial accelerometer and the elevation data of the elevator car's location measured in real time by the barometer are simultaneously acquired. A unified timestamp is marked for each set of synchronously acquired acceleration data and elevation data to form a correlated data pair.

[0048] Step 103: Use the acceleration data and elevation data to identify the elevator's operating status and determine the static elevation data.

[0049] Elevator operation status recognition refers to the process of distinguishing seven motion states of an elevator through the sensitive detection of dynamic acceleration changes by an accelerometer and the stable judgment of static speed by a barometer, in collaboration with "preliminary identification + status correction", thus overcoming the inherent defects of accelerometers.

[0050] Static elevation data refers to elevation data verified by barometers under true static conditions. It has the characteristics of stable elevation values ​​and no dynamic changes, and is the core basis for constructing floor elevation tables.

[0051] Figure 2 and Figure 3 The elevation maps obtained using different sensors for the same motion are incorrect because the initial state is unknown, leading to errors in the state obtained using only the accelerometer. Figure 3 Initially), and the barometer can correct for erroneous conditions ( Figure 2 (At the very beginning).

[0052] like Figures 2-3 As shown, Figure 2The elevator elevation map obtained from the barometer visually illustrates the inherent limitations of the barometer. Different colors represent different states: dark red indicates accelerated descent, light red indicates decelerated descent, red indicates uniform descent, gray indicates stillness, dark green indicates accelerated ascent, light green indicates decelerated ascent, and green indicates uniform ascent. The horizontal axis represents time, and the vertical axis represents height. The elevation data exhibits "step-like jumps" due to lag, and there are obvious spikes at the start and stop, such as the slight fluctuations in the first 0.5 seconds before acceleration. Even in seemingly stable horizontal sections, there is a slow rise due to environmental air pressure drift; for example, the elevation slightly rises from 98.2m to 98.3m within 3 seconds of stillness, making it difficult to directly determine "true stillness."

[0053] Figure 3 The elevator elevation map calculated by the accelerometer (the initial state error was corrected by the barometer data) optimized this problem through collaborative logic: initially, the elevator was misjudged as "stationary" because it was in a uniform speed state when it was turned on, but the state correction function was triggered by the speed of 0.3 m / s calculated by the barometer (>0.2 m / s threshold), which corrected the state to "uniform speed ascent". Subsequently, when the elevator actually came to a stop, the horizontal segment in the map was strictly stable (the elevation was stable at 98.15m±0.02m), clearly distinguishing between "true stationary" and "pseudo-stationary".

[0054] It should be noted that each data segment (0.5s) will not span any three consecutive states of the elevator, and will span at most two states.

[0055] Elevator motion can be divided into seven states: accelerating descent, decelerating descent, constant speed descent, stationary, accelerating ascent, decelerating ascent, and constant speed ascent.

[0056] The elevator is initially set to be stationary. However, if the machine starts when the elevator is moving at a constant speed, the calculation logic above cannot obtain the correct elevator operating state due to the incorrect initial state. Therefore, a state correction function is needed to correct the error as soon as it is detected.

[0057] Although accelerometers cannot distinguish between constant speed and stillness, barometers can. Therefore, the logic for using barometer data to correct the elevator's operating status is as follows: First, the current elevator speed can be obtained by dividing the distance traveled by the barometer between two data points by the time taken. When the barometer detects that the elevator is stationary (<0.2m / s) while the accelerometer indicates that the elevator is moving at a constant speed upwards or downwards, the current elevator state is changed to stationary.

[0058] The reason for not directly using a barometer to calculate the current motion state of the elevator is that the barometer itself has a slow response speed, and the elevation data it collects is prone to large fluctuations during the elevator's short-term start-stop, acceleration and other dynamic processes, making it difficult to accurately capture the instantaneous motion characteristics of the elevator. Just like the "lagging jump" in the acceleration upward segment of the barometer elevation map, it is impossible to accurately determine the real-time status of the elevator.

[0059] Furthermore, step 103 may include the following sub-steps: It should be noted that in elevator operation status identification, the accelerometer cannot distinguish between "average speed" and "stationary". Since the acceleration measured by both is close to 0, it is necessary to combine it with the barometer for correction. Only when it is determined that the elevator is in a truly stationary state can the elevation data measured by the barometer be the true height of the corresponding floor.

[0060] S21. Determine whether the acceleration data is within the preset steady-state range of the gravity component; In this embodiment of the invention, the corrected z-axis acceleration data is extracted, and it is determined whether the acceleration data is within the preset steady-state range of the gravity component. The preset steady-state range of the gravity component is preferably [9.7, 9.9]. This step corresponds to the determination of the horizontal segment in the accelerometer elevation map: when the z-axis acceleration is stable at 9.8 m / s² ± 0.1 m / s², it is initially determined to be "uniform or stationary", and the dynamic data of the green acceleration segment (z-axis acceleration > 9.9 m / s²) and the red deceleration segment (z-axis acceleration < 9.7 m / s²) in the barometer elevation map are filtered out.

[0061] S22. When the acceleration data is within the preset steady-state range of the gravity component, the vertical velocity value of the elevator is calculated using the elevation data of adjacent moments. In this embodiment of the invention, when the acceleration data is within the preset steady-state range of the gravity component, barometer elevation data at an adjacent time point with the same timestamp as the acceleration data are selected, preferably 0.5s apart. The vertical velocity value of the elevator is calculated using the following formula:

[0062] In the formula, This represents the vertical speed of the elevator. This indicates the elevation data measured by the barometer at the current moment. This indicates the elevation data measured by the barometer at the previous moment. This represents the time difference between two elevation data collections. For example, in a seemingly stable 0.5s interval on the barometer elevation map, if the elevation data measured by the barometer at the current moment is 98.3m, and the elevation data measured by the barometer at the previous moment is 98.2m, the calculated velocity value is 0.2m / s. Further verification is needed to determine whether it is truly stationary.

[0063] S23. When the speed value is less than the preset speed threshold, the current elevation data is used as the target elevation data. In this embodiment of the invention, the preset speed threshold is preferably 0.2 m / s. When the calculated speed value is less than this threshold, the elevator is determined to be in a truly stationary state, thereby correcting the defect of the accelerometer in "confusing uniform speed with stationary state". The current elevation data is marked as the target elevation data. Only the elevation when truly stationary has the uniqueness of floor positioning. For example, if the stable horizontal segment in the elevation map after accelerometer correction is calculated to have v = 0.1 m / s < 0.2 m / s, the corresponding elevation of 98.15 m is marked as the target data.

[0064] S24. Collect a preset number of target elevation data to obtain static elevation data.

[0065] In this embodiment of the invention, a preset number of target elevation data are continuously collected, such as 20 times, which can be adjusted as needed. Abnormal values ​​with a single elevation deviation from the mean exceeding 0.3m (such as sudden spurs in the stationary section of the barometer elevation map) are removed to avoid interference from instantaneous air pressure fluctuations. The data are then compiled to form static elevation data, corresponding to 20 stable points (98.15m±0.02m) selected in the elevation map after accelerometer correction.

[0066] It should be noted that when the acceleration data is not within the preset steady-state range of the gravity component, it indicates that the elevator is in a state of accelerating descent, decelerating descent, accelerating ascent, or decelerating ascent. At this time, the elevation data fluctuates rapidly with the acceleration and does not have the stability of floor positioning. Therefore, the elevation data at the current moment is not included in the target elevation data collection range.

[0067] When the speed value is greater than or equal to the preset speed threshold, even if the acceleration data is in the steady state range (corresponding to uniform descent or uniform ascent), the elevator is actually in continuous motion, and the elevation data changes linearly with time (such as the gentle slope of the uniform ascent section in the barometer elevation graph), and is not the floor elevation at a stable stop. Therefore, the elevation data at the current moment is not marked as the target elevation data.

[0068] By filtering these two non-static scenarios, we ensure that only stable elevation data under truly static conditions are collected, just as... Figure 2 and Figure 3 The comparison shows that the final static elevation data all come from a strictly stable segment where the accelerometer determined the steady state and the barometer verified the velocity to be <0.2m / s, providing a clean data source for the subsequent construction of floor elevation tables.

[0069] Step 104: Construct a floor elevation mapping table for the target building based on the static elevation data.

[0070] Initial acquisition of the floor plan Floor location needs to be determined without knowing the total height of the building or the height of each floor. Furthermore, it cannot be assumed that each floor has the same height, nor can it be assumed that there are several underground floors and several above-ground floors; the height of each above-ground floor is also uncertain. Therefore, a floor table recording the height of each floor must be obtained based on previous elevator operation data.

[0071] The steps for calculating the floor plan are as follows: Collect data from N elevator operations, identify each pause as a single operation, and record the elevation information of the paused state.

[0072] The lowest floor of the collected N data points is identified as floor 1 (automatically identified floors do not have underground floors, so the lowest floor is defined as floor 1).

[0073] A histogram of the frequency of elevation occurrences was obtained with a step size of Nm (0.3m in actual use).

[0074] Set a threshold and set the histogram of the portion that does not meet the threshold to 0 to separate the boundaries between floors.

[0075] Adjacent histograms are merged and treated as a single floor. The average value is then taken as the floor height. This yields a floor elevation table.

[0076] Set a reasonable range for floor height, such as [2.3m, 5.5m]. Delete floors with elevations less than the minimum value in the floor elevation table.

[0077] Iterate through the floor elevation table from smallest to largest. When the distance between two floors is greater than the maximum reasonable range, add one floor (the median of the reasonable range), and continue adding floors until the distance between two floors is less than the maximum reasonable range.

[0078] This yields a floor elevation table, which is used to determine the floor level in real time during elevator operation.

[0079] Furthermore, step 104 may include the following sub-steps: S31. Select the minimum value from multiple target elevation data as the benchmark elevation, and construct an elevation distribution histogram using a fixed interval step size; In this embodiment of the invention, multiple target elevation data correspond to N elevation records of elevator stops. From these multiple target elevation data, the elevation with the smallest value is selected as the baseline elevation, defined as the first floor of the building. Following the scenario convention of "no underground floors, the lowest floor is the first floor," a fixed interval step of 0.3 meters is used to divide continuous elevation intervals. An elevation distribution histogram is constructed with each interval as the horizontal axis and the data distribution as the vertical axis, as shown below. Figure 4As shown, Elevation is the elevation of the elevator car (i.e., the vertical position value, which can be understood as altitude or relative height). Elevation Histogram is an elevation distribution histogram that intuitively presents the clustering characteristics of static elevation data.

[0080] S32. Based on the elevation distribution histogram, perform frequency statistics on each target elevation data to obtain the interval frequency of each elevation interval; In this embodiment of the invention, based on the elevation distribution histogram, the frequency of occurrence of target elevation data is counted in each interval to obtain the interval frequency of each elevation interval. The higher the frequency, the more frequently the elevator stops in that interval, and the higher the probability of the corresponding actual floor.

[0081] S33. Based on the comparison results between the frequency of each interval and the preset interval filtering threshold, candidate floor intervals are selected from each elevation interval. In this embodiment of the invention, a preset interval filtering threshold is set, preferably 3 times. If the interval frequency is greater than or equal to the preset interval filtering threshold, it is marked as a candidate floor interval. If the interval frequency is less than the preset interval filtering threshold, it is determined to be a floor gap and is set to 0 to divide the physical boundaries of the floors. At the same time, if the physical distance between adjacent candidate intervals is less than 0.5 meters, they are merged into one candidate floor interval to eliminate the problem of interval fragmentation with small gaps.

[0082] S34. Calculate the average value of multiple target elevation data in each candidate floor interval, and use the average target elevation as the floor standard elevation of the corresponding floor. In this embodiment of the invention, for each candidate floor interval after merging, all target elevation data are extracted and the arithmetic mean is calculated. The calculated average target elevation is used as the standard floor elevation of the corresponding floor.

[0083] S35. Based on the standard elevation of each floor, calculate the standard elevation difference between adjacent candidate floor intervals; In this embodiment of the invention, the standard elevation difference (i.e., floor height) between adjacent candidate floors is calculated based on the standard elevation of each floor; at the same time, abnormal data where the standard elevation of a floor is less than the benchmark elevation (1 floor) are removed to avoid misjudging underground floors.

[0084] S36. When the standard elevation difference is within the preset effective range of floor height, the floor associated with the elevation difference value is marked as a valid floor. In this embodiment of the invention, the floor height range is verified, and valid floors are marked. The preset valid floor height range is set as [2.3 meters, 5.5 meters]. If the standard elevation difference between adjacent candidate floors is within this range, it is marked as a valid floor. If the standard elevation difference exceeds the range, such as <2.3 meters or >5.5 meters, it is determined to be an abnormal floor and is removed.

[0085] S37. Calculate the effective standard elevation difference between adjacent effective floors; In this embodiment of the invention, for the valid floors marked by S36, the effective standard elevation difference between adjacent valid floors is recalculated, and the true and reliable floor heights are screened to eliminate abnormal interference.

[0086] S38. When the effective standard elevation difference is greater than the preset intercalation spacing threshold, the preset intercalation elevation is used to perform intercalation and complete the floor elevation, generating a floor elevation mapping table containing the floor elevation mapping relationship.

[0087] In this embodiment of the invention, a preset interlayer spacing threshold of 5.5 meters is set, which is consistent with the upper limit of the effective floor height range. If the effective standard elevation difference is greater than 5.5 meters, the preset interlayer elevation (the median of the effective floor height range, such as 3.9 meters) is used to interpolate and complete the floor between the two floors. For example, if the distance between the 1st and 3rd floors is 9 meters, the 2nd floor and the 1st floor are interpolated by 3.9 meters. The interlayer operation is repeated until the distance between all adjacent effective floors is less than 5.5 meters. Finally, a floor elevation mapping table containing the mapping relationship of "floor number - standard elevation" is generated.

[0088] Furthermore, it may also include the following steps: Step 105: When the barometer has elevation drift, correct the floor elevation mapping table by the reference elevation frequency offset or elevation scaling factor.

[0089] In this embodiment of the invention, new elevation data of the elevator in a truly stationary state within a collection period (e.g., 24 hours) are collected, and the motion state is filtered to ensure that the data is stable and stationary. The frequency of occurrence of each elevation is counted, and the elevation value with the highest frequency is extracted as the reference elevation after drift.

[0090] The correction of the floor elevation mapping table based on the reference elevation frequency offset specifically includes: Retrieve the initial reference elevation from the floor elevation mapping table, which is the standard elevation of the first floor when the table was created, and denot it as . ; Calculate the reference offset In the formula, This indicates the new elevation data.

[0091] Perform an overlay offset operation on the standard elevations of all floors in the floor elevation mapping table:

[0092] In the formula, Indicates the corrected version Standard elevation of the layer.

[0093] By correcting the floor elevation mapping table using the reference elevation frequency offset, the problem of overall elevation shift caused by drift within the same floor is solved.

[0094] The correction of the floor elevation mapping table based on the elevation scaling factor specifically includes: Based on the newly acquired elevation data under true static conditions, calculate the actual floor height of adjacent floors, such as the elevation difference between 1-2 floors after drift. ; Retrieve the initial floor height from the floor elevation mapping table ,like The value was determined to be "elevation scaling drift"; Using the proportional relationship of the first-level benchmark, the global scaling law is derived, and the elevation scaling factor is calculated. :

[0095] Perform a scaling operation on the standard elevation of all floors in the floor elevation mapping table:

[0096] In the formula, Indicates the corrected version Standard elevation of the layer.

[0097] The floor elevation mapping table is updated by selecting the effective result of either of the two correction methods to ensure that it continues to adapt to the elevator floor detection after air pressure drift.

[0098] This invention has the following advantages: 1. Based on the complementary performance of a triaxial accelerometer and a barometric pressure sensor, a robust method for recognizing the vertical motion state of an elevator is designed, overcoming the limitations of a single sensor in complex environments. 2. A dynamic correction algorithm for zero bias of accelerometer driven by static state is proposed to alleviate zero drift caused by temperature drift, time drift and mechanical vibration in real time, and ensure the long-term stability of acceleration data. 3. Construct a segmented discrimination mechanism of "acceleration trend state machine + air pressure speed assistance", combine multi-time period data to accurately identify seven states such as "acceleration / deceleration / uniform speed rise and fall, and stationary", and eliminate the misjudgment of "uniform speed / stationary" by acceleration through air pressure correction; 4. For unknown buildings, based on histogram statistics of floor elevations, threshold filtering, and spacing rules, a floor elevation mapping table is automatically generated to achieve scene adaptation without prior information. 5. Design a dynamic update mechanism of "bottom high-frequency docking benchmark + floor coding structure alignment" to adaptively correct elevation deviation caused by air pressure drift and continuously maintain floor identification accuracy.

[0099] By fusing features and correcting algorithms from two types of sensor data, this invention can achieve high-precision, real-time monitoring of elevator operating height, floor status, start-up and deceleration processes without modifying the shaft structure, with low-cost deployment and adaptability to complex environmental changes, thereby improving operational safety and intelligent management capabilities.

[0100] Please see Figure 5 , Figure 5 This is a structural block diagram of a floor detection device provided in an embodiment of the present invention.

[0101] The present invention provides a floor detection device, comprising: Correction module 501 is used to correct the triaxial accelerometer of the target building; Data acquisition module 502 is used to synchronously acquire the acceleration data of the corrected triaxial accelerometer and the elevation data of the barometer within a preset time period; The state determination module 503 is used to identify the elevator's operating state using the acceleration data and the elevation data, and to determine the static elevation data. The floor construction module 504 is used to construct a floor elevation mapping table of the target building based on the static elevation data.

[0102] Furthermore, the correction module 501 includes: The static triaxial acceleration mean submodule is used to collect triaxial acceleration data of the target building during a continuous static state segment from the triaxial accelerometer, and perform mean calculation to obtain the static triaxial acceleration mean. The zero-point offset value submodule is used to perform zero-point correction using the static triaxial acceleration mean value to obtain the corrected zero-point offset value. The summation submodule is used to perform a summation operation on the corrected zero-point offset value and the triaxial acceleration data to obtain the corrected triaxial accelerometer acceleration data.

[0103] Furthermore, the correction module 501 also includes: The submodule for removing deviation values ​​is used to perform a difference calculation between the triaxial acceleration data of the stationary state segment and the mean value of the static triaxial acceleration to obtain the removal deviation value associated with each of the triaxial acceleration data. The elimination submodule is used to eliminate the associated triaxial acceleration data when the elimination deviation value is greater than a preset deviation threshold, so as to obtain the triaxial acceleration data of the continuous stationary state segment after elimination.

[0104] Furthermore, the state determination module 503 includes: The judgment submodule is used to determine whether the acceleration data is within the preset steady-state range of the gravity component; The velocity value submodule is used to calculate the vertical velocity value of the elevator using the elevation data at adjacent times when the acceleration data is within the preset steady-state range of the gravity component. The target elevation data submodule is used to take the current elevation data as the target elevation data when the speed value is less than a preset speed threshold. The static elevation data submodule is used to collect a preset number of target elevation data to obtain static elevation data.

[0105] Furthermore, the floor construction module 504 includes: The elevation distribution histogram submodule is used to select the minimum value from multiple target elevation data as the reference elevation and construct an elevation distribution histogram using a fixed interval step size; The interval frequency submodule is used to perform frequency statistics on each of the target elevation data according to the elevation distribution histogram to obtain the interval frequency of each elevation interval; The candidate floor interval submodule is used to filter candidate floor intervals from each elevation interval based on the comparison results between the frequency of each interval and the preset interval filtering threshold. The floor standard elevation submodule is used to perform mean calculation on multiple target elevation data within each candidate floor interval, and use the mean of the target elevation as the floor standard elevation of the corresponding floor. The standard elevation difference submodule is used to calculate the standard elevation difference between adjacent candidate floor intervals based on the standard elevation of each floor. The effective floor submodule is used to mark the floor associated with the elevation difference as an effective floor when the standard elevation difference is within the preset effective floor height range; The effective standard elevation difference submodule is used to calculate the effective standard elevation difference between adjacent effective floors; The interpolation completion submodule is used to perform interpolation completion using the preset interpolation elevation when the effective standard elevation difference is greater than the preset interpolation spacing threshold, thereby generating a floor elevation mapping table containing floor elevation mapping relationships.

[0106] Furthermore, it also includes: An elevation drift module is used to correct the floor elevation mapping table by means of a reference elevation frequency offset or an elevation scaling factor when the barometer exhibits elevation drift.

[0107] Please see Figure 6 , Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of the present invention.

[0108] An electronic device according to an embodiment of the present invention includes: a memory 601 and a processor 602. The memory 601 stores a computer program. When the computer program is executed by the processor 602, the processor 602 performs the floor detection method as described in any of the above embodiments.

[0109] Memory 601 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 601 has storage space 603 for program code 613 for performing any of the method steps described above. For example, storage space 603 for program code may include various program codes 613 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to perform the various steps in the floor detection method described above.

[0110] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the floor detection method as described in any of the above embodiments.

[0111] This invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the floor detection method as described in any of the above embodiments.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A floor detection method characterized by, The method comprises the following steps: correcting a three-axis accelerometer of a target building; synchronously collecting acceleration data of the corrected three-axis accelerometer and altitude data of a barometer in a preset time period; performing elevator running state recognition by using the acceleration data and the altitude data, and determining static altitude data; constructing a floor altitude mapping table of the target building according to the static altitude data.

2. The floor detection method according to claim 1, characterized by, The corrected three-axis accelerometer of the target building comprises: collecting three-axis acceleration data of a continuous static state segment of a three-axis accelerometer of a target building, and performing mean value operation to obtain a static three-axis acceleration mean value; performing zero point correction by using the static three-axis acceleration mean value to obtain a corrected zero point offset value; performing sum value operation on the corrected zero point offset value and the three-axis acceleration data to obtain acceleration data of the corrected three-axis accelerometer.

3. The floor detection method according to claim 2, characterized by, Further comprising: performing difference value operation on the three-axis acceleration data of the static state segment and the static three-axis acceleration mean value to obtain a rejection deviation value associated with each three-axis acceleration data; when the rejection deviation value is greater than a preset deviation threshold value, the associated three-axis acceleration data is rejected to obtain three-axis acceleration data of a continuous static state segment after rejection.

4. The floor detection method according to claim 1, characterized by, The elevator running state recognition by using the acceleration data and the altitude data to determine the static altitude data comprises: judging whether the acceleration data is in a preset gravity component steady state interval; when the acceleration data is in the preset gravity component steady state interval, a vertical direction speed value of the elevator is calculated by using altitude data of adjacent time points; when the speed value is less than a preset speed threshold value, the altitude data of the current time point is taken as target altitude data; a preset number of target altitude data is collected to obtain static altitude data.

5. The floor detection method according to claim 4, characterized by, The floor altitude mapping table of the target building is constructed according to the static altitude data, which comprises: selecting a minimum value from a plurality of target altitude data as a reference altitude, and constructing an altitude distribution histogram by using a fixed interval step; frequency statistics is performed on each target altitude data according to the altitude distribution histogram to obtain interval frequencies of each altitude interval; candidate floor intervals are selected from each altitude interval based on comparison results of each interval frequency and a preset interval filtering threshold value; mean value operation is performed on a plurality of target altitude data in each candidate floor interval, and a target altitude mean value is taken as a floor standard altitude of the corresponding floor; a standard altitude difference between adjacent candidate floor intervals is calculated based on each floor standard altitude; when the standard altitude difference is in a preset layer height effective interval, the floor associated with the altitude difference value is marked as an effective floor; an effective standard altitude difference between adjacent effective floors is calculated; when the effective standard altitude difference is greater than a preset supplemental layer interval threshold value, a preset supplemental layer altitude is used for interlayer completion to generate a floor altitude mapping table containing floor altitude mapping relationship.

6. The floor detection method according to any one of claims 1 to 5, characterized in that, Further comprising: when the barometer has an altitude drift, the floor altitude mapping table is corrected by a reference altitude frequency offset or an altitude scaling coefficient.

7. A floor detection device, characterized by The method comprises the following steps: a correction module is used for correcting a three-axis accelerometer of a target building; The data acquisition module is configured to synchronously acquire acceleration data of the three-axis accelerometer and altitude data of the barometer in a preset time period after correction. The state determination module is configured to identify an operation state of the elevator by using the acceleration data and the altitude data, and determine static altitude data. The floor construction module is configured to construct a floor altitude mapping table of the target building according to the static altitude data.

8. An electronic device, comprising: The computer program is stored in the memory and is executed by the processor, so that the processor executes the steps of the floor detection method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the floor detection method according to any one of claims 1-6.

10. A computer program product, characterised in that, The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the floor detection method according to any one of claims 1-6.

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