A radar ranging method, apparatus, device and storage medium

Abstract

This application discloses a radar ranging method, apparatus, device, and storage medium. A reflector is installed at one end of the elevator shaft, and a radar is installed in a car moving within the shaft. The method includes: controlling the radar to emit electromagnetic wave signals to the reflector and receiving a first echo signal reflected from the shaft; extracting a first spectral feature of the shaft from the first echo signal; matching the first spectral feature with a second spectral feature pre-learned from the shaft, where each second spectral feature is labeled with a reflector; if the match is successful, measuring the car's position in the shaft based on the distance mapping of the labeled reflectors. By matching the first and second spectral features, the position of the car in the shaft can be measured, reducing interference from highly reflective obstacles such as metal objects, ensuring positional accuracy, and guaranteeing the safe operation of the elevator regardless of where the radar is powered on.

Description

Technical Field

[0001] This application relates to the field of elevator technology, and in particular to a radar ranging method, device, equipment and storage medium. Background Technology

[0002] With the development of urbanization, high-rise buildings have become widespread, and elevators have become an important means of transportation for people's lives, studies, and work.

[0003] During elevator operation, in order to monitor the position of the car in the shaft, the current method is mostly to use radar and reflectors for distance measurement. That is, by measuring the time it takes for the radar to emit and receive electromagnetic wave signals, and combining this with the speed of light, the distance between the radar and the reflector is calculated, which is then mapped to the position of the car in the shaft.

[0004] However, in addition to the reflector, there are other metals in the wellbore. These metals reflect some of the electromagnetic waves emitted by the radar, causing interference. This is especially noticeable when the distance between the radar and the reflector is large, which reduces the accuracy of ranging. Summary of the Invention

[0005] This application provides a radar ranging method, apparatus, device, and storage medium to address how to improve the accuracy of measuring the position of a car.

[0006] According to one aspect of this application, a radar ranging method is provided, wherein a reflector is provided at one end of a shaft, and a car moving in the shaft is equipped with a radar, the method comprising:

[0007] The radar is controlled to transmit electromagnetic wave signals to the reflector and receive the first echo signal reflected by the shaft.

[0008] Extract the first spectral feature of the wellbore from the first echo signal;

[0009] The first spectral feature is matched with a second spectral feature pre-learned on the wellbore, and each second spectral feature is labeled with the reflector;

[0010] If a match is successful, the position of the car in the hoistway is measured based on the distance mapping of the labeled reflector.

[0011] According to another aspect of this application, a radar ranging device is provided, wherein a reflector is provided at one end of a shaft, and a radar is provided for a car moving in the shaft, the device comprising:

[0012] A transmission control module is used to control the radar to transmit electromagnetic wave signals to the reflector and receive the first echo signal reflected by the wellbore.

[0013] The first spectral feature extraction module is used to extract the first spectral feature of the wellbore from the first echo signal;

[0014] A matching module is used to match the first spectral feature with a second spectral feature pre-learned on the wellbore, wherein each second spectral feature is labeled with the reflector;

[0015] A lookup module is used to measure the position of the car in the hoistway based on the distance mapping of the labeled reflectors if a match is found.

[0016] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform a radar ranging method according to any embodiment of this application.

[0020] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program configured to cause a processor to execute and implement a radar ranging method according to any embodiment of this application.

[0021] In this embodiment, a reflector is provided at the end of the hoistway, and a radar is provided for the car moving in the hoistway. The radar is controlled to emit electromagnetic wave signals to the reflector and receive the first echo signal reflected by the hoistway. The first spectral feature of the hoistway is extracted from the first echo signal. The first spectral feature is matched with the second spectral feature pre-learned from the hoistway, and each second spectral feature is labeled with a reflector. If the match is successful, the position of the car in the hoistway is measured according to the distance mapping of the labeled reflector. When the car is in the same position, all obstacles within the radar's scanning range are identical, resulting in similar or identical echo signals. This leads to similar or identical spectral characteristics of the hoistway extracted from the echo signals. However, when the car is in different positions, the obstacles within the radar's scanning range differ. Even for the same obstacle, its distance relative to the radar changes, resulting in different echo signals and thus different spectral characteristics of the hoistway extracted from the echo signals. Reflectors and highly reflective obstacles such as metal objects are all part of the hoistway's spectral characteristics. Therefore, by matching the first and second spectral characteristics to measure the car's position in the hoistway, interference from highly reflective obstacles such as metal objects can be reduced, ensuring positional accuracy. Regardless of where the radar is powered on, the car's position in the hoistway can be measured normally, ensuring the safe operation of the elevator.

[0022] Furthermore, compared to methods that indirectly measure the car's position in the hoistway, such as leveling sensors and traction machine encoders, this embodiment uses radar to directly measure the car's position in the hoistway, saving on various switches within the hoistway and significantly reducing costs.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a diagram showing the positional relationship between the car and the hoistway;

[0026] Figure 2 It is a spectrum diagram of the echo signal reflected from the shaft;

[0027] Figure 3 This is a flowchart of a radar ranging method according to Embodiment 1 of this application;

[0028] Figure 4 This is a second spectrum diagram of a radar ranging method provided according to Embodiment 1 of this application;

[0029] Figure 5 This is a flowchart of a radar ranging method according to Embodiment 2 of this application;

[0030] Figure 6 This is a flowchart of a radar ranging method according to Embodiment 3 of this application;

[0031] Figure 7 This is a flowchart of a radar ranging method according to Embodiment 4 of this application;

[0032] Figure 8 This is a schematic diagram of the structure of a radar ranging device provided in Embodiment 5 of this application;

[0033] Figure 9 This is a schematic diagram of the structure of an electronic device provided in Embodiment Six of this application. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] Generally speaking, elevators come in different types and have different functions, such as passenger elevators, freight elevators, and sightseeing elevators. Therefore, the components installed in elevators are also different.

[0037] In one example, the components of a certain type of elevator include a traction machine, control cabinet, speed governor, door operator, car frame, car door, counterweight guide rail, car guide rail, guide rail support, traveling cable, counterweight device, compensating chain (cable), landing door, guide device for compensating chain (cable), and buffer.

[0038] Of course, in some types of elevators, the traction machine, control cabinet, speed governor, and traveling cable can be omitted, and this embodiment does not impose any restrictions on this.

[0039] like Figure 1 As shown, a shaft 110 is installed inside or outside buildings such as teaching buildings, office buildings, and residences. The elevator car 120 is used to move (i.e., glide) in the shaft 110. A control panel is installed on each floor or the first floor of the building. The control panel is generally equipped with buttons for calling the elevator up and down. Passengers can press these buttons to make an elevator call request.

[0040] Of course, in addition to the control panel, passengers can also use a client, remote control, or other means to make an elevator call request, and this embodiment does not limit this.

[0041] When the elevator receives a call request, it controls the car 120 to slide in the shaft 110 to the floor where the passenger 130 is located, opens the car door, and closes the car door after the passenger 130 enters the car 120. A control panel is installed inside the car 120, which is generally equipped with buttons for each floor, open door, close door, alarm, etc. The passenger 130 can press the buttons for each floor to send a call request.

[0042] Of course, in addition to the control panel, passengers can also use a client, remote control, or other means to make an elevator request, and this embodiment does not impose any restrictions on this.

[0043] When the elevator receives a passenger request, it controls the car 120 to slide in the shaft 110 to the destination floor indicated by the passenger 140, opens the car door, and closes the car door after the passenger 130 exits the car 120.

[0044] In this embodiment, in order to measure the position of the car 120 in the hoistway 110, a radar 140 can be installed outside the car 120, especially a millimeter-wave radar using FMCW (Frequency Modulated Continuous Wave). In this case, the radar 140 will move up and down in the hoistway 110 along with the car 120.

[0045] A reflector 150 is installed at the end point of the shaft 110 (i.e., the top or bottom of the shaft). The reflector 150 is an object with a high intensity of electromagnetic wave signal reflection. It can be a passive reflector such as a corner reflector or a flat plate, or it can be an active signal reflector. This embodiment does not limit this.

[0046] In one installation configuration, when the radar 140 is mounted on the top of the car, the reflector 150 can be mounted on the top of the hoistway.

[0047] In another installation method, when the radar is installed at the bottom of the car, the reflector can be installed at the bottom of the hoistway.

[0048] Regardless of the installation method, the radar 140 and the reflector 150 are on the same vertical line, especially the center of the radar 140 and the center of the lens of the reflector 150 are on the same vertical line.

[0049] To enable those skilled in the art to better understand this application, in this embodiment, the radar 140 is installed on the top of the car, and the reflector 150 is installed on the top of the hoistway as an example of an installation method.

[0050] When detecting the position of the car in the hoistway, the radar emits electromagnetic wave signals. These signals are reflected back by obstacles (including reflectors) in the hoistway, forming echo signals. Upon receiving the echo signals, the radar can calculate the distance between the radar and the reflector, thus mapping the absolute position of the car in the hoistway.

[0051] In addition to reflectors, there may be some highly reflective obstacles in the hoistway, especially metal objects with horizontal bottoms and exceeding a certain size, such as guide rail supports and thresholds. These obstacles can also effectively reflect the electromagnetic wave signals emitted by the radar and be received by the radar, thus interfering with the detection of the car's position in the hoistway.

[0052] The echo signal received by the radar is converted into a spectrum diagram. In addition to the peak signal of the reflector, the spectrum diagram also contains the peak signal of these obstacles.

[0053] Furthermore, the equation for radar ranging is as follows:

[0054]

[0055] Among them, P r P represents the radar's receiving power. t Let G represent the radar's transmit power, λ represent the radar's antenna gain, σ represent the wavelength of the electromagnetic signal, σ represent the effective reflective area of ​​the reflector, R represent the distance between the radar and the reflector, and L represent the distance between the radar and the reflector. s L represents system losses. a(R) represents atmospheric attenuation loss.

[0056] As can be seen from the equation, when the radar and reflector are determined, the radar's receiving power is related to the distance. As the radar moves away from the reflector along with the car, the echo signal received by the radar and reflected by the reflector will gradually weaken.

[0057] Therefore, as Figure 2 As shown, when the distance between the radar and the reflector exceeds a certain threshold, in the spectrum diagram (horizontal axis is distance S, vertical axis is amplitude A), the peak signal 210 of the reflector's echo signal may be weaker than the peak signal of the echo signal of other obstacles (especially metal objects) in the shaft. When the radar is powered on again at that location, it cannot determine which peak signal in the spectrum diagram is the peak signal of the echo signal reflected by the reflector, and the initial position cannot be located.

[0058] Since the environment of the hoistway is fixed over a period of time, and the peak signal of the echo signal reflected by reflectors and other obstacles (such as metal objects) is related to distance, when the car moves to the same position in the hoistway each time, the distance and amplitude (also known as intensity) of the peak signal of the echo signal reflected by reflectors and other obstacles (such as metal objects) are basically the same. However, when the car moves to different positions in the hoistway, the distance and amplitude of the peak signal of the echo signal reflected by reflectors and other obstacles (such as metal objects), as well as the number of echo signals reflected by other obstacles (such as metal objects), are mostly different. Therefore, this embodiment can record the distance and amplitude of the peak points (including the peak points of the echo signals reflected by reflectors) where the amplitude exceeds a certain threshold at different positions of the car in the hoistway, and mark the peak points of the echo signals reflected by reflectors as features obtained by radar scanning when the car is at different positions in the hoistway. Subsequently, the position of the car in the hoistway can be searched based on the recorded features.

[0059] Example 1

[0060] Figure 3 This is a flowchart illustrating a radar ranging method provided in an embodiment of this application. This embodiment is applicable to situations where the characteristics of electromagnetic wave signals reflected by radar from various obstacles in a shaft are learned. The radar ranging device can be implemented in hardware and / or software, and can be configured in electronic devices, such as... Figure 3 As shown, the method includes:

[0061] Step 301: Control the car to move sequentially to each marker position in the hoistway.

[0062] When the elevator is in the stage of learning the characteristics of electromagnetic wave signals reflected by radar from various obstacles, it can indicate to the outside that the elevator is under maintenance or is not normally carrying people or goods.

[0063] At this point, multiple marker positions can be divided in the shaft. When the shaft is used as a reference object, the marker positions remain fixed, so that the characteristics of the electromagnetic wave signals of each obstacle reflection radar can be learned at each marker position.

[0064] Generally, the interval between two adjacent markers is a preset step size, that is, the markers are evenly distributed in the wellbore. This step size can be set according to factors such as the accuracy of the radar, such as 1 millimeter.

[0065] Of course, in addition to the markers being evenly distributed in the shaft, the markers can also be distributed non-uniformly in the shaft. For example, the interval between markers can be smaller near the reflector, resulting in a higher density of markers, while the interval between markers can be larger away from the reflector, resulting in a lower density of markers, and so on. This embodiment does not limit this.

[0066] In this embodiment, the car is controlled to move sequentially to each marker position in the hoistway. The starting marker position is closest to the reflector. That is, when the characteristics of the electromagnetic wave signals of each obstacle reflection radar are learned, the initial position of the car is closest to the reflector. Then, the car can be controlled to move sequentially to each marker position in a direction away from the target.

[0067] Because different buildings have different environments, the elevators installed will vary, and the range of motion of the car will also vary. Therefore, the position of the car closest to the reflector refers to the position closest to the reflector within its range of motion.

[0068] like Figure 1 As shown, when the radar 140 is installed on the top of the car and the reflector 150 can be installed on the top of the hoistway, when the characteristics of the electromagnetic wave signals reflected by the radar of each obstacle are learned, the initial position of the car 120 is the top floor (marker position). After that, the car 120 is controlled to move to each mark position from top to bottom.

[0069] Furthermore, since the speed at which the car moves in the hoistway is controllable, and the step size between the marker positions is preset, the car can estimate the distance it moves in a short time using methods such as uniform motion, uniform acceleration motion, and Kalman linear filtering, so that it conforms to the step size between two adjacent marker positions.

[0070] Step 302: In each flag position, control the radar to send electromagnetic wave signals to the reflector and receive the second echo signal reflected by the well passage.

[0071] In this embodiment, the car control radar can be controlled to continuously send electromagnetic wave signals toward the reflector. The electromagnetic wave signals propagate in the atmosphere and reach various obstacles within the radar's scanning range in the hoistway. At this time, the electromagnetic wave signals will be reflected and scattered on the surface of the obstacles.

[0072] These obstacles can include reflectors, guide rails, thresholds and other highly reflective metal objects, as well as side walls and other obstacles with low reflectivity.

[0073] The radar can continuously receive electromagnetic wave signals reflected from within the radar's scan range in the shaft. Because the electromagnetic wave signal absorbs some of its energy by the reflector, it generates an original electromagnetic wave signal with attenuation delay. This original electromagnetic wave signal is superimposed on the original electromagnetic wave signal to form an echo signal. For ease of distinction, this echo signal is denoted as the second echo signal.

[0074] When the car moves to each marker position, the electromagnetic wave signal emitted at that marker position is extracted and the second echo signal reflected by the hoistway is received. The car then waits to learn the second spectral characteristics of the hoistway scanned at that marker position.

[0075] Step 303: Extract the second spectral features of the wellbore from the second echo signal.

[0076] For the second echo signal collected by the car at each marker position by the radar, the frequency domain features can be extracted from the second echo signal according to the preset feature extraction method, which is called the second spectrum feature. The second spectrum feature characterizes the characteristics of each obstacle in the hoistway within the radar scanning range on the reflected radar wave signal.

[0077] When the car is at the same marker position, all obstacles within the radar's scanning range are the same. Therefore, the second echo signals collected when the car is at the same marker position are similar or identical, making the second spectral features of the hoistway extracted from the second echo signals similar or identical as well.

[0078] When the car is in different marker positions, the obstacles within the radar's scanning range are different. Even for the same obstacle, its distance relative to the radar will change. Therefore, the second echo signal collected when the car is in different marker positions will be different, which will also result in different extraction of the second spectral characteristics of the hoistway from the second echo signal.

[0079] In one embodiment of this application, step 303 may include the following steps:

[0080] Step 3031: Convert the second echo signal to the second spectrum diagram.

[0081] In this embodiment, operations such as FT (Fourier Transform) and FFT (Fast Fourier Transform) can be performed on the second echo signal to convert the second echo signal from the time domain to the frequency domain, resulting in a spectrum diagram (also known as a spectrum distribution diagram). This spectrum diagram reflects the relationship between the echo signal in terms of distance (i.e., the distance between the obstacle and the radar) and energy (also known as intensity, amplitude, or magnitude). For ease of distinction, this spectrum diagram is referred to as the second spectrum diagram.

[0082] Step 3032: Locate the multiple second peak points with the largest amplitude in the second spectrum graph.

[0083] In this embodiment, each peak point can be found in the second spectrum and marked as the second peak point.

[0084] By comparing the amplitudes of different second peak points, the multiple second peak points with the largest amplitudes are obtained.

[0085] Generally, the amplitude of the second peak point corresponding to the echo signal reflected by the reflector is relatively high, belonging to the multiple second peak points with the largest amplitude.

[0086] Step 3033: Sort the multiple second peak points according to the distance to obtain the second spectral characteristics of the shaft.

[0087] In this embodiment, multiple second peak points can be sorted according to distance as the second spectral feature of the shaft.

[0088] In practical applications, even if the car is at the same marker position and all obstacles within the radar's scanning range are the same, the second spectral characteristics learned at the same marker position will still have some differences due to factors such as radar noise and electromagnetic wave signal noise during propagation.

[0089] To improve the accuracy of the second spectral features, the second spectral features of multiple wells can be learned at the same flag bit. Statistically significant data such as the average, median, and weighted sum of the second spectral features of multiple wells can be calculated and used as the final second spectral features of the wells.

[0090] After traversing all the flag bits, multiple sets of second spectral characteristics of the shaft can be obtained. At this time, the second spectral characteristics of the shaft can be expressed as (S 1i A 1i ), (S 2i A 2i ), ..., (S Ni A Ni), where S represents distance, A represents amplitude, N (N is a positive integer) represents the number of second peak points, i = 1, 2, ..., n (n is a positive integer) represents the number of groups of second spectral features, and their corresponding flag bits.

[0091] Furthermore, the sorting order can be either sorting multiple second peak points from largest to smallest distance or sorting multiple second peak points from smallest to largest distance; this embodiment does not impose any restrictions on this.

[0092] Step 304: Mark the reflector in the second spectral features.

[0093] In this embodiment, the reflector can be marked in the second spectral feature so that when the second spectral feature is applied later, the distance mapping between the reflector and the radar can be used to obtain the position of the car in the hoistway.

[0094] If the car is in the initial marker position (i.e., the car and its radar are closest to the reflector), most of the electromagnetic wave signal emitted by the radar will illuminate and be reflected by the reflector. Therefore, the intensity of the electromagnetic wave signal received by the radar from the reflector will be significantly higher than the intensity of electromagnetic wave signals emitted by other obstacles. Figure 4 As shown in the second spectrum diagram, the peak signal 410 corresponding to the echo signal reflected by the reflector has the highest amplitude. Therefore, the second peak point with the largest amplitude can be marked as the reflector in the second spectrum feature.

[0095] If the car is not located at the initial marker position, the reflector can be tracked using methods such as uniform motion, uniform acceleration motion, and Kalman linear filtering, based on the reflector already marked at the previous marker position, so as to mark the reflector to the corresponding second peak point.

[0096] Example 2

[0097] Figure 5 This is a flowchart of a radar ranging method provided in Embodiment 2 of this application. This embodiment applies the matching method to detect the position of the car in the hoistway based on Embodiment 1. It is particularly simple in hoistways with low lifting heights (e.g., less than or equal to 20 meters). Figure 5 As shown, the method includes:

[0098] Step 501: Control the radar to transmit electromagnetic wave signals to the reflector and receive the first echo signal reflected by the shaft.

[0099] When the elevator is in normal operation, it carries people or goods. Passengers in the waiting hall and inside the car can control the elevator car to glide in the shaft and move to different floors.

[0100] During normal elevator operation, the car control radar can send electromagnetic wave signals toward the reflector at key points such as initial power-on and at preset intervals. The electromagnetic wave signals propagate through the atmosphere and reach various obstacles within the radar's scanning range in the shaft. At this time, the electromagnetic wave signals will be reflected and scattered on the surface of the obstacles.

[0101] These obstacles can include reflectors, guide rails, thresholds and other highly reflective metal objects, as well as side walls and other obstacles with low reflectivity.

[0102] The radar can receive electromagnetic wave signals reflected from various obstacles within the radar's scan range in the shaft. Because the electromagnetic wave signal absorbs some of its energy through the reflector, it generates an original electromagnetic wave signal with attenuation delay. This original electromagnetic wave signal is superimposed on the original electromagnetic wave signal to form an echo signal. For ease of distinction, this echo signal is denoted as the first echo signal.

[0103] Step 502: Extract the first spectral features of the wellbore from the first echo signal.

[0104] In this embodiment, frequency domain features can be extracted from the first echo signal according to a preset feature extraction method, and are referred to as the first spectral features. The first spectral features characterize the characteristics of each obstacle in the well that is within the radar's scan range on the reflected radar wave signal.

[0105] Furthermore, the feature extraction method for extracting the first spectral feature from the first echo signal is the same as the feature extraction method for extracting the second spectral feature from the second echo signal.

[0106] In one embodiment of this application, step 502 may include the following steps:

[0107] Step 5021: Convert the first echo signal to the first spectrum diagram.

[0108] In this embodiment, operations such as FT and FFT can be performed on the first echo signal to convert the first echo signal from the time domain to the frequency domain and obtain a spectrum diagram. For easy distinction, this spectrum diagram is referred to as the first spectrum diagram.

[0109] Step 5022: Locate the first peak points with the largest amplitude in the first spectrum graph.

[0110] In this embodiment, each peak point can be found in the first spectrum and marked as the first peak point.

[0111] By comparing the amplitudes of different first peak points, the multiple first peak points with the largest amplitudes are obtained.

[0112] Generally, the amplitude of the first peak point corresponding to the echo signal reflected by the reflector is relatively high, belonging to the multiple first peak points with the largest amplitude.

[0113] Step 5023: Sort the multiple first peak points according to the distance to obtain the first spectral features of the shaft.

[0114] In this embodiment, multiple first peak points can be sorted according to distance as the first spectral feature of the shaft.

[0115] At this point, the first spectral feature can be recorded as (S1',A1'), (S2',A2')...(S N ',A N '), where S represents distance, A represents amplitude, and N represents the number of first peak points.

[0116] Furthermore, the sorting order can be either sorting multiple second peak points from largest to smallest distance or sorting multiple second peak points from smallest to largest distance; this embodiment does not impose any restrictions on this.

[0117] Step 503: Match the first spectral feature with the second spectral feature learned in advance from the wellbore.

[0118] In this embodiment, some or all of the second spectral features learned in advance for the wellbore can be queried, and the first spectral features can be matched with each second spectral feature to calculate the similarity between the first spectral feature and each second spectral feature.

[0119] In one embodiment of this application, step 503 may include the following steps:

[0120] Step 5031: Find the second spectral feature of the wellbore learned in advance.

[0121] In this embodiment, some or all of the second spectral features learned in advance for the wellbore can be retrieved from the database or cache, and then matched with the first spectral features.

[0122] The second spectral feature contains multiple second peak points, one of which is marked with a reflector, thus characterizing the echo signal reflected by the transmitter.

[0123] In one scenario, the first spectral feature can be matched with all second spectral features to ensure a comprehensive match.

[0124] In another scenario, the intervals between flags set during learning may be short (e.g., 1 mm), resulting in a large number of groups of second spectral features. If the first spectral feature is matched with all the second spectral features each time, the computational load is high, requiring strong computing power. However, elevators are mass-produced systems, and due to cost considerations, the computing power and storage space are often sufficient to meet the requirements. Their control systems are mostly processors (Microprocessor Units, MPUs) with relatively weak computing power. If the first spectral feature is directly matched with all the second spectral features for computation, it will be time-consuming and may even cause system crashes. Since elevators are real-time control systems, requiring real-time processing of various control signals, high time consumption or crashes will affect the normal operation of the elevator and may even pose safety risks.

[0125] Considering that the speed of the car is limited (e.g., limited to 4 meters per second), the range of movement of the car between two adjacent moments is relatively limited. Therefore, when the car is moving in the hoistway, the position of the car in the hoistway calculated at the previous moment can be determined. The position can be extended upward and / or downward by a certain distance to obtain a candidate range, so that the candidate range can encompass the range of movement of the car.

[0126] At this point, multiple second spectral features pre-learned on the wellbore can be loaded, and the distances of the second peak points of the reflectors marked in each second spectral feature can be traversed. If the distances of the second peak points of the marked reflectors are within the candidate range, the second spectral features are extracted and waited to be matched with the first spectral features.

[0127] This method can significantly reduce the number of second spectral features while ensuring successful matching between the first and second spectral features. This reduces the computational workload of matching the first and second spectral features, ensuring that the elevator control system has the computational capacity to support real-time matching of the first and second spectral features, thus guaranteeing the safe operation of the elevator.

[0128] Step 5032: For each second spectral feature, calculate the overall total difference between the multiple first peak points and the multiple second peak points.

[0129] In this embodiment, the first spectral features can be matched with each extracted second spectral feature according to the sorting order of the second spectral features.

[0130] The first spectral feature contains multiple first peak points with the highest amplitude, and the second spectral feature contains multiple second peak points with the highest amplitude. Therefore, during matching, the overall difference between the multiple first peak points and the multiple second peak points can be calculated and denoted as the total difference.

[0131] In one embodiment of this application, step 5032 may further include the following steps:

[0132] Step 50321: For each second spectral feature, pair multiple first peak points with multiple second peak points to obtain peak pairs.

[0133] The number and sorting of the first peak points in the first spectral feature are the same as the number and sorting of the second peak points in the second spectral feature. Therefore, when matching the first spectral feature with each extracted second spectral feature, each first peak point with the same sorting position in the first and second spectral features can be paired with each second peak point, and recorded as a peak pair.

[0134] Step 50322: For each peak pair, calculate the first difference between the distance between the first peak point and the distance between the second peak point.

[0135] In this embodiment, all peak pairs are traversed, and the difference between the distance between the first peak point and the distance between the second peak point in each peak pair is calculated and denoted as the first difference.

[0136] For example, for each peak pair, a first difference is calculated between the distance between the first peak point and the distance between the second peak point, and the first difference is squared to obtain the first variation.

[0137] Step 50323: For each peak pair, calculate the second difference between the amplitude of the first peak point and the amplitude of the second peak point.

[0138] In this embodiment, all peak pairs are traversed, and the difference between the amplitude of the first peak point and the amplitude of the second peak point in each peak pair is calculated and denoted as the second difference.

[0139] For example, for each peak pair, a second difference is calculated between the amplitude of the first peak point and the amplitude of the second peak point; the second difference is squared to obtain the second variation.

[0140] Step 50324: For each peak pair, merge the first difference and the second difference into a third difference between the first peak point and the second peak point.

[0141] In this embodiment, all peak pairs are traversed, and the first difference in distance and the second difference in amplitude are linearly or nonlinearly fused into the difference between the first peak point and the second peak point, which is denoted as the third difference.

[0142] For example, for each peak pair, the first difference is multiplied by the first weight to obtain the first adjustment value; the second difference is multiplied by the second weight to obtain the second adjustment value.

[0143] Normally, the sum of the first weight and the second weight is 1. Considering that the reflector in the shaft may have tilting, deformation, etc., which may cause the first echo signal to change to a certain extent relative to the second echo signal, and these situations will not affect the distance between the reflector and the radar, that is, the distance generally will not change, therefore, the first weight can be set to be greater than or equal to the second weight.

[0144] The third difference between the first peak point and the second peak point is obtained by taking the square root of the sum between the first and second adjustment weights.

[0145] Step 50325: For all peak pairs, sum all sub-differences to obtain the total overall difference between multiple first peak points and multiple second peak points.

[0146] For the first spectral feature and the predetermined second spectral feature, the differences of all peak pairs can be added together to obtain the total difference between multiple first peak points and multiple second peak points.

[0147] For example, the total difference is calculated as follows:

[0148]

[0149] Where Δ represents the total difference, i is the group number of the second spectral feature, N represents the number of peak points (the first peak point and the second peak point), and S′1, S′2, ..., S′ N S represents the distance between the radar and the reflector at multiple first peak points. 1i S 2i S Ni Let A′1, A′2, ..., A′ be the distances between the radar and the reflector at multiple second peak points. N A represents the amplitude among multiple first peak points. 1i A 2i A Ni Let α be the amplitude among multiple second peak points, β be the first weight, α+β=1, and α≥β.

[0150] Step 5033: Find the second spectral feature with the smallest total difference, and use it as the second spectral feature that successfully matches the first spectral feature.

[0151] The total differences of each second spectral feature are compared. Generally, the greater the total difference, the lower the similarity between the first and second spectral features. Conversely, the smaller the total difference, the higher the similarity between the first and second spectral features.

[0152] In this embodiment, the second spectral feature with the smallest total difference can be used as the second spectral feature that successfully matches the first spectral feature.

[0153] Step 504: If the match is successful, measure the position of the car in the hoistway based on the distance of the marked reflector.

[0154] Each second spectral feature has been labeled with a reflector, that is, a reflector has been labeled at a certain second peak point in the second spectral feature, and the second peak point has distance and amplitude.

[0155] When the first spectral feature and the second spectral feature are successfully matched, the position of the car in the hoistway when the first spectral feature is collected is close to the position of the car in the hoistway when the second spectral feature is collected. Therefore, the current position of the car in the hoistway can be measured by the distance of the reflector marked in the second spectral feature.

[0156] In one scenario, the distance of the reflector already marked in the second spectrum can be directly mapped to the current position of the car in the hoistway, which can reduce the amount of computation and the computation time.

[0157] In another scenario, the second peak point of the labeled reflector can be found in the second spectral feature as a reference peak point (S). 0i A 0i Using methods such as Euclidean distance, the reference peak point (S) is determined. 0i A 0i The distance value is calculated between the first peak point and each of the first spectral features.

[0158] Find the nearest (i.e., closest distance value) reference peak point (S) in the first spectral feature. 0i A 0i The first peak point of ) is taken as the target peak point (S) P A P ), to target peak point (S) P A P The distance S) P This is mapped to the position of the car in the hoistway.

[0159] Considering the influence of noise such as radar noise and atmospheric noise on learning and application, and since the learned second spectral feature is discrete, there may be situations where the car is located between the learned second spectral features. By using the distance of the actual first peak point as the reference, the position of the car in the hoistway can be mapped to avoid the influence of noise and the discreteness of the learned second spectral feature, and improve the accuracy of the car's position in the hoistway.

[0160] In this embodiment, a reflector is provided at the end of the hoistway, and a radar is provided for the car moving in the hoistway. The radar is controlled to emit electromagnetic wave signals to the reflector and receive the first echo signal reflected by the hoistway. The first spectral feature of the hoistway is extracted from the first echo signal. The first spectral feature is matched with the second spectral feature pre-learned from the hoistway, and each second spectral feature is labeled with a reflector. If the match is successful, the position of the car in the hoistway is measured according to the distance mapping of the labeled reflector. When the car is in the same position, all obstacles within the radar's scanning range are identical, resulting in similar or identical echo signals. This leads to similar or identical spectral characteristics of the hoistway extracted from the echo signals. However, when the car is in different positions, the obstacles within the radar's scanning range differ. Even for the same obstacle, its distance relative to the radar changes, resulting in different echo signals and thus different spectral characteristics of the hoistway extracted from the echo signals. Reflectors and highly reflective obstacles such as metal objects are all part of the hoistway's spectral characteristics. Therefore, by matching the first and second spectral characteristics to measure the car's position in the hoistway, interference from highly reflective obstacles such as metal objects can be reduced, ensuring positional accuracy. Regardless of where the radar is powered on, the car's position in the hoistway can be measured normally, ensuring the safe operation of the elevator.

[0161] Furthermore, compared to methods that indirectly measure the car's position in the hoistway, such as leveling sensors and traction machine encoders, this embodiment uses radar to directly measure the car's position in the hoistway, saving on various switches within the hoistway and significantly reducing costs.

[0162] Example 3

[0163] Figure 6 This is a flowchart illustrating a radar distance method provided in Embodiment 3 of this application. This embodiment, based on Embodiment 1, describes the process of detecting the position of a stationary car within the car, particularly ensuring accuracy in shafts with high lifting heights (e.g., greater than 20 meters). Figure 6 As shown, the method includes:

[0164] Step 601: When the car is stationary, control the radar to transmit multiple frames of electromagnetic wave signals to the reflector and receive multiple frames of electromagnetic wave signals reflected by the hoistway.

[0165] Step 602: Coherently accumulate the multi-frame reflected electromagnetic wave signals to obtain the first echo signal.

[0166] In practical applications, the accuracy formula for radar ranging is as follows:

[0167]

[0168] Where c is the speed of light, B is the bandwidth of the radar's electromagnetic wave signal, and SNR is the signal-to-noise ratio of the echo signal.

[0169] In order to ensure the accuracy of ranging, the signal-to-noise ratio (SNR) should be high enough. However, when the radar's transmit power and the size of the reflector are limited, the intensity of the echo signal reflected by the reflector decreases as the distance between them increases. This means that the peak SNR of the echo signal decreases, and the accuracy of ranging decreases.

[0170] Therefore, the accuracy of measuring the position of the car in the hoistway using the matching method is slightly poor.

[0171] To further improve the accuracy of ranging, in this embodiment, the car is kept stationary, such as when the elevator control system is powered on and started, or when the car is idle and waiting to be called on a certain floor. At this time, the car control radar can be controlled to continuously send multiple frames of electromagnetic wave signals in the direction of the reflector. Each frame of electromagnetic wave signal propagates in the atmosphere and reaches various obstacles in the radar's scanning range in the shaft. At this time, the electromagnetic wave signal will be reflected and scattered on the surface of the obstacle.

[0172] These obstacles can include reflectors, guide rails, thresholds and other highly reflective metal objects, as well as side walls and other obstacles with low reflectivity.

[0173] Therefore, the radar can receive multiple frames of electromagnetic wave signals reflected from various obstacles within the radar's scan range in the shaft. Because the electromagnetic wave signals absorb some energy from the reflectors, they generate an original electromagnetic wave signal with attenuation delay, which is then superimposed on the original electromagnetic wave signal.

[0174] During the time the car is stationary, the electromagnetic wave signal reflected in each frame can be regarded as a coherent signal. The electromagnetic wave signals reflected in multiple frames are coherently accumulated and recorded as the first echo signal. This enhances the intensity of the first echo signal reflected by the reflector. The noise can be regarded as Gaussian white noise with an expected value of zero. Therefore, this can improve the signal-to-noise ratio (SNR) of the first echo signal reflected by the reflector.

[0175] Step 603: Extract the first spectral features of the wellbore from the first echo signal.

[0176] In the specific implementation, the first echo signal is converted into a first spectrum; multiple first peak points with the largest amplitude are found in the first spectrum; the multiple first peak points are sorted according to distance to obtain the first spectrum characteristics of the shaft.

[0177] Step 604: Match the first spectral feature with the second spectral feature learned in advance from the wellbore.

[0178] In this embodiment, each second spectral feature is labeled with a reflector.

[0179] In the specific implementation, the first spectral feature contains multiple first peak points; then, when performing matching, a second spectral feature that has been pre-learned on the wellbore can be found. The second spectral feature contains multiple second peak points, one of which has been labeled with a reflector. For each second spectral feature, the total difference between the multiple first peak points and the multiple second peak points is calculated. The second spectral feature with the smallest total difference is found as the second spectral feature that successfully matches the first spectral feature.

[0180] Furthermore, when searching for second spectral features to be matched, as the car moves in the hoistway, the position of the car in the hoistway calculated at the previous moment is determined; the position is expanded upwards and / or downwards to obtain a candidate range; multiple second spectral features pre-learned for the hoistway are loaded; if the distance of the labeled reflector is within the candidate range, the second spectral feature is extracted.

[0181] Furthermore, when calculating the total difference, for each second spectral feature, multiple first peak points are paired with multiple second peak points to obtain peak pairs; for each peak pair, the first difference between the distance between the first peak point and the distance between the second peak point is calculated; for each peak pair, the second difference between the amplitude of the first peak point and the amplitude of the second peak point is calculated; for each peak pair, the first difference and the second difference are fused into a third difference between the first peak point and the second peak point; for all peak pairs, all sub-differences are added to obtain the total difference between multiple first peak points and multiple second peak points as a whole.

[0182] In one example, for each pair of peaks, a first difference is calculated between the distance between the first peak point and the distance between the second peak point; the first difference is squared to obtain the first variation.

[0183] Accordingly, for each peak pair, a second difference is calculated between the amplitude of the first peak point and the amplitude of the second peak point; the second difference is obtained by squaring the second difference.

[0184] In this example, for each peak pair, the first difference is multiplied by the first weight to obtain the first adjustment value; the second difference is multiplied by the second weight to obtain the second adjustment value, wherein the first weight is greater than or equal to the second weight; the square root of the sum of the first adjustment value and the second adjustment value is used to obtain the third difference between the first peak point and the second peak point.

[0185] Step 605: If the match is successful, measure the position of the car in the hoistway based on the distance mapping of the marked reflectors.

[0186] In the specific implementation, the second peak point of the labeled reflector is found in the second spectral features and used as the reference peak point; the first peak point closest to the reference peak point is found in the first spectral features and used as the target peak point; the distance of the target peak point is mapped to the position of the car in the hoistway.

[0187] In this embodiment, since the application is basically similar to that in Embodiment 2, the description is relatively simple. For relevant details, please refer to the description in Embodiment 2. This embodiment will not be described in detail here.

[0188] Example 4

[0189] Figure 7 This is a flowchart of a radar ranging method provided in Embodiment 4 of this application. This embodiment adds a process to Embodiment 3 where the car's position in the hoistway is detected using a linear filtering method during the continuous operation phase. Figure 7 As shown, the method includes:

[0190] Step 701: Query the motion state of the car at the previous moment.

[0191] During the movement of the car in the hoistway, there are certain differences between the reflected electromagnetic wave signals, which cannot be regarded as coherent. Therefore, it is impossible to coherently accumulate the reflected electromagnetic wave signals.

[0192] In this embodiment, the position of the car in the hoistway can be detected at multiple moments. These moments are generally determined by the frequency of the electromagnetic wave signal emitted by the radar. In order to improve the accuracy of distance measurement during the movement of the car, the movement state of the car can be modeled at each moment to obtain the motion state of the car at each moment. The motion state of the car includes at least the distance of the reflector, that is, the distance between the radar and the reflector.

[0193] Considering that the car's movement in the hoistway is affected by mechanical vibrations or other environmental factors, resulting in disturbances during distance measurement, and given that the elevator's operation approximates uniformly accelerated linear motion, linear filtering is used to track the car's motion state at each moment, reducing the noise impact of disturbances. In one modeling approach, using a single angle iron as a reflector, let T0 be the time interval between each distance measurement (i.e., the interval between two adjacent moments), s(k) be the car's actual position at moment kT0, and y(k) be the estimated position at moment kT0. Then, the distance measurement model is as follows:

[0194] y(k)=s(k)+δ(k)

[0195] Where δ(k) represents the observation noise of the ranging.

[0196] The following formula is derived from the formula for uniformly accelerated linear motion:

[0197]

[0198] s(k)′=s(k-1)′+T0a(k-1)

[0199] Where s(k)′ is the speed of the car, and the total acceleration a(k) is composed of the car's own acceleration v(k) and the vibration acceleration w(k) caused by random mechanical vibration.

[0200] The motion state x(k) of the car at time kT0 includes distance and velocity, as shown below:

[0201]

[0202] At this point, the motion state equation of the car can be established:

[0203]

[0204] Therefore, the motion state equation of the car can be transformed into:

[0205]

[0206] y(k)=

[10] x(k)+δ(k)

[0207] Step 702: Perform linear filtering on the motion state of the previous moment to obtain the motion state of the current moment.

[0208] In a specific implementation, the initial moment can be the moment when the car is stationary. Its motion state is calculated by the method in Example 3. For each moment after the initial moment, the motion state of the previous moment is linearly filtered to obtain the motion state of the current moment, until the car is stationary.

[0209] Linear filtering can ensure that the motion state at the previous moment has a linear relationship with the motion state at the current moment.

[0210] In one embodiment of this application, step 702 may include the following steps:

[0211] Step 7021: Based on the predicted motion state of the car at the previous moment, its own acceleration at the previous moment, and the vibration acceleration at the previous moment, perform uniformly accelerated linear motion to obtain the motion state at the current moment.

[0212] In this embodiment, the car can perform uniformly accelerated linear motion between two adjacent moments, and the process of uniformly accelerated linear motion can be modeled to obtain the equation of motion.

[0213] In one example, the equation of motion is as follows:

[0214]

[0215] Given the motion state of the car at the previous moment x(k-1), the motion state of the car at the current moment can be predicted using the equation of motion. This process is represented as x(k|k-1).

[0216] Furthermore, by substituting the motion state x(k-1) of the car at the previous moment, the acceleration v(k-1) of the car at the previous moment, and the vibration acceleration w(k-1) of the car at the previous moment caused by random mechanical vibration, into the motion equation, we can obtain the motion state x(k) at the current moment.

[0217] Step 7022: Add a preset process noise variance matrix to the covariance matrix of the previous time step to predict the covariance matrix of the current time step.

[0218] In practice, each element of the covariance matrix is ​​the covariance between the elements of each vector, which is a natural generalization from scalar random variables to high-dimensional random vectors.

[0219] In this embodiment, the covariance matrix P(k-1) of the previous time step can be queried, and a preset process noise variance matrix Q can be added to the covariance matrix P(k-1) of the previous time step to predict the covariance matrix P(k) of the current time step. This process is expressed as P(k|k-1)=P(k-1)+Q.

[0220] Step 7023: Calculate the ratio between the covariance matrix and the target matrix at the current time, and use it as the linear gain at the current time.

[0221] In this embodiment, the covariance matrix P(k) at the current time can be added to the preset measurement noise variance matrix R to obtain the target matrix. That is, the target matrix is ​​the sum of the covariance matrix at the current time and the preset measurement noise variance matrix.

[0222] At this point, the ratio between the covariance matrix P(k) at the current time and the target matrix can be calculated as the linear gain K at the current time. This process is expressed as K = P(k|k-1) / (P(k|k-1)+R).

[0223] Step 7024: Update the motion state at the current time according to the linear gain at the current time.

[0224] In this embodiment, the current motion state x(k) can be updated using the linear gain K at the current moment, thereby improving the accuracy of the motion state at the current moment and thus improving the accuracy of detecting the position of the car in the hoistway.

[0225] In the specific implementation, since the motion state can include other parameters such as speed in addition to distance, we ignore the other parameters except distance. Then, we add the preset distance observation noise δ(k) to the motion state x(k) at the current moment to obtain the distance y(k) of the reflector at the current moment.

[0226] For example, the motion state x(k) at the current moment can be substituted into the following observation equation to obtain the distance y(k) of the reflector at the current moment:

[0227] y(k)=

[10] x(k)+δ(k) calculates the product of the linear gain K and the motion deviation at the current time, where the motion deviation is the difference between the distance y(k) of the reflector measured at the current time and the motion state x(k-1) at the previous time.

[0228] Add the product to the motion state x(k-1) of the previous moment to obtain the new motion state of the current moment, and update it to the motion state x(k) of the current moment. Then, this update process is expressed as x(k) = x(k-1) + K(y(k) - x(k-1)).

[0229] Step 7025: Multiply the difference between 1 and the linear gain at the current time by the covariance matrix at the current time to update the covariance matrix at the current time.

[0230] In this embodiment, the difference between 1 and the linear gain K at the current time is obtained. This difference is then multiplied by the covariance matrix P(k|k-1) at the current time to obtain the new covariance matrix P(k) at the current time. This is used to update the covariance matrix at the current time, thereby improving the accuracy of the covariance matrix. This process can be expressed as P(k)=(1-K)*P(k|k-1).

[0231] Step 703: Extract the distance of the reflector in the current motion state as the position of the car in the hoistway.

[0232] In this embodiment, the distance of the reflector, i.e. the distance between the radar and the reflector, is extracted from the current motion state, and then the distance of the reflector is mapped to the position of the car in the hoistway.

[0233] Step 704: Calculate the difference between the position calculated based on linear filtering and the position calculated based on the second spectral characteristics, as the position deviation.

[0234] In this embodiment, during the continuous operation phase of the elevator, the position of the car in the shaft can be measured asynchronously using two methods.

[0235] One method is the matching method in Example 2, where the obtained position is recorded as the position calculated based on the spectral features of the second spectrum.

[0236] Another approach is the linear filtering method in Example 4, where the obtained position is recorded as the position calculated based on linear filtering.

[0237] Generally, there are some differences between the matching method and the linear filtering method, but these differences are small. Therefore, in order to further improve the accuracy of ranging, the difference between the position calculated based on linear filtering and the position calculated based on the spectral characteristics of the second spectrum can be calculated and denoted as the position deviation. This position deviation reflects the stability of the matching method and the linear filtering method to a certain extent.

[0238] Of course, if the matching method times out and the position of the car in the hoistway cannot be obtained, the position calculated based on linear filtering can be directly output. This embodiment does not impose any restrictions on this.

[0239] The difference between the position calculated using linear filtering and the position calculated using the second spectral characteristic is calculated simultaneously to improve the measurement accuracy of the car position. The car position calculated using linear filtering is the primary value used to output the measured distance of the car. The car position calculated using the second spectral characteristic is the secondary value used to compare with the car position calculated using linear filtering and the output measured distance of the car, thus obtaining the deviation of the car position.

[0240] Step 705: If the position deviation is within the preset legal range, then the position calculated based on linear filtering is determined to be valid.

[0241] The position deviation is compared with the preset legal range. If the position deviation is within the preset legal range, it means that the position deviation is small, the difference between the matching method and the linear filtering method is small, which is a reasonable error, and the matching method and the linear filtering method are operating stably.

[0242] Considering that the linear filtering method has higher accuracy than the matching method, the linear filtering method can be used as the main method and the matching method as a supplement. The position calculated based on the linear filtering method is valid, and the position calculated based on the linear filtering method is output externally.

[0243] Step 706: If the position deviation is outside the preset legal range, execute the fault alarm operation.

[0244] The position deviation is compared with the preset legal range. If the position deviation is outside the preset legal range, it means that the position deviation is large and the difference between the matching method and the linear filtering method is large, which is an illegal error. At least one of the matching method and the linear filtering method may be malfunctioning. At this time, a fault alarm operation is executed to prompt the management personnel, maintenance personnel and other users to check and repair, so as to ensure the safe operation of the elevator.

[0245] Example 5

[0246] Figure 8 This is a schematic diagram of a radar ranging method device provided in Embodiment 5 of this application. A reflector is provided at one end of the shaft, and a radar is provided for the car moving within the shaft. Figure 8 As shown, the device includes:

[0247] The transmission control module 801 is used to control the radar to transmit electromagnetic wave signals to the reflector and receive the first echo signal reflected by the well channel.

[0248] The first spectral feature extraction module 802 is used to extract the first spectral feature of the wellbore from the first echo signal;

[0249] The matching module 803 is used to match the first spectral feature with a second spectral feature pre-learned on the wellbore, wherein each second spectral feature is labeled with the reflector;

[0250] The lookup module 804 is used to measure the position of the car in the hoistway based on the distance mapping of the labeled reflector if a match is found.

[0251] In one embodiment of this application, the first spectral feature extraction module 802 includes:

[0252] A conversion module is used to convert the first echo signal to a first spectrum diagram;

[0253] The first peak point search module is used to search for multiple first peak points with the largest amplitude in the first spectrum.

[0254] The first peak point sorting module sorts multiple first peak points according to distance to obtain the first spectral features of the wellbore.

[0255] In one embodiment of this application, the first spectral feature includes a plurality of first peak points; the matching module 803 includes:

[0256] The second spectral feature lookup module is used to look up a second spectral feature pre-learned on the wellbore, the second spectral feature containing multiple second peak points, one of which has been labeled with the reflector;

[0257] The total difference calculation module is used to calculate the total difference between multiple first peak points and multiple second peak points for each second spectral feature.

[0258] The search and matching module is used to find the second spectral feature with the smallest total difference, which is then used as the second spectral feature that successfully matches the first spectral feature.

[0259] In one embodiment of this application, the total difference calculation module includes:

[0260] The peak pair acquisition module is used to pair multiple first peak points with multiple second peak points for each second spectral feature to obtain peak pairs;

[0261] The first difference calculation module is used to calculate, for each of the peak pairs, a first difference between the distance between the first peak point and the distance between the second peak point;

[0262] The second difference calculation module is used to calculate a second difference between the amplitude of the first peak point and the amplitude of the second peak point for each peak pair;

[0263] The third difference calculation module is used to merge the first difference and the second difference into a third difference between the first peak point and the second peak point for each peak pair.

[0264] The sub-difference addition module is used to add all the sub-differences for all the peak pairs to obtain the total difference between the multiple first peak points and the multiple second peak points.

[0265] In one embodiment of this application, the first difference calculation module includes:

[0266] The first difference calculation module is used to calculate a first difference between the distance between the first peak point and the distance between the second peak point for each peak pair;

[0267] The first difference square calculation module is used to square the first difference to obtain the first difference;

[0268] The second difference calculation module includes:

[0269] The second difference calculation module is used to calculate a second difference between the amplitude of the first peak point and the amplitude of the second peak point for each peak pair;

[0270] The second difference squaring module is used to square the second difference to obtain the second difference.

[0271] In one embodiment of this application, the third difference calculation module includes:

[0272] The first adjustment value calculation module is used to multiply the first difference by the first weight for each peak pair to obtain the first adjustment value;

[0273] The second adjustment weight calculation module is used to multiply the second difference by the second weight to obtain the second adjustment weight, wherein the first weight is greater than or equal to the second weight.

[0274] The sub-third difference calculation module is used to take the square root of the sum between the first adjustment value and the second adjustment value to obtain the sub-third difference between the first peak point and the second peak point.

[0275] In one embodiment of this application, the second spectral feature lookup module includes:

[0276] The car position determination module is used to determine the position of the car in the hoistway calculated at the previous moment when the car moves in the hoistway.

[0277] A candidate range acquisition module is used to expand the position upwards and / or downwards to obtain a candidate range;

[0278] A loading module is used to load multiple second spectral features pre-learned on the wellbore;

[0279] The second spectral feature extraction module is used to extract the second spectral feature if the distance of the labeled reflector is within the candidate range.

[0280] In one embodiment of this application, the search module 804 includes:

[0281] The reference module is used to find the second peak point of the labeled reflector in the second spectral feature as a reference peak point;

[0282] The target peak point finding module is used to find the first peak point in the first spectral feature that is closest to the reference peak point, as the target peak point;

[0283] The distance setting module is used to map the distance of the target peak point to the position of the car in the shaft.

[0284] In one embodiment of this application, the transmission control module 801 includes:

[0285] An electromagnetic wave signal receiving module is used to control the radar to transmit multiple frames of electromagnetic wave signals to the reflector and receive multiple frames of electromagnetic wave signals reflected by the shaft when the car is stationary.

[0286] The coherent accumulation module is used to coherently accumulate multiple frames of reflected electromagnetic wave signals to obtain the first echo signal.

[0287] In one embodiment of this application, it further includes:

[0288] The motion status query module is used to query the motion status of the car at the previous moment, and the motion status includes the distance of the reflector;

[0289] The filtering module is used to perform linear filtering on the motion state at the previous moment to obtain the motion state at the current moment;

[0290] The distance extraction module is used to extract the distance of the reflector in the current motion state, as the position of the car in the shaft.

[0291] In one embodiment of this application, the filtering module includes:

[0292] The motion state acquisition module is used to predict the motion state of the car at the previous moment, its own acceleration at the previous moment, and its vibration acceleration at the previous moment, and then perform uniformly accelerated linear motion to obtain the motion state at the current moment.

[0293] The covariance matrix prediction module is used to add a preset process noise variance matrix to the covariance matrix of the previous time step to predict the covariance matrix of the current time step.

[0294] The linear gain calculation module is used to calculate the ratio between the covariance matrix and the target matrix at the current time, which is used as the linear gain at the current time. The target matrix is ​​the sum of the covariance matrix at the current time and the preset measurement noise variance matrix.

[0295] The motion state update module is used to update the motion state at the current moment according to the linear gain at the current moment;

[0296] The covariance matrix update module is used to update the covariance matrix at the current time by multiplying the difference between 1 and the linear gain at the current time by the covariance matrix at the current time.

[0297] In one embodiment of this application, the motion state update module includes:

[0298] The reflector distance measurement module adds a preset distance observation noise to the motion state at the current moment to obtain the distance of the reflector at the current moment.

[0299] The product calculation module is used to calculate the product of the linear gain and the motion deviation at the current moment, wherein the motion deviation is the difference between the distance of the reflector measured at the current moment and the motion state at the previous moment;

[0300] The product superposition module is used to add the product to the motion state at the previous moment to update the motion state at the current moment.

[0301] In one embodiment of this application, it further includes:

[0302] The position deviation calculation module is used to calculate the difference between the position calculated based on the linear filtering and the position calculated based on the second spectral feature, as the position deviation;

[0303] The effective position determination module is used to determine that the position calculated according to the linear filter is valid if the position deviation is within a preset legal range.

[0304] The fault alarm operation execution module is used to execute a fault alarm operation if the position deviation is outside the preset legal range.

[0305] In one embodiment of this application, it further includes:

[0306] A moving module is used to control the car to move sequentially to each marker position in the hoistway, wherein the starting marker position is closest to the reflector;

[0307] The second echo signal receiving module is used to control the radar to send electromagnetic wave signals to the reflector and receive the second echo signal reflected by the wellbore in each of the flag bits;

[0308] The second spectral feature extraction module is used to extract the second spectral features of the wellbore from the second echo signal;

[0309] A reflector labeling module is used to label the reflector in the second spectral feature.

[0310] In one embodiment of this application, the second spectrum feature extraction module includes:

[0311] The second echo signal conversion module is used to convert the second echo signal to a second spectrum diagram;

[0312] An amplitude lookup module is used to find multiple second peak points with the largest amplitude in the second spectrum.

[0313] The second peak point sorting module sorts multiple second peak points according to distance to obtain the second spectral characteristics of the wellbore.

[0314] In one embodiment of this application, the second spectral feature includes a plurality of second peak points, and the reflector annotation module includes:

[0315] The initial labeling module is used to label the reflector with the second peak point with the largest amplitude if the car is located at the initial mark position;

[0316] A tracking standard module is used to track the reflector if the car is not located at the initial marker position, so as to mark the reflector to the second peak point.

[0317] The radar ranging method apparatus provided in this application embodiment can execute a radar ranging method provided in any embodiment of this application, and has the functional modules and beneficial effects corresponding to the execution of a radar ranging method.

[0318] Example 6

[0319] Figure 9 A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0320] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0321] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0322] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a radar ranging method.

[0323] In some embodiments, a radar ranging method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of a radar ranging method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a radar ranging method by any other suitable means (e.g., by means of firmware).

[0324] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0325] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0326] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0327] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0328] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0329] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0330] Example 7

[0331] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements a radar ranging method as provided in any embodiment of this application.

[0332] In the implementation of the computer program product, computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0333] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0334] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A radar ranging method, characterized by, The method is characterized in that: a reflector is arranged at the end point of the shaft, a radar is arranged on a car moving in the shaft, the method comprises: controlling the radar to emit an electromagnetic wave signal to the reflector and receive a first echo signal reflected by the shaft; extracting a first spectrum feature of the shaft from the first echo signal; matching the first spectrum feature with a second spectrum feature learned in advance from the shaft, each of the second spectrum features being labeled with the reflector; if the matching is successful, measuring the position of the car in the shaft according to the distance mapping of the labeled reflector; wherein the first spectrum feature comprises a plurality of first peak points; the matching of the first spectrum feature with the second spectrum feature learned in advance from the shaft comprises: finding a second spectrum feature learned in advance from the shaft, the second spectrum feature comprising a plurality of second peak points, wherein one of the second peak points is labeled with the reflector; for each of the second spectrum features, calculating a total difference between the plurality of first peak points and the plurality of second peak points in the whole; finding the second spectrum feature with the minimum total difference as the second spectrum feature matched successfully with the first spectrum feature.

2. The method of claim 1, wherein, the extracting of the first spectrum feature of the shaft from the first echo signal comprises: converting the first echo signal into a first spectrum graph; finding a plurality of first peak points with the maximum amplitude in the first spectrum graph; sorting the plurality of first peak points according to the distance to obtain the first spectrum feature of the shaft.

3. The method of claim 1, wherein, the calculating of the total difference between the plurality of first peak points and the plurality of second peak points in the whole for each of the second spectrum features comprises: for each of the second spectrum features, pairing the plurality of first peak points with the plurality of second peak points respectively to obtain a peak pair; for each of the peak pairs, calculating a first difference between the distance of the first peak point and the distance of the second peak point; for each of the peak pairs, calculating a second difference between the amplitude of the first peak point and the amplitude of the second peak point; for each of the peak pairs, fusing the first difference and the second difference into a third difference between the first peak point and the second peak point; for all of the peak pairs, adding all the sub-differences to obtain the total difference between the plurality of first peak points and the plurality of second peak points in the whole.

4. The method according to claim 3, characterized in that: the calculating of the first difference between the distance of the first peak point and the distance of the second peak point for each of the peak pairs comprises: for each of the peak pairs, calculating a first difference value between the distance of the first peak point and the distance of the second peak point; squaring the first difference value to obtain the first difference; the calculating of the second difference between the amplitude of the first peak point and the amplitude of the second peak point for each of the peak pairs comprises: for each of the peak pairs, calculating a second difference value between the amplitude of the first peak point and the amplitude of the second peak point; Square the second difference value to obtain a second difference.

5. The method of claim 4, wherein, The first difference and the second difference are fused as a third difference between the first peak point and the second peak point for each peak pair, including: For each peak pair, the first difference is multiplied by a first weight to obtain a first weighted value; The second difference is multiplied by a second weight to obtain a second weighted value, wherein the first weight is greater than or equal to the second weight; The sum of the first weighted value and the second weighted value is square rooted to obtain the third difference between the first peak point and the second peak point.

6. The method of claim 1, wherein, The second spectrum feature learned in advance for the shaft includes: When the car moves in the shaft, the position of the car in the shaft calculated at the last time is determined; The position is extended upwards and / or downwards to obtain a candidate range; A plurality of second spectrum features learned in advance for the shaft are loaded; If the distance of the reflector that has been labeled is within the candidate range, the second spectrum feature is extracted.

7. The method of claim 1, wherein, The position of the car in the shaft is mapped according to the distance of the labeled reflector, including: In the second spectrum feature, a second peak point of the reflector that has been labeled is found as a reference peak point; In the first spectrum feature, a first peak point closest to the reference peak point is found as a target peak point; The distance of the target peak point is mapped as the position of the car in the shaft.

8. The method of claim 1, wherein, The radar is controlled to emit an electromagnetic wave signal to the reflector and receive a first echo signal reflected by the shaft, including: When the car is stationary, the radar is controlled to emit multiple frames of electromagnetic wave signals to the reflector and receive multiple frames of electromagnetic wave signals reflected by the shaft; Multiple frames of reflected electromagnetic wave signals are coherently accumulated to obtain a first echo signal.

9. The method according to any one of claims 1-8, characterized in that, Further comprising: The motion state of the car at the last time is queried, and the motion state includes the distance of the reflector; Linear filtering is performed on the motion state at the last time to obtain the motion state at the current time; The distance of the reflector in the motion state at the current time is extracted as the position of the car in the shaft.

10. The method of claim 9, wherein, The linear filtering is performed on the motion state at the last time to obtain the motion state at the current time, including: On the basis of predicting the motion state of the car at the last time, its acceleration at the last time and the vibration acceleration at the last time, uniform acceleration linear motion is performed to obtain the motion state at the current time; On the basis of the covariance matrix at the last time, a preset process noise variance matrix is added to predict the covariance matrix at the current time; The ratio between the covariance matrix at the current time and a target matrix is calculated as a linear gain at the current time, and the target matrix is the sum between the covariance matrix at the current time and a preset measurement noise variance matrix; The motion state at the current time is updated according to the linear gain at the current time; The covariance matrix at the current time is multiplied by the difference between 1 and the linear gain at the current time to update the covariance matrix at the current time.

11. The method of claim 10, wherein, The linear gain at the current time is updated according to the motion state at the current time, comprising: adding a preset observation noise to the motion state at the current time to obtain a distance of the reflector at the current time; calculating a product of the linear gain at the current time and a motion deviation, the motion deviation being a difference between the distance of the reflector at the current time and the motion state at the last time; adding the product to the motion state at the last time to update the motion state at the current time.

12. The method of claim 9, wherein, Further comprising: calculating a difference between the position calculated according to the linear filtering and the position calculated according to the second spectral feature as a position deviation; if the position deviation is within a preset legal range, determining that the position calculated according to the linear filtering is valid; if the position deviation is outside the preset legal range, performing a fault alarm operation.

13. The method according to any one of claims 1-8, 10-12, characterized in that, Further comprising: controlling the car to move to each marker position in the shaft in turn, wherein the starting marker position is closest to the reflector; at each marker position, controlling the radar to send an electromagnetic wave signal to the reflector and receive a second echo signal reflected by the shaft; extracting a second spectral feature of the shaft from the second echo signal; labeling the reflector in the second spectral feature.

14. The method of claim 13, wherein, The second spectral feature of the shaft is extracted from the second echo signal, comprising: converting the second echo signal into a second spectrum diagram; finding a plurality of second peak points with the largest amplitude in the second spectrum diagram; sorting the plurality of second peak points according to the distance to obtain the second spectral feature of the shaft.

15. The method of claim 13, wherein, The second spectral feature contains a plurality of second peak points, and the reflector is labeled in the second spectral feature, comprising: if the car is located at the starting marker position, the second peak point with the largest amplitude is labeled as the reflector; if the car is not located at the starting marker position, the reflector is tracked to label the reflector to the second peak point.

16. A radar ranging device, characterized by A reflector is arranged at an end of a shaft, and a radar is arranged on a car moving in the shaft, and the device comprises: a sending control module, configured to control the radar to send an electromagnetic wave signal to the reflector and receive a first echo signal reflected by the shaft; a first spectral feature extraction module, configured to extract a first spectral feature of the shaft from the first echo signal; a matching module, configured to match the first spectral feature with a second spectral feature learned in advance from the shaft, each second spectral feature having labeled the reflector; a finding module, configured to find a distance of the reflector in the first spectral feature as a position of the car in the shaft if the matching is successful; wherein the first spectral feature contains a plurality of first peak points; and the matching module comprises: a second spectral feature finding module, configured to find a second spectral feature learned in advance from the shaft, the second spectral feature containing a plurality of second peak points, one of which has labeled the reflector. a total difference calculation module configured to calculate, for each of the second spectral features, a total difference between the first peak points and the second peak points as a whole; a searching and matching module configured to search for the second spectral feature with the smallest total difference as the second spectral feature successfully matched with the first spectral feature.

17. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the radar ranging method according to any one of claims 1-15.

18. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is used to enable the processor to implement the radar ranging method according to any one of claims 1-15 when executed.

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