Elevator control system based on UWB technology and control method thereof

Abstract

The invention discloses an elevator control system based on a UWB technology and a control method thereof. The system comprises a UWB positioning tag, a plurality of UWB positioning base stations, an edge computing gateway and an elevator control module. The UWB positioning base stations comprise mobile base stations deployed in an elevator car, and the mobile base stations are used for calibrating the absolute position of the elevator car in a building coordinate system in real time. And the edge computing gateway analyzes the moving track of the user based on the real-time position of the UWB positioning tag and the calibrated absolute position of the elevator car so as to predict the elevator taking intention of the user, and generates a corresponding elevator control instruction. According to the invention, the mobile base station is deployed in the elevator car, so that the technical problem of inaccurate positioning of the elevator in movement is solved, a reliable data basis is provided for realizing high-precision user track analysis and intention prediction, and the reliability of non-inductive passing experience and the operation efficiency of the elevator are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent buildings, and more particularly, to an elevator control system based on UWB technology and a control method thereof. BACKGROUND

[0002] With the acceleration of urbanization and the popularity of high-rise buildings, elevators have become an indispensable vertical transportation tool in people's daily work and life. The traditional elevator control method mainly relies on user manual key operation. In order to improve user experience, some improved technologies based on induction or near field communication have emerged, but there are still problems such as high false triggering rate, the need for active operation, etc., and the real "non-sensing passage" has not been achieved.

[0003] In recent years, high-precision indoor positioning technologies represented by ultra-wideband (UWB) technology have provided new possibilities for intelligent elevator control. In the prior art, schemes have been disclosed for using positioning technology (including UWB) to analyze user movement trajectories and pre-calling elevators when judging that users are walking towards elevators, as well as using user's historical elevator-riding data to predict destination floors. These schemes have proposed the idea of "predictive" control, but in actual application, they face a key technical bottleneck: to accurately predict the user's intention, not only the user's accurate position needs to be known, but also the accurate position of the elevator (especially the moving elevator car) as a reference needs to be known. The traditional elevator system can only know through the sensors in the shaft that the car is roughly at which floor, and cannot provide its real-time absolute position in the building three-dimensional coordinate system at the centimeter level. The problem of inaccurate positioning of the reference itself makes the calculation of the relative position and movement relationship between the user and the elevator have a large error, thereby seriously affecting the reliability and practicality of the prediction algorithm.

[0004] Therefore, how to provide an intelligent elevator control scheme that can first solve the problem of high-precision positioning of the elevator car itself and on this basis realize reliable user intention prediction, thereby providing a stable and efficient non-sensing passage experience, has become a technical problem that needs to be solved in the field. SUMMARY

[0005] The present application aims to solve the problem of low reliability of user intention prediction caused by inaccurate positioning of the elevator car itself in the prior art, thereby providing an elevator control system based on UWB technology and a control method thereof.

[0006] The present application provides an elevator control system based on UWB technology, comprising: a UWB positioning tag configured to be carried by a user and periodically emit a UWB signal; a plurality of UWB positioning base stations deployed in an elevator operating environment and configured to receive the UWB signal emitted by the UWB positioning tag; The edge computing gateway is in communication connection with the plurality of UWB positioning base stations. The elevator control module is in communication connection with the edge computing gateway. The plurality of UWB positioning base stations include fixed base stations deployed in the elevator hall or the floor corridor, and a mobile base station deployed in the elevator car. The mobile base station is configured to communicate with the fixed base stations, so that the edge computing gateway can calibrate the absolute position of the elevator car in the building coordinate system in real time. The edge computing gateway is configured to: Based on the UWB signals received from the plurality of UWB positioning base stations, the real-time three-dimensional spatial position of the UWB positioning tag is calculated. Based on the real-time three-dimensional spatial position and the calibrated absolute position of the elevator car, the moving trajectory of the UWB positioning tag relative to the elevator is analyzed to predict the user's elevator intention, and an elevator control instruction is generated.

[0007] As can be seen, by deploying a mobile base station in the elevator car and enabling it to communicate with the fixed base station network in real time, the elevator car itself is creatively included in the UWB positioning network as a high-precision positioning object, thereby solving the key technical problem of the absolute position of the moving elevator car itself being inaccurate. This scheme provides a unified and accurate coordinate system reference for the entire control system, and the calculation accuracy of key parameters such as the relative position and relative speed between the user and the elevator is fundamentally improved. Based on this high-precision data, the reliability of the system in analyzing the user's moving trajectory and predicting the elevator intention is greatly enhanced, thereby enabling the system to stably implement intelligent functions such as advance call and automatic selection of destination floor, and providing the user with a truly reliable and efficient non-sensing travel experience.

[0008] Optionally, the edge computing gateway analyzes the moving trajectory of the UWB positioning tag to predict the user's elevator intention, including: determining the moving direction and moving speed of the user according to the moving trajectory; when it is judged that the moving direction of the user continuously points to the elevator hall door and the moving speed meets the preset walking speed range, the elevator intention is predicted as advance call.

[0009] As can be seen, by analyzing the moving direction and speed of the user, the user can call the elevator in advance when showing clear elevator behavior, thereby improving the response efficiency of the elevator.

[0010] Optionally, the edge computing gateway is further configured to: when detecting that the UWB positioning tag enters the elevator car, obtain historical elevator data of the user, wherein the UWB positioning tag is pre-bound with the identity information of the user; based on the historical elevator data, predict the destination floor of the user; wherein the elevator control instruction includes controlling the elevator to automatically run to the predicted destination floor.

[0011] It can be seen that by combining the user's historical elevator data to predict the destination floor, intelligent and personalized service of the elevator can be realized, the user's floor selection operation after entering the car is eliminated, and the convenience of non-sensing passing is further improved.

[0012] Optionally, the historical elevator data includes destination floor records corresponding to the user at different time periods; the edge computing gateway predicts the destination floor of the user based on the historical elevator data, including: counting the floor with the highest frequency of visits by the user in the current time period as the high-probability destination floor; when the appearance probability of the high-probability destination floor is greater than a preset probability threshold, determining it as the predicted destination floor.

[0013] It can be seen that by counting the high-probability destination floor and comparing it with the preset probability threshold, a reliable decision basis is provided for destination floor prediction, ensuring the accuracy of the prediction and avoiding invalid operation of the elevator caused by false prediction.

[0014] Optionally, the edge computing gateway is further configured to perform data smoothing processing on the real-time three-dimensional spatial position by a Kalman filtering algorithm to correct positioning errors caused by signal multipath effects.

[0015] It can be seen that by smoothing the positioning data with the Kalman filtering algorithm, the multipath effect interference caused by factors such as metal reflection in the elevator environment is effectively overcome, the positioning accuracy is improved, and a more reliable data basis is provided for subsequent trajectory analysis and intention prediction.

[0016] Optionally, the edge computing gateway is further configured to simultaneously identify multiple UWB positioning tags entering the elevator hall; determine whether there is a UWB positioning tag that is not bound to the user's identity, and if so, generate an anti-tailing alarm instruction.

[0017] It can be seen that by simultaneously identifying multiple UWB tags and determining their identity binding state, the system is given the ability to prevent unauthorized personnel from tailing in, enhancing the safety of elevator use.

[0018] Optionally, the edge computing gateway is further configured to activate a multi-user cooperative scheduling logic when multiple users with elevator intention are detected within a short period of time; the cooperative scheduling logic includes: clustering elevator tasks according to the predicted destination floors of multiple users, and intelligently assigning elevators to each user group or single user in combination with the real-time positions, running directions and car load conditions of all elevators.

[0019] It can be seen that through the multi-user cooperative scheduling mechanism, the control of the elevator can be improved from passive response to a single user to active management of group behavior and optimal allocation of system-level transport resources, improving the overall transport efficiency.

[0020] The application further provides an elevator control method based on UWB technology, comprising the following steps: deploying a fixed base station and a mobile base station in a car; communicating between the mobile base station and the fixed base station to enable the edge computing gateway to calibrate the absolute position of the elevator car in real time; receiving a signal of a UWB positioning tag; the edge computing gateway calculates the real-time position of the UWB positioning tag; and based on the real-time position of the tag and the calibrated car position, analyzing the moving track of the user relative to the elevator to predict the intention of taking the elevator and generating instructions to control the operation of the elevator.

[0021] Optionally, the step of analyzing the moving track of the UWB positioning tag to predict the intention of taking the elevator of the user comprises: determining the moving direction and speed of the user according to the moving track; when it is judged that the moving direction of the user continuously points to the elevator hall door and the moving speed meets the preset walking speed range, the intention of taking the elevator is predicted as early calling.

[0022] It can be seen that the method can call the elevator in advance when the user shows clear taking elevator behavior, thereby improving the response efficiency of the elevator.

[0023] Optionally, before generating the elevator control instruction, it further comprises: when it is detected that the UWB positioning tag enters the elevator car, obtaining the historical taking elevator data of the user, wherein the UWB positioning tag is pre-bound with the identity information of the user; based on the historical taking elevator data, predicting the destination floor of the user; wherein the elevator control instruction comprises controlling the elevator to automatically run to the predicted destination floor.

[0024] It can be seen that the method can realize intelligent and personalized service of the elevator, and further improves the convenience of non-sensing passage.

[0025] Optionally, predicting the destination floor of the user based on the historical taking elevator data comprises: counting the floor with the highest frequency of going to in the current time period as the high-probability destination floor; when the appearance probability of the high-probability destination floor is greater than a preset probability threshold, determining it as the predicted destination floor.

[0026] It can be seen that the method provides a reliable decision basis for destination floor prediction, ensures the accuracy of prediction, and avoids invalid operation of the elevator caused by false prediction. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the application and, together with the description, serve to explain the principles of the application.

[0028] Figure 1 is a system architecture schematic diagram of an elevator control system based on UWB technology provided by an embodiment of the application.

[0029] Figure 2is a functional module schematic diagram of an edge computing gateway provided by an embodiment of the present application.

[0030] Reference signs: 1, UWB positioning tag; 2, UWB positioning base station; 21, fixed base station; 22, mobile base station; 3, edge computing gateway; 4, elevator control module; 5, elevator; 31, data receiving and preprocessing module; 32, positioning solution module; 33, trajectory analysis and intention prediction module; 34, historical data analysis module; 35, decision and instruction generation module. DETAILED DESCRIPTION

[0031] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments are not limiting to the scope of the present application unless otherwise specifically stated.

[0032] The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the scope of the application its application or uses.

[0033] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, techniques, methods, and devices should be considered part of the description of the present application.

[0034] In all of the examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as a limitation. Thus, other examples of exemplary embodiments can have different values.

[0035] Note that similar reference numerals and letters refer to like items in the following drawings, and thus, once an item is defined in one drawing, it need not be discussed further in subsequent drawings.

[0036] Reference will now be made to Figure 1 An elevator control system based on UWB technology is provided by an embodiment of the present application. The system aims to realize a user's non-sensory and efficient elevator riding experience through the combination of high-precision positioning and intelligent algorithms. The overall architecture of the system includes a UWB positioning tag 1, multiple UWB positioning base stations 2, an edge computing gateway 3, and an elevator control module 4, and each component cooperates closely to form a closed-loop control system that reacts quickly and makes intelligent decisions.

[0037] The UWB positioning tag 1 is a user's personalized beacon, as the sensing end of the system, which can be flexibly integrated into the user's mobile phone case, name card, bracelet or other portable items. Each UWB positioning tag 1 is burned with a globally unique ID at the factory, which is bound with specific user identity information (such as employee number, name, etc.) through the management background when the system is initially deployed or a new user is registered. This binding relationship is the basis for realizing personalized services and security management. In work, the UWB chip in the tag periodically transmits nanosecond-level UWB pulse signals conforming to the IEEE 802.15.4a / z standard at a preset update frequency (for example, 5-10 Hz can be set to balance real-time performance and power consumption).

[0038] The UWB positioning base station 2 is the receiving and sensing network of UWB signals, and its deployment method is the key to solving the core technical problems of the present application. The UWB positioning base station 2 is divided into two types: fixed base stations 21 and mobile base stations 22. The fixed base stations 21 are permanently installed on the fixed structures of the building, such as the ceiling of each floor elevator hall, the wall of the main passage and corridor, and together form a stable positioning reference network covering the public areas of the building. In order to ensure the accuracy of three-dimensional positioning solution, each key positioning area (especially the elevator hall) is covered by at least three fixed base stations 21 arranged in a non-linear layout (preferably an equilateral triangle or a more optimal polygonal layout) to maximize the geometric dilution of precision (GDOP).

[0039] The mobile base station 22 is specially installed inside each elevator car, preferably at the center of the car roof, to obtain the best signal coverage inside the car. It moves vertically in the shaft together with the elevator car.

[0040] The mobile base station 22 has a dual and equally important function. First, it serves as a "mobile anchor point" inside the car, used to accurately locate the user's UWB positioning tag 1 that has entered the elevator car. Second, it itself is a "dynamic tag" that is tracked in real time, which continuously interacts with the fixed base stations 21 on both sides of the shaft or adjacent floors through UWB signals, and is also positioned with high precision by the positioning solution module 32, so as to calculate the absolute position (x, y, z) of the elevator car itself in the three-dimensional coordinate system of the entire building in real time and centimeter level. This step realizes the dynamic real-time calibration of the moving elevator car, completely solves the pain point of the traditional elevator system that can only know the floor number through the encoder or floor sensor, and provides a solid and accurate reference for the subsequent relative motion analysis between all users and the elevator.

[0041] The edge computing gateway 3 is the "brain" of the whole system, which is a high-performance computing unit deployed locally in the building (for example, in the weak current room or server cabinet of the building), and its hardware can include multi-core processors, large-capacity memories and high-speed network interfaces. The design idea of local deployment is to ensure low delay of data processing and avoid sluggish system response due to public network fluctuations. Please refer to Figure 2 The software functions inside the edge computing gateway 3 can be divided into the following several logically independent but closely coupled modules: Data receiving and preprocessing module 31: This module serves as the data entrance of the system, receiving raw signal data packets with accurate hardware timestamps from all fixed base stations 21 and mobile base stations 22 in the network through Ethernet. Considering that the complex electromagnetic environment of the elevator shaft and the car will cause serious signal multipath effect, the module preferably uses Kalman filter algorithm to preprocess the received raw data stream. The Kalman filter can predict the current state based on the previous state and correct it combined with the current observation value, effectively filtering out abnormal jump points caused by signal reflection and diffraction, and outputting more stable and reliable data stream to the subsequent modules. Those skilled in the art can understand that in addition to Kalman filter, other algorithms with data smoothing function such as particle filter, mean filter, etc. can also be used to correct positioning error.

[0042] Positioning solution module 32: The module is the core computing engine to realize high-precision positioning. It receives the preprocessed data and uses TDOA (Time Difference of Arrival) algorithm to simultaneously solve the positions of two different types of objects in the unified, pre-calibrated three-dimensional coordinate system of the building: one is to solve the real-time three-dimensional spatial positions of all effective UWB positioning tags 1 within the field of view; the other is to solve the real-time three-dimensional spatial positions of all mobile base stations 22 (i.e. elevator cars). This synchronous solving mechanism ensures that the position information of users and elevators has high time and space consistency, which is the basis for subsequent accurate analysis. Those skilled in the art can understand that in addition to TDOA algorithm, other positioning algorithms such as Time of Arrival (TOA), Angle of Arrival (AOA) or the fusion of multiple algorithms can also be used to realize position solving.

[0043] Trajectory analysis and intent prediction module 33: This module is the key to the system's "proactive" intelligence. It performs high-reliability relative motion analysis based on the user position and elevator car position output by the positioning solution module 32, which is accurate to the centimeter level. For example, by continuously calculating the rate of change of the distance between the user's position coordinates and the precise coordinates of the target elevator door, as well as the angle between the user's movement vector and the normal vector of the elevator door, it can very accurately determine whether the user is walking towards the elevator. When it detects that the user's movement direction is consistently (e.g., for more than 2 seconds) pointing towards the elevator hall door and the movement speed conforms to typical walking characteristics (e.g., 0.5-1.5 meters per second), the system predicts that the user has a clear "advance call" intent.

[0044] Historical data analysis module 34: This module is used to provide personalized destination floor prediction services. It is activated when the trajectory analysis and intent prediction module 33 confirms that the user has entered the car (i.e., the position coordinates of the UWB positioning tag 1 have entered the coordinate range of the car where the mobile base station 22 is located). It accesses the local database associated with the user ID, which stores the user's historical elevator records. Based on contextual information such as the current time and date, it predicts the most likely destination floor using probability statistics. For example, it can statistically determine the floor with the highest frequency of travel when the user departs from the first floor between 8:00 AM and 9:00 AM on weekdays. If the probability of the floor appearing is greater than a preset confidence threshold (e.g., 70%), it is determined as the predicted destination floor for this trip.

[0045] Decision and instruction generation module 35: This module serves as the decision center of the system. Based on the results of intent prediction and floor prediction, it generates the final, executable elevator control instructions and executes them through the elevator control module 4. For example, when it receives an "advance call" intent, it generates an instruction to summon the elevator. When the user enters the elevator and the destination floor is successfully predicted, it generates a composite instruction to close the door and run to the floor.

[0046] Elevator control module 4 is the execution unit of the physical action of the elevator, responsible for receiving and executing instructions from the edge computing gateway 3 to control the operation of the elevator 5.

[0047] In a preferred embodiment, the system of the present application is also equipped with security protection function. Specifically, the decision and instruction generation module 35 is further configured to execute anti-tail logic. When the positioning solution module 32 simultaneously identifies multiple UWB positioning tags 1 in the elevator hall area, the decision and instruction generation module 35 will query whether each tag ID is bound with the user identity information in the system. If it is detected that the ID of a certain UWB positioning tag 1 is not registered in the user database, or the tag moves synchronously with the authorized user tag in very close distance (e.g. less than 0.5 meters), and the authorized user has triggered the boarding intention, the system judges that there may be tailing behavior of unauthorized personnel. At this time, the decision and instruction generation module 35 will generate anti-tail alarm instructions. The instructions can include but are not limited to: issuing a voice prompt through the loudspeaker in the elevator hall, "Please note that there is unauthorized personnel approaching"; controlling the elevator door to close quickly after the authorized user enters; and / or sending an alarm message containing time, location and related tag ID to the monitoring system of the building security center through the network for verification and processing by security personnel.

[0048] In a preferred embodiment, the decision and instruction generation module 35 also includes multi-user collaborative scheduling logic to deal with complex scenarios where multiple users approach the elevator at the same time. When the trajectory analysis and intention prediction module 33 detects multiple users with boarding intentions within a short period of time (e.g. within 3 seconds), the collaborative scheduling logic inside the decision and instruction generation module 35 is activated. Its workflow is as follows: Firstly, the system will quickly query the historical data of multiple users to try to predict their respective destination floors.

[0049] Secondly, the system will "cluster the boarding tasks" of multiple users according to the predicted destination floors. For example, users with consistent or similar destinations (e.g. going to high zone floors 15-20) are divided into a group.

[0050] Then, the system will combine the real-time positions, running directions and car load conditions of all elevators to intelligently assign elevators to each group or individual user. The optimization goal is to minimize the total waiting time of all users and the total running energy consumption of the elevators. For example, for a group going to the high zone, the system will preferentially assign an elevator that is already running in the high zone or is idle and heading to the high zone, rather than dispatching from the bottom, thereby achieving the "piggyback" effect. For users with scattered destinations, the system may assign different elevators to serve, avoiding frequent stops by one elevator and improving overall transportation efficiency.

[0051] Finally, the system can also push the assignment information to the user's mobile phone through the linkage with the user's mobile phone APP, such as "Please take the left elevator, estimated waiting time 15 seconds", to guide the user to board the elevator in an orderly manner.

[0052] To illustrate the cooperative scheduling logic more specifically, let's consider the following application scenario: an office building is equipped with two elevators (denoted as Elevator A and Elevator B, respectively). During the morning rush hour, the system detects that User A, User B, and User C are all walking towards the elevator at the 1st floor hall, triggering the multi-user cooperative scheduling logic.

[0053] At this time, the system obtains the real-time status of each unit: User prediction: Based on the prediction of the historical data analysis module 34, User A's destination floor is the 18th floor, User B's destination floor is the 20th floor, and User C's destination floor is the 5th floor.

[0054] Elevator status: Elevator A is at the 10th floor and is descending, with no one in the car; Elevator B is at the 3rd floor and is ascending, with 2 people in the car, with a load rate of 30%.

[0055] The cooperative scheduling logic execution process is as follows: 1. Clustering of elevator tasks: The system classifies User A (18th floor) and User B (20th floor) with similar destination floors into a "high zone task group", and User C (5th floor) as an independent "low zone task".

[0056] 2. Intelligent dispatching decision: The system evaluates the cost of serving each task for the two elevators. For the "high zone task group", although Elevator A is at the 10th floor, it can serve User A and User B directly at the 1st floor after descending, which is efficient. However, Elevator B needs to stop at the existing passengers' destination floor and User C's 5th floor, which will significantly increase the waiting and riding time of high zone users. For the "low zone task", Elevator B is ascending and is on the way, so the cost of serving User C is extremely low.

[0057] 3. Instruction generation and delivery: Therefore, the system makes the optimal decision to generate instructions: assign Elevator A to descend to the 1st floor to serve User A and User B; assign Elevator B to continue ascending and pick up User C when it stops at the 5th floor. At the same time, the system can prompt through the display screen of the elevator hall or the user's mobile phone APP: "User A and User B please take Elevator A", "User C please take Elevator B".

[0058] As can be seen from this embodiment, the multi-user cooperative scheduling mechanism can effectively integrate elevator demand and elevator capacity, achieve optimal matching of resources, and significantly improve the efficiency of elevator operation and user experience during peak hours.

[0059] The multi-user cooperative scheduling mechanism improves elevator control from passive response and simple prediction for individual users to active management of group behavior and optimal allocation of system-level capacity resources, which is an important expansion of the invention in the aspect of intelligence.

[0060] The present invention also provides an elevator control method based on UWB technology, the process of which is as follows: Step S801: First, fixed base stations 21 and mobile base stations 22 are deployed in the elevator operating environment to build a complete UWB positioning network.

[0061] Step S802: After the system starts, the mobile base station 22 deployed in the car continuously interacts with the surrounding fixed base stations 21, and the positioning calculation module 32 in the edge computing gateway 3 calculates and calibrates the accurate absolute position of the elevator car in real time.

[0062] Step S803: At the same time, all UWB positioning base stations 2 in the network continuously receive the signals emitted by the user UWB positioning tag 1, and the positioning calculation module 32 synchronously calculates the real-time three-dimensional spatial position of the user.

[0063] Step S804: The trajectory analysis and intention prediction module 33 analyzes the relative movement trajectory between the user and the elevator car in real time based on the accurate position data of the two.

[0064] Step S805: The system predicts the user's intention to take the elevator according to the trajectory analysis result. If it is predicted that the user has a clear intention to take the elevator (for example, stably walking towards the elevator), the process enters the next step. In this step, if multiple user intentions are detected, the above-mentioned multi-user cooperative scheduling logic will be activated for intelligent dispatching.

[0065] Step S806: The decision and instruction generation module 35 generates an early call instruction to call the elevator through the elevator control module 4.

[0066] Step S807: The system continues to monitor, and when it is detected that the user's accurate position has entered the accurate position range of the elevator car, that is, the user has entered the elevator, the process enters the next step.

[0067] Step S808: The historical data analysis module 34 is activated to predict the most likely destination floor of the user according to the user's historical records and the current situation.

[0068] Step S809: The decision and instruction generation module 35 generates an instruction to close the door and run to the predicted destination floor to control the elevator to automatically deliver the user.

[0069] In summary, the application fundamentally improves the accuracy and reliability of the intelligent elevator system in predicting user behavior by setting a mobile base station in the elevator car and performing real-time position calibration, and provides a more technically advanced and effectively stable solution for non-sensing passage. Although some specific embodiments of the application have been described in detail through examples, those skilled in the art should understand that the above examples are only for illustration, not for limiting the scope of the application. Those skilled in the art should understand that the above embodiments can be modified without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims.

[0070] Although some specific embodiments of the application have been described in detail through examples, those skilled in the art should understand that the above examples are only for illustration, not for limiting the scope of the application. Those skilled in the art should understand that the above embodiments can be modified without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims.

Claims

1. An elevator control system based on UWB technology, characterized in that, include: UWB positioning tags are configured to be carried by users and periodically transmit UWB signals; Multiple UWB positioning base stations are deployed in the elevator operating environment and configured to receive the UWB signals transmitted by the UWB positioning tag; An edge computing gateway, which is communicatively connected to multiple UWB positioning base stations; The elevator control module is communicatively connected to the edge computing gateway; Among them, the multiple UWB positioning base stations include fixed base stations deployed in elevator lobbies or floor corridors, and mobile base stations deployed inside elevator cars; The mobile base station is configured to communicate with the fixed base station so that the edge computing gateway can calibrate the absolute position of the elevator car in the building coordinate system in real time; The edge computing gateway is configured as follows: The real-time three-dimensional spatial position of the UWB positioning tag is calculated based on the UWB signals received from multiple UWB positioning base stations. Based on the real-time three-dimensional spatial position and the calibrated absolute position of the elevator car, the movement trajectory of the UWB positioning tag relative to the elevator is analyzed to predict the user's intention to ride the elevator and generate elevator control commands.

2. The system according to claim 1, characterized in that, The edge computing gateway analyzes the movement trajectory of the UWB positioning tag to predict the user's elevator usage intention, including: The user's movement direction and speed are determined based on the movement trajectory; When it is determined that the user's movement direction is continuously pointing towards the elevator hall door and the movement speed is within the preset walking speed range, the intention to ride the elevator is predicted as an advance call for the elevator.

3. The system according to claim 1 or 2, characterized in that, The edge computing gateway is also configured to: When the UWB positioning tag is detected to have entered the elevator car, the user's historical elevator ride data is obtained, wherein the UWB positioning tag is pre-bound to the user's identity information; Based on the historical elevator usage data, predict the user's destination floor; The elevator control command includes controlling the elevator to automatically run to the predicted destination floor.

4. The system according to claim 1, characterized in that, The edge computing gateway is also configured to: The real-time three-dimensional spatial position is smoothed using a Kalman filter algorithm to correct positioning errors caused by signal multipath effects.

5. The system according to claim 1, characterized in that, The edge computing gateway is also configured to: Simultaneously identify multiple UWB positioning tags entering the elevator lobby; Determine if there is a UWB location tag that is not bound to the user's identity. If it exists, generate an anti-tailgating alarm command.

6. The system according to claim 1, characterized in that, The edge computing gateway is also configured to: When multiple users with intent to use the elevator are detected within a short period of time, the multi-user collaborative scheduling logic is activated. The collaborative scheduling logic includes: clustering elevator ride tasks based on the predicted destination floors of multiple users, and intelligently dispatching elevators for each user group or individual user by combining the real-time location, direction of operation, and load status of all elevators in the car.

7. An elevator control method based on UWB technology, characterized in that, Includes the following steps: Multiple UWB positioning base stations are deployed in the elevator operating environment, including fixed base stations and mobile base stations deployed inside the elevator car; The mobile base station communicates with the fixed base station so that the edge computing gateway can calibrate the absolute position of the elevator car in the building coordinate system in real time; The UWB signals transmitted by the UWB positioning tag carried by the user are received by multiple UWB positioning base stations; The edge computing gateway calculates the real-time three-dimensional spatial position of the UWB positioning tag based on the UWB signal; The edge computing gateway analyzes the movement trajectory of the UWB positioning tag relative to the elevator based on the real-time three-dimensional spatial position and the calibrated absolute position of the elevator car to predict the user's intention to ride the elevator, and generates elevator control commands to control the operation of the elevator.

8. The method according to claim 7, characterized in that, The step of analyzing the movement trajectory of the UWB positioning tag to predict the user's elevator usage intention includes: The user's movement direction and speed are determined based on the movement trajectory; When it is determined that the user's movement direction is continuously pointing towards the elevator hall door and the movement speed is within the preset walking speed range, the intention to ride the elevator is predicted as an advance call for the elevator.

9. The method according to claim 7 or 8, characterized in that, Before generating elevator control commands, the following is also included: When the UWB positioning tag is detected to have entered the elevator car, the user's historical elevator ride data is obtained, wherein the UWB positioning tag is pre-bound to the user's identity information; Based on the historical elevator usage data, predict the user's destination floor; The elevator control command includes controlling the elevator to automatically run to the predicted destination floor.

10. The method according to claim 7 or 8, characterized in that, When predicting the user's elevator usage intention, if multiple users with elevator usage intentions are detected, the method further includes: Activate multi-user collaborative scheduling logic; The collaborative scheduling logic includes: clustering elevator ride tasks based on the predicted destination floors of multiple users, and intelligently dispatching elevators for each user group or individual user by combining the real-time location, direction of operation, and load status of all elevators in the car.

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