Personnel flow control method and system based on escalator taking

Abstract

The invention provides a passenger flow control method and system based on escalator taking, and the method comprises the steps that the bearing capacity of an escalator and the number and the weight of passengers taking the escalator are obtained, and the bearing capacity of the escalator comprises the number and the weight of the passengers capable of being borne by the escalator; according to the method, the number of people taking the escalator or the weight information of the people is judged in real time, when at least one condition of the number or the weight of the people taking the escalator meets the bearing capacity of the escalator, a people flow control decision of the escalator is started, and dynamic control over the people flow of the escalator is achieved. According to the method, the number or the weight of people taking the escalator is controlled, the upper limit of the weight of the people taking the escalator is adjusted in real time according to the bearing capacity of the escalator or the estimated bearing capacity of the escalator, under the condition that the safety of the people is guaranteed, the fatigue running time of the escalator is shortened, and the service life of the escalator is prolonged; and the dynamic control of the flow of people taking the escalator can be realized.

Description

Technical Field

[0001] This application relates to the field of personnel flow control technology, specifically a method and system for personnel flow control based on escalator use. Background Technology

[0002] Currently, escalators serve as a convenient and efficient vertical transportation tool in public places such as shopping malls, subway stations, and large office buildings, undertaking the task of managing large crowds. However, a significant problem in daily use is the lack of crowd control during escalator use. This is mainly reflected in the fact that, in most cases, passengers can freely choose when to step onto the escalator without following any form of crowd control mechanism.

[0003] This disorderly state often leads to a large number of pedestrians flooding into the same escalator almost simultaneously during peak hours, such as rush hour or holidays, causing the escalator to bear a weight far exceeding its design load. Although the escalator design has taken into account a certain safety margin, long-term and frequent overload operation undoubtedly poses a severe challenge to its mechanical structure and power system. Over time, the wear and tear of escalator components accelerates, and maintenance cycles are forced to be shortened, which not only increases operating costs, but more importantly, significantly reduces the overall stability and safety of the escalator.

[0004] More seriously, overloaded escalators suffer from impaired operational stability, potentially leading to sudden stops, uneven acceleration, or abnormal noises. These issues significantly increase the risk of falls, collisions, and other safety accidents for passengers. This chaotic and high-density environment is particularly problematic for the elderly, children, and those with mobility impairments, exacerbating their sense of insecurity and making escalation more difficult. Therefore, the lack of effective passenger flow control not only affects the lifespan and efficiency of escalators but, more importantly, directly impacts the personal safety of every passenger. It is a critical issue that urgently needs attention and improvement, and dynamic control of escalator passenger flow is essential. Summary of the Invention

[0005] To overcome the aforementioned problems in the existing technology, this application provides a method and system for controlling passenger flow based on escalator use, which adopts the following technical solution:

[0006] Firstly, this application provides a method for controlling passenger flow based on escalator use, including:

[0007] The load-bearing capacity of the escalator and the number and weight of people riding the escalator are obtained, where the load-bearing capacity of the escalator includes the number and weight of people that the escalator can carry.

[0008] The system makes real-time judgments on the number or weight of people riding the escalator. When at least one of the conditions for the number or weight of people riding the escalator meets the escalator's load-bearing capacity, the system initiates a passenger flow control decision, thereby achieving dynamic control of passenger flow on the escalator.

[0009] Furthermore, obtaining the load-bearing capacity of the escalator includes: re-estimating the load-bearing capacity of the escalator based on the escalator's operating time, and obtaining the estimated load-bearing capacity of the escalator.

[0010] Furthermore, the process of re-estimating the escalator's load-bearing capacity based on its operating time to obtain an estimated escalator load-bearing capacity includes:

[0011] Real-time acquisition of escalator operation data, including sensor data during operation and maintenance data imported by maintenance personnel;

[0012] The data information of escalator operation is filtered to obtain filtered data of escalator operation;

[0013] Wavelet transform is performed on the vibration data obtained from vibration sensors in the filtered data to obtain the abnormal vibration characteristics of the escalator operation;

[0014] Feature engineering is performed on the temperature, power, and current data in the filtered data to obtain the changing trends of temperature, power, and current data during escalator operation as key features of escalator operation;

[0015] The maintenance data, vibration anomaly characteristics, and key features imported by the operation and maintenance personnel are used as inputs to a preset machine learning model, which outputs the current health status indicators of the escalator.

[0016] Based on the current health status indicators of the escalator, the load-bearing capacity attenuation ratio of the escalator is obtained, and the estimated remaining load-bearing capacity of the escalator is dynamically calculated.

[0017] Furthermore, the number of people riding the escalator was obtained, including:

[0018] Real-time video streams within the escalator riding area are acquired, and people are detected in the real-time video stream using a deep learning object detection model. A unique ID is assigned to each person detected using a pre-set tracking module to identify the movement trajectory of the same pedestrian. A first virtual detection line is set at the escalator entrance, and a second virtual detection line is set at the escalator exit. The number of people within the first and second virtual detection lines is counted based on their IDs to determine the total number of people riding the escalator.

[0019] Furthermore, the weight of the people riding the escalator is obtained, including:

[0020] If there is an estimated remaining load capacity of the current escalator, the weight data in the estimated remaining load capacity of the current escalator will be used as the weight limit for passengers riding the escalator.

[0021] When there is no estimated remaining load capacity of the escalator, the weight of passengers set at the factory shall be used as the weight limit for passengers riding the escalator.

[0022] Furthermore, the system performs real-time assessments of the number or weight of passengers using the escalator. When at least one of these conditions—the number or weight of passengers—meets the escalator's load-bearing capacity, the system initiates passenger flow control decisions, achieving dynamic control of passenger flow on the escalator. This includes:

[0023] During the process of determining the number of people, when the number of people entering the virtual detection line within a preset time period meets the preset threshold, the escalator's personnel flow control decision is activated to achieve dynamic control of the escalator's personnel flow.

[0024] During the process of judging the weight of people, when the weight information of the escalator gravity sensor meets the preset threshold, the escalator's personnel flow control decision is activated to achieve dynamic control of the escalator's personnel flow.

[0025] Secondly, this application also provides a passenger flow control system based on escalator use, comprising:

[0026] The passenger flow acquisition module is used to acquire the escalator's load-bearing capacity and the number and weight of passengers riding the escalator. The escalator's load-bearing capacity includes the number and weight of passengers that the escalator can carry.

[0027] The passenger flow control module is used to make real-time judgments on the number or weight of people riding the escalator. When at least one of the conditions of the number or weight of people riding the escalator meets the escalator's load-bearing capacity, the passenger flow control decision is activated to achieve dynamic control of the passenger flow on the escalator.

[0028] Thirdly, this application provides an electronic device, comprising:

[0029] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0030] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0031] Fifthly, this application provides a computer program that, when executed by a computer, performs the method described in the first aspect.

[0032] In one possible design, the program in the fifth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor.

[0033] This application has the following beneficial effects:

[0034] This application obtains the escalator's load-bearing capacity and the number and weight of passengers, where the escalator's load-bearing capacity includes the number and weight of passengers it can carry. It performs real-time judgment on the number or weight of passengers, and when at least one condition—the number or weight of passengers—meets the escalator's load-bearing capacity, it initiates passenger flow control decisions, achieving dynamic control of passenger flow. By controlling the number or weight of passengers and adjusting the upper limit of passenger weight in real-time based on the escalator's load-bearing capacity or estimated load-bearing capacity, this application reduces the escalator's fatigue operating time and increases its service life while ensuring passenger safety. Attached Figure Description

[0035] Figure 1 This is an exemplary system architecture diagram to which embodiments of this application can be applied;

[0036] Figure 2 This is a flowchart of a method for controlling passenger flow based on escalator use, according to an embodiment of this application.

[0037] Figure 3 This is a flowchart illustrating the process of obtaining the estimated load-bearing capacity of an escalator according to an embodiment of this application.

[0038] Figure 4 This is a system flowchart of an embodiment of this application;

[0039] Figure 5 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0041] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0042] 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.

[0043] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0044] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0045] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0046] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0047] It should be noted that the escalator-based passenger flow control method provided in this application is generally executed by a server / terminal device, and correspondingly, the escalator-based passenger flow control system is generally set in the server / terminal device.

[0048] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0049] Continue to refer to Figure 2 The figure shows a flowchart of a method for controlling passenger flow based on escalator use, which includes the following steps:

[0050] Step 201: Obtain the load-bearing capacity of the escalator and the number and weight of people riding the escalator, wherein the load-bearing capacity of the escalator includes the number and weight of people that the escalator can carry.

[0051] As the load-bearing capacity of an escalator changes over time, the load-bearing capacity of the escalator is re-estimated based on its operating time to obtain an estimated load-bearing capacity.

[0052] In one possible implementation, the escalator's load-bearing capacity is re-estimated based on its operating time to obtain an estimated load-bearing capacity. Please refer to [reference needed]. Figure 3 The specific content includes:

[0053] Step 31: Acquire real-time escalator operation data, including sensor data during operation and maintenance data imported by maintenance personnel. The sensor data includes vibration, temperature, and electrical sensors. Vibration data is acquired through vibration sensors, temperature data through temperature sensors, and power and current data through electrical sensors. This application can acquire operational data from escalator sensors and obtain aging-related information from maintenance records.

[0054] Step 32 involves filtering the escalator operation data to obtain filtered data that eliminates operating noise, environmental interference, and abnormal fluctuations, thus obtaining filtered data reflecting the escalator's true operating status. It should be noted that bandpass filtering is used to filter the escalator operation data. Bandpass filtering is a mature technology for data filtering and will not be elaborated upon here.

[0055] Step 33: Perform wavelet transform on the vibration data obtained from vibration sensors in the filtered data to obtain the vibration anomaly characteristics of the escalator operation. The vibration anomaly characteristics obtained by wavelet transform are used to characterize the energy distribution changes of key mechanical components of the escalator at different operating stages, thereby identifying potential abnormal states caused by wear, loosening, or structural fatigue.

[0056] Step 34 involves performing feature engineering to extract the temperature, power, and current data from the filtered data, obtaining the changing trends of these data during escalator operation as key features of the escalator's operation. In this embodiment, it should be noted that obtaining the changing trends of the temperature, power, and current data during escalator operation includes statistical data such as the peak value, mean, and variance of the temperature, power, and current data.

[0057] Step 35: The maintenance data, vibration anomaly features, and key features imported by the maintenance personnel are used as input to the preset machine learning model, and the current health status index of the escalator is output. It should be noted that the preset machine learning model in this application can be a neural network, support vector machine, random forest, etc. Before obtaining the current health status index of the escalator through the preset machine learning model, the preset machine learning model learns from a large amount of historical data to establish a nonlinear relationship between the escalator's vibration anomaly features, key features, and health status index. Then, the vibration anomaly features and key features extracted from the current escalator are used as input to the preset machine learning model, and the current health status index of the escalator is output.

[0058] Step 36: Obtain the escalator's load-bearing capacity attenuation ratio based on the escalator's current health status indicators, and dynamically calculate the estimated remaining load-bearing capacity of the escalator. In this embodiment, obtaining the escalator's load-bearing capacity attenuation ratio based on the escalator's current health status indicators can be based on a preset formula. ,in This refers to various health indicators of the escalator. These are the weight values ​​for each health indicator. This indicates the percentage decrease in the escalator's load-bearing capacity. Health indicators refer to electrical parameters such as motor current and voltage, and operational status indicators such as temperature and vibration. In this embodiment, the estimated remaining load-bearing capacity of the escalator is calculated as: Original design load-bearing capacity × (1 - Load-bearing capacity decrease percentage). For example, if the original design load-bearing capacity is 1000 kg and the calculated load-bearing capacity decrease percentage is 20%, then the estimated remaining load-bearing capacity is... .

[0059] In this embodiment, the escalator load-bearing capacity attenuation ratio will decrease with long-term operation of the escalator, component aging, or changes in operating conditions. By associating and mapping health status indicators with set rated load parameters, the load-bearing capacity of the escalator is dynamically corrected, thereby obtaining an estimated value of the remaining load-bearing capacity that conforms to the current operating state.

[0060] This application achieves real-time assessment of the actual load-bearing capacity of escalators as their operating status changes by extracting dynamic features and evaluating their health status, thus avoiding safety hazards caused by judging load capacity using fixed parameters.

[0061] The machine learning approach in this application establishes a mapping relationship between operational characteristics and escalator health status by training on historical samples of normal and abnormal operation states.

[0062] In one possible implementation, obtaining the number of people riding the escalator includes:

[0063] The method acquires real-time video streams within the escalator riding area and performs object detection on the people in the real-time video stream based on a deep learning object detection model. It should be noted that the deep learning object detection model in this application can use algorithms such as YOLO and SSD; object detection is a relatively mature technology and will not be elaborated upon here.

[0064] Based on the preset tracking module, a unique ID is assigned to the same person in the target detection to identify the movement trajectory of the same pedestrian. The preset tracking module of this application can use the SORT algorithm to perform multi-target tracking of pedestrians. By assigning a unique ID to the same person in the target detection, this application avoids double counting of people riding escalators when obtaining the number of people on the escalator.

[0065] A first virtual detection line is set up at the escalator entrance, and a second virtual detection line is set up at the escalator exit; the number of people within the first and second virtual detection lines is counted based on the personnel ID as the number of people riding the escalator.

[0066] In one possible implementation, obtaining the weight of a person riding the escalator includes:

[0067] If there is an estimated remaining load capacity of the current escalator, the weight data in the estimated remaining load capacity of the current escalator will be used as the weight limit for passengers riding the escalator.

[0068] When there is no estimated remaining load capacity of the escalator, the weight of passengers set at the factory shall be used as the weight limit for passengers riding the escalator.

[0069] It should be noted that the weight of passengers riding the escalator is obtained based on the estimated remaining load capacity of the current escalator. If there is no estimated remaining load capacity data for the current escalator, and the escalator is likely new, the weight of passengers riding the escalator should be obtained based on the number of passengers set at the factory.

[0070] Step 202: Real-time judgment is made on the number or weight of people riding the escalator. When at least one condition of the number or weight of people riding the escalator meets the escalator's load-bearing capacity, the escalator's passenger flow control decision is activated to achieve dynamic control of the escalator's passenger flow.

[0071] It should be noted that this application, by real-time judgment of the weight or number of people riding the escalator, can highlight the dynamic characteristics of escalator passenger detection and achieve dynamic control of escalator passenger flow based on the real-time judgment results. The escalator passenger flow control in this application includes the number or weight of people riding the escalator. In densely populated places such as stations and shopping malls, especially during morning and evening rush hours, escalators, as key vertical transportation tools, often have their carrying capacity near its limit. To ensure public safety and improve traffic efficiency, this application adopts a dynamic escalator passenger flow control strategy, that is, by dynamically controlling the flow of people on the escalator to ensure public safety and order. When the number or weight of people on the escalator meets a preset threshold, this application activates the escalator passenger flow control decision, which can effectively avoid safety hazards caused by overcrowding, improve overall traffic efficiency and passenger experience, ensure the safety and comfort of every user, and extend the service life of the escalator. This intelligent escalator passenger flow control management method not only improves traffic efficiency but also provides passengers with a safer and more comfortable travel experience.

[0072] In one possible implementation, the number or weight of people riding the escalator is determined in real time, including:

[0073] Furthermore, the real-time judgment of the number or weight of people riding the escalator described in this application is not based on the static detection results at a single moment, but rather on a dynamic assessment of the escalator's load-bearing status by comprehensively analyzing the changing trends of the number or weight of people within a continuous time window.

[0074] Specifically, by continuously sampling the number or weight of people within a preset time window, calculating the corresponding rate of change, growth rate, or cumulative load value, and combining this with the current escalator operating status, the risk of the escalator reaching or approaching its load capacity can be judged in advance, thus providing a feedforward basis for personnel flow control decisions.

[0075] In this application embodiment, when at least one condition—the number of people riding the escalator or the weight of the people—meets the escalator's load-bearing capacity, at least one of the following situations is included:

[0076] (1) The number of people riding the escalator reaches or exceeds the corresponding threshold;

[0077] (2) The weight of the person detected by the escalator reaches or exceeds the corresponding weight threshold;

[0078] (3) The combined load value of the number of personnel and the weight of personnel reaches the preset comprehensive load threshold.

[0079] The comprehensive load-bearing threshold can be adaptively adjusted based on the escalator model, operating speed, operating direction, or historical operating data.

[0080] In one possible implementation, when at least one condition—the number of people or their weight—meets the escalator's load-bearing capacity is met, the escalator's passenger flow control decision is activated to achieve dynamic control of passenger flow, including:

[0081] During the process of determining the number of people, when the number of people entering the virtual detection line within a preset time period meets the preset threshold, the escalator's personnel flow control decision is activated to achieve dynamic control of the escalator's personnel flow.

[0082] During the process of judging the weight of people, when the weight information of the escalator gravity sensor meets the preset threshold, the weight information obtained by the escalator gravity sensor is processed by outlier removal and smoothing to eliminate the impact of instantaneous fluctuations caused by short-term jumping of people, dragging of luggage or vibration of escalator, so as to obtain a stable weight assessment result. Based on the weight assessment result, the escalator's personnel flow control decision is activated to realize the dynamic control of escalator personnel flow.

[0083] In this embodiment of the application, the decision to control the flow of people when the escalator is activated includes implementing control measures through turnstiles, prompting devices, escalator speed control, etc.

[0084] In one embodiment of this application, the personnel flow control decision is a hierarchical control strategy, including:

[0085] When the load level is within the warning range, a prompt message is displayed, guiding people to use the escalator during off-peak hours. When the load level increases further, entry of new people to the escalator entrance is restricted. When the load level approaches or reaches the critical load range, the escalator speed is adjusted or temporarily stopped. Through this tiered control approach, safety is ensured while minimizing disruption to normal traffic flow.

[0086] In one possible implementation, the virtual detection line is positioned at a predetermined location in the escalator entrance area, corresponding to the escalator's direction of travel. By identifying individuals crossing the virtual detection line and combining this with information about their movement direction, instances of unauthorized entry, loitering, or going against the flow are filtered out, and only the number of individuals who have effectively entered the escalator is counted, thereby improving the accuracy of personnel count assessment.

[0087] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0088] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0089] Continued reference Figure 4 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a passenger flow control system based on escalator use. This system embodiment is similar to... Figure 2 Corresponding to the illustrated method embodiment, this system can be specifically applied to various electronic devices, including: a people flow acquisition module 401 and a people flow control module 402. Wherein:

[0090] The personnel flow acquisition module 401 is used to acquire the escalator's load-bearing capacity and the number and weight of people riding the escalator, wherein the escalator's load-bearing capacity includes the number and weight of people that the escalator can carry.

[0091] The passenger flow control module 402 is used to make real-time judgments on the number or weight of passengers riding the escalator. When at least one condition, such as the number or weight of passengers riding the escalator, meets the escalator's load-bearing capacity, the passenger flow control decision is activated to achieve dynamic control of passenger flow on the escalator.

[0092] The specific implementation process of the escalator-based passenger flow control system is as follows:

[0093] Real-time acquisition of escalator operation data, including collected sensor data and maintenance data imported by maintenance personnel.

[0094] The operating data is filtered and preprocessed to obtain filtered data that reflects the actual operating status of the escalator.

[0095] Based on the abnormal vibration characteristics and operational trends of escalators, and combined with maintenance data input into a preset machine learning model, the current health status indicators of the escalators are output.

[0096] The escalator's load-bearing capacity attenuation rate is calculated based on health status indicators, and the estimated remaining load-bearing capacity of the escalator is dynamically obtained.

[0097] The system makes real-time judgments on the number and / or weight of people riding the escalator. When the number and / or weight of people meet the dynamic load conditions determined based on the estimated remaining load capacity, the system initiates a passenger flow control decision for the escalator.

[0098] This application obtains the escalator's load-bearing capacity and the number and weight of passengers using the escalator. The escalator's load-bearing capacity includes the number and weight of passengers it can carry. It performs real-time judgment on the number or weight of passengers using the escalator. When at least one condition—the number or weight of passengers—meets the escalator's load-bearing capacity, it initiates passenger flow control decisions, achieving dynamic control of passenger flow. This application controls the number or weight of passengers using the escalator and adjusts the upper limit of passenger weight in real time based on the escalator's load-bearing capacity or estimated load-bearing capacity. While ensuring passenger safety, it reduces the escalator's fatigue operating time, increases its service life, and achieves dynamic control of passenger flow.

[0099] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 5 , Figure 5 This is a basic structural block diagram of the computer device in this embodiment.

[0100] The computer device 5 includes a memory 5a, a processor 5b, and a network interface 5c that are interconnected via a system bus. It should be noted that only the computer device 5 with components 5a-5c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0101] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0102] The memory 5a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 5a may be an internal storage unit of the computer device 5, such as the hard disk or memory of the computer device 5. In other embodiments, the memory 5a may also be an external storage device of the computer device 5, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 5. Of course, the memory 5a may also include both the internal storage unit and its external storage device of the computer device 5. In this embodiment, the memory 5a is typically used to store the operating system and various application software installed on the computer device 5, such as program code for a method for controlling passenger flow based on escalator use. In addition, the memory 5a can also be used to temporarily store various types of data that have been output or will be output.

[0103] In some embodiments, the processor 5b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 5b is typically used to control the overall operation of the computer device 5. In this embodiment, the processor 5b is used to run program code stored in the memory 5a or process data, for example, to run the program code of the escalator-based passenger flow control method.

[0104] The network interface 5c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 5 and other electronic devices.

[0105] This application also provides another embodiment, namely, a non-volatile computer-readable storage medium storing a program for a method of controlling passenger flow based on escalator riding, the method of controlling passenger flow based on escalator riding being executable by at least one processor to perform the steps of the method of controlling passenger flow based on escalator riding as described above.

[0106] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0107] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for controlling passenger flow based on escalator use, characterized in that, include: The load-bearing capacity of the escalator and the number and weight of people riding the escalator are obtained, where the load-bearing capacity of the escalator includes the number and weight of people that the escalator can carry. The system makes real-time judgments on the number or weight of people riding the escalator. When at least one of the conditions for the number or weight of people riding the escalator meets the escalator's load-bearing capacity, the system initiates a passenger flow control decision, thereby achieving dynamic control of passenger flow on the escalator.

2. The method for controlling passenger flow based on escalator use according to claim 1, characterized in that, The process of obtaining the escalator's load-bearing capacity includes: re-estimating the escalator's load-bearing capacity based on the escalator's operating time, and obtaining the estimated load-bearing capacity of the escalator.

3. The method for controlling passenger flow based on escalator use according to claim 2, characterized in that, The process of re-estimating the escalator's load-bearing capacity based on its operating time to obtain the estimated load-bearing capacity includes: Real-time acquisition of escalator operation data, including sensor data during operation and maintenance data imported by maintenance personnel; The data information of escalator operation is filtered to obtain filtered data of escalator operation; Wavelet transform is performed on the vibration data obtained from vibration sensors in the filtered data to obtain the abnormal vibration characteristics of the escalator operation; Feature engineering is performed on the temperature, power, and current data in the filtered data to extract the changing trends of temperature, power, and current data during escalator operation as key features of escalator operation; The maintenance data, vibration anomaly characteristics, and key features imported by the operation and maintenance personnel are used as inputs to a preset machine learning model, which outputs the current health status indicators of the escalator. Based on the current health status indicators of the escalator, the load-bearing capacity attenuation ratio of the escalator is obtained, and the estimated value of the remaining load-bearing capacity of the current escalator is dynamically calculated.

4. The method for controlling passenger flow based on escalator use according to claim 1, characterized in that, Obtain the number of people riding the escalator, including: The system acquires real-time video streams within the escalator riding area and performs target detection on people in the real-time video stream based on a deep learning target detection model. It assigns a unique ID to the same person in the target detection based on a preset tracking module to identify the movement trajectory of the same pedestrian. It sets up a first virtual detection line at the escalator entrance and a second virtual detection line at the escalator exit. It counts the number of people within the first and second virtual detection lines based on the person IDs to determine the number of people riding the escalator.

5. The method for controlling passenger flow based on escalator use according to claim 3, characterized in that, Obtain the weight of people riding the escalator, including: The weight of passengers riding the escalator is obtained through the escalator's weight sensor; When there is an estimated remaining load capacity of the current escalator, the weight data in the estimated remaining load capacity of the current escalator is used as the weight limit for passengers riding the escalator. When there is no estimated remaining load capacity of the escalator, the weight of passengers set at the factory shall be used as the weight limit for passengers riding the escalator.

6. The method for controlling passenger flow based on escalator use according to claim 1, characterized in that, The system performs real-time assessments of the number and weight of passengers using the escalator. When at least one of these conditions—the number of passengers or their weight—meets the escalator's load-bearing capacity, the system initiates passenger flow control decisions, enabling dynamic control of passenger flow on the escalator. This includes: During the process of determining the number of people, when the number of people entering the virtual detection line within a preset time period meets the preset threshold, the escalator's personnel flow control decision is activated to achieve dynamic control of the escalator's personnel flow. During the process of judging the weight of people, when the weight information of the escalator gravity sensor meets the preset threshold, the escalator's personnel flow control decision is activated to achieve dynamic control of the escalator's personnel flow.

7. A passenger flow control system based on escalator use, used to implement the passenger flow control method based on escalator use as described in claims 1-6, characterized in that, include: The passenger flow acquisition module is used to acquire the escalator's load-bearing capacity and the number and weight of passengers riding the escalator. The escalator's load-bearing capacity includes the number and weight of passengers that the escalator can carry. The passenger flow control module is used to make real-time judgments on the number or weight of people riding the escalator. When at least one of the conditions of the number or weight of people riding the escalator meets the escalator's load-bearing capacity, the passenger flow control decision is activated to achieve dynamic control of the passenger flow on the escalator.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-6.

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