CN112785759B

CN112785759B - System and method for passenger flow statistics

Info

Publication number: CN112785759B
Application number: CN202110085015.2A
Authority: CN
Inventors: 续立军
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2021-01-22
Filing date: 2021-01-22
Publication date: 2023-05-23
Anticipated expiration: 2041-01-22
Also published as: CN112785759A

Abstract

The system and method for passenger flow statistics provided by the specification can monitor a plurality of target areas in a detected area through a plurality of sensing systems in the detected area. Each sensing system comprises at least one sensing sensor and at least one processor, each sensing sensor monitors whether a target object exists in a corresponding monitoring range, and the processor performs living body detection on each sensor data to determine whether the target object in the monitoring range of each sensing sensor is a living body or not, and the living body is only counted into the passenger flow volume when the target object is the living body. The control terminal receives the living body detection result of each sensing system, and determines the living body user quantity of each target area, so as to determine the total user quantity in the detected area.

Description

System and method for passenger flow statistics

Technical Field

The present disclosure relates to the field of internet technologies, and in particular, to a system and a method for passenger flow statistics.

Background

Along with the rapid development of the Internet and information technology, the passenger flow statistics is widely applied to various scenes such as catering industry, smart retail, security monitoring, market, hotels, roads, scenic spots, vehicles and the like, and merchants can be assisted to know the conditions in the stores more accurately through the passenger flow statistics and conduct corresponding operation and marketing decisions. Especially for the catering industry, the passenger flow statistics can help the manager to sense the number of customers eating in the store in real time, and has important significance in predicting the future dining flow, improving the business strategy of the merchant and improving the business benefit and the customer experience of the merchant.

The passenger flow statistics scheme commonly used at present mainly adopts the technical scheme that a range finder or visual monitoring is arranged at an entrance and exit, and image recognition is combined, the passenger flow in a store is monitored through the data of an induction sensor monitoring entrance such as an infrared sensor or a visual sensor, the data output by the sensor is processed through a processor, the current passenger flow number is obtained through the data processing output by the sensor, and the passenger flow number is uploaded to a cloud server. If the total number of people in the store needs to be calculated, the accumulated calculation is needed for the sensor data in a period of time. However, over time, the cumulative calculation may lead to error accumulation, resulting in inaccurate passenger flow statistics. In addition, the method for combining visual monitoring with image recognition needs to arrange a plurality of cameras, and has the advantages of high hardware arrangement cost, complex calculation, high power consumption, inconvenient installation and difficult maintenance.

Accordingly, there is a need to provide a low cost, high accuracy system and method for distributed passenger flow statistics.

Disclosure of Invention

The present specification provides a low cost, high accuracy system and method for distributed passenger flow statistics.

In a first aspect, the present disclosure provides a system for passenger flow statistics, including a plurality of sensing systems and a control terminal, the plurality of sensing systems being distributed in a plurality of target areas within a detected area, each of the plurality of sensing systems including at least one sensing sensor and at least one processor, each of the at least one sensing sensors being operative to monitor whether a target object is present within a corresponding monitoring range and to generate sensor data, the target object including a living user and a non-living user; the processor is used for receiving the sensor data sent by each sensing sensor, performing living detection on the sensor data based on a preset identification model and generating an identification result so as to determine whether the target object is the living user; the control terminal is respectively in communication connection with each sensing system when in work, acquires the identification result of each sensing sensor in each sensing system, and determines the total number of users in the detected area based on the identification result.

In some embodiments, the plurality of target areas at least partially cover the detected area.

In some embodiments, the measured area is a dining area of a restaurant, and the monitoring range corresponding to each sensing sensor includes a dining chair.

In some embodiments, the identification model is obtained through training based on sample data corresponding to the living user and sample data corresponding to the non-living user, wherein the sample data comprises a mapping relation between signal frequency and signal amplitude in the sensor data.

In some embodiments, the performing the living body detection on the sensor data based on a preset recognition model includes: determining the mapping relation between the signal frequency and the signal amplitude of the sensor data in a preset time window; and performing living body detection on the mapping relation based on the identification model, and determining whether the sensing sensor corresponding to the sensor data senses the living body user.

In some embodiments, the preset time window includes a preset duration prior to the current time.

In some embodiments, at most one of the living users is within the monitoring range of each of the sensing sensors.

In some embodiments, the determining the total number of users within the detected area includes: determining a number of living users in each of the plurality of target areas based on the recognition result of each of the sensing sensors; and determining the total number of users based on the number of living users in each target area.

In some embodiments, each of the sensing systems further comprises a wireless communication module operative to establish the communication connection with the control terminal, the communication connection comprising a wireless communication connection.

In some embodiments, each of the sensing systems further comprises a power module in operative electrical communication with the at least one sensing sensor and the at least one processor.

In some embodiments, the obtaining the identification result of the each sensing sensor in the each sensing system includes: and acquiring the identification result of each sensing sensor based on a preset time period.

In some embodiments, the at least one sensing sensor comprises at least one of at least one radar sensor, at least one infrared sensor, at least one pressure sensor, at least one temperature sensor, at least one vibration sensor, and at least one electric field sensor.

In a second aspect, the present disclosure provides a method for passenger flow statistics, applied to the system for passenger flow statistics described in the first aspect of the present disclosure, including performing, by the at least one processor: acquiring the sensor data of each sensing sensor in the corresponding sensing system; performing living body detection on the sensor data of each sensing sensor based on the identification model, and generating an identification result of each sensing sensor; and sending the identification result of each sensing sensor to the control terminal.

In some embodiments, the detecting the sensor data of each of the sensing sensors in vivo based on the recognition model includes: determining the mapping relation between the signal frequency and the signal amplitude of the sensor data in a preset time window; and performing living body detection on the mapping relation based on the identification model, and determining whether the sensing sensor corresponding to the sensor data senses the living body user.

In some embodiments, the sending the identification result of each sensing sensor to the control terminal includes: and sending the identification result of each perception sensor to the control terminal based on a preset time period.

In a third aspect, the present disclosure provides a method for passenger flow statistics, which is applied to the passenger flow statistics system described in the first aspect of the present disclosure, and includes executing, by the control terminal: acquiring the identification result of each sensing sensor in each sensing system; and determining the total number of users in the detected area based on the identification result.

According to the technical scheme, the passenger flow statistics system and the passenger flow statistics method provided by the specification can monitor a plurality of target areas in the detected area through a plurality of sensing systems in the detected area. Each sensing system monitors whether a target object exists in a monitoring range corresponding to each sensing sensor through at least one sensing sensor, generates sensor data, and sends the sensor data to a processor, and the processor carries out living body detection on each sensor data according to the sensor data so as to determine whether the target object in the monitoring range of each sensing sensor is a living body, and only when the target object is a living body, the living body is counted into passenger flow. And the control terminal determines the number of living users in each target area according to the living body detection result of the processor in each sensing system, so as to determine the total number of users in the detected area. The system and the method can acquire the absolute value of the passenger flow in the detected area by simultaneously monitoring the passenger flow in a plurality of target areas in the detected area, but not measuring the relative value of the passenger flow, and acquire the total passenger flow through accumulation calculation, thereby avoiding error accumulation and improving the accuracy of passenger flow statistics. Meanwhile, the system and the method can prevent non-human bodies from being counted into the passenger flow volume by performing living detection on the user so as to improve the comprehensiveness and the accuracy of passenger flow statistics.

Additional functionality of the system and method of passenger flow statistics provided herein will be set forth in part in the description which follows. The following numbers and examples presented will be apparent to those of ordinary skill in the art in view of the description. The inventive aspects of the systems and methods of passenger flow statistics provided herein may be fully explained by practicing or using the methods, devices, and combinations described in the following detailed examples.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present description, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 shows a schematic view of an application scenario of a system for passenger flow statistics according to an embodiment of the present disclosure;

FIG. 2 illustrates a schematic diagram of a device of a perception system provided in accordance with an embodiment of the present disclosure;

FIG. 3 illustrates a schematic diagram of an installation location of a perception sensor provided in accordance with an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of sensor data frequency distribution of a living user provided in accordance with an embodiment of the present disclosure;

fig. 5 shows a schematic device diagram of a control terminal according to an embodiment of the present disclosure;

FIG. 6 illustrates a flow chart of a method of passenger flow statistics provided in accordance with an embodiment of the present description;

FIG. 7 shows a schematic diagram of an effective signal and an interference signal provided in accordance with an embodiment of the present description; and

fig. 8 shows a flow chart of another method of passenger flow statistics provided in accordance with an embodiment of the present description.

Detailed Description

The following description is presented to enable one of ordinary skill in the art to make and use the invention, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the disclosure. Thus, the present description is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. For example, as used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. The terms "comprises," "comprising," "includes," and/or "including," when used in this specification, are taken to specify the presence of stated integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

These and other features of the present specification, as well as the operation and function of the related elements of structure, as well as the combination of parts and economies of manufacture, may be significantly improved upon in view of the following description. All of which form a part of this specification, reference is made to the accompanying drawings. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the description. It should also be understood that the drawings are not drawn to scale.

The flowcharts used in this specification illustrate operations implemented by systems according to some embodiments in this specification. It should be clearly understood that the operations of the flow diagrams may be implemented out of order. Rather, operations may be performed in reverse order or concurrently. Further, one or more other operations may be added to the flowchart. One or more operations may be removed from the flowchart.

The present disclosure provides a system and method for passenger flow statistics, which can monitor passenger flow in a detected area through a distributed sensing system. Specifically, the system and method for passenger flow statistics provided in the present disclosure may arrange sensing systems at a plurality of positions in a detected area to monitor passenger flow in the area where the sensing systems are located, thereby obtaining absolute values of passenger flow in the detected area and avoiding accumulated errors. And the system and the method for passenger flow statistics provided by the specification can carry out living body detection on each user, and only the users detected as living bodies can be counted into the passenger flow so as to improve the comprehensiveness and the accuracy of the passenger flow statistics. The scheme solves the problems of laying and maintaining cost of arranging a plurality of cameras in a shop scene, and has the characteristics of simple implementation and easy popularization. The scheme can also provide a low-cost and reproducible in-store passenger flow solution for merchants, and can help the merchants to quickly realize the digitization of off-line scenes.

Fig. 1 shows an application scenario schematic of a passenger flow statistics system 001 according to an embodiment of the present disclosure. A system 001 for passenger flow statistics (hereinafter referred to as system 001) may be used for passenger flow statistics in the region 003 to be tested. The detected area 003 may be any space area, such as a supermarket, a market, a restaurant, etc., and further, such as a supermarket living goods area, a cosmetics area, a clothing area, etc., and further, such as a market children area, a footwear area, a decoration area, etc., and further, such as a restaurant dining area, a checkout area, a waiting area, etc. For convenience of presentation, the following description will take the area 003 to be measured as a restaurant as an example. The system 001 may include a plurality of sensing systems 200 and control terminals 300.

The sensing systems 200 are respectively distributed at a plurality of different positions in the detected region 003, and for convenience of description, the position where each sensing system 200 is distributed in the detected region 003 is referred to as a target position. Each perception system 200 may monitor the flow of people within a preset range of the target location. I.e., each perception system 200 may monitor the number of users within the target area 202 around the corresponding target location. The plurality of perception systems 200 corresponds to the plurality of target areas 202. When a target object is present within the target area 202 of the perception system 200, the presence of the target object may cause a change in the output signal of the perception system 200. It should be noted that the target object may be a living user, such as a human body, or may be a non-living user, such as a bag, clothes, or other sundries placed on a seat in a restaurant, or the like. The change in the output signal when the sensing system 200 senses a living user and the change in the output signal when a non-living user is sensed are different. When the target object is a living user, that is, when the target object is a human body, body movement (limb movement, heartbeat) of the human body, temperature change due to the occurrence of the human body, and the like may cause the output signal of the sensing system 200 to be continuously changed along with the movement of the human body. When the target object is a non-living user, the change condition of the output signal caused by the non-living user is single. The perception system 200 may have stored therein data or instructions for performing the methods of passenger flow statistics described herein, and may execute or be used to perform the data and/or instructions. Specifically, each sensing system 200 may generate sensing data in real time according to the change of the output signal in the corresponding target area 202, and may perform living body detection on each sensing data in the plurality of sensing data based on a preset recognition model, for recognizing human body features, and generate a recognition result to determine whether the target object is a living body, so as to perform passenger flow statistics.

The target area 202 may be a monitoring range of the sensing system 200, or may be an effective working area of the sensing system 200, i.e. a sensing range of the sensing system 200. The target area 202 may be preset or modified. For example, we can alter the range of the target area 202 by adjusting the sensitivity or detection range of the sensor. For example, by adjusting the transmission distance of the radar sensor or the infrared sensor, the range of the target area 202 is adjusted. Each perception system 200 corresponds to a target area 202. The plurality of perception systems 200 corresponds to the plurality of target areas 202.

As shown in fig. 1, a plurality of target areas 202 may at least partially cover the detected area 003. The plurality of target areas 202 may cover a portion of the served area 003, such as when the served area 003 is a space area within an entire restaurant, the plurality of target areas 202 may cover a dining area within the served area 003, i.e., the plurality of target areas 202 partially cover the served area 003. The plurality of target areas 202 may also cover the entire area of the area 003 being served, such as the plurality of target areas 202 may cover the entire area within the area 003 being served, including a dining area, a checkout area, a waiting area, and so forth. The coverage of the multiple target areas 202 of the multiple sensing systems 200 can be changed by adjusting the distribution position of the sensing systems 200, the monitoring range (target areas 202) of the sensing systems 200 and the distribution density of the sensing systems 200, so that the multiple target areas 202 of the multiple sensing systems 200 can cover any area to meet the use requirement. Meanwhile, the area which does not need to be counted of the passenger flow can be avoided, so that the cost is reduced, the accuracy of the passenger flow statistics is improved, and erroneous data are prevented from being counted into statistical data.

Adjacent target areas 202 of the plurality of target areas 202 may or may not overlap. When adjacent target areas 202 are partially overlapped, the area of the overlapped portion does not exceed a preset threshold value, so as to avoid inaccurate statistical results caused by repeated identification and counting of the same user. When the adjacent target areas 202 do not overlap, the gap area between the adjacent target areas 202 should not exceed a preset threshold value, so as to avoid missing statistics and inaccurate statistics results.

The control terminal 300 may store data or instructions for performing the method P200 of passenger flow statistics described in the present specification and may perform or be used to perform the data and/or instructions. The method P200 concerning the passenger flow statistics will be described in detail in the following description. The control terminal 300 may be in communication connection with the plurality of sensing systems 200 during operation, so as to obtain an identification result of each sensing system 200 in the plurality of sensing systems 200, and calculate, based on the identification result, the number of living users in each target area 202, that is, the number of passenger flows in each target area 202, so as to determine the passenger flow in the detected area 003, that is, the total number of users. By communication connection is meant any form of connection capable of directly or indirectly receiving information. In some embodiments, the control terminal 300 may communicate data with each other through a wireless communication connection with the plurality of sensing systems 200; in some embodiments, the control terminal 300 may also communicate data with each other by directly connecting with the plurality of sensing systems 200 through wires; in some embodiments, the control terminal 300 may also establish indirect connections to multiple sensing systems 200 via wires directly connected to other circuits to effect the transfer of data to each other. For convenience of description, the present description will take an example in which the control terminal 300 communicates the recognition result with the plurality of sensing systems 200 through wireless communication connection. The wireless communication connection is established between the control terminal 300 and the plurality of sensing systems 200, so that the installation is simple and convenient, the adaptability is high, and the difficulty brought by wiring of wired communication connection can be avoided.

The control terminal 300 may include a hardware device having a data information processing function and a program necessary for driving the hardware device to operate. Of course, the control terminal 300 may be only a hardware device having data processing capability, or only a program running in the hardware device. In some embodiments, the control terminal 300 may include a mobile device, a tablet computer, a notebook computer, a built-in device of a motor vehicle, or the like, or any combination thereof. In some embodiments, the mobile device may comprise a smart home device, a smart mobile device, or the like, or any combination thereof. In some embodiments, the smart home device may include a smart television, a desktop computer, or the like, or any combination. In some embodiments, the smart mobile device may include a smart phone, personal digital assistant, gaming device, navigation device, etc., or any combination thereof, in some embodiments, the built-in device in the motor vehicle may include an on-board computer, on-board television, etc. In some embodiments, the control terminal 300 may be a device with positioning technology for positioning the position of the control terminal 300.

As shown in fig. 1, the control terminal 300 may include a local device 301. In some embodiments, the control terminal 300 may also include a cloud device 302. The control terminal 300 is in said wireless communication connection with the plurality of sensing systems 200 via a local device 301. The local device 301 and the cloud device 302 may exchange information or data through a network. For example, the cloud device 302 may obtain the recognition result from the local device 301 through a network. In some embodiments, the network may be any type of wired or wireless network, or a combination thereof. For example, the network may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, or the like. In some embodiments, the network may include one or more network access points.

The data processing algorithm for the control terminal 300 to perform passenger flow statistics according to the identification result may be performed in the local device 301 or may be performed in the cloud device 302. Cloud device 302 may have more powerful, faster computing capabilities relative to local device 301. When the calculation amount of the data processing algorithm of the passenger flow statistics is small, the data processing algorithm of the passenger flow statistics can be performed in the local device 301. When the calculation amount of the data processing algorithm of the passenger flow statistics is larger, the data processing algorithm of the passenger flow statistics may be performed in the cloud device 302, and the local device 301 may send the identification result to the cloud device 302 for calculation. The local device 301 may not perform data operation, and the local device 301 may send all the recognition results obtained from the multiple sensing systems 200 to the cloud device 302 for passenger flow statistics, so as to reduce the cost of the local device 301.

As shown in fig. 1, a system 001 described in the present specification monitors target objects in a plurality of target areas 202 in real time by arranging a plurality of sensing systems 200 in a detected area 003, obtains monitored sensing data, and performs living detection on the sensing data through a preset identification model to determine whether the target object is a living user; the control terminal 300 obtains the identification results of the plurality of sensing systems 200 through wireless communication connection with the plurality of sensing systems 200, and determines the absolute value of the number of the passenger flows in each target area 202, thereby obtaining the absolute value of the passenger flow in the detected area 003, avoiding cumulative errors and non-living body false identification, and improving the accuracy of passenger flow statistics.

Fig. 2 shows a schematic device diagram of a perception system 200 provided in accordance with an embodiment of the present description. As shown in fig. 2, the perception system 200 may include at least one perception sensor 220. In some embodiments, the perception system 200 may also include at least one processor 280. In some embodiments, the perception system 200 may also include a wireless communication module 240. In some embodiments, the perception system 200 may also include a power module 260.

Each of the at least one sensor 220 is operable to monitor whether a target object is present within a corresponding monitoring range and to receive signals within the monitoring range to generate sensor data. The sensor data may be a time dependent relationship of the signal. The signal comprises an amplitude of the signal and a frequency distribution of the signal. The sensor data may be used to perform in vivo detection. Each sensing system 200 may include one or more sensing sensors 220. Each of the sensing sensors 220 is configured to monitor a target object within its corresponding monitoring range. Each of the sensing sensors 220 may generate one sensor data. At least one sensor 220 corresponds one-to-one with the at least one sensor data. The at least one sensor 220 may generate at least one sensor data. The combination of the monitoring ranges of at least one of the sensing sensors 220 in each sensing system 200 constitutes a target area 202 corresponding to the sensing system 200. Specifically, the at least one perception sensor 220 may monitor changes in the target object within the corresponding target area 202 and generate corresponding sensor data based on the changes in the target object.

To facilitate passenger flow statistics, at most one living user is within the monitoring range of each of the at least one sensor 220. Fig. 3 shows a schematic view of an installation position of a sensing sensor 220 provided according to an embodiment of the present specification. The perception sensor 220 may be mounted anywhere within the detected area 003. Taking the area 003 as a dining area of a restaurant as an example, the monitoring range of the sensor 220 may be the dining chair 006 of the dining area. The perception sensor 220 may be used to monitor whether a target object is currently on the dining chair. The sensing sensor 220 may be mounted on a dining table 005 or a dining chair 006 of the dining area. As shown in fig. 3, a sensing sensor 220 may be mounted below the dining table 005 and aligned with the dining chair 006 to sense whether a living user is dining on the dining chair 006. Dining chair 006 may also be mounted on the back of dining chair 006 and facing dining table 005, or mounted below dining chair 006, to sense whether a living user is dining in the current position. When a living user is on the dining chair 006, the body movement (limb movement, heartbeat) of the human body or the like causes a change in the output signal of the dining chair 006, sensor data is generated, and the control terminal 300 can receive the sensor data and perform living detection based on the preset identification model to identify the living user.

Of course, the sensing sensor 220 may be installed at other locations, for example, the sensing sensor 220 may be installed at a location near the dining user, for example, on the ceiling or above the dining chair 006, and sense whether the living user is dining on the dining chair 006, etc.

For example, when the area 003 to be measured is a restaurant, each sensing system 200 may be installed under one dining table 005, each sensing system 200 may include a plurality of sensing sensors 220, each sensing sensor 220 facing one dining chair 006 around the current dining table 005, or directly installed on the dining chair 006 to monitor whether the living user is on the corresponding dining chair 006. When the target object appears in the monitoring range of the sensing sensor 220, the signal received by the sensing sensor 220 will change. The law of variation of the signal caused by the living user and the non-living user is also different. The control terminal can recognize whether the target object on the current dining chair 006 is a living user or a non-living user according to the change rule of the signal in the sensor data.

The system 001 may include a plurality of sensing systems 200, each sensing system 200 corresponding to a set of sensor data, each set of sensor data may include the at least one sensor data.

When the number of users in the target area 202 changes, a series of data changes in the target area 202, such as gravity changes, distance changes, vibration changes, even temperature changes, and changes in the dielectric constant in the surrounding environment, etc., may be caused. The at least one sensor 220 may be any form of sensor that senses changes in environmental data. For example, the at least one sensing sensor 220 may be at least one of at least one radar sensor, at least one infrared sensor, at least one pressure sensor, at least one temperature sensor, at least one vibration sensor, and at least one electric field sensor.

The radar sensor may emit electromagnetic wave signals outwards and receive electromagnetic wave signals reflected back by other objects. When the sensing sensor 220 is a radar sensor, the radar sensor may transmit electromagnetic wave signals in a predetermined direction and receive reflected electromagnetic wave signals reflected back by an object. When a target object appears in a preset direction of the radar sensor, a reflected electromagnetic wave signal received by the radar sensor in the preset direction changes. The control terminal 300 may determine whether a target object exists in the preset direction according to the reflected electromagnetic wave signal, and determine whether the target object is a living user according to a change rule of the reflected electromagnetic wave signal. The preset direction may be one direction or a plurality of different directions. The radar sensor data may be one radar sensor that can emit electromagnetic wave signals in a plurality of directions, or may be a plurality of radar sensors that can emit electromagnetic wave signals in a single direction.

When the target object is a living user (human body) and a non-living user, the reflected electromagnetic wave signals received by the radar sensor are also different. When the target object is a non-living user, the non-living user does not move by itself, so that the reflected electromagnetic wave signal received by the radar sensor changes smoothly. When the target object is a living user, the reflected electromagnetic wave signal received by the radar sensor changes more complicated because the movement of limbs or the heartbeat of the human body causes the change of the reflected electromagnetic wave signal.

Taking a radar sensor as an example, fig. 4 shows a schematic diagram of sensor data frequency distribution of a living user according to an embodiment of the present specification. As shown in fig. 4, the horizontal axis represents the frequency f, and the vertical axis represents the output signal amplitude a of the sensor 220. Usually, when a human body waits in a restaurant or takes a meal, signals with lower frequency and higher amplitude can be generated due to limb movement; at the same time, signals with relatively high frequency but small amplitude are generated due to the respiration and heartbeat of the human body. The distribution of limb movement signals 1, respiration signals 2 and heartbeat signals 3 over the frequency spectrum is shown in fig. 4, wherein the respiration frequency is about 0.13-0.4 Hz, the heartbeat frequency is about 0.8-3.3 Hz, and the limb movement frequency is less than 0.1Hz. However, due to the very weak respiration and heartbeat signals, the signals are easily submerged in the signals of limb movements due to the continuous movement of the human body. Meanwhile, the human body is in a stationary state for a long time in the dining process, and the movement signals of the limbs cannot be detected. Therefore, the three data are needed to be combined to judge whether the current radar sensor detects the human body.

Therefore, the sensing system 200 can determine whether the target object is a human body according to the mapping relationship between the frequency of the signal and the amplitude of the signal in the sensor data, so as to avoid the non-living target object from being counted into the passenger flow volume, and improve the accuracy of passenger flow statistics.

The infrared sensor may emit infrared signals outward and receive infrared signals reflected back by other objects. When the sensing sensor 220 is an infrared sensor, the infrared sensor may emit an infrared signal in a preset direction and receive a reflected infrared signal reflected back by an object. When a target object appears in a preset direction of the infrared sensor, the reflected infrared signal received by the infrared sensor in the preset direction changes. The control terminal 300 may determine whether a target object exists in the preset direction according to the reflected infrared signal, and determine whether the target object is a living user according to a change rule of the infrared signal. The preset direction may be one direction or a plurality of different directions. The infrared sensor data may be one infrared sensor that emits infrared signals in a plurality of directions, or a plurality of infrared sensors that emit infrared signals in a single direction. The infrared sensor may be an infrared pyroelectric sensor or an infrared thermopile sensor, and the present specification is not limited herein.

The at least one pressure sensor may measure the pressure change data experienced. When the sensing sensor 220 is a pressure sensor, the pressure sensor may be mounted on the dining chair 006 to measure the pressure changes experienced by the dining chair 006. The pressure data measured by the pressure sensor may also change when a target object is present within the monitoring range of the sensor 220. The control terminal 300 may determine whether the target object exists in the monitoring range according to the change of the pressure data, and determine whether the target object is a living user according to the change rule of the pressure signal.

The temperature sensor may measure temperature change data within the monitoring range. When the sensing sensor 220 is a temperature sensor, the temperature data measured by the temperature sensor may also change when the target object appears in the monitoring range. The control terminal 300 may determine whether the target object exists in the monitoring range according to the change of the temperature data, and determine whether the target object is a living user according to the change rule of the temperature signal.

The vibration sensor may measure vibration variation data within the monitoring range. When the sensing sensor 220 is a vibration sensor, when the target object appears in the monitoring range, vibration data measured by the vibration sensor may also change. The control terminal 300 may determine whether the target object exists in the monitoring range according to the change of the vibration data, and determine whether the target object is a living user according to the change rule of the vibration signal.

The electric field sensor may measure voltage variation data within the monitoring range. When the sensing sensor 220 is an electric field sensor, the dielectric constant in the monitoring range will also change when the target object appears in the monitoring range, and the voltage data measured by the electric field sensor will also change. The control terminal 300 may determine whether the target object exists in the monitoring range according to the change of the voltage data, and determine whether the target object is a living user according to the change rule of the voltage signal.

The sensing sensor 220 may be any one of the above sensors, or may be a combination of the above sensors, or may be any other sensor that can sense information of a human body, such as a distance sensor, an ultrasonic sensor, a sound sensor, a light sensor, and the like. The sensor data may be generated by the sensor 220 based on the number of user changes within the monitoring range. The plurality of sensory data includes at least the plurality of sets of sensor data. The sensor data may be generated in real time by the perception sensor 220.

The at least one processor 280 may store data or instructions for performing the method of passenger flow statistics P100 described herein, and may execute or be used to perform the data and/or instructions. The method P100 for passenger flow statistics will be described in detail in the following description. The at least one processor 280 may be communicatively coupled to each of the at least one sensor 220 and receive the sensor data transmitted by each sensor 220. As previously described, the perception sensor 220 may monitor whether a target object is present within a corresponding monitoring range and generate the sensor data. The at least one processor 280 may acquire and store sensor data of each of the sensing sensors 220 from the at least one sensing sensor 220, and perform a living body detection on the sensor data based on a preset recognition model, generating a recognition result to determine whether the target object is the living body user.

The recognition model is used for performing living detection on the sensor data. As previously described, the output signal of the sensor data changes when the sensing sensor 220 senses the presence of a target object within the monitoring range. Specifically, the change rule of the sensor data when the target object is a living user is different from the change rule when the target object is a non-living user. Taking the sensing sensor 220 as a radar sensor for example, the mapping relationship between the signal frequency and the signal amplitude in the sensor data is different.

The identification model is obtained through training based on sample data corresponding to the living user and sample data corresponding to the non-living user. The sample data may include a mapping of signal frequencies and signal amplitudes in the sensor data. Specifically, a developer may install the sensing sensor 220 in different types of scenarios (such as a plurality of different restaurants) and perform sample data collection, where the sample data includes sensor data corresponding to a plurality of marked living users and sensor data corresponding to non-living users; acquiring a mapping relation between signal frequency and signal amplitude in each sensor data, and training a classified neural network model by taking the mapping relation as training data; after training, the neural network classification model is deployed in the processor 280 to conduct real-time prediction classification, the identification result is generated, and whether a human body exists in the monitoring range of the current perception sensor 280 is output.

The at least one processor 280 may also transmit the recognition result to the control terminal 300 based on a preset time period. Specifically, the processor 280 is an embedded low power processor. The wireless communication module 240 and the at least one processor 280 within the perception system 200 are normally in a standby mode. The control terminal 300 wakes up the sensing system 200 at intervals of a preset time, i.e., the control terminal 300 may wake up the sensing system 200 based on a preset time period; after the sensing system 200 receives the wake-up signal sent by the control terminal 300, the processor 280 in the sensing system 200 may upload the identification result to the control terminal 300, reenter the sleep or standby state, and wait for the next wake-up. Therefore, the low-power processor 280 can save the power consumption of the sensing system 200 and prolong the standby time.

The wake-up signal may be a signal transmitted by the control terminal 300 to the perception system 200. The preset time may be set or changed. Specifically, the control terminal 300 may obtain the preset time through machine learning according to the history data of the passenger flow volume in the detected region 003. The preset time for different time periods may be different, for example, the preset time may be shorter during peak hours of meals, for example, the preset time may be 10s, 1min, 10min, etc. in noon or evening; for example, the preset time may be longer during the low peak of the meal, such as in the morning or late night, the preset time may be 1h, 2h, etc.

When the sensing system 200 is in a standby state, the processor 280 may store the sensor data and the recognition result; when the sensing system 200 is awakened, the processor 280 may delete the transmitted recognition result and the corresponding sensor data after transmitting the stored recognition result to the control terminal 300.

In some embodiments, the perception system 200 may also include at least one storage medium (not shown in fig. 2). The storage medium may include a data storage device that may be used to store the recognition model and at least one instruction set. The instructions are computer program code that may include programs, routines, objects, components, data structures, procedures, modules, etc. that perform the method P100 of passenger flow statistics provided herein.

The wireless communication module 240 may be electrically connected to the at least one processor 280. When the sensing system 200 works, the wireless communication connection can be established with the control terminal 300 through the wireless communication module 240, so that wiring is avoided, cost is reduced, manpower is saved, and appearance attractiveness is improved. The wireless communication may be WiFi communication, bluetooth communication, NFC communication, optical communication, or the like. The sensing system 200 may send the identification result corresponding to the sensing system 200 to the control terminal 300 through the wireless communication module 240.

As shown in fig. 2, the power module 260 may be electrically connected to at least one sensing sensor 220 and at least one sensor 280 to power the entire sensing system 200. The power module 260 may be battery powered, may be wire powered, may be self-powered, and is not limited herein. Wherein, battery power supply and self-powered can realize that the equipment installation exempts from the wiring, increases the aesthetic property when saving the cost, uses more convenient, and the popularization is easier, reduces the deployment and the service condition of system 001.

Fig. 5 shows a schematic device diagram of a control terminal 300. The control terminal 300 may perform the method P200 of passenger flow statistics described in the present specification. The passenger flow statistics method P200 is described elsewhere in this specification. The device schematic shown in fig. 5 may be used for the local device 301 and also for the cloud device 302.

As shown in fig. 5, the control terminal 300 may include at least one storage medium 330 and at least one processor 320. In some embodiments, the control terminal 300 may also include a communication port 350 and an internal communication bus 310.

Internal communication bus 310 may connect the different system components including storage medium 330, processor 320, and communication ports 350.

The communication port 350 is used for controlling data communication between the terminal 300 and the outside, for example, the communication port 350 may be used for controlling data communication between the terminal 300 and the plurality of sensing systems 200. The communication port 350 may also be used for data communication between the local device 301 and the cloud device 302. The communication port 350 may be a wired communication port or a wireless communication port. In this specification, the communication port 350 is described as an example of a wireless communication port. The control terminal 300 receives the recognition results of the plurality of sensing systems 200 through the wireless communication connection.

Storage medium 330 may include a data storage device. The data storage device may be a non-transitory storage medium or a transitory storage medium. For example, the data storage device may include one or more of a magnetic disk 332, a read-only storage medium (ROM) 334, or a random access storage medium (RAM) 336. The storage medium 330 also includes at least one set of instructions stored in the data storage device. The instructions are computer program code that may include programs, routines, objects, components, data structures, procedures, modules, etc. that perform the methods of passenger flow statistics provided herein.

The at least one processor 320 may be communicatively coupled with at least one storage medium 330 and a communication port 350 via an internal communication bus 310. The at least one processor 320 is configured to execute the at least one instruction set. When the system 001 is running, the at least one processor 320 reads the at least one instruction set, and obtains the recognition results of the plurality of sensing systems 200 through the communication port 350 according to the instruction of the at least one instruction set, and performs the method of passenger flow statistics provided in the present specification. The processor 320 may perform all the steps involved in the method of passenger flow statistics. Processor 320 may be in the form of one or more processors, in some embodiments processor 320 may include one or more hardware processors, such as microcontrollers, microprocessors, reduced Instruction Set Computers (RISC), application Specific Integrated Circuits (ASIC), application specific instruction set processors (ASIP), central Processing Units (CPU), graphics Processing Units (GPU), physical Processing Units (PPU), microcontroller units, digital Signal Processors (DSP), field Programmable Gate Arrays (FPGA), advanced RISC Machines (ARM), programmable Logic Devices (PLD), any circuit or processor capable of executing one or more functions, or the like, or any combination thereof. For illustrative purposes only, only one processor 320 is depicted in the control terminal 300 in this specification. However, it should be noted that the control terminal 300 in the present specification may further include a plurality of processors, and thus, the operations and/or method steps disclosed in the present specification may be performed by one processor as described in the present specification, or may be performed by a plurality of processors in combination. For example, if the processor 320 of the control terminal 300 performs steps a and B in this specification, it should be understood that steps a and B may also be performed by two different processors 320 in combination or separately (e.g., a first processor performs step a, a second processor performs step B, or the first and second processors perform steps a and B together).

It should be noted that, when the local device 301 does not have the computing capability, the local device 301 may not include the processor 320.

Fig. 6 shows a flowchart of a method P100 for passenger flow statistics provided according to an embodiment of the present description. The passenger flow statistics method P100 is applied to a passenger flow statistics system 001. The system 001 for passenger flow statistics may perform the method P100 for passenger flow statistics provided in the present specification. Specifically, the at least one processor 280 may read a set of instructions stored in its local storage medium and then execute the method P100 of passenger flow statistics provided herein according to the specification of the set of instructions. The method P100 may include performing, by the at least one processor 280:

s120: the processor 280 obtains sensor data for each of the at least one sensor 220 in the corresponding sensing system 200.

As previously described, each of the sensing sensors 220 may generate sensor data in real time, which may be acquired by the processor 280 in real time.

S140: the processor 280 performs in-vivo detection of the sensor data of each of the sensing sensors 220 based on the recognition model, and generates the recognition result of each of the sensing sensors 220.

Specifically, step S140 may be: determining a mapping relationship between signal frequency and signal amplitude of the sensor data within a preset time window based on the sensor data of each sensing sensor 220; and performing living body detection on the mapping relation based on the identification model, and determining whether the living body user is perceived in the monitoring range of the perception sensor 220 corresponding to each sensor data.

The preset time window includes a preset duration prior to the current time. Specifically, when the processor 280 performs living detection on the sensor data, it may intercept sensor data in a time window from the current time to the past time (i.e., the preset time window), input the sensor data in the time window into the recognition model, and output the recognition result by the recognition model, where the recognition result is whether there is a living user in the detection range of the current sensing sensor 220.

The output signal of at least one of the sensing sensors 220 contains erroneous interfering signals due to the unavoidable presence of various forms of interference in the environment. Fig. 7 shows a schematic diagram of an effective signal and an interference signal provided according to an embodiment of the present specification. As shown in fig. 7, the horizontal axis represents time t, and the vertical axis represents the output signal amplitude a of the sensor 220. The effective signal 5, in which the motion of the user is sensed by the sensing sensor 220, overlaps with the disturbing signal 6. To ensure accuracy of the passenger flow statistics, the processor 280 may filter the sensor data. Specifically, before the living body detection of the sensor data, step S140 may further include: the processor 280 filters the sensor data of each of the sensing sensors 220 to eliminate the interference signal 6; and performing the living body detection based on the filtered sensor data and the preset identification model.

S160: the processor 280 transmits the recognition result of each of the sensing sensors 220 to the control terminal 300.

As previously described, the recognition results may be stored in the processor 280. The control terminal 300 may acquire the recognition result from the processor 280 at predetermined intervals. Specifically, step S160 may include: the processor 280 receives a wake-up signal transmitted from the control terminal 300 to the processor 280 based on a preset time period; the processor 280 transmits the recognition result to the control terminal 300 after receiving the wake-up signal.

Fig. 8 shows a flowchart of a method P200 for passenger flow statistics provided according to an embodiment of the present disclosure. The passenger flow statistics method P200 is applied to the control terminal 300. Specifically, the processor 320 in the control terminal 300 may read the instruction set stored in its local storage medium, and then execute the method P200 of passenger flow statistics provided in the present specification according to the specification of the instruction set. The method P200 may include performing, by the control terminal 300:

s220: the control terminal 300 obtains the identification result of each of the sensing sensors 220 in each of the sensing systems 200.

As previously described, a plurality of sensing systems 220 may be included in the detected region 003. The control terminal 300 may be in communication with each of the sensing systems 200, and may acquire an identification result corresponding to each of the at least one sensing sensor 220 in each of the sensing systems 200. As previously described, the recognition results may be stored in the processor 280. The control terminal 300 may acquire the recognition result from the processor 280 at predetermined intervals. Specifically, step S220 may include: the control terminal 300 obtains the identification result corresponding to each of the sensing sensors 220 based on a preset time period. That is, the control terminal 300 may transmit a wake-up signal to each of the sensing systems 200 at intervals of the preset time; after receiving the wake-up signal, the sensing system 200 sends the identification result to the control terminal 300.

S240: the control terminal 300 determines the total number of users in the detected area based on the recognition result.

Specifically, step S240 may include:

s242: the control terminal 300 determines the number of living users in each target area 202 of the plurality of target areas 202 based on the recognition result of each of the sensing sensors 220.

As described above, at most one living user can be within the detection range of each of the sensing sensors 220. The recognition result of each of the sensing sensors 220 includes whether or not there is a living user within the detection range corresponding to the current sensing sensor 220. When the identification result of the sensing sensor 220 is that there is a living user in the monitoring range of the current sensing sensor 220, it represents that there is a living user in the detection range of the current sensing sensor 220. The control terminal 300 may determine the number of living users in each target area 202 according to the recognition result of each of the sensing sensors 220 in each of the sensing systems 200.

S244: the control terminal 300 determines the total number of users based on the number of living users in each target area 202.

Specifically, the control terminal 300 may superimpose the number of living users in each of the plurality of target areas 202 to determine the total number of users. In some embodiments, the control terminal 300 may also adjust the number of users after stacking, that is, by adjusting the adjustment coefficient, to eliminate errors caused by stacking or spacing of the target area 202. The adjustment coefficient may be obtained by machine learning.

In summary, the passenger flow statistics methods P100, P200 and the system 001 provided in the present disclosure can monitor a plurality of target areas 202 in the detected area 002 through a plurality of sensing systems 200 in the detected area 003. Each sensing system 200 monitors passenger flow in the corresponding target area 202 through at least one sensing sensor 220, performs living detection on each sensor data through a preset identification model, and generates an identification result to determine whether a living user exists in a detection range corresponding to each sensing sensor 220. The control terminal 300 calculates the passenger flow volume in the target area according to the identification result. The system 001, the method P100 and the method P200 can obtain the absolute value of the passenger flow in the detected region 003 by simultaneously monitoring the passenger flow in a plurality of target regions 202 in the detected region 003 instead of measuring the relative value of the passenger flow, and obtain the total passenger flow by accumulation calculation, thereby avoiding error accumulation and improving the accuracy of passenger flow statistics. Meanwhile, the system 001, the method P100 and the method P200 may perform living detection on the sensor data of each sensing sensor 220 to determine that the target object detected by the sensing sensor 220 is a living user, so as to avoid that non-living users are counted into the passenger flow volume, and improve the accuracy of passenger flow statistics.

Another aspect of the present disclosure provides a non-transitory storage medium storing at least one set of executable instructions for passenger flow statistics, which when executed by a processor, direct the processor 280 to implement the steps of the method P100 for passenger flow statistics described herein. In some possible implementations, aspects of the specification can also be implemented in the form of a program product including program code. The program code is for causing the processor 280 to perform the steps of passenger flow statistics described herein when the program product is run on the perception system 200. The program product for implementing the above-described method may employ a portable compact disc read only memory (CD-ROM) comprising program code and may run on the perception system 200. However, the program product of this specification is not limited thereto, and in this specification, the readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system (e.g., processor 280). The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations of the present specification may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the sensing system 200, partly on the sensing system 200, as a stand-alone software package, partly on the sensing system 200, partly on a remote computing device, or entirely on the remote computing device.

Another aspect of the present disclosure provides a non-transitory storage medium storing at least one set of executable instructions for passenger flow statistics, which when executed by a processor, direct the processor 320 to implement the steps of the method P200 of passenger flow statistics described herein. In some possible implementations, aspects of the specification can also be implemented in the form of a program product including program code. The program code is for causing the processor 320 to perform the steps of passenger flow statistics described in this specification when the program product is run on the control terminal 300. The program product for implementing the above-described method may employ a portable compact disc read only memory (CD-ROM) including program code and may be run on the control terminal 300. However, the program product of this specification is not limited thereto, and in this specification, the readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system (e.g., processor 320). The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations of the present specification may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the control terminal 300, partly on the control terminal 300, as a stand-alone software package, partly on the control terminal 300, partly on a remote computing device, or entirely on the remote computing device.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In view of the foregoing, it will be evident to a person skilled in the art that the foregoing detailed disclosure may be presented by way of example only and may not be limiting. Although not explicitly described herein, those skilled in the art will appreciate that the present description is intended to encompass various adaptations, improvements, and modifications of the embodiments. Such alterations, improvements, and modifications are intended to be proposed by this specification, and are intended to be within the spirit and scope of the exemplary embodiments of this specification.

Furthermore, certain terms in the present description have been used to describe embodiments of the present description. For example, "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present description. Thus, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined as suitable in one or more embodiments of the invention.

It should be appreciated that in the foregoing description of embodiments of the present specification, various features have been combined in a single embodiment, the accompanying drawings, or description thereof for the purpose of simplifying the specification in order to assist in understanding one feature. However, this is not to say that a combination of these features is necessary, and it is entirely possible for a person skilled in the art to extract some of them as separate embodiments to understand them upon reading this description. That is, embodiments in this specification may also be understood as an integration of multiple secondary embodiments. While each secondary embodiment is satisfied by less than all of the features of a single foregoing disclosed embodiment.

Each patent, patent application, publication of patent application, and other materials, such as articles, books, specifications, publications, documents, articles, etc., cited herein are hereby incorporated by reference. The entire contents for all purposes, except for any prosecution file history associated therewith, may be any identical prosecution file history inconsistent or conflicting with this file, or any identical prosecution file history which may have a limiting influence on the broadest scope of the claims. Now or later in association with this document. For example, if there is any inconsistency or conflict between the description, definition, and/or use of terms associated with any of the incorporated materials, the terms in the present document shall prevail.

Finally, it is to be understood that the embodiments of the application disclosed herein are illustrative of the principles of the embodiments of the present specification. Other modified embodiments are also within the scope of this specification. Accordingly, the embodiments disclosed herein are by way of example only and not limitation. Those skilled in the art can adopt alternative arrangements to implement the application in the specification based on the embodiments in the specification. Therefore, the embodiments of the present specification are not limited to the embodiments precisely described in the application.

Claims

1. A system of passenger flow statistics, comprising:

a plurality of perception systems distributed over a plurality of target areas within a region to be examined, each of the plurality of perception systems comprising:

at least one sensing sensor mounted at an arbitrary position of the detected area, each of the at least one sensing sensors being operative to monitor whether a target object is present within a corresponding monitoring range and to generate sensor data, the target object including a living user and a non-living user, the sensor data including a signal frequency and a signal amplitude; and

the processor is used for receiving the sensor data sent by each sensing sensor, performing living detection on the sensor data based on a preset identification model, generating an identification result to determine whether the target object is the living user or not, wherein the identification model is obtained by training based on sample data corresponding to the living user and sample data corresponding to the non-living user, and the sample data comprises a mapping relation between signal frequency and signal amplitude in the sensor data; and

The control terminal is respectively in communication connection with each sensing system when in operation, acquires the identification result of each sensing sensor in each sensing system, and determines the total number of users in the detected area based on the identification result.

2. The system of passenger flow statistics of claim 1, wherein the plurality of target areas at least partially cover the detected area.

3. The system of passenger flow statistics of claim 1, wherein the area under test is a dining area of a restaurant, and the monitoring range corresponding to each sensor comprises a dining chair.

4. The system of passenger flow statistics of claim 1, wherein the in-vivo detection of the sensor data based on a preset recognition model comprises:

determining the mapping relation between the signal frequency and the signal amplitude of the sensor data in a preset time window; and

and performing living body detection on the mapping relation based on the identification model, and determining whether the sensing sensor corresponding to the sensor data senses the living body user.

5. The system of passenger flow statistics of claim 4, wherein the preset time window comprises a preset duration prior to the current time.

6. A system of passenger flow statistics as recited in claim 1, wherein at most one of said living users is within a monitoring range of each of said sensing sensors.

7. The system of passenger flow statistics of claim 1, wherein the determining the total number of users within the area to be treated comprises:

determining a number of living users in each of the plurality of target areas based on the recognition result of each of the sensing sensors; and

the total number of users is determined based on the number of living users in each target area.

8. The system for passenger flow statistics of claim 1, wherein each of the perception systems further comprises:

and the wireless communication module is used for establishing communication connection with the control terminal during working, and the communication connection comprises wireless communication connection.

9. The system for passenger flow statistics of claim 1, wherein each of the perception systems further comprises:

and the power supply module is electrically connected with the at least one sensing sensor and the at least one processor in operation.

10. The system of passenger flow statistics of claim 1, wherein the obtaining the identification of the each of the sensing sensors in the each sensing system comprises:

And acquiring the identification result of each sensing sensor based on a preset time period.

11. The system of passenger flow statistics of claim 1, wherein the at least one sensing sensor comprises at least one of at least one radar sensor, at least one infrared sensor, at least one pressure sensor, at least one temperature sensor, at least one vibration sensor, and at least one electric field sensor.

12. A method of passenger flow statistics, for use in the system of passenger flow statistics of claim 1, comprising performing, by the at least one processor:

acquiring the sensor data of each sensing sensor in a corresponding sensing system, wherein the sensor data comprises signal frequency and signal amplitude;

performing living body detection on the sensor data of each sensing sensor based on the identification model, and generating the identification result of each sensing sensor, wherein the identification model is obtained by training based on sample data corresponding to the living body user and sample data corresponding to the non-living body user, and the sample data comprises a mapping relation between signal frequency and signal amplitude in the sensor data; and

And sending the identification result of each sensing sensor to the control terminal.

13. The method of passenger flow statistics of claim 12, wherein the plurality of target areas at least partially cover the detected area.

14. A method of passenger flow statistics as recited in claim 12 wherein the area under test is a dining area of a restaurant and the corresponding monitoring range of each sensor comprises a dining chair.

15. The method of passenger flow statistics of claim 12, wherein the in-vivo detection of the sensor data of each of the perception sensors based on the recognition model comprises:

16. A method of passenger flow statistics as recited in claim 15, wherein the preset time window comprises a preset duration prior to the current time.

17. A method of passenger flow statistics as recited in claim 12, wherein there is at most one of said living users within the monitoring range of each of said sensing sensors.

18. A method of passenger flow statistics according to claim 12, wherein said transmitting the identification of each of the sensor to the control terminal comprises:

and sending the identification result of each perception sensor to the control terminal based on a preset time period.

19. A method of traffic statistics, applied to the system of traffic statistics of claim 1, comprising performing, by the control terminal:

acquiring the identification result of each sensing sensor in each sensing system; and

and determining the total number of users in the detected area based on the identification result.

20. The method of passenger flow statistics of claim 19, wherein the determining the total number of users within the area to be detected comprises:

21. The method of passenger flow statistics of claim 19, wherein the obtaining the identification of the each of the sensing sensors in the each sensing system comprises: