Vehicle behavior detection method, device and equipment

Abstract

The present application provides a vehicle behavior detection method, apparatus, and device. The method includes: determining the presence of a target vehicle in a parking area based on first distance data; determining an estimated distance between the target vehicle and a distance detection sensor based on the first distance data; if the difference between the estimated distance and the target distance is greater than a distance threshold, sending a wake-up command to an image acquisition sensor so that the image acquisition sensor acquires and stores a first image of the parking area and turns off the image acquisition sensor; after a preset time interval, acquiring second distance data of the parking area via the distance detection sensor; if it is determined based on the second distance data that the target vehicle does not exist in the parking area, determining that the target vehicle has left the parking area, and sending a wake-up command to the image acquisition sensor so that the image acquisition sensor sends the first image to a management device. The technical solution of the present application eliminates the need to turn on the image acquisition sensor in real time, thus saving power resources.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation, and in particular to a vehicle behavior detection method, device and equipment. Background Art

[0002] As human society continues to develop, cities will increasingly accommodate a growing population. To achieve sustainable urban development and enhance their overall competitiveness, building smart cities is imperative. The concept of "intelligence" is to enable humans to manage production and daily life in a more refined and flexible manner through the application of next-generation information technology. By embedding or equipping sensors in various facilities, such as power supply systems, water supply systems, and transportation systems, the resulting Internet of Things (IoT) will be connected to the internet, integrating human society with physical systems. By integrating the IoT through computers and cloud computing, smart cities can be realized.

[0003] Smart transportation is a crucial component of smart cities. The essence of smart transportation lies in vehicle management, such as the management of moving and parked vehicles. For the management of moving vehicles, cameras can be used to capture vehicle images and analyze vehicle behavior based on these images to achieve vehicle management.

[0004] For the management of parked vehicles, such as those within a parking area, manual detection can be used to detect when a vehicle enters or leaves the parking area, and then charge the vehicle. However, this method requires the presence of staff, and when staff are not on site, vehicle charges cannot be processed.

[0005] Alternatively, cameras can be deployed to capture real-time images of the parking area. Based on these images, the camera can analyze when a vehicle enters or leaves the parking area, and then charge the vehicle. However, in this method, the camera must be always on to capture real-time images of the parking area. This means that the camera must be connected to a power supply system and powered by a power supply. This connection method is complex, requires multiple wires, and consumes a lot of power. Summary of the Invention

[0006] The present application provides a vehicle behavior detection method, which is applied to an Internet of Things device, wherein the Internet of Things device includes an image acquisition sensor and a distance detection sensor. The method includes:

[0007] collecting first distance data of the parking space area by the distance detection sensor;

[0008] If it is determined based on the first distance data that a target vehicle exists in the parking space area, determining an estimated distance between the target vehicle and the distance detection sensor based on the first distance data;

[0009] If the parking space area is in a parked state and the difference between the estimated distance and the target distance is greater than a distance threshold, sending a wake-up command to the image acquisition sensor to cause the image acquisition sensor to acquire and store a first image of the parking space area, and turning off the image acquisition sensor; wherein the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is parked in the parking space area;

[0010] After a preset time interval, the distance detection sensor collects second distance data of the parking area; if it is determined based on the second distance data that there is no target vehicle in the parking area, it is determined that the target vehicle has left the parking area, the status of the parking area is changed to no parked vehicle, and a wake-up command is sent to the image acquisition sensor to enable the image acquisition sensor to send the first image to the management device and turn off the image acquisition sensor.

[0011] The present application provides a vehicle behavior detection device, which is applied to an Internet of Things device. The Internet of Things device includes an image acquisition sensor and a distance detection sensor. The device includes:

[0012] an acquisition module, configured to acquire first distance data of a parking space area through the distance detection sensor;

[0013] a determination module configured to determine whether a target vehicle exists in the parking area based on the first distance data; and if it is determined that a target vehicle exists in the parking area based on the first distance data, determine an estimated distance between the target vehicle and the distance detection sensor based on the first distance data;

[0014] a sending module, configured to send a wake-up command to the image acquisition sensor, if the parking space area is in a parked state and the difference between the estimated distance value and the target distance is greater than a distance threshold, so that the image acquisition sensor acquires and stores a first image of the parking space area and turns off the image acquisition sensor; wherein the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is parked in the parking space area;

[0015] The acquisition module is further configured to acquire second distance data of the parking area through the distance detection sensor after a preset time interval; the determination module is further configured to, if it is determined based on the second distance data that the target vehicle does not exist in the parking area, determine that the target vehicle has left the parking area and change the state of the parking area to no parked vehicle;

[0016] The sending module is further configured to send a wake-up command to the image acquisition sensor, so that the image acquisition sensor sends the first image to the management device and turns off the image acquisition sensor.

[0017] The present application provides an Internet of Things device, which includes at least a processor, an image acquisition sensor, and a distance detection sensor, wherein: the distance detection sensor is used to collect first distance data of a parking area and send the first distance data to the processor; the processor is used to determine, based on the first distance data, an estimated distance between the target vehicle and the distance detection sensor if it is determined that a target vehicle exists in the parking area; if the parking area is in a parked state and the difference between the estimated distance and a target distance is greater than a distance threshold, send a wake-up command to the image acquisition sensor; wherein the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is already parked in the parking area; the image acquisition sensor is used to, after receiving the wake-up command, collect and store a first image of the parking area and turn off the image acquisition sensor;

[0018] The distance detection sensor is used to collect second distance data of the parking area after a preset time interval and send the second distance data to the processor; the processor is used to determine that the target vehicle has left the parking area if it is determined based on the second distance data that there is no target vehicle in the parking area, change the status of the parking area to no parked vehicle, and send a wake-up command to the image acquisition sensor; the image acquisition sensor is used to send the first image to the management device after receiving the wake-up command and turn off the image acquisition sensor.

[0019] As can be seen from the above technical solutions, in the embodiments of the present application, an image acquisition sensor and a distance detection sensor can be deployed on the Internet of Things device, and the distance data of the parking area can be collected based on the distance detection sensor. Based on the distance data, it is determined whether the target vehicle enters or leaves the parking area. Only when the target vehicle enters or leaves the parking area, a wake-up command is sent to the image acquisition sensor to enable the image acquisition sensor to collect an image of the parking area, and the image acquisition sensor is turned off after the image acquisition is completed, so that there is no need to turn on the image acquisition sensor in real time, saving power resources. Without turning on the image acquisition sensor in real time, it is also possible to collect images of the target vehicle entering or leaving the parking area, and based on these images, it is analyzed when the vehicle enters the parking area and when the vehicle leaves the parking area, and then the vehicle is charged. The above method does not require the Internet of Things device to be connected to the power supply system, and does not require the use of a power supply to power the Internet of Things device. The Internet of Things device can be powered by a battery, and the connection method of the Internet of Things device is relatively simple. Leveraging data processing technology, the system detects vehicle entry and exit behavior in parking areas, improving both accuracy and sensitivity. This significantly enhances various metrics for identifying vehicle entry and exit behavior, increasing both vehicle entry and exit accuracy and demonstrating strong anti-interference capabilities. Focusing on power consumption control, the system minimizes the operating hours of the image acquisition and distance detection sensors, maintaining overall power consumption and delivering superior vehicle entry and exit detection capabilities. By capturing images first and then verifying the data, the system ensures both timely image acquisition and accuracy, reducing false alarm rates in vehicle exit detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flowchart of a vehicle behavior detection method in one embodiment of the present application;

[0021] Figure 2A-2C is a schematic diagram of the deployment location of the Internet of Things devices in one embodiment of the present application;

[0022] Figure 3 It is a flowchart of a vehicle behavior detection method in one embodiment of the present application;

[0023] Figure 4 It is a flowchart of a vehicle behavior detection method in one embodiment of the present application;

[0024] Figure 5 is a processing schematic diagram of a data processing unit in one embodiment of the present application;

[0025] Figure 6 This is a processing diagram of a vehicle entry determination unit in one embodiment of the present application;

[0026] Figure 7is a processing schematic diagram of a distance calibration unit in one embodiment of the present application;

[0027] Figure 8 This is a processing diagram of a vehicle departure determination unit in one embodiment of the present application;

[0028] Figure 9 is a processing diagram of a timing selection unit in one embodiment of the present application;

[0029] Figure 10 This is a processing diagram of a state switching unit in one embodiment of the present application;

[0030] Figure 11 It is a structural diagram of a vehicle behavior detection device in one embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms "a," "the," and "the" used in this application and claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to any or all possible combinations of one or more associated listed items.

[0032] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" used may also be interpreted as "at the time of" or "when" or "in response to determining".

[0033] In an embodiment of the present application, a vehicle behavior detection method is proposed. This method can be applied to an IoT device, which may include an image acquisition sensor and a distance detection sensor. For example, the IoT device may be a battery-powered IoT device, or alternatively, a mains-powered IoT device. There is no restriction on the type of IoT device, as long as it can detect vehicle behavior. The IoT device may communicate with a management device via a wireless network, or alternatively, via a wired network, without restriction on this communication method.

[0034] For example, the IoT device can be located on the front side of the vehicle in the parking area, or the IoT device can be located on the rear side of the vehicle in the parking area, or the IoT device can be located between one parking area and another parking area. There is no restriction on the deployment location of the IoT device.

[0035] The image acquisition sensor may include a camera, and the type of the image acquisition sensor is not limited, as long as it can capture images of the parking area. The distance detection sensor may include an ultrasonic radar, and the type of the distance detection sensor is not limited, as long as it can capture distance data of the parking area.

[0036] See also Figure 1 FIG. 1 is a flow chart of a vehicle behavior detection method, which may include:

[0037] Step 101: collect first distance data of a parking area through a distance detection sensor.

[0038] Step 102 : If it is determined based on the first distance data that a target vehicle exists in the parking space area, an estimated distance between the target vehicle and the distance detection sensor is determined based on the first distance data.

[0039] For example, in determining the presence of a target vehicle in a parking area based on the first distance data, if the first distance data includes multiple distance values, the number of valid distance values ​​among the multiple distance values ​​can be determined. If the parking area is unparked and the number of valid distance values ​​is greater than a first threshold, the presence of the target vehicle in the parking area can be determined. If the parking area is parked and the number of valid distance values ​​is greater than a second threshold, the presence of the target vehicle in the parking area can be determined. In the above embodiment, the first threshold can be greater than the second threshold.

[0040] Exemplarily, in determining the estimated distance between the target vehicle and the distance detection sensor based on the first distance data, if the first distance data includes multiple distance values ​​collected by the distance detection sensor during a current acquisition cycle, the standard deviation of the multiple distance values ​​is determined, and a noise covariance is determined based on the standard deviation. Subsequently, a distance estimate for the current acquisition cycle is determined based on the initial estimate, the multiple distance values ​​from the current acquisition cycle, and the noise covariance. For example, if the parking area is unparked, the initial estimate may be a valid distance value from the multiple distance values. If the parking area is parked, the initial estimate may be a distance estimate from a previous acquisition cycle.

[0041] In one possible implementation, determining the noise covariance based on the standard deviation may include, but is not limited to: if the standard deviation is greater than a standard deviation threshold, determining the noise covariance based on the standard deviation and a preset weight, where the preset weight is a value between 0 and 1; and if the standard deviation is not greater than the standard deviation threshold, determining the noise covariance based on a fixed covariance. Exemplarily, the noise covariance determined based on the fixed covariance is smaller than the noise covariance determined based on the standard deviation and the preset weight.

[0042] Step 103: If the status of the parking area is that a vehicle is parked, and the difference between the distance estimate and the target distance is greater than the distance threshold, a wake-up command is sent to the image acquisition sensor to enable the image acquisition sensor to capture and store a first image of the parking area, and the image acquisition sensor is turned off.

[0043] In a possible implementation, the target distance may be used to indicate the distance between the target vehicle and the distance detection sensor when the target vehicle has been parked in the parking space area.

[0044] Step 104: After a preset time interval, the distance detection sensor collects second distance data of the parking area; if it is determined based on the second distance data that there is no target vehicle in the parking area, it is determined that the target vehicle has left the parking area, the state of the parking area is changed to no parked vehicle, and a wake-up command is sent to the image acquisition sensor to enable the image acquisition sensor to send the first image (i.e., the image stored by the image acquisition sensor) to the management device, and the image acquisition sensor is turned off after the first image is sent.

[0045] In one possible implementation, for step 104, after collecting the second distance data of the parking area through the distance detection sensor, if it is determined based on the second distance data that there is a target vehicle in the parking area (i.e., not that there is no target vehicle in the parking area), the status of the parking area can be maintained as a parked vehicle, and a wake-up command is sent to the image acquisition sensor to cause the image acquisition sensor to discard the first image (i.e., the image stored by the image acquisition sensor) and turn off the image acquisition sensor.

[0046] In one possible implementation, for step 102, after determining the estimated distance between the target vehicle and the distance detection sensor based on the first distance data, if the status of the parking area is that there is no parked vehicle (i.e., not a parked vehicle) and the estimated distance is within the vehicle entry distance range, it is determined that the target vehicle has entered the parking area, the status of the parking area is changed to a parked vehicle, and a wake-up command is sent to the image acquisition sensor so that the image acquisition sensor captures a second image of the parking area, sends the second image to the management device, and turns off the image acquisition sensor after the second image is sent.

[0047] For example, if the estimated distance value is within the vehicle entry distance range, then the target vehicle is determined to have entered the parking space. This may include, but is not limited to: after determining the estimated distance value based on the first distance data, storing the estimated distance value in a previously created data buffer pool. The data buffer pool may be used to store the estimated distance values ​​for the most recent N acquisition cycles. Based on this, if the estimated distance values ​​for the N acquisition cycles in the data buffer pool are all within the vehicle entry distance range, then the target vehicle is determined to have entered the parking space.

[0048] In one possible implementation, with respect to step 101, collecting first distance data of the parking area using a distance detection sensor may include, but is not limited to: determining a collection time based on a data collection interval of the distance detection sensor, waking up the distance detection sensor at each collection time, collecting first distance data of the parking area using the distance detection sensor, and deactivating the distance detection sensor after completing the collection of the first distance data. For example, if the parking area is not parked, the data collection interval may be a first interval; if the parking area is parked and the target distance between the target vehicle and the distance detection sensor has not been acquired, the data collection interval may be a second interval; if the parking area is parked and the target distance between the target vehicle and the distance detection sensor has been acquired, the data collection interval may be a third interval. In the above embodiment, the first interval may be greater than the third interval, the third interval may be greater than or equal to the second interval, and the third interval is inversely proportional to the target distance, i.e., the greater the target distance, the smaller the third interval, and the smaller the target distance, the larger the third interval.

[0049] In one possible implementation, after determining that the target vehicle has entered the parking space area (if the status of the parking space area is that there is no parked vehicle and the distance estimation value is within the vehicle entry distance range, then it is determined that the target vehicle has entered the parking space area), the target distance between the target vehicle and the distance detection sensor can be obtained. The method for obtaining the target distance may include but is not limited to: after determining the distance estimation value based on the first distance data, the distance estimation value is stored in a created data buffer pool, and the data buffer pool is used to store the distance estimation values ​​of M acquisition cycles; if the distance estimation values ​​in the data buffer pool reach M, then the maximum distance estimation value among the M distance estimation values ​​in the data buffer pool is determined as the target distance.

[0050] For example, after each distance estimate is stored in a created data buffer pool, it can be determined whether the data buffer pool has reached M distance estimates. If not, the distance estimate (i.e., the distance estimate obtained based on the next set of first distance data) is stored in the data buffer pool, and so on. If so, the largest distance estimate can be selected from the distance estimates of the M acquisition cycles in the data buffer pool and determined as the target distance.

[0051] As can be seen from the above technical solutions, in the embodiments of the present application, an image acquisition sensor and a distance detection sensor can be deployed on the Internet of Things device, and the distance data of the parking area can be collected based on the distance detection sensor. Based on the distance data, it is determined whether the target vehicle enters or leaves the parking area. Only when the target vehicle enters or leaves the parking area, a wake-up command is sent to the image acquisition sensor to enable the image acquisition sensor to collect an image of the parking area, and the image acquisition sensor is turned off after the image acquisition is completed, so that there is no need to turn on the image acquisition sensor in real time, saving power resources. Without turning on the image acquisition sensor in real time, it is also possible to collect images of the target vehicle entering or leaving the parking area, and based on these images, it is analyzed when the vehicle enters the parking area and when the vehicle leaves the parking area, and then the vehicle is charged. The above method does not require the Internet of Things device to be connected to the power supply system, and does not require the use of a power supply to power the Internet of Things device. The Internet of Things device can be powered by a battery, and the connection method of the Internet of Things device is relatively simple. Leveraging data processing technology, the system detects vehicle entry and exit behavior in parking areas, improving both accuracy and sensitivity. This significantly enhances various metrics for identifying vehicle entry and exit behavior, increasing both vehicle entry and exit accuracy and demonstrating strong anti-interference capabilities. Focusing on power consumption control, the system minimizes the operating hours of the image acquisition and distance detection sensors, maintaining overall power consumption and delivering superior vehicle entry and exit detection capabilities. By capturing images first and then verifying the data, the system ensures both timely image acquisition and accuracy, reducing false alarm rates in vehicle exit detection.

[0052] The above technical solutions of the embodiments of the present application are described below in conjunction with specific application scenarios.

[0053] The IoT device may include an MCU (Micro Controller Unit), an image acquisition sensor, and a distance detection sensor. The image acquisition sensor may be turned on when the target vehicle enters or leaves the parking area, rather than in real time. This saves power resources and power consumption on the basis of being able to collect images of the target vehicle entering or leaving the parking area. The distance detection sensor may be turned on when it is necessary to collect distance data, rather than in real time. This saves power resources and power consumption on the basis of being able to collect distance data. The MCU may be turned on in real time and decide whether the distance detection sensor is appropriate to be turned on. When the distance detection sensor needs to be turned on, the distance detection sensor is triggered to be turned on and to collect distance data. Based on the distance data collected by the distance detection sensor, the MCU may decide whether the image acquisition sensor is appropriate to be turned on. When the image acquisition sensor needs to be turned on, the image acquisition sensor is triggered to be turned on and to collect images.

[0054] The image acquisition sensor may include a camera, and the type of the image acquisition sensor is not limited, as long as it can capture images of the parking area. The distance detection sensor may include an ultrasonic radar, and the type of the distance detection sensor is not limited, as long as it can capture distance data of the parking area.

[0055] In summary, in the embodiments of the present application, IoT devices can detect vehicle entry and exit behaviors using ultrasonic radar and cameras, and with the help of data processing technology, detect vehicle entry and exit behaviors in parking areas (such as curbside parking spaces, etc.). Vehicle entry behavior is the behavior of a target vehicle entering a parking area (such as a curbside parking space), and vehicle exit behavior is the behavior of a target vehicle leaving a parking area (such as a curbside parking space).

[0056] For example, ultrasonic radar collects distance data from a parking area and detects the entry or exit of a target vehicle based on this distance data. When the target vehicle enters or leaves the parking area, a camera captures an image of the target vehicle, and based on this image, it is possible to determine when the target vehicle entered or left the parking area.

[0057] For example, since the image acquisition sensor and the distance detection sensor are not turned on in real time, only the MCU is turned on in real time, the power consumption of the IoT device is relatively low, so it can be powered by a battery instead of a power supply. In other words, the IoT device can be a battery-powered IoT device. Of course, the IoT device can also be a power-powered IoT device, and there is no restriction on this.

[0058] For example, to simplify the deployment environment of IoT devices and avoid complex wiring scenarios, IoT devices can communicate with management devices via a wireless network. Specifically, the IoT devices can send images to the management device via the wireless network, which then stores the images. Of course, IoT devices can also communicate with management devices via a wired network. This communication method is not limited.

[0059] See also Figure 2A The figure shows the deployment location of IoT devices. IoT devices can be located at the front of the vehicle in the parking area. That is, the ultrasonic radar (i.e., the distance detection sensor) collects the distance data of the front position of the vehicle in the parking area. Alternatively, see Figure 2B As shown, the IoT device can also be located at the rear side of the vehicle in the parking area, that is, the ultrasonic radar collects distance data of the rear position of the vehicle in the parking area. Alternatively, see Figure 2C As shown, the IoT device can also be located in the middle of the two parking areas, that is, the two IoT devices are integrated and deployed. Each IoT device includes an MCU, a camera, and an ultrasonic radar. The ultrasonic radar of the left IoT device collects distance data of the rear position of the vehicle in the left parking area, and the ultrasonic radar of the right IoT device collects distance data of the front position of the vehicle in the right parking area.

[0060] In actual applications, the relative horizontal angle between the main detection direction of the ultrasonic radar and the driving direction needs to be between 0 degrees and 180 degrees. As long as this relative relationship condition is met, there is no need to distinguish the specific usage scenarios of parking spaces. For example, the relative horizontal angle between the main detection direction of the ultrasonic radar and the driving direction is between 0 degrees and 45 degrees, and the ultrasonic radar is installed on the rear side of the vehicle. Figure 2B Alternatively, the relative horizontal angle between the main detection direction of the ultrasonic radar and the driving direction is between 135 degrees and 180 degrees, and the ultrasonic radar is installed on the front side of the vehicle. Figure 2A Alternatively, two ultrasonic radars are installed at the center line of adjacent parking spaces, that is, two ultrasonic radars are integrated and deployed, with the main detection direction of one ultrasonic radar at a relative horizontal angle to the driving direction of 0 degrees to 45 degrees, and the main detection direction of the other ultrasonic radar at a relative horizontal angle to the driving direction of 135 degrees to 180 degrees, see Figure 2C shown.

[0061] In the embodiment of the present application, the ultrasonic radar is a distance detection sensor that is based on the principle of ultrasonic ranging and can return the distance value of the nearest obstacle within the detection range, that is, the ultrasonic radar returns the distance value of the nearest obstacle to itself. Assuming that the ranging range of the ultrasonic radar is 0-255cm, when there is no obstacle blocking the ranging range, that is, the distance value of the nearest obstacle to the ultrasonic radar is greater than 255cm, the distance value returned by the ultrasonic radar is 0. When there is an obstacle blocking the ranging range, that is, the distance value of the nearest obstacle to the ultrasonic radar is not greater than 255cm, the distance value returned by the ultrasonic radar is between 0-255cm. Assuming that the distance value returned by the ultrasonic radar is 100cm, it means that the distance value of the nearest obstacle to the ultrasonic radar is 100cm. Of course, the above values ​​are only examples and are not limited to these values.

[0062] In the above application scenario, a vehicle behavior detection method is proposed in an embodiment of the present application. The vehicle behavior detection method can be applied to Internet of Things devices. The image acquisition sensor collects images of the target vehicle when it enters or leaves the parking area, and sends the images to a management device (such as a cloud platform, etc.). The distance detection sensor collects distance data of the parking area, and the MCU implements the data processing flow of the vehicle behavior detection method. For example, based on the distance data of the parking area, the MCU identifies the moment when the target vehicle enters or leaves the parking area, and triggers the image acquisition sensor to collect images of the parking area.

[0063] In one possible implementation, the MCU may include, but is not limited to, the following functional units: a data processing unit, a vehicle entry determination unit, a timing selection unit, a distance calibration unit, a vehicle exit determination unit, and a state switching unit. Of course, the above functional units are merely examples, and a functional unit may be split into more functional units, or multiple functional units may be combined into a single functional unit. This is not a limitation.

[0064] The state switching unit is used to maintain the state of the parking area, which can be a state where a vehicle is parked (i.e., a parked state) or an unparked vehicle (i.e., an empty state). A parked vehicle indicates that a vehicle is already parked in the parking area. For example, when the vehicle entry determination unit determines that the target vehicle has entered the parking area (i.e., there is a vehicle entry behavior), the state of the parking area is marked. For example, the mark can be a first value, indicating that the state of the parking area is a parked vehicle. An unparked vehicle indicates that no vehicle is parked in the parking area. For example, when the vehicle exit determination unit determines that the target vehicle has left the parking area (i.e., there is a vehicle exit behavior), or when there is no vehicle entry behavior temporarily, the state of the parking area is marked. For example, the mark can be a second value, indicating that the state of the parking area is an unparked vehicle.

[0065] The data processing unit is used to obtain distance data of the parking space area from the distance detection sensor, and determine an estimated distance value between the target vehicle and the distance detection sensor at a current moment based on the distance data.

[0066] The timing selection unit is used to determine the data collection interval of the distance detection sensor, and determine the collection time based on the data collection interval, and trigger the distance detection sensor to collect distance data at each collection time.

[0067] The vehicle entry determination unit is used to determine whether the target vehicle enters the parking space area based on the distance estimation value, that is, whether there is a vehicle entry behavior. If so, the image acquisition sensor is triggered to collect an image of the parking space area.

[0068] The vehicle exit determination unit is used to determine whether the target vehicle has left the parking area based on the distance estimation value, that is, whether there is a vehicle exit behavior. If so, the image acquisition sensor is triggered to collect an image of the parking area.

[0069] The distance calibration unit is used to calibrate the target distance between the target vehicle and the distance detection sensor based on the distance estimation value and store the target distance. The target distance is used to represent the distance between the target vehicle and the distance detection sensor when the target vehicle has been parked in the parking space area (i.e., no longer moving).

[0070] In a possible implementation, the flow chart of the vehicle behavior detection method can be found in Figure 3 shown.

[0071] In step 301, a distance detection sensor collects distance data of a parking area. A data processing unit obtains the distance data from the distance detection sensor. If a target vehicle is determined to be present in the parking area based on the distance data, an estimated distance between the target vehicle and the distance detection sensor is determined based on the distance data.

[0072] In step 302, when the parking area is empty, the data processing unit inputs the estimated distance value to the vehicle entry determination unit. Based on the estimated distance value, the vehicle entry determination unit determines whether the target vehicle has entered the parking area, that is, whether there has been a vehicle entry. If so, step 303 is executed. If not, the data processing unit waits for the next acquisition moment, where it reacquires distance data and determines a distance estimate based on the distance data. This distance estimate is then input to the vehicle entry determination unit, which then re-determines whether the target vehicle has entered the parking area based on the estimated distance value. This process continues in this manner.

[0073] In step 303, when the vehicle entry determination unit determines that the target vehicle has entered the parking area, it sends a wake-up command to the image acquisition sensor. The image acquisition sensor captures an image of the parking area and sends the image to the management device. After the image is sent, the image acquisition sensor shuts down to conserve power. The state switching unit changes the state of the parking area to parked, switching from an unparked vehicle to a parked vehicle.

[0074] In step 304, the distance detection sensor collects distance data from the parking area. The data processing unit obtains multiple distance data from the distance detection sensor, determines a distance estimate based on each distance data, and inputs all obtained distance estimates to the distance calibration unit. The distance calibration unit determines the target distance between the target vehicle and the distance detection sensor based on the multiple distance estimates and stores the target distance.

[0075] Exemplarily, the distance calibration unit determines the most accurate distance at which the target vehicle is parked, that is, the target distance between the target vehicle and the distance detection sensor, by counting a preset number (e.g., 200) of distance estimates. This target distance is used as a distance threshold for the vehicle departure determination unit to reduce false alarms caused by data jitter.

[0076] In step 305, if the parking area is parked, the data processing unit inputs the estimated distance value to the exit determination unit. The exit determination unit then determines whether the target vehicle has left the parking area based on the estimated distance value, that is, whether an exit attempt has occurred. If so, step 306 is executed. If not, the data processing unit waits for the next acquisition moment, where it reacquires distance data, determines a distance estimate based on the distance data, and inputs the estimated distance estimate to the exit determination unit. The exit determination unit then re-determines whether the target vehicle has left the parking area based on the estimated distance value, and so on.

[0077] Exemplarily, the distance calibration unit may send the target distance to the vehicle exit determination unit. Based on the target distance, the vehicle exit determination unit determines whether the target vehicle leaves the parking space area based on the distance estimation value.

[0078] In step 306, when the vehicle exit determination unit determines that the target vehicle has left the parking area, it sends a wake-up command to the image acquisition sensor. The image acquisition sensor captures an image of the parking area and sends the image to the management device. After the image is sent, the image acquisition sensor shuts down to conserve device power. The state switching unit changes the state of the parking area to no parked vehicles, switching from parked to no parked vehicles.

[0079] To sum up, the complete process of a vehicle (referred to as the target vehicle) from entering the parking area to leaving the parking area is shown. When another vehicle (referred to as the target vehicle) enters the parking area to leave the parking area, the implementation process is shown in steps 301 to 306, which will not be repeated here.

[0080] Exemplarily, the timing selection unit can also determine the data collection interval of the distance detection sensor, and determine the collection time based on the data collection interval, triggering the distance detection sensor to collect distance data at each collection time, that is, the distance detection sensor collects distance data of the parking space area at each collection time.

[0081] In a possible implementation, the flow chart of the vehicle behavior detection method can be found in Figure 4 shown.

[0082] Step 401: The IoT device starts running.

[0083] In step 402, the data processing unit collects the distance data of the parking area collected by the distance detection sensor. The data processing unit determines the estimated distance between the target vehicle and the distance detection sensor based on the distance data. The estimated distance is used as the estimated distance at time t1, that is, the optimal estimated value of the obstacle ahead.

[0084] In step 403, the data processing unit inputs the estimated distance value at time t1 to the vehicle entry determination unit. Based on the estimated distance value, the vehicle entry determination unit determines whether the target vehicle has entered the parking space, that is, whether there has been a vehicle entry. If so, step 404 is executed. If not, the timing selection unit determines the next collection time, triggers the distance detection sensor to collect distance data at this time, and repeats step 402.

[0085] In step 404, when the vehicle entry determination unit determines that the target vehicle has entered the parking area, it triggers the image acquisition sensor to capture an image of the parking area and transmits the image and information about the vehicle's entry to the management device. After the image is transmitted, the image acquisition sensor is deactivated to conserve power. Furthermore, the state switching unit changes the parking area's status to "parked," switching it from "unparked" to "parked."

[0086] In step 405, the data processing unit collects the distance data of the parking area collected by the distance detection sensor, determines a distance estimate based on the distance data, and uses the distance estimate as the distance estimate at time t2. The distance estimate at time t2 (e.g., multiple distance estimates) is then input to the distance calibration unit. The distance calibration unit determines the target distance between the target vehicle and the distance detection sensor based on the distance estimate at time t2 and stores the target distance. The distance calibration unit calculates a preset number of distance estimates to determine the most accurate distance at which the target vehicle is parked, i.e., the target distance between the target vehicle and the distance detection sensor.

[0087] In step 406, the data processing unit inputs the estimated distance value at time t2 to the exit determination unit. The distance calibration unit inputs the target distance to the exit determination unit. The exit determination unit then determines whether the target vehicle has left the parking space based on the estimated distance value and the target distance, that is, whether an exit attempt has occurred. If so, step 407 is executed. If not, the timing selection unit determines the next collection time, triggers the distance detection sensor to collect distance data at this collection time, and repeats step 405.

[0088] In step 407, when the vehicle exit determination unit determines that the target vehicle has left the parking area, it triggers the image acquisition sensor to capture an image of the parking area and transmits the exit information and the image to the management device. After the image is transmitted, the image acquisition sensor is deactivated to conserve power. Furthermore, the state switching unit changes the state of the parking area from parked to unparked.

[0089] In summary, after the IoT device starts running, steps 402 to 407 can be executed cyclically to achieve cyclic detection of the entry and exit behaviors of all target vehicles in the parking area, which will not be repeated here.

[0090] In one possible implementation, the data processing unit may acquire distance data, determine whether a target vehicle exists in the parking space area based on the distance data, and determine an estimated distance between the target vehicle and the distance detection sensor based on the distance data. Figure 5 The figure shows a processing diagram of the data processing unit.

[0091] In step 501, the data processing unit obtains distance data of the parking area from the distance detection sensor. That is, each time the distance detection sensor collects distance data of the parking area, it sends the distance data to the data processing unit. The distance data includes multiple distance values, and the number of distance values ​​can be selected based on experience.

[0092] For example, since the distance detection sensor is the main part of power consumption, the working time of the distance detection sensor for collecting distance data at a time should be strictly limited to achieve a balance between accuracy and power consumption. Assuming that the distance detection sensor collects K distance values ​​within the working time of the distance detection sensor (such as 200ms), then these K distance values ​​are used as distance data. The distance data obtained by the data processing unit from the distance detection sensor each time includes K distance values, taking the case where K distance values ​​are 3 distance values ​​as an example.

[0093] For each distance value, assuming the distance detection sensor has a range of 0-255cm, if there are no obstacles within the range, the distance value is 0. If there are obstacles within the range, the distance value represents the distance to the obstacle closest to the distance detection sensor, and the distance value is within the range of 0-255cm.

[0094] In step 502, if the distance data includes multiple distance values, the data processing unit determines the number of valid distance values ​​among the multiple distance values. For example, for each distance value, if the distance value is 0, it indicates that the distance value is an invalid distance value. If the distance value is greater than an upper limit (e.g., 255 cm), it indicates that the distance value is an invalid distance value. If the distance value is between 0 and 255 cm, it indicates that the distance value is a valid distance value. In summary, the data processing unit can count the number of valid distance values ​​among the multiple distance values.

[0095] In step 503, the data processing unit determines whether the number of valid distance values ​​is greater than a threshold. If so, step 504 is executed. If not, the estimated distance between the target vehicle and the distance detection sensor is determined to be a preset value (e.g., 0). This preset value indicates that the target vehicle is not present in the parking area. Based on this estimated distance value, it can be determined that there is no vehicle entering or exiting the parking space, and the target vehicle is not present.

[0096] For example, the number threshold may be related to the status of the parking area. When the parking area is unparked, the number threshold may be a first number threshold. When the parking area is parked, the number threshold may be a second number threshold, with the first number threshold being greater than the second number threshold. In summary, if the parking area is unparked and the number of valid distance values ​​is greater than the first number threshold, then the target vehicle is present in the parking area. If the parking area is parked and the number of valid distance values ​​is greater than the second number threshold, then the target vehicle is present in the parking area.

[0097] For example, when the parking area is unoccupied, the first quantity threshold may be 2 / 3 of the total number of distance values ​​(i.e., K). If K is 3, the first quantity threshold is 2. When the parking area is parked, the second quantity threshold may be 1 / 3 of the total number of distance values ​​(i.e., K). If K is 3, the second quantity threshold is 1. Obviously, the first quantity threshold is greater than the second quantity threshold.

[0098] Of course, the above are just examples of the first quantity threshold and the second quantity threshold. As long as the first quantity threshold is less than or equal to the total number of distance values ​​K, the second quantity threshold is less than the total number of distance values ​​K, and the first quantity threshold is greater than the second quantity threshold, there is no restriction on the value of this quantity threshold.

[0099] Exemplarily, the reason for adopting the above-mentioned first and second thresholds is that: if the parking area is unparked, it is necessary to detect whether a target vehicle has entered the parking area. After entering the parking area, the target vehicle is stationary, and the object reflection received by the distance detection sensor is strong and the value is stable. Therefore, the first threshold number of distance values ​​is required to be a valid distance value, and the first threshold number is 2 / 3 of the total number of distance values ​​K. If the parking area is parked, it is necessary to detect whether a target vehicle has left the parking area. The target vehicle leaving the parking area is a rapid movement process, and the object reflection received by the distance detection sensor is weak, and the value stability is reduced. Therefore, the second threshold number of distance values ​​is required to be a valid distance value, and the second threshold number is 1 / 3 of the total number of distance values ​​K.

[0100] In step 504, if the distance data includes multiple distance values ​​collected by the distance detection sensor in the current collection period, such as K distance values, the data processing unit determines the standard deviation of the multiple distance values. For example, the standard deviation of the multiple distance values ​​can be determined using the following formula: In the above formula, S represents the standard deviation, K represents the total number of distance values, X represents the average value of all distance values, and X represents the distance between the two distances. i Represents the distance value. The standard deviation can reflect the stability of this set of distance data. The smaller the value, the higher the stability.

[0101] Step 505: Determine the noise covariance based on the standard deviation. For example, if the standard deviation is greater than a standard deviation threshold, the noise covariance is determined based on the standard deviation and a preset weight, where the preset weight is between 0 and 1. If the standard deviation is not greater than the standard deviation threshold, the noise covariance is determined based on a fixed covariance. The noise covariance determined based on the fixed covariance is smaller than the noise covariance determined based on the standard deviation and the preset weight.

[0102] For example, in a vehicle behavior detection scenario, a set of distance data (i.e., K distance values) collected by a distance detection sensor is collected within a relatively short period of time, such as within 200ms. Therefore, it can be assumed that the relative position of the target vehicle and the distance detection sensor has not changed, that is, the K distance values ​​in the distance data are exactly the same. However, in actual applications, different distance values ​​or jumps in distance values ​​may occur. In such cases, standard deviation can be introduced to account for fluctuations between the K distance values ​​in the same set of distance data.

[0103] On this basis, if the standard deviation is greater than the standard deviation threshold (which can be configured based on experience), it means that the distance data of this group is significantly affected by noise interference, and the noise covariance R of this group of distance data is determined to be the standard deviation of this group of distance data * weight, that is, the noise covariance is determined based on the standard deviation and the preset weight. If the standard deviation is not greater than the standard deviation threshold, it means that the distance data of this group is relatively stable and not significantly affected by noise interference, and the noise covariance R of this group of distance data is determined using a fixed covariance (i.e., the default value). The noise covariance determined based on the fixed covariance can be smaller than the noise covariance determined based on the standard deviation and the preset weight.

[0104] Step 506 determines a distance estimate for the current acquisition cycle based on the initial estimate, the multiple distance values ​​for the current acquisition cycle, and the noise covariance. For example, if the parking area is unparked, the initial estimate may be a valid distance value from the multiple distance values. If the parking area is parked, the initial estimate may be a distance estimate from the previous acquisition cycle.

[0105] For example, an initial estimate, multiple distance values ​​for the current acquisition cycle, and noise covariance may be input to a Kalman filter, which then outputs a distance estimate for the current acquisition cycle. In other words, the data processing unit uses the Kalman filter's calculation formula to determine the distance estimate for the current acquisition cycle. Of course, the Kalman filter is merely an example of determining a distance estimate, and this determination method is not limited.

[0106] Since the processing of multiple distance values ​​is only one-dimensional data processing, a simplified one-dimensional Kalman filter can be used, see the following formula: In the above formula, E k Represents the optimal estimate at the current moment, that is, the distance estimate of the current acquisition cycle. E k-1 represents the initial estimate, R represents the noise covariance, K represents the total number of distance values, X i In summary, the distance estimation value can be determined based on the initial estimation value, the multiple distance values ​​and the noise covariance.

[0107] In the above formula, the initial estimate E k-1It is related to the state of the parking area. If the state of the parking area is that a vehicle is parked, the initial estimated value E k-1 It can be the distance estimate of the previous acquisition cycle, that is, E k , represents the optimal estimate at the previous moment. If the state of the parking area is no parked vehicles, then the initial estimate E k-1 It can be a valid distance value X1 among multiple distance values. X1 can be any valid distance value among all distance values ​​(such as K distance values), such as the first valid distance value among all distance values, that is, the first valid distance value X1 is used as the optimal estimate at the previous moment.

[0108] Using the above initial estimate E k-1 The reason is that if the parking space area is in the state of a parked vehicle, the collection interval of two adjacent sets of distance data is short and highly correlated with each other. The optimal estimate of the previous collection cycle (tn-1 moment) can be used as the initial estimate E of the current collection cycle (tn moment). k-1 If the parking area is unparked, in order to reduce the high power consumption of the distance detection sensor and the interference caused by pedestrians or other environmental factors, the detection frequency of the distance data is low and the correlation between two adjacent sets of distance data is low. Therefore, the first valid distance value X1 of this set of distance data is used as the initial estimated value E. k-1 .

[0109] In summary, the distance estimation value for the current acquisition cycle can be obtained and output. For example, the data processing unit can obtain the distance estimation value for each acquisition cycle and input the distance estimation value for each acquisition cycle to the vehicle entry determination unit, the vehicle exit determination unit, the distance calibration unit, etc.

[0110] In a possible implementation, the vehicle entry determination unit may determine whether the target vehicle enters the parking space area based on the distance estimation value, see Figure 6 The figure shows a processing diagram of the vehicle entry determination unit.

[0111] Step 601: After receiving the distance estimation value, the vehicle entry determination unit stores the distance estimation value in a created data buffer pool. The data buffer pool is used to store the distance estimation values ​​of the latest N acquisition cycles.

[0112] For example, the vehicle entry determination unit can create and maintain a data buffer pool to store the distance estimates of the most recent N acquisition cycles. N can be configured based on experience, such as 12, 16, etc., with N being 12 as an example. After the vehicle entry determination unit stores the distance estimate of the 13th acquisition cycle in the data buffer pool, it deletes the distance estimate of the first acquisition cycle from the data buffer pool. Similarly, the vehicle entry determination unit only stores the distance estimates of the last 12 acquisition cycles in the data buffer pool.

[0113] For example, when the status of the parking space area is that there is no parked vehicle, the vehicle entry determination unit can receive the distance estimation value input by the data processing unit. Each time the vehicle entry determination unit receives the distance estimation value, it stores the distance estimation value in the data buffer pool, and the data buffer pool can store up to 12 distance estimation values.

[0114] In step 602, if the parking area is unoccupied and the estimated distance values ​​for the N acquisition cycles in the data buffer (i.e., all estimated distance values) are within the vehicle entry distance range, the vehicle entry determination unit determines that the target vehicle has entered the parking area, i.e., a vehicle entry has occurred, and the process proceeds to step 603. If the estimated distance value for any acquisition cycle in the data buffer is not within the vehicle entry distance range, then no vehicle entry has occurred.

[0115] For example, the vehicle entry distance range can be configured based on experience, such as the vehicle entry distance range can be [30, 200]. Of course, the vehicle entry distance range [30, 200] is just an example and there is no restriction on this, as long as the minimum value of the vehicle entry distance range is greater than 0 and the maximum value of the vehicle entry distance range is less than 255 (that is, the maximum value of the distance value).

[0116] The reason for using the vehicle entry distance range of [30,200] is that when the relative position of the target vehicle and the distance detection sensor is less than 30 cm, the target vehicle's acquisition angle is incomplete. Therefore, the vehicle entry distance range should not be less than 30 cm. When the relative position of the target vehicle and the distance detection sensor is greater than 200 cm, the reflection intensity becomes unstable due to the increased distance, and the value fluctuations are prone to cause interference. Therefore, the vehicle entry distance range should not be greater than 200 cm.

[0117] In step 603, the vehicle entry determination unit sends a wake-up command to the image acquisition sensor so that the image acquisition sensor acquires an image of the parking area (i.e., an image of the vehicle entry behavior), sends the image to the management device, and turns off the image acquisition sensor after the image is sent, thereby saving power consumption.

[0118] In one possible implementation, after the vehicle entry determination unit determines that the target vehicle has entered the parking space, that is, after the vehicle entry occurs, the distance calibration unit may determine the target distance between the target vehicle and the distance detection sensor (that is, the distance between the target vehicle and the distance detection sensor after the target vehicle has parked in the parking space) based on the multiple distance estimation values, and store the target distance. Figure 7 The figure shows a processing diagram of the distance calibration unit.

[0119] In step 701, after receiving the estimated distance value, the distance calibration unit determines whether the target vehicle has completed distance calibration, that is, whether the target distance between the target vehicle and the distance detection sensor has been determined. If so, the target distance determination process is exited. If not, the target distance determination process is continued, that is, step 702 is executed.

[0120] For example, when the status of the parking space area is that a vehicle is parked, after the data processing unit obtains the distance estimation value of each acquisition cycle, the distance estimation value can be sent to the distance calibration unit. After receiving the distance estimation value, the distance calibration unit first determines whether the target vehicle has completed distance calibration.

[0121] In step 702, the distance calibration unit stores the distance estimation value into the created data buffer pool, which is used to store the distance estimation values ​​of M acquisition cycles. That is, the data buffer pool stores the distance estimation values ​​of M acquisition cycles at most. M can be a positive integer, such as 200, 210, etc., and there is no limit on this.

[0122] For example, the distance calibration unit can create and maintain a data buffer pool that is different from the data buffer pool created by the vehicle entry determination unit. The data buffer pool created by the distance calibration unit is used to store distance estimates for M acquisition cycles. M can be configured based on experience. Therefore, each time the distance calibration unit receives a distance estimate, it can store the distance estimate in the data buffer pool.

[0123] Step 703: The distance calibration unit determines whether the number of distance estimation values ​​in the data buffer pool reaches M.

[0124] For example, each time the distance calibration unit stores the distance estimation value in the data buffer pool, it can determine whether the distance estimation values ​​in the data buffer pool reach M. If so, execute step 704; if not, wait for the next time to store the distance estimation value in the data buffer pool and continue to execute step 703.

[0125] In step 704 , the distance calibration unit determines the maximum distance estimation value among the M distance estimation values ​​in the data buffer pool as the target distance between the target vehicle and the distance detection sensor.

[0126] For example, after determining the maximum distance estimate as the target distance, the target vehicle can be marked as having completed calibration, and distance calibration will not be repeated for the target vehicle. The target vehicle is then sent to the vehicle departure determination unit. Furthermore, the distance calibration unit can clear the data buffer so that after the current target vehicle leaves, the next target vehicle enters the vehicle and uses fresh accumulated data for distance calibration.

[0127] In a possible implementation, the vehicle exit determination unit may determine whether the target vehicle has left the parking space based on the distance estimation value. Figure 8 The figure shows a processing diagram of the vehicle departure determination unit.

[0128] In step 801, when the parking area is in a parked state, the vehicle exit determination unit may receive a distance estimation value input by the data processing unit. After receiving the distance estimation value, the vehicle exit determination unit determines whether the difference between the distance estimation value and the target distance is greater than a distance threshold. If so, step 802 is executed. If not, step 802 is executed. If not, step 802 is executed. If not, step 802 is executed. If not, step 802 is executed. If not, step 802 is executed. If it is not 0 ... 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802, step 802 is executed. If it is 802,

[0129] For example, if the difference between the estimated distance value and the target distance is greater than a distance threshold, it indicates that the distance between the target vehicle and the distance detection sensor is increasing, and the target vehicle may leave the parking space area.

[0130] In step 802, the vehicle departure determination unit sends a wake-up command to the image acquisition sensor so that the image acquisition sensor captures an image of the parking area (i.e., an image of the vehicle departure behavior) and stores the image of the vehicle departure behavior. After the image storage is completed, the image acquisition sensor is turned off to save power consumption.

[0131] For example, in order to capture an image of the parking area (which may include the license plate) as the target vehicle is leaving, high requirements are placed on timeliness and data stability. Therefore, when the target vehicle is likely to leave the parking area, a wake-up command needs to be sent to the image acquisition sensor immediately to enable it to capture an image of the parking area. However, after image acquisition is completed, the image is not immediately sent to the management device. Instead, the image is stored and awaits the results of the subsequent vehicle departure review.

[0132] The reason for adopting the above method is: in order to ensure that images are captured during the vehicle's departure, if the difference between the distance estimate and the target distance is greater than the distance threshold, or the distance estimate is 0, that is, it is known that the target vehicle may leave the parking area, it is necessary to immediately send a wake-up command to the image acquisition sensor to enable the image acquisition sensor to capture images of the parking area, otherwise the target vehicle may leave quickly. However, in actual scenarios, the distance estimate may increase abnormally due to noise data, which in turn causes a false alarm of the vehicle's departure. Therefore, after the image acquisition is completed, the image is not immediately sent to the management device, but the image is first stored and the result of the subsequent vehicle departure review is awaited. A false alarm is an erroneous result. For example, the target vehicle did not leave, but was mistakenly identified as having left.

[0133] In step 803, after a preset time interval, the vehicle departure determination unit may receive the distance estimate input from the data processing unit. Upon receiving the distance estimate, if the difference between the distance estimate and the target distance is greater than a distance threshold, or if the distance estimate is zero (indicating the target vehicle is not present in the parking area), the vehicle departure determination unit determines that the target vehicle has left the parking area and executes step 804. If the difference between the distance estimate and the target distance is not greater than the distance threshold, and the distance estimate is not zero, the target vehicle has not left the parking area, i.e., a false alarm has occurred, and the process proceeds to step 805.

[0134] Exemplarily, after the image acquisition sensor is triggered to capture an image of the parking area, a timer can be started, and the timeout period of the timer is a preset duration, which can be configured based on experience, such as 3s, 4s, etc., and there is no limit on this. After the timer timeout period is reached, the data processing unit obtains the distance data of the parking area, determines the distance estimate based on the distance data, and sends the distance estimate to the vehicle exit determination unit. After the vehicle exit determination unit receives the distance estimate, if the difference between the distance estimate and the target distance is greater than the distance threshold, or the distance estimate is 0 (i.e., there is no target vehicle in the parking area), it is determined that the vehicle exit has occurred. If the difference between the distance estimate and the target distance is not greater than the distance threshold, and the distance estimate is not 0, it is determined that the vehicle exit has not occurred, i.e., a false alarm has occurred.

[0135] In another possible implementation, after the vehicle departure determination unit receives the estimated distance value, if the estimated distance value is 0 (i.e., the target vehicle does not exist in the parking area), it determines that a vehicle departure has occurred. Alternatively, if the estimated distance value is not 0 (i.e., the target vehicle exists in the parking area), regardless of whether the difference between the estimated distance value and the target distance is greater than a distance threshold, it is determined that no vehicle departure has occurred. Obviously, in the above method, determining whether a vehicle departure has occurred is only related to whether the target vehicle exists in the parking area.

[0136] In step 804 , the vehicle dispatch determination unit sends a wake-up command to the image acquisition sensor so that the image acquisition sensor sends the image to the management device, and turns off the image acquisition sensor after the image is sent.

[0137] In step 805, the vehicle dispatch determination unit sends a wake-up command to the image acquisition sensor, causing it to discard the stored image and shut down. For example, because the aforementioned target distance can lead to false alarms, the vehicle dispatch determination unit can also adjust the target distance, such as by increasing the target distance value. Subsequently, the aforementioned process is executed based on the adjusted target distance.

[0138] In a possible implementation, the timing selection unit may determine the data collection interval of the distance detection sensor, and determine the collection time based on the data collection interval, and trigger the distance detection sensor to collect distance data at each collection time, see Figure 9 The figure shows a processing diagram of the timing selection unit.

[0139] Step 901: The timing selection unit determines the status of the parking space area.

[0140] In step 902, if the parking area is unparked, the timing selection unit determines the data collection interval as a first interval, i.e., the interval between two adjacent collection moments is the first interval. For example, after waking up the distance detection sensor, collecting distance data for the parking area via the distance detection sensor, and then turning off the distance detection sensor, after the first interval, the distance detection sensor is woken up again, collecting distance data for the parking area via the distance detection sensor, and then turning off the distance detection sensor, and so on.

[0141] In step 903 , if the parking space area is in a parked state, the timing selection unit determines whether a target distance between the target vehicle and the distance detection sensor has been acquired.

[0142] If not, step 904 may be executed, and if so, step 905 may be executed.

[0143] In step 904, if the target distance between the target vehicle and the distance detection sensor is not obtained, the timing selection unit determines the data collection interval to be the second interval, that is, the interval between two adjacent collection moments is the second interval. For example, after collecting distance data of the parking area using the distance detection sensor and then turning off the distance detection sensor, after the second interval, the distance detection sensor is awakened again, and distance data of the parking area is collected using the distance detection sensor, and then the distance detection sensor is turned off, and so on.

[0144] In step 905, if the target distance between the target vehicle and the distance detection sensor has been acquired, the timing selection unit determines the data collection interval to be the third interval, i.e., the interval between two adjacent collection moments is the third interval. For example, after collecting distance data for the parking area using the distance detection sensor and then turning it off, the distance detection sensor is awakened again after the third interval, and distance data for the parking area is collected using the distance detection sensor, and then turned off, and so on.

[0145] In the above embodiment, the first interval may be greater than the third interval, the third interval may be greater than or equal to the second interval, and the third interval may be inversely proportional to the target distance. That is, the greater the target distance, the smaller the third interval, and the smaller the target distance, the larger the third interval. The above intervals are adopted because:

[0146] If the status of the parking area is that there is no parked vehicle, image acquisition needs to be triggered after the next target vehicle enters the parking area and stops steadily. The relative position of the target vehicle to the distance detection sensor after it stops steadily is random and fixed. Therefore, the timeliness requirement for triggering image acquisition will be much lower than the image acquisition triggered by the target vehicle leaving. Considering timeliness and power consumption, the data acquisition interval in the unparked vehicle state is relatively large. Therefore, the first interval will be greater than the third interval, and the first interval will be greater than the second interval. For example, the first interval can be 5s. Of course, 5s is just an example of the first interval and there is no limitation on this.

[0147] If the status of the parking area is parked and the target distance is not obtained, it means that the target vehicle has just entered the parking area, and the target distance between the target vehicle and the distance detection sensor needs to be quickly calibrated. Therefore, by accelerating data iteration, data that truly reflects the target distance can be obtained, that is, the data collection interval is relatively small, so the second interval will be smaller than the first interval, and the second interval will be smaller than the third interval. For example, the second interval can be 500ms. Of course, 500ms is just an example and there is no limitation to this.

[0148] If the parking space is parked and the target distance has been obtained, a data collection interval can be selected based on the target distance. This data collection interval is the third interval, and the third interval is inversely proportional to the target distance. For example, when the target distance is 0-120cm, the third interval can be 1s; when the target distance is 120-150cm, the third interval can be 800ms; when the target distance is greater than 150, the third interval can be 600ms. Of course, the above target distance and third interval values ​​are only examples and are not limited to this.

[0149] The reason for selecting different data collection intervals based on the target distance is that in actual scenarios, the target vehicle's departure speed is constant, and the distance detection sensor has a limited ranging range. Therefore, when the target distance is greater, the time window in which the target vehicle can be discovered during the departure process is shorter. When the target distance is smaller, the time window in which the target vehicle can be discovered during the departure process is larger. Therefore, different data collection intervals can be selected based on the target distance to improve the image acquisition rate during the target vehicle's departure process.

[0150] In a possible implementation, when the state of the parking area is that no vehicle is parked, the state switching unit may change the state of the parking area to that of a parked vehicle, or when the state of the parking area is that a vehicle is parked, the state switching unit may change the state of the parking area to that of no vehicle. Figure 10 FIG. 1 is a processing diagram of a state switching unit.

[0151] Step 1001: The state switching unit determines the state of the parking area.

[0152] In step 1002, if the status of the parking area is that there is no parked vehicle, and the vehicle entry determination unit determines that the target vehicle has entered the parking area (i.e., there is a vehicle entry behavior), the state switching unit changes the status of the parking area to a parked vehicle and marks the target vehicle as uncalibrated. The distance calibration unit calibrates the target vehicle and determines the target distance between the target vehicle and the distance detection sensor.

[0153] In step 1003, if the parking area is parked and the vehicle exit determination unit determines that the target vehicle has left the parking area (i.e., a vehicle exit has occurred), the state switching unit changes the parking area to unparked. For example, the state switching unit may also clear the data buffer of the vehicle entry determination unit to begin the entry determination process for a new target vehicle. This process is not further described.

[0154] As can be seen from the above technical solutions, in the embodiments of the present application, the various measurement indicators for identifying vehicle entry and exit behaviors using distance detection sensors are greatly improved, the accuracy of vehicle entry identification is increased to more than 95%, and vehicle exit identification is calculated from the actual departure action of the vehicle, and the vehicle exit behavior can be identified in a short time with an accuracy of more than 90%. The image acquisition can be triggered at the moment of vehicle exit, and the algorithm has strong anti-interference ability. The algorithm is centered on power consumption control, and controls the power consumption of the entire machine by minimizing the working time of the distance detection sensor. At the same time, it has a good ability to identify vehicle entry and exit behaviors, and can achieve better recognition rate and timeliness of vehicle entry and exit behaviors. Based on discrete distance data, the standard deviation of the same group of distance data is associated with the noise covariance of the Kalman filter to achieve data noise suppression. Through the process of collecting images first and then reviewing the data, the timeliness and accuracy of image acquisition are compatible; through the feedback mechanism of data review failure, the false alarm rate of detecting vehicle exit behaviors is reduced. By identifying data characteristics for both empty and parked vehicles, the system differentially correlates the optimal estimate of adjacent moments, the standard deviation of the same set of sampled data, and the Kalman filter's noise covariance to achieve superior data noise reduction. High-frequency data collection and iteration accurately estimates target distance, suppresses the impact of noise on target distance, reduces the false alarm rate for vehicle dispatch, and improves dispatch detection sensitivity. The raw data sampling interval frequency is differentiated based on the relative distance between the actual parked vehicle and the distance detection sensor, balancing power consumption control with the timeliness of dispatch triggering.

[0155] Based on the same application concept as the above method, a vehicle behavior detection device is proposed in the embodiment of the present application, which is applied to an Internet of Things device, wherein the Internet of Things device includes an image acquisition sensor and a distance detection sensor. Figure 11 FIG. 1 is a schematic structural diagram of the device, which may include:

[0156] An acquisition module 1101 is configured to acquire first distance data of a parking area through the distance detection sensor; a determination module 1102 is configured to determine whether a target vehicle exists in the parking area based on the first distance data; if it is determined that a target vehicle exists in the parking area based on the first distance data, an estimated distance between the target vehicle and the distance detection sensor is determined based on the first distance data; a sending module 1103 is configured to send a wake-up command to the image acquisition sensor if the parking area is in a parked state and the difference between the estimated distance and the target distance is greater than a distance threshold, so that the image acquisition sensor acquires and stores a first image of the parking area and turns off the image acquisition sensor; wherein the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is already parked in the parking area;

[0157] The acquisition module 1101 is further used to collect second distance data of the parking area through the distance detection sensor after a preset time interval; the determination module 1102 is further used to determine that the target vehicle has left the parking area if it is determined based on the second distance data that there is no target vehicle in the parking area, and change the status of the parking area to no parked vehicle; the sending module 1103 is further used to send a wake-up command to the image acquisition sensor so that the image acquisition sensor sends the first image to the management device and turns off the image acquisition sensor.

[0158] In one possible embodiment, the determination module 1102 is further used to determine that the target vehicle has entered the parking area if the status of the parking area is that there is no parked vehicle and the distance estimation value is within the vehicle entry distance range, and change the status of the parking area to a parked vehicle; the sending module 1103 is further used to send a wake-up command to the image acquisition sensor so that the image acquisition sensor captures a second image of the parking area, sends the second image to the management device, and turns off the image acquisition sensor after the second image is sent.

[0159] In a possible implementation, when the determining module 1102 determines that a target vehicle exists in the parking space area based on the first distance data, it is specifically configured to:

[0160] If the first distance data includes multiple distance values, the number of valid distance values ​​among the multiple distance values ​​is determined; if the status of the parking area is that there is no parked vehicle and the number of valid distance values ​​is greater than a first quantity threshold, it is determined that there is a target vehicle in the parking area; if the status of the parking area is that there is a parked vehicle and the number of valid distance values ​​is greater than a second quantity threshold, it is determined that there is a target vehicle in the parking area; wherein the first quantity threshold is greater than the second quantity threshold.

[0161] In a possible implementation manner, when the determination module 1102 determines the estimated distance between the target vehicle and the distance detection sensor based on the first distance data, it is specifically configured to:

[0162] If the first distance data includes a plurality of distance values ​​collected by the distance detection sensor in a current collection period, determining a standard deviation of the plurality of distance values, and determining a noise covariance based on the standard deviation;

[0163] Based on the initial estimate value, multiple distance values ​​of the current acquisition cycle and the noise covariance, the distance estimate value of the current acquisition cycle is determined; wherein, if the status of the parking space area is that there is no parked vehicle, the initial estimate value is a valid distance value among the multiple distance values; if the status of the parking space area is that there is a parked vehicle, the initial estimate value is the distance estimate value of the previous acquisition cycle.

[0164] In one possible implementation, the determination module 1102 is specifically used to determine the noise covariance based on the standard deviation: if the standard deviation is greater than the standard deviation threshold, determine the noise covariance based on the standard deviation and a preset weight, where the preset weight is a value between 0 and 1; if the standard deviation is not greater than the standard deviation threshold, determine the noise covariance based on a fixed covariance; wherein the noise covariance determined based on the fixed covariance is smaller than the noise covariance determined based on the standard deviation and the preset weight.

[0165] In a possible embodiment, when the acquisition module 1101 collects the first distance data of the parking space area through the distance detection sensor, it is specifically used to: determine the acquisition time based on the data acquisition interval of the distance detection sensor, wake up the distance detection sensor at each acquisition time, collect the first distance data of the parking space area through the distance detection sensor, and turn off the distance detection sensor after the first distance data collection is completed; wherein: if the status of the parking space area is that there is no parked vehicle, the data acquisition interval is the first interval; if the status of the parking space area is that there is a parked vehicle and the target distance between the target vehicle and the distance detection sensor is not obtained, the data acquisition interval is the second interval; if the status of the parking space area is that there is a parked vehicle and the target distance between the target vehicle and the distance detection sensor is obtained, the data acquisition interval is the third interval; wherein, the first interval is greater than the third interval, the third interval is greater than or equal to the second interval, and the third interval is inversely proportional to the target distance.

[0166] An embodiment of the present application provides an Internet of Things device, which includes at least a processor (i.e., the MCU of the above embodiment), an image acquisition sensor, and a distance detection sensor, wherein: the distance detection sensor is used to collect first distance data of a parking area and send the first distance data to the processor; the processor is used to determine, based on the first distance data, an estimated distance between the target vehicle and the distance detection sensor if it is determined that a target vehicle exists in the parking area; if the parking area is in a parked state and the difference between the estimated distance and a target distance is greater than a distance threshold, send a wake-up command to the image acquisition sensor; the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is already parked in the parking area; the image acquisition sensor is used to, after receiving the wake-up command, collect and store a first image of the parking area and turn off the image acquisition sensor;

[0167] The distance detection sensor is used to collect second distance data of the parking area after a preset time interval and send the second distance data to the processor; the processor is used to determine that the target vehicle has left the parking area if it is determined based on the second distance data that there is no target vehicle in the parking area, change the status of the parking area to no parked vehicle, and send a wake-up command to the image acquisition sensor; the image acquisition sensor is used to send the first image to the management device after receiving the wake-up command and turn off the image acquisition sensor.

[0168] In one possible embodiment, the processor is further configured to determine that the target vehicle has entered the parking area if the status of the parking area is that there is no parked vehicle and the distance estimation value is within the vehicle entry distance range, change the status of the parking area to a parked vehicle, and send a wake-up command to the image acquisition sensor; the image acquisition sensor is further configured to, after receiving the wake-up command, capture a second image of the parking area, send the second image to the management device, and turn off the image acquisition sensor after the second image is sent.

[0169] In a possible implementation, the IoT device may further include a machine-readable storage medium storing machine-executable instructions that can be executed by the processor; the processor is configured to execute the machine-executable instructions to implement the above operations, which will not be described in detail.

[0170] Based on the same application concept as the above method, an embodiment of the present application also provides a machine-readable storage medium, on which a number of computer instructions are stored. When the computer instructions are executed by a processor, the vehicle behavior detection method disclosed in the above example of the present application can be implemented.

[0171] The machine-readable storage medium may be any electronic, magnetic, optical, or other physical storage device that may contain or store information, such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, a storage drive (such as a hard disk drive), a solid-state drive, any type of storage disk (such as a CD, DVD, etc.), or similar storage media, or a combination thereof.

[0172] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email transceiver, game console, tablet computer, wearable device, or any combination of these devices.

[0173] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0174] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0176] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A vehicle behavior detection method, characterized in that: Applied to an Internet of Things device, the Internet of Things device includes an image acquisition sensor and a distance detection sensor, and the method includes: collecting first distance data of the parking space area by the distance detection sensor; If it is determined based on the first distance data that there is a target vehicle in the parking area, then an estimated distance value between the target vehicle and the distance detection sensor is determined based on the first distance data; wherein, if the first distance data includes multiple distance values ​​collected by the distance detection sensor in the current collection period, then the standard deviation of the multiple distance values ​​is determined, and the noise covariance is determined based on the standard deviation; based on the initial estimate, the multiple distance values ​​of the current collection period and the noise covariance, the distance estimate value of the current collection period is determined; wherein, if the status of the parking area is not parked vehicle, the initial estimated value is a valid distance value among the multiple distance values; if the status of the parking space area is a parked vehicle, the initial estimated value is a distance estimated value of the previous acquisition cycle; wherein, if the standard deviation is greater than a standard deviation threshold, the noise covariance is determined based on the standard deviation and a preset weight, and the preset weight is a value between 0 and 1; if the standard deviation is not greater than the standard deviation threshold, the noise covariance is determined based on a fixed covariance; wherein the noise covariance determined based on the fixed covariance is less than the noise covariance determined based on the standard deviation and the preset weight; If the parking space area is in a parked state and the difference between the estimated distance and the target distance is greater than a distance threshold, sending a wake-up command to the image acquisition sensor to cause the image acquisition sensor to acquire and store a first image of the parking space area, and turning off the image acquisition sensor; wherein the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is parked in the parking space area; After a preset time interval, the distance detection sensor collects second distance data of the parking area; if it is determined based on the second distance data that there is no target vehicle in the parking area, it is determined that the target vehicle has left the parking area, the status of the parking area is changed to no parked vehicle, and a wake-up command is sent to the image acquisition sensor to enable the image acquisition sensor to send the first image to the management device and turn off the image acquisition sensor.

2. The method according to claim 1, characterized in that After collecting the second distance data of the parking space area by the distance detection sensor, the method further includes: If it is determined based on the second distance data that there is a target vehicle in the parking area, the state of the parking area is maintained as a parked vehicle, and a wake-up command is sent to the image acquisition sensor so that the image acquisition sensor discards the first image and turns off the image acquisition sensor.

3. The method according to claim 1, characterized in that After determining the estimated distance between the target vehicle and the distance detection sensor based on the first distance data, the method further includes: If the status of the parking area is that there is no parked vehicle and the distance estimation value is within the vehicle entry distance range, it is determined that the target vehicle has entered the parking area, the status of the parking area is changed to a parked vehicle, and a wake-up command is sent to the image acquisition sensor so that the image acquisition sensor captures a second image of the parking area, sends the second image to the management device, and turns off the image acquisition sensor after the second image is sent.

4. The method according to any one of claims 1 to 3, characterized in that The determining, based on the first distance data, that a target vehicle exists in the parking space area includes: If the first distance data includes multiple distance values, the number of valid distance values ​​among the multiple distance values ​​is determined; if the status of the parking area is that there is no parked vehicle and the number of valid distance values ​​is greater than a first quantity threshold, it is determined that there is a target vehicle in the parking area; if the status of the parking area is that there is a parked vehicle and the number of valid distance values ​​is greater than a second quantity threshold, it is determined that there is a target vehicle in the parking area; wherein the first quantity threshold is greater than the second quantity threshold.

5. The method according to claim 3, characterized in that If the estimated distance value is within the vehicle entry distance range, determining that the target vehicle has entered the parking space area includes: After determining a distance estimation value based on the first distance data, storing the distance estimation value in a created data buffer pool, wherein the data buffer pool is used to store distance estimation values ​​of the most recent N acquisition cycles; If the distance estimation values ​​of the N acquisition cycles in the data buffer pool are all within the vehicle entry distance range, it is determined that the target vehicle enters the parking space area.

6. The method according to any one of claims 1 to 3, characterized in that The collecting first distance data of the parking space area by the distance detection sensor includes: Determine a collection time based on the data collection interval of the distance detection sensor, wake up the distance detection sensor at each collection time, collect first distance data of the parking space area through the distance detection sensor, and shut down the distance detection sensor after the first distance data collection is completed; wherein: If the state of the parking area is that no vehicle is parked, the data collection interval is the first interval; If the state of the parking space area is that a vehicle is parked, and the target distance between the target vehicle and the distance detection sensor is not obtained, the data collection interval is the second interval; If the state of the parking space area is that a vehicle is parked, and the target distance between the target vehicle and the distance detection sensor has been acquired, the data collection interval is a third interval; The first interval is greater than the third interval, the third interval is greater than or equal to the second interval, and the third interval is inversely proportional to the target distance.

7. The method according to claim 3 or 5, characterized in that After determining that the target vehicle has entered the parking space, the method further includes: acquiring the target distance between the target vehicle and the distance detection sensor, wherein the target distance is acquired by: After determining a distance estimation value based on the first distance data, storing the distance estimation value in a created data buffer pool, wherein the data buffer pool is used to store the distance estimation values ​​of M acquisition cycles; If the number of distance estimation values ​​in the data buffer pool reaches M, the maximum distance estimation value among the M distance estimation values ​​in the data buffer pool is determined as the target distance.

8. The method according to any one of claims 1 to 3, characterized in that The IoT device is located on the front side of the vehicle in the parking area, or the IoT device is located on the rear side of the vehicle in the parking area, or the IoT device is located between the parking area and another parking area; The IoT device is a battery-powered IoT device; The IoT device communicates with the management device via a wireless network.

9. A vehicle behavior detection device, characterized in that: Applied to an Internet of Things device, the Internet of Things device includes an image acquisition sensor and a distance detection sensor, and the device includes: an acquisition module, configured to acquire first distance data of a parking space area through the distance detection sensor; a determination module configured to determine whether a target vehicle exists in the parking area based on the first distance data; if the target vehicle is determined to exist in the parking area based on the first distance data, determine an estimated distance between the target vehicle and the distance detection sensor based on the first distance data; wherein, if the first distance data includes multiple distance values ​​collected by the distance detection sensor in a current collection period, determine a standard deviation of the multiple distance values ​​and determine a noise covariance based on the standard deviation; and determine the estimated distance for the current collection period based on an initial estimate, the multiple distance values ​​for the current collection period, and the noise covariance; Wherein, if the state of the parking space area is that no vehicle is parked, the initial estimated value is a valid distance value among the multiple distance values; if the state of the parking space area is that a vehicle is parked, the initial estimated value is a distance estimated value of the previous acquisition cycle; wherein, if the standard deviation is greater than a standard deviation threshold, the noise covariance is determined based on the standard deviation and a preset weight, and the preset weight is a value between 0 and 1; if the standard deviation is not greater than the standard deviation threshold, the noise covariance is determined based on a fixed covariance; wherein the noise covariance determined based on the fixed covariance is less than the noise covariance determined based on the standard deviation and the preset weight; a sending module, configured to send a wake-up command to the image acquisition sensor, if the parking space area is in a parked state and the difference between the estimated distance value and the target distance is greater than a distance threshold, so that the image acquisition sensor acquires and stores a first image of the parking space area and turns off the image acquisition sensor; wherein the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is parked in the parking space area; The acquisition module is further configured to acquire second distance data of the parking area through the distance detection sensor after a preset time interval; the determination module is further configured to, if it is determined based on the second distance data that the target vehicle does not exist in the parking area, determine that the target vehicle has left the parking area and change the state of the parking area to no parked vehicle; The sending module is further configured to send a wake-up command to the image acquisition sensor, so that the image acquisition sensor sends the first image to the management device and turns off the image acquisition sensor.

10. The device according to claim 9, characterized in that The determining module is further configured to determine that the target vehicle has entered the parking area if the state of the parking area is that no vehicle is parked and the estimated distance value is within a vehicle entry distance range, and change the state of the parking area to that of a parked vehicle; The sending module is further used to send a wake-up command to the image acquisition sensor so that the image acquisition sensor acquires a second image of the parking space area, sends the second image to the management device, and turns off the image acquisition sensor after the second image is sent.

11. An Internet of Things device, characterized in that: The IoT device includes at least a processor, an image acquisition sensor, and a distance detection sensor, wherein: The distance detection sensor is used to collect first distance data of the parking area and send the first distance data to the processor; the processor is used to determine the distance estimate between the target vehicle and the distance detection sensor based on the first distance data if it is determined that there is a target vehicle in the parking area based on the first distance data; wherein, if the first distance data includes multiple distance values ​​collected by the distance detection sensor in the current collection period, the standard deviation of the multiple distance values ​​is determined, and the noise covariance is determined based on the standard deviation; the current collection period is determined based on the initial estimate, the multiple distance values ​​of the current collection period and the noise covariance. a distance estimation value of a period; wherein, if the state of the parking space area is that no vehicle is parked, the initial estimation value is a valid distance value among the multiple distance values; if the state of the parking space area is that a vehicle is parked, the initial estimation value is a distance estimation value of a previous acquisition period; wherein, if the standard deviation is greater than a standard deviation threshold, the noise covariance is determined based on the standard deviation and a preset weight, and the preset weight is a value between 0 and 1; if the standard deviation is not greater than the standard deviation threshold, the noise covariance is determined based on a fixed covariance; wherein the noise covariance determined based on the fixed covariance is less than the noise covariance determined based on the standard deviation and the preset weight; the processor being configured to send a wake-up command to the image acquisition sensor if the parking space area is in a parked vehicle state and the difference between the distance estimate and the target distance is greater than a distance threshold; wherein the target distance represents the distance between the target vehicle and the distance detection sensor when the target vehicle is parked in the parking space area; The image acquisition sensor is configured to acquire and store a first image of the parking area and turn off the image acquisition sensor after receiving the wake-up command; The distance detection sensor is used to collect second distance data of the parking area after a preset time interval and send the second distance data to the processor; the processor is used to determine that the target vehicle has left the parking area if it is determined based on the second distance data that there is no target vehicle in the parking area, change the status of the parking area to no parked vehicle, and send a wake-up command to the image acquisition sensor; the image acquisition sensor is used to send the first image to the management device after receiving the wake-up command and turn off the image acquisition sensor.

12. The device according to claim 11, characterized in that The processor is further configured to, if the state of the parking area is that no vehicle is parked and the distance estimation value is within a vehicle entry distance range, determine that the target vehicle has entered the parking area, change the state of the parking area to that of a parked vehicle, and send a wake-up command to the image acquisition sensor; The image acquisition sensor is further used to acquire a second image of the parking area after receiving the wake-up command, send the second image to the management device, and turn off the image acquisition sensor after the second image is sent.

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