Networking method based on Internet of Things and related device

Abstract

The invention provides a networking method based on the Internet of Things and a related device. The method comprises the following steps: marking a target position corresponding to a target object in an indoor map; obtaining millimeter wave radars corresponding to the first area to obtain a millimeter wave radars; determining the distance between each millimeter-wave radar in the a millimeter-wave radars and the target position to obtain a distances; determining detection precision corresponding to the a millimeter wave radars according to the a distances to obtain a detection precision; selecting the detection precision greater than a preset threshold in the a detection precision to obtain b detection precision, and waking up b millimeter wave radars corresponding to the b detection precision; and networking the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and controlling the b millimeter-wave radars to perform health monitoring on the target object by using the second Internet of Things system. According to the embodiment of the invention, low power consumption and reasonable networking can be realized to realize health monitoring on the old people.

Description

Technical Field

[0001] This application relates to the field of communication technologies or the field of Internet of Things technologies, and particularly relates to a networking method based on the Internet of Things and related devices. Background Art

[0002] The essence of the Internet of Things is a network that connects electronic products with communication functions (such as tablet computers, mobile phones, etc.) to the Internet through information sensing devices according to various communication protocols for data interaction to achieve functions such as intelligent identification, positioning, tracking, monitoring, and management.

[0003] In practical applications, since there is also data transmission in the connection between things, it will also bring some unnecessary power consumption to the Internet of Things system. Especially in the elderly care scenario, if various sensors in the indoor environment are connected to the Internet of Things system, it will also generate more unnecessary functions. Therefore, the problem of how to achieve low power consumption and reasonable networking to monitor the health of the elderly needs to be solved urgently. Summary of the Invention

[0004] Embodiments of this application provide a networking method based on the Internet of Things and related devices, which can achieve low power consumption and reasonable networking to monitor the health of the elderly.

[0005] In a first aspect, embodiments of this application provide a networking method based on the Internet of Things, which is applied to a master device in a first Internet of Things system. The first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponding to an indoor area, and each indoor area including multiple millimeter-wave radars. The method includes:

[0006] Obtain a first image of a target object through a first camera in the i-th group of cameras, and use image recognition technology to identify the target area and the background area of the target object in the first image. The i-th group of cameras is one group of the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras;

[0007] Obtain the shooting angle parameter when the first camera shoots the first image, determine the first area where the target object is located and the target position in the first area according to the shooting angle parameter, the target area, and the background area, and obtain the indoor map of the first area, and mark the target position corresponding to the target object in the indoor map;

[0008] Obtain the millimeter-wave radars corresponding to the first area, and obtain a millimeter-wave radars, where a is a positive integer;

[0009] Determine the distance between each millimeter-wave radar in the a millimeter-wave radars and the target position, and obtain a distances;

[0010] Determine the detection accuracy corresponding to the a millimeter-wave radars according to the a distances, and obtain a detection accuracies;

[0011] Select the detection accuracies greater than a preset threshold among the a detection accuracies, obtain b detection accuracies, and wake up the b millimeter-wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a;

[0012] Network the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object.

[0013] In a second aspect, an Internet of Things-based networking device provided by an embodiment of the present application is applied to a master device in a first Internet of Things system. The first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponding to an indoor area, and each indoor area including multiple millimeter-wave radars; the device includes: an identification unit, a determination unit, an acquisition unit, a selection unit, and a networking unit, where,

[0014] The identification unit is configured to obtain a first image of a target object through a first camera in the i-th group of cameras, and use image recognition technology to identify the target area and the background area of the target object in the first image; the i-th group of cameras is one group of cameras among the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras;

[0015] The determination unit is configured to obtain a shooting angle parameter when the first camera shoots the first image, determine a first area where the target object is located and a target position in the first area according to the shooting angle parameter, the target area, and the background area, and obtain an indoor map of the first area, and mark the target position corresponding to the target object in the indoor map;

[0016] The acquisition unit is configured to acquire millimeter-wave radars corresponding to the first area, and obtain a millimeter-wave radars, where a is a positive integer;

[0017] The determination unit is further configured to determine the distance between each millimeter-wave radar in the a millimeter-wave radars and the target position, and obtain a distances; determine the detection accuracy corresponding to the a millimeter-wave radars according to the a distances, and obtain a detection accuracies;

[0018] The selection unit is configured to select the detection accuracies greater than a preset threshold among the a detection accuracies, obtain b detection accuracies, and wake up the b millimeter-wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a;

[0019] The networking unit is used to network the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object.

[0020] In a third aspect, an embodiment of the present application provides a main control device, including a processor, a memory, a communication interface, and one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above processor. The above programs include instructions for executing the steps in the first aspect of the embodiments of the present application.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium. Among them, the above computer-readable storage medium stores a computer program for electronic data exchange. Among them, the above computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product. Among them, the above computer program product includes a non-transitory computer-readable storage medium storing a computer program. The above computer program is operable to enable a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product can be a software installation package.

[0023] Implementing the embodiments of the present application has the following beneficial effects:

[0024] It can be seen that the networking method and related devices described in the embodiments of the present application are applied to the master device in the first Internet of Things system. The first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponding to an indoor area, and each indoor area includes multiple millimeter-wave radars. The first image of the target object is obtained through the first camera in the i-th group of cameras, and the target area and background area of the target object in the first image are identified by using image recognition technology; the i-th group of cameras is one group of the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras. The shooting angle parameter when the first camera shoots the first image is obtained, and the first area where the target object is located and the target position in the first area are determined according to the shooting angle parameter, the target area and the background area, and the indoor map of the first area is obtained. The target position corresponding to the target object is marked in the indoor map, the millimeter-wave radar corresponding to the first area is obtained, and a millimeter-wave radars are obtained, where a is a positive integer. The distance between each millimeter-wave radar in the a millimeter-wave radars and the target position is determined, and a distances are obtained. The detection accuracy corresponding to the a millimeter-wave radars is determined according to the a distances, and a detection accuracies are obtained. The detection accuracies greater than the preset threshold are selected from the a detection accuracies, and b detection accuracies are obtained. The b millimeter-wave radars corresponding to the b detection accuracies are awakened; b is a positive integer less than or equal to a. The b millimeter-wave radars and the first Internet of Things system are networked to obtain a second Internet of Things system, and the second Internet of Things system is used to control the b millimeter-wave radars to perform health monitoring on the target object. First, based on the shooting angle parameter, the target area of the target object, and the background area, environmental positioning (image recognition positioning) can accurately locate the position where the millimeter-wave radar needs to detect and the position of the target object with low power consumption, ensuring the accuracy of the millimeter-wave radar detection. Second, the millimeter-wave radar with higher detection accuracy is selected by using the positioning result (target position, position of the millimeter-wave radar), distance, and detection accuracy to ensure that the detection result has high credibility. Third, the millimeter-wave radar with high credibility is networked into the first Internet of Things system to quickly network on the basis of ensuring the original Internet of Things architecture, so as to complete the health monitoring of the target object. In this way, low-power and reasonable networking can be achieved to realize the health monitoring of the target object (the elderly). BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a schematic diagram of the architecture of a first Internet of Things system provided by an embodiment of the present application;

[0027] Figure 2 It is a schematic flowchart of a networking method based on the Internet of Things provided by an embodiment of the present application;

[0028] Figure 3 It is a schematic structural diagram of a main control device provided by an embodiment of the present application;

[0029] Figure 4 It is a block diagram of the functional units of a networking device based on the Internet of Things provided by an embodiment of the present application. Detailed implementation manners

[0030] Terms such as "first" and "second" in the description and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may further include unlisted steps or units in one possible example, or other steps or units inherent to these processes, methods, products or devices in one possible example.

[0031] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0033] The IoT devices and master devices involved in the embodiments of the present application can all include various devices with communication functions. The IoT devices can include but are not limited to: smartphones, tablets, intelligent robots, in-vehicle devices, intelligent gateways, cameras, wearable devices, computing devices, intelligent switches, intelligent gateways, intelligent routers, intelligent vehicles, intelligent driving recorders, intelligent sensors, smart home devices, or other processing devices connected to a wireless modem, as well as various forms of user equipment (UE), mobile stations (MS), terminal devices, etc. There is no limitation here. The IoT device can also be a server.

[0034] Among them, the smart home devices can include at least one of the following: smart TVs, smart sockets, smart rice cookers, smart projectors, smart table lamps, smart massage chairs, smart washing machines, smart refrigerators, etc. There is no limitation here.

[0035] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the architecture of a first IoT system provided by the embodiments of the present application. The first IoT system further includes multiple groups of cameras. Each group of cameras corresponds to an indoor area, and each indoor area includes multiple millimeter-wave radars. The following functions are realized based on this first IoT system:

[0036] Obtain a first image of a target object through a first camera in the i-th group of cameras, and use image recognition technology to identify the target area and the background area of the target object in the first image. The i-th group of cameras is one group of the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras;

[0037] Obtain the shooting angle parameter when the first camera shoots the first image, determine the first area where the target object is located and the target position in the first area according to the shooting angle parameter, the target area, and the background area, and obtain the indoor map of the first area, and mark the target position corresponding to the target object in the indoor map;

[0038] Obtain the millimeter-wave radars corresponding to the target area, and obtain a millimeter-wave radars, where a is a positive integer;

[0039] Determine the distance between each millimeter-wave radar in the a millimeter-wave radars and the target position, and obtain a distances;

[0040] Determine the detection accuracy corresponding to the a millimeter-wave radars according to the a distances, and obtain a detection accuracies;

[0041] Select a detection accuracies greater than a preset threshold from the a detection accuracies to obtain b detection accuracies, and wake up the b millimeter-wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a;

[0042] Network the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object.

[0043] Among them, the master device can communicate directly or indirectly with the camera. For example, the master device can be directly connected to the camera, or the master device can be indirectly connected to the camera through a router or gateway.

[0044] Among them, the camera can be a visible light camera or an infrared camera.

[0045] It can be seen that in the embodiments of the present application, firstly, based on the shooting angle parameter, the target area and the background area of the target object, environmental positioning (image recognition positioning) can accurately locate the position that needs to be detected by the millimeter-wave radar and the position of the target object with low power consumption, ensuring the accuracy of the millimeter-wave radar detection. Secondly, the millimeter-wave radar with higher detection accuracy is selected by using the positioning result (target position, position of the millimeter-wave radar), distance and detection accuracy to ensure that the detection result has high credibility. Thirdly, the millimeter-wave radar with high credibility is networked into the first Internet of Things system to quickly network on the basis of ensuring the original Internet of Things architecture to complete the health monitoring of the target object. In this way, low-power consumption and reasonable networking can be achieved to realize the health monitoring of the target object (the elderly).

[0046] Please refer to Figure 2 , Figure 2 FIG. is a schematic flowchart of a networking method based on the Internet of Things provided by the embodiments of the present application, which is applied to a master device in a first Internet of Things system. The first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponds to an indoor area, and each indoor area includes multiple millimeter-wave radars. This networking method based on the Internet of Things includes:

[0047] 201. Obtain a first image of the target object through a first camera in the i-th group of cameras, and use image recognition technology to identify the target area and the background area of the target object in the first image; the i-th group of cameras is one group of cameras among the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras.

[0048] Among them, the target object can be a person whose age is within a preset age range and / or has a preset type of disease. Among them, the preset age range and the preset type of disease can be set in advance or defaulted by the system.

[0049] Among them, the i-th group of cameras is one group of multiple groups of cameras, and the first camera is one camera in the i-th group of cameras. Each group of cameras can include at least one camera. The camera can be a single camera or a multi-camera (dual-camera). The camera can be a visible light camera or an infrared camera. The camera can be a rotatable camera or a non-rotatable camera. The camera can also be used to implement a ranging function. For example, the camera is a depth camera.

[0050] Among them, the first image can include at least one of the following features of the target object: face, body shape, action, posture, etc., which are not limited here. In a specific implementation, the at least one feature can be used to identify whether it is the target object (the person himself).

[0051] In an embodiment of the present application, the first image of the target object can be obtained through the first camera in the i-th group of cameras. Image recognition technology is used to identify the target area and the background area of the target object in the first image. The image recognition technology can be used to identify whether the target object is the person himself, or the image recognition technology can be used to implement image segmentation to segment out the area where the target object is located (the target area) and the area where the non-target object is located (the background area). Among them, the background area is further object-recognized by using the image recognition technology to identify the type, attribute and position of the object. The attribute can include at least one of the following: color, volume, whether it is movable, whether it is fixed, etc., which are not limited here.

[0052] 202. Obtain the shooting angle parameter when the first camera shoots the first image, determine the first area where the target object is located and the target position in the first area according to the shooting angle parameter, the target area and the background area, and obtain the indoor map of the first area, and mark the target position corresponding to the target object in the indoor map.

[0053] In an embodiment of the present application, the shooting angle parameter can include at least one of the following: shooting angle, shooting direction, etc., which are not limited here.

[0054] In specific implementation, the shooting angle parameter when the first camera shoots the first image can be obtained, and based on the shooting angle parameter, the target area, and the background area, the first area where the target object is located and the target position in the first area can be determined. That is, based on the shooting angle parameter, the target area of the target object, and the background area, environmental positioning is utilized to identify the first area where the target object is located. The first area is an area in the indoor area. The target position in the first area can be a position in the target area where the target object is located, and this position can be a 2D position or a 3D position. For example, the target position can be the position where a certain position (such as the heart, face, etc.) on the body of the target object is located. Due to the camera (image recognition and positioning), no other positioning sensors are required for auxiliary positioning, which can ensure low-power and precise positioning.

[0055] Furthermore, the indoor map of the first area can be obtained. The indoor map can be a 2D map or a 3D map. The target position corresponding to the target object is marked in the indoor map. In this way, the position that needs to be detected by the millimeter-wave radar can be accurately positioned, thereby ensuring the accuracy of the millimeter-wave radar detection.

[0056] 203. Obtain the millimeter-wave radars corresponding to the first area, and obtain a millimeter-wave radars, where a is a positive integer.

[0057] In the embodiments of the present application, since each indoor area includes multiple millimeter-wave radars, different millimeter-wave radars can be responsible for different indoor areas. Due to the possible influence of walls and other interferences (electromagnetic waves) between different areas, different millimeter-wave radars can be set for different indoor areas.

[0058] Among them, the first area can be understood as an indoor area.

[0059] In specific implementation, the mapping relationship between the preset indoor area and the millimeter-wave radar set can be stored in advance. Furthermore, based on this mapping relationship, the millimeter-wave radars corresponding to the first area can be determined, and a millimeter-wave radars can be obtained, where a is a positive integer. Each millimeter-wave radar set can include at least one millimeter-wave radar.

[0060] Among them, in the embodiments of the present application, in order to reduce the power consumption of the Internet of Things system, the a millimeter-wave radars can be not connected to the Internet of Things system in advance and are in a sleep state.

[0061] 204. Determine the distance between each millimeter-wave radar among the a millimeter-wave radars and the target position, and obtain a distances.

[0062] In the embodiments of the present application, the positions of the a millimeter-wave radars in the indoor map can be known. Therefore, the distance between each millimeter-wave radar among the a millimeter-wave radars and the target position can be determined, and a distances can be obtained.

[0063] 205. Determine the detection accuracies corresponding to the a millimeter-wave radars according to the a distances, and obtain a detection accuracies.

[0064] In specific implementation, considering different distances, the detection accuracies corresponding to millimeter-wave radars are different. Therefore, the mapping relationship between preset distances and detection accuracies can be stored in advance. Furthermore, the detection accuracy corresponding to each of the a distances can be determined based on this mapping relationship, and a detection accuracies are obtained.

[0065] 206. Select the detection accuracies greater than a preset threshold among the a detection accuracies, obtain b detection accuracies, and wake up the b millimeter-wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a.

[0066] Among them, the preset threshold can be set in advance or be the system default. The preset threshold can be a fixed value, or the preset threshold can also be related to the distance, and different distances correspond to different preset thresholds. Each detection accuracy can be compared with its corresponding preset threshold to identify whether the detection result of the millimeter-wave radar is credible.

[0067] Among them, if the detection accuracy of a certain millimeter-wave radar is greater than the preset threshold, it means that the detection result of this millimeter-wave radar is credible. On the contrary, if the detection accuracy of a certain millimeter-wave radar is less than or equal to the preset threshold, it means that the detection result of this millimeter-wave radar is not credible.

[0068] In the embodiment of the present application, the detection accuracies greater than the preset threshold can be selected among the a detection accuracies, b detection accuracies are obtained, and the b millimeter-wave radars corresponding to the b detection accuracies are woken up, where b is a positive integer less than or equal to a.

[0069] 207. Network the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object.

[0070] In the embodiment of the present application, since the first Internet of Things system needs to add b millimeter-wave radars, re-networking is required. Therefore, the b millimeter-wave radars and the first Internet of Things system can be networked to obtain a second Internet of Things system, and the second Internet of Things system is used to control the b millimeter-wave radars to perform health monitoring on the target object. In this way, low power consumption and reasonable networking can be achieved to realize health monitoring of the target object (the elderly).

[0071] Optionally, in step 207 above, networking the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system may include the following steps:

[0072] Determine the positions of the b millimeter-wave radars to obtain b positions;

[0073] Determine the shortest distance between each position among the b positions and the devices in the first Internet of Things system to obtain b shortest distances, and acquire the devices corresponding to the b shortest distances to obtain b Internet of Things devices;

[0074] Connect each Internet of Things device among the b Internet of Things devices to the corresponding millimeter-wave radar to obtain the second Internet of Things system.

[0075] Among them, there may be identical Internet of Things devices among the b Internet of Things devices, or there may be no identical Internet of Things devices among the b Internet of Things devices.

[0076] In a specific implementation, it is possible to determine the positions of the b millimeter-wave radars to obtain b positions, and determine the shortest distance between each position among the b positions and the devices in the first Internet of Things system to obtain b shortest distances, and acquire the devices corresponding to the b shortest distances to obtain b Internet of Things devices, and then connect each Internet of Things device among the b Internet of Things devices to the corresponding millimeter-wave radar to obtain the second Internet of Things system. In this way, it is possible to identify the Internet of Things device closest to each millimeter-wave radar among the b millimeter-wave radars and use this Internet of Things device to form a network with the corresponding millimeter-wave radar. That is, it is possible to quickly connect new devices while keeping the original Internet of Things architecture unchanged, achieve rapid networking, and also reduce the data transmission distance of the millimeter-wave radar to ensure data transmission efficiency.

[0077] Optionally, in step 207 above, using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object may include the following steps:

[0078] Obtain the b distances and b detection precisions corresponding to the b millimeter-wave radars;

[0079] Determine the detection frequency corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances and the b detection precisions to obtain b detection frequencies;

[0080] Determine the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances to obtain b transmission powers;

[0081] Determine b transmission directions according to the b positions and the target position;

[0082] Determine the communication paths between the master device and the b millimeter-wave radars according to the second Internet of Things system to obtain b communication paths;

[0083] Determine b health monitoring instructions according to the b detection frequencies, the b transmission powers, and the b transmission directions, where each of the b health monitoring instructions carries the detection frequency, the transmission power, and the transmission direction corresponding to a respective millimeter-wave radar;

[0084] Send the b health monitoring instructions to the b millimeter-wave radars through the b communication paths to control the corresponding millimeter-wave radars to perform health monitoring at the corresponding detection frequencies, transmission powers, and transmission directions.

[0085] Among them, there may be identical communication paths among the b communication paths, or the communication paths among the b communication paths are all different.

[0086] In the embodiments of the present application, the distance and the detection accuracy corresponding to each millimeter-wave radar among the b millimeter-wave radars can be obtained, resulting in b distances and b detection accuracies, where each millimeter-wave radar corresponds to a distance and a detection accuracy.

[0087] Next, the distance reflects the actual environment (spatial environment (distance)), and the interference corresponding to different distances is different, so different detection frequencies are required. The detection accuracy reflects the inherent property of the millimeter-wave radar, and different detection accuracies may also correspond to different detection frequencies. Furthermore, the detection frequency corresponding to each millimeter-wave radar among the b millimeter-wave radars can be determined according to the b distances and the b detection accuracies, resulting in b detection frequencies. In this way, the detection frequency can be deeply adapted to the actual environment (spatial environment (distance)) and the inherent property of the millimeter-wave radar.

[0088] In specific implementation, the mapping relationship between the preset distance and the transmission power can be stored in advance. Furthermore, the transmission power corresponding to each of the b distances can be determined based on this mapping relationship, so that the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars can be obtained, that is, b transmission powers, making the transmission power deeply adapted to the distance, which can not only ensure the detection efficiency of the millimeter-wave radar but also make the power consumption most reasonable.

[0089] Next, the b transmission directions can also be determined according to the b positions and the target position, where each transmission direction can correspond to the direction from the position corresponding to each millimeter-wave radar to the target position, thereby ensuring the detection efficiency of the millimeter-wave radar.

[0090] Next, it is also possible to determine the communication paths between the master device and the b millimeter-wave radars according to the second Internet of Things system, obtaining b communication paths. Then, b health monitoring instructions can be determined based on the b detection frequencies, b transmission powers, and b transmission directions. Each of the b health monitoring instructions carries the detection frequency, transmission power, and transmission direction corresponding to a respective millimeter-wave radar. Subsequently, the b health monitoring instructions are sent to the b millimeter-wave radars through the b communication paths to control the corresponding millimeter-wave radars to perform health monitoring at the corresponding detection frequencies, transmission powers, and transmission directions. In this way, firstly, the corresponding detection frequencies can be adapted based on the distance and detection accuracy, enabling the detection effect to deeply conform to the actual environment and the inherent properties of the millimeter-wave radars. Secondly, the corresponding transmission distance and transmission direction can be adapted based on the actual distance and positional relationship. Thirdly, based on the network architecture of the Internet of Things system and reasonable path planning, it is ensured that each millimeter-wave radar can be independently controlled. Thus, the health monitoring instructions can be sent synchronously to ensure the consistency and synchrony of health monitoring. In this way, low power consumption and reasonable networking can be achieved to realize health monitoring of the target object (the elderly).

[0091] Optionally, for the above step of determining the detection frequency corresponding to each of the b millimeter-wave radars according to the b distances and the b detection accuracies, obtaining b detection frequencies, it can be implemented as follows:

[0092] Determine b first reference detection frequencies corresponding to the b distances;

[0093] Determine b second reference detection frequencies corresponding to the b detection accuracies;

[0094] Determine the remaining service lives corresponding to the b millimeter-wave radars, obtaining b remaining service lives;

[0095] Determine the weight pairs corresponding to the b remaining service lives, obtaining b weight pairs;

[0096] Perform a weighted operation based on the b first reference detection frequencies, the b second reference detection frequencies, and the b weight pairs to obtain the b detection frequencies.

[0097] In specific implementation, the first mapping relationship between the preset distance and the detection frequency can be pre-stored. Furthermore, based on this first mapping relationship, the detection frequency corresponding to each of the b distances can be determined, obtaining b first reference detection frequencies. Then, the second mapping relationship between the preset detection accuracy and the detection frequency can also be pre-stored. Furthermore, based on this second mapping relationship, the detection frequency corresponding to each of the b detection accuracies can be determined, obtaining b second reference detection frequencies.

[0098] Next, the remaining service life corresponding to b millimeter-wave radars can also be determined to obtain b remaining service lives. Due to the influence of the usage duration of the millimeter-wave radar, there will also be a certain attenuation in the millimeter-wave radar itself, and the weights of distance and detection accuracy will also fluctuate dynamically. Therefore, the mapping relationship between the preset remaining service life and the weight pair can be pre-stored. Furthermore, based on this mapping relationship, the weight pair corresponding to each remaining service life among the b remaining service lives can be determined to obtain b weight pairs. Each weight pair among the b weight pairs includes two weights, and the sum of the two weights is 1. The two weights are the weights corresponding to distance and detection accuracy respectively. Finally, weighted operations can be performed based on the b first reference detection frequencies, the b second reference detection frequencies, and the b weight pairs to obtain b detection frequencies, that is, each first reference detection frequency is weighted with its corresponding second reference detection frequency and weight pair to obtain the corresponding detection frequency. It can not only adapt the corresponding detection frequency based on distance and detection accuracy, but also make the weights corresponding to distance and detection accuracy consistent with the usage conditions of the corresponding millimeter-wave radar, so that the detection effect deeply conforms to the actual environment and the inherent properties of the millimeter-wave radar. Thus, it helps to ensure low power consumption and reasonable networking to achieve precise health monitoring of the target object (the elderly).

[0099] Optionally, the above step of determining the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances to obtain b transmission powers can be implemented as follows:

[0100] Determine the first reference transmission power corresponding to the first distance, where the first distance is any one of the b distances;

[0101] Send a first test signal to the target position at the first reference transmission power through the first millimeter-wave radar corresponding to the first distance, and receive a first feedback signal;

[0102] Determine the first signal-to-noise ratio of the first feedback signal;

[0103] When the first signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, use the first reference transmission power as the transmission power corresponding to the first distance;

[0104] When the first signal-to-noise ratio is less than the preset signal-to-noise ratio, determine the first deviation degree between the first signal-to-noise ratio and the preset signal-to-noise ratio;

[0105] Determine the first adjustment parameter corresponding to the first deviation degree;

[0106] Adjust the first reference transmission power according to the first adjustment parameter to obtain a second reference transmission power, and use the second reference transmission power as the transmission power corresponding to the first distance.

[0107] Among them, the preset signal-to-noise ratio can be set in advance or be the system default.

[0108] In a specific implementation, the mapping relationship between the preset distance and the transmission power can be stored in advance. Taking the first distance as an example, the first distance is any one of the b distances, and the first reference transmission power corresponding to the first distance can be determined based on this mapping relationship.

[0109] In a specific implementation, the first millimeter-wave radar corresponding to the first distance can send a first test signal to the target position at the first reference transmission power, receive the first feedback signal, and determine the first signal-to-noise ratio of the first feedback signal. When the first signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, it indicates that the millimeter-wave radar is not interfered or the interference is small, and the corresponding detection result is credible. Furthermore, the first reference transmission power can be directly used as the transmission power corresponding to the first distance.

[0110] Correspondingly, when the first signal-to-noise ratio is less than the preset signal-to-noise ratio, the first deviation degree between the first signal-to-noise ratio and the preset signal-to-noise ratio can be determined. The first deviation degree = (preset signal-to-noise ratio - first signal-to-noise ratio) / preset signal-to-noise ratio. Furthermore, the mapping relationship between the preset deviation degree and the adjustment parameter can be stored in advance, and the first adjustment parameter corresponding to the first deviation degree can be determined based on this mapping relationship. The first adjustment parameter is greater than 0. For example, the first adjustment parameter can be from 0 to 0.2. Then, the first reference transmission power is adjusted according to the first adjustment parameter to obtain the second reference transmission power. The second reference transmission power = (1 + first adjustment parameter) * second reference transmission power. The second reference transmission power is used as the transmission power corresponding to the first distance. In this way, after initially determining the transmission power of the millimeter-wave radar, it can be tested. When the test result meets the conditions, the initially determined transmission power of the millimeter-wave radar is directly used as the final transmission power of the millimeter-wave radar. On the contrary, when the test result does not meet the conditions, the transmission power of the millimeter-wave radar is adjusted to improve its signal anti-interference ability, thereby ensuring the detection accuracy of the millimeter-wave radar.

[0111] Optionally, the above steps of using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object can be implemented in the following ways:

[0112] Perform health monitoring on the target object through the b millimeter-wave radars to obtain b health monitoring results;

[0113] Determine the physical condition evaluation values corresponding to the b health monitoring results to obtain b physical condition evaluation values;

[0114] Determine the target weight set corresponding to the b millimeter-wave radars;

[0115] Perform operations based on the target weight set and the b physical condition evaluation values to obtain a target physical condition evaluation value.

[0116] Among them, the health monitoring results may include at least one of the following: heart rate data, respiratory data, etc., which are not limited here.

[0117] In the embodiments of the present application, each of the b millimeter-wave radars can be used to perform health monitoring on the target object to obtain b health monitoring results, and each health monitoring result corresponds to a millimeter-wave radar. A mapping relationship between the preset health monitoring results and the physical condition evaluation values can also be pre-stored. Furthermore, based on this mapping relationship, the physical condition evaluation value corresponding to each of the b health monitoring results can be determined to obtain b physical condition evaluation values.

[0118] Next, a target weight set corresponding to the b millimeter-wave radars can also be determined. The target weight set may include b weights, and the sum of the b weights is 1. Then, perform operations based on the target weight set and the b physical condition evaluation values to obtain a target physical condition evaluation value. In this way, based on the health monitoring results of the b millimeter-wave radars and jointly monitoring the target object by multiple millimeter-wave radars, it helps to ensure low power consumption and reasonable networking to achieve accurate health monitoring of the target object (the elderly).

[0119] Furthermore, when the target physical condition evaluation value is less than a certain threshold, health warning can be carried out, and a remote alarm function can also be carried out, thereby ensuring the safety of the elderly. On the contrary, it means that the target object is in a healthy state.

[0120] Optionally, when the target object is in the first region, the following steps may further be included:

[0121] Determine the b positions corresponding to the b millimeter-wave radars;

[0122] Determine the center of the b positions;

[0123] Record the first position of the target position of the target object;

[0124] Determine the distance x between the center and the first position;

[0125] Update the target position at each preset time interval to obtain a second position;

[0126] Determine the distance y between the center and the second position;

[0127] Determine the difference between the distance y and the distance x;

[0128] When the difference is greater than a preset distance, change the second Internet of Things system back to the first Internet of Things system, and according to the second position, perform the step of determining the distance between each of the a millimeter wave radars and the target position to obtain a distances.

[0129] Among them, the preset time interval can be set in advance or be the system default. The preset distance can be set in advance or be the system default, and the preset distance is greater than 0.

[0130] In the embodiments of the present application, when the target object is in the first region, b positions corresponding to b millimeter wave radars can be determined, the center of the b positions can be determined, and the center can be the geometric center. Record the first position of the target position of the target object, that is, the target position in step 202. The distance x between the center and the first position can also be determined. The target position can also be updated every preset time interval to obtain the second position, and then the distance y between the center and the second position can be determined. Then, the difference between the distance y and the distance x can be determined, and the difference = distance y - distance x. When the difference is greater than the preset distance, change the second Internet of Things system back to the first Internet of Things system, that is, disconnect the communication connection with the b millimeter wave radars and retain the original first Internet of Things system. This indicates that the position of the target object has changed. To ensure the accuracy of health monitoring, new millimeter wave radars need to be re-determined for health monitoring. Then, according to the second position, perform the step of determining the distance between each of the a millimeter wave radars and the target position to obtain a distances, that is, use the second position as the target position of the new target object and re-organize the network. In this way, low-power and reasonable networking can be achieved to monitor the health of the target object (the elderly).

[0131] Further, the b health monitoring results may include multiple health monitoring results. Each health monitoring result corresponds to a sampling moment, and each sampling moment corresponds to an evaluation value of the target physical condition. Furthermore, based on the evaluation value of the target physical condition and the corresponding sampling moment, a fitting can be performed. Specifically, the evaluation value of the target physical condition and the corresponding sampling moment can be mapped to a coordinate system. The horizontal axis of this coordinate system is time and the vertical axis is the evaluation value of the target physical condition. That is, each evaluation value of the target physical condition and the corresponding sampling moment can correspond to a coordinate point. Thus, multiple coordinate points can be obtained. By fitting these multiple coordinate points, a fitting line is obtained. Determine the first absolute value of the slope of the fitting line. According to the preset mapping relationship between the absolute value and the time interval, based on this mapping relationship, determine the preset time interval corresponding to the first absolute value. In this way, the first absolute value reflects the physical change trend of the target object. The larger the first absolute value, the lower the safety, and the smaller the preset time interval. On the contrary, the smaller the first absolute value, the greater the safety, and the corresponding preset time interval is larger. This makes the time interval deeply related to the safety of the target object, thereby ensuring the safety of the target object.

[0132] Optionally, for the above steps, to perform health monitoring on the target object through the b millimeter-wave radars and obtain b health monitoring results, it can be implemented in the following manner:

[0133] Send a second test signal to the target position through the first millimeter-wave radar at the second reference transmission power and receive a second feedback signal;

[0134] Determine the second signal-to-noise ratio of the second feedback signal;

[0135] According to the preset mapping relationship between the accuracy and the weight, determine the reference weight corresponding to the accuracy of the first distance;

[0136] When the second signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, perform health monitoring on the target object through the first millimeter-wave radar to obtain the health monitoring result corresponding to the first millimeter-wave radar;

[0137] When the second signal-to-noise ratio is less than the preset signal-to-noise ratio, determine the second deviation degree between the second signal-to-noise ratio and the first signal-to-noise ratio;

[0138] When the second deviation degree is greater than the preset deviation degree, execute the step of performing health monitoring on the target object through the first millimeter-wave radar to obtain the health monitoring result corresponding to the first millimeter-wave radar;

[0139] When the second deviation degree is less than or equal to the preset deviation degree, continue to put the first millimeter-wave radar into sleep and set the health monitoring result corresponding to the first millimeter-wave radar to 0.

[0140] In a specific implementation, taking the first millimeter-wave radar as an example, the first millimeter-wave radar can send a second test signal to the target position at a second reference transmission power and receive a second feedback signal to determine the second signal-to-noise ratio of the second feedback signal. That is, based on the adjusted transmission power, the test can continue to detect whether the power adjustment is effective. Since the transmission power increases, the signal-to-noise ratio of the feedback signal also increases, and the second signal-to-noise ratio is greater than the first signal-to-noise ratio.

[0141] Furthermore, the mapping relationship between the preset precision and the weight can be pre-stored, and then according to this mapping relationship, the reference weight corresponding to the precision corresponding to the first distance is determined. Different precisions correspond to different weights, making the detection result suitable for the actual situation.

[0142] Next, when the second signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, it indicates that the adjusted transmission power is effective, and the corresponding detection result is credible. That is, the first millimeter-wave radar can be allowed to perform health monitoring, that is, the first millimeter-wave radar performs health monitoring on the target object to obtain the health monitoring result corresponding to the first millimeter-wave radar.

[0143] Furthermore, when the second signal-to-noise ratio is less than the preset signal-to-noise ratio, the second deviation degree between the second signal-to-noise ratio and the first signal-to-noise ratio can be determined. The second deviation degree = (second signal-to-noise ratio - first signal-to-noise ratio) / first signal-to-noise ratio. The second deviation degree reflects the improvement effect of the feedback signal after the transmission power adjustment.

[0144] Correspondingly, when the second deviation degree is greater than the preset deviation degree, it indicates that the millimeter-wave radar may be interfered, but the improvement effect of the feedback signal after the transmission power adjustment meets the expectation and has a certain credibility. Then, the step of performing health monitoring on the target object through the first millimeter-wave radar to obtain the health monitoring result corresponding to the first millimeter-wave radar can be continued. On the contrary, when the second deviation degree is less than or equal to the preset deviation degree, it indicates that the millimeter-wave radar may be interfered, and the improvement effect of the feedback signal after the transmission power adjustment does not meet the expectation, that is, the detection result of the first millimeter-wave radar is not credible. Then, the first millimeter-wave radar can be continued to be in a sleep state, and the health monitoring result corresponding to the first millimeter-wave radar is set to 0. In this way, the power consumption of the IoT system can be reduced, and it also helps to ensure low power consumption and reasonable networking to achieve precise health monitoring of the target object (the elderly).

[0145] Optionally, for the above step of determining the target weight set corresponding to the b millimeter-wave radars, it can be implemented in the following manner:

[0146] Determine the weight corresponding to each millimeter-wave radar among the b millimeter-wave radars in the manner of the following steps S1 - S5 to obtain b weights, specifically as follows:

[0147] S1. Determine the reference weight value of the accuracy corresponding to the first distance according to the mapping relationship between the preset accuracy and the weight value.

[0148] S2. When the second signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, use the reference weight value as the weight value corresponding to the first millimeter-wave radar.

[0149] S3. When the second signal-to-noise ratio is less than the preset signal-to-noise ratio and the second deviation is less than or equal to the preset deviation, determine the first feedback adjustment parameter corresponding to the second deviation.

[0150] S4. Determine the weight value corresponding to the first millimeter-wave radar according to the first feedback adjustment parameter and the reference weight value.

[0151] S5. When the second deviation is less than or equal to the preset deviation, use 0 as the weight value corresponding to the first millimeter-wave radar.

[0152] Determine the sum of the b weight values to obtain the weight sum.

[0153] Determine the target weight value set according to the b weight values and the weight sum.

[0154] In the embodiments of the present application, the weights corresponding to each of the b millimeter-wave radars can be determined in the following manner to obtain b weights. Specifically, the mapping relationship between the preset accuracy and the weights can be stored in advance. The greater the accuracy, the more credible the detection result, and correspondingly, the greater the weight. Then, based on this mapping relationship, the reference weight corresponding to the accuracy corresponding to the first distance is determined. When the second signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, it indicates that the detection result is credible, and the reference weight can be used as the weight corresponding to the first millimeter-wave radar. When the second signal-to-noise ratio is less than the preset signal-to-noise ratio and the second deviation is greater than the preset deviation, it indicates that the millimeter-wave radar may be interfered, but the improvement effect of the feedback signal after the transmission power adjustment meets the expectation. Furthermore, the mapping relationship between the preset deviation and the feedback adjustment parameter can be stored in advance. The greater the deviation, the greater its credibility and the smaller the gap from being completely credible. On the contrary, the smaller the deviation, the greater its credibility and the larger the gap from being completely credible. The value range of the feedback adjustment parameter can be 0 to 0.15. The greater the deviation, the smaller the feedback adjustment parameter. On the contrary, the smaller the deviation, the greater the feedback adjustment parameter. Furthermore, the first feedback adjustment parameter corresponding to the second deviation can be determined based on this mapping relationship, and then the weight corresponding to the first millimeter-wave radar can be determined according to the first feedback adjustment parameter and the reference weight, that is, the weight corresponding to the first millimeter-wave radar = (1 - the first feedback adjustment parameter) * the reference weight. In this way, when the second signal-to-noise ratio is less than the preset signal-to-noise ratio and the second deviation is greater than the preset deviation, the detection result in this case has a certain credibility and there is a certain gap from being completely credible. Furthermore, its corresponding weight can be feedback-adjusted (reduced) so that the final weight depth conforms to the actual situation.

[0155] Correspondingly, when the second deviation is greater than the preset deviation, it indicates that the detection result of the first millimeter-wave radar is not credible, and 0 is used as the weight corresponding to the first millimeter-wave radar. In this way, the power consumption of the Internet of Things system can be reduced, and it also helps to ensure low power consumption and reasonable networking to achieve precise health monitoring of the target object (such as the elderly).

[0156] Next, the weights of each of the b millimeter-wave radars can be determined in the above manner, and then the sum of the b weights is determined to obtain the weight sum. The target weight set is determined according to the b weights and the weight sum, that is, the ratio between each of the b weights and the weight sum is determined to obtain b ratios. Each ratio corresponds to a weight, and the b ratios are the b final weights, that is, the target weight set.

[0157] It can be seen that the networking method based on the Internet of Things described in the embodiments of the present application is applied to the master device in the first Internet of Things system. The first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponding to an indoor area, and each indoor area includes multiple millimeter-wave radars. The first image of the target object is obtained through the first camera in the i-th group of cameras, and the target area and background area of the target object in the first image are identified by using image recognition technology; the i-th group of cameras is one of the multiple groups of cameras, and the first camera is one of the cameras in the i-th group of cameras. The shooting angle parameter when the first camera shoots the first image is obtained, and based on the shooting angle parameter, the target area and the background area, the first area where the target object is located and the target position in the first area are determined, and the indoor map of the first area is obtained. The target position corresponding to the target object is marked in the indoor map, the millimeter-wave radar corresponding to the first area is obtained, and a millimeter-wave radars are obtained, where a is a positive integer. The distance between each millimeter-wave radar in the a millimeter-wave radars and the target position is determined, and a distances are obtained. Based on the a distances, the detection accuracies corresponding to the a millimeter-wave radars are determined, and a detection accuracies are obtained. The detection accuracies greater than the preset threshold are selected from the a detection accuracies, and b detection accuracies are obtained. The b millimeter-wave radars corresponding to the b detection accuracies are awakened; b is a positive integer less than or equal to a. The b millimeter-wave radars and the first Internet of Things system are networked to obtain a second Internet of Things system, and the second Internet of Things system is used to control the b millimeter-wave radars to perform health monitoring on the target object. First, based on the shooting angle parameter, the target area of the target object, and the background area, environmental positioning (image recognition positioning) can accurately locate the position that needs to be detected by the millimeter-wave radar and the position of the target object with low power consumption, ensuring the accuracy of the millimeter-wave radar detection. Second, the millimeter-wave radars with higher detection accuracies are selected by using the positioning results (target position, position of the millimeter-wave radar), distance, and detection accuracy to ensure that the detection results have high credibility. Third, the millimeter-wave radars with high credibility are networked into the first Internet of Things system to achieve fast networking on the basis of ensuring the original Internet of Things architecture, so as to complete the health monitoring of the target object. In this way, low-power and reasonable networking can be achieved to realize the health monitoring of the target object (the elderly).

[0158] Consistently with the above embodiments, please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a master device provided by an embodiment of the present application. The master device includes a processor, a memory, a communication interface, and one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above processor. In the embodiments of the present application, it is applied to the first Internet of Things system, and the first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponding to an indoor area, and each indoor area includes multiple millimeter-wave radars; the above programs include instructions for performing the following steps:

[0159] Obtain a first image of a target object through a first camera in the i-th group of cameras, and use image recognition technology to identify the target area and background area of the target object in the first image; the i-th group of cameras is a group of cameras among the multiple groups of cameras, and the first camera is a camera in the i-th group of cameras;

[0160] Obtain the shooting angle parameter when the first camera shoots the first image, determine the first area where the target object is located and the target position in the first area according to the shooting angle parameter, the target area and the background area, and obtain the indoor map of the first area, and mark the target position corresponding to the target object in the indoor map;

[0161] Obtain the millimeter-wave radar corresponding to the first area, and obtain a millimeter-wave radars, where a is a positive integer;

[0162] Determine the distance between each millimeter-wave radar in the a millimeter-wave radars and the target position, and obtain a distances;

[0163] Determine the detection accuracy corresponding to the a millimeter-wave radars according to the a distances, and obtain a detection accuracies;

[0164] Select the detection accuracies greater than a preset threshold among the a detection accuracies, and obtain b detection accuracies, and wake up the b millimeter-wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a;

[0165] Network the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object.

[0166] Optionally, in terms of networking the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, the above program includes instructions for performing the following steps:

[0167] Determine the positions of the b millimeter-wave radars, and obtain b positions;

[0168] Determine the shortest distance between each position in the b positions and the devices in the first Internet of Things system, and obtain b shortest distances, and obtain the devices corresponding to the b shortest distances, and obtain b Internet of Things devices;

[0169] Connect each Internet of Things device in the b Internet of Things devices to the corresponding millimeter-wave radar to obtain the second Internet of Things system.

[0170] Optionally, in the aspect of using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object, the above program includes instructions for performing the following steps:

[0171] Obtain b distances and b detection precisions corresponding to the b millimeter-wave radars;

[0172] Determine the detection frequency corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances and the b detection precisions, and obtain b detection frequencies;

[0173] Determine the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances, and obtain b transmission powers;

[0174] Determine b transmission directions according to the b positions and the target position;

[0175] Determine the communication paths between the master control device and the b millimeter-wave radars according to the second Internet of Things system, and obtain b communication paths;

[0176] Determine b health monitoring instructions according to the b detection frequencies, the b transmission powers, and the b transmission directions, and each of the b health monitoring instructions carries the detection frequency, transmission power, and transmission direction corresponding to a respective millimeter-wave radar;

[0177] Send the b health monitoring instructions to the b millimeter-wave radars through the b communication paths, so as to control the corresponding millimeter-wave radars to perform health monitoring at the corresponding detection frequencies, transmission powers, and transmission directions.

[0178] Optionally, in the aspect of determining the detection frequency corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances and the b detection precisions, and obtaining b detection frequencies, the above program includes instructions for performing the following steps:

[0179] Determine b first reference detection frequencies corresponding to the b distances;

[0180] Determine b second reference detection frequencies corresponding to the b detection precisions;

[0181] Determine the remaining service lives corresponding to the b millimeter-wave radars, and obtain b remaining service lives;

[0182] Determine weight pairs corresponding to the b remaining service lives, and obtain b weight pairs;

[0183] Perform weighted operations according to the b first reference detection frequencies, the b second reference detection frequencies, and the b weight pairs, and obtain the b detection frequencies.

[0184] Optionally, in the aspect of determining the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances to obtain b transmission powers, the above program includes instructions for performing the following steps:

[0185] Determine a first reference transmission power corresponding to a first distance, where the first distance is any one of the b distances;

[0186] Send a first test signal from the first millimeter-wave radar corresponding to the first distance to the target position at the first reference transmission power and receive a first feedback signal;

[0187] Determine a first signal-to-noise ratio of the first feedback signal;

[0188] When the first signal-to-noise ratio is greater than or equal to a preset signal-to-noise ratio, use the first reference transmission power as the transmission power corresponding to the first distance;

[0189] When the first signal-to-noise ratio is less than the preset signal-to-noise ratio, determine a first deviation degree between the first signal-to-noise ratio and the preset signal-to-noise ratio;

[0190] Determine a first adjustment parameter corresponding to the first deviation degree;

[0191] Adjust the first reference transmission power according to the first adjustment parameter to obtain a second reference transmission power, and use the second reference transmission power as the transmission power corresponding to the first distance.

[0192] Optionally, in the aspect of using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object, the above program includes instructions for performing the following steps:

[0193] Perform health monitoring on the target object through the b millimeter-wave radars to obtain b health monitoring results;

[0194] Determine body condition evaluation values corresponding to the b health monitoring results to obtain b body condition evaluation values;

[0195] Determine a target weight set corresponding to the b millimeter-wave radars;

[0196] Perform an operation according to the target weight set and the b body condition evaluation values to obtain a target body condition evaluation value.

[0197] Optionally, in the aspect of performing health monitoring on the target object through the b millimeter-wave radars to obtain b health monitoring results, the above program includes instructions for performing the following steps:

[0198] Transmit a second test signal to the target position at the second reference transmission power through the first millimeter-wave radar, and receive a second feedback signal;

[0199] Determine the second signal-to-noise ratio of the second feedback signal;

[0200] Determine the reference weight of the accuracy corresponding to the first distance according to the mapping relationship between the preset accuracy and the weight;

[0201] When the second signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, perform health monitoring on the target object through the first millimeter-wave radar to obtain the health monitoring result corresponding to the first millimeter-wave radar;

[0202] When the second signal-to-noise ratio is less than the preset signal-to-noise ratio, determine the second deviation between the second signal-to-noise ratio and the first signal-to-noise ratio;

[0203] When the second deviation is greater than the preset deviation, execute the step of performing health monitoring on the target object through the first millimeter-wave radar to obtain the health monitoring result corresponding to the first millimeter-wave radar;

[0204] When the second deviation is less than or equal to the preset deviation, continue to put the first millimeter-wave radar into sleep, and set the health monitoring result corresponding to the first millimeter-wave radar to 0.

[0205] It can be seen that the master device described in the embodiments of the present application is applied to a first Internet of Things system, which further includes multiple groups of cameras. Each group of cameras corresponds to an indoor area, and each indoor area includes multiple millimeter-wave radars. A first image of a target object is obtained through a first camera in the i-th group of cameras, and the target area and background area of the target object in the first image are identified using image recognition technology; the i-th group of cameras is one group of the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras. The shooting angle parameter when the first camera shoots the first image is obtained, and based on the shooting angle parameter, the target area and the background area, the first area where the target object is located and the target position in the first area are determined, and the indoor map of the first area is obtained. The target position corresponding to the target object is marked in the indoor map, the millimeter-wave radars corresponding to the first area are obtained, and a millimeter-wave radars are obtained, where a is a positive integer. The distance between each millimeter-wave radar in the a millimeter-wave radars and the target position is determined, and a distances are obtained. Based on the a distances, the detection accuracies corresponding to the a millimeter-wave radars are determined, and a detection accuracies are obtained. The detection accuracies greater than a preset threshold among the a detection accuracies are selected, and b detection accuracies are obtained. The b millimeter-wave radars corresponding to the b detection accuracies are awakened; b is a positive integer less than or equal to a. The b millimeter-wave radars and the first Internet of Things system are networked to obtain a second Internet of Things system, and the second Internet of Things system is used to control the b millimeter-wave radars to perform health monitoring on the target object. First, based on the shooting angle parameter, the target area of the target object, and the background area, environmental positioning (image recognition positioning) can accurately locate the position where the millimeter-wave radar needs to detect and the position of the target object with low power consumption, ensuring the accuracy of the millimeter-wave radar detection. Second, the millimeter-wave radars with higher detection accuracies are selected using the positioning results (target position, position of the millimeter-wave radar), distance, and detection accuracy to ensure that the detection results have high credibility. Third, the millimeter-wave radars with high credibility are networked into the first Internet of Things system to quickly network on the basis of ensuring the original Internet of Things architecture to complete the health monitoring of the target object. In this way, low-power and reasonable networking can be achieved to realize the health monitoring of the target object (the elderly).

[0206] Figure 4 It is a functional unit composition block diagram of a networking device 400 based on the Internet of Things involved in the embodiments of the present application, which is applied to the master device in the first Internet of Things system. The first Internet of Things system further includes multiple groups of cameras. Each group of cameras corresponds to an indoor area, and each indoor area includes multiple millimeter-wave radars; the networking device 400 based on the Internet of Things includes: an identification unit 401, a determination unit 402, an acquisition unit 403, a selection unit 404, and a networking unit 405, where

[0207] The recognition unit 401 is configured to obtain a first image of a target object through a first camera in the i-th group of cameras, and use image recognition technology to recognize the target area and the background area of the target object in the first image; the i-th group of cameras is a group of cameras among the multiple groups of cameras, and the first camera is a camera in the i-th group of cameras;

[0208] The determination unit 402 is configured to obtain the shooting angle parameter when the first camera shoots the first image, determine the first area where the target object is located and the target position in the first area according to the shooting angle parameter, the target area and the background area, and obtain the indoor map of the first area, and mark the target position corresponding to the target object in the indoor map;

[0209] The acquisition unit 403 is configured to acquire millimeter wave radars corresponding to the first area, and obtain a millimeter wave radars, where a is a positive integer;

[0210] The determination unit 402 is further configured to determine the distance between each millimeter wave radar in the a millimeter wave radars and the target position, and obtain a distances; determine the detection accuracy corresponding to the a millimeter wave radars according to the a distances, and obtain a detection accuracies;

[0211] The selection unit 404 is configured to select the detection accuracies greater than a preset threshold from the a detection accuracies, obtain b detection accuracies, and wake up the b millimeter wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a;

[0212] The networking unit 405 is configured to network the b millimeter wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter wave radars to perform health monitoring on the target object.

[0213] Optionally, in terms of networking the b millimeter wave radars and the first Internet of Things system to obtain a second Internet of Things system, the networking unit 405 is specifically configured to:

[0214] Determine the positions of the b millimeter wave radars to obtain b positions;

[0215] Determine the shortest distance between each position in the b positions and the devices in the first Internet of Things system to obtain b shortest distances, and acquire the devices corresponding to the b shortest distances to obtain b Internet of Things devices;

[0216] Connect each millimeter wave radar through each Internet of Things device in the b Internet of Things devices to obtain the second Internet of Things system.

[0217] Optionally, in the aspect of using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object, the networking unit 405 is specifically configured to:

[0218] Obtain b distances and b detection precisions corresponding to the b millimeter-wave radars;

[0219] Determine the detection frequency corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances and the b detection precisions, and obtain b detection frequencies;

[0220] Determine the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances, and obtain b transmission powers;

[0221] Determine b transmission directions according to the b positions and the target position;

[0222] Determine the communication paths between the master control device and the b millimeter-wave radars according to the second Internet of Things system, and obtain b communication paths;

[0223] Determine b health monitoring instructions according to the b detection frequencies, the b transmission powers, and the b transmission directions, and each of the b health monitoring instructions carries the detection frequency, transmission power, and transmission direction corresponding to a respective millimeter-wave radar;

[0224] Send the b health monitoring instructions to the b millimeter-wave radars through the b communication paths, so as to control the corresponding millimeter-wave radars to perform health monitoring at the corresponding detection frequencies, transmission powers, and transmission directions.

[0225] Optionally, in the aspect of determining the detection frequency corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances and the b detection precisions, and obtaining b detection frequencies, the networking unit 405 is specifically configured to:

[0226] Determine b first reference detection frequencies corresponding to the b distances;

[0227] Determine b second reference detection frequencies corresponding to the b detection precisions;

[0228] Determine the remaining service lives corresponding to the b millimeter-wave radars, and obtain b remaining service lives;

[0229] Determine weight pairs corresponding to the b remaining service lives, and obtain b weight pairs;

[0230] Perform weighted operations according to the b first reference detection frequencies, the b second reference detection frequencies, and the b weight pairs to obtain the b detection frequencies.

[0231] Optionally, in the aspect of determining the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances to obtain b transmission powers, the networking unit 405 is specifically configured to:

[0232] Determine a first reference transmission power corresponding to a first distance, where the first distance is any one of the b distances;

[0233] Send a first test signal to the target location at the first reference transmission power through the first millimeter-wave radar corresponding to the first distance, and receive a first feedback signal;

[0234] Determine a first signal-to-noise ratio of the first feedback signal;

[0235] When the first signal-to-noise ratio is greater than or equal to a preset signal-to-noise ratio, use the first reference transmission power as the transmission power corresponding to the first distance;

[0236] When the first signal-to-noise ratio is less than the preset signal-to-noise ratio, determine a first deviation degree between the first signal-to-noise ratio and the preset signal-to-noise ratio;

[0237] Determine a first adjustment parameter corresponding to the first deviation degree;

[0238] Adjust the first reference transmission power according to the first adjustment parameter to obtain a second reference transmission power, and use the second reference transmission power as the transmission power corresponding to the first distance.

[0239] Optionally, in the aspect of using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object, the networking unit 405 is specifically configured to:

[0240] Perform health monitoring on the target object through the b millimeter-wave radars to obtain b health monitoring results;

[0241] Determine physical condition evaluation values corresponding to the b health monitoring results to obtain b physical condition evaluation values;

[0242] Determine a target weight set corresponding to the b millimeter-wave radars;

[0243] Perform an operation according to the target weight set and the b physical condition evaluation values to obtain a target physical condition evaluation value.

[0244] Optionally, in the aspect of performing health monitoring on the target object through the b millimeter-wave radars to obtain b health monitoring results, the networking unit 405 is specifically configured to:

[0245] Send a second test signal to the target location at the second reference transmission power through the first millimeter-wave radar, and receive a second feedback signal;

[0246] Determine the second signal-to-noise ratio of the second feedback signal;

[0247] Determine the reference weight corresponding to the accuracy corresponding to the first distance according to the mapping relationship between the preset accuracy and the weight;

[0248] When the second signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, perform health monitoring on the target object through the first millimeter-wave radar to obtain the health monitoring result corresponding to the first millimeter-wave radar;

[0249] When the second signal-to-noise ratio is less than the preset signal-to-noise ratio, determine the second deviation between the second signal-to-noise ratio and the first signal-to-noise ratio;

[0250] When the second deviation is greater than the preset deviation, execute the step of performing health monitoring on the target object through the first millimeter-wave radar to obtain the health monitoring result corresponding to the first millimeter-wave radar;

[0251] When the second deviation is less than or equal to the preset deviation, continue to put the first millimeter-wave radar into sleep and set the health monitoring result corresponding to the first millimeter-wave radar to 0.

[0252] It can be seen that the networking device based on the Internet of Things described in the embodiments of the present application is applied to the master device in the first Internet of Things system. The first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponding to an indoor area, and each indoor area including multiple millimeter-wave radars. A first image of a target object is obtained through a first camera in the i-th group of cameras, and the target area and background area of the target object in the first image are identified using image recognition technology; the i-th group of cameras is one group of the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras. The shooting angle parameter when the first camera shoots the first image is obtained, and based on the shooting angle parameter, the target area and the background area, the first area where the target object is located and the target position in the first area are determined, and the indoor map of the first area is obtained. The target position corresponding to the target object is marked in the indoor map, the millimeter-wave radars corresponding to the first area are obtained, and a millimeter-wave radars are obtained, where a is a positive integer. The distance between each millimeter-wave radar in the a millimeter-wave radars and the target position is determined, and a distances are obtained. Based on the a distances, the detection accuracies corresponding to the a millimeter-wave radars are determined, and a detection accuracies are obtained. The detection accuracies greater than a preset threshold are selected from the a detection accuracies, and b detection accuracies are obtained. The b millimeter-wave radars corresponding to the b detection accuracies are awakened; b is a positive integer less than or equal to a. The b millimeter-wave radars and the first Internet of Things system are networked to obtain a second Internet of Things system, and the second Internet of Things system is used to control the b millimeter-wave radars to perform health monitoring on the target object. First, based on the shooting angle parameter, the target area of the target object, and the background area, environmental positioning (image recognition positioning) can accurately locate the position where the millimeter-wave radar needs to detect and the position of the target object with low power consumption, ensuring the accuracy of the millimeter-wave radar detection. Second, the millimeter-wave radars with higher detection accuracies are selected using the positioning results (target position, position of the millimeter-wave radar), distance, and detection accuracy to ensure that the detection results have high credibility. Third, the millimeter-wave radars with high credibility are networked into the first Internet of Things system to quickly network on the basis of ensuring the original Internet of Things architecture to complete the health monitoring of the target object. In this way, low-power and reasonable networking can be achieved to realize the health monitoring of the target object (the elderly).

[0253] It can be understood that the functions of the respective program modules of the networking device based on the Internet of Things in this embodiment can be specifically implemented according to the methods in the above method embodiments, and the specific implementation process can refer to the relevant descriptions in the above method embodiments, which will not be elaborated here.

[0254] The embodiments of the present application further provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, and the computer program causes the computer to execute some or all of the steps of any of the methods recorded in the above method embodiments, and the above computer includes a master device.

[0255] An embodiment of the present application further provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to execute some or all of the steps of any one of the methods described in the foregoing method embodiments. The computer program product may be a software installation package, and the computer includes a main control device.

[0256] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0257] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0258] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces. The indirect coupling or communication connection of the device or unit may be in an electrical or other form.

[0259] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0260] In addition, the functional units in the various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0261] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), external hard drives, magnetic disks, or optical discs.

[0262] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory. The memory can include: USB flash drives, read-only memories (abbreviation: ROM, English: Read-Only Memory), random access memories (abbreviation: RAM, English: Random Access Memory), magnetic disks, or optical discs, etc.

[0263] The above has introduced the embodiments of this application in detail. Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A networking method based on the Internet of Things, characterized in that, The master device applied to the first Internet of Things system, the first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponds to an indoor area, and each indoor area includes multiple millimeter-wave radars; The method includes: Obtain the first image of the target object through the first camera in the i-th group of cameras, and use image recognition technology to identify the target area and the background area of the target object in the first image; The i-th group of cameras is one group of cameras among the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras; Obtain the shooting angle parameter when the first camera shoots the first image, determine the first area where the target object is located and the target position in the first area according to the shooting angle parameter, the target area and the background area, and obtain the indoor map of the first area, and mark the target position corresponding to the target object in the indoor map; Obtain the millimeter-wave radar corresponding to the first area, and obtain a millimeter-wave radars, where a is a positive integer; Determine the distance between each millimeter-wave radar in the a millimeter-wave radars and the target position, and obtain a distances; Determine the detection accuracy corresponding to the a millimeter-wave radars according to the a distances, and obtain a detection accuracies; Select the detection accuracies greater than the preset threshold among the a detection accuracies, and obtain b detection accuracies, and wake up the b millimeter-wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a; Network the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object.

2. The method according to claim 1, wherein The networking of the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system includes: Determine the positions of the b millimeter-wave radars, and obtain b positions; Determine the shortest distance between each position in the b positions and the devices in the first Internet of Things system, and obtain b shortest distances, and obtain the devices corresponding to the b shortest distances, and obtain b Internet of Things devices; Connect each Internet of Things device in the b Internet of Things devices to the corresponding millimeter-wave radar to obtain the second Internet of Things system.

3. The method according to claim 1 or 2, characterized in that, The use of the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object includes: Obtain the b distances and b detection accuracies corresponding to the b millimeter-wave radars; Determine the detection frequency corresponding to each millimeter-wave radar in the b millimeter-wave radars according to the b distances and the b detection accuracies, and obtain b detection frequencies; Determine the transmission power corresponding to each millimeter-wave radar in the b millimeter-wave radars according to the b distances, and obtain b transmission powers; Determine b transmission directions according to the b positions and the target position; Determine the communication paths between the master device and the b millimeter-wave radars according to the second Internet of Things system, and obtain b communication paths; Determine b health monitoring instructions according to the b detection frequencies, the b transmission powers, and the b transmission directions. Each of the b health monitoring instructions carries the detection frequency, transmission power, and transmission direction corresponding to a respective millimeter-wave radar. Send the b health monitoring instructions to the b millimeter-wave radars through the b communication paths to control the corresponding millimeter-wave radars to perform health monitoring at the corresponding detection frequencies, transmission powers, and transmission directions.

4. The method according to claim 3, characterized in that, The determining the detection frequency corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances and the b detection precisions to obtain b detection frequencies includes: Determine b first reference detection frequencies corresponding to the b distances. Determine b second reference detection frequencies corresponding to the b detection precisions. Determine the remaining service lives corresponding to the b millimeter-wave radars to obtain b remaining service lives. Determine weight pairs corresponding to the b remaining service lives to obtain b weight pairs. Perform weighted operations according to the b first reference detection frequencies, the b second reference detection frequencies, and the b weight pairs to obtain the b detection frequencies.

5. The method according to claim 3, wherein The determining the transmission power corresponding to each millimeter-wave radar among the b millimeter-wave radars according to the b distances to obtain b transmission powers includes: Determine a first reference transmission power corresponding to a first distance, where the first distance is any one of the b distances. Send a first test signal to the target position through the first millimeter-wave radar corresponding to the first distance at the first reference transmission power and receive a first feedback signal. Determine a first signal-to-noise ratio of the first feedback signal. When the first signal-to-noise ratio is greater than or equal to a preset signal-to-noise ratio, use the first reference transmission power as the transmission power corresponding to the first distance. When the first signal-to-noise ratio is less than the preset signal-to-noise ratio, determine a first deviation degree between the first signal-to-noise ratio and the preset signal-to-noise ratio. Determine a first adjustment parameter corresponding to the first deviation degree. Adjust the first reference transmission power according to the first adjustment parameter to obtain a second reference transmission power, and use the second reference transmission power as the transmission power corresponding to the first distance.

6. The method according to claim 5, wherein The using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object includes: Perform health monitoring on the target object through the b millimeter-wave radars to obtain b health monitoring results. Determine body condition evaluation values corresponding to the b health monitoring results to obtain b body condition evaluation values. Determine a target weight set corresponding to the b millimeter-wave radars. Perform operations according to the target weight set and the b body condition evaluation values to obtain a target body condition evaluation value.

7. The method according to claim 6, characterized in that The performing health monitoring on the target object through the b millimeter-wave radars to obtain b health monitoring results includes: Send a second test signal to the target position through the first millimeter-wave radar at the second reference transmission power and receive a second feedback signal. Determine a second signal-to-noise ratio of the second feedback signal. Determine a reference weight value corresponding to the accuracy corresponding to the first distance according to a preset mapping relationship between accuracy and weight value; When the second signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio, perform health monitoring on the target object through the first millimeter-wave radar to obtain a health monitoring result corresponding to the first millimeter-wave radar; When the second signal-to-noise ratio is less than the preset signal-to-noise ratio, determine a second deviation degree between the second signal-to-noise ratio and the first signal-to-noise ratio; When the second deviation degree is greater than the preset deviation degree, execute the step of performing health monitoring on the target object through the first millimeter-wave radar to obtain a health monitoring result corresponding to the first millimeter-wave radar; When the second deviation degree is less than or equal to the preset deviation degree, continue to put the first millimeter-wave radar into sleep, and set the health monitoring result corresponding to the first millimeter-wave radar to 0.

8. An Internet of Things-based networking device, characterized in that, Applied to a master device in a first Internet of Things system, the first Internet of Things system further includes multiple groups of cameras, each group of cameras corresponding to an indoor area, and each indoor area includes multiple millimeter-wave radars; the device includes: an identification unit, a determination unit, an acquisition unit, a selection unit, and a networking unit, where, The identification unit is configured to obtain a first image of a target object through a first camera in the i-th group of cameras, and use image recognition technology to identify a target area and a background area of the target object in the first image; the i-th group of cameras is one group of cameras among the multiple groups of cameras, and the first camera is one camera in the i-th group of cameras; The determination unit is configured to obtain a shooting angle parameter when the first camera shoots the first image, determine a first area where the target object is located and a target position in the first area according to the shooting angle parameter, the target area, and the background area, and obtain an indoor map of the first area, and mark the target position corresponding to the target object in the indoor map; The acquisition unit is configured to acquire millimeter-wave radars corresponding to the first area, and obtain a millimeter-wave radars, where a is a positive integer; The determination unit is further configured to determine the distance between each millimeter-wave radar in the a millimeter-wave radars and the target position to obtain a distances; determine the detection accuracy corresponding to the a millimeter-wave radars according to the a distances to obtain a detection accuracies; The selection unit is configured to select detection accuracies greater than a preset threshold from the a detection accuracies to obtain b detection accuracies, and wake up the b millimeter-wave radars corresponding to the b detection accuracies; b is a positive integer less than or equal to a; The networking unit is configured to network the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, and use the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object.

9. The device according to claim 8, characterized in that In terms of networking the b millimeter-wave radars and the first Internet of Things system to obtain a second Internet of Things system, the networking unit is specifically configured to: Determine the positions of the b millimeter-wave radars to obtain b positions; Determine the shortest distance between each of the b positions and the devices in the first Internet of Things system, obtain b shortest distances, and acquire the devices corresponding to the b shortest distances to obtain b Internet of Things devices; Connect each of the b Internet of Things devices to the corresponding millimeter-wave radar to obtain the second Internet of Things system.

10. The device according to claim 8 or 9, characterized in that, In terms of using the second Internet of Things system to control the b millimeter-wave radars to perform health monitoring on the target object, the networking unit is specifically configured to: Obtain the b distances and b detection precisions corresponding to the b millimeter-wave radars; Determine the detection frequency corresponding to each of the b millimeter-wave radars according to the b distances and the b detection precisions to obtain b detection frequencies; Determine the transmission power corresponding to each of the b millimeter-wave radars according to the b distances to obtain b transmission powers; Determine b transmission directions according to the b positions and the target position; Determine the communication paths between the master device and the b millimeter-wave radars according to the second Internet of Things system to obtain b communication paths; Determine b health monitoring instructions according to the b detection frequencies, the b transmission powers, and the b transmission directions, and each of the b health monitoring instructions carries the detection frequency, transmission power, and transmission direction corresponding to a corresponding millimeter-wave radar; Send the b health monitoring instructions to the b millimeter-wave radars through the b communication paths to control the corresponding millimeter-wave radars to perform health monitoring at the corresponding detection frequencies, transmission powers, and transmission directions.

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