CN113612974B

CN113612974B - Internet of things energy saving method, device and system based on position prediction

Info

Publication number: CN113612974B
Application number: CN202110998278.2A
Authority: CN
Inventors: 王滨; 张峰; 王星; 赵海涛; 周梦影; 张晖; 夏文超
Original assignee: Hangzhou Hikvision Digital Technology Co Ltd
Current assignee: Hangzhou Hikvision Digital Technology Co Ltd
Priority date: 2021-08-27
Filing date: 2021-08-27
Publication date: 2023-05-12
Anticipated expiration: 2041-08-27
Also published as: CN113612974A

Abstract

The application discloses an energy-saving method, equipment and a system of the Internet of things based on position prediction, relates to the technical field of video monitoring, and is beneficial to reducing energy consumption of the Internet of things. The method comprises the following steps: after the motion detection node detects that the monitored area has an intrusion target, the first communication node receives a first message which is sent by the motion detection node and contains measurement information of the current position, wherein the video node is in a closed state when the intrusion target is not detected in the monitored area; the first communication node sends a second message containing measurement information of the current position to the controller; the second communication node controls the first video node to be started based on a third message from the controller to monitor the intrusion target, wherein the third message is obtained by the controller based on measurement information of the current position and prediction information of the current position, the prediction information of the current position is obtained based on the last position of the intrusion target, and the second communication node is the communication node closest to the first video node.

Description

Internet of things energy saving method, device and system based on position prediction

Technical Field

The application relates to the technical field of video monitoring, in particular to an energy-saving method, equipment and system of the internet of things based on position prediction.

Background

A wireless sensor network (wireless sensor networks, WSN) is used to collect and propagate information characterizing physical phenomena around the sensor. Recent technological advances have made low cost wireless multimedia sensor networks (wireless multimedia sensor networks, WMSN) possible. The WMSN is a novel sensor network capable of collecting, transmitting and processing multimedia information such as audio and video, has flexible coverage range, does not need to preset network infrastructure, has certain self-organizing and multi-hop transmission capacity, and has great application potential in environment monitoring, intelligent transportation, safety production and various emergency communication occasions. Compared with the traditional WSN, the WMSN has the capability of providing richer data and wider coverage, and provides more possibility for the application of the Internet of things technology. Therefore, wi-Fi based WMSN is an effective and flexible solution for monitoring intrusion targets in a selected monitoring area.

However, WMSN always turns on the video node therein even if data is not transmitted, so as to ensure monitoring of an intrusion target. This can result in greater energy consumption for the internet of things.

Disclosure of Invention

The embodiment of the application provides an energy-saving method, equipment and system for the Internet of things based on position prediction, which are beneficial to reducing the energy consumption of the Internet of things.

In order to achieve the above purpose, the embodiments of the present application adopt the following technical solutions:

in a first aspect, an energy saving method of the internet of things based on position prediction is provided, including: after the motion detection node detects that the monitored area has an intrusion target, the first communication node receives a first message sent by the motion detection node, wherein the first message comprises measurement information of the current position of the intrusion target, and the video node is in a closed state when the intrusion target is not detected in the monitored area; the first communication node sends a second message containing measurement information of the current position to the controller; the second communication node controls the first video node to be started based on a third message from the controller to monitor the intrusion target, wherein the third message is obtained by the controller based on the measurement information of the current position and the prediction information of the current position, the prediction information of the current position is obtained based on the last position of the intrusion target, and the second communication node is the communication node closest to the first video node.

According to the technical scheme, after the motion detection node detects that an intrusion target exists in a monitoring area, the first communication node receives measurement information of the current position of the intrusion target sent by the motion detection node, and reports the measurement information to the controller. Based on the measurement information of the current position of the intrusion target and the prediction information of the current position, the controller controls the opening of the corresponding video node through the second communication node, so that the intrusion target is monitored. Wherein the video node is in a closed state when no intrusion target is detected in the monitored area. Because the energy consumption of the motion detection node in the opening state is lower than that of the video node in the opening state, compared with the prior art that the video node is always in the opening state, the scheme reduces the number of the video nodes participating in the warning activity, and is beneficial to reducing the energy consumption of the Internet of things.

In one possible implementation, the method further includes: the third communication node controls the second video node to be started on the basis of a fourth message from the controller so as to monitor the intrusion target, wherein the fourth message is obtained by the controller on the basis of the predicted information of the next position of the intrusion target, the predicted information of the next position is obtained on the basis of the current position, and the third communication node is the communication node closest to the second video node.

According to the possible implementation mode, the controller predicts the next position of the intrusion target based on the current position of the intrusion target, and opens the corresponding video node through the third communication node. In other words, before the intrusion target reaches the next location, the video node is turned on based on the predicted next location of the intrusion target to enable continuous monitoring of the intrusion target. Thus, the method is beneficial to improving the accuracy of monitoring and tracking the intrusion target.

In a second aspect, an energy saving method of the internet of things based on position prediction is provided, including: after the motion detection node detects that the monitored area has an intrusion target, the first gateway receives (particularly indirectly receives, such as through a communication node connected with both the first gateway and the motion detection node) a first message from the motion detection node, wherein the first message comprises measurement information of the current position of the intrusion target, and the video node is in a closed state when the intrusion target is not detected in the monitored area; the first gateway sends a second message containing measurement information of the current position to the controller; the second gateway controls the first video node to be opened based on a third message from the controller to monitor the intrusion target, wherein the third message is obtained by the controller based on measurement information of the current position and prediction information of the current position, the prediction information of the current position is obtained based on the last position of the intrusion target, and the second gateway is a gateway connected with a communication node nearest to the first video node.

According to the technical scheme, after the motion detection node detects that an intrusion target exists in a monitoring area, the first gateway receives measurement information of the current position of the intrusion target sent by the motion detection node and reports the measurement information to the controller. Based on the measurement information of the current position of the intrusion target and the prediction information of the current position, the controller controls the opening of the corresponding video node through the second gateway, so that the intrusion target is monitored. Wherein the video node is in a closed state when no intrusion target is detected in the monitored area. Because the energy consumption of the motion detection node in the opening state is lower than that of the video node in the opening state, compared with the prior art that the video node is always in the opening state, the scheme reduces the number of the video nodes participating in the warning activity, and is beneficial to reducing the energy consumption of the Internet of things.

In one possible implementation, the method further includes: and the third gateway receives a fourth message from the controller and controls the second video node to be started based on the fourth message so as to monitor the intrusion target, wherein the fourth message is obtained by the controller based on the predicted information of the next position of the intrusion target, the predicted information of the next position is obtained based on the current position, and the third gateway is a gateway connected with the communication node closest to the second video node.

This possible implementation helps to improve the accuracy of monitoring and tracking of intrusion targets.

In one possible implementation, after the first gateway receives the first message from the motion detection node, the method further includes: the first gateway determines target information of the current position based on measurement information of the current position and prediction information of the current position; the first gateway adopts a greedy algorithm, and distributes spread spectrum factors to a plurality of communication nodes connected with the first gateway based on target information of the current position, wherein the plurality of communication nodes comprise the first communication node; the spreading factor allocated to the first communication node is used for the first communication node to send subsequent information to the first gateway.

In this possible implementation, the first gateway allocates a spreading factor based on the target information of the current location. And, the spreading factor allocated to the first communication node is used for the first communication node to send the subsequent information to the first gateway. Therefore, the method is beneficial to improving the high efficiency of message transmission in the Internet of things, and the accuracy of monitoring and tracking the intrusion target by the Internet of things is improved.

In one possible implementation, the first gateway employs a greedy algorithm, and allocates spreading factors for a plurality of communication nodes connected to the first gateway based on target information of a current location, including: the method comprises the steps that a first gateway obtains a current state, wherein the current state is constructed based on position information of a plurality of communication nodes and target information of a current position; the first gateway distributes spread spectrum factors for a plurality of communication nodes by utilizing a greedy algorithm and combining a current state and a Q table, wherein the Q table comprises a plurality of Q values, one Q value corresponds to one candidate state and one candidate action, the candidate state is constructed based on position information of the plurality of communication nodes and position information of a candidate area of an intrusion target, the candidate area corresponds to the communication nodes one by one, and the candidate action is a distribution action corresponding to a candidate mode adopted by the first gateway for distributing the spread spectrum factors for the plurality of communication nodes.

The possible implementation manner provides a specific implementation manner for distributing the spreading factor based on the greedy algorithm and the target information of the current position of the intrusion target.

In one possible implementation, the method further includes: the first gateway calculates a probability of success for the plurality of communication nodes to transmit information using the allocated spreading factor and updates the Q table based on the probability of success.

Therefore, preparation can be made for next spreading factor allocation, so that accuracy of next spreading factor allocation is improved, and further high efficiency of message transmission in the Internet of things is improved.

In a third aspect, an energy saving method of the internet of things based on position prediction is provided, including: the controller receives a second message from the first communication node, wherein the second message contains measurement information of the current position of the intrusion target, the measurement information of the current position is contained in the first message and is sent to the first communication node when the motion detection node detects that the intrusion target exists in the monitoring area, and the video node is in a closed state when the intrusion target is not detected in the monitoring area; the controller determines a third message based on the measurement information of the current position and the prediction information of the current position, wherein the prediction information of the current position is obtained based on the last position of the intrusion target; the controller sends a third message to the second communication node, wherein the third message is used for controlling the first video node to be started by the second communication node so as to monitor an intrusion target, and the second communication node is the communication node closest to the first video node.

In one possible implementation, the method further includes: the controller determines target information of the current position based on the measurement information of the current position and the prediction information of the current position; the controller determines the predicted information of the next position of the intrusion target based on the target information of the current position; the controller determines a fourth message based on the predicted information of the next position; the controller sends a fourth message to the third communication node, wherein the fourth message is used for controlling the second video node to be started by the third communication node so as to monitor an intrusion target, and the third communication node is the communication node closest to the second video node.

In one possible implementation, the controller determines, based on target information of the current location, prediction information of a next location of the intrusion target, including: the controller predicts the predicted information of the next position of the intrusion target based on the target information of the current position and the KF algorithm. This possible implementation provides a specific implementation of the prediction information that determines the next location of the intrusion target.

The explanation of the relevant content and the description of the beneficial effects and the like in the method provided by the third aspect and any possible implementation manner thereof may refer to the corresponding content provided by the first aspect or the possible implementation manner thereof, and are not repeated here.

In a fourth aspect, a communication node is provided, which is configured to perform any one of the energy saving methods of the internet of things based on location prediction provided in the first aspect or a corresponding possible implementation manner thereof.

In one possible implementation manner, the present application may divide the functional modules of the communication node according to corresponding steps in the method provided in the first aspect or corresponding possible implementation manner thereof. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated in one processing module. By way of example, the present application may functionally divide the communication node into a receiving module, a transmitting module, a control module, and the like. The description of possible technical solutions and beneficial effects executed by each of the above-divided functional modules may refer to the technical solutions provided by the above first aspect or corresponding possible implementation manners thereof, or refer to the following detailed description sections, which are not repeated herein.

In one possible implementation, the communication node includes: memory and a processor. The memory is coupled to the processor. The memory is for storing computer program code, the computer program code comprising computer instructions. When executed by a processor, causes the communication node to perform any one of the location prediction based internet of things energy saving methods as provided in the first aspect or its corresponding possible implementation forms.

In a fifth aspect, a gateway is provided, where the gateway is configured to perform any one of the energy saving methods of the internet of things based on location prediction provided in the second aspect or a corresponding possible implementation manner thereof.

In one possible implementation manner, the gateway may be divided into functional modules according to corresponding steps in the method provided by the second aspect or corresponding possible implementation manner of the second aspect. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated in one processing module. By way of example, the gateway may be functionally divided into a receiving module, a transmitting module, a control module, and the like. The description of possible technical solutions and beneficial effects executed by each of the above-divided functional modules may refer to the technical solutions provided by the above second aspect or corresponding possible implementation manners thereof, or refer to the following detailed description section, which is not repeated herein.

In one possible implementation, the gateway includes: memory and a processor. The memory is coupled to the processor. The memory is for storing computer program code, the computer program code comprising computer instructions. The computer instructions, when executed by a processor, cause the gateway to perform any of the location prediction based internet of things energy saving methods as provided in the second aspect or its corresponding possible implementation.

In a sixth aspect, a controller is provided, where the controller is configured to perform any one of the energy saving methods of the internet of things based on location prediction provided in the third aspect or its corresponding possible implementation manner.

In one possible implementation manner, the present application may divide the functional modules of the controller according to corresponding steps in the method provided by the third aspect or corresponding possible implementation manner thereof. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated in one processing module. By way of example, the present application may divide the controller into a receiving module, a determining module, a transmitting module, and the like according to functions. The description of possible technical solutions and beneficial effects executed by each of the above-divided functional modules may refer to the technical solutions provided by the above third aspect or corresponding possible implementation manners thereof, or refer to the following detailed description section, which is not repeated herein.

In one possible implementation, the controller includes: memory and a processor. The memory is coupled to the processor. The memory is for storing computer program code, the computer program code comprising computer instructions. When the processor executes the computer instructions, the controller is caused to perform any one of the energy saving methods of the internet of things based on location prediction as provided in the third aspect or its corresponding possible implementation manner.

In a seventh aspect, there is provided a location prediction based energy saving system for internet of things, comprising: and the motion detection node is used for sending a first message to the first communication node after detecting that the monitored area has the intrusion target, wherein the first message comprises measurement information of the current position of the intrusion target, and the video node is in a closed state when the intrusion target is not detected in the monitored area. The first communication node is configured to send a second message containing measurement information of the current location to the controller. And a controller for determining a third message based on the measurement information of the current position and the prediction information of the current position, and transmitting the third message to a second communication node, the prediction information of the current position being obtained based on the last position of the intrusion target, the second communication node being the communication node closest to the first video node. And the second communication node is used for controlling the first video node to be started based on the third message so as to monitor the intrusion target.

Optionally, the above nodes included in the system may perform other steps, and the other steps and their corresponding advantageous effects may refer to the corresponding method parts above, or refer to the following detailed description parts. Optionally, the system may further comprise other nodes, the steps performed by the other nodes and their corresponding advantageous effects may be referred to in the method sections corresponding to above, or in the detailed description section below,

In an eighth aspect, a system on a chip is provided, the system on a chip being applied to a communication node/gateway/controller. The system-on-chip includes one or more interface circuits, and one or more processors. The interface circuit and the processor are interconnected through a circuit; the interface circuit is for receiving signals from the memory of the communication node/gateway/controller and transmitting the signals to the processor, the signals including computer instructions stored in the memory.

When the chip system is applied to a communication node, when the processor executes the computer instructions, the controller executes the energy saving method of the internet of things based on the position prediction as provided in the first aspect or a corresponding possible implementation manner thereof.

When the chip system is applied to a gateway, when the processor executes the computer instructions, the controller executes the energy saving method of the internet of things based on the position prediction as provided in the second aspect or a corresponding possible implementation manner thereof.

When the chip system is applied to a controller, when the processor executes the computer instructions, the controller executes the energy saving method of the internet of things based on the position prediction as provided in the third aspect or a corresponding possible implementation manner thereof.

In a ninth aspect, there is provided a computer readable storage medium comprising computer instructions which, when run on a computer, cause the computer to perform any of the above provided location prediction based internet of things energy conservation methods.

It is to be understood that any of the above-mentioned communication nodes, gateways, controllers, chip systems or computer readable storage media may be applied to the corresponding methods provided above, and therefore, the advantages achieved by the above-mentioned communication nodes, gateways, controllers, chip systems or computer readable storage media may refer to the advantages in the corresponding methods, and are not repeated herein.

These and other aspects of the present application will be more readily apparent from the following description.

Drawings

Fig. 1 is a schematic architecture diagram of an energy saving system of the internet of things based on position prediction according to an embodiment of the present application;

fig. 2 is a deployment schematic diagram of the internet of things according to an embodiment of the present application;

fig. 3 is a schematic hardware structure of a communication device applicable to an embodiment of the present application;

fig. 4 is a flowchart of an energy saving method of the internet of things based on position prediction according to an embodiment of the present application;

FIG. 5 is a graph of the relationship between measured information, predicted information and target information for the location of an intruding object, and the use of these values, as provided herein;

fig. 6 is a flowchart of another energy saving method of the internet of things based on position prediction according to an embodiment of the present application;

fig. 7 is a flowchart of a method for allocating SF according to an embodiment of the present application;

Fig. 8 is a schematic structural diagram of a communication node according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a gateway according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of a controller according to an embodiment of the present application.

Detailed Description

The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or for distinguishing between different processes of the same object and not for describing a particular sequential order of objects.

Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the description of the present application, unless otherwise indicated, the meaning of "a plurality" means two or more.

Fig. 1 is a schematic architecture diagram of an energy saving system of the internet of things based on position prediction according to an embodiment of the present application. The system can be applied to scenes such as a large warehouse, a supermarket and the like which need to monitor and track the intrusion target.

The system shown in fig. 1 includes: controller 10, gateway 20, communication node 30, motion detection node 40, and video node 50. Wherein the controller 10 may be connected to one or more gateways 20, each gateway 20 may be connected to one or more communication nodes 30. As an example, each communication node 30 may connect one motion detection node 40 and one video node 50. Illustratively, the communication node 30 may be connected to the video node 50 via an ethernet cable.

The controller 10 may remotely control the turning on of the video node 50 in cooperation with the motion detection node 40, the gateway 20 and the communication node 30. Specific: the motion detection node 40 detects the presence and activity (or motion) of the intrusion target within its sensing range, and reports the position information of the intrusion target and the like to the communication node 30. The communication node 30 communicates with the controller 10 through the gateway 20 and controls the video node 50 to be turned on. Video node 50 may record the activity of an intrusion target in a video manner after it is turned on. Communication between video node 50 and communication node 30 may be based on wireless communication technology, such as Wi-Fi technology.

Wherein, the video node 50 is turned on, which may include: the radio interface of video node 50 (i.e., the interface where video node 50 connects with communication node 30) is turned on. After the video node 50 is turned on, the video node 50 is in a listening state and participates in a vigilance activity (i.e., participates in recording the activity of an intrusion target).

Optionally, when the internet of things is deployed, the video node 50 is in a closed state, that is, in the initial state, the video node 50 is in a closed state. In addition, the video node 50 may be in an on state under the control of the internet of things energy saving method based on the position prediction provided in the embodiments of the present application. Subsequently, the controller 10 controls to turn off a certain video node 50 if it is determined that information from the video node 50 is not received within a preset period of time from the turning on of the video node 50. Thus, the energy consumption of the Internet of things can be saved.

It should be noted that, in consideration of network security and unified management, in the technical solution provided in the embodiments of the present application, the controller 10 controls the on/off of the video node 50.

According to the position prediction-based energy saving system of the Internet of things, monitoring and tracking of an intrusion target are achieved through deployment of the motion detection node 40, measurement information of the current position of the intrusion target, which is obtained in the process of monitoring and tracking the intrusion target, is obtained by the motion detection node 40, and then the measurement information of the current position of the intrusion target is reported to the controller 10; then, the controller 10 controls the corresponding video node 50 to be turned on based on the measurement information of the current position of the intrusion target and the prediction information of the current position, so as to realize the monitoring of the intrusion target by the video node 50. Because the energy consumption of the motion detection node 40 in the on state is lower than that of the video node 50 in the on state, compared with the situation that the video node is always in the on state in the prior art, the energy-saving system of the internet of things based on the position prediction provided by the embodiment of the application reduces the number of video nodes participating in the alert activity, and is beneficial to reducing the energy consumption of the internet of things.

In order to improve accuracy in monitoring and tracking of intrusion targets in the internet of things, optionally, when the internet of things is deployed, the motion detection node 40 may be deployed in a manner as shown in fig. 2. Specific: assuming that the total monitoring area of the internet of things (such as the area where a large warehouse or supermarket is located) has a width W and a length L, and the entire monitoring area is divided into equal squares, then the motion detection node 40 may be disposed at the center of the squares to ensure coverage. Fig. 2 is merely an example, and is not intended to limit the manner in which embodiments of the present application may be deployed.

It should be noted that, each video node 50 has a certain monitoring area, and each motion detection node 40 has a certain monitoring area. There may or may not be overlap in the monitored areas of the different video nodes 50. There may or may not be overlap in the monitored areas of the different motion detection nodes 40. For ease of description, the monitoring areas of the different video nodes 50 are not overlapping and the monitoring areas of the different motion detection nodes 40 are not overlapping.

In order to distinguish the monitoring area of the internet of things from the monitoring area of the video node 50 (or the monitoring area of the motion detection node 40), in the embodiment of the present application, the monitoring area of the internet of things is referred to as a total monitoring area. As an example, the total monitoring area of the internet of things may be understood as the sum of the monitoring areas of all the video nodes 50 deployed in the internet of things, and the total monitoring area of the internet of things may be understood as the sum of the monitoring areas of all the motion detection nodes 40 deployed in the internet of things.

The remote wide area network (long range wide area network, loRa) is one of low-power consumption wide area networks (low power wide area network, LPWAN), is suitable for the remote connection of low-bit rate equipment, has the characteristics of low power consumption and the like, and can be well suitable for the application of the Internet of things technology. In this case, in conjunction with fig. 1, the gateway 20 may be a LoRa gateway, the communication node 30 may be a LoRa node, and the controller 10 uses the LoRa technology to control the opening of the video node 50, where the LoRa gateway and the LoRa node are always activated during the control process. Of course, in the specific implementation, the LoRa may be replaced by other similar techniques.

By way of example, the WSN or WMSN may be applied to the location prediction-based energy saving system of the internet of things provided in the embodiments of the present application. In this case, the motion detection node 40 may be a motion sensor. Video node 50 may be a video camera, a still camera, or the like.

It should be noted that the internet of things energy saving system based on the location prediction shown in fig. 1 is only an example, and in actual implementation, the internet of things energy saving system based on the location prediction may further include more or fewer devices/apparatuses than those shown in fig. 1. In addition, the names of the devices in the energy-saving system of the internet of things based on the position prediction are not limited, and for example, the above-mentioned controller may also be referred to as a control device, a control node, a central processor, a central controller or a general controller.

In addition, it should be noted that, other functions of the internet of things energy saving system based on position prediction provided in the embodiments of the present application may be obtained based on the internet of things energy saving method based on position prediction described below, and for brevity, will not be described here again.

Illustratively, the controller 10, the gateway 20 and the communication node 30 in the embodiments of the present application may be implemented by a hardware schematic of the communication device 60 shown in fig. 3, where the communication device 60 includes a processor 601, a memory 602 and a communication interface 603 as shown in fig. 3.

Wherein the processor 601 comprises one or more central processing units (central processing unit, CPU). The CPU may be a single-core CPU (single-CPU) or a multi-core CPU (multi-CPU).

Memory 602 includes, but is not limited to, RAM, ROM, EPROM, flash memory, or optical memory, among others. The memory 602 stores attribute information of a storage block in the SSD, which is not limited in the embodiment of the application.

Optionally, the processor 601 implements the energy saving method of the internet of things based on position prediction provided in the embodiments of the present application by reading the instruction stored in the memory 602, or the processor 601 implements the energy saving method of the internet of things based on position prediction provided in the embodiments of the present application by internal stored instructions. In the case where the processor 601 implements the method in the above embodiment by reading the instruction stored in the memory 602, the instruction for implementing the location prediction-based energy saving method of the internet of things provided in the embodiment of the present application is stored in the memory 602.

The communication interface 603 is a wired interface (or port) such as a fiber optic distributed data interface (fiber distributed data Interface, FDDI), gigabit Ethernet (GE) interface. Alternatively, the communication interface 603 is a wireless interface. It should be appreciated that the communication interface 603 includes a plurality of physical ports, and that the communication interface 603 is configured to receive/transmit information for transmission with other nodes. For example, when the communication device 60 is specifically the controller 10, the communication interface 603 may be configured to receive measurement information of the current position of the intrusion target sent by the gateway, or send a message to the gateway for turning on the video node. As another example, when the communication device 60 is in particular a gateway, the communication interface 603 may be configured to receive measurement information of the current position of the intrusion target sent by the communication node, or send a message to the communication node for turning on the video node.

Optionally, the communication device 60 further comprises a bus 604, and the processor 601, the memory 602, and the communication interface 603 are typically interconnected by the bus 604 or otherwise.

The energy-saving method of the internet of things based on the position prediction provided by the embodiment of the application is described below with reference to the accompanying drawings. The method can be applied to the energy-saving system of the Internet of things based on the position prediction shown in fig. 1.

As shown in fig. 4, a flowchart of an energy saving method of the internet of things based on position prediction is provided in an embodiment of the present application. The method can be applied to the Internet of things shown in fig. 1, and comprises the following steps:

s101: the motion detection node obtains measurement information of the current position of the intrusion target.

The motion detection node can be any motion detection node which is deployed in the position prediction-based internet of things node system and detects an intrusion target. The intrusion target can be any person, object and the like entering the monitoring area of the motion detection node.

Optionally, the current location of the intrusion target is a location of the intrusion target within the current monitoring period. In this case, the next position of the intrusion target is the position of the intrusion target in the next monitoring period of the current monitoring period, and the previous position of the intrusion target is the position of the intrusion target in the previous monitoring period of the current monitoring period. The duration of one monitoring period is not limited in the embodiment of the present application.

The embodiment of the application does not limit the specific implementation manner of the measurement information of the current position of the intrusion target. For example, in order to achieve simplicity and improve accuracy, the motion detection node may represent measurement information of the current position of the intrusion target in a rectangular coordinate manner.

S102: the motion detection node sends a first message to the first communication node. Wherein the first communication node is a communication node connected to the motion detection node. The first message includes measurement information of a current location of the intrusion target.

S103: the first communication node sends a second message containing measurement information of the current location to the controller through the first gateway. Wherein the first gateway is a gateway connected to the first communication node.

Optionally, the first communication node sends the second message to the first gateway using a Spreading Factor (SF).

Wherein the spreading factor may be preset if the current monitoring period is the first monitoring period.

Wherein if the current monitoring period is not the first monitoring period, the spreading factor may be a spreading factor allocated to the first communication node by the first gateway based on the location of the intrusion target in the last monitoring period. A method in which the first gateway allocates a spreading factor to a communication node to which it is connected may refer to fig. 7.

S104: the controller determines target information of the current position of the intrusion target according to the measurement information of the current position of the intrusion target and the prediction information of the current position of the intrusion target. The prediction information of the current position is obtained based on the last position of the intrusion target.

The current position of the intrusion target is the position of the intrusion target in the current monitoring period:

if the current monitoring period is the first monitoring period, the target information of the position of the intrusion target in the current monitoring period may be measurement information of the position of the intrusion target in the current monitoring period.

If the current monitoring period is not the first monitoring period, the predicted information of the position of the intrusion target in the current monitoring period may be obtained by the controller based on the position of the intrusion target in the previous monitoring period (e.g., the target information of the position of the intrusion target in the previous monitoring period). For example, prediction information of the position of the intrusion target in the current monitoring period is obtained based on the following equation 7.

It is to be understood that the target information of the position of the intrusion target means: the present embodiments contemplate more accurate values than measurement information and prediction information, where the characterized position does not represent the absolute position of the intrusion target.

S105: the controller determines a third message based on target information of a current location of the intrusion target. The third message is used for controlling the first video node to be started.

Specific: the controller determines a video node in a monitoring area where a position indicated by target information of a current position of an intrusion target is located as a first video node. As shown in fig. 5, determining a first video node based on target information of a current location of an intrusion target is illustrated. The controller then determines a third message, which may contain an identification of the first video node.

In the technical scheme, the controller takes measurement information of the current position and prediction information of the current position as references, determines target information of the current position, and further determines a first video node based on the position indicated by the target information of the current position; instead of determining as the first video node directly based on measurement information of the current location or prediction information of the current location, it helps to improve the accuracy of the current location of the intrusion target determined by the controller.

S106: the controller sends a third message to the second gateway. Wherein the second gateway is a gateway connecting "the communication node closest to the first video node (i.e. the second communication node herein)".

For example, the third message is an on command or a wake-up instruction, the third message comprising an identification of the first video node.

S107: the second gateway controls the first video node to be started through the second communication node based on the third message so as to monitor the intrusion target.

Specifically, the second gateway broadcasts the third message. A communication node (e.g., a second communication node herein) that receives the third message generates a wake-up Packet (Magic Packet) to turn on the first video node when it determines that the identity of the video node to which it is connected is the same as the identity of the first video node that the third message contains.

Illustratively, when an intrusion object enters a monitoring area of a motion detection node, the motion detection node monitors the intrusion object and generates an alert message containing current location information of the intrusion object, which is forwarded to the controller via a gateway connected to the motion detection node, in particular indirectly via a communication node. If the intrusion target continues to move and passes the monitoring area of the other motion detection node, the other motion detection node generates an alarm message and forwards the alarm message to the controller via a gateway connected to the other motion detection node, in particular indirectly via the communication node. The controller can start the corresponding video node based on the received alarm message, so that the video node can monitor the intrusion target.

According to the energy-saving method of the Internet of things based on the position prediction, the motion detection node is used for acquiring and reporting the measurement information of the position of the intrusion target to the controller, and the controller is used for controlling the opening of the corresponding video node based on the measurement information of the current position of the intrusion target and the prediction information of the current position, so that the intrusion target is monitored. Because the energy consumption of the motion detection node in the opening state is lower than that of the video node in the opening state, compared with the method in the prior art that the video node is always in the opening state, the method provided by the embodiment of the application reduces the number of the video nodes participating in the warning activity, and is beneficial to reducing the energy consumption of the Internet of things.

The energy-saving method of the internet of things based on the position prediction, which is provided by the embodiment of the application, is based on formula analysis, so that the energy consumption of the video internet of things is reduced:

define E as the energy consumption of the internet of things, as shown in formula 1:

E＝E _tx +E _rx +E _oh +E _idle 1 (1)

Wherein E is _tx ，E _rx Energy consumed by video nodes in transmitting data mode and receiving data mode, respectively, E _oh Is the total energy consumed by the video node in receiving a pattern of messages destined for other nodes, E _idle Is the energy consumed by the video node in idle mode.

Further, E may be represented as formula 2 below:

E＝P _tx T _tx +P _rx T _rx +P _oh T _oh +P _idle T _idle 2, 2

Wherein P is _tx ，P _rx ，P _oh ，P _idle Respectively corresponding power consumption of video node in each mode, T _tx ，T _rx ，T _oh ，T _idle The time it takes for the video node to go through each of the modes described above, respectively.

In the energy-saving method of the internet of things based on the position prediction provided in the embodiment of the application, the video node is turned off when not sending or not receiving data, and does not consume energy, further, the above formula 1 may be represented as the following formula 3:

E＝P _tx T _tx +P _rx T _rx 3

Therefore, the energy-saving method of the Internet of things based on the position prediction is adopted, and the energy consumption of the Internet of things is reduced by reducing the number of video nodes participating in the alert activity.

Optionally, in order to improve accuracy of monitoring and tracking of an intrusion target by the internet of things, in the energy-saving method of the internet of things based on position prediction provided by the embodiment of the invention, the controller can also predict a next position of a current position of the intrusion target and start a corresponding video node. Specifically, as shown in fig. 6, based on fig. 4, the method for energy saving of the internet of things based on position prediction provided in the embodiment of the present application may further include the following steps S108 to S111:

s108: the controller determines predicted information of a next position of the intrusion target based on target information of the current position.

Optionally, the controller determines the predicted information of the next position of the intrusion target based on target information of the current position of the intrusion target in combination with a Kalman Filter (KF) algorithm.

The following is an exemplary description of a method of predicting using KF algorithm:

definition Y _t Is the state vector of the intrusion target in the t-th monitoring period, given by equation 4:

Y _t ＝[x _t ，y _t ，v _x，t ，v _y，t ] ^T 4. The method is to

Wherein x is _t And y _t Is the abscissa value of the position of the intrusion target (i.e. the target information) in the t-th monitoring period, v _x，t And v _y，t Is the abscissa value of the speed of the intrusion target in the t-th monitoring period.

Definition W _t For having zero mean and covariance matrix Q _t Process noise matrix, V _t Is a matrix R with zero mean and covariance _t And W is the measurement noise matrix of _t And V _t Uncorrelated. In addition, define P _t Covariance is estimated for states in the t-th monitoring period. Definition Z _t Is the measurement information of the position of the intrusion target in the t-th monitoring period.

The controller obtains a state transition matrix A according to the motion of the intrusion target and the state vector shown in the formula 4, and the state transition matrix A is shown as the formula 5:

where Δt is the monitoring period.

Defining a measurement transfer matrix H, given by equation 6:

the KF algorithm mainly includes a prediction step and a correction step.

Taking the example that the current monitoring period is the t-1 th monitoring period, the following describes the predicting step and the correcting step:

first, the predicting step may include:

firstly, a controller uses a system model to carry out recursive estimation on state variables to obtain a priori state estimated value

As shown in formula 7:

wherein, the liquid crystal display device comprises a liquid crystal display device,

is the state vector Y of the controller in the t-1 monitoring period by using the intrusion target _t-1 (including target information of the position of the intrusion target in the t-1 th monitoring period), and prediction information of the state vector of the determined intrusion target in the t-1 th monitoring period (including prediction information of the position of the intrusion target in the t-1 th monitoring period).

Second, the controller estimates a priori error covariance matrix

As shown in formula 8:

it should be appreciated that the controller may perform the above-described predictive steps during the t-1 th monitoring cycle.

Second, the correcting step may include:

first, the controller calculates the kalman gain as in equation 9:

next, the controller updates the error covariance matrix P _t For the next prediction, as in equation 10:

finally, the controller is based on

And Z _t Obtaining a state vector Y of an intrusion target in a t-th monitoring period _t (including target information of the position of the intrusion target within the t-th monitoring period) as shown in formula 11:

it should be appreciated that the controller may perform the corrective steps described above during the t-th monitoring period.

It should be noted that, after the controller performs the prediction step, the prediction information of the next position of the intrusion target is obtained. The controller performs a corrective step to obtain target information for the next location of the intrusion target in preparation for the next prediction.

S109: the controller determines a fourth message based on the predicted information of the location information of the next location. The fourth message is used for controlling the second video node to be started.

Optionally, the fourth message includes an identification of the second video node.

Optionally, the controller determines a monitoring area where the position indicated by the target information of the current position is located, and determines a video node corresponding to the monitoring area as a second video node, as shown in fig. 5.

The first video node and the second video node may be the same video node or may be different vision sensor video nodes. When the two are different video nodes, the two may be connected to the same communication node or to different communication nodes.

S110: the controller sends a fourth message to the third gateway.

S111: and the third gateway controls the second video node to be started based on the fourth message through the third communication node so as to monitor the intrusion target. The third communication node is the communication node closest to the second video node, and the third gateway is a gateway connected with the third communication node.

The specific implementation of S111 may refer to S107, which is not described herein.

Optionally, the method for saving energy of the internet of things based on the position prediction in any one of fig. 4 or fig. 6 may further include: and if the controller does not receive the information (such as the position information of the invasion target) sent by the motion detection node corresponding to a certain video node within the preset time period for starting the video node, sending a message to the video node so as to control the video node to be closed.

Therefore, the number of video nodes participating in the alert activity is reduced, and the energy consumption of the Internet of things is reduced. The video node may be any video node in the internet of things, such as the first video node or the second video node. If a video node and a motion detection node are both connected to the same communication node, the video node corresponds to the motion detection node.

It will be appreciated that the controller may turn on the corresponding video node in accordance with the method shown in fig. 4 or 6 and turn off the corresponding video node based on the method provided by the alternative implementation.

Fig. 7 is a flowchart of a method for allocating a Spreading Factor (SF) according to an embodiment of the present application. The method may comprise the steps of:

s201: the gateway obtains the current state. The current state is constructed based on the position information of a plurality of communication nodes connected by the gateway and the target information of the current position of the intrusion target. The plurality of communication nodes may be all communication nodes to which the gateway is connected.

For example, if the gateway is the first gateway described above, the plurality of communication nodes includes the first communication node described above.

The location information of the plurality of communication nodes to which the gateway is connected may be stored in advance in the gateway before S201 is performed. For example, when the internet of things is deployed, the internet of things is stored in the gateway.

In one example: s201 may include the steps of:

step one: the gateway determines the current area position information Location of the intrusion target based on the target information of the current position of the intrusion target _tar 。

The target information of the current position of the intrusion target is used to indicate the current specific position of the intrusion target. Here, the "current specific position of the intrusion target" is used to distinguish the "current area position of the intrusion target" described below.

The current zone location of the intrusion target is a candidate zone location of the intrusion target. The candidate area position of the intrusion target is related to a motion detection node in the Internet of things. One candidate region position corresponds to one motion detection node. For example, a specific position (such as a center position) in the monitored area of each motion detection node is taken as one candidate area position.

For example, assuming that a general monitoring area of the internet of things is divided into 16 sub-areas, and one motion detection node is deployed in each sub-area, there are 16 candidate area positions of the intrusion target. Wherein each candidate region location is used to indicate a sub-region. The current zone location of the intrusion target may be one of the 16 candidate zone locations.

Step two: the gateway uses a set of position information of a plurality of communication nodes connected by the gateway and current area position information of an intrusion target as a current state.

For example, the current state may be expressed as: s is S ^(k) ＝{Location ₁ ，Location ₂ …Location _N ，Location _tar }。

Wherein S is ^(k) The current state, i.e. the state of the current monitoring period, is indicated, and the superscript k indicates that the current monitoring period is the kth monitoring period. N represents the number of communication nodes connected with the gateway and Location _i And the position information of the ith communication node connected with the gateway is represented, i is more than or equal to 1 and less than or equal to N, and i is an integer.

S202: the gateway utilizes a greedy algorithm and combines the current state and the Q table to allocate SFs to the plurality of communication nodes. The Q table comprises a plurality of Q values, and each Q value corresponds to one candidate state and one candidate action. The candidate state is constructed based on the position information of the plurality of communication nodes connected by the gateway and the candidate area position information of the intrusion target. The candidate actions are allocation actions corresponding to candidate modes adopted by the gateway for allocating the spread spectrum factors to the plurality of communication nodes connected by the gateway.

The Q table is a table constructed in Q learning (QLearning) and includes a plurality of Q values. Q learning is the value-based algorithm in the reinforcement learning algorithm. The Q value, Q (S, a), represents the expectation that the action of a (a. Epsilon. A) will be taken to obtain a benefit in the S state (S. Epsilon. S) at a certain moment. Where S is the set of candidate states and a is the set of candidate actions. The environment feeds back corresponding report r according to the action of the agent. Therefore, the main idea of the algorithm is to construct a Q table (Q-table) based on the candidate State (State) and the candidate Action (Action) to store the Q value, and then select the Action that can obtain the maximum benefit according to the Q value.

In the embodiment of the application, the Q table may be obtained based on transfer learning.

By way of example, a Q table may be as shown in table 1:

table 1: q meter

	X1	X2	X3	……	Xj
						S1	7	100	6	……	95
S2	6	41	13	……	89
						S3	78	9	3	……	2
……	……	……	……	……	……
						Sk	54	19	38	……	49

The Q table shown in Table 1 is exemplified by k candidate states (labeled S1-Sk) and j candidate actions (labeled X1-Xj). Wherein k and j are integers of 2 or more.

The maximum value of k is the number of motion detection nodes.

The maximum value of j is related to the total number of candidate SFs and the total number of communication nodes to which the gateway is connected. Taking the SF value of 7-12 as an example, the number of candidate SF is 6. Assuming that the gateway connects 4 communication nodes in total, since the SF allocated to different communication nodes may be the same or different, 6 is a total for the 4 communication nodes ⁴ The number of candidate allocation methods is one, and the maximum value of j is 6 ⁴ 。

A combination of one candidate state and one candidate action corresponds to one Q value. The elements in the other cells in the first row and the first column in table 1 represent Q values. For example, the Q value corresponding to candidate state S1 and candidate action X1 is 7.

It should be noted that the present embodiment may be applied to the LoRa, and the major disadvantage of the LoRa wan is that the unregulated transmission policy is pure ALOHA transmission. When traffic in the network is high, this strategy can lead to a high probability of collisions, resulting in reduced performance in terms of throughput and fairness of the network as a whole. In general, all communication nodes in the LoRa have the same transmission parameter, which may be in particular the transmit power. When all communication nodes in the LoRa have the same transmission parameters, in order to improve the efficiency of message transmission in the internet of things, SF needs to be allocated to each communication node.

SF is the ratio between symbol rate and chip rate. Higher spreading factors increase the signal-to-noise ratio (signal to noise ratio, SNR), but also increase the broadcast time of the data packets. Currently, the spreading factor may be selected between 7 and 12.

Taking the example that the value of SF is 7-12, the relationship between the SF allocation table, that is, all candidate actions (including X1-Xj) and the SF allocated to each communication node in the allocation manner corresponding to the candidate actions (including X1-Xj) may be as shown in table 2:

table 2: SF allocation table

Communication node 1

……

Communication node N

X1

SF7

SF10

SF12

SF10

……

SF7

X2

SF10

SF7

SF9

SF7

……

SF11

……

Xj

SF10

SF7

SF9

SF7

……

SF11

The gateway can select an action from the Q table by using an E-greedy algorithm and combining the current state; then based on the selected action X ^(k) That is, the action selected in the kth monitoring period, the corresponding SF in the SF allocation table in the allocation scheme corresponding to the action is determined as the SF allocated by the gateway to the communication node to which the gateway is connected.

For example, the gateway may select an action from the Q table based on equation 12 as follows:

wherein, formula 1 represents: based on the maximum Q value, from the current state S ^(k) Of the corresponding actions, action X is selected ^(k) Is 1-epsilon, and action X is selected from other actions ^(k) Is epsilon. Epsilon is a positive number.

For example, assuming that the current state is S1, the gateway selects an action X from the actions corresponding to S1 ^(k) Is 1-epsilon, and action X is selected from other actions ^(k) Is epsilon. Based on this example, referring to Table 1, assume that the gateway selects X ^(k) X2, the gateway is the communication nodes 1 to 2 based on Table 2The SF allocated by the communication node N is SF10, SF7, SF9, SF7, … …, SF11 in order.

Optionally, after S202, the method may further include the following step S203:

s203: the gateway calculates a transmission success rate of the communication nodes (e.g., all communication nodes) connected to the gateway, and updates the Q table based on the calculated transmission success rate.

The updated Q table is used for allocating SF to the communication node next time by the gateway.

In the following, taking "N communication nodes in total are uniformly distributed in a region with a radius d centered on a gateway, and the value of SF is 7-12, namely SF7-SF712", the transmission success rate of the communication nodes and updating the Q table are described as follows:

assuming that each communication node independently transmits a data packet with an intensity θ, the intensity θ satisfies the following equation 13:

where τ is the data collection time and K is the number of packets sent during the data collection time τ.

The packet generation procedure of the communication node in the gateway-centric region of radius d follows the parameter θμ _f Poisson distribution of N, where μ _f To configure SF _f Is subject to

Assuming a communication node with a distance x from the gateway, by configuring SF _f Sending a message to a gateway; and at transmission time T _f In the absence of other configuration SF _f Overlapping the data packets sent by the current communication node, or other configuration SF _f At least P in the gateway transmission power exceeding the current communication node _th The method comprises the steps of carrying out a first treatment on the surface of the And, it is assumed that all communication nodes have the same transmission parameters (specifically, transmission power). In this caseIn the case, the potential source of interference for the communication node is the other communication node at the distance xR from the gateway, due to the path loss characteristics of the signal, wherein,

gamma is the path loss parameter. Considering configuration of SF _f The number of potential interference sources of the communication node is thus +.>

Then if at duration 2T _f If no internal potential interference source starts to transmit, the communication node can successfully transmit, and the probability of successful transmission of the communication node is +.>

Satisfying the following formula 14:

The gateway calculates the configuration SF in the region with the radius d centered on the gateway by the method of 14 _f Is determined by the average probability of success between the communication nodes of (a) as shown in equation 15:

to sum up, the gateway updates the benefit U in the kth monitoring period first ^(k) The following formula 16:

the gateway then monitors the Q value (i.e., the state S in the kth monitoring period ^(k) And action X ^(k) The corresponding Q value).

Alternatively, the gateway may update the Q value in the kth monitoring period based on the maximum value of Q in the kth+1th monitoring period and the Q value in the kth monitoring period. For example, the gateway may update the Q value in the kth monitoring period based on equation 17:

Q(S ^(k) ，X ^(k) )＝Q(S ^(k) ，X ^(k) )+α[U ^(k) +βmax(Q(S ^k+1 ，X)-Q(S ^(k) ，X ^(k) ))]17 (17)

Where α is the learning rate and β is the discount factor. Alpha and beta are preset during the initialization phase of reinforcement learning. Equation 17 is specifically understood to mean that the result on the right side of the equal sign is assigned to state S ^(k) And action X ^(k) Corresponding Q values.

The foregoing description of the solution provided in the embodiments of the present application has been mainly presented in terms of a method. To achieve the above functions, it includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

Fig. 8 is a schematic structural diagram of a communication node according to an embodiment of the present application. The communication node 80 shown in fig. 8 includes: a receiving module 801, a transmitting module 802 and a control module 803. The receiving module 801 is configured to receive, after the motion detection node detects that the monitored area has an intrusion target, a first message sent by the motion detection node, where the first message includes measurement information of a current position of the intrusion target, and when the monitored area does not detect the intrusion target, the video node is in a closed state. A sending module 802, configured to send a second message including measurement information of the current location to the controller. The control module 803 is configured to control the first video node to be turned on based on a third message from the controller to monitor the intrusion target, where the third message is obtained by the controller based on measurement information of a current position of the intrusion target and prediction information of the current position of the intrusion target, the prediction information of the current position of the intrusion target is obtained based on a previous position of the intrusion target, and the communication node 80 is a communication node closest to the first video node.

In actual implementation, the control module 803 may be implemented by the processor 601 shown in fig. 3 invoking program code in the memory 602. The receiving module 801 and the transmitting module 802 may be implemented by the communication interface 603 shown in fig. 3.

The explanation of the communication node 80 and the description of the beneficial effects provided above refer to the corresponding method embodiments described above, and are not repeated here.

Fig. 9 is a schematic structural diagram of a gateway according to an embodiment of the present application. The gateway 90 shown in fig. 9 includes: a receiving module 901, a transmitting module 902 and a control module 903. The receiving module 901 is configured to receive, after the motion detection node detects that the monitored area has an intrusion target, a first message from the motion detection node, where the first message includes measurement information of a current position of the intrusion target, and the video node is in a closed state when the monitored area does not detect the intrusion target. A sending module 902, configured to send a second message including measurement information of the current location to the controller. The control module 903 is configured to control the first video node to be turned on based on a third message from the controller to monitor the intrusion target, where the third message is obtained by the controller based on measurement information of a current location and prediction information of the current location, the prediction information of the current location is obtained based on a previous location of the intrusion target, and the gateway is a gateway connected to a communication node closest to the first video node.

Optionally, the gateway 90 further includes: a determination module 904 and an allocation module 905. Wherein, the determining module 904 is configured to determine target information of the current location based on measurement information of the current location and prediction information of the current location. An allocation module 905, configured to allocate spreading factors to a plurality of communication nodes connected to the gateway, where the plurality of communication nodes includes a first communication node, by using a greedy algorithm and based on target information of a current location; the spreading factor allocated to the first communication node is used for the first communication node to send subsequent information to the gateway.

Optionally, the allocation module 905 is specifically configured to: the method comprises the steps of obtaining a current state, wherein the current state is constructed based on position information of a plurality of communication nodes and target information of a current position. Then, a greedy algorithm is utilized, and a current state and a Q table are combined, so that spread spectrum factors are distributed to a plurality of communication nodes, wherein the Q table comprises a plurality of Q values, one Q value corresponds to one candidate state and one candidate action, the candidate state is constructed based on position information of the plurality of communication nodes and position information of candidate areas of invasion targets, the candidate areas correspond to the communication nodes one by one, and the candidate actions are distribution actions corresponding to candidate modes adopted by a gateway for distributing the spread spectrum factors to the plurality of communication nodes.

Optionally, the gateway 90 further includes: an updating module 906, configured to calculate a success probability of the plurality of communication nodes transmitting information using the allocated spreading factors, and update the Q table based on the success probability.

In actual implementation, the control module 903, the determination module 904, the allocation module 905, and the update module 906 may all be implemented by the processor 601 shown in fig. 3 calling program code in the memory 602. The receiving module 901 and the transmitting module 902 may be implemented by the communication interface 603 shown in fig. 3.

For a specific description of the above alternative modes, reference may be made to the foregoing method embodiments, and details are not repeated here. In addition, the explanation of the gateway 90 and the description of the beneficial effects provided above refer to the corresponding method embodiments described above, and are not repeated here.

Fig. 10 is a schematic structural diagram of a controller according to an embodiment of the present application. The controller 100 shown in fig. 10 includes: a receiving module 1001, a determining module 1002 and a transmitting module 1003. The receiving module 1001 is configured to receive a second message from the first communication node, where the second message includes measurement information of a current location of an intrusion target, where the measurement information of the current location is included in the first message and sent to the first communication node when the motion detection node detects that the monitored area has the intrusion target, and the video node is in a closed state when the monitored area does not detect the intrusion target. A determining module 1002, configured to determine a third message based on measurement information of a current location and prediction information of the current location, where the prediction information of the current location is obtained based on a previous location of the intrusion target. And a sending module 1003, configured to send a third message to a second communication node, where the third message is used by the second communication node to control the first video node to be turned on so as to monitor the intrusion target, and the second communication node is the communication node closest to the first video node.

Optionally, the determining module 1002 is further configured to determine target information of the current location based on the measurement information of the current location and the prediction information of the current location; determining prediction information of a next position of the intrusion target based on target information of the current position; a fourth message is determined based on the predicted information for the next location. The sending module 1003 is further configured to send a fourth message to a third communication node, where the fourth message is used for the third communication node to control the second video node to be turned on so as to monitor the intrusion target, and the third communication node is a communication node closest to the second video node.

Optionally, the determining module 1002 is specifically configured to predict prediction information of a next location of the intrusion target based on target information of the current location and a kalman filter KF algorithm.

In actual implementation, the determination module 1002 may be implemented by the processor 601 shown in fig. 3 invoking program code in the memory 602. The receiving module 1001 and the transmitting module 1003 may be implemented by the communication interface 603 shown in fig. 3.

For a specific description of the above alternative modes, reference may be made to the foregoing method embodiments, and details are not repeated here. In addition, the explanation and the description of the beneficial effects of the controller 100 provided above refer to the corresponding method embodiments described above, and are not repeated herein.

In addition, the embodiment of the application also provides an energy-saving system of the internet of things based on position prediction, which comprises:

and the motion detection node is used for sending a first message to the first communication node after detecting that the monitored area has the intrusion target, wherein the first message comprises measurement information of the current position of the intrusion target, and the video node is in a closed state when the intrusion target is not detected in the monitored area.

The first communication node is configured to send a second message containing measurement information of the current location to the controller.

And a controller for determining a third message based on the measurement information of the current position and the prediction information of the current position, and transmitting the third message to a second communication node, the prediction information of the current position being obtained based on the last position of the intrusion target, the second communication node being the communication node closest to the first video node.

And the second communication node is used for controlling the first video node to be started based on the third message so as to monitor the intrusion target.

The motion detection node may be, for example, any of the motion detection nodes 40 of fig. 1. The first communication node is a communication node connected to the motion detection node. The controller may be the controller 10 of fig. 1.

Optionally, the system further comprises a third communication node. The controller is further used for determining target information of the current position based on the measurement information of the current position and the prediction information of the current position; determining prediction information of a next position of the intrusion target based on target information of the current position; determining a fourth message based on the predicted information of the next location; and sending a fourth message to a third communication node, the third communication node being the closest communication node to the second video node. And the third communication node is used for controlling the second video node to be started based on the fourth message so as to monitor the intrusion target.

Optionally, the first communication node is specifically configured to send the second message to the controller via the gateway. The gateway is further used for determining target information of the current position based on the measurement information of the current position and the prediction information of the current position; a greedy algorithm is adopted, and spreading factors are distributed to a plurality of communication nodes connected with the gateway based on target information of the current position, wherein the communication nodes comprise first communication nodes; the spreading factor allocated to the first communication node is used for the first communication node to send subsequent information to the gateway.

Optionally, the gateway is specifically configured to: acquiring a current state, wherein the current state is constructed based on the position information of a plurality of communication nodes and the target information of the current position; and allocating spreading factors to the plurality of communication nodes by using a greedy algorithm and combining the current state and a Q table, wherein the Q table comprises a plurality of Q values, one Q value corresponds to one candidate state and one candidate action, the candidate state is constructed based on the position information of the plurality of communication nodes and the position information of a candidate area of an intrusion target, the candidate area corresponds to the communication nodes one by one, and the candidate action is an allocation action corresponding to a candidate mode adopted by a gateway for allocating the spreading factors to the plurality of communication nodes.

Optionally, the gateway is further configured to calculate a success probability of the plurality of communication nodes transmitting information using the allocated spreading factor, and update the Q table based on the success probability.

For a specific description of the above alternative modes, reference may be made to the foregoing method embodiments, and details are not repeated here. In addition, the explanation and the description of the location prediction-based energy saving system for the internet of things provided above may refer to the corresponding method embodiments, and are not repeated herein.

The embodiment of the application further provides a computer readable storage medium, where computer instructions are stored, when the computer instructions are executed on a computer device, to cause the computer device to execute each step executed by the computer device in the method flow shown in the above method embodiment. The computer device here may be a controller or a gateway or a communication node.

The embodiment of the application also provides a chip system, which is applied to the equipment. The system-on-chip includes one or more interface circuits, and one or more processors. The interface circuit and the processor are interconnected by a wire. The interface circuit is for receiving signals from a memory of the computer device and transmitting the signals to the processor, the signals including computer instructions stored in the memory. When the processor executes the computer instructions, the computer device performs the steps performed by the computer device in the method flow shown in the method embodiment described above. The computer device here may be a controller or a gateway or a communication node.

There is also provided in an embodiment of the present application a computer program product comprising instructions which, when run on a computer device, cause the computer device to perform the steps performed by the computer device in the method flow shown in the above-described method embodiment. The computer device here may be a controller or a gateway or a communication node.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer-executable instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, a website, computer, server, or data center via a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.

The foregoing is merely a specific embodiment of the present application. Variations and alternatives will occur to those skilled in the art from the detailed description provided herein and are intended to be included within the scope of the present application.

Claims

1. The energy-saving method of the Internet of things based on the position prediction is characterized by comprising the following steps of:

after the motion detection node detects that an intrusion target exists in a monitoring area, a first communication node receives a first message sent by the motion detection node, wherein the first message comprises measurement information of the current position of the intrusion target, and a video node is in a closed state when the intrusion target is not detected in the monitoring area;

the first communication node sends a second message containing measurement information of the current position to the controller through a first gateway; the first gateway is used for adopting a greedy algorithm and distributing spread spectrum factors to a plurality of communication nodes connected with the first gateway based on the target information of the current position; wherein the target information of the current position is determined by the first gateway based on the measurement information of the current position and the prediction information of the current position; the plurality of communication nodes includes the first communication node; a spreading factor allocated to the first communication node, configured to send subsequent information to the first gateway by the first communication node;

And the second communication node controls the first video node to be started on the basis of a third message from the controller so as to monitor the intrusion target, wherein the third message is obtained by the controller on the basis of the measurement information of the current position and the prediction information of the current position, the prediction information of the current position is obtained on the basis of the last position of the intrusion target, and the second communication node is the communication node closest to the first video node.

2. The method according to claim 1, wherein the method further comprises:

and a third communication node controls a second video node to be started on the basis of a fourth message from the controller so as to monitor the intrusion target, wherein the fourth message is obtained by the controller on the basis of the predicted information of the next position of the intrusion target, the predicted information of the next position is obtained on the basis of the current position, and the third communication node is the communication node closest to the second video node.

3. The energy-saving method of the Internet of things based on the position prediction is characterized by comprising the following steps of:

after the motion detection node detects that an intrusion target exists in a monitoring area, a first gateway receives a first message from the motion detection node, wherein the first message comprises measurement information of the current position of the intrusion target, and the video node is in a closed state when the intrusion target is not detected in the monitoring area;

The first gateway determines target information of the current position based on measurement information of the current position and prediction information of the current position;

the first gateway adopts a greedy algorithm, and distributes spread spectrum factors to a plurality of communication nodes connected with the first gateway based on the target information of the current position, wherein the plurality of communication nodes comprise a first communication node; a spreading factor allocated to the first communication node, configured to send subsequent information to the first gateway by the first communication node;

the first gateway sends a second message containing measurement information of the current position to a controller;

and a second gateway controls the first video node to be started on the basis of a third message from the controller so as to monitor the intrusion target, wherein the third message is obtained by the controller on the basis of the measurement information of the current position and the prediction information of the current position, the prediction information of the current position is obtained on the basis of the last position of the intrusion target, and the second gateway is a gateway connected with a communication node nearest to the first video node.

4. A method according to claim 3, characterized in that the method further comprises:

And a third gateway receives a fourth message from the controller and controls a second video node to be started based on the fourth message so as to monitor the intrusion target, wherein the fourth message is obtained by the controller based on the predicted information of the next position of the intrusion target, the predicted information of the next position is obtained based on the current position, and the third gateway is a gateway connected with a communication node closest to the second video node.

5. The method of claim 4, wherein the first gateway employs a greedy algorithm and assigns spreading factors to a plurality of communication nodes connected to the first gateway based on target information for the current location, comprising:

the first gateway obtains a current state, wherein the current state is constructed based on the position information of the plurality of communication nodes and the target information of the current position;

the first gateway allocates spreading factors to the plurality of communication nodes by utilizing a greedy algorithm and combining the current state and a Q table, wherein the Q table comprises a plurality of Q values, one Q value corresponds to one candidate state and one candidate action, the candidate state is constructed based on the position information of the plurality of communication nodes and the position information of the candidate area of the invasion target, the candidate areas are in one-to-one correspondence with the communication nodes, and the candidate actions are allocation actions corresponding to candidate modes adopted by the first gateway for allocating the spreading factors to the plurality of communication nodes.

6. The method of claim 5, wherein the method further comprises:

the first gateway calculates a success probability of the plurality of communication nodes transmitting information using the allocated spreading factors, and updates the Q table based on the success probability.

7. The energy-saving method of the Internet of things based on the position prediction is characterized by comprising the following steps of:

the controller receives a second message containing measurement information of the current position of an intrusion target from a first communication node through a first gateway, the measurement information of the current position is contained in the first message and sent to the first communication node when the motion detection node detects that the intrusion target exists in a monitoring area, and the video node is in a closed state when the intrusion target is not detected in the monitoring area; the first gateway is configured to adopt a greedy algorithm, and allocate spreading factors to a plurality of communication nodes connected to the first gateway based on the target information of the current position, where the plurality of communication nodes include the first communication node; a spreading factor allocated to the first communication node, configured to send subsequent information to the first gateway by the first communication node;

The controller determines a third message based on the measurement information of the current position and the prediction information of the current position, wherein the prediction information of the current position is obtained based on the last position of the intrusion target;

the controller sends the third message to a second communication node, wherein the third message is used for controlling the first video node to be started by the second communication node so as to monitor the intrusion target, and the second communication node is the communication node closest to the first video node.

8. The method of claim 7, wherein the method further comprises:

the controller determines target information of the current position based on the measurement information of the current position and the prediction information of the current position;

the controller determines the prediction information of the next position of the intrusion target based on the target information of the current position;

the controller determines a fourth message based on the predicted information of the next location;

the controller sends the fourth message to a third communication node, wherein the fourth message is used for controlling a second video node to be started by the third communication node so as to monitor the intrusion target, and the third communication node is the communication node closest to the second video node.

9. The method of claim 8, wherein the controller determining predicted information for a next location of the intrusion target based on target information for the current location comprises:

the controller predicts the predicted information of the next position of the intrusion target based on the target information of the current position and a Kalman filtering KF algorithm.

10. A gateway, comprising:

the device comprises a receiving module, a detecting module and a processing module, wherein the receiving module is used for receiving a first message from a motion detection node after the motion detection node detects that an intrusion target exists in a monitoring area, wherein the first message comprises measurement information of the current position of the intrusion target, and the video node is in a closed state when the intrusion target is not detected in the monitoring area;

the control module is used for determining target information of the current position based on the measurement information of the current position and the prediction information of the current position;

the control module is further configured to allocate spreading factors to a plurality of communication nodes connected to the gateway, where the plurality of communication nodes include a first communication node, by adopting a greedy algorithm, and based on the target information of the current position; the spreading factor allocated to the first communication node is used for the first communication node to send subsequent information to the gateway;

A sending module, configured to send a second message containing measurement information of the current position to a controller;

the control module is further configured to control a first video node to be turned on based on a third message from the controller to monitor the intrusion target, where the third message is obtained by the controller based on measurement information of the current position and prediction information of the current position, the prediction information of the current position is obtained based on a previous position of the intrusion target, and the gateway is a gateway connected to a communication node closest to the first video node.

11. A controller, comprising:

the receiving module is used for receiving a second message containing measurement information of the current position of an intrusion target from a first communication node through a first gateway, wherein the measurement information of the current position is contained in the first message and sent to the first communication node when the motion detection node detects that the intrusion target exists in a monitoring area, and the video node is in a closed state when the intrusion target is not detected in the monitoring area; the first gateway is configured to adopt a greedy algorithm, and allocate spreading factors to a plurality of communication nodes connected to the first gateway based on the target information of the current position, where the plurality of communication nodes include the first communication node; a spreading factor allocated to the first communication node, configured to send subsequent information to the first gateway by the first communication node;

A determining module, configured to determine a third message based on measurement information of the current location and prediction information of the current location, where the prediction information of the current location is obtained based on a previous location of the intrusion target;

and the sending module is used for sending the third message to a second communication node, wherein the third message is used for controlling the first video node to be started by the second communication node so as to monitor the intrusion target, and the second communication node is the communication node closest to the first video node.

12. The utility model provides an thing networking economizer system based on position prediction which characterized in that includes:

the motion detection node is used for sending a first message to the first communication node after detecting that the monitored area has an intrusion target, wherein the first message comprises measurement information of the current position of the intrusion target, and the video node is in a closed state when the monitored area does not detect the intrusion target;

the first communication node is used for sending a second message containing the measurement information of the current position to the controller through the first gateway; the first gateway is used for adopting a greedy algorithm and distributing spread spectrum factors to a plurality of communication nodes connected with the first gateway based on the target information of the current position; wherein the target information of the current position is determined by the first gateway based on the measurement information of the current position and the prediction information of the current position; the plurality of communication nodes includes the first communication node; a spreading factor allocated to the first communication node, configured to send subsequent information to the first gateway by the first communication node;

The controller is configured to determine a third message based on measurement information of the current position and prediction information of the current position, and send the third message to a second communication node, where the prediction information of the current position is obtained based on a previous position of the intrusion target, and the second communication node is a communication node closest to the first video node;