Low-power-consumption wide-area Internet of Things communication method and device, electronic equipment and storage medium

Abstract

The invention discloses a low-power-consumption wide-area Internet of Things communication method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a resource allocation optimization model of a target network environment according to a current equipment state and a current network state, and determining the network resource allocation amount of each target RedCap equipment; constructing a transmission power optimization model and a retransmission strategy optimization model of each target RedCap device according to the current device state and the network resource allocation amount, and determining the device transmission power and the target retransmission strategy of each target RedCap device; and constructing a working mode optimization model of each target RedCap device according to the current device state, the device transmission power and the target retransmission strategy, and determining a target working mode of each target RedCap device. According to the invention, the energy efficiency and connection performance of the Internet of Things equipment in the 5G network are improved, and the method can be widely applied to the technical field of Internet of Things.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a low-power wide-area Internet of Things communication method, device, electronic equipment and storage medium. Background Art

[0002] With the development of 5G technology and the popularization of the Internet of Things (IoT), a large number of low-power devices need to access the 5G network. The 5G network can provide a new generation of wireless communication services with high speed, large capacity and low latency for IoT terminals. However, the traditional 5G network design does not fully consider the need to access a large number of low-power devices. How to significantly reduce device power consumption and extend battery life while maintaining communication quality is a problem that the 5G communication system needs to solve when facing large-scale access of IoT devices.

[0003] The current low-power wide area network technologies mainly include LoRa, Sigfox, NB-IoT, etc. They can provide low-power consumption and long-distance communication solutions in specific scenarios, but in the face of the changing wireless environment of 5G networks, such as different channel conditions, frequent network interference, and rich and diverse IoT application requirements, the challenge of providing stable, reliable, and efficient communication services is still very arduous. In addition, for large-scale deployment of IoT devices, how to effectively schedule and manage resources to ensure device communication performance while minimizing energy consumption has also become an urgent problem to be solved.

[0004] Therefore, in response to the above problems, 3GPP proposed the RedCap device scenario under study in the 5G evolution. The goal of RedCap is to provide a solution with low speed, low power consumption, high frequency coverage and large number of connections, mainly used to support IoT applications with medium-speed data transmission requirements, such as smart homes, industrial automation, etc. However, how to use RedCap technology to efficiently meet the communication needs of low-power IoT devices in 5G networks also requires further research and discussion.

[0005] Terminology explanation:

[0006] RedCap (Reduced Capability): A term specific to 5G network technology. RedCap provides support for Internet of Things (IoT) devices in 5G networks, especially those with lower data rate requirements and / or lower device complexity. Summary of the invention

[0007] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0008] To this end, an object of an embodiment of the present invention is to provide a low-power wide-area Internet of Things communication method, which improves the energy efficiency and connection performance of Internet of Things devices in a 5G network.

[0009] Another object of an embodiment of the present invention is to provide a low-power wide-area Internet of Things communication device.

[0010] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0011] On the one hand, an embodiment of the present invention provides a low-power wide-area Internet of Things communication method, comprising the following steps:

[0012] Acquire the current device status of multiple target RedCap devices and the current network status of the target network environment, construct a resource allocation optimization model of the target network environment according to the current device status and the current network status, and determine the network resource allocation amount of each target RedCap device according to the resource allocation optimization model;

[0013] Construct a transmission power optimization model and a retransmission strategy optimization model for each of the target RedCap devices according to the current device state and the network resource allocation amount, and determine the device transmission power and target retransmission strategy of each of the target RedCap devices according to the transmission power optimization model and the retransmission strategy optimization model;

[0014] Constructing a working mode optimization model for each of the target RedCap devices according to the current device state, the device transmission power, and the target retransmission strategy, and determining a target working mode for each of the target RedCap devices according to the working mode optimization model;

[0015] Network resources are allocated to each of the target RedCap devices according to the network resource allocation amount, the working mode of each of the target RedCap devices is adjusted according to the target working mode, and then each of the target RedCap devices is controlled to perform signal transmission according to the device transmission power and the target retransmission strategy.

[0016] Further, in one embodiment of the present invention, the obtaining of the current device status of multiple target RedCap devices and the current network status of the target network environment, and constructing a resource allocation optimization model of the target network environment according to the current device status and the current network status, specifically includes:

[0017] Obtaining the current device status of each target RedCap device, wherein the current device status includes battery power, data transmission requirements, and signal strength;

[0018] Acquire the current network status of the target network environment, wherein the current network status includes network congestion level and network signal quality;

[0019] Determine the device power consumption function of each target RedCap device according to the current device state, and determine the upper and lower limits of network load and the upper and lower limits of device demand according to the current network state;

[0020] Minimizing the sum of the device power consumption functions of each of the target RedCap devices is taken as the optimization goal, the network load constraint is determined according to the sum of the network load variables of each of the target RedCap devices and the upper and lower limits of the network load, and the device demand constraint is determined according to the sum of the transmission demand variables of each of the target RedCap devices and the upper and lower limits of the device demand, so as to obtain the resource allocation optimization model.

[0021] Furthermore, in one embodiment of the present invention, the resource allocation optimization model is:

[0022]

[0023] L≤Σl i ≤U

[0024] D≤Σd i ≤V

[0025] Where E represents the total energy consumption function, N represents the total number of devices, and l i represents the network load variable of the ith device, d i represents the transmission demand variable of the ith device, e i Represents the network load variable l of the i-th device i and the transmission demand variable d i The device power consumption function, L and U represent the lower and upper limits of the network load, D and V represent the lower and upper limits of the device demand, respectively;

[0026] Determining the network resource allocation amount of each target RedCap device according to the resource allocation optimization model specifically includes:

[0027] Converting the resource allocation optimization model into a Lagrangian function form to obtain a target Lagrangian function and a target equation group;

[0028] The target equation group is optimized and solved by Lagrange multiplier method to obtain the optimal network load of each target RedCap device;

[0029] Determine the network resource allocation amount of each target RedCap device according to the optimal network load;

[0030] The target Lagrangian function is:

[0031] F(e i ,l i ,d i ,λ,μ,ν,θ)=E-λ(L-Σl i )-μ(U-∑l i )-ν(D-∑d i )-θ(V-∑d i )

[0032] Among them, F(e i ,l i ,d i ,λ,μ,ν,θ) represents the target Lagrangian function, λ,μ,ν and θ represent the Lagrangian multipliers;

[0033] The target equations are:

[0034]

[0035] in, as well as They represent the target Lagrangian function for e i , λ, μ, ν and the first-order partial derivatives of θ.

[0036] Further, in one embodiment of the present invention, the transmission power optimization model and the retransmission strategy optimization model of each target RedCap device are constructed according to the current device state and the network resource allocation, which specifically includes:

[0037] Determine the signal-to-noise ratio variable and the noise variable of the target RedCap device under different transmission power variables according to the current device state;

[0038] Determine a signal quality function according to the signal-to-noise ratio variable, the noise variable and the transmission power variable;

[0039] Determine the upper and lower limits of the transmission power of the target RedCap device according to the network resource allocation amount;

[0040] Taking the maximization of the signal quality function as the optimization goal, determining the transmission power constraint condition according to the transmission power variable and the transmission power upper and lower limits, and obtaining the transmission power optimization model;

[0041] Determine the single transmission energy consumption variable of the target RedCap device under different transmission power variables and the single waiting energy consumption variable under different retransmission interval variables according to the current device state;

[0042] Determine a retransmission energy consumption function according to the retransmission number variable, the single transmission energy consumption variable and the single waiting energy consumption variable;

[0043] Determine the total time consumed for retransmission according to the current device state;

[0044] The minimization of the retransmission energy consumption function is taken as the optimization goal, and the retransmission time constraint condition is determined according to the retransmission number variable, the retransmission interval variable and the total retransmission time to obtain the retransmission strategy optimization model.

[0045] Furthermore, in one embodiment of the present invention, the transmission power optimization model is:

[0046]

[0047] p min ≤p≤p max

[0048] Where Q represents the signal quality, p represents the transmission power variable, g and n represent the signal-to-noise ratio variable and noise variable of the device under the transmission power variable p, respectively. min and p max Respectively represent the lower and upper limits of the transmission power;

[0049] The retransmission strategy optimization model is:

[0050] min E1=R(E r +E t )

[0051] T=R*t

[0052] Among them, E1 represents the retransmission energy consumption function, R represents the retransmission number variable, t represents the retransmission interval variable, and E r It represents the single transmission energy consumption variable of the device under the transmission power variable p, E t represents the single waiting energy consumption variable of the device under the retransmission interval variable t, and T represents the total retransmission time;

[0053] The determining of the device transmission power and the target retransmission strategy of each target RedCap device according to the transmission power optimization model and the retransmission strategy optimization model specifically includes:

[0054] Solving the transmission power optimization model to obtain the optimal transmission power of the target RedCap device;

[0055] Solving the retransmission strategy optimization model according to the optimal transmission power to obtain the optimal number of retransmissions for the target RedCap device;

[0056] Determine the current transmission power and the current number of retransmissions of the target RedCap device;

[0057] Determine a first weight parameter according to the current device state and the network resource allocation amount;

[0058] Performing a weighted summation of the current transmission power and the optimal transmission power according to the first weight parameter to obtain the device transmission power;

[0059] According to the first weight parameter, a weighted sum is taken for the current number of retransmissions and the optimal number of retransmissions to obtain a target number of retransmissions, a target retransmission interval is determined according to the target number of retransmissions and the total retransmission time, and the target retransmission strategy is determined according to the target number of retransmissions and the target retransmission interval.

[0060] Further, in one embodiment of the present invention, the working mode optimization model of each target RedCap device is constructed according to the current device state, the device transmission power and the target retransmission strategy, and the target working mode of each target RedCap device is determined according to the working mode optimization model, which specifically includes:

[0061] Determine the performance indicator variables and device energy consumption variables of the target RedCap device in different working modes according to the current device state, the device transmission power and the target retransmission strategy;

[0062] Construct binary variable vectors for different working modes and determine the corresponding vector constraints;

[0063] Determine a device energy consumption function according to the binary variable vector and the device energy consumption variable;

[0064] Taking the minimization of the device energy consumption function as the optimization goal, determining the performance indicator constraint conditions according to the performance indicator variables and the data transmission requirements of the target RedCap device, and obtaining the working mode optimization model;

[0065] The working mode optimization model is solved to obtain the target working mode.

[0066] Furthermore, in one embodiment of the present invention, the working mode optimization model is:

[0067]

[0068] X=[x1,x2,...,x M ]

[0069] x1+x2+...+x M =1

[0070] x j ∈{0,1}

[0071] x j *P j ≥C

[0072] Among them, E2 represents the energy consumption function of the device, M represents the total number of working modes, and x j =1 means selecting the jth working mode, x j =0 means not selecting the jth working mode, E j represents the energy consumption variable of the equipment in the jth working mode, X represents the binary variable vector, P j Represents the performance indicator variable under the jth working mode.

[0073] On the other hand, an embodiment of the present invention provides a low-power wide-area Internet of Things communication device, including:

[0074] A resource allocation optimization module, used to obtain the current device status of multiple target RedCap devices and the current network status of the target network environment, build a resource allocation optimization model of the target network environment according to the current device status and the current network status, and determine the network resource allocation amount of each target RedCap device according to the resource allocation optimization model;

[0075] A transmission power and retransmission strategy optimization module, used to construct a transmission power optimization model and a retransmission strategy optimization model for each of the target RedCap devices according to the current device state and the network resource allocation, and determine the device transmission power and target retransmission strategy of each of the target RedCap devices according to the transmission power optimization model and the retransmission strategy optimization model;

[0076] A working mode optimization module, used to construct a working mode optimization model of each target RedCap device according to the current device state, the device transmission power and the target retransmission strategy, and determine the target working mode of each target RedCap device according to the working mode optimization model;

[0077] The communication control module is used to allocate network resources to each of the target RedCap devices according to the network resource allocation amount, adjust the working mode of each of the target RedCap devices according to the target working mode, and then control each of the target RedCap devices to perform signal transmission according to the device transmission power and the target retransmission strategy.

[0078] On the other hand, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein the program, when executed by the processor, realizes the low-power wide-area Internet of Things communication method as described above.

[0079] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the low-power wide-area Internet of Things communication method as described above.

[0080] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0081] The embodiment of the present invention obtains the current device status of multiple target RedCap devices and the current network status of the target network environment, constructs a resource allocation optimization model of the target network environment according to the current device status and the current network status, determines the network resource allocation amount of each target RedCap device according to the resource allocation optimization model, constructs a transmission power optimization model and a retransmission strategy optimization model for each target RedCap device according to the current device status and the network resource allocation amount, determines the device transmission power and the target retransmission strategy of each target RedCap device according to the transmission power optimization model and the retransmission strategy optimization model, constructs a working mode optimization model for each target RedCap device according to the current device status, the device transmission power and the target retransmission strategy, determines the target working mode of each target RedCap device according to the working mode optimization model, allocates network resources to each target RedCap device according to the network resource allocation amount, adjusts the working mode of each target RedCap device according to the target working mode, and then controls each target RedCap device to perform signal transmission according to the device transmission power and the target retransmission strategy. The embodiments of the present invention dynamically adjust the network resource allocation of each device through a resource allocation optimization model, dynamically adjust the device transmission power and retransmission strategy of each device through a transmission power optimization model and a retransmission strategy optimization model, and dynamically adjust the working mode of each device through a working mode optimization model. This can effectively reduce the power consumption of IoT devices, thereby extending the battery life of the devices and reducing the cost of using the devices. At the same time, it can improve the efficiency and stability of data transmission, thereby improving the energy efficiency and connection performance of IoT devices in 5G networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0083] Figure 1 A flowchart of a low-power wide-area Internet of Things communication method provided by an embodiment of the present invention;

[0084] Figure 2 A step flow chart of step S101 provided in an embodiment of the present invention;

[0085] Figure 3 Another step flow chart of step S101 provided in an embodiment of the present invention;

[0086] Figure 4 A step flow chart of step S102 provided in an embodiment of the present invention;

[0087] Figure 5 Another step flow chart of step S102 provided in an embodiment of the present invention;

[0088] Figure 6 A step flow chart of step S103 provided in an embodiment of the present invention;

[0089] Figure 7 A schematic diagram of the structure of a low-power wide-area Internet of Things communication device provided by an embodiment of the present invention;

[0090] Figure 8 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention;

[0091] Fig. 9 A schematic diagram of the structure of a storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0092] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limitations on the present application. It should be noted that, although the functional module division is performed in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order from the module division in the system schematic diagram or the flow chart. For the step numbers in the following embodiments, they are only set for the convenience of explanation, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0093] In the description of the present invention, the meaning of "a plurality" is two or more. If there is a description of the first or the second, it is only for the purpose of distinguishing the technical features, and it cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used in this document are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0094] The low-power wide-area Internet of Things communication method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a set-top box, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the low-power wide-area Internet of Things communication method, etc., but is not limited to the above forms.

[0095] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0096] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0097] like Figure 1 FIG. 1 is a flow chart showing a method for low power consumption wide area Internet of Things communication provided by an embodiment of the present invention, referring to FIG. Figure 1 The embodiment of the present invention provides a low-power wide-area Internet of Things communication method, which specifically includes the following steps:

[0098] S101, obtaining the current device status of multiple target RedCap devices and the current network status of the target network environment, building a resource allocation optimization model of the target network environment according to the current device status and the current network status, and determining the network resource allocation amount of each target RedCap device according to the resource allocation optimization model;

[0099] S102, constructing a transmission power optimization model and a retransmission strategy optimization model for each target RedCap device according to the current device status and network resource allocation, and determining the device transmission power and target retransmission strategy of each target RedCap device according to the transmission power optimization model and the retransmission strategy optimization model;

[0100] S103, constructing a working mode optimization model for each target RedCap device according to the current device status, device transmission power and target retransmission strategy, and determining the target working mode of each target RedCap device according to the working mode optimization model;

[0101] S104. Allocate network resources to each target RedCap device according to the network resource allocation amount, adjust the working mode of each target RedCap device according to the target working mode, and then control each target RedCap device to perform signal transmission according to the device transmission power and the target retransmission strategy.

[0102] The purpose of the present invention is to improve the energy efficiency and connection performance of IoT devices in 5G networks. First, the present invention optimizes the device resource allocation strategy. By designing a dynamic resource allocation algorithm, the resource allocation of RedCap devices can be intelligently adjusted according to the current state of the device and the network load to reduce unnecessary energy consumption. The optimization of this device resource allocation strategy can minimize energy consumption while ensuring the normal operation of IoT devices and the quality of data transmission, thereby extending the service life of the device; secondly, the present invention improves the signal processing mechanism, with the goal of consuming as little energy as possible, evaluates the feasible transmission power range according to the current network environment and device status, and selects the smallest possible transmission power on the premise of meeting the signal quality requirements. When the network environment or device status changes, the above-mentioned transmission power is re-evaluated in real time and adjusted; finally, the present invention proposes a dynamic adjustment method for the working mode, which can automatically switch the working mode of the RedCap device according to the environment in which the device is located and the task requirements. This strategy of automatically switching the working mode can be flexibly adjusted according to the environmental status and task requirements of the device to achieve automatic optimization of power consumption and performance without human intervention.

[0103] The embodiment of the present invention dynamically adjusts the network resource allocation of each device through a resource allocation optimization model, dynamically adjusts the device transmission power and retransmission strategy of each device through a transmission power optimization model and a retransmission strategy optimization model, and dynamically adjusts the working mode of each device through a working mode optimization model, which can effectively reduce the power consumption of IoT devices, thereby extending the battery life of the device and reducing the cost of using the device. At the same time, it can improve the efficiency and stability of data transmission, thereby improving the energy efficiency and connection performance of IoT devices in 5G networks. By implementing the present invention, the deployment of large-scale IoT devices can be supported, which helps to promote the widespread application of IoT technology in various fields.

[0104] like Figure 2 FIG. 1 is a flowchart of step S101 provided in an embodiment of the present invention, referring to FIG. Figure 2As an optional implementation, the current device status of multiple target RedCap devices and the current network status of the target network environment are obtained, and a resource allocation optimization model of the target network environment is constructed according to the current device status and the current network status, which specifically includes:

[0105] S1011, obtaining the current device status of each target RedCap device, the current device status including battery power, data transmission requirements and signal strength;

[0106] S1012, obtaining the current network status of the target network environment, where the current network status includes network congestion level and network signal quality;

[0107] S1013, determining the device power consumption function of each target RedCap device according to the current device status, and determining the upper and lower limits of the network load and the upper and lower limits of the device demand according to the current network status;

[0108] S1014. Minimizing the sum of the device power consumption functions of each target RedCap device is taken as the optimization goal. The network load constraint is determined according to the sum of the network load variables of each target RedCap device and the upper and lower limits of the network load. The device demand constraint is determined according to the sum of the transmission demand variables of each target RedCap device and the upper and lower limits of the device demand, so as to obtain a resource allocation optimization model.

[0109] like Figure 3 FIG. 1 is another step flow chart of step S101 provided in an embodiment of the present invention, referring to FIG. Figure 3 , further as an optional implementation, the resource allocation optimization model is:

[0110]

[0111] L≤∑l i ≤U

[0112] D≤∑d i ≤V

[0113] Where E represents the total energy consumption function, N represents the total number of devices, and l i represents the network load variable of the ith device, d i represents the transmission demand variable of the ith device, e i Represents the network load variable l of the i-th device i and the transmission demand variable d i The device power consumption function, L and U represent the lower and upper limits of the network load, D and V represent the lower and upper limits of the device demand, respectively;

[0114] The network resource allocation amount of each target RedCap device is determined according to the resource allocation optimization model, which specifically includes:

[0115] S1015, converting the resource allocation optimization model into a Lagrangian function form to obtain a target Lagrangian function and a target equation group;

[0116] S1016, optimizing and solving the target equations by Lagrange multiplier method to obtain the optimal network load of each target RedCap device;

[0117] S1017, determining the network resource allocation amount of each target RedCap device according to the optimal network load;

[0118] The target Lagrangian function is:

[0119] F(e i ,l i ,d i ,λ,μ,ν,θ)=E-λ(L-∑l i )-μ(U-∑l i )-ν(D-∑d i )-θ(V-∑d i )

[0120] Among them, F(e i ,l i ,d i ,λ,μ,ν,θ) represents the target Lagrangian function, λ,μ,ν and θ represent the Lagrangian multipliers;

[0121] The target equations are:

[0122]

[0123]

[0124] in, as well as They represent the target Lagrangian function for e i , λ, μ, ν and the first-order partial derivatives of θ.

[0125] Specifically, the embodiment of the present invention intelligently adjusts the resource allocation of the RedCap device according to the current state of the IoT device and the load of the network environment. Through this optimization, the system can reduce unnecessary energy consumption. The specific process is as follows:

[0126] First, data is collected based on the current status of IoT devices and the load of the network environment, including factors such as the device's battery level, data transmission requirements, signal strength, as well as network congestion and signal quality.

[0127] Then, the collected data is processed and analyzed. Take the power of the device as an example. Generally speaking, the lower the power, the stricter the control of energy consumption. At this time, the algorithm may choose to allocate fewer resources to the device to reduce energy consumption. However, if the data transmission demand of the device is very large, the algorithm may prioritize transmission efficiency and appropriately increase resource allocation.

[0128] This process can be expressed as an optimization problem, which is to minimize the energy consumption of the device while satisfying the network load and device demand constraints:

[0129]

[0130] L≤Σl i ≤U

[0131] D≤∑d i ≤V

[0132] Where E represents the total energy consumption function, N represents the total number of devices, and l i represents the network load variable of the ith device, d i represents the transmission demand variable of the ith device, e i Represents the network load variable l of the i-th device i and the transmission demand variable d i The device power consumption function is shown in Figure 2, where L and U represent the lower and upper limits of the network load, respectively, and D and V represent the lower and upper limits of the device demand, respectively.

[0133] The embodiment of the present invention uses the Lagrange Multiplier Method to solve the above optimization problem.

[0134] The Lagrangian function of this optimization problem can be formalized as:

[0135] F(e i ,l i ,d i ,λ,μ,ν,θ)=E-λ(L-∑l i )-μ(U-∑l i )-ν(D-∑d i )-θ(V-∑d i )

[0136] Here, λ, μ, ν, and θ represent Lagrange multipliers.

[0137] The optimization problem is transformed into solving the following system of equations:

[0138]

[0139]

[0140] By iterative solution, the network load variable l of interest can be obtained i The value of is used to determine the network resource allocation for each target RedCap device.

[0141] Finally, based on the analysis results of the algorithm, the system will adjust the resource allocation of the RedCap device to minimize energy consumption. This method can effectively avoid resource waste, improve network resource utilization, and thus reduce device power consumption. This optimization of the device resource allocation strategy based on the dynamic resource allocation algorithm can minimize energy consumption while ensuring the normal operation of IoT devices and data transmission quality, thereby extending the service life of the device and meeting the needs of low-power wide-area IoT communication systems.

[0142] like Figure 4 FIG. 1 is a flow chart of step S102 provided in an embodiment of the present invention, referring to FIG. Figure 4 As an optional implementation, a transmission power optimization model and a retransmission strategy optimization model for each target RedCap device are constructed according to the current device status and the network resource allocation, which specifically includes:

[0143] S10211. Determine the signal-to-noise ratio variable and the noise variable of the target RedCap device under different transmission power variables according to the current device state;

[0144] S10212, determining a signal quality function according to a signal-to-noise ratio variable, a noise variable, and a transmission power variable;

[0145] S10213. Determine the upper and lower limits of the transmission power of the target RedCap device according to the network resource allocation;

[0146] S10214, taking the maximization of the signal quality function as the optimization goal, determining the transmission power constraint condition according to the transmission power variable and the upper and lower limits of the transmission power, and obtaining a transmission power optimization model;

[0147] S10215, determining a single transmission energy consumption variable of the target RedCap device under different transmission power variables and a single waiting energy consumption variable under different retransmission interval variables according to the current device state;

[0148] S10216, determining a retransmission energy consumption function according to a retransmission number variable, a single transmission energy consumption variable, and a single waiting energy consumption variable;

[0149] S10217, determining the total time consumed for retransmission according to the current device status;

[0150] S10218. Minimizing the retransmission energy consumption function is taken as the optimization goal, and the retransmission time constraint is determined according to the retransmission number variable, the retransmission interval variable, and the total retransmission time, to obtain a retransmission strategy optimization model.

[0151] like Figure 5 FIG. 1 is another step flow chart of step S102 provided in an embodiment of the present invention, referring to FIG. Figure 5 , further as an optional implementation, the transmission power optimization model is:

[0152]

[0153] p min ≤p≤p max

[0154] Where Q represents the signal quality, p represents the transmission power variable, g and n represent the signal-to-noise ratio variable and noise variable of the device under the transmission power variable p, respectively. min and p max Respectively represent the lower and upper limits of the transmission power;

[0155] The retransmission strategy optimization model is:

[0156] min E1=R(E r +E t )

[0157] T=R*t

[0158] Among them, E1 represents the retransmission energy consumption function, R represents the retransmission number variable, t represents the retransmission interval variable, and E r It represents the single transmission energy consumption variable of the device under the transmission power variable p, E t represents the single waiting energy consumption variable of the device under the retransmission interval variable t, and T represents the total retransmission time;

[0159] The device transmission power and target retransmission strategy of each target RedCap device are determined according to the transmission power optimization model and the retransmission strategy optimization model, which specifically include:

[0160] S10221. Solve the transmission power optimization model to obtain the optimal transmission power of the target RedCap device;

[0161] S10222. Solve the retransmission strategy optimization model according to the optimal transmission power to obtain the optimal number of retransmissions for the target RedCap device;

[0162] S10223. Determine the current transmission power and current number of retransmissions of the target RedCap device;

[0163] S10224. Determine a first weight parameter according to the current device state and the network resource allocation amount;

[0164] S10225. Perform a weighted summation of the current transmission power and the optimal transmission power according to the first weight parameter to obtain the device transmission power;

[0165] S10226. Perform weighted summation of the current number of retransmissions and the optimal number of retransmissions according to the first weight parameter to obtain a target number of retransmissions, determine a target retransmission interval according to the target number of retransmissions and the total retransmission time, and determine a target retransmission strategy according to the target number of retransmissions and the target retransmission interval.

[0166] Specifically, the main goal of optimizing the signal processing algorithm is to improve data transmission efficiency and reduce the number of retransmissions. The goal is to consume as little energy as possible, which usually means that the power needs to be reduced during transmission to adapt to various network environments, but this may affect the transmission quality. To address this issue, the feasible transmission power range is evaluated based on the current network environment and device status. This can be achieved by the device sensing information such as the signal-to-noise ratio (SNR) of its environment. In the signal processing algorithm, the main goal of the embodiment of the present invention is to reduce the transmission power to save energy while meeting the signal quality requirements. The specific process is as follows:

[0167] First, the device needs to collect and process enough environmental information to determine the device's current state and predict its future behavior. This includes the device's battery level, data transmission requirements, signal strength, and various environmental factors that may affect the device's communication effectiveness. The main task at this stage is to select sufficient and effective features to reflect the device's current state.

[0168] Secondly, it is necessary to evaluate the current signal quality of the device and use the gradient descent method to optimize the signal quality Q. Signal quality can be considered as a function of device power and communication environment noise during the optimization process. The main thing is to select the appropriate power, reduce noise interference during communication, and improve signal quality. The goal of this stage is to find the device transmission power that optimizes the signal quality.

[0169] Assuming that the signal-to-noise ratio (SNR) of the current device is g, the transmission power is p, and the noise is n, we need to maximize the signal quality Q, then:

[0170]

[0171] Here Q can be regarded as the signal quality, and the larger the value, the better the signal quality. Therefore, our goal is to find the best p to maximize Q, which can be formalized as the following optimization problem:

[0172]

[0173] p min ≤p≤p max

[0174] This is an optimization problem under constraints, solved using the gradient descent method.

[0175] Then, for data loss caused by signal quality problems, you need to design an effective retransmission strategy to reduce the data loss rate. Please select an appropriate retransmission interval and number of retransmissions to minimize the energy loss caused by retransmissions. At the same time, the impact of retransmissions also needs to be considered at this stage to avoid network congestion caused by frequent retransmissions.

[0176] Design an efficient retransmission strategy for data loss caused by poor signal quality, and adopt appropriate retransmission intervals and retransmission times. The optimization goal of the retransmission strategy is to reduce the overall energy consumption E, as follows:

[0177] The energy consumption of R retransmissions is R*E r , where E r is the energy consumption of a single transmission;

[0178] Overall energy consumption E1 = R (Er + Et), where E t It is the single waiting energy consumption caused by the retransmission interval;

[0179] We need to find a suitable R to minimize E, which can be formalized as the following optimization problem:

[0180] min E1=R(E r +E t )

[0181] T=R*t

[0182] Among them, E1 represents the retransmission energy consumption function, R represents the retransmission number variable, t represents the retransmission interval variable, and E r It represents the single transmission energy consumption variable of the device under the transmission power variable p, E t It represents the single waiting energy consumption variable of the device under the retransmission interval variable t, and T represents the total retransmission time.

[0183] By solving the above optimization problem, the optimal transmission power and the optimal number of retransmissions can be obtained.

[0184] Finally, the device's transmit power and selective retransmission strategy are adjusted in an adaptive manner. The device's transmit power and selective retransmission strategy are adjusted in real time through the device's operation log or real-time feedback information to adapt to the device's environmental changes and business needs.

[0185] Specifically, the transmission power p and the retransmission count R of the device will be dynamically adjusted according to the network environment and the device status. Introducing a weight coefficient w (0 < w < 1), the update rules for the transmission power p and the retransmission count R of the device can be described as follows:

[0186] p new = w * p old + (1 - w) * p opt

[0187] R new = w * R old + (1 - w) * R opt

[0188] Wherein, p opt and R opt are the optimal transmission power and the optimal retransmission count obtained through the above optimization problem, and p old and R old are the previous parameter settings of the device. w is the weight coefficient, which can be dynamically adjusted according to the device status and the network environment, such as the device power, network load, etc.

[0189] Through this adaptive adjustment method, the device can automatically adjust its own parameter settings according to the current environmental status and device status to achieve the minimization of energy consumption and the maximization of communication quality.

[0190] As Figure 6 shown is a step flowchart of step S103 provided by an embodiment of the present invention. Referring to Figure 6 , further as an optional implementation manner, an operation mode optimization model of each target RedCap device is constructed according to the current device status, device transmission power, and target retransmission strategy, and the target operation mode of each target RedCap device is determined according to the operation mode optimization model, which specifically includes:

[0191] S1031. Determine the performance index variables and device energy consumption variables of the target RedCap device in different operation modes according to the current device status, device transmission power, and target retransmission strategy;

[0192] S1032. Construct a binary variable vector for different operation modes and determine the corresponding vector constraint conditions;

[0193] S1033. Determine the device energy consumption function according to the binary variable vector and the device energy consumption variables;

[0194] S1034. Take the minimization of the device energy consumption function as the optimization goal, and determine the performance index constraint conditions according to the performance index variables and the data transmission requirements of the target RedCap device to obtain the operation mode optimization model;

[0195] S1035. Solve the working mode optimization model to obtain the target working mode.

[0196] As an optional implementation, the working mode optimization model is:

[0197]

[0198] X=[x1,x2,...,x M ]

[0199] x1+x2+...+x M =1

[0200] x j ∈{0,1}

[0201] x j *P j ≥C

[0202] Where E2 represents the device energy consumption function, M represents the total number of working modes, and x j =1 means selecting the jth working mode, x j =0 means not selecting the jth working mode, E j represents the energy consumption variable of the equipment in the jth working mode, X represents the binary variable vector, P j Represents the performance indicator variable under the jth working mode.

[0203] Specifically, the system can automatically switch the working mode of the RedCap device according to the environment and task requirements of the IoT device. For example, when the device is under low load, the system can switch the device to sleep mode to reduce the energy consumption of the device; for another example, when the device requires efficient data transmission, the system can switch the device to high-efficiency mode to ensure the stability and efficiency of data transmission.

[0204] The dynamic adjustment of the device working mode is mainly achieved by designing a new working mode switching mechanism. This mechanism mainly considers the current environmental conditions and task requirements of the device, as well as the energy consumption of the device itself, and reduces the power consumption of the device as much as possible while ensuring the normal operation of the device.

[0205] In the present invention, the RedCap device may have a variety of different working modes, such as low power mode, high performance mode, power saving mode, etc. By automatically switching different working modes, the present invention aims to balance the power consumption and performance of the device. For the dynamic adjustment of the working mode, the current environmental status and task requirements of the device are mainly considered. According to the environmental status and task requirements of the device, the working mode of the RedCap device is automatically switched to balance the power consumption and performance. The specific process is as follows:

[0206] First, the system needs to monitor the working status and environmental information of each RedCap device in real time. The working status includes the current battery power of the device, task requirements, working mode of the device, etc.; environmental information includes the network environment, signal quality, environmental noise, etc. All this information will affect the working mode and energy consumption of the device.

[0207] Secondly, the system needs to process and analyze the collected information. The system can select a device operating mode that minimizes total energy consumption.

[0208] Then, the system needs to feed back the analysis results to each RedCap device. Each device automatically adjusts its own working mode based on the feedback results. For example, when the device's battery is low, the task demand is small, and the network environment is stable, the device can switch to low-power mode; conversely, when the device's battery is high, the task demand is large, and the network environment is unstable, the device can switch to high-performance mode.

[0209] Finally, the system needs to continuously monitor the working status and environmental information of the equipment and regularly adjust the working mode of the equipment based on the new information. This ensures that the equipment always runs in the optimal working mode to achieve the lowest energy consumption.

[0210] The process can be expanded into the following steps:

[0211] A1. Real-time monitoring of the device's environmental status and current task requirements. This includes information such as the device's current battery level, surrounding network conditions, and the computing requirements of the current task. At the same time, multiple working modes are preset, and each mode corresponds to the corresponding power consumption and performance under the predetermined task requirements and battery level.

[0212] A2. According to the current environment status and task requirements, the algorithm will find the most suitable working mode through optimization problems. If there are M working modes, each working mode corresponds to a performance indicator P j and power consumption E j ,This process is to select an optimal working mode under the premise of meeting the task requirements and minimizing energy consumption.

[0213] Since the decision variables involved in this problem are discrete variables, that is, the choice of each working mode, more specifically, this is an integer linear programming (ILP) problem. Below is an ILP solution process:

[0214] Define decision variables: The decision variable here is which working mode to choose, which can be defined as a binary variable vector X = [x1, x2, ..., x M ], where x j=1 means selecting the jth working mode, x j =0 means that the jth working mode is not selected. In order to ensure that only one working mode is selected, we also need to add a constraint: x1+x2+...+x M =1.

[0215] Constructing the objective function: The objective function is the total energy consumption that needs to be minimized, which can be expressed as: Where E j Represents the energy consumption variable of the equipment in the jth working mode.

[0216] Add constraints: The constraints here are to meet the task requirements, that is, the performance index P of the jth working mode j Need to be greater than or equal to the task requirement C. For all working modes, the constraint can be expressed as: j *P j ≥C.

[0217] Putting the above content together, the entire ILP problem can be expressed as:

[0218]

[0219] X=[x1,x2,...,x M ]

[0220] x1+x2+...+x M =1

[0221] x j ∈{0,1}

[0222] x j *P j ≥C

[0223] This problem is solved using methods such as the Branch and Bound method. The basic idea of ​​the Branch and Bound method is to decompose the problem into several smaller sub-problems (branches), and exclude some feasible solutions by finding the upper and lower bounds (bounds) of the sub-problems, gradually narrowing the scope of the problem, and finally finding the global optimal solution.

[0224] A3. Finally, according to the solution of the above optimization problem, the algorithm will automatically switch to the best working mode to meet the task requirements and reduce power consumption.

[0225] This strategy of automatically switching working modes can not only be flexibly adjusted according to the environmental status and task requirements of the device, but also can achieve automatic optimization of power consumption and performance without human intervention to meet the needs of low-power wide-area IoT communication systems.

[0226] The above is a description of the method steps of the embodiment of the present invention. It can be recognized that the embodiment of the present invention dynamically adjusts the network resource allocation of each device through the resource allocation optimization model, dynamically adjusts the device transmission power and retransmission strategy of each device through the transmission power optimization model and the retransmission strategy optimization model, and dynamically adjusts the working mode of each device through the working mode optimization model, which can effectively reduce the power consumption of the Internet of Things device, thereby extending the battery life of the device and reducing the cost of using the device. At the same time, it can improve the efficiency and stability of data transmission, thereby improving the energy efficiency and connection performance of the Internet of Things device in the 5G network. By implementing the present invention, it can support the deployment of large-scale Internet of Things devices, which helps to promote the widespread application of Internet of Things technology in various fields.

[0227] Compared with the prior art, the embodiments of the present invention also have the following advantages:

[0228] 1) Reduce energy consumption: Through optimized device resource allocation strategy and signal processing mechanism, and dynamically adjusting the working mode according to device status and task requirements, the present invention can effectively reduce the power consumption of IoT devices, thereby extending the battery life of the device and reducing the cost of device use.

[0229] 2) Improving communication efficiency: The improved signal processing mechanism in the present invention can improve data transmission efficiency and stability, reduce data retransmission, and thus improve the communication efficiency of the device.

[0230] 3) Strong adaptability: Due to the adoption of a strategy for dynamically switching working modes, the present invention has strong adaptability, is compatible with various network environments and device states, and adapts to the deployment requirements of large-scale Internet of Things devices.

[0231] 4) Improve the application value of IoT technology: This invention can provide a more efficient and energy-saving 5G network connection method, which will help promote the wider application of low-power IoT devices in various industries and increase their economic and social value.

[0232] 5) Improve user experience: Through the present invention, users can enjoy a longer usage time due to reduced device power consumption while ensuring the communication quality of IoT devices, which greatly improves user experience.

[0233] like Figure 7 FIG. 1 is a schematic diagram of the structure of a low-power wide-area Internet of Things communication device provided by an embodiment of the present invention, referring to FIG. Figure 7 , an embodiment of the present invention provides a low-power wide-area Internet of Things communication device, comprising:

[0234] A resource allocation optimization module is used to obtain the current device status of multiple target RedCap devices and the current network status of the target network environment, build a resource allocation optimization model of the target network environment according to the current device status and the current network status, and determine the network resource allocation amount of each target RedCap device according to the resource allocation optimization model;

[0235] The transmission power and retransmission strategy optimization module is used to construct the transmission power optimization model and retransmission strategy optimization model of each target RedCap device according to the current device status and network resource allocation, and determine the device transmission power and target retransmission strategy of each target RedCap device according to the transmission power optimization model and the retransmission strategy optimization model;

[0236] The working mode optimization module is used to build a working mode optimization model for each target RedCap device according to the current device status, device transmission power and target retransmission strategy, and determine the target working mode of each target RedCap device according to the working mode optimization model;

[0237] The communication control module is used to allocate network resources to each target RedCap device according to the network resource allocation amount, adjust the working mode of each target RedCap device according to the target working mode, and then control each target RedCap device to perform signal transmission according to the device transmission power and target retransmission strategy.

[0238] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0239] The embodiment of the present invention also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned low-power wide-area Internet of Things communication method. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0240] like Figure 8 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention, referring to FIG. Figure 8 , an embodiment of the present invention provides an electronic device, including:

[0241] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0242] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the low-power wide-area Internet of Things communication method of the embodiment of the present invention;

[0243] Input / output interface 803, used to implement information input and output;

[0244] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0245] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0246] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0247] like Fig. 9 FIG. 1 is a schematic diagram of a storage medium according to an embodiment of the present invention. Fig. 9 An embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs 901, and the one or more programs 901 can be executed by one or more processors to implement the above-mentioned low-power wide-area Internet of Things communication method.

[0248] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0249] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0250] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0251] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0252] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0253] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0254] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0255] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0256] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0257] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0258] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A low-power wide-area Internet of Things communication method, characterized in that: The following steps are involved: Acquire the current device status of multiple target RedCap devices and the current network status of the target network environment, construct a resource allocation optimization model of the target network environment according to the current device status and the current network status, and determine the network resource allocation amount of each target RedCap device according to the resource allocation optimization model; Construct a transmission power optimization model and a retransmission strategy optimization model for each of the target RedCap devices according to the current device state and the network resource allocation amount, and determine the device transmission power and target retransmission strategy of each of the target RedCap devices according to the transmission power optimization model and the retransmission strategy optimization model; Constructing a working mode optimization model for each of the target RedCap devices according to the current device state, the device transmission power, and the target retransmission strategy, and determining a target working mode for each of the target RedCap devices according to the working mode optimization model; Network resources are allocated to each of the target RedCap devices according to the network resource allocation amount, the working mode of each of the target RedCap devices is adjusted according to the target working mode, and then each of the target RedCap devices is controlled to perform signal transmission according to the device transmission power and the target retransmission strategy.

2. A low power consumption wide area Internet of Things communication method according to claim 1, characterized in that: The step of obtaining the current device status of multiple target RedCap devices and the current network status of the target network environment, and constructing a resource allocation optimization model of the target network environment according to the current device status and the current network status, specifically includes: Obtaining the current device status of each target RedCap device, wherein the current device status includes battery power, data transmission requirements, and signal strength; Acquire the current network status of the target network environment, wherein the current network status includes network congestion level and network signal quality; Determine the device power consumption function of each target RedCap device according to the current device state, and determine the upper and lower limits of network load and the upper and lower limits of device demand according to the current network state; Minimizing the sum of the device power consumption functions of each of the target RedCap devices is taken as the optimization goal, the network load constraint is determined according to the sum of the network load variables of each of the target RedCap devices and the upper and lower limits of the network load, and the device demand constraint is determined according to the sum of the transmission demand variables of each of the target RedCap devices and the upper and lower limits of the device demand, so as to obtain the resource allocation optimization model.

3. A low power consumption wide area Internet of Things communication method according to claim 2, characterized in that: The resource allocation optimization model is: L≤∑l i ≤U D≤∑d i ≤V Where E represents the total energy consumption function, N represents the total number of devices, and l i represents the network load variable of the ith device, d i represents the transmission demand variable of the ith device, e i Represents the network load variable l of the i-th device i and the transmission demand variable d i The device power consumption function, L and U represent the lower and upper limits of the network load, D and V represent the lower and upper limits of the device demand, respectively; Determining the network resource allocation amount of each target RedCap device according to the resource allocation optimization model specifically includes: Converting the resource allocation optimization model into a Lagrangian function form to obtain a target Lagrangian function and a target equation group; The target equation group is optimized and solved by Lagrange multiplier method to obtain the optimal network load of each target RedCap device; Determine the network resource allocation amount of each target RedCap device according to the optimal network load; The target Lagrangian function is: F(e i ,l i ,d i ,λ,μ,ν,θ)=E-λ(L-Σl i )-μ(U-Σl i )-ν(D-∑d i )-θ(V-Σd i ) Among them, F(e i ,l i ,d i ,λ,μ,ν,θ) represents the target Lagrangian function, λ,μ,ν and θ represent the Lagrangian multipliers; The target equations are: in, as well as They represent the target Lagrangian function for e i , λ, μ, ν and the first-order partial derivatives of θ.

4. A low power consumption wide area Internet of Things communication method according to claim 1, characterized in that: The transmission power optimization model and retransmission strategy optimization model of each target RedCap device are constructed according to the current device state and the network resource allocation, which specifically includes: Determine the signal-to-noise ratio variable and the noise variable of the target RedCap device under different transmission power variables according to the current device state; Determine a signal quality function according to the signal-to-noise ratio variable, the noise variable and the transmission power variable; Determine the upper and lower limits of the transmission power of the target RedCap device according to the network resource allocation amount; Taking the maximization of the signal quality function as the optimization goal, determining the transmission power constraint condition according to the transmission power variable and the transmission power upper and lower limits, and obtaining the transmission power optimization model; Determine the single transmission energy consumption variable of the target RedCap device under different transmission power variables and the single waiting energy consumption variable under different retransmission interval variables according to the current device state; Determine a retransmission energy consumption function according to the retransmission number variable, the single transmission energy consumption variable and the single waiting energy consumption variable; Determine the total time consumed for retransmission according to the current device state; The retransmission energy consumption function minimization is taken as the optimization goal, and the retransmission time constraint is determined according to the retransmission number variable, the retransmission interval variable and the total retransmission time to obtain the retransmission strategy optimization model.

5. A low power consumption wide area Internet of Things communication method according to claim 4, characterized in that: The transmission power optimization model is: p min ≤p≤p max Where Q represents the signal quality, p represents the transmission power variable, g and n represent the signal-to-noise ratio variable and noise variable of the device under the transmission power variable p, respectively. min and p max Respectively represent the lower and upper limits of the transmission power; The retransmission strategy optimization model is: min E1=R(E r +E t ) T=R*t Among them, E1 represents the retransmission energy consumption function, R represents the retransmission number variable, t represents the retransmission interval variable, and E r It represents the single transmission energy consumption variable of the device under the transmission power variable p, E t represents the single waiting energy consumption variable of the device under the retransmission interval variable t, and T represents the total retransmission time; The determining of the device transmission power and the target retransmission strategy of each target RedCap device according to the transmission power optimization model and the retransmission strategy optimization model specifically includes: Solving the transmission power optimization model to obtain the optimal transmission power of the target RedCap device; Solving the retransmission strategy optimization model according to the optimal transmission power to obtain the optimal number of retransmissions for the target RedCap device; Determine the current transmission power and the current number of retransmissions of the target RedCap device; Determine a first weight parameter according to the current device state and the network resource allocation amount; Performing a weighted summation of the current transmission power and the optimal transmission power according to the first weight parameter to obtain the device transmission power; According to the first weight parameter, a weighted sum is taken for the current number of retransmissions and the optimal number of retransmissions to obtain a target number of retransmissions, a target retransmission interval is determined according to the target number of retransmissions and the total retransmission time, and the target retransmission strategy is determined according to the target number of retransmissions and the target retransmission interval.

6. A low power consumption wide area Internet of Things communication method according to claim 1, characterized in that: The step of constructing a working mode optimization model for each of the target RedCap devices according to the current device state, the device transmission power, and the target retransmission strategy, and determining a target working mode for each of the target RedCap devices according to the working mode optimization model specifically includes: Determine the performance indicator variables and device energy consumption variables of the target RedCap device in different working modes according to the current device state, the device transmission power and the target retransmission strategy; Construct binary variable vectors for different working modes and determine the corresponding vector constraints; Determine a device energy consumption function according to the binary variable vector and the device energy consumption variable; Taking the minimization of the device energy consumption function as the optimization goal, determining the performance indicator constraint conditions according to the performance indicator variables and the data transmission requirements of the target RedCap device, and obtaining the working mode optimization model; The working mode optimization model is solved to obtain the target working mode.

7. A low power consumption wide area Internet of Things communication method according to claim 6, characterized in that: The working mode optimization model is: X=[x1,x2,...,x M ] x1+x2+...+x M =1 x j ∈{0,1} x j *P j ≥C Where E2 represents the device energy consumption function, M represents the total number of working modes, and x j =1 means selecting the jth working mode, x j =0 means not selecting the jth working mode, E j represents the energy consumption variable of the equipment in the jth working mode, X represents the binary variable vector, P j Represents the performance indicator variable under the jth working mode.

8. A low-power wide-area Internet of Things communication device, characterized in that: include: A resource allocation optimization module, used to obtain the current device status of multiple target RedCap devices and the current network status of the target network environment, build a resource allocation optimization model of the target network environment according to the current device status and the current network status, and determine the network resource allocation amount of each target RedCap device according to the resource allocation optimization model; A transmission power and retransmission strategy optimization module, used to construct a transmission power optimization model and a retransmission strategy optimization model for each of the target RedCap devices according to the current device state and the network resource allocation, and determine the device transmission power and target retransmission strategy of each of the target RedCap devices according to the transmission power optimization model and the retransmission strategy optimization model; A working mode optimization module, used to construct a working mode optimization model of each target RedCap device according to the current device state, the device transmission power and the target retransmission strategy, and determine the target working mode of each target RedCap device according to the working mode optimization model; The communication control module is used to allocate network resources to each of the target RedCap devices according to the network resource allocation amount, adjust the working mode of each of the target RedCap devices according to the target working mode, and then control each of the target RedCap devices to perform signal transmission according to the device transmission power and the target retransmission strategy.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory. When the program is executed by the processor, the steps of the low-power wide-area Internet of Things communication method as described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the low-power wide-area Internet of Things communication method as described in any one of claims 1 to 8.

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