A loRa network throughput optimization method based on many-to-one matching theory

Abstract

The application provides a LoRa network throughput optimization method based on a many-to-one matching theory, relates to the technical field of Internet communication, and comprises the following steps: initially allocating a spread spectrum factor based on an equal-area scheme, determining initial spread spectrum factors of each terminal node in a LoRa network, obtaining spread spectrum factors corresponding to the highest throughput of each terminal node, replacing the initial spread spectrum factors with the spread spectrum factors, determining a plurality of groups of node SF pairs, selecting at least any two groups of node SF pairs from the plurality of groups of node SF pairs to exchange the spread spectrum factors, determining a target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determining the final spread spectrum factors of each terminal node based on the target node SF pair. On the basis of realizing the maximum throughput of each node, the application continuously performs matching exchange to eliminate blocking pairs in the LoRa network, maximizes the total throughput in the network, and improves the overall performance of the LoRa network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet communication, and in particular to a LoRa network throughput optimization method based on many-to-one matching theory. BACKGROUND

[0002] LoRa (Long Range) is a physical layer modulation and demodulation technology. LoRaWAN (LoRa Wide Area Network) is a low-power wide-area network standard based on an open-source MAC layer protocol proposed by the LoRa Alliance. The main goal is to build a large-capacity, long-range, and low-power star network to meet the needs of IoT (Internet of Things) applications. The LoRa gateway is a bridge between the device (i.e. LoRa node) and the LoRaWAN core network. The device uses a low-power network (LoRaWAN) to connect to the LoRa gateway, and the LoRa gateway uses a high-bandwidth network (such as WiFi, Ethernet, or cellular network) to connect to the core network. The communication capability requirements of the LoRa gateway are different in different scenarios. For example, in a high-density LoRa node scenario, the LoRa gateway needs to have strong communication capabilities, while in a low-density LoRa node scenario, the communication capability requirements of the LoRa gateway are relatively low. Currently, most LoRa gateways use standard LoRa gateway structures. A standard LoRa gateway is generally composed of one or more wireless transceiver modules. For example, for the most common LoRa gateway composed of one SX1301, it has the ability to receive data packets at 6 rates (SF7-12) in parallel on 8 channels, and has the ability to transmit at a specified modulation rate on one channel. However, as the number of terminal nodes in the LoRa network continues to increase and the radius continues to increase, the average packet arrival rate Psp and the throughput will both decrease, and the network performance will decrease accordingly. SUMMARY

[0003] The purpose of the present application is to solve the problem that as the number of terminal nodes in the LoRa network continues to increase and the radius continues to increase, the average Psp and the throughput will both decrease, and the network performance will decrease accordingly. The present application provides a LoRa network throughput optimization method based on many-to-one matching theory.

[0004] The technical scheme of the present application embodiment is implemented as follows:

[0005] The first aspect of the present application provides a LoRa network throughput optimization method based on many-to-one matching theory, comprising:

[0006] initially allocate the spreading factor based on the equal-area scheme to determine the initial spreading factor of each terminal node in the LoRa network;

[0007] acquire the spreading factor corresponding to the highest throughput of each terminal node, and replace the initial spreading factor with the spreading factor to determine a plurality of node SF pairs;

[0008] select at least any two node SF pairs from the plurality of node SF pairs to perform spreading factor exchange, determine the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determine the final spreading factor of each terminal node based on the target node SF pair.

[0009] Optionally, the initial allocation of the spreading factor based on the equal-area scheme to determine the initial spreading factor of each terminal node in the LoRa network comprises:

[0010] acquire the maximum coverage range of the LoRa network;

[0011] According to the node density and communication demand in the LoRa network, the maximum coverage range is divided into multiple regions, and the same spreading factor resource is allocated to each region.

[0012] Optionally, the acquisition of the spreading factor corresponding to the highest throughput of each terminal node, and the replacement of the initial spreading factor with the spreading factor to determine a plurality of node SF pairs comprises:

[0013] Adjust the spreading factor of each terminal node, and record the throughput data of each terminal node under different spreading factors to acquire the spreading factor corresponding to the highest throughput of each terminal node;

[0014] Replace the initial spreading factor with the spreading factor, and determine a plurality of node SF pairs based on each terminal node and the corresponding spreading factor.

[0015] Optionally, the selection of at least any two node SF pairs from the plurality of node SF pairs to perform spreading factor exchange, the determination of the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and the determination of the final spreading factor of each terminal node based on the target node SF pair comprises:

[0016] select at least any two node SF pairs from the plurality of node SF pairs to perform spreading factor exchange, determine the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determine the final spreading factor of each terminal node based on the target node SF pair.

[0017] Optionally, the selection of at least any two node SF pairs from the plurality of node SF pairs to perform spreading factor exchange, the determination of the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and the determination of the final spreading factor of each terminal node based on the target node SF pair comprises:

[0018] selecting at least any two groups of node SF pairs from the groups of node SF pairs for spreading factor exchange, and determining the overall throughput of the LoRa network after the exchange;

[0019] If the sum of the throughputs of the nodes in a target node SF pair is greater than the sum of the throughputs of the nodes in any other node SF pair, the spreading factor corresponding to each terminal node in the target node SF pair is determined as the final spreading factor.

[0020] The second aspect of the embodiment of the application provides a LoRa network throughput optimization device based on a many-to-one matching theory, which comprises an allocation module, a replacement module and a determination module, wherein,

[0021] The allocation module is configured to initially allocate spreading factors based on an equal-area scheme, and determine initial spreading factors of each terminal node in the LoRa network.

[0022] The replacement module is configured to obtain the spreading factors corresponding to the highest throughputs of each terminal node, replace the initial spreading factors with the spreading factors, and determine groups of node SF pairs.

[0023] The determination module is configured to select at least any two groups of node SF pairs from the groups of node SF pairs for spreading factor exchange, determine a target node SF pair corresponding to the highest throughput of the overall LoRa network, and determine the final spreading factors of each terminal node based on the target node SF pair.

[0024] Optionally, the allocation module is specifically configured to:

[0025] Obtain the maximum coverage range of the LoRa network.

[0026] According to the node density and communication requirements in the LoRa network, the maximum coverage range is divided into multiple regions, and the same spreading factor resources are allocated to each region.

[0027] Optionally, the replacement module is specifically configured to:

[0028] Adjust the spreading factors of each terminal node, record the throughput data of each terminal node under different spreading factors, and obtain the spreading factors corresponding to the highest throughputs of each terminal node.

[0029] Replace the initial spreading factors with the spreading factors, and determine groups of node SF pairs based on each terminal node and the corresponding spreading factor.

[0030] The third aspect of the embodiment of the present application provides an electronic device, comprising a processor and a memory; the memory has a computer program stored therein, wherein the computer program, when executed by the processor, implements the LoRa network throughput optimization method based on the many-to-one matching theory of the first aspect.

[0031] The fourth aspect of the embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of the first aspect.

[0032] Compared with the prior art, the technical scheme provided by the present application has the beneficial effects that:

[0033] The present application provides a LoRa network throughput optimization method based on a many-to-one matching theory, which initially allocates a spreading factor based on an equal-area scheme, determines the initial spreading factor of each terminal node in the LoRa network, obtains the spreading factor corresponding to the highest throughput of each terminal node, replaces the initial spreading factor with the spreading factor, determines a plurality of node SF pairs, selects at least any two node SF pairs from the plurality of node SF pairs for spreading factor exchange, determines the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determines the final spreading factor of each terminal node based on the target node SF pair. On the basis of achieving the maximum throughput at each node, matching exchange is continuously performed to eliminate the blocking pairs in the LoRa network, so as to maximize the total throughput in the network and improve the overall performance of the LoRa network. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 A flowchart of a LoRa network throughput optimization method based on a many-to-one matching theory provided by the embodiment of the present application is shown;

[0035] Figure 2 A structural diagram of a LoRa network throughput optimization device based on a many-to-one matching theory provided by the embodiment of the present application is shown;

[0036] Figure 3 A structural diagram of an electronic device provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] In the following detailed description, many specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, that one or more embodiments can be practiced without these specific details. In other instances, well-known structures and functions have not been described in detail in order to avoid obscuring the concepts of the present application.

[0038] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the terms "comprises", "comprising", "includes", "including" and the like are specifically intended to be open-ended terms meaning that other elements can be added.

[0039] All terms used herein, including technical and scientific terms, have the meanings commonly understood by one of ordinary skill in the art unless otherwise defined herein. It should be noted that the terms "comprise", "comprising", "comprises" and the like can be used interchangeably with the term "include" or "including". The terms "comprise", "comprising", "comprises" and the like are used synonymously with the term "include" or "including" and are intended to have the inclusive meaning rather than the exclusive meaning when used in connection with the terms "comprise", "comprising", "comprises" and the like.

[0040] Some of the blocks of the diagrams and / or flowcharts, and combinations of them, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the block diagrams and / or flowchart block or blocks.

[0041] In some embodiments, referring to Figure 1 , Figure 1 A flowchart of a LoRa network throughput optimization method based on a many-to-one matching theory provided by embodiments of the application; the LoRa network throughput optimization method based on a many-to-one matching theory provided by embodiments of the application comprises:

[0042] S110, initial allocation of the spreading factor based on the equal-area scheme, to determine the initial spreading factor of each terminal node in the LoRa network.

[0043] In the LoRa network, the commonly used values of the spreading factor (SF) are 7 to 12. Different spreading factors correspond to different symbol rates and coding rates, thereby affecting the communication distance and anti-interference capability. Here, by dividing the LoRa network into multiple regions, demand analysis is performed for each region, including communication distance, signal quality, and node quantity, etc., on the basis of which the initial spreading factor is allocated to determine the initial spreading factor of each terminal node in the LoRa network.

[0044] In some embodiments, S110, initial allocation of the spreading factor based on the equal-area scheme, to determine the initial spreading factor of each terminal node in the LoRa network, comprises:

[0045] Obtaining the maximum coverage range of the LoRa network;

[0046] According to the node density and communication requirements within the LoRa network, the maximum coverage range is divided into multiple regions, and each region is allocated with equivalent spreading factor resources.

[0047] In this embodiment, considering that the requirements of different regions are different, for regions that require long-distance communication or high anti-interference capability, a higher spreading factor (such as SF12) can be allocated. For regions with dense nodes and short communication distance, a lower spreading factor (such as SF7 or SF8) can be allocated to improve the data transmission rate. Considering the overall performance and resource utilization of the network, the allocation of spreading factors can be balanced among different regions to determine a more optimal initial allocation scheme.

[0048] S120, obtaining the spreading factor corresponding to the highest throughput of each terminal node, and replacing the initial spreading factor to determine a plurality of groups of node SF pairs.

[0049] Here, a preference list between the terminal node and the SF can be established. Each terminal node establishes a preference list according to the throughput when selecting different SFs, and the first SF value in each row of the preference list has the highest preference value, that is, the corresponding throughput is the largest. It should be noted that the highest preference value of different terminal nodes can be corresponding to the same SF value, or different SF values.

[0050] In some embodiments, S120, obtaining the spreading factor corresponding to the highest throughput of each terminal node, and replacing the initial spreading factor to determine a plurality of groups of node SF pairs, includes:

[0051] Adjusting the spreading factor of each terminal node and recording the throughput data of each terminal node under different spreading factors, obtaining the spreading factor corresponding to the highest throughput of each terminal node;

[0052] Replacing the initial spreading factor with the spreading factor, and determining a plurality of groups of node SF pairs based on each terminal node and its corresponding spreading factor.

[0053] In this embodiment, the throughput of each terminal node under different spreading factors can be recorded, and then the spreading factor corresponding to the highest throughput is determined. The initial spreading factor before is replaced with the spreading factor to maximize the throughput of a single terminal node.

[0054] S130, selecting at least any two groups of node SF pairs from the plurality of groups of node SF pairs for spreading factor exchange to determine a target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determining the final spreading factor of each terminal node based on the target node SF pair.

[0055] In the LoRa network, the total throughput of each terminal node is not a simple superposition relationship. In the case of a large coverage range of the LoRa network, the average data packet arrival rate Psp and the total throughput are reduced, and the network performance is reduced accordingly. Therefore, the SF value of each terminal node needs to be adjusted to maximize the total throughput.

[0056] In this embodiment, each terminal node and its corresponding SF value can constitute a node SF pair. By exchanging the SF values in different node SF pairs and comparing the total throughput of all terminal nodes before and after the exchange, the SF value optimization direction can be determined.

[0057] In some embodiments, S130, at least any two groups of node SF pairs are selected from the plurality of groups of node SF pairs for spreading factor exchange, a target node SF pair corresponding to the highest throughput of the LoRa network is determined, and the final spreading factor of each terminal node is determined based on the target node SF pair, including:

[0058] At least any two groups of node SF pairs with different spreading factors are selected from the plurality of groups of node SF pairs for spreading factor exchange, a target node SF pair corresponding to the highest throughput of the LoRa network is determined, and the final spreading factor of each terminal node is determined based on the target node SF pair.

[0059] In this embodiment, considering that there are some terminal nodes with the same SF value, the SF values in the node SF pairs with different SF values are exchanged to improve the exchange efficiency. Here, the SF values in two groups of node SF pairs can be exchanged at the same time, or the SF values in multiple groups of node SF pairs can be exchanged at the same time, and the total throughput before and after the exchange is compared to retain the target node SF pair corresponding to the highest throughput.

[0060] In some embodiments, S130, at least any two groups of node SF pairs are selected from the plurality of groups of node SF pairs for spreading factor exchange, a target node SF pair corresponding to the highest throughput of the LoRa network is determined, and the final spreading factor of each terminal node is determined based on the target node SF pair, including:

[0061] At least any two groups of node SF pairs are selected from the plurality of groups of node SF pairs for spreading factor exchange, and the throughput of the exchanged LoRa network is determined. The throughput of the LoRa network includes the sum of the throughputs of all nodes.

[0062] All node SF pairs are traversed, and if the sum of the throughputs of the nodes in the target node SF pair is greater than the sum of the throughputs of the nodes in any other node SF pair, the spreading factor corresponding to each terminal node in the target node SF pair is determined as the final spreading factor.

[0063] In the embodiment, the spreading factors in the selected at least two groups of node SF pairs are exchanged, the throughput data of all nodes after the exchange of the spreading factors is recorded, the throughputs of all nodes are added to obtain the overall throughput of the LoRa network. The step of exchanging the spreading factors is repeated to traverse all possible combinations of the exchange of the node SF pairs, and in all traversed exchanges, the node SF pair configuration that maximizes the overall throughput of the LoRa network, that is, the target node SF pair, is determined, the spreading factors corresponding to each terminal node of the target node SF pair are determined as the final spreading factors, and the final spreading factors are applied to the LoRa network.

[0064] The embodiment determines the initial spreading factors of the terminal nodes in the LoRa network based on the equal-area scheme, obtains the spreading factors corresponding to the highest throughput of each terminal node, replaces the initial spreading factors with the spreading factors, determines a plurality of groups of node SF pairs, selects at least any two groups of node SF pairs from the plurality of groups of node SF pairs for exchange of the spreading factors, determines the target node SF pair corresponding to the highest throughput of the LoRa network, and determines the final spreading factors of each terminal node based on the target node SF pair. On the basis of achieving the maximum throughput at each node, matching and exchange are continuously performed to eliminate the blocking pairs in the LoRa network, so that the total throughput in the network is maximized, and the overall performance of the LoRa network is improved.

[0065] In some embodiments, referring to Figure 2 , Figure 2 The structure diagram of the LoRa network throughput optimization device based on the many-to-one matching theory provided by the embodiments of the present application; the LoRa network throughput optimization device 200 based on the many-to-one matching theory provided by the embodiments of the present application comprises a distribution module 210, a replacement module 220, and a determination module 230, wherein

[0066] The distribution module 210 is configured to initially distribute the spreading factors based on the equal-area scheme and determine the initial spreading factors of the terminal nodes in the LoRa network.

[0067] The replacement module 220 is configured to obtain the spreading factors corresponding to the highest throughput of each terminal node, replace the initial spreading factors with the spreading factors, and determine a plurality of groups of node SF pairs.

[0068] The determination module 230 is configured to select at least any two groups of node SF pairs from the plurality of groups of node SF pairs for exchange of the spreading factors, determine the target node SF pair corresponding to the highest throughput of the LoRa network, and determine the final spreading factors of each terminal node based on the target node SF pair.

[0069] In some embodiments, the distribution module 210 is specifically configured to:

[0070] acquire the maximum coverage range of the LoRa network;

[0071] According to the node density and communication requirements within the LoRa network, the maximum coverage range is divided into multiple areas, and each area is allocated with equivalent spreading factor resources.

[0072] In some embodiments, the replacing module 220 is specifically configured to:

[0073] adjust the spreading factor of each terminal node, and record the throughput data of each terminal node under different spreading factors to acquire the spreading factor corresponding to the highest throughput of each terminal node;

[0074] replace the initial spreading factor with the spreading factor, and determine multiple groups of node SF pairs based on each terminal node and the corresponding spreading factor.

[0075] In some embodiments, the determining module 230 is specifically configured to:

[0076] select at least any two groups of node SF pairs with different spreading factors from the multiple groups of node SF pairs for spreading factor exchange, determine the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determine the final spreading factor of each terminal node based on the target node SF pair.

[0077] In some embodiments, the determining module 230 is specifically configured to:

[0078] select at least any two groups of node SF pairs from the multiple groups of node SF pairs for spreading factor exchange, determine the throughput of the exchanged LoRa network as a whole; the throughput of the LoRa network as a whole includes the sum of the throughputs of all nodes;

[0079] traverse all node SF pairs, if the sum of the throughputs of the nodes in the target node SF pair is greater than the sum of the throughputs of the nodes in any other node SF pair, then the spreading factor corresponding to each terminal node in the target node SF pair is determined as the final spreading factor.

[0080] The LoRa network throughput optimization device based on the many-to-one matching theory provided in the embodiments of the present application is based on the above-mentioned LoRa network throughput optimization method based on the many-to-one matching theory. To avoid repetition, it will not be repeated here.

[0081] It should be noted that the LoRa network throughput optimization device based on the many-to-one matching theory provided in the embodiments of the present application and the LoRa network throughput optimization method based on the many-to-one matching theory provided in the embodiments of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the aforementioned LoRa network throughput optimization method based on the many-to-one matching theory, and the repeated parts will not be repeated.

[0082] In some embodiments, referring to Figure 3 , Figure 3 A structural schematic diagram of an electronic device is provided in embodiments of the present application. An electronic device 300 is provided in embodiments of the present application, comprising a processor 310 and a memory 320; the memory 320 has a computer program stored therein, wherein the computer program, when executed by the processor, implements the LoRa network throughput optimization method based on the many-to-one matching theory described above.

[0083] Specifically, the processor 310 may, for example, include a general purpose microprocessor, an instruction set processor, and / or a related chip set and / or a specialized microprocessor, such as an application specific integrated circuit (ASIC), etc. The processor 310 can also include on-board memory for cache use. The processor 310 can be a single processing unit or a plurality of processing units for performing different actions of the method flow according to embodiments of the present application.

[0084] The memory 320 may, for example, be any medium capable of containing, storing, communicating, propagating or transferring instructions. For example, the memory 320 may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices, devices or propagation media. Specific examples of the memory 320 include magnetic storage devices such as magnetic tapes or hard disk drives (HDDs); optical storage devices such as compact discs (CD-ROMs); also random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0085] The present application also provides a computer readable medium having a computer program stored thereon, which, when executed by a processor, implements the LoRa network throughput optimization method based on the many-to-one matching theory described above. The computer readable medium can be included in the device / system described in the above embodiments; or it can exist separately and not be assembled into the device / system. The above computer readable medium carries one or more programs, which, when executed, implement the method according to embodiments of the present application.

[0086] According to the embodiments of the present application, the computer readable medium can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, the computer readable storage medium can be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus or device. In this application, the computer readable signal medium can include a computer readable program code transmitted in baseband or as part of a carrier wave in which the computer readable program code is digitally modulated and transmits over a carrier wave. Such a transmitted program code can take any number of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber cable, RF, and the like, or any suitable combination thereof.

[0087] Those skilled in the art will appreciate that features recited in the various embodiments and / or claims of this application can be combined and / or interchanged, even if this is not explicitly stated in the application. In particular, the features of the various embodiments and / or claims of this application can be combined and / or interchanged, without departing from the spirit and teachings of this application. All such combinations and / or interchanges are intended to fall within the scope of this application. Accordingly, the scope of the application should not be limited to the above-described embodiments, but should be determined by the appended claims and their equivalents.

Claims

1. A LoRa network throughput optimization method based on many-to-one matching theory, characterized in that, The method comprises the following steps: initially allocating the spreading factor based on an equal-area scheme to determine the initial spreading factor of each terminal node in the LoRa network; acquiring the spreading factor corresponding to the highest throughput of each terminal node, and replacing the initial spreading factor with the spreading factor to determine a plurality of node SF pairs; each terminal node and its corresponding SF value form a node SF pair; selecting at least any two node SF pairs from the plurality of node SF pairs to exchange the spreading factors, determining the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determining the final spreading factor of each terminal node based on the target node SF pair. 2.The LoRa network throughput optimization method based on the many-to-one matching theory of claim 1, wherein, The method of initially allocating the spreading factor based on the equal-area scheme to determine the initial spreading factor of each terminal node in the LoRa network comprises the following steps: acquiring the maximum coverage range of the LoRa network; dividing the maximum coverage range into a plurality of regions according to the node density and communication demand in the LoRa network, and allocating equal spreading factor resources to each region. 3.The LoRa network throughput optimization method based on the many-to-one matching theory of claim 2, wherein, The method of acquiring the spreading factor corresponding to the highest throughput of each terminal node, and replacing the initial spreading factor with the spreading factor to determine a plurality of node SF pairs comprises the following steps: adjusting the spreading factor of each terminal node, recording the throughput data of each terminal node under different spreading factors, and acquiring the spreading factor corresponding to the highest throughput of each terminal node; replacing the initial spreading factor with the spreading factor, and determining a plurality of node SF pairs based on each terminal node and its corresponding spreading factor. 4.The LoRa network throughput optimization method based on the many-to-one matching theory of claim 1, wherein, The method of selecting at least any two node SF pairs from the plurality of node SF pairs to exchange the spreading factors, determining the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determining the final spreading factor of each terminal node based on the target node SF pair comprises the following steps: selecting at least any two node SF pairs with different spreading factors from the plurality of node SF pairs to exchange the spreading factors, determining the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determining the final spreading factor of each terminal node based on the target node SF pair. 5.The LoRa network throughput optimization method based on the many-to-one matching theory of claim 1, wherein, The method of selecting at least any two node SF pairs from the plurality of node SF pairs to exchange the spreading factors, determining the target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determining the final spreading factor of each terminal node based on the target node SF pair comprises the following steps: selecting at least any two node SF pairs from the plurality of node SF pairs to exchange the spreading factors, determining the throughput of the exchanged LoRa network as a whole; the throughput of the LoRa network as a whole comprises the sum of the throughputs of all nodes; traversing all the node SF pairs, if the sum of the throughputs of the nodes in the target node SF pair is greater than the sum of the throughputs of the nodes in any other node SF pair, then the spreading factor corresponding to each terminal node in the target node SF pair is determined as the final spreading factor.

6. A LoRa network throughput optimization device based on many-to-one matching theory, characterized in that, The method comprises the following steps: The allocation module, the replacement module, and the determination module are used to perform the following steps: The distribution module is configured to initially distribute the spreading factor based on an equal-area scheme, and determine initial spreading factors of each terminal node in the LoRa network; The replacement module is configured to obtain the spreading factor corresponding to the highest throughput of each terminal node, and replace the initial spreading factor with the spreading factor, to determine multiple groups of node SF pairs; each terminal node and its corresponding SF value form a group of node SF pairs; The determination module is configured to select at least any two groups of node SF pairs from the multiple groups of node SF pairs for spreading factor exchange, determine a target node SF pair corresponding to the highest throughput of the LoRa network as a whole, and determine the final spreading factor of each terminal node based on the target node SF pair. 7.The LoRa network throughput optimization device based on the many-to-one matching theory of claim 6, wherein, The distribution module is specifically further configured to: Obtain the maximum coverage range of the LoRa network; According to the node density and communication demand in the LoRa network, divide the maximum coverage range into multiple areas, and allocate equal spreading factor resources to each area. 8.The LoRa network throughput optimization device based on the many-to-one matching theory of claim 6, wherein, The replacement module is specifically configured to: Adjust the spreading factor of each terminal node, record the throughput data of each terminal node under different spreading factors, and obtain the spreading factor corresponding to the highest throughput of each terminal node; Replace the initial spreading factor with the spreading factor, and determine multiple groups of node SF pairs based on each terminal node and its corresponding spreading factor.

9. An electronic device comprising a processor and a memory; said memory having stored a computer program, wherein, The computer program, when executed by the processor, implements the LoRa network throughput optimization method based on the many-to-one matching theory according to any one of claims 1 to 5.

10. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

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