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

Abstract

The invention provides a LoRa network throughput optimization method based on a many-to-one matching theory, and relates to the technical field of Internet communication, and the method comprises the steps: carrying out the initial distribution of spreading factors based on an equal-area scheme, determining the initial spreading factor of each terminal node in a LoRa network, and carrying out the optimization of the throughput of the LoRa network; acquiring a spreading factor corresponding to the maximum throughput of each terminal node, replacing the initial spreading factor with the spreading factor, 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 perform spreading factor exchange, and determining a corresponding target node SF pair when the whole LoRa network reaches the highest throughput, and determining a final spreading factor of each terminal node based on the target node SF pair. According to the invention, on the basis of realizing the maximum throughput of each node, the matching exchange is continuously carried out 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.

Description

Technical Field

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

[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-distance and low-power star network to meet the application requirements of IoT (Internet of Things). The LoRa gateway is a bridge between the device (also known as the 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 is required to have strong communication capabilities, while in a low-density LoRa node scenario, the communication capability requirements of the LoRa gateway are lower. At present, most LoRa gateways use the standard LoRa gateway structure. A standard LoRa gateway is generally composed of one or more wireless transceiver modules. For example, the most common LoRa gateway composed of one SX1301 has the ability to receive 6-rate (SF7-12) data packets in parallel on 8 channels, and has the ability to send data packets at a specified modulation mode and 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 throughput will decrease, and the network performance will decrease accordingly. Summary of the invention

[0003] The purpose of the present invention 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 throughput will decrease, and the network performance will decrease accordingly. The present invention provides a LoRa network throughput optimization method based on many-to-one matching theory.

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

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

[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] Obtaining a spreading factor corresponding to each of the terminal nodes achieving the highest throughput, and replacing the initial spreading factor with the spreading factor to determine multiple groups of node SF pairs;

[0008] At least any two groups of node SF pairs are selected from the multiple groups of node SF pairs to exchange spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

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

[0010] Obtain the maximum coverage of the LoRa network;

[0011] According to the node density and communication requirements in the LoRa network, the maximum coverage range is divided into multiple areas, and equal spreading factor resources are allocated to each area.

[0012] Optionally, the acquiring a spreading factor corresponding to each of the terminal nodes achieving a maximum throughput, and replacing the initial spreading factor with the spreading factor to determine multiple groups of node SF pairs includes:

[0013] Adjusting the spreading factor of each of the terminal nodes, and recording the throughput data of each of the terminal nodes under different spreading factors, and obtaining the spreading factor corresponding to the highest throughput of each of the terminal nodes;

[0014] The initial spreading factor is replaced by the spreading factor, and multiple groups of node SF pairs are determined based on the spreading factor corresponding to each terminal node.

[0015] Optionally, selecting at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange spreading factors, determining the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determining the final spreading factor of each terminal node based on the target node SF pairs, includes:

[0016] At least any two groups of node SF pairs with different spreading factors are selected from the multiple groups of node SF pairs to exchange the spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole achieves the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

[0017] Optionally, selecting at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange spreading factors, determining the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determining the final spreading factor of each terminal node based on the target node SF pairs, includes:

[0018] Select at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange spreading factors, and determine the overall throughput of the LoRa network after the exchange; the overall throughput of the LoRa network includes the sum of the throughputs of all nodes;

[0019] All the 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 pairs, the spreading factors corresponding to the terminal nodes in the target node SF pair are determined as the final spreading factors.

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

[0021] The allocation module is configured to 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;

[0022] The replacement module is configured to obtain a spreading factor corresponding to each of the terminal nodes achieving the highest throughput, and replace the initial spreading factor with the spreading factor to determine multiple groups of node SF pairs;

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

[0024] Optionally, the allocation module is further configured as follows:

[0025] Obtain the maximum coverage 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 areas, and equal spreading factor resources are allocated to each area.

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

[0028] Adjusting the spreading factor of each of the terminal nodes, and recording the throughput data of each of the terminal nodes under different spreading factors, and obtaining the spreading factor corresponding to the highest throughput of each of the terminal nodes;

[0029] The initial spreading factor is replaced by the spreading factor, and multiple groups of node SF pairs are determined based on the spreading factor corresponding to each terminal node.

[0030] A third aspect of an embodiment of the present application provides an electronic device, comprising a processor and a memory; the memory stores 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 described in the first aspect.

[0031] A fourth aspect of an 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 described in the first aspect.

[0032] Compared with the prior art, the technical solution provided by this application has the following beneficial effects:

[0033] The present invention provides a LoRa network throughput optimization method based on many-to-one matching theory, by initially allocating the spreading factor based on the equal area scheme, determining the initial spreading factor of each terminal node in the LoRa network, obtaining the spreading factor corresponding to the highest throughput of each terminal node, and replacing the initial spreading factor with the spreading factor, determining multiple groups of node SF pairs, selecting at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange the spreading factors, determining the target node SF pairs 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 pairs. By continuously performing matching exchanges to eliminate blocking pairs in the LoRa network on the basis of achieving the maximum throughput of each node, the total throughput in the network is maximized, thereby improving the overall performance of the LoRa network. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0035] Figure 2 A schematic diagram of the structure of a LoRa network throughput optimization device based on many-to-one matching theory provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] Below, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present application.

[0038] The terms used herein are only for describing specific embodiments and are not intended to limit the present application. The terms "include", "comprising", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0039] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0040] Some block diagrams and / or flow charts are shown in the accompanying drawings. It should be understood that some blocks or combinations thereof in the block diagrams and / or flow charts may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that these instructions, when executed by the processor, may create a device for implementing the functions / operations described in these block diagrams and / or flow charts.

[0041] In some embodiments, see Figure 1 , Figure 1 A flow chart of a LoRa network throughput optimization method based on many-to-one matching theory provided in an embodiment of the present application; a LoRa network throughput optimization method based on many-to-one matching theory provided in an embodiment of the present application includes:

[0042] S110, 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.

[0043] In the LoRa network, the common values ​​of the spreading factor (SF) are 7 to 12. Different spreading factors correspond to different symbol rates and coding rates, which affect the communication distance and anti-interference ability. Here, by dividing the LoRa network into multiple areas, the demand analysis is performed for each area, including communication distance, signal quality, and number of nodes, etc. On this basis, the spreading factor is initially allocated to determine the initial spreading factor of each terminal node in the LoRa network.

[0044] In some embodiments, S110, 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 includes:

[0045] Get the maximum coverage of the LoRa network;

[0046] According to the node density and communication requirements within the LoRa network, the maximum coverage area is divided into multiple areas, and equal spreading factor resources are allocated to each area.

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

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

[0049] Here, a preference list between terminal nodes and SFs can be established. Each terminal node establishes a preference list based on the throughput when selecting different SFs. 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 values ​​of different terminal nodes can correspond to the same SF value or different SF values.

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

[0051] 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;

[0052] The initial spreading factor is replaced by the spreading factor, and multiple groups of node SF pairs are determined 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 can be determined, and the spreading factor can be used to replace the previous initial spreading factor to maximize the throughput of a single terminal node.

[0054] S130, select at least any two groups of node SF pairs from multiple groups of node SF pairs to exchange spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

[0055] In the LoRa network, the total throughput of each terminal node is not a simple superposition relationship. When the coverage of the LoRa network is large, the average packet arrival rate PSP and the total throughput will decrease, and the network performance will decrease 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 may constitute a set of node SF pairs. By exchanging SF values ​​in different node SF pairs and comparing the total throughput of all terminal nodes before and after the exchange, the optimization direction of the SF value may be determined.

[0057] In some embodiments, S130, selecting at least any two groups of node SF pairs from multiple groups of node SF pairs to exchange spreading factors, determining the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determining the final spreading factor of each terminal node based on the target node SF pairs, including:

[0058] At least two groups of node SF pairs with different spreading factors are selected from multiple groups of node SF pairs to exchange the spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole achieves the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

[0059] In this embodiment, considering that the SF values ​​corresponding to some terminal nodes are the same, only 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, and the target node SF pair corresponding to the highest throughput is retained.

[0060] In some embodiments, S130, selecting at least any two groups of node SF pairs from multiple groups of node SF pairs to exchange spreading factors, determining the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determining the final spreading factor of each terminal node based on the target node SF pairs, including:

[0061] Select at least any two groups of node SF pairs from multiple groups of node SF pairs to exchange spreading factors, and determine the overall throughput of the LoRa network after the exchange; the overall throughput of the LoRa network includes the sum of the throughputs of all nodes;

[0062] All node SF pairs are traversed. 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 pairs, the spreading factors corresponding to the terminal nodes in the target node SF pair are determined as the final spreading factors.

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

[0064] This embodiment initially allocates the spreading factor based on the 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, and replaces the initial spreading factor with the spreading factor, determines multiple groups of node SF pairs, selects at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange the spreading factors, determines the target node SF pairs 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 pairs. By continuously performing matching exchanges to eliminate blocking pairs in the LoRa network on the basis of achieving the maximum throughput of each node, the total throughput in the network is maximized, thereby improving the overall performance of the LoRa network.

[0065] In some embodiments, see Figure 2 , Figure 2 A schematic diagram of the structure of a LoRa network throughput optimization device based on many-to-one matching theory provided in an embodiment of the present application; a LoRa network throughput optimization device 200 based on many-to-one matching theory provided in an embodiment of the present application comprises: an allocation module 210, a replacement module 220 and a determination module 230, wherein:

[0066] The allocation module 210 is configured to initially allocate the spreading factors based on the equal area scheme and determine the initial spreading factor of each terminal node in the LoRa network;

[0067] The replacement module 220 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;

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

[0069] In some embodiments, the allocation module 210 is specifically configured as follows:

[0070] Get the maximum coverage of the LoRa network;

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

[0072] In some embodiments, the replacement module 220 is specifically configured as follows:

[0073] 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;

[0074] The initial spreading factor is replaced by the spreading factor, and multiple groups of node SF pairs are determined based on the spreading factor corresponding to each terminal node.

[0075] In some embodiments, the determination module 230 is specifically configured as follows:

[0076] At least two groups of node SF pairs with different spreading factors are selected from multiple groups of node SF pairs to exchange the spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole achieves the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

[0077] In some embodiments, the determination module 230 is specifically configured as follows:

[0078] Select at least any two groups of node SF pairs from multiple groups of node SF pairs to exchange spreading factors, and determine the overall throughput of the LoRa network after the exchange; the overall throughput of the LoRa network includes the sum of the throughputs of all nodes;

[0079] All node SF pairs are traversed. 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 pairs, the spreading factors corresponding to the terminal nodes in the target node SF pair are determined as the final spreading factors.

[0080] The LoRa network throughput optimization device based on many-to-one matching theory provided in the embodiment of the present application is based on the above-mentioned LoRa network throughput optimization method based on 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 embodiment of the present application and the LoRa network throughput optimization method based on the many-to-one matching theory provided in the embodiment of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer 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, see Figure 3 , Figure 3 The present invention provides a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 300 provided in an embodiment of the present invention includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program implements the LoRa network throughput optimization method based on the many-to-one matching theory when executed by the processor.

[0083] Specifically, the processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 310 may also include an onboard memory for cache purposes. The processor 310 may be a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiment of the present application.

[0084] The memory 320 may be any medium capable of containing, storing, conveying, propagating or transmitting instructions. For example, the memory 320 may include, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device, component or propagation medium. Specific examples of the memory 320 include: a magnetic storage device, such as a magnetic tape or a hard disk (HDD); an optical storage device, such as a compact disk (CD-ROM); a random access memory (RAM) or flash memory; and / or a wired / wireless communication link.

[0085] The present application also provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned LoRa network throughput optimization method based on the many-to-one matching theory is implemented. The computer-readable medium may be included in the device / system described in the above embodiment; or it may exist alone without being assembled into the device / system. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed, the method according to the embodiment of the present application is implemented.

[0086] According to an embodiment of the present application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical cable, radio frequency signal, etc., or any suitable combination of the above.

[0087] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above-described embodiments, but should be determined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A LoRa network throughput optimization method based on many-to-one matching theory, characterized in that: include: 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; Obtaining a spreading factor corresponding to each of the terminal nodes achieving the highest throughput, and replacing the initial spreading factor with the spreading factor to determine multiple groups of node SF pairs; At least any two groups of node SF pairs are selected from the multiple groups of node SF pairs to exchange spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

2. The LoRa network throughput optimization method based on many-to-one matching theory according to claim 1, characterized in that, 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 includes: Obtain the maximum coverage of the LoRa network; According to the node density and communication requirements in the LoRa network, the maximum coverage range is divided into multiple areas, and equal spreading factor resources are allocated to each area.

3. The LoRa network throughput optimization method based on many-to-one matching theory according to claim 2, characterized in that, The obtaining of the spreading factor corresponding to the highest throughput of each of the terminal nodes, and replacing the initial spreading factor with the spreading factor to determine multiple groups of node SF pairs, includes: Adjusting the spreading factor of each of the terminal nodes, and recording the throughput data of each of the terminal nodes under different spreading factors, and obtaining the spreading factor corresponding to the highest throughput of each of the terminal nodes; The initial spreading factor is replaced by the spreading factor, and multiple groups of node SF pairs are determined based on the spreading factor corresponding to each terminal node.

4. The LoRa network throughput optimization method based on many-to-one matching theory according to claim 1, characterized in that: The selecting at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange spreading factors, determining the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determining the final spreading factor of each terminal node based on the target node SF pairs, includes: At least any two groups of node SF pairs with different spreading factors are selected from the multiple groups of node SF pairs to exchange the spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole achieves the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

5. The LoRa network throughput optimization method based on many-to-one matching theory according to claim 1, characterized in that, The selecting at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange spreading factors, determining the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determining the final spreading factor of each terminal node based on the target node SF pairs, includes: Select at least any two groups of node SF pairs from the multiple groups of node SF pairs to exchange spreading factors, and determine the overall throughput of the LoRa network after the exchange; the overall throughput of the LoRa network includes the sum of the throughputs of all nodes; All the 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 pairs, the spreading factors corresponding to the terminal nodes in the target node SF pair are determined as the final spreading factors.

6. A LoRa network throughput optimization device based on many-to-one matching theory, characterized in that: include: An allocation module, a replacement module and a determination module, wherein: The allocation module is configured to 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; The replacement module is configured to obtain a spreading factor corresponding to each of the terminal nodes achieving the highest throughput, and replace the initial spreading factor with the spreading factor to determine multiple groups 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 to exchange spreading factors, determine the target node SF pairs corresponding to when the LoRa network as a whole reaches the highest throughput, and determine the final spreading factor of each terminal node based on the target node SF pairs.

7. The LoRa network throughput optimization system based on many-to-one matching theory according to claim 6, characterized in that: The allocation module is further configured as follows: Obtain the maximum coverage of the LoRa network; According to the node density and communication requirements in the LoRa network, the maximum coverage range is divided into multiple areas, and equal spreading factor resources are allocated to each area.

8. The LoRa network throughput optimization system based on many-to-one matching theory according to claim 6, characterized in that: The replacement module is specifically configured as follows: Adjusting the spreading factor of each of the terminal nodes, and recording the throughput data of each of the terminal nodes under different spreading factors, and obtaining the spreading factor corresponding to the highest throughput of each of the terminal nodes; The initial spreading factor is replaced by the spreading factor, and multiple groups of node SF pairs are determined based on the spreading factor corresponding to each terminal node.

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

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

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