Intelligent management and control system based on LoRa wireless communication

Abstract

The invention relates to the technical field of communication technologies, and discloses an intelligent management and control system based on LoRa wireless communication, and the system comprises a multi-band adaptive communication module which is used for monitoring the interference intensity of a plurality of ISM bands in real time, and judging whether a transmission mode needs to be switched or not based on a comprehensive evaluation model; the enhanced edge computing node is used for executing dynamic frequency band switching based on a Q-Learning algorithm and performing adaptive adjustment on a transmission mode; the hybrid topology networking module supports dynamic switching between a star network and a Mesh network and is used for optimizing a routing path through a link quality index; the cross-protocol data packaging unit is used for defining a unified data frame structure and supporting mutual conversion of LoRaWAN, MQTT and CoAP protocols; the unified data frame structure comprises a frame header, a load and a CRC16 check code; long-acting and stable operation of the equipment in a complex deployment environment can be guaranteed, the endurance time of the terminal is effectively prolonged through intelligent resource scheduling, and reliable technical support is provided for construction of the low-power-consumption wide-area Internet of Things.

Description

Technical Field

[0001] This application relates to the technical field of communication technologies, and in particular, to an intelligent control system based on LoRa wireless communication. Background Art

[0002] In the field of low-power wide-area Internet of Things technologies, traditional wireless communication systems face significant challenges in complex electromagnetic environments. Typical LoRa devices often adopt a fixed frequency band communication mechanism, making it difficult to cope with the co-frequency interference of randomly appearing devices such as WiFi and Bluetooth within the ISM frequency band, resulting in a sharp decline in the reliability of data transmission as the environment deteriorates.

[0003] LoRa wireless communication technology is a low-power wide-area network (LPWAN) communication technology based on spread spectrum technology. Its core principle is to adopt Chirp Spread Spectrum (CSS) modulation technology to achieve long-distance transmission and low-power characteristics through linear frequency modulation signals. Its operating frequency range is wide, usually operating within the ISM frequency band, supporting the use of globally free frequency bands. Its intelligent control system usually adopts a distributed architecture and realizes the overall function through modules such as data collection, processing, analysis, and visualization. It can also include an access layer, a front-end UI layer, a business layer, a service layer, a data layer, a database layer, and an infrastructure layer to support multi-terminal access and data management.

[0004] However, existing dynamic channel selection schemes mostly rely on periodic polling detection, suffering from problems such as high response latency and rigid handover decisions, and are unable to meet the stringent requirements for real-time performance in industrial scenarios; at the network topology level, a single star structure is limited by the coverage range of the central node, while a pure Mesh network can expand coverage but leads to complex routing paths and lacks the adaptive ability to dynamically reconstruct the topology according to link quality, making it difficult to balance transmission efficiency and network robustness. In addition, Internet of Things terminal devices form data islands due to differences in manufacturer protocols, and existing gateway devices mostly adopt a protocol stack superposition method to achieve heterogeneous network access, resulting in increased data encapsulation redundancy and reduced parsing efficiency. Therefore, the above technical bottlenecks seriously restrict the reliability and scalability of large-scale Internet of Things deployments, and there is an urgent need to build an intelligent communication control system with autonomous decision-making capabilities. Summary of the Invention

[0005] In order to provide an intelligent communication control system with autonomous decision-making capabilities, this application provides an intelligent control system based on LoRa wireless communication, adopting the following technical solutions:

[0006] An intelligent control system based on LoRa wireless communication, comprising:

[0007] A multi-band adaptive communication module, configured to monitor the interference intensity of multiple ISM frequency bands in real time and determine whether to switch the transmission mode based on a comprehensive evaluation model;

[0008] An enhanced edge computing node for performing dynamic frequency band switching based on the Q-Learning algorithm and adaptively adjusting the transmission mode.

[0009] A hybrid topology networking module that supports dynamic switching between star and Mesh networks and is used to optimize the routing path through link quality metrics.

[0010] A cross-protocol data encapsulation unit for defining a unified data frame structure and supporting the mutual conversion of LoRaWAN, MQTT, and CoAP protocols; the unified data frame structure is a frame header, payload, and CRC16 checksum.

[0011] By adopting the above technical solutions, the communication reliability in complex electromagnetic environments is effectively improved through multi-band real-time monitoring and comprehensive evaluation models, autonomously judging the channel state and triggering dynamic switching strategies to ensure the continuous stability of signal transmission. The hybrid network topology design combines the complementary advantages of star and Mesh architectures, dynamically reconstructs the network path according to real-time link quality, and effectively expands the coverage and disaster tolerance capabilities in large-scale Internet of Things scenarios; through a standardized data frame format, multi-protocol transparent conversion is achieved, significantly reducing the access threshold for heterogeneous devices and providing flexible and scalable data interaction capabilities for different application scenarios; the entire solution has significant application value in fields such as industrial Internet of Things and smart cities, can ensure the long-term stable operation of devices in complex deployment environments, and at the same time effectively extends the battery life of terminals through intelligent resource scheduling, providing reliable technical support for the construction of low-power wide-area Internet of Things.

[0012] Optionally, the multi-band adaptive communication module includes:

[0013] An interference intensity measurement unit for obtaining the comprehensive instantaneous interference intensity I of all the ISM frequency bands f , where

[0014] A comprehensive evaluation model for calculating and obtaining the switching necessity score Q channel :

[0015]

[0016] where S i is the signal intensity of the i-th ISM frequency band, N i is the noise floor, n is the total number of ISM frequency bands, SNR is the signal-to-noise ratio, SNR th is the preset signal-to-noise ratio, and PLR is the packet loss rate;

[0017] A switching trigger module that determines whether Q channel > Q th holds, and Q this a preset switching threshold; if it holds, a transmission mode switching instruction is triggered.

[0018] Optionally, the reward function R(s,a) of the Q-Learning algorithm is defined as:

[0019]

[0020] where ω1 + ω2 + ω3 = 1, PLR is the packet loss rate, Econ is the energy consumption value, Emax is the maximum energy consumption value, P tx is the transmission power, and P max is the maximum transmission power; s is the state space and a is the action set.

[0021] By adopting the above technical solution, Q-Learning does not require an environmental model, is applicable to dynamically changing environments and supports online learning; when the state space is too large (such as continuous states), the traditional Q-table is no longer applicable, and a deep neural network (DNN) can be used to replace the Q-table. By training the DNN to approximate the Q function, that is, DeepQ-Network (DQN). DQN uses Experience Replay and Target Network to improve stability.

[0022] Optionally, the process of optimizing the routing path through the link quality index includes:

[0023] Obtain the physical layer parameters of the link at a specified time period;

[0024] Based on the multi-variable collaborative path cost function, perform link quality level division within the lowest cost TOP-N, and select the highest quality link for communication;

[0025] The physical layer parameters include received signal strength RSSI, signal-to-noise ratio SNR, link quality index LQI, expected transmission times ETX, and air time ToA.

[0026] By adopting the above technical solution, the hybrid topology networking module is built with a dual-mode network protocol stack. Under the star topology, the TDMA mechanism is used to achieve timing synchronization between the terminal node and the gateway, and a heat map of the terminal device location is constructed through received signal strength indication (RSSI) and link quality indication (LQI). It is also possible to automatically activate the Mesh network mode to establish a dynamic routing table when the round-trip delay between the edge node and the central gateway exceeds the set threshold. By periodically broadcasting link detection messages, calculate the path stability coefficient and hop count weighted value, select the relay path with the optimal comprehensive quality, and smoothly switch back to the star architecture after the channel condition improves.

[0027] Optionally, the multi-variable collaborative path cost function includes:

[0028]

[0029] where n is the total number of hops of the path, and C path is the path cost, LQI j is the link quality index of the j-th hop path, ToA j is the air time of the j-th hop path, ETX j is the expected number of transmissions of the j-th hop path, SNR j is the signal-to-noise ratio of the j-th hop path, and α, β, γ, and δ are the preset weighting coefficients for the corresponding items respectively.

[0030] Optionally, the method for obtaining the received signal strength RSSI is RSSI = 10log 10 (P r / P0), where P r is the received power and P0 is the specified power.

[0031] Optionally, the method for obtaining the signal-to-noise ratio SNR is SNR = RSSI - N i .

[0032] Optionally, the method for obtaining the expected number of transmissions ETX is ETX = 1 / (p f *p r ), where p f is the forward delivery rate and p r is the reverse acknowledgment rate.

[0033] Optionally, the method for obtaining the air time ToA is ToA = N sym (2 SF / BW), where N sym is the number of symbols, BW is the bandwidth, and SF is the spreading factor.

[0034] Optionally, the encryption method for the cross-protocol data encapsulation unit is as follows:

[0035] Encrypt using the AES-128-CTR mode, and the initialization vector IV is generated by the exclusive OR operation of the device ID and the timestamp.

[0036] By adopting the above technical solutions, the uniqueness of each encryption operation is ensured through the dynamic IV generation mechanism, combined with the protocol-adaptive security encapsulation strategy, achieving a high-level security standard while meeting the low-power consumption requirements.

[0037] In summary, the present application includes at least one of the following beneficial technical effects:

[0038] 1. This application effectively improves the communication reliability in complex electromagnetic environments through multi-band real-time monitoring and comprehensive evaluation models, autonomously judges the channel status and triggers dynamic switching strategies to ensure the continuous stability of signal transmission. The hybrid network topology design combines the complementary advantages of star and Mesh architectures, dynamically reconstructs network paths according to real-time link quality, and effectively expands the coverage and disaster tolerance capabilities in large-scale Internet of Things scenarios. It realizes multi-protocol transparent conversion through standardized data frame formats, significantly reduces the access threshold of heterogeneous devices, and provides flexible and scalable data interaction capabilities for different application scenarios. The entire solution has significant application value in fields such as industrial Internet of Things and smart cities, can ensure the long-term stable operation of devices in complex deployment environments, and effectively extends the battery life of terminals through intelligent resource scheduling, providing reliable technical support for the construction of low-power wide-area Internet of Things.

[0039] 2. The hybrid topology networking module is built with a dual-mode network protocol stack. Under the star topology, it uses the TDMA mechanism to achieve timing synchronization between terminal nodes and the gateway, and constructs a heat map of terminal device locations through received signal strength indication (RSSI) and link quality indication (LQI). It can also automatically activate the Mesh network mode to establish a dynamic routing table when the round-trip delay between the edge node and the central gateway exceeds the set threshold. By periodically broadcasting link detection messages, calculating the path stability coefficient and hop count weighted value, selecting the relay path with the best overall quality, and smoothly switching back to the star architecture after the channel conditions improve. Description of the Drawings

[0040] Figure 1 It is a schematic diagram of the modules of an intelligent control system based on LoRa wireless communication. Detailed Implementation Modes

[0041] The following details the implementation modes of this application, and the examples of the implementation modes are shown in the drawings.

[0042] In the description of this specification, the description with reference to the terms "certain implementation modes", "one implementation mode", "some implementation modes", "schematic implementation modes", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the implementation mode or example are included in at least one implementation mode or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same implementation mode or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more implementation modes or examples.

[0043] LoRa wireless communication technology is a low-power wide-area network (LPWAN) communication technology based on spread spectrum technology. Its core principle is to use Chirp Spread Spectrum (CSS) modulation technology to achieve long-distance transmission and low-power characteristics through linear frequency modulation signals. Its operating frequency range is wide, usually operating within the ISM band (such as 433 MHz, 868 MHz, 915 MHz), supporting the use of globally free frequency bands. Its intelligent management and control system usually adopts a distributed architecture, and realizes the overall function through modules such as data acquisition, processing, analysis, and visualization. It can also include an access layer, a front-end UI layer, a business layer, a service layer, a data layer, a database layer, and an infrastructure layer to support multi-terminal access and data management.

[0044] In the fields of industrial and energy management, such as scenarios like smart grids, coal mines, and smart parks, it realizes the automated and intelligent management of the production process through an intelligent management and control system; in the fields of home and office automation, it can achieve centralized control of devices such as lighting, air conditioners, and security through an intelligent terminal system, improving the user experience and energy efficiency.

[0045] This application embodiment discloses an intelligent management and control system based on LoRa wireless communication, such as Figure 1 shown, including:

[0046] A multi-band adaptive communication module, used to real-time monitor the interference intensity of multiple ISM bands, and judge whether to switch the transmission mode based on a comprehensive evaluation model;

[0047] An enhanced edge computing node, used to perform dynamic band switching based on the Q-Learning algorithm and adaptively adjust the transmission mode;

[0048] A hybrid topology networking module, supporting dynamic switching between star and Mesh networks, and used to optimize the routing path through link quality indicators;

[0049] A cross-protocol data encapsulation unit, used to define a unified data frame structure, supporting the mutual conversion of LoRaWAN, MQTT, and CoAP protocols; the unified data frame structure is a frame header (2-byte protocol identifier, QoS level marker, and payload type encoding), a payload (storing the original application data in TLV format), and a CRC16 checksum, and the checksum supports the forward error correction function.

[0050] During protocol conversion, first parse information such as the device address and security context in the source protocol frame header, and map them to standard metadata fields. For downlink data, reconstruct the frame structure according to the target protocol specification - for example, add the DevEUI identifier when converting to LoRaWAN, encapsulate the topic path when converting to MQTT, and automatically complete the lossless conversion between binary data and JSON format to achieve two-way compatibility at the protocol semantic layer.

[0051] In the present embodiment of the present application, the signal characteristic parameters of the 433 MHz, 868 MHz, and 915 MHz ISM frequency bands can be synchronously collected with a period of 10 ms through the spectrum scanning module built in the SX1278 chipset. Combining the comprehensive evaluation model effectively improves the communication reliability in a complex electromagnetic environment, autonomously judges the channel state and triggers the dynamic switching strategy to ensure the continuous stability of signal transmission. The hybrid network topology design is compatible with the complementary advantages of the star and Mesh architectures, dynamically reconstructs the network path according to the real-time link quality, and effectively expands the coverage and disaster tolerance capabilities in large-scale Internet of Things scenarios; realizes multi-protocol transparent conversion through the standardized data frame format, greatly reduces the access threshold of heterogeneous devices, and provides flexible and scalable data interaction capabilities for different application scenarios; the whole set of solutions has significant application value in the fields of industrial Internet of Things, smart cities, etc., can ensure the long-term stable operation of devices in complex deployment environments, and at the same time effectively extends the battery life of terminals through intelligent resource scheduling, providing reliable technical support for the construction of low-power wide-area Internet of Things.

[0052] Optionally, the multi-band adaptive communication module includes:

[0053] An interference intensity measurement unit for obtaining the comprehensive instantaneous interference intensity I of all the ISM frequency bands f where

[0054] A comprehensive evaluation model for calculating and obtaining the switching necessity score Q channel :

[0055]

[0056] where S i is the signal intensity of the i-th ISM frequency band, and the received signal strength RSSI can be used, with a dynamic range of -148 dBm to +20 dBm; N i is the noise floor, and the background noise power spectral density within a 1 MHz bandwidth can be calculated through FFT. n is the total number of ISM frequency bands, SNR is the signal-to-noise ratio, and SNR th is the preset signal-to-noise ratio, and PLR is the packet loss rate;

[0057] A switching trigger module that judges whether Q channel > Q th holds, where Q th is the preset switching threshold; if it holds, a transmission mode switching instruction is triggered.

[0058] Optionally, the reward function R(s,a) of the Q-Learning algorithm is defined as:

[0059]

[0060] where ω1 + ω2 + ω3 = 1, specifically, 0.6, 0.3, and 0.1 can be selected in sequence, PLR is the packet loss rate, Econ is the energy consumption value, Emax is the maximum energy consumption value, P tx is the transmission power, P max is the maximum transmission power; s is the state space, which is composed of a triple a is the action set, including the following operations:

[0061] a1: Switch to the 433 MHz frequency band (SF = 12, BW = 125 kHz);

[0062] a2: Switch to the 868 MHz frequency band (SF = 9, BW = 250 kHz);

[0063] a3: Switch to the 915 MHz frequency band (SF = 7, BW = 500 kHz);

[0064] a4: Keep the current frequency band but adjust the spreading factor (SF ± 1).

[0065] When the algorithm decision triggers a frequency band switch (such as selecting action a2), the system performs the following atomic operations:

[0066] 1. Spectrum reservation: Confirm that the target frequency band is idle through the CAD (Channel Activity Detection) function of LoRaWAN. If a conflict is detected, wait for T backoff = 2 r * 1 ms (r is the backoff count);

[0067] 2. Parameter reconfiguration:

[0068] Adjust the register settings of SX1278: center frequency f c = 868.3 MHz, bandwidth BW = 250 kHz, coding rate CR = 4 / 8;

[0069] Set the physical layer frame format: preamble length 8 symbols, header explicit mode, CRC check enabled;

[0070] 3. Smooth transition:

[0071] Adopt a double-buffer mechanism to retain the current frequency band data packets for 500 ms to prevent data loss during the switch;

[0072] Synchronize the new frequency band parameters to the gateway through the Beacon frame to ensure that the downlink synchronization time T sync < 50 ms;

[0073] 4. Energy efficiency optimization:

[0074] Dynamically adjust the transmission power P tx= 10 * log 10 (d 2.7 / G), where d is the node - gateway distance (km), and G = 3 dBi antenna gain;

[0075] Enable the deep - sleep mode, and the power consumption during the idle period is reduced to 1 μA.

[0076] The hybrid - topology networking module is built - in with a dual - mode network protocol stack. Under the star topology, it uses the TDMA mechanism to achieve timing synchronization between the terminal nodes and the gateway, and constructs a heat map of the terminal device locations through the received signal strength indication (RSSI) and the link quality indication (LQI). It can also automatically activate the Mesh network mode to establish a dynamic routing table when the round - trip delay between the edge node and the central gateway is detected to exceed the set threshold. By periodically broadcasting link - probing messages, calculate the path stability coefficient and the hop - count weighted value, select the relay path with the optimal comprehensive quality, and smoothly switch back to the star architecture after the channel condition improves.

[0077] Optionally, the process of optimizing the routing path through the link quality metrics includes:

[0078] Obtain the physical - layer parameters of the link at a specified time period;

[0079] Based on the multi - variable collaborative path - cost function, perform link - quality level division within the lowest - cost TOP - N, and select the highest - quality link for communication;

[0080] The physical - layer parameters include the received signal strength RSSI, the signal - to - noise ratio SNR, the link - quality index LQI, the expected transmission count ETX, and the air time ToA.

[0081] The method for obtaining the received signal strength RSSI is RSSI = 10 log 10 (P r / P0), where P r is the received power, and P0 is the specified power, optionally 1 wM; the method for obtaining the signal - to - noise ratio SNR is SNR = RSSI - N i ; the method for obtaining the expected transmission count ETX is ETX = 1 / (p f * p r ), where p f is the forward delivery rate, and p r is the reverse confirmation rate; the method for obtaining the air time ToA is ToA = N sym (2 SF / BW), where N sym is the number of symbols, BW is the bandwidth, and SF is the spreading factor.

[0082] Optionally, the multi - variable collaborative path - cost function includes:

[0083]

[0084] Among them, n is the total number of hops of the path, C path is the path cost, LQI j is the link quality index of the j-th hop path, ToA j is the air time of the j-th hop path, ETX j is the expected number of transmissions of the j-th hop path, SNR j is the signal-to-noise ratio of the j-th hop path, and α, β, γ, and δ are the preset weighting coefficients for the corresponding items respectively.

[0085] α = 0.4: LQI weight, reflecting the reliability of link decoding;

[0086] β = 0.3: ToA weight, optimizing the overall network delay and energy consumption;

[0087] γ = 0.2: ETX weight, suppressing high retransmission links;

[0088] δ = 0.1: SNR weight, avoiding low signal-to-noise ratio paths;

[0089] n is the number of hops of the path, ∑ is the accumulation of multi-hop link metrics, and max / min is used to identify bottleneck links.

[0090] The definition methods of link quality levels include:

[0091] High-quality link: LQI >= 200, SNR >= 10dB, ETX <= 1.2;

[0092] Medium-quality link: 150 < LQI < 200, 5dB < SNR < 10dB, 1.2 < ETX < 2.0;

[0093] Low-quality link: LQI <= 150, SNR <= 5dB, ETX >= 2.0;

[0094] Select the top 3 links with the lowest cost and sort them according to the number of physical layer parameters corresponding to the links that fall into the above link quality level definitions. For example:

[0095] The high-quality link index of the lowest-cost TOP1 link with LQI of 220, SNR of 10, and ETX of 1.3 is 1;

[0096] The high-quality link index of the lowest-cost TOP2 link with LQI of 210, SNR of 10, and ETX of 1.3 is 2;

[0097] The high-quality link index of the lowest-cost TOP3 link with LQI of 201, SNR of 10, and ETX of 1.2 is 3;

[0098] In this case, the TOP3 link is selected. Considering comprehensive indicators such as RSSI (Received Signal Strength Indicator), SNR (Signal-to-Noise Ratio), and ETX (Expected Transmission Count), a link with high RSSI but low SNR may have a high actual packet loss rate due to interference, and traditional solutions are prone to misjudgment. The dynamic ETX calculation can predict path stability. Preferentially select a link with a historical retransmission count < 2 to avoid the limitations of single-parameter evaluation. Transmit critical data (such as alarm signals) over the superior link, and enable forward error correction (FEC) for the intermediate link to achieve on-demand resource allocation. Through the energy weight factor in the path cost function, in practical applications, preferentially select nodes with remaining battery power > 40% as relays or low-power paths with single-hop energy consumption < 5mJ to extend the service life of field monitoring nodes powered by solar energy.

[0099] The TOP-3 link redundancy path allows for a quick selection of an alternative route for switching when the primary path fails.

[0100] Optionally, the encryption method of the cross-protocol data encapsulation unit is as follows:

[0101] Encrypt using the AES-128-CTR mode. The initialization vector IV is generated by the exclusive OR operation of the device ID and the timestamp. The AES-128-CTR mode has a no-padding mechanism, adapts to variable-length IoT data payloads, can improve the encryption efficiency of edge devices by leveraging the block independence of the CTR mode, provides support for parallel computing, and additionally allows decryption operations at any position of the encrypted data, with random access characteristics.

[0102] LoRaWAN adaptation: Encapsulate the CTR-mode ciphertext into the FRMPayload, retaining the native MAC-layer encryption

[0103] MQTT / CoAP conversion: Embed a security header in the application-layer payload, including: [protocol version][key index][IV][ciphertext][HMAC-SHA256]. Session recovery enables key synchronization during cross-gateway communication through the pre-shared key index.

[0104] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An intelligent control system based on LoRa wireless communication, characterized in that, Including: A multi-band adaptive communication module, which is used to monitor the interference intensity of multiple ISM bands in real time and determine whether to switch the transmission mode based on a comprehensive evaluation model; An enhanced edge computing node, which is used to perform dynamic band switching based on the Q-Learning algorithm and adaptively adjust the transmission mode; A hybrid topology networking module, which supports dynamic switching between star and Mesh networks and is used to optimize the routing path through link quality indicators; A cross-protocol data encapsulation unit, which is used to define a unified data frame structure and support the mutual conversion of LoRaWAN, MQTT, and CoAP protocols; the unified data frame structure is a frame header, a payload, and a CRC16 checksum.

2. The intelligent control system based on LoRa wireless communication according to claim 1, wherein The multi-band adaptive communication module includes: An interference intensity measurement unit for obtaining the comprehensive instantaneous interference intensity I of all the ISM frequency bands f , where Comprehensive evaluation model for calculating and obtaining the switching necessity score Q channel : Among them, S i is the signal strength of the i-th ISM band, N i is the noise floor, n is the total number of ISM bands, SNR is the signal-to-noise ratio, and SNR th is the preset signal-to-noise ratio, and PLR is the packet loss rate; The switching trigger module determines whether Q channel > Q th is established, where Q th is a preset switching threshold; if it is established, a transmission mode switching instruction is triggered.

3. The intelligent management and control system based on LoRa wireless communication according to claim 1, characterized in that: The reward function R(s,a) of the Q-Learning algorithm is defined as: where ω1 + ω2 + ω3 = 1, PLR is the packet loss rate, Econ is the energy consumption value, Emax is the maximum energy consumption value, P tx is the transmission power, and P max is the maximum transmission power; s is the state space and a is the action set.

4. The intelligent control system based on LoRa wireless communication according to claim 2, wherein The process of optimizing the routing path through link quality indicators includes: Obtaining the physical layer parameters of the link at a specified time period; Performing link quality level division within the lowest cost TOP-N based on a multi-variable collaborative path cost function and selecting the highest quality link for communication; The physical layer parameters include received signal strength RSSI, signal-to-noise ratio SNR, link quality index LQI, expected transmission times ETX, and air time ToA.

5. The intelligent control system based on LoRa wireless communication according to claim 4, wherein, The multi-variable collaborative path cost function includes: Where n is the total number of hops in the path, C path is the path cost, LQI j is the link quality index of the j-th hop, ToA j is the air time of the j-th jump, ETX j is the expected number of transmissions for the j-th hop, SNR j is the signal-to-noise ratio of the j-th hop, and α, β, γ, and δ are the preset weighting coefficients of the corresponding items.

6. The intelligent control system based on LoRa wireless communication according to claim 4, characterized in that, The method for obtaining the received signal strength RSSI is RSSI=10log 10 (P r / P0), where P r Receive power, P0 is the specified power.

7. The intelligent control system based on LoRa wireless communication according to claim 6, wherein The method for obtaining the signal-to-noise ratio SNR is SNR = RSSI - N i .

8. The intelligent control system based on LoRa wireless communication according to claim 4, characterized in that The method for obtaining the expected transmission count ETX is ETX = 1 / (p f *p r ), where p f is the forward delivery rate, and p r is the reverse confirmation rate.

9. The intelligent control system based on LoRa wireless communication according to claim 4, characterized in that, The method for obtaining the time of arrival ToA in the air is ToA = N sym (2 SF / BW), where N sym is the number of symbols, BW is the bandwidth, and SF is the spreading factor.

10. An intelligent control system based on LoRa wireless communication, characterized in that, The encryption method of the cross-protocol data encapsulation unit is: Adopt AES-128-CTR mode encryption, and the initialization vector IV is generated by the exclusive OR operation of the device ID and the timestamp.

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