Fixed Mesh node device and communication system

Abstract

The invention belongs to the technical field of network equipment, and discloses a fixed Mesh node device and a communication system. The fixed Mesh node device provided by the invention comprises a communication module, an antenna module and a power supply module, the communication module comprises a processing unit and a radio frequency front end connected with the processing unit, and the processing unit is configured to perform congestion control on Mesh nodes; the antenna module is connected with the communication module and is used for communicating with other Mesh nodes; and the power supply module is used for supplying power to the fixed Mesh node device. The communication system provided by the invention comprises the fixed Mesh node device. According to the invention, the anti-interference capability and the stability of the fixed Mesh node device can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of network devices, and more specifically, relates to a fixed Mesh node device and a communication system. Background Art

[0002] With the development of the new energy industry, higher requirements are put forward for data collection and transmission in new energy power stations. The existing multiple main communication means have the following problems: The initial deployment cost of optical fiber communication is high, and it may be difficult for cellular networks or satellite communication to achieve full coverage. Power line carrier communication is easily affected by electromagnetic interference from the power grid and other electronic devices. In addition, existing wireless communication devices are also easily affected by factors such as electromagnetic interference and have poor stability. While the fixed Mesh node device has a strong network self-healing ability and can achieve more reliable communication. However, in the new energy power station scenario, due to environmental particularities (such as strong winds, sandstorms, extreme temperatures, etc.), higher requirements are placed on the anti-interference ability and stability of the fixed Mesh node device. Summary of the Invention

[0003] The present invention solves the problem that the anti-interference ability and stability of the fixed Mesh node device in the prior art need to be further improved by providing a fixed Mesh node device and a communication system.

[0004] The present invention provides a fixed Mesh node device, including:

[0005] A communication module, including a processing unit and a radio frequency front end connected to the processing unit, where the processing unit is configured to perform congestion control on the Mesh node;

[0006] An antenna module, connected to the communication module, for communicating with other Mesh nodes;

[0007] A power module, for supplying power to the fixed Mesh node device.

[0008] Preferably, the congestion control on the Mesh node includes: calculating the bandwidth occupancy rate and the queue occupancy rate respectively; calculating the congestion degree based on the bandwidth occupancy rate and the queue occupancy rate; determining the congestion level according to the congestion degree; and adjusting the transmission rate of the node according to the congestion level.

[0009] Preferably, the bandwidth occupancy rate is obtained by performing exponential weighted average calculation on the historical bandwidth occupancy rate and the current instantaneous bandwidth occupancy rate;

[0010] The queue occupancy rate is obtained by performing exponential weighted average calculation on the historical queue occupancy rate and the current instantaneous queue occupancy rate;

[0011] The congestion degree is calculated using the following formula: In the formula, CD is the congestion degree, BU is the bandwidth occupancy rate, and BU max is the maximum value of the bandwidth occupancy rate, and W BU is the first weight corresponding to the bandwidth occupancy rate, QO is the queue occupancy rate, QOmax is the maximum value of the queue occupancy rate, and W QO is the second weight corresponding to the queue occupancy rate;

[0012] Based on the calculated congestion degree and multiple set congestion degree thresholds, determine the congestion level corresponding to the current congestion status, and adjust the sending rate of the node to the sending speed set for the determined congestion level.

[0013] Preferably, the processing unit is further configured to select an optimal path based on a hybrid routing decision method; the hybrid routing decision method includes:

[0014] Initialize the relevant parameters of the Q-learning algorithm, including the learning rate α 3 , the discount factor γ, and the initial Q value; set the weight set {w 1 , w 2 , w 3 , w 4} for multi-dimensional path evaluation. The weight set corresponds to the quantization impact factors of the path length L, available bandwidth B, transmission delay D, and packet loss rate P respectively, and w 1 + w 2 + w 3 + w 4 = 1;

[0015] Collect the quality of service parameters of each link in the network topology in real time, construct a set of network states s including node load and link quality indicators, and generate a set of forwarding paths a;

[0016] Use the Q-learning algorithm to explore different forwarding paths, select the forwarding path a through the ε-greedy strategy, and update Q(s,a) based on Q(s,a) = Q(s,a) + α 3 ×[r + γ × max a' Q(s',a') - Q(s,a)], to obtain the path value prediction value Q corresponding to the screened potential path; in the formula, Q(s,a) represents the path value prediction value of the node selecting the forwarding path a in the network state s; r is the immediate reward, s' represents the next network state, and a' represents the next forwarding path; max a' Q(s',a') represents the maximum Q value corresponding to all possible next forwarding paths a' in the next network state s';

[0017] For all potential paths, based on multi-metric comprehensive decision-making, calculate the multi-dimensional evaluation score S of each path: Wherein, L max is the maximum path length, and L min is the minimum path length, B max is the maximum available bandwidth, and B min is the minimum available bandwidth, D max is the maximum transmission delay, and D min is the minimum transmission delay, P max is the maximum packet loss rate, and P min is the minimum packet loss rate;

[0018] Fuse the path value prediction value Q generated by the Q-learning algorithm and the multi-dimensional evaluation score S obtained by multi-metric comprehensive decision-making to determine the optimal forwarding path.

[0019] Preferably, after determining the optimal forwarding path, it further includes: detecting whether the optimal forwarding path is the shortest path; if it is detected that it is not the shortest path, trigger the Dijkstra algorithm for topology verification.

[0020] Preferably, the antenna module includes an antenna array, and the layout of the antenna array corresponds to the distribution of several Mesh nodes constituting the Mesh network; the antenna array includes high-gain microstrip antennas and dipole antennas; the housing of the antenna is made of waterproof materials, a lightning rod or a lightning protection strip is installed on the top of the antenna, and a lightning protection device is provided inside the antenna.

[0021] Preferably, the fixed Mesh node device is installed in a new energy station, and the power module draws power from a photovoltaic panel or a wind turbine.

[0022] Preferably, the power module includes a conversion unit and a battery pack; the input end of the conversion unit is connected to the photovoltaic panel or the wind turbine, and the output end of the conversion unit is connected to the charging management chip of the battery pack.

[0023] Preferably, the fixed Mesh node device further includes: a mounting bracket; the mounting bracket is used to mount the fixed Mesh node device on the ground or a tower, and the mounting bracket is made of stainless steel or alloy materials.

[0024] On the other hand, the present invention provides a communication system including the above-mentioned fixed Mesh node device.

[0025] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0026] The fixed Mesh node device provided by the present invention includes a communication module, an antenna module, and a power module. The communication module includes a processing unit and a radio frequency front end connected to the processing unit. The processing unit is configured to perform congestion control on the Mesh node. By configuring the processing unit to perform congestion control on the Mesh node, the present invention can dynamically adjust the transmission rate of the node according to the congestion situation, optimize resource utilization; make full use of the multi-path transmission ability to maximize the overall network throughput; avoid data queuing in the buffer for a long time, and significantly reduce the transmission delay; enhance the anti-interference ability and stability of the network, providing a reliable technical guarantee for wireless communication in complex environments. In addition, the processing unit in the communication module of the present invention can also be configured to select the optimal path based on a hybrid routing decision method. The present invention combines the path value prediction value Q generated by the Q-learning algorithm and the multi-dimensional evaluation score S obtained by multi-metric comprehensive decision-making to determine the optimal forwarding path, enabling intelligent routing selection. Combining congestion control, the present invention can improve the stability and anti-interference ability of data transmission. Description of the Drawings

[0027] Figure 1 FIG. is a schematic framework diagram of a fixed Mesh node device provided in Embodiment 1 of the present invention. Detailed Embodiments

[0028] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0029] Embodiment 1:

[0030] Embodiment 1 provides a fixed Mesh node device, see Figure 1 , mainly including:

[0031] A communication module, including a processing unit and a radio frequency front end connected to the processing unit, where the processing unit is configured to perform congestion control on the Mesh node;

[0032] An antenna module, connected to the communication module, for communicating with other Mesh nodes;

[0033] A power module, for supplying power to the fixed Mesh node device.

[0034] The communication module in the present invention is the core of the fixed Mesh node device, responsible for receiving, processing, and sending data. It adopts advanced Mesh network technology and supports multi-hop relay function.

[0035] Among them, the congestion control of the Mesh node includes the following steps:

[0036] (1) Calculate the bandwidth utilization rate and the queue occupancy rate respectively.

[0037] Specifically, the bandwidth utilization rate (Bandwidth Utilization, BU) refers to the ratio of the currently used bandwidth to the total bandwidth.

[0038] The bandwidth utilization rate is obtained by calculating the exponential weighted average of the historical bandwidth utilization rate and the current instantaneous bandwidth occupancy rate. The calculation formula used is as follows:

[0039] UsedRate_BWn(t) = α 1 ×UsedRate_BWn(t - 1)+(1 - α 1 )×UsedRate_BWn_instant(t);

[0040] In the formula, UsedRate_BWn(t) represents the smoothed bandwidth occupancy rate of the nth node at time t, UsedRate_BWn(t - 1) represents the smoothed bandwidth occupancy rate of the nth node at time t - 1, UsedRate_BWn_instant(t) represents the instantaneous bandwidth occupancy rate of the nth node at time t, and α 1 represents the first smoothing factor, and the value range of α 1 is 0.1 ≤ α 1 ≤ 0.3.

[0041] Specifically, the queue occupancy rate (Queue Occupancy, QO) represents the ratio of the number of data packets waiting to be processed in the network device to its maximum capacity.

[0042] The queue occupancy rate is obtained by calculating the exponential weighted average of the historical queue occupancy rate and the current instantaneous queue occupancy rate. The calculation formula used is as follows:

[0043] QRn(t) = α 2 ×QRn(t - 1)+(1 - α 2 )×QRn_instant(t);

[0044] In the formula, QRn(t) represents the smoothed queue occupancy rate of the nth node at time t, QRn(t - 1) represents the smoothed queue occupancy rate of the nth node at time t - 1, QRn_instant(t) represents the instantaneous queue occupancy rate of the nth node at time t, and α 2 represents the second smoothing factor, and the value range of α 2 is 0.2 ≤ α 2 ≤ 0.4.

[0045] (2) Calculate the congestion degree based on the bandwidth utilization rate and the queue occupancy rate.

[0046] Specifically, the congestion degree (CD) is calculated using the following formula:

[0047]

[0048] In the formula, CD is the congestion degree, BU is the bandwidth occupancy rate, and BU max is the maximum value of the bandwidth occupancy rate, and W BU is the first weight corresponding to the bandwidth occupancy rate, QO is the queue occupancy rate, QOmax is the maximum value of the queue occupancy rate, and W QO is the second weight corresponding to the queue occupancy rate.

[0049] For W BU and W QO , the weights can be adjusted according to the actual situation or experience to more accurately reflect the congestion situation. For example, in some cases, the queue occupancy rate may better reflect the degree of network congestion than the bandwidth occupancy rate. In this case, a higher weight can be assigned to the queue occupancy rate.

[0050] (3) Determine the congestion level based on the congestion degree, and adjust the sending rate of the node according to the congestion level.

[0051] Specifically, based on the calculated congestion degree and multiple set congestion degree thresholds, determine the congestion level corresponding to the current congestion situation, and adjust the sending rate of the node to the sending speed set for the determined congestion level.

[0052] That is, preset congestion degree thresholds to determine whether the network is in a congested state, and the congestion level can be further divided. The congestion degree thresholds can be set according to historical data.

[0053] For example, the congestion levels are divided as follows:

[0054] Normal: When the congestion degree is lower than the first threshold (e.g., lower than 20%), it indicates that the network is in a normal state and there is no need to adjust the sending rate.

[0055] Mild congestion: When the congestion degree is lower than the second threshold and higher than the first threshold (e.g., between 20% and 50%), at this time, measures are taken to slow down the growth rate of the sending rate or slightly reduce the sending rate. The sending rate can be gradually reduced in a linearly decreasing manner. For example, each time the current rate is reduced by 10% until the congestion degree falls back to the normal range.

[0056] Moderate congestion: When the congestion degree is lower than the third threshold and higher than the second threshold (e.g., between 50% and 80%), the sending rate is significantly reduced to relieve network pressure. An exponential decreasing method can be used to quickly reduce the sending rate. For example, each time the rate is reduced to 50% of the original rate.

[0057] Severe congestion: When the congestion level is higher than the third threshold (e.g., higher than 80%), the transmission rate is significantly reduced until the congestion condition is alleviated, or the data transmission is paused for a period of time, and the transmission is resumed after waiting for the network condition to improve.

[0058] In addition, the processing unit in the present invention can also be configured to select an optimal path based on a hybrid routing decision method.

[0059] Specifically, the hybrid routing decision method includes the following steps:

[0060] (1) Initialize the relevant parameters of the Q-learning algorithm, including the learning rate α 3 , the discount factor γ and the initial Q value; set the weight set {w 1 , w 2 , w 3 , w 4} for multi-dimensional path evaluation. The weight set corresponds to the quantization impact factors of the path length L, available bandwidth B, transmission delay D, and packet loss rate P respectively, and w 1 + w 2 + w 3 + w 4 = 1.

[0061] (2) Collect the quality of service parameters of each link in the network topology in real time, construct a set of network states s including node load and link quality indicators, and generate a set of forwarding paths a.

[0062] (3) Use the Q-learning algorithm to explore different forwarding paths, select the forwarding path a through the ε-greedy strategy, and update Q(s,a) based on Q(s,a) = Q(s,a) + α 3 × [r + γ × max a' Q(s',a') - Q(s,a)], and obtain the path value prediction value Q corresponding to the screened potential path.

[0063] In the formula, Q(s,a) represents the path value prediction value of the node selecting the forwarding path a in the network state s; r is the immediate reward, s' represents the next network state, and a' represents the next forwarding path; max a' Q(s',a') represents the maximum Q value corresponding to all possible next forwarding paths a' in the next network state s'.

[0064] Specifically, Q(s,a) is the expectation of obtaining benefits by taking action a in the s state at a certain moment. In the present invention, Q(s,a) represents the expected revenue valuation of the node selecting a certain forwarding path a in a specific network state s, that is, the path value prediction value.

[0065] s represents the state. In the present invention, s represents the set of network environment parameters currently sensed by the node, that is, the network state. The network environment parameters include, for example, link quality, node load, etc.

[0066] a represents the action. In the present invention, a represents the forwarding path decision that the node can choose. For example, select node A as the next hop, select node B as the next hop.

[0067] α 3 is the learning rate, and the value range of α 3 is 0.1 ≤ α 3 ≤ 0.3.

[0068] r is the immediate reward, and r is the direct feedback obtained after executing the action a. r is related to the available bandwidth B, transmission delay D, and packet loss rate P. In the formula, w B , w D , w P are the weight coefficients of the available bandwidth, transmission delay, and packet loss rate respectively, which are used to adjust the importance of each factor when calculating the reward; B max is the maximum available bandwidth in the system, indicating that a higher bandwidth corresponds to a higher reward; D max is the maximum transmission delay in the system, indicating that a lower delay will bring a higher reward; w P ×(1 - P) indicates that a lower packet loss rate (i.e., a more reliable connection) corresponds to a higher reward.

[0069] γ is the discount factor, and the value range of γ is 0.9 ≤ γ ≤ 1.

[0070] s' represents the next state, that is, after executing the action a, the new network state when the data packet arrives at the next-hop node.

[0071] a' represents the next action.

[0072] max a' Q(s', a') represents the maximum expected value, that is, in the next state s', the maximum Q value among all possible actions a'.

[0073] (4) For all potential paths, based on the multi-metric comprehensive decision-making, calculate the multi-dimensional evaluation score S of each path:

[0074]

[0075] In the formula, L max is the maximum path length, L min is the minimum path length, B max is the maximum available bandwidth, B minis the minimum available bandwidth, D max is the maximum transmission delay, D min is the minimum transmission delay, P max is the maximum packet loss rate, P min is the minimum packet loss rate.

[0076] (5) Integrate the path value prediction Q generated by the Q-learning algorithm and the multi-dimensional evaluation score S obtained by multi-metric comprehensive decision-making to determine the optimal forwarding path.

[0077] Specifically, the weighted decision function that can be used to integrate the path value prediction Q generated by the Q-learning algorithm and the multi-dimensional evaluation score S obtained by multi-metric comprehensive decision-making is expressed as:

[0078]

[0079] In the formula, β is an adjustment factor. In a preferred solution, β can also be dynamically adjusted according to the network state.

[0080] In addition to the above steps, in a preferred solution, after determining the optimal forwarding path, it may further include: detecting whether the optimal forwarding path is the shortest path; if it is detected that it is not the shortest path, trigger the Dijkstra algorithm for topology verification.

[0081] In summary, the present invention can combine congestion control and intelligent routing selection. According to the calculated congestion metric results, the congestion level is divided into different levels, and the sending rate of nodes is adjusted accordingly. For example, when the congestion level is high, the sending rate is reduced to reduce the network load, and information such as link quality, node load, and path diversity is used to dynamically select the optimal path. Data can be relayed through multiple nodes, effectively expanding the communication coverage range, and having higher transmission and reception speeds, which can improve the stability and anti-interference ability of data transmission, and is especially suitable for new energy power station areas with complex terrain and difficult to cover by traditional communication.

[0082] Among them, the antenna module includes an antenna array, and the layout of the antenna array corresponds to the distribution of several Mesh nodes constituting the Mesh network; the antenna array includes high-gain microstrip antennas and dipole antennas; the housing of the antenna is made of waterproof material, a lightning rod or lightning protection strip is installed on the top of the antenna, and a lightning protection device is provided inside the antenna.

[0083] Specifically, the present invention adopts a high-performance and multi-directional antenna design. By adjusting the structural parameters and array layout of the antenna, and according to the coverage requirements and node distribution of the Mesh network, the scale, shape, and layout of the antenna array are determined to achieve multi-directional coverage of the antenna, which can improve communication efficiency. The high-gain antenna unit microstrip antenna and dipole antenna and corresponding array technology adopted by the present invention not only improve the gain of the antenna, enhance the transmission distance of the signal, but also improve the anti-interference ability. In terms of antenna design, the overall stability of the antenna can be improved by adding a support structure and strengthening the connection components. The antenna housing adopts waterproof materials and sealing technology, such as stainless steel or aluminum alloy, which can prevent rainwater from entering the antenna interior. And a lightning rod or lightning protection strip is installed on the top of the antenna, which can guide the lightning current to safely discharge into the ground. A lightning protection device is arranged inside the antenna, which can limit the damage of lightning overvoltage to the internal circuit of the antenna. The above design can greatly improve the anti-wind, anti-rain, and lightning protection capabilities, ensuring normal operation under various harsh weather conditions.

[0084] Among them, the fixed Mesh node device is installed in the new energy station, and the power module draws power from the photovoltaic panel or the wind turbine. The power module includes a conversion unit and a battery pack; the input end of the conversion unit is connected to the photovoltaic panel or the wind turbine, and the output end of the conversion unit is connected to the charging management chip of the battery pack.

[0085] Specifically, considering that new energy stations are often located in remote areas with unstable power supply, the present invention designs a reliable power system that directly draws power from the photovoltaic panel or the wind turbine nearby, and is built-in with an efficient inverter and rectifier. The rectifier can convert the alternating current provided by the wind turbine into direct current, while the inverter can convert the direct current of the photovoltaic panel into alternating current, so as to ensure that the power module has appropriate power factor correction and overload protection functions when powered on; according to the load demand and the instability of the power supply, a battery pack is set in the power module for power storage, and an intelligent control system or device can be carried to monitor the power status, load demand, and the status of the energy storage module, which can ensure that the device can continuously supply power in various environments and enhance the self-sufficiency ability.

[0086] In addition, the fixed Mesh node device may further include: a mounting bracket; the mounting bracket is designed to adapt to complex geographical environments and is made of high-strength and corrosion-resistant materials such as stainless steel and alloy materials, which can ensure that the fixed Mesh node device can be firmly installed on the ground, tower racks and other various scenarios, and is convenient for installation and maintenance.

[0087] In summary, the fixed Mesh node device provided in Embodiment 1 can operate stably in complex and changeable geographical environments, and at the same time supports multi-hop relay, expanding the communication coverage range, and can provide strong support for the intelligent, networked, and digital transformation of new energy stations.

[0088] Embodiment 2:

[0089] Embodiment 2 provides a communication system, including the fixed Mesh node device as described in Embodiment 1.

[0090] For example, the communication system includes multiple fixed Mesh node devices. A backbone network of a wireless Mesh network can be formed through the multiple fixed Mesh node devices. Each fixed Mesh node device can serve as a Mesh node in the network. In this network, each fixed Mesh node device can establish a wireless communication link with the surrounding fixed Mesh node devices to achieve wireless communication.

[0091] When applying the present invention to a new energy station, the following advantages are also achieved:

[0092] (1) Improving communication stability and reliability: The new energy station has extremely high requirements for communication stability and reliability because any communication interruption may lead to the failure of equipment not being detected and processed in time, thus affecting the operation efficiency of the entire station. The present invention optimizes the signal transceiver efficiency and enhances the signal penetration and stability by integrating high-performance communication modules and antenna modules, and can ensure the continuity and reliability of communication.

[0093] (2) Expanding the communication range: There are numerous internal devices in the new energy station, and they are widely distributed. The traditional point-to-point communication method is difficult to meet the information interaction requirements among a large number of devices. The present invention adopts Mesh network technology, which supports multi-hop relay function, that is, data can be relayed through multiple nodes, thereby effectively expanding the communication coverage. This can not only ensure seamless communication among the internal devices of a large new energy station, but also achieve a stable connection with the remote monitoring center, improving the overall operation and maintenance efficiency.

[0094] (3) Reducing the operation and maintenance cost: The operation and maintenance cost of traditional communication devices in the new energy station is relatively high, mainly because the devices are widely distributed, the environment is harsh, and the maintenance is difficult. The present invention uses the fixed Mesh node device to realize functions such as remote configuration and automatic repair, greatly reducing the operation and maintenance cost. At the same time, the design of the device is also convenient for installation and maintenance, reducing manual intervention and downtime.

[0095] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the examples, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A fixed Mesh node device, characterized in that: include: A communication module, comprising a processing unit and a radio frequency front end connected to the processing unit, wherein the processing unit is configured to perform congestion control on the Mesh node; An antenna module, connected to the communication module, for communicating with other Mesh nodes; Power module, used to power fixed Mesh node devices.

2. The fixed Mesh node device according to claim 1, characterized in that: The congestion control of the Mesh node includes: calculating bandwidth occupancy and queue occupancy respectively; calculating congestion degree according to the bandwidth occupancy and the queue occupancy; determining congestion level according to the congestion degree; and adjusting the sending rate of the node according to the congestion level.

3. The fixed Mesh node device according to claim 2, characterized in that: The bandwidth occupancy rate is obtained by performing exponential weighted average calculation on the historical bandwidth occupancy rate and the current instantaneous bandwidth occupancy rate; The queue occupancy is obtained by performing exponential weighted average calculation on the historical queue occupancy and the current instantaneous queue occupancy; The congestion degree is calculated using the following formula: In the formula, CD is the congestion degree, BU is the bandwidth occupancy rate, and BU max is the maximum bandwidth occupancy, W BU is the first weight corresponding to the bandwidth occupancy, QO is the queue occupancy, QOmax is the maximum queue occupancy, W QO is the second weight corresponding to the queue occupancy rate; Based on the calculated congestion degree and the set multiple congestion degree thresholds, a congestion level corresponding to the current congestion condition is determined, and the sending rate of the node is adjusted to the sending speed set by the determined congestion level.

4. The fixed Mesh node device according to claim 1, characterized in that: The processing unit is further configured to select an optimal path based on a hybrid routing decision method; the hybrid routing decision method comprises: Initialize the relevant parameters of the Q-learning algorithm, including the learning rate α3, the discount factor γ and the initial Q value; set the weight set {w1, w2, w3, w4} of the multi-dimensional path evaluation, where the weight set corresponds to the quantitative impact factors of the path length L, the available bandwidth B, the transmission delay D and the packet loss rate P, respectively, w1+w2+w3+w4=1; Collect the service quality parameters of each link in the network topology in real time, build a set of network states s including node load and link quality indicators, and generate a set of forwarding paths a; The Q-learning algorithm is used to explore different forwarding paths, and the forwarding path a is selected through the ε-greedy strategy based on Q(s,a)=Q(s,a)+α3×[r+γ×max a' Q(s',a')-Q(s,a)], update Q(s,a), and obtain the path value prediction value Q corresponding to the screened potential path; where Q(s,a) represents the path value prediction value of the node selecting the forwarding path a under the network state s; r is the immediate reward, s' represents the next network state, and a' represents the next forwarding path; max a' Q(s',a') represents the maximum Q value corresponding to all possible next forwarding paths a' in the next network state s'; For all potential paths, based on multi-metric comprehensive decision-making, the multi-dimensional evaluation score S of each path is calculated: Where, L max is the maximum path length, L min is the minimum path length, B max is the maximum available bandwidth, B min is the minimum available bandwidth, D max is the maximum transmission delay, D min For the minimum transmission delay, P max is the maximum packet loss rate, P min is the minimum packet loss rate; The optimal forwarding path is determined by integrating the path value prediction value Q generated by the Q-learning algorithm and the multi-dimensional evaluation score S obtained by multi-metric comprehensive decision-making.

5. The fixed Mesh node device according to claim 4, characterized in that: After determining the optimal forwarding path, the method further includes: detecting whether the optimal forwarding path is the shortest path; if the optimal forwarding path is detected to be a non-shortest path, triggering the Dijkstra algorithm to perform topology verification.

6. The fixed Mesh node device according to claim 1, characterized in that: The antenna module includes an antenna array, the layout of which corresponds to the distribution of several Mesh nodes constituting the Mesh network; the antenna array includes a high-gain microstrip antenna and a dipole antenna; the antenna shell is made of waterproof material, a lightning rod or a lightning strip is installed on the top of the antenna, and a lightning protection device is arranged inside the antenna.

7. The fixed Mesh node device according to claim 1, characterized in that: The fixed Mesh node device is installed in a new energy station, and the power module draws power from a photovoltaic panel or a wind turbine.

8. The fixed Mesh node device according to claim 7, characterized in that: The power module includes a conversion unit and a battery pack; the input end of the conversion unit is connected to the photovoltaic panel or the wind turbine, and the output end of the conversion unit is connected to the charging management chip of the battery pack.

9. The fixed Mesh node device according to claim 1, characterized in that: Also includes: Mounting bracket: The mounting bracket is used to install the fixed Mesh node device on the ground or a tower, and the mounting bracket is made of stainless steel or alloy material.

10. A communication system, characterized in that: It comprises a fixed Mesh node device as described in any one of claims 1 to 9.

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