Wireless communication method and system applied to distributed scene

Abstract

The invention provides a wireless communication method and system applied to a distributed scene, and the method comprises the steps: obtaining feature parameters in a LoRa network, and calculating the membership degrees of a first feature parameter and a third feature parameter one by one based on a membership degree function; constructing a first feature matrix; calculating according to the first feature matrix to obtain a first weight coefficient of the first feature parameter or the second feature parameter, and calculating according to the first weight coefficient and the first feature matrix to obtain a second feature matrix of the first feature parameter or the second feature parameter; a weight matrix of the characteristic parameters is calculated according to the second characteristic matrix, a communication path selection matrix is constructed according to the weight matrix and the second characteristic matrix, and an optimal communication path is selected according to the communication path selection matrix. According to the invention, relay communication between devices can be realized, and the network coverage range and stability are improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a wireless communication method and system applied to a distributed scenario. Background Art

[0002] In a distributed photovoltaic scenario, traditional devices usually connect a distributed power access unit and a photovoltaic inverter through a 485 communication line. However, due to the complex installation environment of the devices, wiring is difficult and the communication line may be long, which easily leads to signal attenuation or communication anomalies. At the same time, as the number of photovoltaic inverters, energy storage devices, and charging piles within a user increases, the flexibility and scalability of traditional wired communication methods are insufficient to meet the requirements of complex distributed energy scenarios.

[0003] LoRa wireless communication technology has become an ideal choice for solving communication problems in distributed energy scenarios due to its long-distance transmission, low power consumption, and anti-interference capabilities. However, the LoRa single-point communication mode may have signal attenuation problems, especially when the devices are widely distributed or the environment has obstructions. Summary of the Invention

[0004] The purpose of the present invention is to provide a wireless communication method and system applied to a distributed scenario, aiming to achieve relay communication between devices, improve network coverage and stability, and thus solve the problems of signal attenuation or communication anomalies existing in traditional communication technologies.

[0005] In a first aspect, the present invention provides a wireless communication method applied to a distributed scenario, the method comprising: Obtaining characteristic parameters in a LoRa network every first preset time, the characteristic parameters including a first characteristic parameter and a second characteristic parameter, each of the second characteristic parameters including a plurality of third characteristic parameters, and calculating the membership degrees of the first characteristic parameter and the third characteristic parameters one by one based on a membership function; Constructing a first characteristic matrix corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership degrees, the number of rows of the first characteristic matrix corresponding to the second characteristic parameter being equal to the number of third characteristic parameters included in the second characteristic parameter; Calculating a first weight coefficient of the first characteristic parameter or the second characteristic parameter according to the first characteristic matrix, the k-th row in the first characteristic matrix corresponding to the second characteristic parameter corresponding to the weight coefficient of the k-th third characteristic parameter included in the second characteristic parameter, and calculating a second characteristic matrix of the first characteristic parameter or the second characteristic parameter according to the first weight coefficient and the first characteristic matrix; Calculate the weight matrix of the characteristic parameters according to the second characteristic matrix, and construct a communication path selection matrix according to the weight matrix and the second characteristic matrix, so as to select the optimal communication path according to the communication path selection matrix.

[0006] Further, the method further includes: Construct a Mesh network, which includes multiple nodes. One node corresponds to a distributed power access unit, and other nodes correspond to one of a photovoltaic inverter, an energy storage device, and a charging pile. LoRa wireless relay units are installed on other nodes, and a LoRa antenna is deployed on the distributed power access unit. The LoRa wireless relay unit is communicatively connected to the LoRa antenna.

[0007] Further, the first characteristic parameters include signal strength and communication energy consumption, the second characteristic parameters include data transmission delay and reliability, the data transmission delay includes time delay and delay jitter, and the reliability includes packet loss rate, number of retransmissions, and bit error rate; The step of calculating the membership degrees of the first characteristic parameters and the third characteristic parameters one by one based on the membership function includes: Calculate the membership degree of any one of the characteristic parameters of communication energy consumption, time delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate according to the following formula: ; Wherein, is the membership degree of the corresponding characteristic parameter under the adjacent i-th node and j-th node, and are the maximum value and the minimum value of the corresponding characteristic parameter under the adjacent nodes respectively, is the value of the corresponding characteristic parameter under the adjacent i-th node and j-th node; Calculate the membership degree of signal strength according to the following formula: ; Wherein, is the membership degree of signal strength under the adjacent i-th node and j-th node, is the value of signal strength under the adjacent i-th node and j-th node, and are the maximum value and the minimum value of signal strength under the adjacent nodes respectively.

[0008] Further, the step of constructing the first characteristic matrix corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership degrees includes: The expression of the first characteristic matrix corresponding to signal strength is: ; The expression of the first characteristic matrix corresponding to communication energy consumption is: ; The expression of the first characteristic matrix corresponding to data transmission delay is: ; The expression of the first characteristic matrix corresponding to reliability is: ; Wherein, , are respectively the membership degrees of the signal strengths under the adjacent i-th node and the k-th node, and the n-th node, , , are respectively the membership degrees of the communication energy consumptions under the adjacent i-th node and the j-th node, the k-th node, and the n-th node, , , are respectively the membership degrees of the time delays under the adjacent i-th node and the j-th node, the k-th node, and the n-th node, , , are respectively the membership degrees of the delay jitters under the adjacent i-th node and the j-th node, the k-th node, and the n-th node, , , are respectively the membership degrees of the packet loss rates under the adjacent i-th node and the j-th node, the k-th node, and the n-th node, , , are respectively the membership degrees of the retransmission times under the adjacent i-th node and the j-th node, the k-th node, and the n-th node, , , are respectively the membership degrees of the bit error rates under the adjacent i-th node and the j-th node, the k-th node, and the n-th node, , , , are respectively the first characteristic matrices corresponding to the signal strength, communication energy consumption, data transmission delay, and reliability with the i-th node as the starting transmission node, and n is the total number of nodes.

[0009] Furthermore, the step of calculating the first weight coefficient of the first characteristic parameter or the second characteristic parameter according to the first characteristic matrix includes: The first weight coefficient of the first characteristic parameter is 1; Calculate the membership degree variance of each third characteristic parameter according to the first characteristic matrix: ; Among them, is the membership variance of the third characteristic parameter, is the membership of the third characteristic parameter under the adjacent i-th node and j-th node, is the average value of the memberships of the third characteristic parameter under all adjacent nodes; Obtain the sum of the membership variances of all third characteristic parameters under the same second characteristic parameter, and use the ratio of the membership variance of the third characteristic parameter to the sum of the membership variances as the first weight of the third characteristic parameter, and generate the first weight coefficient of the second characteristic parameter according to the weight of the third characteristic parameter: ; Among them, is the first weight coefficient of data transmission delay, is the first weight coefficient of reliability, , , , , are the first weights of delay, delay jitter, packet loss rate, retransmission times, and bit error rate respectively.

[0010] Furthermore, the step of calculating the second characteristic matrix of the first characteristic parameter or the second characteristic parameter according to the first weight coefficient and the first characteristic matrix includes: Calculate the second characteristic matrix of data transmission delay according to the following formula: ; Calculate the second characteristic matrix of reliability according to the following formula: ; Calculate the second characteristic matrix of signal strength according to the following formula: ; Calculate the second characteristic matrix of communication energy consumption according to the following formula: ; Among them, , , , are the second characteristic matrices of data transmission delay, reliability, signal strength, and communication energy consumption with the i-th node as the starting emission point respectively, , , are the overall characteristic values of data transmission delay from the i-th node to the j-th node, k-th node, and n-th node respectively, , , They are the overall characteristic values regarding reliability from the \(i\)-th node to the \(j\)-th node, the \(k\)-th node, and the \(n\)-th node respectively.

[0011] Furthermore, the step of calculating the weight matrix of the characteristic parameters according to the second characteristic matrix includes: Obtain the membership variance of the first characteristic parameter or the second characteristic parameter according to the following formula: ; where , , , are the membership variances of signal strength, communication energy consumption, data transmission delay, and reliability respectively, , are the average values of the memberships of signal strength and communication energy consumption under all adjacent nodes respectively, , are the average values of the overall characteristic values of data transmission delay and reliability under all adjacent nodes respectively; Obtain the second weight of the corresponding characteristic parameter according to the membership variances of signal strength, communication energy consumption, data transmission delay, and reliability, and obtain the weight matrix according to the second weight.

[0012] Furthermore, the step of constructing a communication path selection matrix according to the weight matrix and the second characteristic matrix to select an optimal communication path according to the communication path selection matrix includes: When the device priority is the first threshold, calculate the link evaluation matrix according to the following formula: ; When the device priority is the second threshold, calculate the link evaluation matrix according to the following formula: ; where , , , are the second weights of signal strength, communication energy consumption, data transmission delay, and reliability respectively, is the weight matrix, is the link evaluation matrix with the \(i\)-th node as the starting transmitting node, , , are the wireless transmission link evaluation values from the \(i\)-th node to the \(j\)-th node, the \(k\)-th node, and the \(n\)-th node respectively.

[0013] Further, the step of constructing a communication path selection matrix according to the weight matrix and the second feature matrix to select an optimal communication path according to the communication path selection matrix further includes: Constructing a communication path selection matrix according to a link evaluation matrix: ; Wherein, is the communication path selection matrix with the i-th node as the starting transmitting node. Each row of the communication path selection matrix represents the comprehensive evaluation value under the corresponding path. is the wireless transmission link evaluation value from the j-th node to the n-th node. is the wireless transmission link evaluation value from the j-th node to the l-th node, is the wireless transmission link evaluation value from the k-th node to the n-th node; Obtain the maximum comprehensive evaluation value from all the comprehensive evaluation values, and use the communication path corresponding to the maximum comprehensive evaluation value as the optimal path for communication.

[0014] In a second aspect, the present invention provides a wireless communication system applied to a distributed scenario. The system includes: A feature parameter selection module, configured to obtain feature parameters in the LoRa network every first preset time. The feature parameters include first feature parameters and second feature parameters. Each second feature parameter includes multiple third feature parameters, and calculate the membership degrees of the first feature parameters and the third feature parameters one by one based on a membership function; A membership degree calculation module, configured to construct a first feature matrix corresponding to the first feature parameter and the second feature parameter respectively according to the calculated membership degrees. The number of rows of the first feature matrix corresponding to the second feature parameter is equal to the number of third feature parameters included in the second feature parameter; A feature matrix construction module, configured to calculate a first weight coefficient of the first feature parameter or the second feature parameter according to the first feature matrix. The k-th row in the first feature matrix corresponding to the second feature parameter corresponds to the weight coefficient of the k-th third feature parameter included in the second feature parameter, and calculate a second feature matrix of the first feature parameter or the second feature parameter according to the first weight coefficient and the first feature matrix; An optimal path selection module, configured to calculate a weight matrix of the feature parameters according to the second feature matrix, and construct a communication path selection matrix according to the weight matrix and the second feature matrix, so as to select an optimal communication path according to the communication path selection matrix.

[0015] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned wireless communication method applied to a distributed scenario.

[0016] In a fourth aspect, the present invention provides an electronic device, which includes a memory and a processor, wherein: The memory is used for storing a computer program; When the processor executes the computer program stored on the memory, it implements the above-mentioned wireless communication method applied to a distributed scenario.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Traditional communication path optimization algorithms usually only focus on a single objective (such as the fastest or the shortest). The present invention conducts a multi-objective comprehensive evaluation (signal strength RSSI, delay T, jitter ΔT, packet loss rate Floss, etc.), performs normalization processing using a membership function, and combines dynamic weights (adjusted in real time based on parameters such as node congestion, energy consumption, or bit error rate fluctuations) to achieve more accurate path evaluation and selection.

[0018] 2. Compared with the defect of the cumulative model being vulnerable to local degradation and resulting in inaccurate overall evaluation, the present invention prefers to use the product model in a multi-hop relay network to ensure the balance of each link performance and end-to-end quality.

[0019] 3. The present invention supports weight calculation between neighbor nodes and dynamic routing selection. If a certain node has signal attenuation or excessive energy consumption, it can be timely switched to a better neighbor node to achieve adaptive scheduling and fault redundancy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of a wireless communication method applied to a distributed scenario proposed in an embodiment of the present invention; Figure 2 is a schematic structural diagram of a Mesh network exemplified in an embodiment of the present invention; Figure 3 is a schematic structural diagram of a wireless communication system applied to a distributed scenario proposed in an embodiment of the present invention.

[0021] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the art to which the present invention pertains. The words such as "including" used herein are intended to mean that the elements or items appearing before this word cover the elements or items listed after this word and their equivalents, without excluding other elements or items.

[0023] As Figure 1 shown, an embodiment of the present invention provides a wireless communication method applied to a distributed scenario. The method includes steps S101 to S104, where: Step S101: Obtain characteristic parameters in the LoRa network every first preset time. The characteristic parameters include a first characteristic parameter and a second characteristic parameter. Each second characteristic parameter includes a plurality of third characteristic parameters, and calculate the membership degrees of the first characteristic parameter and the third characteristic parameters one by one based on a membership function; It should be noted that in a scenario composed of multiple devices such as distributed photovoltaic, energy storage, and charging piles, the traditional wired 485 communication method has complex wiring and weak adaptability to complex environments, and is easily affected by cable deployment, length limitations, and interference factors. To overcome the wiring limitations and improve the deployment flexibility, it is necessary to use a LoRa wireless transfer unit to replace or be parallel with the traditional 485 communication interface on devices such as photovoltaic inverters, energy storage devices, and charging piles; at the distributed power access unit, monitoring center, or routing aggregation node, corresponding LoRa antennas and transfer devices are also deployed to form a complete LoRa network coverage. Specifically, as Figure 2 shown, a Mesh network is constructed. The Mesh network includes multiple nodes, where one node corresponds to a distributed power access unit, and the other nodes correspond to one of a photovoltaic inverter, an energy storage device, and a charging pile. LoRa wireless transfer units are installed on the other nodes, and a LoRa antenna is deployed on the distributed power access unit. The LoRa wireless transfer unit is communicatively connected to the LoRa antenna. In addition, regarding the LoRa wireless transfer unit, it is a device based on LoRa (LongRange) wireless communication technology, used to realize wireless data transmission and conversion between different communication interfaces or protocols. For the distributed scenario, by constructing a Mesh network, the limitations of the traditional wired topology are overcome, the communication coverage breadth is significantly improved, the cable cost is reduced, and a flexible wireless node access environment is provided for the subsequent multi-objective routing algorithm.

[0024] In addition, in this step, the LoRa wireless relay unit is first powered on and initialized, and then characteristic parameters are selected. These characteristic parameters are obtained by each node regularly broadcasting its own status. In some embodiments, the first characteristic parameter includes signal strength and communication energy consumption, and the second characteristic parameter includes data transmission delay and reliability. The data transmission delay includes latency and jitter, and the reliability includes packet loss rate, number of retransmissions, and bit error rate. Then, the membership degree of any one of the characteristic parameters such as communication energy consumption, latency, jitter, packet loss rate, number of retransmissions, and bit error rate is calculated according to the following formula: ; Wherein, is the membership degree of the corresponding characteristic parameter under the adjacent i-th node and j-th node, , are respectively the maximum value and the minimum value of the corresponding characteristic parameter under the adjacent nodes, is the value of the corresponding characteristic parameter under the adjacent i-th node and j-th node; Exemplarily, the membership degree calculation formula of communication energy consumption (transmission energy consumption) is: ; Wherein, is the membership degree of the communication energy consumption under the adjacent i-th node and j-th node, , are respectively the maximum value and the minimum value of the communication energy consumption under the adjacent nodes, is the value of the communication energy consumption under the adjacent i-th node and j-th node.

[0025] The membership degree of the signal strength is calculated according to the following formula: ; Wherein, is the membership degree of the signal strength under the adjacent i-th node and j-th node, is the value of the signal strength under the adjacent i-th node and j-th node, , are respectively the maximum value and the minimum value of the signal strength under the adjacent nodes.

[0026] In addition, the first preset time is set to continuously detect the status of the LoRa network in real time, so that when there are abnormal nodes or the currently selected communication path is abnormal, an alternative optimal communication path can be quickly selected. In some embodiments, the first preset time is related to specific usage requirements and is not specifically limited in this embodiment.

[0027] In summary, by calculating the membership degrees of features such as signal strength and communication energy consumption, values between 0 and 1 are obtained for subsequent routing evaluation. In addition, traditional point-to-point or tree network structures are prone to central node overload or single-point bottlenecks when the number of nodes increases, and when a single path fails, it is unable to quickly switch to an alternative path, resulting in insufficient disaster tolerance. By dynamically weighting based on information such as RSSI and energy consumption between adjacent nodes, the self-healing ability and load balancing of the overall network are improved, enabling each node to act as a forwarding node. Once a node or communication path becomes abnormal, it can quickly switch to other feasible paths according to the weights assigned to neighbor nodes, significantly enhancing the network robustness and scalability.

[0028] Step S102: Construct a first feature matrix corresponding to the first feature parameter and the second feature parameter respectively according to the calculated membership degrees. The number of rows of the first feature matrix corresponding to the second feature parameter is equal to the number of third feature parameters included in the second feature parameter. In some embodiments, the expression of the first feature matrix corresponding to the signal strength is: ; The expression of the first feature matrix corresponding to the communication energy consumption is: ; The expression of the first feature matrix corresponding to the data transmission delay is: ; The expression of the first feature matrix corresponding to the reliability is: ; Wherein, and are the membership degrees of the signal strength under the adjacent i-th node and k-th node, n-th node respectively, and and are the membership degrees of the communication energy consumption under the adjacent i-th node and j-th node, k-th node, n-th node respectively, and and are the membership degrees of the time delay under the adjacent i-th node and j-th node, k-th node, n-th node respectively, and and are the membership degrees of the delay jitter under the adjacent i-th node and j-th node, k-th node, n-th node respectively, and and are the membership degrees of the packet loss rate under the adjacent i-th node and j-th node, k-th node, n-th node respectively, and , are the membership degrees of the retransmission times under the adjacent ith node, jth node, and kth node, and nth node respectively. , , are the membership degrees of the bit error rates under the adjacent ith node, jth node, and kth node, and nth node respectively. , , , are the first feature matrices corresponding to signal strength, communication energy consumption, data transmission delay, and reliability respectively with the ith node as the starting transmitting node, and n is the total number of nodes.

[0029] It should be noted that in this step, by constructing the first feature matrix regarding the first feature parameter and the second feature parameter, it is to comprehensively consider the feature parameters in multiple dimensions such as signal strength, communication energy consumption, data transmission delay, and reliability, which is beneficial to subsequently screening out a more reliable and representative optimal communication path.

[0030] Step S103: Calculate the first weight coefficient of the first feature parameter or the second feature parameter according to the first feature matrix, which corresponds to the kth row in the first feature matrix corresponding to the second feature parameter and the weight coefficient of the kth third feature parameter included in the second feature parameter, and calculate the second feature matrix of the first feature parameter or the second feature parameter according to the first weight coefficient and the first feature matrix; It should be noted that in this step, since the first feature parameter only contains one parameter, the first weight coefficient of the first feature parameter is 1.

[0031] In addition, calculate the membership degree variance of each third feature parameter according to the first feature matrix: ; where is the membership degree variance of the third feature parameter, is the membership degree of the third feature parameter under the adjacent ith node and jth node, is the average value of the membership degrees of the third feature parameter under all adjacent nodes.

[0032] Obtain the sum of the membership degree variances of all third feature parameters under the same second feature parameter, and use the ratio of the membership degree variance of the third feature parameter to the sum of the membership degree variances as the first weight of the third feature parameter, and generate the first weight coefficient of the second feature parameter according to the weight of the third feature parameter: ; where is the first weight coefficient of the data transmission delay, is the first weight coefficient for reliability, , , , , are respectively the first weights for delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate.

[0033] Exemplarily, the first weight for delay is specifically: .

[0034] Wherein, is the first weight for delay, is the membership variance of delay, is the membership variance of delay jitter.

[0035] In addition, in some embodiments, the second characteristic matrix of data transmission delay is calculated according to the following formula: ; The second characteristic matrix of reliability is calculated according to the following formula: ; The second characteristic matrix of signal strength is calculated according to the following formula: ; The second characteristic matrix of communication energy consumption is calculated according to the following formula: ; Wherein, , , , are respectively the second characteristic matrices of data transmission delay, reliability, signal strength, and communication energy consumption with the i-th node as the starting transmission point, , , are respectively the overall characteristic values of data transmission delay from the i-th node to the j-th node, k-th node, and n-th node, , , are respectively the overall characteristic values of reliability from the i-th node to the j-th node, k-th node, and n-th node.

[0036] Step S104: Calculate the weight matrix of characteristic parameters according to the second characteristic matrix, and construct a communication path selection matrix based on the weight matrix and the second characteristic matrix, so as to select the optimal communication path according to the communication path selection matrix.

[0037] It should be noted that, similarly, the membership variance of the first characteristic parameter or the second characteristic parameter is obtained according to the following formula: ; wherein, 、 、 、 are the membership variances of signal strength, communication energy consumption, data transmission delay, and reliability respectively, 、 are the average values of the memberships of signal strength and communication energy consumption under all adjacent nodes respectively, 、 are the average values of the overall characteristic values of data transmission delay and reliability under all adjacent nodes respectively; The second weight of the corresponding characteristic parameter is obtained according to the membership variances of signal strength, communication energy consumption, data transmission delay, and reliability. The calculation method of this second weight is exactly the same as that of the first weight, and the weight matrix is obtained according to the second weight .

[0038] In summary, since fixed weights cannot reflect the current network conditions when facing real-time changes (such as node congestion, signal fluctuations, sudden increase in energy consumption), it is easy to cause the selected path to fail or the efficiency to decrease in the actual environment. Based on this, by introducing a dynamic weight strategy, characteristic parameters such as delay variance, packet loss rate, and bit error rate are continuously obtained in real time. Once the network state fluctuates, the weights of these indicators in the comprehensive evaluation can be increased or decreased in a timely manner, that is, the weight matrix is updated, avoiding misjudgment of the overall path due to a bottleneck in a single node and improving the system's adaptability to environmental changes.

[0039] In addition, in some embodiments, the characteristic parameter further includes device priority. When the device priority is the highest, only data transmission delay and reliability are considered for communication path evaluation, and the comprehensive evaluation value of data transmission delay and reliability is the largest. For example: for a user with integrated photovoltaics, energy storage, and charging, for the device acquisition priority, when the numerical value of the user's photovoltaic data acquisition priority is the highest, to ensure the success rate of photovoltaic device data acquisition, the requirements for data communication delay and data transmission reliability are the highest for this device. Specifically, it is defined that:

[0040] wherein, a refers to the first threshold, and b refers to the second threshold. For example, the first threshold is 1 and the second threshold is 0.

[0041] The evaluation parameters for the overall path are signal strength, energy consumption, data transmission delay, reliability, and device priority. The comprehensive evaluation value from the i-th node to any adjacent node is used as the overall link evaluation value. Specifically, when the device priority is the first threshold, the link evaluation matrix is calculated according to the following formula: ; When the device priority is the second threshold, the link evaluation matrix is calculated according to the following formula: ; Among them, , , , are the second weights of signal strength, communication energy consumption, data transmission delay, and reliability respectively, is the weight matrix, is the link evaluation matrix with the i-th node as the starting transmitting node, , , are the wireless transmission link evaluation values from the i-th node to the j-th node, k-th node, and n-th node respectively. Here, the n-th node corresponds to the distributed power access unit.

[0042] In addition, in a multi-hop network, the performance balance of each segment of the end-to-end path is the key to ensuring stable transmission. Based on this, the product model can be used to fully reflect the importance of each segment of the link. If a single segment deteriorates severely, it will significantly reduce the evaluation score of the entire path. Specifically, the communication path selection matrix is constructed according to the link evaluation matrix: ; Among them, is the communication path selection matrix with the i-th node as the starting transmitting node. Each row of the communication path selection matrix represents the comprehensive evaluation value under the corresponding path, is the wireless transmission link evaluation value from the j-th node to the n-th node, is the wireless transmission link evaluation value from the j-th node to the l-th node, is the wireless transmission link evaluation value from the k-th node to the n-th node; the product of the evaluation values from the i-th node to the adjacent relay node and then from the relay node to the final distributed power access unit is used as the measurement standard for each communication path, that is, the maximum comprehensive evaluation value is obtained from all the comprehensive evaluation values, and the communication path corresponding to the maximum comprehensive evaluation value is used as the optimal path for communication. Furthermore, an alternative path can be automatically searched for after a node fails to achieve fault redundancy.

[0043] In summary, in the traditional technology, due to the multi-hop accumulation model being vulnerable to the "dilution" effect of local degradation indicators, a link segment with a large gap is not given sufficient attention, and the end-to-end transmission quality cannot be effectively reflected. Based on this, the product model adopted in the present invention can ensure that each link segment is given sufficient attention. Once the performance of a certain segment deteriorates significantly, its corresponding product value will significantly reduce the overall path score, thereby promptly exposing the weak link. In the end-to-end path evaluation, the comprehensive evaluation values of each link segment are multiplied (instead of accumulated). If any node or link segment fails or has too low performance, the product result will rapidly decay to reflect this problem. Thus, it can more accurately reflect the quality differences of each link segment, avoid the evaluation distortion caused by "averaging", provide better disaster tolerance and fault redundancy capabilities for the multi-hop Mesh network, and improve the stability of overall data transmission.

[0044] In addition, in some embodiments, when a new device is connected or the position of an existing device changes, only the LoRa wireless relay unit needs to be added or moved to quickly integrate into the existing Mesh network; the background monitoring system can visually display the status (RSSI, energy consumption, packet loss rate) of each node, facilitating the operation and maintenance personnel to promptly detect abnormalities.

[0045] According to the above wireless communication method applied to a distributed scenario, it has the following beneficial effects: 1. Traditional communication path optimization algorithms usually only focus on a single objective (such as the fastest or the shortest). The present invention conducts multi-objective comprehensive evaluation (signal strength RSSI, delay T, jitter ΔT, packet loss rate Floss, etc.), performs normalization processing using the membership function, and combines dynamic weights (adjusted in real time based on parameters such as node congestion, energy consumption, or bit error rate fluctuations) to achieve more accurate path evaluation and selection.

[0046] 2. Compared with the defect that the accumulation model is vulnerable to local degradation and causes inaccurate overall evaluation, the present invention prefers to use the product model in the multi-hop relay network to ensure the balance of each link segment performance and end-to-end quality.

[0047] 3. The present invention supports the weight calculation between neighbor nodes and dynamic route selection. If a certain node has signal attenuation or too high energy consumption, it can be promptly switched to a better neighbor node to achieve adaptive scheduling and fault redundancy.

[0048] As Figure 3 shown, an embodiment of the present invention also proposes a wireless communication system applied to a distributed scenario, and the system includes: The feature parameter selection module 10 is configured to obtain the feature parameters in the LoRa network every first preset time. The feature parameters include a first feature parameter and a second feature parameter. Each second feature parameter includes a plurality of third feature parameters, and the membership degrees of the first feature parameter and the third feature parameters are calculated one by one based on the membership function. The membership degree calculation module 20 is configured to construct a first feature matrix corresponding to the first feature parameter and the second feature parameter respectively according to the calculated membership degrees. The number of rows of the first feature matrix corresponding to the second feature parameter is equal to the number of third feature parameters included in the second feature parameter. The feature matrix construction module 30 is configured to calculate the first weight coefficient of the first feature parameter or the second feature parameter according to the first feature matrix. The k-th row in the first feature matrix corresponding to the second feature parameter corresponds to the weight coefficient of the k-th third feature parameter included in the second feature parameter, and calculate the second feature matrix of the first feature parameter or the second feature parameter according to the first weight coefficient and the first feature matrix. The optimal path selection module 40 is configured to calculate the weight matrix of the feature parameters according to the second feature matrix, and construct a communication path selection matrix according to the weight matrix and the second feature matrix, so as to select the optimal communication path according to the communication path selection matrix.

[0049] On the other hand, the present invention also proposes a storage medium, on which one or more programs are stored, and when the program is executed by a processor, the above-mentioned wireless communication method applied to a distributed scenario is implemented.

[0050] On the other hand, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the above-mentioned wireless communication method applied to a distributed scenario.

[0051] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0052] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0053] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0054] Although the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention. Moreover, the present invention described herein can have other embodiments and can be implemented or realized in various ways.

Claims

1. A wireless communication method applied to a distributed scenario, characterized in that The method includes: Obtaining feature parameters in the LoRa network every first preset time, where the feature parameters include a first feature parameter and a second feature parameter, each of the second feature parameters includes a plurality of third feature parameters, and calculating the membership degrees of the first feature parameter and the third feature parameters one by one based on a membership function; Constructing a first feature matrix corresponding to the first feature parameter and the second feature parameter respectively according to the calculated membership degrees, and the number of rows of the first feature matrix corresponding to the second feature parameter is equal to the number of third feature parameters included in the second feature parameter; Calculating a first weight coefficient of the first feature parameter or the second feature parameter according to the first feature matrix, where the k-th row in the first feature matrix corresponding to the second feature parameter corresponds to the weight coefficient of the k-th third feature parameter included in the second feature parameter, and calculating a second feature matrix of the first feature parameter or the second feature parameter according to the first weight coefficient and the first feature matrix; Calculating a weight matrix of the feature parameters according to the second feature matrix, and constructing a communication path selection matrix according to the weight matrix and the second feature matrix to select an optimal communication path according to the communication path selection matrix.

2. The wireless communication method applied to a distributed scenario according to claim 1, wherein The method further includes: Constructing a Mesh network, where the Mesh network includes a plurality of nodes, one of the nodes corresponds to a distributed power access unit, and the other nodes correspond to one of a photovoltaic inverter, an energy storage device, and a charging pile. LoRa wireless relay units are installed on the other nodes, a LoRa antenna is deployed on the distributed power access unit, and the LoRa wireless relay unit is communicatively connected to the LoRa antenna.

3. The wireless communication method applied to a distributed scenario according to claim 2, characterized in that, The first feature parameters include signal strength and communication energy consumption, the second feature parameters include data transmission delay and reliability, the data transmission delay includes time delay and delay jitter, and the reliability includes packet loss rate, number of retransmissions, and bit error rate; The step of calculating the membership degrees of the first feature parameter and the third feature parameters one by one based on the membership function includes: Calculating the membership degree of any one of the feature parameters such as communication energy consumption, time delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate according to the following formula: ； Among them, is the membership degree of the corresponding characteristic parameter under the adjacent i-th node and j-th node, , are respectively the maximum value and the minimum value of the corresponding characteristic parameter under the adjacent nodes, is the value of the corresponding characteristic parameter under the adjacent i-th node and j-th node; Calculating the membership degree of signal strength according to the following formula: ； Among them, is the membership degree of the signal strength under the adjacent i-th node and j-th node, is the value of the signal strength under the adjacent i-th node and j-th node, and are the maximum value and minimum value of the signal strength under the adjacent nodes, respectively.

4. The wireless communication method applied to a distributed scenario according to claim 3, wherein The step of constructing a first feature matrix corresponding to the first feature parameter and the second feature parameter respectively according to the calculated membership degrees includes: The expression of the first feature matrix corresponding to signal strength is: ； The expression of the first feature matrix corresponding to communication energy consumption is: ； The expression of the first feature matrix corresponding to data transmission delay is: ； The expression of the first feature matrix corresponding to reliability is: ； wherein, and are the membership degrees of the signal strengths under the adjacent i-th node and k-th node, and n-th node respectively, and and are the membership degrees of the communication energy consumptions under the adjacent i-th node and j-th node, k-th node, and n-th node respectively, and and are the membership degrees of the time delays under the adjacent i-th node and j-th node, k-th node, and n-th node respectively, and and are the membership degrees of the delay jitters under the adjacent i-th node and j-th node, k-th node, and n-th node respectively, and and are the membership degrees of the packet loss rates under the adjacent i-th node and j-th node, k-th node, and n-th node respectively, and and are the membership degrees of the retransmission times under the adjacent i-th node and j-th node, k-th node, and n-th node respectively, and and are the membership degrees of the bit error rates under the adjacent i-th node and j-th node, k-th node, and n-th node respectively, and and and are the first feature matrices corresponding to the signal strength, communication energy consumption, data transmission delay, and reliability with the i-th node as the starting transmission node, and n is the total number of nodes.

5. The wireless communication method applied to a distributed scenario according to claim 4, characterized in that, The step of calculating a first weight coefficient of the first feature parameter or the second feature parameter according to the first feature matrix includes: The first weight coefficient of the first feature parameter is 1; Calculating the membership degree variance of each third feature parameter according to the first feature matrix: ； Among them, is the membership variance of the third characteristic parameter, is the membership of the third characteristic parameter under the adjacent ith node and jth node, is the average value of the memberships of the third characteristic parameter under all adjacent nodes; Obtain the sum of the membership variances of all third characteristic parameters under the same second characteristic parameter, and use the ratio of the membership variance of the third characteristic parameter to the sum of the membership variances as the first weight of the third characteristic parameter. Generate the first weight coefficient of the second characteristic parameter according to the weight of the third characteristic parameter: ； Among them, is the first weight coefficient for data transmission delay, is the first weight coefficient for reliability, , , , , are the first weights for latency, latency jitter, packet loss rate, number of retransmissions, and bit error rate, respectively.

6. The wireless communication method applied to a distributed scenario according to claim 5, wherein The step of calculating the second characteristic matrix of the first characteristic parameter or the second characteristic parameter according to the first weight coefficient and the first characteristic matrix includes: Calculate the second characteristic matrix of the data transmission delay according to the following formula: ； Calculate the second characteristic matrix of the reliability according to the following formula: ； Calculate the second characteristic matrix of the signal strength according to the following formula: ； Calculate the second characteristic matrix of the communication energy consumption according to the following formula: ； Among them, , , , are the second characteristic matrices of data transmission delay, reliability, signal strength, and communication energy consumption with the i-th node as the starting emission point, respectively. , , are the overall characteristic values of the data transmission delay from the i-th node to the j-th node, k-th node, and n-th node, respectively. , , are the overall characteristic values of the reliability from the i-th node to the j-th node, k-th node, and n-th node, respectively.

7. The wireless communication method applied to a distributed scenario according to claim 6, wherein The step of calculating the weight matrix of the characteristic parameter according to the second characteristic matrix includes: Obtain the membership variance of the first characteristic parameter or the second characteristic parameter according to the following formula: ； Among them, , , , are the membership variances of signal strength, communication energy consumption, data transmission delay, and reliability respectively, , are the average values of the membership degrees of signal strength and communication energy consumption under all adjacent nodes respectively, , are the average values of the overall characteristic values of data transmission delay and reliability under all adjacent nodes respectively; Obtain the second weight of the corresponding characteristic parameter according to the membership variances of the signal strength, communication energy consumption, data transmission delay, and reliability, and obtain the weight matrix according to the second weight.

8. The wireless communication method applied to a distributed scenario according to claim 7, wherein The step of constructing the communication path selection matrix according to the weight matrix and the second characteristic matrix to select the optimal communication path according to the communication path selection matrix includes: When the device priority is the first threshold, calculate the link evaluation matrix according to the following formula: ； When the device priority is the second threshold, calculate the link evaluation matrix according to the following formula: ； Among them, , , , are the second weights of signal strength, communication energy consumption, data transmission delay, and reliability respectively, is the weight matrix, is the link evaluation matrix with the i-th node as the starting transmitting node, , , are the wireless transmission link evaluation values from the i-th node to the j-th node, the k-th node, and the n-th node respectively.

9. The wireless communication method applied to a distributed scenario according to claim 8, wherein The step of constructing the communication path selection matrix according to the weight matrix and the second characteristic matrix to select the optimal communication path according to the communication path selection matrix further includes: Construct the communication path selection matrix according to the link evaluation matrix: ； Among them, is the communication path selection matrix with the i-th node as the starting transmitting node. Each row of the communication path selection matrix represents the comprehensive evaluation value under the corresponding path. is the wireless transmission link evaluation value from the j-th node to the n-th node. is the wireless transmission link evaluation value from the j-th node to the l-th node, is the wireless transmission link evaluation value from the k-th node to the n-th node; Obtain the maximum comprehensive evaluation value from all the comprehensive evaluation values, and use the communication path corresponding to the maximum comprehensive evaluation value as the optimal path for communication.

10. A wireless communication system applied to a distributed scenario, characterized in that, The system includes: A characteristic parameter selection module, configured to obtain the characteristic parameters in the LoRa network every first preset time. The characteristic parameters include the first characteristic parameter and the second characteristic parameter. Each second characteristic parameter includes a plurality of third characteristic parameters, and calculate the membership degrees of the first characteristic parameter and the third characteristic parameters one by one based on the membership function; A membership degree calculation module, configured to construct a first characteristic matrix corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership degrees. The number of rows of the first characteristic matrix corresponding to the second characteristic parameter is equal to the number of third characteristic parameters included in the second characteristic parameter; A characteristic matrix construction module, configured to calculate the first weight coefficient of the first characteristic parameter or the second characteristic parameter according to the first characteristic matrix. The k-th row in the first characteristic matrix corresponding to the second characteristic parameter corresponds to the weight coefficient of the k-th third characteristic parameter included in the second characteristic parameter, and calculate the second characteristic matrix of the first characteristic parameter or the second characteristic parameter according to the first weight coefficient and the first characteristic matrix; The optimal path selection module is used to calculate the weight matrix of the feature parameters according to the second feature matrix, and construct a communication path selection matrix based on the weight matrix and the second feature matrix, so as to select the optimal communication path according to the communication path selection matrix.

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