Wireless communication method and system for distributed scenarios

Abstract

The present invention provides a wireless communication method and system for use in distributed scenarios. The method includes: obtaining characteristic parameters in a LoRa network, calculating the membership of a first characteristic parameter and a third characteristic parameter one by one based on a membership function; constructing a first characteristic matrix; calculating a first weight coefficient of the first characteristic parameter or the second characteristic parameter based on the first characteristic matrix, and calculating a second characteristic matrix of the first characteristic parameter or the second characteristic parameter based on the first weight coefficient and the first characteristic matrix; calculating a weight matrix of the characteristic parameters based on the second characteristic matrix, and constructing a communication path selection matrix based on the weight matrix and the second characteristic matrix to select an optimal communication path based on the communication path selection matrix. The present invention can realize relay communication between devices and improve network coverage and stability.

Description

Technical Field

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

[0002] In distributed photovoltaic scenarios, traditional equipment typically connects distributed power access units (DGUs) to PV inverters via 485 communication lines. However, due to the complex installation environment, cabling difficulties, and potentially long communication lines, signal attenuation and communication anomalies are common. Furthermore, as the number of PV inverters, energy storage devices, and charging stations within user facilities increases, traditional wired communication methods lack flexibility and scalability, failing to meet the demands of complex distributed energy scenarios.

[0003] LoRa wireless communication technology, with its long-range transmission, low power consumption, and anti-interference capabilities, is an ideal choice for solving communication problems in distributed energy scenarios. However, LoRa's single-point communication mode may suffer from signal attenuation, especially when devices are widely distributed or there are obstructions in the environment. Summary of the Invention

[0004] The purpose of the present invention is to provide a wireless communication method and system for use in distributed scenarios, 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 in a distributed scenario, the method comprising:

[0006] Acquire characteristic parameters in the LoRa network at first preset time intervals, 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 calculate the membership of the first characteristic parameter and the third characteristic parameter one by one based on a membership function;

[0007] Constructing first characteristic matrices corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership degree, wherein the number of rows of the first characteristic matrix corresponding to the second characteristic parameter is equal to the number of third characteristic parameters contained in the second characteristic parameter;

[0008] A first weight coefficient of the first characteristic parameter or the second characteristic parameter is calculated based on the first characteristic matrix, the kth row in the first characteristic matrix corresponding to the second characteristic parameter corresponds to the weight coefficient of the kth third characteristic parameter included in the second characteristic parameter, and a second characteristic matrix of the first characteristic parameter or the second characteristic parameter is calculated based on the first weight coefficient and the first characteristic matrix;

[0009] A weight matrix of characteristic parameters is calculated according to the second characteristic matrix, and a communication path selection matrix is ​​constructed according to the weight matrix and the second characteristic matrix, so as to select an optimal communication path according to the communication path selection matrix.

[0010] Furthermore, the method further comprises:

[0011] A Mesh network is constructed, wherein the Mesh network includes multiple nodes, one of which corresponds to a distributed power access unit, and the other nodes correspond to one of photovoltaic inverters, energy storage devices, and charging piles. LoRa wireless switching units are installed on the other nodes, and a LoRa antenna is deployed on the distributed power access unit. The LoRa wireless switching unit is communicatively connected to the LoRa antenna.

[0012] Furthermore, 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 time delay and delay jitter, and the reliability includes packet loss rate, number of retransmissions and bit error rate;

[0013] The step of calculating the membership of the first characteristic parameter and the third characteristic parameter one by one based on the membership function includes:

[0014] The membership degree of any characteristic parameter among communication energy consumption, delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate is calculated according to the following formula:

[0015] ;

[0016] in, is the membership degree of the corresponding characteristic parameters under the adjacent i-th node and j-th node, 、 are the maximum and minimum values ​​of the corresponding characteristic parameters under adjacent nodes, is the value of the corresponding characteristic parameter under the adjacent i-th node and j-th node;

[0017] The membership degree of the signal strength is calculated according to the following formula:

[0018] ;

[0019] in, is the membership degree of the signal strength between the adjacent i-th node and the j-th node, is the value of the signal strength between the adjacent i-th node and j-th node, 、 are the maximum and minimum signal strengths of adjacent nodes respectively.

[0020] Furthermore, the step of constructing a first characteristic matrix corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership degree includes:

[0021] The expression of the first characteristic matrix corresponding to the signal strength is:

[0022] ;

[0023] The expression of the first characteristic matrix corresponding to communication energy consumption is:

[0024] ;

[0025] The expression of the first characteristic matrix corresponding to data transmission delay is:

[0026] ;

[0027] The expression of the first characteristic matrix corresponding to reliability is:

[0028] ;

[0029] in, 、 are the membership degrees of the signal strengths of the adjacent i-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the communication energy consumption between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delays between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delay jitter between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the packet loss rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 are the membership of the number of retransmissions under the adjacent i-th node and j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the bit error rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 、 are the first characteristic matrices corresponding to signal strength, communication energy consumption, data transmission delay, and reliability with the i-th node as the starting transmitting node, and n is the total number of nodes.

[0030] Furthermore, the step of calculating a first weight coefficient of the first characteristic parameter or the second characteristic parameter according to the first characteristic matrix includes:

[0031] The first weight coefficient of the first characteristic parameter is 1;

[0032] Calculate the membership variance of each third feature parameter based on the first feature matrix:

[0033] ;

[0034] in, is the membership variance of the third characteristic parameter, is the membership degree of the third characteristic parameter under the adjacent i-th node and j-th node, is the average membership value of the third characteristic parameter under all adjacent nodes;

[0035] Obtain the sum of the membership variances of all third feature parameters under the same second feature parameter, and use the ratio of the membership variance of the third feature parameter to the sum of the membership variances as the first weight of the third feature parameter. Generate the first weight coefficient of the second feature parameter based on the weight of the third feature parameter:

[0036] ;

[0037] in, is the first weight coefficient of data transmission delay, is the first weight coefficient of reliability, 、 、 、 、 They are the first weights of delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate respectively.

[0038] Furthermore, the step of 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 includes:

[0039] The second characteristic matrix of data transmission delay is calculated according to the following formula:

[0040] ;

[0041] The second characteristic matrix of reliability is calculated according to the following formula:

[0042] ;

[0043] The second characteristic matrix of signal strength is calculated according to the following formula:

[0044] ;

[0045] The second characteristic matrix of communication energy consumption is calculated according to the following formula:

[0046] ;

[0047] in, 、 、 、 are 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 the overall characteristic values ​​of data transmission delay from the i-th node to the j-th node, the k-th node, and the n-th node, respectively. 、 、 are the overall eigenvalues ​​of reliability from the i-th node to the j-th node, the k-th node, and the n-th node respectively.

[0048] Furthermore, the step of calculating a weight matrix of characteristic parameters according to the second characteristic matrix includes:

[0049] The membership variance of the first characteristic parameter or the second characteristic parameter is obtained according to the following formula:

[0050] ;

[0051] in, 、 、 、 They are the membership variance of signal strength, communication energy consumption, data transmission delay, and reliability, 、 are the average membership of signal strength and communication energy consumption of all adjacent nodes, 、 are the average values ​​of the overall characteristic values ​​of data transmission delay and reliability under all adjacent nodes respectively;

[0052] According to the membership variance of signal strength, communication energy consumption, data transmission delay and reliability, a second weight of the corresponding characteristic parameter is obtained, and a weight matrix is ​​obtained according to the second weight.

[0053] Furthermore, 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 includes:

[0054] When the device priority is the first threshold, the link evaluation matrix is ​​calculated according to the following formula:

[0055] ;

[0056] When the device priority is the second threshold, the link evaluation matrix is ​​calculated according to the following formula:

[0057] ;

[0058] in, 、 、 、 They are signal strength, communication energy consumption, data transmission delay, and the second weight of reliability. is the weight matrix, is the link evaluation matrix with the i-th node as the starting transmitting node, 、 、 are the evaluation values ​​of the wireless transmission links from the i-th node to the j-th node, the k-th node, and the n-th node respectively.

[0059] Furthermore, 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:

[0060] Construct the communication path selection matrix based on the link evaluation matrix:

[0061] ;

[0062] in, is the communication path selection matrix with the i-th node as the starting transmitting node, and each row of the communication path selection matrix represents the comprehensive evaluation value under the corresponding path, is the evaluation value of the wireless transmission link from the jth node to the nth node, is the evaluation value of the wireless transmission link from the jth node to the lth node, is the evaluation value of the wireless transmission link from the kth node to the nth node;

[0063] A maximum comprehensive evaluation value is obtained from all comprehensive evaluation values, and communication is performed using a communication path corresponding to the maximum comprehensive evaluation value as an optimal path.

[0064] In a second aspect, the present invention provides a wireless communication system for use in a distributed scenario, the system comprising:

[0065] A characteristic parameter selection module is used to obtain characteristic parameters in the LoRa network at a first preset time interval, wherein the characteristic parameters include a first characteristic parameter and a second characteristic parameter, each of the second characteristic parameters includes multiple third characteristic parameters, and calculate the membership of the first characteristic parameter and the third characteristic parameter one by one based on a membership function;

[0066] A membership calculation module is used to construct a first characteristic matrix corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership, wherein 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;

[0067] a characteristic matrix construction module, configured to calculate a first weight coefficient of the first characteristic parameter or the second characteristic parameter based on the first characteristic matrix, wherein the kth row in the first characteristic matrix corresponding to the second characteristic parameter corresponds to the weight coefficient of the kth third characteristic parameter contained in the second characteristic parameter, and calculate a second characteristic matrix of the first characteristic parameter or the second characteristic parameter based on the first weight coefficient and the first characteristic matrix;

[0068] The optimal path selection module is used to calculate a weight matrix of 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 an optimal communication path according to the communication path selection matrix.

[0069] 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 in a distributed scenario.

[0070] In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein:

[0071] The memory is used to store computer programs;

[0072] When the processor is used to execute the computer program stored in the memory, the above-mentioned wireless communication method applied in a distributed scenario is implemented.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] 1. Traditional communication path optimization algorithms typically focus on a single objective (such as fastest or shortest). This invention achieves more accurate path evaluation and selection through a comprehensive evaluation of multiple objectives (such as signal strength RSSI, latency T, jitter ΔT, packet loss rate Floss, etc.), normalization using a membership function, and dynamic weighting (real-time adjustment based on parameters such as node congestion, energy consumption, or bit error rate fluctuations).

[0075] 2. Compared with the cumulative model, which is susceptible to local degradation and leads to inaccurate overall evaluation, the present invention prefers to use the product model in multi-hop relay networks to ensure the balance of performance of each link and end-to-end quality.

[0076] 3. The present invention supports weight calculation and dynamic routing selection between neighboring nodes. If a node experiences signal attenuation or excessive energy consumption, it can be switched to a better neighboring node in a timely manner to achieve adaptive scheduling and fault redundancy. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a flow chart of a wireless communication method applied in a distributed scenario proposed in one embodiment of the present invention;

[0078] Figure 2 A schematic diagram of the structure of a Mesh network according to an embodiment of the present invention;

[0079] Figure 3 This is a structural diagram of a wireless communication system applied in a distributed scenario proposed in one embodiment of the present invention.

[0080] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0081] In order to make the purpose, 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. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0082] like Figure 1 As shown, an embodiment of the present invention provides a wireless communication method applied in a distributed scenario, the method comprising steps S101 to S104, wherein:

[0083] Step S101: acquiring characteristic parameters in the LoRa network at first preset intervals, 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 of the first characteristic parameter and the third characteristic parameter one by one based on a membership function;

[0084] It should be pointed out that in scenarios consisting of multiple devices such as distributed photovoltaics, energy storage, and charging piles, the traditional wired 485 communication method has complex wiring and poor adaptability to complex environments, and is easily affected by cable deployment, length limitations, and interference factors. In order to overcome wiring limitations and improve deployment flexibility, it is necessary to use LoRa wireless adapter units to replace or parallelize traditional 485 communication interfaces on photovoltaic inverters, energy storage devices, charging piles, and other devices; and deploy corresponding LoRa antennas and adapters at distributed power access units, monitoring centers, or routing aggregation nodes to form complete LoRa network coverage. Specifically, if Figure 2 As shown, a Mesh network is constructed, which includes multiple nodes, one of which corresponds to a distributed power access unit, and the other nodes correspond to one of photovoltaic inverters, energy storage devices, and charging piles. LoRa wireless switching units are installed on the other nodes, and a LoRa antenna is deployed on the distributed power access unit. The LoRa wireless switching unit is communicatively connected to the LoRa antenna. In addition, the LoRa wireless switching unit is a device based on LoRa (LongRange) wireless communication technology, which is used to realize wireless data transmission and conversion between different communication interfaces or protocols. For distributed scenarios, by constructing a Mesh network, the limitations of traditional wired topology are broken away, the communication coverage is significantly improved, the cable cost is reduced, and a flexible wireless node access environment is provided for subsequent multi-target routing algorithms.

[0085] In addition, in this step, the LoRa wireless adapter is first powered on and initialized, and then characteristic parameters are selected. These characteristic parameters are obtained by each node periodically 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 time delay and time delay jitter, and the reliability includes packet loss rate, number of retransmissions, and bit error rate. The membership of any one of the characteristic parameters of communication energy consumption, time delay, time delay jitter, packet loss rate, number of retransmissions, and bit error rate is then calculated according to the following formula:

[0086] ;

[0087] in, is the membership degree of the corresponding characteristic parameters under the adjacent i-th node and j-th node, 、 are the maximum and minimum values ​​of the corresponding characteristic parameters under adjacent nodes, is the value of the corresponding characteristic parameter under the adjacent i-th node and j-th node;

[0088] For example, the membership calculation formula of communication energy consumption (transmission energy consumption) is:

[0089] ;

[0090] in, is the membership degree of the communication energy consumption between the adjacent i-th node and the j-th node, 、 are the maximum and minimum communication energy consumption of adjacent nodes respectively. is the value of the communication energy consumption between the adjacent i-th node and the j-th node.

[0091] The membership degree of the signal strength is calculated according to the following formula:

[0092] ;

[0093] in, is the membership degree of the signal strength between the adjacent i-th node and the j-th node, is the value of the signal strength between the adjacent i-th node and j-th node, 、 are the maximum and minimum signal strengths of adjacent nodes respectively.

[0094] Furthermore, the first preset time is set to continuously monitor the status of the LoRa network in real time, so that when certain nodes exist 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.

[0095] In summary, by calculating the membership degree of features such as signal strength and communication energy consumption, a value between 0 and 1 is obtained for subsequent routing evaluation. Furthermore, traditional point-to-point or tree-like network structures are prone to central node overload or single-point bottlenecks as the number of nodes increases. Furthermore, when a single path fails, it is impossible to quickly switch to an alternative path, resulting in insufficient disaster recovery capabilities. Dynamically weighting adjacent nodes based on information such as RSSI and energy consumption improves the overall network's self-healing capabilities and load balancing, enabling each node to function as a forwarding node. If a node or communication path experiences an anomaly, it can quickly switch to another feasible path based on the weights assigned by neighboring nodes, significantly improving network robustness and scalability.

[0096] Step S102: constructing first characteristic matrices corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership degree, wherein 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;

[0097] In some embodiments, the expression of the first characteristic matrix corresponding to the signal strength is:

[0098] ;

[0099] The expression of the first characteristic matrix corresponding to communication energy consumption is:

[0100] ;

[0101] The expression of the first characteristic matrix corresponding to data transmission delay is:

[0102] ;

[0103] The expression of the first characteristic matrix corresponding to reliability is:

[0104] ;

[0105] in, 、 are the membership degrees of the signal strengths of the adjacent i-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the communication energy consumption between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delays between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delay jitter between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the packet loss rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 are the membership of the number of retransmissions under the adjacent i-th node and j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the bit error rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 、 are the first characteristic matrices corresponding to signal strength, communication energy consumption, data transmission delay, and reliability with the i-th node as the starting transmitting node, and n is the total number of nodes.

[0106] It should be pointed out that this step constructs a first characteristic matrix about the first characteristic parameter and the second characteristic parameter in order to comprehensively consider characteristic parameters of multiple dimensions such as signal strength, communication energy consumption, data transmission delay, and reliability, which is conducive to the subsequent screening of a more reliable and representative optimal communication path.

[0107] Step S103: Calculating a first weight coefficient of the first characteristic parameter or the second characteristic parameter based on the first characteristic matrix, wherein 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 calculating a second characteristic matrix of the first characteristic parameter or the second characteristic parameter based on the first weight coefficient and the first characteristic matrix;

[0108] It should be noted that, in this step, since the first characteristic parameter only includes one parameter, the first weight coefficient of the first characteristic parameter is 1.

[0109] In addition, the membership variance of each third eigenvalue is calculated based on the first eigenvalue matrix:

[0110] ;

[0111] in, is the membership variance of the third characteristic parameter, is the membership degree of the third characteristic parameter under the adjacent i-th node and j-th node, is the average membership degree of the third characteristic parameter of all adjacent nodes.

[0112] Obtain the sum of the membership variances of all third feature parameters under the same second feature parameter, and use the ratio of the membership variance of the third feature parameter to the sum of the membership variances as the first weight of the third feature parameter. Generate the first weight coefficient of the second feature parameter based on the weight of the third feature parameter:

[0113] ;

[0114] in, is the first weight coefficient of data transmission delay, is the first weight coefficient of reliability, 、 、 、 、 They are the first weights of delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate respectively.

[0115] Exemplarily, the first weight of the delay is specifically:

[0116] .

[0117] in, is the first weight of the delay, is the membership variance of the delay, is the membership variance of delay jitter.

[0118] In addition, in some embodiments, the second characteristic matrix of data transmission delay is calculated according to the following formula:

[0119] ;

[0120] The second characteristic matrix of reliability is calculated according to the following formula:

[0121] ;

[0122] The second characteristic matrix of signal strength is calculated according to the following formula:

[0123] ;

[0124] The second characteristic matrix of communication energy consumption is calculated according to the following formula:

[0125] ;

[0126] in, 、 、 、 are 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 the overall characteristic values ​​of data transmission delay from the i-th node to the j-th node, the k-th node, and the n-th node, respectively. 、 、 are the overall eigenvalues ​​of reliability from the i-th node to the j-th node, the k-th node, and the n-th node respectively.

[0127] Step S104: obtaining a weight matrix of characteristic parameters according to the second characteristic matrix, and constructing a communication path selection matrix according to the weight matrix and the second characteristic matrix, so as to select an optimal communication path according to the communication path selection matrix.

[0128] 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:

[0129] ;

[0130] in, 、 、 、 They are the membership variance of signal strength, communication energy consumption, data transmission delay, and reliability, 、 are the average membership of signal strength and communication energy consumption of all adjacent nodes, 、 are the average values ​​of the overall characteristic values ​​of data transmission delay and reliability under all adjacent nodes respectively;

[0131] The second weight of the corresponding characteristic parameter is obtained according to the membership variance of signal strength, communication energy consumption, data transmission delay and reliability. The calculation method of the second weight is exactly the same as that of the first weight, and the weight matrix is ​​obtained according to the second weight .

[0132] In summary, fixed weights cannot reflect the current network status when facing real-time changes (such as node congestion, signal fluctuations, and sudden increases in energy consumption), which can easily cause the selected path to fail or become inefficient in actual environments. Based on this, by introducing a dynamic weight strategy, characteristic parameters such as delay variance, packet loss rate, and bit error rate are continuously acquired in real time. Once the network status fluctuates, the weights of these indicators in the comprehensive evaluation can be promptly increased or decreased, that is, the weight matrix is ​​updated to avoid overall path judgment errors caused by bottlenecks at a single node, thereby improving the system's adaptability to environmental changes.

[0133] In addition, in some embodiments, the characteristic parameters also include 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, if the user is a photovoltaic storage and charging integrated user, for device collection priority, when the value of the user's photovoltaic data collection priority is the highest, in order to ensure the success rate of photovoltaic device data collection, the data communication delay and data transmission reliability are required to be the highest for the device collection success rate. Specifically, the definition is:

[0134]

[0135] Here, 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.

[0136] 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 evaluation value of the link. Specifically, when the device priority is the first threshold, the link evaluation matrix is ​​calculated according to the following formula:

[0137] ;

[0138] When the device priority is the second threshold, the link evaluation matrix is ​​calculated according to the following formula:

[0139] ;

[0140] in, 、 、 、 They are signal strength, communication energy consumption, data transmission delay, and the second weight of reliability. is the weight matrix, is the link evaluation matrix with the i-th node as the starting transmitting node, 、 、 are the evaluation values ​​of the wireless transmission links from the i-th node to the j-th node, the k-th node, and the n-th node, respectively. Here, the n-th node corresponds to the distributed power access unit.

[0141] Furthermore, in multi-hop networks, balanced performance across all segments of an end-to-end path is crucial for ensuring stable transmission. Therefore, the product model fully reflects the importance of each link segment. Severe degradation in a single segment significantly reduces the overall path evaluation score. Specifically, a communication path selection matrix is ​​constructed based on the link evaluation matrix:

[0142] ;

[0143] in, is the communication path selection matrix with the i-th node as the starting transmitting node, and each row of the communication path selection matrix represents the comprehensive evaluation value under the corresponding path, is the evaluation value of the wireless transmission link from the jth node to the nth node, is the evaluation value of the wireless transmission link from the jth node to the lth node, is the evaluation value of the wireless transmission link from the kth node to the nth 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 metric 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, so that an alternative path can be automatically found after a node fails to achieve fault redundancy.

[0144] In summary, in traditional technologies, the multi-hop accumulation model is susceptible to the "dilution" of local degradation indicators, resulting in a link segment with a large gap not being given enough attention, and unable to effectively reflect the end-to-end transmission quality. Based on this, the present invention adopts a product model to ensure that each link segment is given enough attention. Once the performance of a certain segment degrades significantly, its corresponding product value will significantly reduce the overall path score, thereby promptly exposing the weak links. In the end-to-end path evaluation, the comprehensive evaluation values ​​of each link segment are multiplied (rather than accumulated). If any node or link segment fails or has too low performance, the product result will decay rapidly to reflect the problem. This can more accurately reflect the quality differences of each link segment, avoid 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.

[0145] In addition, in some embodiments, when a new device is connected or the location of an existing device changes, it only needs to add or move a LoRa wireless adapter unit to quickly integrate into the existing Mesh network; the background monitoring system can visualize the status of each node (RSSI, energy consumption, packet loss rate), making it easier for operation and maintenance personnel to promptly detect anomalies.

[0146] The above-mentioned wireless communication method applied in a distributed scenario has the following beneficial effects:

[0147] 1. Traditional communication path optimization algorithms typically focus on a single objective (such as fastest or shortest). This invention achieves more accurate path evaluation and selection through a comprehensive evaluation of multiple objectives (such as signal strength RSSI, latency T, jitter ΔT, packet loss rate Floss, etc.), normalization using a membership function, and dynamic weighting (real-time adjustment based on parameters such as node congestion, energy consumption, or bit error rate fluctuations).

[0148] 2. Compared with the cumulative model, which is susceptible to local degradation and leads to inaccurate overall evaluation, the present invention prefers to use the product model in multi-hop relay networks to ensure the balance of performance of each link and end-to-end quality.

[0149] 3. The present invention supports weight calculation and dynamic routing selection between neighboring nodes. If a node experiences signal attenuation or excessive energy consumption, it can be switched to a better neighboring node in a timely manner to achieve adaptive scheduling and fault redundancy.

[0150] like Figure 3 As shown, an embodiment of the present invention further provides a wireless communication system applied in a distributed scenario, the system comprising:

[0151] A characteristic parameter selection module 10 is configured to obtain characteristic parameters in the LoRa network at first preset intervals, wherein the characteristic parameters include a first characteristic parameter and a second characteristic parameter, each of the second characteristic parameters includes a plurality of third characteristic parameters, and calculate the membership of the first characteristic parameter and the third characteristic parameter one by one based on a membership function;

[0152] A membership calculation module 20 is configured to construct a first characteristic matrix corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership, wherein 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;

[0153] a characteristic matrix construction module 30, configured to calculate a first weight coefficient of the first characteristic parameter or the second characteristic parameter based on the first characteristic matrix, wherein 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 a second characteristic matrix of the first characteristic parameter or the second characteristic parameter based on the first weight coefficient and the first characteristic matrix;

[0154] The optimal path selection module 40 is configured to calculate a weight matrix of 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 an optimal communication path according to the communication path selection matrix.

[0155] On the other hand, the present invention further proposes a storage medium on which one or more programs are stored. When the programs are executed by a processor, the above-mentioned wireless communication method applied in a distributed scenario is implemented.

[0156] On the other hand, the present invention further proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned wireless communication method applied in a distributed scenario.

[0157] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0158] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0159] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0160] While 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 of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A wireless communication method applied in a distributed scenario, characterized in that: The method comprises: Acquire characteristic parameters in the LoRa network at first preset time intervals, 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 calculate the membership of the first characteristic parameter and the third characteristic parameter one by one based on a membership function; Constructing a Mesh network, wherein the Mesh network includes multiple nodes, one of which corresponds to a distributed power access unit, and the other nodes correspond to one of photovoltaic inverters, energy storage devices, and charging piles. The other nodes are all equipped with LoRa wireless adapter units, and the distributed power access unit is deployed with a LoRa antenna, and the LoRa wireless adapter unit is communicatively connected to the LoRa antenna; 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 time delay and time delay jitter, and the reliability includes packet loss rate, number of retransmissions and bit error rate. The membership degree of any characteristic parameter among communication energy consumption, delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate is calculated according to the following formula: ； in, is the membership degree of the corresponding characteristic parameters under the adjacent i-th node and j-th node, 、 are the maximum and minimum values ​​of the corresponding characteristic parameters under adjacent nodes, is the value of the corresponding characteristic parameter under the adjacent i-th node and j-th node; The membership degree of the signal strength is calculated according to the following formula: ； in, is the membership degree of the signal strength between the adjacent i-th node and the j-th node, is the value of the signal strength between the adjacent i-th node and j-th node, 、 are the maximum and minimum signal strengths of adjacent nodes respectively; Constructing first characteristic matrices corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership degree, wherein the number of rows of the first characteristic matrix corresponding to the second characteristic parameter is equal to the number of third characteristic parameters contained in the second characteristic parameter; The expression of the first characteristic matrix corresponding to the 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: ； in, 、 are the membership degrees of the signal strengths of the adjacent i-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the communication energy consumption between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delays between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delay jitter between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the packet loss rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 are the membership of the number of retransmissions under the adjacent i-th node and j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the bit error rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 、 are the first characteristic matrices corresponding to signal strength, communication energy consumption, data transmission delay, and reliability with the i-th node as the starting transmitting node, and n is the total number of nodes; A first weight coefficient of the first characteristic parameter or the second characteristic parameter is calculated based on the first characteristic matrix, the kth row in the first characteristic matrix corresponding to the second characteristic parameter corresponds to the weight coefficient of the kth third characteristic parameter included in the second characteristic parameter, and a second characteristic matrix of the first characteristic parameter or the second characteristic parameter is calculated based on the first weight coefficient and the first characteristic matrix; A weight matrix of characteristic parameters is calculated according to the second characteristic matrix, and a communication path selection matrix is ​​constructed according to the weight matrix and the second characteristic matrix, so as to select an optimal communication path according to the communication path selection matrix.

2. The wireless communication method for distributed scenarios according to claim 1, characterized in that: 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 variance of each third feature parameter based on the first feature matrix: ； in, is the membership variance of the third characteristic parameter, is the membership degree of the third characteristic parameter under the adjacent i-th node and j-th node, is the average membership value of the third characteristic parameter under all adjacent nodes; Obtain the sum of the membership variances of all third feature parameters under the same second feature parameter, and use the ratio of the membership variance of the third feature parameter to the sum of the membership variances as the first weight of the third feature parameter. Generate the first weight coefficient of the second feature parameter based on the weight of the third feature parameter: ； in, is the first weight coefficient of data transmission delay, is the first weight coefficient of reliability, 、 、 、 、 They are the first weights of delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate respectively.

3. The wireless communication method for distributed scenarios according to claim 2, wherein: The step of 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 includes: 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: ； in, 、 、 、 are 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 the overall characteristic values ​​of data transmission delay from the i-th node to the j-th node, the k-th node, and the n-th node, respectively. 、 、 are the overall eigenvalues ​​of reliability from the i-th node to the j-th node, the k-th node, and the n-th node respectively.

4. The wireless communication method for distributed scenarios according to claim 3, wherein: The step of calculating a weight matrix of characteristic parameters according to the second characteristic matrix includes: The membership variance of the first characteristic parameter or the second characteristic parameter is obtained according to the following formula: ； in, 、 、 、 They are the membership variance of signal strength, communication energy consumption, data transmission delay, and reliability, 、 are the average membership of signal strength and communication energy consumption of all adjacent nodes, 、 are the average values ​​of the overall characteristic values ​​of data transmission delay and reliability under all adjacent nodes respectively; According to the membership variance of signal strength, communication energy consumption, data transmission delay and reliability, a second weight of the corresponding characteristic parameter is obtained, and a weight matrix is ​​obtained according to the second weight.

5. The wireless communication method for distributed scenarios according to claim 4, characterized in that: 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 includes: 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: ； in, 、 、 、 They are signal strength, communication energy consumption, data transmission delay, and the second weight of reliability. is the weight matrix, is the link evaluation matrix with the i-th node as the starting transmitting node, 、 、 are the evaluation values ​​of the wireless transmission links from the i-th node to the j-th node, the k-th node, and the n-th node respectively.

6. The wireless communication method for distributed scenarios according to claim 5, characterized in that: 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: Construct the communication path selection matrix based on the link evaluation matrix: ； in, is the communication path selection matrix with the i-th node as the starting transmitting node, and each row of the communication path selection matrix represents the comprehensive evaluation value under the corresponding path, is the evaluation value of the wireless transmission link from the jth node to the nth node, is the evaluation value of the wireless transmission link from the jth node to the lth node, is the evaluation value of the wireless transmission link from the kth node to the nth node; A maximum comprehensive evaluation value is obtained from all comprehensive evaluation values, and communication is performed using a communication path corresponding to the maximum comprehensive evaluation value as an optimal path.

7. A wireless communication system applied in a distributed scenario, characterized in that: The system comprises: A characteristic parameter selection module is used to obtain characteristic parameters in the LoRa network at a first preset time interval, wherein the characteristic parameters include a first characteristic parameter and a second characteristic parameter, each of the second characteristic parameters includes multiple third characteristic parameters, and calculate the membership of the first characteristic parameter and the third characteristic parameter one by one based on a membership function; Constructing a Mesh network, wherein the Mesh network includes multiple nodes, one of which corresponds to a distributed power access unit, and the other nodes correspond to one of photovoltaic inverters, energy storage devices, and charging piles. The other nodes are all equipped with LoRa wireless adapter units, and the distributed power access unit is deployed with a LoRa antenna, and the LoRa wireless adapter unit is communicatively connected to the LoRa antenna; 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 time delay and time delay jitter, and the reliability includes packet loss rate, number of retransmissions and bit error rate. The membership degree of any characteristic parameter among communication energy consumption, delay, delay jitter, packet loss rate, number of retransmissions, and bit error rate is calculated according to the following formula: ； in, is the membership degree of the corresponding characteristic parameters under the adjacent i-th node and j-th node, 、 are the maximum and minimum values ​​of the corresponding characteristic parameters under adjacent nodes, is the value of the corresponding characteristic parameter under the adjacent i-th node and j-th node; The membership degree of the signal strength is calculated according to the following formula: ； in, is the membership degree of the signal strength between the adjacent i-th node and the j-th node, is the value of the signal strength between the adjacent i-th node and j-th node, 、 are the maximum and minimum signal strengths of adjacent nodes respectively; A membership calculation module is used to construct a first characteristic matrix corresponding to the first characteristic parameter and the second characteristic parameter respectively according to the calculated membership, wherein 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; The expression of the first characteristic matrix corresponding to the 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: ； in, 、 are the membership degrees of the signal strengths of the adjacent i-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the communication energy consumption between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delays between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the delay jitter between the adjacent i-th node and the j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the packet loss rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 are the membership of the number of retransmissions under the adjacent i-th node and j-th node, k-th node, and n-th node, respectively. 、 、 are the membership degrees of the bit error rates of the adjacent i-th node, j-th node, k-th node, and n-th node, respectively. 、 、 、 are the first characteristic matrices corresponding to signal strength, communication energy consumption, data transmission delay, and reliability with the i-th node as the starting transmitting node, and n is the total number of nodes; a characteristic matrix construction module, configured to calculate a first weight coefficient of the first characteristic parameter or the second characteristic parameter based on the first characteristic matrix, wherein the kth row in the first characteristic matrix corresponding to the second characteristic parameter corresponds to the weight coefficient of the kth third characteristic parameter contained in the second characteristic parameter, and calculate a second characteristic matrix of the first characteristic parameter or the second characteristic parameter based on the first weight coefficient and the first characteristic matrix; The optimal path selection module is used to calculate a weight matrix of 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 an optimal communication path according to the communication path selection matrix.

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