Multi-device cooperative communication method of 5G router

Abstract

The embodiment of the invention provides a multi-device cooperative communication method of a 5G router. The multi-device cooperative communication method comprises the following steps: acquiring current position information and transmission rate requirements of multiple devices; calculating an expected delay of each candidate routing path based on the inter-device distance and the transmission rate requirement; screening out an available routing path set meeting a preset delay threshold according to the expected delay; and selecting a target path with the optimal optimization index in the available routing path set for data transmission. According to the scheme provided by the embodiment of the invention, the routing path selection can be intelligently adjusted according to the distance between the devices and the transmission rate requirement so as to solve the problem of overhigh delay.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular to a multi-device collaborative communication method for a 5G router. Background Art

[0002] The multi-device collaborative communication method for a 5G router utilizes the characteristics of high speed and low latency of the 5G network, combined with the collaborative cooperation between devices, to achieve optimized resource allocation and efficient data transmission. This method can dynamically allocate network bandwidth and flexibly adjust the connection mode between devices according to the actual scenario to improve the overall communication efficiency and stability. However, this technology faces a problem, that is, how to intelligently select a routing path based on the distance between devices and specific transmission rate requirements to avoid excessive latency. In a complex multi-device environment, if the routing selection is not accurate or timely enough, it may lead to an increase in data transmission latency, thus affecting the user experience. Therefore, a targeted algorithm is needed to optimize the path decision-making process. Summary of the Invention

[0003] In view of this, embodiments of the present disclosure provide a multi-device collaborative communication method for a 5G router, which at least partially solves the problems existing in the prior art.

[0004] A multi-device collaborative communication method for a 5G router includes:

[0005] Obtain the current location information and transmission rate requirements of multiple devices;

[0006] Calculate the expected latency of each candidate routing path based on the distance between devices and transmission rate requirements;

[0007] Filter out a set of available routing paths that meet a preset latency threshold according to the expected latency;

[0008] Select the target path with the optimal optimization metric in the set of available routing paths for data transmission.

[0009] According to one embodiment, calculating the expected latency of each candidate routing path based on the distance between devices and transmission rate requirements further includes:

[0010] Obtain the number of devices N on the candidate routing path;

[0011] Determine the initial maximum hop count Hmax and record the current hop count H;

[0012] Calculate the dynamic weight W of the path based on the following formula: W = D × R + β × (N - H), where D is the distance between devices, R is the device transmission rate, and β is a weighting parameter;

[0013] If W exceeds the preset weight threshold Wth, then eliminate this path.

[0014] According to one embodiment, calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement further includes:

[0015] Recording the device distribution density σ of each candidate route;

[0016] Obtaining the priority coefficient η according to the transmission requirements between devices;

[0017] Evaluating the path comprehensive score S = η × ∑(Dk / Vk) based on the following formula, where Dk is the distance requirement of device k and Vk is the corresponding transmission rate;

[0018] If S is greater than or equal to the path score upper limit Smax, the path is unavailable, otherwise it is retained.

[0019] According to one embodiment, calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement further includes:

[0020] Defining the payload L_load of each candidate route;

[0021] Setting the congestion coefficient γ in combination with the current network congestion status;

[0022] Updating the delay estimation value of the path Td = α × (D × γ / R)^(1 / 2) based on the following formula, where α is a proportionality constant, D is the distance between devices, and R is the device transmission rate;

[0023] Comparing the Td of the path with the user demand delay Tmax, and retaining the path set lower than Tmax.

[0024] According to one embodiment, calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement further includes:

[0025] Obtaining the path loss exponent P_L of each candidate route;

[0026] Calculating the total path length L_total and its normalized value ρ = L_total / ∑(L_k, distance between devices);

[0027] Optimizing the path selection function C = λ × [ρ + (1 - λ) × (R / D)] based on the following formula, where λ is the weight;

[0028] For any path k, if it satisfies the condition Ck > C_threshold, it is added to the available path group.

[0029] According to one embodiment, calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement further includes:

[0030] Statistically calculate the actual throughput Q_avg and the historical throughput standard deviation σ_Q of all candidate routes;

[0031] Calculate the real-time stability score V_stable of each path = σ_Q / Q_avg;

[0032] Use the following constraint relationship to screen the target path G = max(Rδ × |V_stable|^2), where δ ≥ 0 represents the sensitivity factor;

[0033] When G of the candidate route ≥ G_threshold, save it for subsequent screening.

[0034] According to one embodiment, calculating the expected delay of each candidate route path based on the distance between devices and the transmission rate requirement further includes:

[0035] Obtain the interference degree I_interf of each pair of nodes through signal strength prediction, denoted as an interference matrix;

[0036] Introduce the maximum hop limit M_hop_max within the time window τ;

[0037] Calculate the comprehensive performance evaluation Y of each path = min{[D × (I_interf)^×(τ / T_hop)], where D and τ are defined consistently, and T_hop represents the delay value of a single hop;

[0038] Find the optimal solution that satisfies Y ≤ Y_limit on the path and output it.

[0039] According to one embodiment, calculating the expected delay of each candidate route path based on the distance between devices and the transmission rate requirement further includes:

[0040] Record the average link quality index Link_quality of each path, represented in the range of [0, 1];

[0041] Based on parameters such as the signal-to-noise ratio SNk, correct the value of the above Link_quality Z′k = exp[a × (SN_min - SNk)^b], and the a and b parameters need to be calibrated;

[0042] For any candidate, set a dynamic threshold Link_quality_ther to prevent low-quality links from contaminating the result pool;

[0043] Only allow candidate paths that meet the coverage rate of Z′k ≥ Link_quality_ther to pass the verification.

[0044] According to one embodiment, calculating the expected delay of each candidate route path based on the distance between devices and the transmission rate requirement further includes:

[0045] Introduce the path connectivity probability P_connect and evaluate each path;

[0046] Combine the current network bandwidth B_curr and the theoretical bandwidth upper and lower limits to define the bandwidth efficiency Eff_bw = max(B_curr / B_max, B_min / B_curr, 0);

[0047] Verify using the following constraint rule: Q_val = [(P_connect^2 * P_fail_thresh) / (Eff_bw * b_bw)], if Q_val ≤ ε;

[0048] Retain the feasible paths with sufficient reliable bandwidth support to continue the evaluation process.

[0049] According to one embodiment, calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement further includes:

[0050] Construct a multivariate regression model to simulate the real delay situation Delay_real on different links, and the model parameters are trained by historical samples;

[0051] Calculate the deviation of the model output error Δ_model_err compared with the measured result;

[0052] Introduce the fitness judgment item J_adapt = |Δ_model_err| / Delay_expected, which holds only if its absolute value is less than the tolerance ∈_tol;

[0053] Use the judgment basis of J_adapt < ∈_tol as the basic configuration for the next data packet transmission.

[0054] The embodiments of the present disclosure provide a multi-device collaborative communication method for a 5G router, including: obtaining the current location information and transmission rate requirements of multiple devices; calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement; screening out an available routing path set that meets a preset delay threshold according to the expected delay; and selecting a target path with the optimal optimization index in the available routing path set for data transmission. Through the solution of the embodiments of the present disclosure, it is possible to intelligently adjust the routing path selection according to the distance between devices and the transmission rate requirement to solve the problem of excessive delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In the drawings, unless otherwise specified, the same reference numerals throughout the several views refer to the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0056] Figure 1 It is a flowchart of a multi-device collaborative communication method for a 5G router;

[0057] Figure 2 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0058] Figure 3 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0059] Figure 4 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0060] Figure 5 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0061] Figure 6 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0062] Figure 7 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0063] Figure 8 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0064] Figure 9 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0065] Figure 10 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements;

[0066] Figure 11 It is a further flowchart for calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements. Detailed implementation manner

[0067] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0068] Next, referring to the figures, the specific implementation steps and implementation details of a multi-device collaborative communication method for a 5G router of the present invention will be described. The present invention provides a method for intelligent path selection, aiming to optimize the communication delay between multiple devices and improve the user experience.

[0069] First, referring to Figure 1 , a multi-device collaborative communication method for a 5G router of the present invention will be described, which includes:

[0070] S101: Obtain the current location information and transmission rate requirements of multiple devices. Specifically, through the location awareness module built into the 5G base station or router in combination with GPS or other positioning technologies, the spatial coordinate data of each connected device is obtained. In addition, each device reports to the router key information such as the minimum bandwidth, real-time transmission rate, and other QoS requirements required for its current task. For example, a device may require a stable and low-latency high-bandwidth connection during a video conference, while other non-sensitive tasks (such as file synchronization) have relatively relaxed requirements for speed and latency. This stage ensures that the system can accurately understand the basic attributes and priority settings of each connected object in the current network environment.

[0071] S102: Based on the obtained location information and the specific conditions of each device link, the system will further analyze the distance between devices and comprehensively consider the rate characteristics under different transmission schemes, and calculate the expected delay value of the candidate routing path. In the actual operation process, the 5G router uses the built-in algorithm framework, combined with technical means such as link quality estimation and signal attenuation modeling, to predict the overall duration generated by various alternative channels from the source device to the target receiver. To improve the prediction accuracy, in one embodiment, a set of parameter models can be trained through machine learning algorithms to characterize the degree of influence of the complex environment in the real world on the wireless channel propagation time. At the same time, considering the problem that the actual physical conditions change relatively fast, it is also necessary to regularly refresh the measurement results to update the relevant estimated values to adapt to the dynamic working condition conversion requirements.

[0072] S103: Screen out the set of available routing paths that meet the preset delay threshold according to the expected delay. Find out which of all the obtained possible options can be accepted as the basis resource set for the next decision, that is, screen out the part exceeding the preset threshold according to the expected delay value obtained in the previous stage. This helps to filter out some option combinations that do not meet the performance standard constraints and are even poor or unable to complete the specified task level. Specifically, assume that we set the maximum tolerable delay time as x milliseconds. After comparing various statistical indicators, directly exclude all the lines that exceed the given limit value to form the remaining qualified standby group list - that is, the set of available routing paths that meet the preset delay requirements mentioned above.

[0073] S104: Select the target path with the optimal optimization index in the set of available routing paths for data transmission. Then enter the final decisive step, that is, select the most suitable path to carry out the actual data stream distribution work to achieve the goal of efficient communication; during this period, specific criteria for measuring advantages and disadvantages, such as energy consumption ratio efficiency weights and other elements, need to be introduced for sorting and determination to select the final output item to be determined as the target execution plan. Take an example: If there are many types of Internet of Things devices such as automated manipulators and surveillance cameras installed and running in an intelligent factory area, and the frequency of information interaction between them is relatively high and the types are complex, then our technology can well demonstrate its own value at this time - by quickly evaluating and screening these massive associated terminals and making appropriate selections, the advantages in minimizing the reaction time of the entire system and improving the operation stability and reliability can be reflected.

[0074] Therefore, under the coordinated cooperation and joint action of the above-mentioned stages, one of the effective ways to successfully solve the problem of making intelligent automatic adjustments according to the comprehensive considerations of the relative distance differences between devices and the different required bandwidth capacity levels, and selecting the most suitable optimal transmission route to avoid the occurrence of the phenomenon of unnecessary high waiting period extension caused by improper selection is to adopt the novel, unique, innovative, far-reaching and widely influential solution method, measure, approach, form, measure and way introduced, proposed, described, detailed, explained and elaborated here.

[0075] Next, refer to Figure 2 to describe the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements of the present invention.

[0076] S201: Obtain the number of devices N on the candidate routing path. This means clarifying the number of network nodes included in a certain route, which will directly affect the overall efficiency and resource consumption of the entire path. Usually, N is a positive integer and is related to the specific topological environment. For example, in a 5G router network covering a city, N may vary from a few to dozens.

[0077] S202: Determine the initial maximum hop count Hmax and record the current hop count H. This operation sets the maximum path limit tolerable by the network to prevent overly long paths from introducing excessive latency or reliability issues. The value of Hmax should be determined according to application requirements. For example, for video calls that may require shorter paths, a lower Hmax (such as 10) can be set, while for general data file sharing, a higher Hmax value can be accepted. The recorded current hop count H starts from 1 and grows to dynamically monitor the situation of each step of the path.

[0078] S203: Calculate the dynamic weight W of the path based on the following formula: W = D × R + β × (NH), where D is the distance between devices, R is the device transmission rate, and β is the weighting parameter; calculate the dynamic weight W of the path based on the given formula: W = D × R + β × (N^H), where D is the physical distance between adjacent devices, R is the transmission rate between these devices, and β is the weighting parameter. Here, the selection of β has a great impact on the overall result, and its optimal value can be determined through empirical testing or theoretical analysis. Specifically, if it is desired to reduce the proportion of long-distance communication, β can be appropriately increased; conversely, reducing β can increase the distance sensitivity. This formula aims to comprehensively consider the impacts of both transmission performance and network scale: D × R represents the basic performance loss of a single device connection, while β × (N^H) indicates that the path will quickly accumulate more costs as the number of hops increases.

[0079] S204: If W exceeds the preset weight threshold Wth, then eliminate this path.

[0080] Eliminate the path whose weight W exceeds the threshold Wth. Here, Wth is used to define the unsatisfactory path conditions outside the allowable range, and it should be flexibly set according to the requirements of the target service. In one embodiment, assume that in a certain calculation, W is 180 and the preset Wth value is 150, then this path does not meet the conditions and is eliminated.

[0081] All the above steps ensure that the key characteristics of the path are fully considered during the screening process. For example, if there are three devices forming a candidate routing path, and it is known that their distances are all 10 unit lengths, the average transmission rate is 2 Gbps, the initial setting is β = 0.3 and Hmax = 5, and they are judged one by one according to the above method, it is possible to find the optimal available combinations that meet the multi-device collaboration requirements, while excluding the inferior alternatives to improve the overall efficiency of the system.

[0082] Next, with reference to Figure 3 , the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement of the present invention are described, which include:

[0083] S301: Record the device distribution density σ of each candidate route;

[0084] S302: Obtain the priority coefficient η according to the transmission requirements between devices;

[0085] S303: Evaluate the path comprehensive score S = η × ∑(Dk / Vk) based on the following formula, where Dk is the distance requirement of device k, and Vk is the corresponding transmission rate;

[0086] S304: If S is greater than or equal to the path score upper limit Smax, the path is unavailable; otherwise, it is retained.

[0087] The specific meanings of the above steps are explained as follows. The purpose of recording the device distribution density σ of the candidate route is to analyze the density of devices in the path, so as to better measure the impact of factors such as signal interference and network load on communication quality. The density value is usually in the interval [0,1], and the optimal value varies according to different application scenarios. For low-density areas, communication may be affected by insufficient coverage; while for high-density areas, the possibility of more devices competing for channel bandwidth needs to be considered.

[0088] Obtaining the priority coefficient η according to the transmission requirements between devices is a method for quantifying the importance of communication tasks. For example, real-time high-definition video transmission requires a higher priority. The priority coefficient can be a positive real number, and the range is generally set between [0.5,2]. Its role is to assign a greater weight to tasks with higher requirements.

[0089] Among the parameters of the path comprehensive score formula, Dk represents the actual physical distance requirement between devices, usually in meters; Vk specifies the theoretical maximum transmission rate (bps) corresponding to a specific device on the path. The core meaning of the formula is to obtain the overall performance index of a route by accumulating the weighted distance requirements and rate ratios and combining the task priority weight η.

[0090] In one embodiment, assume that there is a candidate path involving three key node devices A, B, and C, and the distances between them in pairs are known to be D1 = 25 meters, D2 = 30 meters, and D3 = 45 meters respectively, and the corresponding optimal transmission rates are V1 = 5 Mbps, V2 = 7 Mbps, and V3 = 6 Mbps respectively. In addition, the evaluation value of the priority coefficient η is 1.5, and when the device density σ within this path is relatively high and close to full load, Smax is set to approximately 8. Specifically, applying the formula: S = η × ((25 / 5) + (30 / 7) + (45 / 6)) = 1.5 × (5 + 4.29 + 7.5) ≈ 20.68. Since the calculated comprehensive score of the path far exceeds the upper limit value Smax = 8, it is directly determined that this path is invalid and excluded.

[0091] The reason for setting the path score formula in this way is to take into account the weight ratio of the influence of distance, transmission capacity, and priority level on the final effect. By normalizing the distance requirement with respect to the available transmission rate first and then superimposing the priority adjustment, the actual feasibility of each routing selection can be judged more fairly and justly, improving the resource utilization efficiency and the dynamic adaptability to meet more high-quality communication requests in a complex network structure.

[0092] Next, refer to Figure 4 , and describe the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements of the present invention. This process is divided into several steps, defining parameters, setting coefficients, calculating formula values, and screening the path set in sequence. The execution order of these steps is crucial for the effectiveness of the multi-device cooperative communication method.

[0093] S401: Define the payload L_load of each candidate route. This payload is used to measure the total amount or size of the data packets transmitted by the path, and it directly affects the data processing complexity and the required resource allocation strategy in the actual communication requirements. In one embodiment, for example, L_load can represent any value within the range from the minimum packet size to the maximum bearable packet size involved in a single data transmission request in bytes.

[0094] S402: Set the congestion coefficient γ in combination with the current congestion state of the network. The network congestion state here reflects the real-time bandwidth occupancy ratio of the system and the risk index that may cause transmission blockage. By adjusting γ, the adaptability of the path selection to the delay performance can be dynamically evaluated under different network load conditions. Generally, the value range of γ is [0, 1], and the optimal value depends on the specific traffic fluctuation level. If the network is relatively idle, γ is relatively small; on the contrary, when approaching the overload threshold, the value of γ should be increased to compensate the delay prediction result.

[0095] S403: Update the delay estimate value Td of the path based on the following formula: Td = α×(D×γ / R)^(1 / 2), where α is a proportionality constant associated with the system architecture and hardware specifications, D represents the physical separation in the Euclidean space between two nodes, i.e., the distance between devices, usually in standard distance units such as meters (m), etc.; R represents the theoretical maximum transmission rate on the link, for example, in the form of megabits per second (Mbps), etc. This formula construction synthesizes the combined effects of geographical layout constraints, signal attenuation characteristics, and its speed limit. This square root form design takes into account the increasing change pattern caused by the expansion of distance or the reduction of bandwidth, but does not show an overly sensitive trend, thus avoiding the deviation impact on stability judgment caused by overly aggressive estimation. For example, specifically, on a candidate path constructed by three-hop nodes A->B->C, if it is known that AB = 150 meters and the peak 1 Gbps data stream is supported to connect BC in a relatively sparse environment, only 80 meters apart, allowing up to 300 Mbps, and assuming that the network is at medium load at this moment, γ is located at the default reference value of 0.7, and then select the standardized proportional correction parameter to be a fixed 1.2, approximately obtaining the corresponding estimated values TD_Ab = 0.6 s and TD_bC≈0.9 s, etc., to provide a quantitative comparison basis.

[0096] S404: Compare the Td of the path with the user's required delay Tmax, and retain the set of paths lower than Tmax. Finally, the relative size of the obtained result value Td and the user's pre-set required delay time limit Tmax determines which options meet the qualifications and continue to be retained in the available path list for subsequent operations. If, according to the above example, the overall cumulative waiting time across multiple links is finally solved to be less than the specified upper limit, it is successfully confirmed as an object to be retained; otherwise, it is eliminated and no longer considered for participation in the transaction processing within the combined configuration plan arrangement.

[0097] Next, refer to Figure 5 , and describe the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements of the present invention.

[0098] S501: Obtain the path loss exponent P_L of each candidate route. This step aims to quantify the degree of energy attenuation during the propagation of wireless signals. Specifically, path loss is related to the wireless environment, transmission frequency, and communication distance. For example, in one embodiment, the value of the path loss exponent can be determined by the measured signal strength or theoretical calculation formula through experiments, usually in the range of 2 to 5, and the optimal value depends on the measured empirical data in a specific scenario.

[0099] S502: Calculate the total path length \(L_{total}\) and its normalized value \(\rho = L_{total} / \sum(L_k,\text{ distance between devices})\). This step evaluates the rationality of the routing path by measuring the total distance between all devices passed by the route and its proportional relationship to all possible distances. The path length \(L_{total}\) is in meters and is the weighted sum of the straight-line distances between devices in each segment; the \(\rho\) value is used to standardize the influence of different network scales, making the algorithm more universal. In one embodiment, if a path contains three nodes and connects distances of 10 meters and 20 meters, then \(L_{total}=30\), and when all possible path lengths are 50 meters, \(\rho = 0.6\).

[0100] S503: Optimize the path selection function \(C=\lambda\times[\rho+(1 - \lambda)\times(R / D)]\) based on the following formula, where \(\lambda\) is an important weight parameter for balancing the distance factor and the rate factor, generally taking values between 0 and 1, and the optimal value is adjusted according to the test results; \(R\) represents the actual transmission rate (such as Mbps) and \(D\) is the required minimum transmission rate; this function comprehensively considers the path distance and bandwidth matching situation to obtain the selection standard score of the candidate path. For example, setting \(\lambda = 0.7\) emphasizes the influence of distance on the selection.

[0101] S504: For any path \(k\) that satisfies \(C_k > C_{threshold}\), it is marked as an available path group and added to it. Set a reasonable \(C_{threshold}\) reference value (which can be initialized according to empirical values), ensuring that only paths that perform excellently and meet the expected goals will be adopted into the effective set to complete the subsequent multi-device collaborative communication process management. For example, after assuming \(C_{threshold}=0.8\), check each \(C_k\) and retain the paths that meet the inequality as one of the preferred solutions.

[0102] Next, refer to Figure 6 , describe the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements of the present invention. This process mainly includes the following steps:

[0103] S601: First, count the actual throughput \(Q_{avg}\) and the historical throughput standard deviation \(\sigma_Q\) of all candidate routes;

[0104] S602: Calculate the real-time stability score \(V_{stable}=\sigma_Q / Q_{avg}\) of each path;

[0105] S603: Filter out candidate paths that meet the stability using the objective function G = max(Rδ × |V_stable|²), where δ is the sensitivity factor and δ ≥ 0; S604: Save the candidate routes when G ≥ G_threshold for subsequent filtering, that is, judge whether the path G meets the preset threshold G_threshold and use the result for the subsequent filtering process.

[0106] The first step involves collecting historical data of candidate routes, mainly the actual throughput Q_avg on the candidate routes and the corresponding throughput fluctuation (denoted by σ_Q). Here, Q_avg represents the size of the data traffic that the candidate route can carry on average over a period of time (unit: Mbps or Gbps), and the range varies according to the system capacity; the optimal value depends on the specific network environment but is usually better if it is higher. At the same time, σ_Q is the standard deviation obtained by quantifying the throughput fluctuation degree in several past observations, which is used to evaluate the performance stability. For example, a high-quality path will show a small σ_Q and maintain a stable Q_avg, which means better network service quality.

[0107] The subsequent step is to derive the stability index V_stable of each path using the above statistical parameters. It is defined as the ratio of the throughput standard deviation to the mean, that is, V_stable = σ_Q / Q_avg. The significance of this formula is to represent the relative instability degree (or jitter) of the candidate route in a dimensionless way. The closer it is to zero, the more constant the route behaves and the more it meets the requirements of high-reliability application scenarios.

[0108] The next step introduces an optimization method to select the optimal route G = max(Rδ × |V_stable|²). In this formula, R is a weight variable that reflects factors such as the device interval and signal strength attenuation on the current path, usually between 0 and 1, and the lower it is, the worse the channel condition. δ ≥ 0 is an adjustable sensitivity factor used to fine-tune the algorithm focus - if δ is increased, the impact of non-optimal solutions on the result will be amplified, making the selection more stringent and thus improving the accuracy at the cost of ignoring more alternatives. The purpose of setting this objective equation is to balance stability and path quality to obtain an equilibrium solution, so as to ensure efficient operation under the communication requirements of multiple devices.

[0109] In the last operation, compare the generated value with the preset threshold G_threshold. Only those paths that pass the test and reach or exceed the threshold can be retained as the final valid options and serve for subsequent finer-grained analysis. Specifically, in one embodiment, if a router connects three substations to form several potential paths A, B, and C with corresponding G values of {8.2, 7.5, 6.1}, and the critical level is given as G_threshold = 6, then the first two are retained for further decision-making, and the latter is directly excluded because it is below the requirement.

[0110] Next, with reference to Figure 7 , the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement of the present invention are described. Through multiple steps in this process, an optimal routing scheme that meets specific transmission performance can be obtained.

[0111] S701: Obtain the interference degree I_interf of each pair of nodes through signal strength prediction, denoted as an interference matrix. First, obtain the interference degree matrix I_interf of each pair of nodes. This process is achieved through signal strength prediction. Each node calculates the interference degree with other surrounding nodes based on the strength of the received signal and records these results in matrix form. The main purpose of this step is to quantify the quality of the channel environment, so as to provide a reference basis for subsequent path evaluation.

[0112] S702: Introduce the maximum hop limit M_hop_max within the time window τ. Second, introduce the time window τ and the maximum hop limit M_hop_max. The time window τ specifies the maximum time allowed for a message to travel from the starting point to the ending point; while M_hop_max limits the maximum number of hops that a single route can tolerate in the physical network. The purpose of these two is to constrain the length and transmission time limit of the candidate path, ensuring that the communication neither exceeds the network load capacity nor violates the real-time requirement.

[0113] S703: Calculate the comprehensive performance evaluation Y of each path = min{[D×(I_interf)^(1)×(τ / T_hop)]}, where D and τ are defined as above, and T_hop represents the delay value per unit segment. Third, use the formula Y = min{[D×(I_interf)^1×(τ / T_hop)]} to evaluate the comprehensive performance value. Among them, the parameter D is the actual physical distance between devices; T_hop represents the fixed delay required for each hop on average, which is closely related to the characteristics of the network hardware design; Y_limit is defined as the threshold of the target acceptable performance, used to screen suitable routing options. The logic of this formula is to combine the distance loss, channel interference level, and hop delay effect of the path, and finally form a single index to reflect the overall performance.

[0114] S704: Find the optimal solution that satisfies Y≤Y_limit on the path and output it. Fourth, find the solution that satisfies Y≤Y_limit in the set of all potential paths as the final output.

[0115] For example, in a specific embodiment, assume that there is a distributed sensor system that uses a multi-hop mode to transmit information to a central controller. The direct distance D between each sensor is relatively small and the fluctuation is not significant. The typical numerical range may be from 30 meters to 200 meters. At this time, the corresponding signal-to-noise ratio is relatively stable. The value of I_interf generally falls within the range of 0.5 to 1, indicating a moderate to high signal-to-noise situation. Suppose the given time period τ is 20 milliseconds, and if the basic delay T_hop in each hop stage is approximately 1 - 3 milliseconds, then inputting the various parameters into the above equation to calculate the scores of different routing paths, and then selecting one or more routes with the lowest scores and not exceeding the preset upper limit for use, ensuring that the entire communication process meets the standards of high efficiency and reliability.

[0116] Next, refer to Figure 8 to describe the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements of the present invention.

[0117] S801: Record the average link quality index Link_quality of each path, expressed in the range of [0, 1];

[0118] S802: Modify the value of the above Link_quality based on parameters such as the signal-to-noise ratio SNk, Z′k = exp[a×(SN_min - SNk)^b], and the parameters a and b need to be calibrated;

[0119] S803: For any candidate, set a dynamic threshold Link_quality_ther to prevent low-quality links from contaminating the result pool;

[0120] S804: Only allow candidate paths that meet the coverage rate of Z′k ≥ Link_quality_ther to pass the verification.

[0121] First, record the average link quality index Link_quality of each path. Its numerical range is strictly limited to [0, 1]. Link_quality represents the evaluation value of the communication ability of the path. The larger the value, the better the communication quality. For example, the average channel stability and bandwidth performance of the path from A to B in a 5G network will be abstracted into this index value.

[0122] Secondly, the recorded Link_quality values are corrected using the formula Z′k = exp[a×(SN_min - SNk)^b], where a and b are two pre-calibrated parameters that respectively adjust the smoothness of the exponential change and the degree of non-linearity. a adjusts the amplitude of the logarithmic scale, and its optimal value usually lies within the range of [1, 5]. b determines the sensitivity of the difference magnitude, and its optimal value may be within the range of [2, 4]. In this formula, SN_min represents the lowest tolerable signal-to-noise ratio lower limit of the system, and SNk is the actual signal-to-noise ratio of the candidate path. The purpose of the entire formula is to dynamically feedback the signal-to-noise ratio characteristics at the physical layer to the abstract quality evaluation value Link_quality, enabling it to more accurately reflect the changes in the real-world channel conditions.

[0123] Furthermore, a dynamic threshold Link_quality_ther is set for any candidate path to eliminate those path options that may cause a significant drop in performance or become unavailable, ensuring that the overall algorithm does not introduce more risk factors of instability or high latency due to low-quality connections. For example, when it is found that the ambient noise in the current environment increases, causing the signal-to-noise ratio of most candidate paths to decrease, this threshold is appropriately lowered; conversely, when the signal environment is good, the standard can be moderately increased to pursue a higher probability of achieving the goal of a more efficient service experience and strengthening the implementation of the strategy.

[0124] Finally, a candidate set that only meets the condition of Z′k ≥ Link_quality_ther and has a relatively high coverage ratio is selected and retained for further analysis and comparison to determine the final best solution. In one embodiment, specifically considering the multicast task requirement scenario of sending messages from a certain node to a group of multiple remote nodes, it is necessary to comprehensively evaluate multiple constraint indicators such as the delay contribution and the satisfaction level of the total throughput at each forwarding and jumping link, and jointly participate in the trade-off to select the result generation mechanism and demonstrate the actual effect to verify the effectiveness.

[0125] Next, refer to Figure 9 to describe the steps of calculating the expected delay of each candidate routing path based on the device-to-device distance and transmission rate requirements of the present invention. This process completes the evaluation through a series of logical steps, including introducing the path connectivity probability, defining the bandwidth efficiency formula, verifying the constraint rules, and retaining the feasible paths that meet the conditions.

[0126] S901: Introduce the path connectivity probability P_connect and evaluate each path. The path connectivity probability reflects the possibility of establishing a communication link between two devices, and its value range is from 0 to 1. Among them, the higher the P_connect value, the more stable the path and the higher the reliability of the connection between the devices.

[0127] S902: Combine the current network bandwidth B_curr with the upper and lower limits of the theoretical bandwidth (B_max and B_min respectively), and define the bandwidth efficiency Eff_bw = max(B_curr / B_max, B_min / B_curr, 0). The bandwidth efficiency formula aims to measure whether the existing network conditions are close to the optimal value and zero out outliers. For example, the efficiency will be affected when the bandwidth is too low or too high.

[0128] S903: Conduct verification using constraint rules: Q_val = [(P_connect^2 * P_fail_thresh) / (Eff_bw * b_bw)]. If Q_val ≤ ε, the path is considered reliable and proceeds to the next step. Each parameter in the formula has a clear meaning. P_connect represents the path connectivity probability mentioned above; P_fail_thresh is the failure threshold set by the system (ranging from 0 to 1, usually default set to 0.1); Eff_bw is calculated as described above; b_bw is the target bandwidth requirement per unit time (in Gbps, related to the application requirements); and ε is a small positive value preset by the system (such as 0.05), representing the maximum acceptable uncertainty limit. The design of the entire formula takes into account the comprehensive weights of connectivity stability and actual bandwidth utilization effect, and improves the judgment sensitivity through non-linearity.

[0129] S904: Retain the feasible paths with sufficient reliable bandwidth support to continue the evaluation process.

[0130] In a specific example, for instance, a given scenario involves five devices collaborating to transmit data. In one embodiment, these devices are located at different distances, and their path connectivity probabilities are 0.8, 0.7, 0.6, 0.5, and 0.9 respectively. At the same time, in the network environment, B_max = 5 Gbps, B_min = 0.5 Gbps, and the target bandwidth requirement b_bw = 1 Gbps. Assume B_curr are 3.5 Gbps, 1.8 Gbps, 2.5 Gbps, 1.2 Gbps, and 4 Gbps respectively. Specifically, first calculate P_connect and the bandwidth efficiency Eff_bw item by item, and then substitute them into the verification constraint rule to check if Q_val meets the criteria. Finally, only select those paths that satisfy the final constraints for the next operation.

[0131] Next, refer to Figure 10 , describe the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements of the present invention. This process mainly includes the following four steps: constructing a regression model based on multiple variables, calculating the model output error, introducing a fitness judgment item, and determining based on the packet sending configuration that meets the standards.

[0132] S1001: Build a regression model based on multiple variables to simulate the real delay situation Delay_real on different links. This model synthesizes the combined effects of multiple variables such as the distance between devices and the transmission rate requirements on the delay. For example, determine the optimal values of the model parameters through the historical samples in the training set and verify the accuracy of the model. Among them, the range of Delay_real is real numbers greater than or equal to zero, and the optimal value is a specific value consistent with the actual network conditions, which is used to approximate the actual performance of the link in the real communication environment.

[0133] S1002: Calculate the deviation of the model output error Δ_model_err compared with the measured result. This is the result obtained by comparing the predicted value Delay_real_predicted of the regression model with the measured delay value Delay_real_actual. The formula is Δ_model_err = |Delay_real_actual - Delay_real_predicted|. The parameter Δ_model_err is also a non - negative value, and the optimal value approaches zero because the closer it is to zero, the more consistent the prediction result is with the actual situation. In an embodiment, the measured delay between two nodes in a 5G router is 2ms, and the delay predicted by the model is 2.1ms, then the calculated error value Δ_model_err is 0.1ms.

[0134] S1003: Introduce a fitness judgment term J_adapt to evaluate whether the model accuracy meets the requirements. Specifically, the fitness formula is J_adapt = |Δ_model_err| / Delay_expected. In the formula, Delay_expected is the target delay expectation value estimated according to the theoretical model, usually taking a non - zero positive value within a reasonable design range, and ε_tol is a predefined tolerance value. For example, in an experimental scenario, when Delay_expected is set to 3ms and the value of Δ_model_err is 0.1ms, if ∈_tol = 0.05 is set, then J_adapt = 0.03 can be calculated, which is less than ∈_tol, indicating that the current path model is available. This setting is to ensure that the path selection has high reliability and at the same time ensure the network transmission efficiency.

[0135] S1004: Based on whether the fitness meets the standard, perform a basic screening of the data packet sending configuration. If a path meets the judgment basis of J_adapt < ∈_tol, then use it as the basic path for the next data packet sending. Specifically, in practical applications, in combination with the 5G network environment, test the candidate paths respectively. For example, for multiple paths, finally only select the path with the smallest error to achieve an efficient and stable multi - device collaborative communication task. This step aims to ensure the data transmission efficiency and quality stability among multiple devices from a technical implementation perspective.

[0136] Next, refer to Figure 11 to describe the steps of calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirement in the present invention. This method first records the retry success rate of the link as a consideration point for path stability; then evaluates the overall performance of the candidate path based on the global path health factor formula; when a non-healthy link is detected, attempts to disconnect and reconstruct; repeats the iteration until the task is completed.

[0137] S1101: Record the retry success rate SR_retry of the link, which is used to measure path stability. The retry success rate represents the probability that data is successfully sent after one or more failed attempts. This value usually ranges from [0, 1], where the closer to 1 indicates more stable and reliable. The optimal value can be regarded as 1 theoretically when the network environment is extremely good.

[0138] S1102: Define the global path health factor Health_factor, which is calculated by the formula Health_factor = sum{(1D_k / R) / SR_retry}, where the parameters D_k and R correspond to the distance between specific nodes and the specified minimum required transmission rate respectively. The design of the formula aims to integrate three important factors: the signal attenuation effect D_k caused by the link distance, the rate requirement R corresponding to the user service scenario demand, and the previously mentioned path reliability SR_retry. Such a construction method ensures that a low score is assigned to those routes that have both long-distance communications and high bandwidth pressure but unstable actual conditions.

[0139] For example, in an embodiment, if the actual measured average distance between devices on a specific link is 50m, the service needs to ensure at least 2Mbps bandwidth, and its retry rate is known to be 0.9, then the sub-item contribution of this segment will be calculated as (1×50÷2)÷0.9, which is approximately equal to 27.78. Add similar values of multiple sub-links to obtain the final complete index number.

[0140] S1103: If a non-healthy link Health_factor ≥ F_critical is encountered, actively disconnect the connection and attempt to reconstruct other alternative link paths.

[0141] S1104: Repeat the iteration until the overall path meets the minimum health threshold limit value to complete the path adjustment task.

[0142] In the case of encountering a non-healthy link, that is, when the result of Health_factor is greater than the pre-determined important threshold F_critical, take actions to actively cut off the problematic connection, and instead explore and activate possible alternative solutions until the overall system state meets the pre-specified minimum passing line to complete the entire process control link work.

[0143] Specifically, assume that the entire route consists of four sub - intervals. After analysis, it is found that the above - mentioned calculation summary of the third sub - interval exceeds the warning threshold. Immediately switch to use another pre - reserved detour path to meet the requirements of the overall service quality target.

[0144] A multi - device collaborative communication method for a 5G router according to the present invention includes: First, by obtaining the current location information and transmission rate requirements of multiple devices participating in communication, a basic data model of the dynamic communication state between devices is established. These information can reflect the spatial distribution of different devices and the data throughput performance requirements, which are important bases for subsequent path calculation. Then, combining key factors such as the distance between devices and transmission rate, each potential candidate routing path is analyzed one by one, and the expected delay value of this path is quantified. By comparing these expected delays with a pre - set delay threshold, a set of available routing paths that meet the real - time performance requirements is screened out.

[0145] Finally, based on the above - mentioned available routing paths that meet the conditions, according to a certain optimization objective function, a target path with the optimal index characteristics is selected from multiple candidate solutions to execute the specific data packet sending task. This method effectively solves the technical challenge of how to intelligently adjust the routing path selection according to the distance between devices and transmission rate requirements to cope with the high - delay problem. Specifically, it is to avoid the problem of low information interaction efficiency caused by network congestion and excessive transmission delay by means of a strategy dynamically adjusted based on the actual operating conditions. At the same time, it can also take into account the different transmission capacity characteristics and geographical distribution characteristics of various devices, ensuring that the stability and reliability of the overall communication system are significantly enhanced. This technical solution shows unique advantages in improving the quality of data transfer and is especially suitable for complex and changeable actual application scenarios.

[0146] The methods, programs, systems, devices, etc. in the embodiments of the present invention can be executed or implemented in a single or multiple networked computers, and can also be practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks can be executed by remote processing devices connected through a communication network.

[0147] Those skilled in the art should understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, those skilled in the art can think that the implementation of the above - mentioned functional modules / units or controllers and related method steps can be realized in a software, hardware, or software / hardware combination manner.

[0148] Unless explicitly stated, the actions or steps of the methods and programs recorded according to the embodiments of the present invention do not necessarily have to be executed in a specific order and can still achieve the desired results. In some embodiments, multi - tasking and parallel processing are also possible or may be beneficial.

[0149] In this document, multiple embodiments of the present invention are described. For the sake of brevity, the description of each embodiment is not exhaustive, and features or parts that are the same or similar between the various embodiments may be omitted. In this document, "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean applicable to at least one embodiment or example according to the present invention, rather than all embodiments. The above terms do not necessarily refer to the same embodiment or example. Without contradiction, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples.

[0150] Exemplary systems and methods of the present invention have been specifically shown and described with reference to the above embodiments, which are only examples of the best mode for implementing the systems and methods. Those skilled in the art can understand that various changes can be made to the embodiments of the systems and methods described herein when implementing the systems and / or methods without departing from the spirit and scope of the present invention defined in the appended claims.

Claims

1. A multi-device collaborative communication method for a 5G router, characterized in that, Including: Obtain the current location information and transmission rate requirements of multiple devices; Calculate the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements; Filter out a set of available routing paths that meet the preset delay threshold according to the expected delay; Select the target path with the optimal optimization metric in the set of available routing paths for data transmission.

2. The multi-device collaborative communication method of a 5G router according to claim 1, characterized in that Calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements further includes: Obtain the number of devices N on the candidate routing path; Determine the initial maximum hop count Hmax and record the current hop count H; Calculate the dynamic weight W of the path based on the following formula: W = D × R + β × (N - H), where D is the distance between devices, R is the device transmission rate, and β is the weighting parameter; If W exceeds the preset weight threshold Wth, then eliminate this path.

3. A multi-device collaborative communication method for a 5G router according to claim 1, characterized in that, Calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements further includes: Record the device distribution density σ of each candidate route; Obtain the priority coefficient η according to the transmission requirements between devices; Evaluate the comprehensive path score S = η × ∑(Dk / Vk) based on the following formula, where Dk is the distance requirement of device k and Vk is the corresponding transmission rate; If S is greater than or equal to the path score upper limit Smax, then the path is unavailable, otherwise it is retained.

4. A multi-device collaborative communication method for a 5G router according to claim 1, characterized in that Calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements further includes: Define the payload L_load of each candidate route; Set the congestion coefficient γ in combination with the current congestion state of the network; Update the delay estimate value Td of the path based on the following formula: Td = α × (D × γ / R)^(1 / 2), where α is a proportionality constant, D is the distance between devices, and R is the device transmission rate; Compare the Td of the path with the user required delay Tmax and retain the set of paths lower than Tmax.

5. A multi-device collaborative communication method for a 5G router according to claim 1, characterized in that Calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements further includes: Obtain the path loss exponent P_L of each candidate route; Calculate the total path length L_total and its normalized value ρ = L_total / ∑(L_k, distance between devices); Optimize the path selection function C = λ × [ρ + (1 - λ) × (R / D)] based on the following formula, where λ is the weight; For any path k, those that meet the condition Ck > C_threshold are added to the available path group.

6. A multi-device collaborative communication method for a 5G router according to claim 1, characterized in that, Calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements further includes: Statistically calculate the actual throughput Q_avg and the historical throughput standard deviation σ_Q of all candidate routes; Calculate the real-time stability score V_stable of each path: V_stable = σ_Q / Q_avg; Use the following constraint relationship to screen the target path: G = max(Rδ × |V_stable|^2), where δ ≥ 0 represents the sensitivity factor; When G of the candidate route ≥ G_threshold, save it for subsequent screening use.

7. A multi-device collaborative communication method for a 5G router according to claim 6, characterized in that, Calculating the expected delay of each candidate routing path based on the distance between devices and the transmission rate requirements further includes: Obtain the interference degree I_interf for each pair of nodes through signal strength prediction, denoted as an interference matrix; Introduce the maximum hop limit M_hop_max within the time window τ; Calculate the comprehensive performance evaluation Y of each path = min{[D×(I_interf)^(1)×(τ / T_hop)], where D and τ are defined consistently, and T_hop represents the delay value of a unit segment; Find the optimal solution that satisfies Y ≤ Y_limit on the path and output it.

8. A multi-device collaborative communication method for a 5G router according to claim 1, characterized in that Calculating the expected delay of each candidate routing path based on the distance between devices and transmission rate requirements further includes: Record the average link quality index Link_quality of each path, represented in the range [0,1]; Modify the value of the above Link_quality based on parameters such as the signal-to-noise ratio SNk, Z′k = exp[a×(SN_min SNk)^b], and the parameters a and b need to be calibrated; For any candidate, set a dynamic threshold Link_quality_ther to prevent low-quality links from contaminating the result pool; Only allow candidate paths that meet the coverage rate of Z′k ≥ Link_quality_ther to pass the verification.

9. A multi-device collaborative communication method for a 5G router according to claim 1, characterized in that, Calculating the expected delay of each candidate routing path based on the distance between devices and transmission rate requirements further includes: Introduce the path connectivity probability P_connect and evaluate each path; Define the bandwidth efficiency Eff_bw = max(B_curr / B_max B_min / B_curr,0) by combining the current network bandwidth B_curr and the upper and lower limits of the theoretical bandwidth; Verify using the following constraint rule: Q_val = [(P_connect^2P_fail_thresh) / (Eff_bw b_bw)], if Q_val ≤ ε; Retain the feasible paths with sufficient reliable bandwidth support to continue the evaluation process.

10. A multi-device collaborative communication method for a 5G router according to claim 1, characterized in that, Calculating the expected delay of each candidate routing path based on the distance between devices and transmission rate requirements further includes: Construct a regression model based on multiple variables to simulate the real delay situation Delay_real on different links, and the model parameters are trained by historical samples; Calculate the deviation of the model output error Δ_model_err compared with the measured result; Introduce the fitness judgment term J_adapt = |Δ_model_err| / Delay_expected, which holds only if its absolute value is less than the tolerance ∈_tol; Use the determination basis of J_adapt < ∈Tol as the basic configuration for the next data packet transmission.

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