Method, device and medium for data communication transmission in a wireless communication network

Abstract

This invention discloses a data communication transmission method, device, and medium in a wireless communication network, relating to the field of resource scheduling technology. The method includes: acquiring multi-source heterogeneous security data and calculating the comprehensive risk value of each coordinate point within the coverage area of ​​the wireless communication network; constructing a dynamic risk topology field based on the comprehensive risk value; using the dynamic risk topology field to perform risk-aware trajectory prediction analysis on real-time trajectory data to generate preset communication demand entries; performing risk gradient-aware routing decisions on preset communication data packets corresponding to the preset communication demand entries to obtain forwarding paths; and performing signalless implicit resource scheduling on the wireless communication network according to the preset communication demand entries and forwarding paths to complete the transmission of the preset communication data packets. By constructing a dynamic risk topology field, a refined characterization of the comprehensive risk value of each coordinate point within the coverage area of ​​the wireless communication network is achieved, significantly improving the security of data transmission and resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling technology, and in particular to a data communication transmission method, device and medium in a wireless communication network. Background Technology

[0002] In wireless communication networks, data communication transmission methods must address multiple challenges, including high node mobility, time-varying channel characteristics, resource constraints, and security sensitivity. To ensure communication reliability and efficiency, transmission mechanisms should be able to dynamically perceive the complex risk situation within the cyberspace, comprised of the physical environment, device status, mobile behavior, and security policies, and based on this, achieve proactive path planning and resource pre-allocation. Especially in dense deployments or emergency communication scenarios, the transmission process not only requires low latency and high throughput but also needs to ensure that the entire path remains within acceptable security boundaries, thereby avoiding data interruption, leakage, or retransmission due to localized high-risk areas.

[0003] Existing technologies typically simplify risk factors to single indicators such as link quality or node remaining energy, which makes it difficult to fully reflect the comprehensive risk situation under the combined effect of multiple heterogeneous factors such as abnormal movement trajectory, equipment condition degradation, environmental interference and security policy conflicts during the communication process. This results in a lack of global security considerations in path selection and resource allocation. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a data communication transmission method in a wireless communication network to solve the problem of lack of global security considerations in path selection and resource allocation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a data communication transmission method in a wireless communication network, comprising,

[0008] Acquire multi-source heterogeneous security data and calculate the comprehensive risk value of each coordinate point within the coverage area of ​​the wireless communication network. Construct a dynamic risk topology field based on the comprehensive risk value. The multi-source heterogeneous security data includes real-time trajectory data, status monitoring data, environmental perception data, and security management rule data.

[0009] By utilizing dynamic risk topology fields, trajectory prediction analysis is performed on real-time trajectory data to generate pre-defined communication requirement entries.

[0010] For the pre-defined communication data packets corresponding to the pre-defined communication requirement entries, perform risk gradient-aware routing decisions to obtain forwarding paths;

[0011] Based on the pre-defined communication requirements and forwarding paths, implicit resource scheduling without signaling is performed on the wireless communication network to complete the transmission of pre-defined communication data packets.

[0012] As a preferred embodiment of the data communication transmission method in the wireless communication network of the present invention, the multi-source heterogeneous security data is generated by time synchronization and alignment of real-time trajectory data, status monitoring data, environmental perception data, and security management rule data.

[0013] As a preferred embodiment of the data communication transmission method in the wireless communication network described in this invention, the step of constructing a dynamic risk topology field based on a comprehensive risk value specifically involves:

[0014] Risk factors are extracted from multi-source heterogeneous security data on a preset geographic grid, and the extracted risk factors are normalized to generate a normalized risk factor vector for each geographic grid.

[0015] The normalized risk factor vectors are weighted and fused to generate a comprehensive risk value for each geographic grid.

[0016] Based on the comprehensive risk value of all geographic grids, a dynamic risk topology field is constructed within the coverage area of ​​the wireless communication network.

[0017] As a preferred embodiment of the data communication transmission method in the wireless communication network described in this invention, the step of generating preset communication requirement entries specifically involves...

[0018] Real-time trajectory data is discretized at fixed time intervals to generate motion state sequences;

[0019] The future trajectory of the motion state sequence is predicted, and a trajectory tree is generated;

[0020] The dynamic risk topology field is used to perform risk perception on the trajectory branches in the trajectory tree, and the trajectory branches that meet the risk tolerance conditions are selected to output the risk perception trajectory set.

[0021] In the risk perception trajectory set, high-probability prediction trajectories are selected based on the joint probability of trajectory branches occurring.

[0022] Communication requirements are inferred from high-probability predicted trajectories, and preset communication requirement entries are generated.

[0023] As a preferred embodiment of the data communication transmission method in the wireless communication network of the present invention, the future trajectory prediction refers to predicting the movement path within a future period based on the motion state sequence, according to kinematic laws and historical movement patterns, and organizing the movement path into a trajectory tree.

[0024] As a preferred embodiment of the data communication transmission method in the wireless communication network of the present invention, the risk perception refers to spatially matching the trajectory branches in the trajectory tree with the dynamic risk topology field, obtaining the comprehensive risk value of the coordinate points traversed by the trajectory branches, and judging whether the trajectory branches meet the movement path requirements required for safe communication based on the preset risk tolerance conditions.

[0025] As a preferred embodiment of the data communication transmission method in the wireless communication network described in this invention, wherein obtaining the forwarding path specifically involves:

[0026] Receive the preset communication data packets corresponding to the preset communication requirement entries;

[0027] Obtain the geographic coordinates of the current forwarding node and read the comprehensive risk value of the coordinate point where the current forwarding node is located from the dynamic risk topology field;

[0028] Iterate through all adjacent next-hop candidate nodes of the current forwarding node and obtain the geographical coordinates of the adjacent next-hop candidate nodes. Read the comprehensive risk value of the coordinates of the adjacent next-hop candidate nodes from the dynamic risk topology field.

[0029] Among all adjacent next-hop candidate nodes, select a set of candidate nodes whose comprehensive risk value is less than the comprehensive risk value of the current forwarding node;

[0030] When the candidate node set is not empty, select the neighboring node with the smallest comprehensive risk value from the candidate node set as the next hop node;

[0031] When the candidate node set is empty, the preset communication data packet is copied into multiple copies, and all adjacent next-hop candidate nodes are used as next-hop nodes to forward different copies;

[0032] The current forwarding node and the selected next-hop node are combined in the transmission order, and a copy identifier corresponding to the pre-set communication data packet is attached to form a forwarding path.

[0033] In a preferred embodiment of the data communication transmission method in the wireless communication network described in this invention, the step of performing implicit resource scheduling without signaling on the wireless communication network to complete the transmission of preset communication data packets specifically involves:

[0034] Monitor the current time and compare it with the expected occurrence time in the preset communication requirement entries, capture the preset communication requirement entries that have reached the expected occurrence time, and mark the status of the preset communication requirement entries as active;

[0035] Map the Quality of Service (QoS) level in the activated preset communication requirement entries to radio resource requirements, and associate the forwarding path corresponding to the preset communication requirement entries.

[0036] Extract the next-hop node from the associated forwarding path and allocate corresponding contiguous resource blocks to the next-hop node according to the radio resource requirements;

[0037] Through the downlink control channel of the wireless communication network, resource authorization information is silently sent to the next hop node without the next hop node sending any resource request;

[0038] After receiving the resource authorization information, the next-hop node directly sends the corresponding pre-set communication data packet on the authorized contiguous resource block to complete the transmission of the pre-set communication data packet.

[0039] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the data communication transmission method in a wireless communication network as described in the first aspect of the present invention.

[0040] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the data communication transmission method in a wireless communication network as described in the first aspect of the present invention.

[0041] The beneficial effects of this invention are as follows: By constructing a dynamic risk topology field, a refined characterization of the comprehensive risk value of each coordinate point within the coverage area of ​​the wireless communication network is achieved, expanding risk representation from single link quality to multi-dimensional security situation awareness; the dynamic risk topology field, as a unified spatial risk benchmark, supports the coordinated operation of subsequent trajectory prediction, routing decision-making, and resource scheduling under the same risk semantics, avoiding the path selection and resource allocation mismatch caused by the fragmentation of risk information in traditional methods; thus, in highly dynamic scenarios, the communication path is kept in a low-risk area throughout, and a reliable spatiotemporal basis is provided for signaling-free implicit scheduling, significantly improving the security of data transmission and the efficiency of resource utilization. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a data communication transmission method in a wireless communication network.

[0044] Figure 2 A flowchart for constructing a dynamic risk topology field.

[0045] Figure 3A flowchart for generating preset communication requirement entries.

[0046] Figure 4 This is a flowchart of routing decisions and resource scheduling. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a data communication transmission method in a wireless communication network, comprising the following steps:

[0051] S1. Acquire multi-source heterogeneous security data and calculate the comprehensive risk value of each coordinate point within the coverage area of ​​the wireless communication network. Construct a dynamic risk topology field based on the comprehensive risk value. The multi-source heterogeneous security data includes real-time trajectory data, status monitoring data, environmental perception data, and security management rule data.

[0052] Real-time trajectory data is collected through location tags (e.g., the location coordinate sequence [(x1, y1, t1), (x2, y2, t2)] reported by a mobile terminal via GPS location tags), status monitoring data is collected through device sensors (e.g., CPU utilization of user equipment is 80%, memory utilization is 75%, and link interruption times are 2 per hour), environmental perception data is collected through environmental sensors (e.g., electromagnetic interference intensity measured by environmental sensors is -70dBm, temperature is 35°C, humidity is 60%, and signal multipath attenuation is 15dB), and security management rule data is retrieved from the security management database (e.g., access control and violation judgment rules defined in the SQL policy library such as "prohibit unauthorized devices from accessing area A" and "maximum of 3 authentication failures allowed per hour").

[0053] Among them, the security management database is an existing database, such as a rule repository commonly used in wireless communication security, such as an SQL-based policy database, used to manage access control and risk rules; all data sources (i.e., multi-source heterogeneous security data) are uniformly converted to the same geographic coordinate system (such as WGS-84) before access, and the logical areas in the security management rules are mapped to the corresponding geographic grids through predefined geofences to ensure that cross-source data are spatially and semantically aligned; the predefined geofences are delineated based on the management needs and business scenarios of the wireless communication network coverage area, and the boundaries of the geofences are aligned with the preset geographic grids (such as 50 meters × 50 meters), and the size is usually one or more continuous geographic grid units.

[0054] Real-time trajectory data, status monitoring data, environmental perception data, and safety management rule data are synchronized and aligned in time to generate multi-source heterogeneous safety data.

[0055] It should be noted that, based on time synchronization and alignment, the multi-source heterogeneous security data undergoes standardized processing including semantic mapping, format normalization, and unit unification: real-time trajectory data is unified into a spatiotemporal point sequence in the WGS-84 coordinate system; status monitoring data is mapped to a predefined set of general indicators (such as CPU utilization and link interruption frequency) according to device type; environmental perception data is converted into standard physical units and registered to the same geographic grid coordinate system; and security management rule data is structured into quantifiable factors (such as the number of violations and permission matching degree) through rule parsing, thereby generating semantically consistent, format-unified, and fusionable multi-source heterogeneous security data.

[0056] Specifically, this involves: adding timestamps for the collection times of real-time trajectory data, status monitoring data, environmental perception data, and safety management rule data; matching the timestamps of real-time trajectory data with those of status monitoring data, matching the timestamps of status monitoring data with those of environmental perception data, and matching the timestamps of environmental perception data with those of safety management rule data; and based on the matched timestamps, combining real-time trajectory data and status monitoring data under the same timestamp, and combining status monitoring data with environmental perception data... Sensing data is combined under the same timestamp. Specifically, environmental sensing data and safety management rule data are combined under the same timestamp. The sliding window nearest neighbor alignment strategy is adopted. For any two types of real-time trajectory data, status monitoring data, environmental sensing data and safety management rule data, if the timestamp difference is within the preset tolerance window (set based on the typical collection period and transmission delay statistical distribution of various data sources in the wireless communication network, and the value is a certain percentage of the minimum collection period), it is considered to be successfully matched and combined. If the timestamp difference does not fall into the preset tolerance window, the original timestamp is retained but does not participate in the current fusion. It is temporarily stored in the buffer to wait for retry in subsequent periods or discarded after timeout.

[0057] By combining real-time trajectory data, status monitoring data, environmental perception data, and safety management rule data, multi-source heterogeneous safety data is formed.

[0058] It should be noted that the combination order is aligned step by step according to the data dependency chain: real-time trajectory data and status monitoring data are matched first because they belong to the same node ontology perception layer; status monitoring data and environmental perception data are associated secondarily because they co-occur at the same physical location and time; and environmental perception data and safety management rule data are finally connected because the rule triggering depends on the environmental context. This ensures that multi-source heterogeneous safety data are consistent in spatiotemporal semantics layer by layer, rather than being arbitrarily spliced ​​together in pairs.

[0059] Risk factors are extracted from multi-source heterogeneous security data on a preset geographic grid, and the extracted risk factors are normalized to generate a normalized risk factor vector for each geographic grid. The preset geographic grid is set based on the boundary and resolution of the wireless communication network coverage area, such as 50 meters × 50 meters, to discretize the continuous space into uniform units, which facilitates the mapping of multi-source heterogeneous security data and the extraction and fusion of risk factors at each coordinate point.

[0060] Specifically, this involves: mapping multi-source heterogeneous security data to each geographic grid location within a pre-defined geographic grid; extracting a trajectory velocity factor (range 0 to 1) from the real-time trajectory data mapped to each geographic grid location (e.g., a trajectory velocity factor of 0.3 indicating a low risk level when a node moves linearly at a constant speed of 5 m / s); extracting a trajectory direction factor (range 0 to 1, e.g., a trajectory direction factor of 0.5 indicating a medium risk level when the trajectory direction deviates from the historical average direction by 15 degrees); extracting a failure rate factor (range 0 to 1, e.g., a failure rate factor of 0.5 indicating a medium risk level when the probability of equipment failure in the past hour is 5%) from the status monitoring data; and extracting a load factor (range 0 to 1, e.g., a load factor of 0 to 1 when the node resource utilization rate reaches 8%). At 0%, the load factor is 0.8, indicating a high-risk level. An interference intensity factor, ranging from 0 to 1, is extracted from environmental perception data. For example, when the interference signal power is 10 dB higher than the noise power in a wireless environment, the interference intensity factor is 0.7, indicating a relatively high-risk level. A signal attenuation factor, also ranging from 0 to 1, is extracted from security management rule data. For example, when a signal attenuates by 15 dB on a multipath propagation path, the signal attenuation factor is 0.6, indicating a medium-risk level. A violation frequency factor, ranging from 0 to 1, is extracted from security management rule data. For example, when two violations occur in the past hour, the violation frequency factor is 0.4, indicating a low-risk level. Finally, an access control factor, ranging from 0 to 1, is extracted. For example, when the access permission matching rate is 85%, the access control factor is 0.15, indicating a low-risk level.

[0061] It should be noted that the trajectory speed factor is determined based on the relative position of the current moving speed within the historical speed range; the trajectory direction factor is determined based on the degree of deviation of the current moving direction from the historical average direction; the failure rate factor is determined based on the frequency of failures of each node device in the wireless communication network, including base stations, user terminals, and relay equipment, in recent operation; the load factor is determined based on the relative position of the node resource utilization level between the normal and overload boundaries; the interference intensity factor is determined based on the strength of the interference signal relative to the noise in the wireless environment; the signal attenuation factor is determined based on the degree of signal weakening in the propagation path; and the violation frequency factor is determined based on the unit The access control factor is determined by the number of times security rules, such as access control policies (e.g., "only authorized devices are allowed to access in designated areas"), behavioral compliance constraints (e.g., "a single terminal may not attempt authentication more than 3 times per minute"), and resource usage restrictions (e.g., "high-priority services may exclusively use specific frequency bands") are violated within a given time period.

[0062] The extracted trajectory velocity factor, trajectory direction factor, failure rate factor, load factor, interference intensity factor, signal attenuation factor, violation frequency factor, and access control factor are adjusted (i.e., using a minimum-maximum normalization method, the original value of each risk factor is linearly mapped to the minimum and maximum values ​​in historical operating data, so that the result strictly falls within the 0 to 1 range) to a unified range of zero to one, forming normalized trajectory velocity factor, normalized trajectory direction factor, normalized failure rate factor, normalized load factor, normalized interference intensity factor, normalized signal attenuation factor, normalized violation frequency factor, and normalized access control factor for each geographic grid location. The normalized trajectory velocity factor, normalized trajectory direction factor, normalized failure rate factor, normalized load factor, normalized interference intensity factor, normalized signal attenuation factor, normalized violation frequency factor, and normalized access control factor for each geographic grid location are arranged in a fixed order (i.e., arranged according to the extraction order of multi-source heterogeneous security data sources), generating a normalized risk factor vector corresponding to each geographic grid.

[0063] It should be noted that the values ​​of the trajectory speed factor, trajectory direction factor, failure rate factor, load factor, interference intensity factor, signal attenuation factor, violation frequency factor, and access control factor are all in the range of 0-1. This is because the normalization process scales the original values ​​of different dimensions to the [0, 1] interval, which facilitates the subsequent weighted fusion to generate a comprehensive risk value and to compare risks.

[0064] The normalized risk factor vectors are weighted and fused to generate a comprehensive risk value for each geographic grid, expressed as follows:

[0065] ;

[0066] In the formula, For geographic grids The overall risk value, For geographic grids The Normalized risk factors ( This is the normalized trajectory velocity factor. This is the normalized trajectory direction factor. The normalized failure rate factor, For the normalized load factor, This is the normalized interference intensity factor. This is the normalized signal attenuation factor. To normalize the violation frequency factor, (for normalized access control factors) For the first The weights of each normalized risk factor (e.g., satisfy ), 8 represents the total number of normalized risk factors, namely 8 specific factors: normalized trajectory speed factor, normalized trajectory direction factor, normalized failure rate factor, normalized load factor, normalized interference intensity factor, normalized signal attenuation factor, normalized violation frequency factor, and normalized access control factor.

[0067] It should be noted that the risk factor normalization process adopts the minimum-maximum normalization method. For each risk factor among trajectory speed factor, trajectory direction factor, failure rate factor, load factor, interference intensity factor, signal attenuation factor, violation frequency factor, and access control factor, the minimum and maximum values ​​of the risk factor during the historical operation of the wireless communication network are first determined. Then, the original risk factor value extracted at the current moment is linearly mapped between the minimum and maximum values, so that the mapped value strictly falls within the range of 0 to 1. Specifically, when the original risk factor value is equal to the historical minimum value, it is mapped to 0; when the original risk factor value is equal to the historical maximum value, it is mapped to 1; and when the original risk factor value is between the minimum and maximum values, it is proportionally mapped to the range of 0 to 1, thereby completing the normalization process of all risk factors.

[0068] It should also be noted that the trajectory speed factor, for example, is set to 0.3, based on the fact that the node moves at a constant linear speed of 5 m / s, which is relatively low within the historical speed range; the trajectory direction factor, for example, is set to 0.5, based on the fact that the current direction of movement deviates from the historical average direction by 15 degrees, which is a moderate deviation; the failure rate factor, for example, is set to 0.5, based on the fact that the frequency of node device failures in the past hour is in the middle of the historical common range; the load factor, for example, is set to 0.8, based on the fact that the node resource utilization rate has reached the high load area in historical operation; the interference intensity factor, for example, is set to 0.7, based on the fact that the power of the interference signal in the wireless environment is significantly higher than the noise floor, indicating a strong interference state; the signal attenuation factor, for example, is set to 0.6, based on the fact that the signal experiences a moderate degree of attenuation in the multipath propagation path; the violation frequency factor, for example, is set to 0.4, based on the fact that there were 2 security rule violation events in the past hour, which is a low frequency; and the access control factor is set to 0.15, based on the fact that the access request matches the authorization policy well and deviates from the authorized range little.

[0069] The weights of the normalized risk factors are determined using a combination of offline training and online adaptation. Offline statistics are performed on historical datasets before the deployment of the wireless communication network to rank the correlation between each normalized risk factor and actual communication failure events. Risk factors with stronger correlations are assigned larger initial weights, resulting in eight initial weight values, with the sum of the eight initial weights guaranteed to be 1. Subsequently, during the actual operation of the wireless communication network, the correlation between recent communication failure events and each normalized risk factor is re-statistically analyzed at fixed intervals. Based on the statistical results, the eight weight values ​​are slightly adjusted, while still maintaining the sum of the eight weights at 1. This allows the weights to adaptively update with changes in the network environment, while ensuring that the weighted fusion formula remains effective.

[0070] It should be noted that the initial weights are determined by analyzing the co-occurrence patterns and trends of historical communication failure events and various normalized risk factors during the offline training phase, and the weights are fine-tuned based on the dynamic correlation strength between recent communication anomalies and each normalized risk factor during the online adaptive phase.

[0071] Based on the comprehensive risk value of all geographic grids, a dynamic risk topology field is constructed within the coverage area of ​​the wireless communication network.

[0072] Specifically, the process involves: associating the comprehensive risk value of all geographic grids with the coordinates of each grid; organizing the associated comprehensive risk values ​​and coordinates of all geographic grids into a continuous distribution according to the boundary of the wireless communication network coverage area. The boundary is set based on the geographic coordinates of the effective coverage of the base station signal, for example, a radius of 1-2 kilometers in urban 5G networks and a radius of 5-10 kilometers in suburban 4G networks, to confine the dynamic risk topology field within a reliable communication area; expanding the organized comprehensive risk values ​​and coordinates of all geographic grids into a planar representation covering the entire wireless communication network coverage area; and forming a two-dimensional risk numerical field by assigning a comprehensive risk value to each coordinate position in the planar representation. This two-dimensional risk numerical field is the dynamic risk topology field.

[0073] S2. Utilize dynamic risk topology fields to perform risk perception trajectory prediction analysis on real-time trajectory data and generate pre-set communication requirement entries.

[0074] Real-time trajectory data is discretized and sampled at fixed time intervals to generate motion state sequences.

[0075] Specifically, the real-time trajectory data is divided into multiple continuous time segments (e.g., 5 time segments with 1-second intervals within a 5-second observation window) according to fixed time intervals (e.g., 1 second for urban vehicle trajectories or 0.5 seconds for high-speed drone trajectories). The position coordinates, velocity magnitude, velocity direction, and acquisition time are extracted from the real-time trajectory data within each time segment. The extracted position coordinates, velocity magnitude, velocity direction, and acquisition time are arranged in chronological order of the time segments. The combined and arranged position coordinates, velocity magnitude, velocity direction, and acquisition time form a motion state sequence.

[0076] The motion state sequence is used to predict future trajectories and generate trajectory trees. Future trajectory prediction refers to predicting all possible movement paths within a certain period of time based on the motion state sequence, according to kinematic laws and historical movement patterns, and organizing the movement paths into trajectory trees.

[0077] Specifically, starting from the last record of the motion state sequence, extract the current position coordinates, current velocity magnitude, and current velocity direction; using the current position coordinates as the starting point, generate the first predicted path segment based on the current velocity magnitude and current velocity direction. Generating the first predicted path segment means extending a distance forward from the current position coordinates along the current velocity direction. This extension distance is determined by the current velocity magnitude and a fixed prediction time step (e.g., 1 second), yielding the next position coordinates and forming the first predicted path segment; from the end point of the first predicted path segment, continue generating multiple different directions (e.g., using the current velocity direction as a reference, generate deflections of -30°, -15°, and 0° respectively). The system generates five predicted path branches (in five directions: +15°, +30°, +15°, and +30°), each corresponding to a different velocity direction variation range. This velocity direction variation range is determined based on kinematic laws and historical movement patterns. For example, in urban vehicle scenarios, the predicted steering angle variation range for each step is -30° to +30°, while in drone scenarios it is -45° to +45°. Each predicted path branch is repeatedly extended to generate subsequent branches, forming a multi-layered predicted path. All predicted path branches are organized into a multi-branched tree structure that expands outward from the root node, using the position coordinates of the last record in the motion state sequence as the root node, according to the hierarchical connection relationship between the start and end points. This is the trajectory tree.

[0078] The dynamic risk topology field is used to perform risk perception on the trajectory branches in the trajectory tree, filter out the trajectory branches that meet the risk tolerance conditions, and output the risk perception trajectory set. Risk perception refers to spatially matching the trajectory branches in the trajectory tree with the dynamic risk topology field, obtaining the comprehensive risk value of the coordinate points passed by the trajectory branch, and judging whether the trajectory branch meets the movement path requirements required for secure communication based on the preset risk tolerance conditions.

[0079] Specifically, the process involves: mapping each trajectory branch in the trajectory tree to a corresponding coordinate position sequence in the dynamic risk topology field; extracting the comprehensive risk value sequence of the coordinate position sequence traversed by each trajectory branch from the dynamic risk topology field; comparing the comprehensive risk value sequence of each trajectory branch with a preset risk tolerance condition; retaining the trajectory branch if all comprehensive risk values ​​in the comprehensive risk value sequence of each trajectory branch are lower than the preset risk tolerance threshold; and combining all retained trajectory branches into a risk-aware trajectory set.

[0080] It should be noted that the risk tolerance threshold is a specific numerical value, while the risk tolerance condition is a judgment rule defined based on the risk tolerance threshold. The two are not the same thing.

[0081] The preset risk tolerance condition requires that the comprehensive risk value of all coordinate points traversed by each trajectory branch must be lower than the preset risk tolerance threshold. This preset risk tolerance condition is determined based on the security transmission requirements of the wireless communication network and is used to ensure that the risk is controllable throughout the communication path. The security transmission requirements of the wireless communication network ensure that the bit error rate, latency, and resistance to illegal interception and tampering during data transmission meet the reliability and confidentiality standards agreed upon by the network protocol and service level. The preset risk tolerance threshold is set based on the distribution characteristics of the wireless communication network, and its value is taken from the percentile or mean of the risk value sequence plus a certain number of standard deviations to retain a sufficient number of feasible trajectory branches while ensuring communication security. If the value is too low, only extremely low-risk paths will be retained, resulting in insufficient prediction coverage; if the value is too high, unacceptably high-risk areas will be included, weakening transmission reliability.

[0082] In the risk perception trajectory set, high-probability prediction trajectories are selected based on the joint probability of trajectory branches occurring.

[0083] The joint probability of trajectory branches occurring is expressed as follows:

[0084] ;

[0085] In the formula, 'b' represents the joint probability of a trajectory branch from its starting point to its ending point, ranging from 0 to 1. It is used to filter high-probability predicted trajectories from the risk-aware trajectory set. 'b' represents a specific trajectory branch, i.e., the complete path from the root node of the trajectory tree to a certain leaf node. This represents the node index in the trajectory branch, starting from the root node and incrementing sequentially from 1. It is used to identify the node position where the joint probability is currently being calculated. This represents the total number of nodes contained in the trajectory branch, that is, the number of nodes in the trajectory branch from the root node to the leaf node. This is a probability function used to calculate the likelihood of the next node state occurring given a sequence of historical node states. For the first branch of the trajectory The state of each node specifically includes its position coordinates, velocity magnitude, and velocity direction. This refers to the state of the first node in the trajectory branch, i.e., the state of the root node, specifically including the starting position coordinates, the starting velocity magnitude, and the starting velocity direction. For the current node The total number of nodes that have appeared so far is used to define the range of the historical node state sequence given in the conditional probability, i.e., the previous... The state sequence of each node arrive Conditional probability is given before the known trajectory branches. In the case of the nth node state sequence, the nth node... The probability of each node's state occurring is determined by analyzing the same previous node states in the historical movement pattern dataset. The frequency of the kth node state following the sequence of node states is determined by statistical analysis.

[0086] It should be noted that the joint probability of trajectory branches is obtained through statistical analysis of historical mobility pattern data. Historical mobility pattern data originates from long-term recorded trajectory logs of terminals or nodes in wireless communication networks. This includes location, speed, and direction information reported by vehicles, drones, or user equipment at fixed time intervals (e.g., 1 second for urban vehicle trajectories, 0.5 seconds for high-speed drone trajectories, consistent with the discretized sampling interval of real-time trajectory data) in different scenarios. The sample size covers typical operating cycles over multiple days (e.g., 7 or 30 consecutive days), and is discretized according to geographic grids and motion states for statistical modeling. The trajectory sample data in the historical mobility pattern data is discretized according to the same time intervals as the real-time trajectory, and each time step is mapped to a discrete motion state based on the location grid, speed range, and direction range. Any two adjacent time steps of discrete motion states are considered as a pair of state transitions. The number of times the previous discrete motion state and the next discrete motion state appear together in the trajectory sample data of all historical mobility pattern data is counted, along with the total number of times the previous discrete motion state appears alone. For state transitions, the ratio of the number of times the state transition occurs to the total number of times the corresponding previous discrete motion state occurs is used. , representing the conditional probability of a state transition occurring given a previous discrete motion state; for a single trajectory branch, multiple discrete motion states are sequentially passed from the starting point to the ending point of the trajectory branch, with each adjacent discrete motion state corresponding to a conditional probability. The joint probability of the trajectory branch represents the comprehensive probability that all state transitions will occur consecutively along the trajectory branch from the starting point to the ending point in a predetermined order (the temporal order from the root node to the leaf node in the trajectory branch, i.e., the order of the discrete motion state sequence that progresses sequentially according to the predicted time step). Specifically, it involves sequentially combining the conditional probabilities corresponding to each segment of state transition to obtain the joint probability value; in actual practice... During the process, to avoid the joint probability being zero due to extremely low-frequency state transitions or unobserved state transitions, a smoothing constant is introduced when counting the occurrences of state transitions. The value is determined based on the sparsity of the historical movement pattern data and the size of the state space. Typically, Laplace smoothing (i.e., adding 1) or a small positive number based on the proportion of the total number of state transitions is used to ensure that unobserved but physically feasible state transitions have a non-zero probability. Physically possible state transitions that do not appear in the trajectory sample data of the historical movement pattern data are assigned a very small but non-zero probability value (i.e., assigning an unobserved but physically possible state transition a positive probability value close to zero, such as 10). -6 Alternatively, the conditional probability is based on the minimum allocatable probability unit after normalization of the total number of states; through the above statistical and combination processes, the joint probability of candidate trajectory branches is calculated based on historical movement pattern data, and the calculated joint probability is compared with a preset high probability threshold to select high probability predicted trajectories from the candidate trajectory branches.

[0087] Specifically, the joint probability of each trajectory branch in the risk-aware trajectory set is compared with a preset high-probability threshold. If the joint probability of a trajectory branch in the risk-aware trajectory set is higher than the preset high-probability threshold, then that trajectory branch in the risk-aware trajectory set is retained. All retained trajectory branches in the risk-aware trajectory set are combined into a high-probability prediction trajectory. The preset high-probability threshold is determined based on the percentile (e.g., 90th percentile) or the mean plus several times the standard deviation of the joint probability distribution of trajectory branches in historical movement patterns, to ensure that the retained trajectory branches have a high probability of occurrence in actual operation, while avoiding the situation where there are no trajectories to choose from due to an excessively high high-probability threshold or the introduction of too many low-confidence paths due to an excessively low high-probability threshold.

[0088] Communication requirements are inferred from high-probability predicted trajectories, and preset communication requirement entries are generated.

[0089] Specifically, the process involves: extracting the estimated arrival time and estimated dwell area for each trajectory branch in the high-probability predicted trajectory. The estimated arrival time and dwell area are determined based on the path direction, movement trend, and historical dwell behavior patterns of each trajectory branch. Based on the service type within the estimated dwell area (including voice communication, video transmission, control signaling transmission, sensor data reporting, and file download), corresponding service quality levels are matched, including low-latency high-reliability, normal reliability, and high-throughput low-priority levels. The estimated arrival time, estimated dwell area, and service quality level are combined into a pre-defined communication requirement entry. Finally, the pre-defined communication requirement entries generated by all trajectory branches in the high-probability predicted trajectory are arranged in chronological order of estimated arrival time to form a complete set of pre-defined communication requirement entries.

[0090] S3. For the preset communication data packets corresponding to the preset communication requirement items, perform risk gradient-aware routing decisions to obtain the forwarding path.

[0091] Receive the preset communication data packet corresponding to the preset communication requirement entry; wherein, the preset communication data packet is the specific data content that is prepared when the preset communication requirement entry is generated and is waiting for the expected arrival time in the future to trigger the transmission.

[0092] The system obtains the geographic coordinates of the current forwarding node and reads the comprehensive risk value of the coordinates of the current forwarding node from the dynamic risk topology field. The geographic coordinates of the current forwarding node are obtained in real time through the node's built-in positioning tag (such as GPS or Beidou).

[0093] Iterate through all adjacent next-hop candidate nodes of the current forwarding node and obtain the geographical coordinates of the adjacent next-hop candidate nodes. Read the comprehensive risk value of the coordinates of the adjacent next-hop candidate nodes from the dynamic risk topology field.

[0094] Among all adjacent next-hop candidate nodes, select a set of candidate nodes whose overall risk value is less than that of the current forwarding node.

[0095] Specifically, the process involves: comparing the overall risk value of the current forwarding node with the overall risk values ​​of all adjacent next-hop candidate nodes one by one; selecting all adjacent next-hop candidate nodes whose overall risk value is less than that of the current forwarding node; and combining all selected adjacent next-hop candidate nodes into a candidate node set.

[0096] When the candidate node set is not empty, the neighboring node with the smallest comprehensive risk value is selected as the next hop node from the candidate node set. The candidate node set is considered non-empty when it contains at least one neighboring next hop candidate node. The smallest neighboring node is selected by comparing the comprehensive risk values ​​of all neighboring next hop candidate nodes in the candidate node set and choosing the neighboring next hop candidate node with the smallest value as the next hop node.

[0097] When the candidate node set is empty, the preset communication data packet is copied into multiple copies, and all adjacent next-hop candidate nodes are used as next-hop nodes to forward different copies. The candidate node set is empty when the comprehensive risk value of all adjacent next-hop candidate nodes is not less than the comprehensive risk value of the current forwarding node.

[0098] The current forwarding node and the selected next-hop node are combined in the transmission order, and a copy identifier corresponding to the pre-set communication data packet is attached to form a forwarding path. The copy identifier is generated by the source node when the data packet is copied. The format is a combination of source node ID, timestamp and branch sequence number. It is used by the receiving end to identify different copies of the same data packet and perform deduplication and valid path confirmation.

[0099] S4. Based on the preset communication requirement entries and forwarding paths, perform implicit resource scheduling without signaling on the wireless communication network to complete the transmission of preset communication data packets.

[0100] The system monitors the current time and compares it with the expected occurrence time in the preset communication requirement entries. It captures the preset communication requirement entries that have reached the expected occurrence time and marks the status of the preset communication requirement entries as active. Among them, each node in the wireless communication network and the central scheduling node adopt a network-wide clock synchronization mechanism based on the network time protocol to ensure that the time base of each node is consistent with that of the central scheduling unit, so as to support the accurate matching and activation of the expected occurrence time in the preset communication requirement entries.

[0101] Specifically, the current time is matched one by one with the expected occurrence time in the preset communication requirement entries (i.e., the time predicted in the preset communication requirement entries when the node arrives at the specified area and needs to start transmitting data, such as "2020-11-10 14:30:27"); when the current time is equal to the expected occurrence time in the preset communication requirement entries, the matched preset communication requirement entries are captured; the captured preset communication requirement entries are marked as active; all captured preset communication requirement entries are marked as active to form active preset communication requirement entries.

[0102] Map the Quality of Service (QoS) level in the activated preset communication requirement entries to radio resource requirements, and associate the forwarding path corresponding to the preset communication requirement entries.

[0103] Specifically, the following steps are taken: The service quality level in the activated preset communication requirement entries is matched with a preset resource mapping table; the low-latency, high-reliability level is matched with a high-priority contiguous resource block and a low-order modulation and coding scheme; the normal reliability level is matched with a medium-priority contiguous resource block and a medium-order modulation and coding scheme; the high-throughput, low-priority level is matched with a low-priority contiguous resource block and a high-order modulation and coding scheme; the matched high-priority contiguous resource block and low-order modulation and coding scheme, or medium-priority contiguous resource block and medium-order modulation and coding scheme, or low-priority contiguous resource block and high-order modulation and coding scheme, are determined as radio resource requirements; the forwarding path corresponding to the activated preset communication requirement entry is bound to the determined radio resource requirement, forming complete scheduling information including the forwarding path and the radio resource requirement.

[0104] The pre-defined resource mapping table is based on the correspondence between QoS identifiers and physical resource block allocation in 3GPP, explicitly linking service type, service quality level, resource block priority, and modulation and coding scheme: low latency and high reliability level corresponds to scenarios such as control signaling transmission or emergency sensor data reporting, and is mapped to high-priority contiguous resource blocks and low-order modulation and coding schemes to ensure extremely low latency and high transmission reliability; normal reliability level corresponds to scenarios such as regular voice communication or video transmission, and is mapped to medium-priority contiguous resource blocks and medium-order modulation and coding schemes to balance latency and throughput requirements (i.e., the amount of data that the service needs to transmit per unit time, reflecting the requirements for network bandwidth and transmission efficiency); high throughput and low priority level corresponds to scenarios such as file download or non-real-time data reporting, and is mapped to low-priority contiguous resource blocks and high-order modulation and coding schemes to prioritize improving spectrum efficiency rather than latency performance; the correspondence between service type and resource allocation strategy is explicitly defined by the pre-defined resource mapping table.

[0105] Extract the next-hop node from the associated forwarding path and allocate corresponding contiguous resource blocks to the next-hop node according to the radio resource requirements.

[0106] Specifically, the process involves: extracting the first-hop node from the forwarding path as the next-hop node from the complete scheduling information containing the forwarding path and radio resource requirements; selecting unoccupied continuous resource blocks from the available resource block pool that meet the priority requirements (based on the scheduling priority order of resource blocks determined by the quality of service level, i.e., the specific number of continuous resource blocks, priority, and modulation and coding scheme combination required by the next-hop node according to the quality of service level); binding the selected continuous resource blocks with the next-hop node; and recording the bound next-hop node and continuous resource blocks in the resource authorization information.

[0107] Resource grant information is silently sent to the next hop node via the downlink control channel of the wireless communication network, even if the next hop node does not send any resource request.

[0108] Specifically, the process involves: encapsulating the next-hop node and contiguous resource blocks recorded in the resource authorization information into a downlink control channel message; directly sending the encapsulated downlink control channel message to the next-hop node via the downlink control channel of the wireless communication network; receiving the downlink control channel message without actively sending any resource requests; and parsing the start position and length of the contiguous resource blocks from the received downlink control channel message (i.e., the next-hop node directly reads the start position field value and length field value of the contiguous resource blocks from the fixed fields of the message according to the downlink control channel message format agreed upon by the wireless communication network) to form complete resource authorization information.

[0109] After receiving the resource authorization information, the next-hop node directly sends the corresponding pre-set communication data packet on the authorized contiguous resource block to complete the transmission of the pre-set communication data packet.

[0110] Specifically: the next-hop node reads the start position and length of the contiguous resource block from the parsed resource authorization information; starting from the start position of the read contiguous resource block, the next-hop node directly puts the corresponding preset communication data packet into the contiguous resource block; after the contiguous resource block is completely filled with the preset communication data packet, the next-hop node sends it out through the wireless channel; after the next-hop node completes the transmission, it marks the corresponding preset communication data packet transmission status as completed; after receiving the preset communication data packet, the receiving node returns an acknowledgment message to the source node, completing the transmission of the preset communication data packet.

[0111] This embodiment also provides a computer device applicable to a data communication transmission method in a wireless communication network, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the data communication transmission method in a wireless communication network as proposed in the above embodiment.

[0112] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0113] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the data communication transmission method in a wireless communication network as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0114] In summary, this invention achieves a refined characterization of the comprehensive risk value of each coordinate point within the coverage area of ​​a wireless communication network by constructing a dynamic risk topology field, thus expanding risk representation from single link quality to multi-dimensional security situation awareness. The dynamic risk topology field serves as a unified spatial risk benchmark, supporting subsequent trajectory prediction, routing decisions, and resource scheduling to operate collaboratively under the same risk semantics, avoiding path selection and resource allocation mismatches caused by fragmented risk information in traditional methods. Therefore, in highly dynamic scenarios, the communication path remains in a low-risk area throughout, providing a reliable spatiotemporal basis for signaling-free implicit scheduling, significantly improving data transmission security and resource utilization efficiency.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A data communication transmission method in a wireless communication network, characterized in that: include, Acquire multi-source heterogeneous security data and calculate the comprehensive risk value of each coordinate point within the coverage area of ​​the wireless communication network. Construct a dynamic risk topology field based on the comprehensive risk value. The multi-source heterogeneous security data includes real-time trajectory data, status monitoring data, environmental perception data, and security management rule data; By utilizing dynamic risk topology fields, trajectory prediction analysis is performed on real-time trajectory data to generate pre-defined communication requirement entries. For the pre-defined communication data packets corresponding to the pre-defined communication requirement entries, perform risk gradient-aware routing decisions to obtain forwarding paths; Based on the pre-set communication requirement entries and forwarding paths, implicit resource scheduling without signaling is performed on the wireless communication network to complete the transmission of pre-set communication data packets; The construction of a dynamic risk topology field based on comprehensive risk values ​​specifically involves... Risk factors are extracted from multi-source heterogeneous security data on a preset geographic grid, and the extracted risk factors are normalized to generate a normalized risk factor vector for each geographic grid. The normalized risk factor vectors are weighted and fused to generate a comprehensive risk value for each geographic grid. Based on the comprehensive risk value of all geographic grids, a dynamic risk topology field is constructed within the coverage area of ​​the wireless communication network; The generation of preset communication requirement entries specifically refers to... Real-time trajectory data is discretized at fixed time intervals to generate motion state sequences; The future trajectory of the motion state sequence is predicted, and a trajectory tree is generated; The dynamic risk topology field is used to perform risk perception on the trajectory branches in the trajectory tree, and the trajectory branches that meet the risk tolerance conditions are selected to output the risk perception trajectory set. In the risk perception trajectory set, high-probability prediction trajectories are selected based on the joint probability of trajectory branches occurring. Communication requirements are inferred from high-probability predicted trajectories, and preset communication requirement entries are generated.

2. The data communication transmission method in a wireless communication network as described in claim 1, characterized in that: The multi-source heterogeneous security data is generated by synchronizing and aligning real-time trajectory data, status monitoring data, environmental perception data, and security management rule data in time.

3. The data communication transmission method in a wireless communication network as described in claim 1, characterized in that: The future trajectory prediction refers to predicting the movement path within a certain period of time based on the sequence of motion states, according to kinematic laws and historical movement patterns, and organizing the movement path into a trajectory tree.

4. The data communication transmission method in a wireless communication network as described in claim 1, characterized in that: The risk perception refers to spatially matching the trajectory branches in the trajectory tree with the dynamic risk topology field, obtaining the comprehensive risk value of the coordinate points traversed by the trajectory branch, and judging whether the trajectory branch meets the movement path requirements for secure communication based on the preset risk tolerance conditions.

5. The data communication transmission method in a wireless communication network as described in claim 1, characterized in that: The obtained forwarding path is specifically as follows: Receive the preset communication data packets corresponding to the preset communication requirement entries; Obtain the geographic coordinates of the current forwarding node and read the comprehensive risk value of the coordinate point where the current forwarding node is located from the dynamic risk topology field; Iterate through all adjacent next-hop candidate nodes of the current forwarding node and obtain the geographical coordinates of the adjacent next-hop candidate nodes. Read the comprehensive risk value of the coordinates of the adjacent next-hop candidate nodes from the dynamic risk topology field. Among all adjacent next-hop candidate nodes, select a set of candidate nodes whose overall risk value is less than that of the current forwarding node; When the candidate node set is not empty, select the neighboring node with the smallest comprehensive risk value from the candidate node set as the next hop node; When the candidate node set is empty, the preset communication data packet is copied into multiple copies, and all adjacent next-hop candidate nodes are used as next-hop nodes to forward different copies; The current forwarding node and the selected next-hop node are combined in the transmission order, and a copy identifier corresponding to the pre-set communication data packet is attached to form a forwarding path.

6. The data communication transmission method in a wireless communication network as described in claim 1, characterized in that: The aforementioned implicit resource scheduling without signaling for the wireless communication network to complete the transmission of pre-set communication data packets specifically involves: Monitor the current time and compare it with the expected occurrence time in the preset communication requirement entries, capture the preset communication requirement entries that have reached the expected occurrence time, and mark the status of the preset communication requirement entries as active; Map the Quality of Service (QoS) level in the activated preset communication requirement entries to radio resource requirements, and associate the forwarding path corresponding to the preset communication requirement entries. Extract the next-hop node from the associated forwarding path and allocate corresponding contiguous resource blocks to the next-hop node according to the radio resource requirements; Through the downlink control channel of the wireless communication network, resource authorization information is silently sent to the next hop node without the next hop node sending any resource request; After receiving the resource authorization information, the next-hop node directly sends the corresponding pre-set communication data packet on the authorized contiguous resource block to complete the transmission of the pre-set communication data packet.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the data communication transmission method in the wireless communication network according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the data communication transmission method in the wireless communication network according to any one of claims 1 to 6.

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