Method and system for optimizing allocation of communication resources based on wireless network cooperation

Abstract

This invention relates to the field of telecommunications technology, specifically to a method and system for optimizing the allocation of communication resources based on wireless network collaboration. The method includes acquiring physical motion state information of a moving target, channel state information of a millimeter-wave link, spatial structure information of the communication path, and service delay constraint information; constructing a first Fresnel zone spatial region based on the communication path and establishing an occupancy state model to calculate the degree of occupancy and its changing trend; predicting the evolution stage and duration of obstruction events by combining the changing trend of the channel state information; generating resource scheduling decision results; and executing a hierarchical resource scheduling strategy according to the scheduling strategy identifier to dynamically adjust millimeter-wave link resources. This method can distinguish the evolution stage of obstruction events and predict their duration, ensuring that the resource scheduling decision results match the service delay constraint information, reducing unnecessary cross-link resource switching, and improving the utilization efficiency and communication stability of millimeter-wave link resources.

Description

Technical Field

[0001] This invention relates to the field of telecommunications, specifically to a method and system for optimizing the allocation of communication resources based on wireless network collaboration. Background Technology

[0002] In industrial wireless communication scenarios, with the development of intelligent manufacturing and collaborative equipment operations, millimeter-wave frequency band-based communication technologies are widely used to meet the demands for high-bandwidth, low-latency data transmission. This type of communication typically relies on the spatial propagation path between the base station and the communication terminal to complete data transmission. Especially in high-frequency millimeter-wave links, communication performance is highly sensitive to the spatial structure of the propagation path, with the direct path playing a dominant role in the overall signal transmission process.

[0003] In existing technologies, resource allocation for wireless communication links is typically based on channel state information for scheduling and control. This involves real-time monitoring of parameters such as received signal strength, channel quality indicators, or link bit error rate. When a decline in channel state is detected, the system triggers corresponding resource adjustment mechanisms, including reducing modulation and coding levels, adjusting transmission power, switching communication frequency bands, or performing cross-base station link handover. This type of technical solution usually uses changes in channel state as a direct decision-making basis, determining channel quality by setting thresholds and executing resource scheduling strategies accordingly. In multi-link environments, communication path switching and compensation can also be achieved through backup links or reflection paths.

[0004] The inventors of this application have discovered the following technical problems with the aforementioned technology in industrial millimeter-wave communication scenarios: Existing systems primarily rely on channel state information for resource scheduling, lacking the ability to model the temporal structure of channel changes and failing to distinguish between short-term disturbances and sustained attenuation, resulting in a lack of targeted resource scheduling strategy selection. In other words, the system assumes the link is "broken" as soon as it sees a signal degradation, unaware that it is only temporarily blocked. Furthermore, during obstruction caused by random personnel movement, the attenuation of the communication link exhibits a gradual change characteristic, but existing technologies typically employ instantaneous threshold triggering mechanisms, failing to continuously track and predict the evolution of the obstruction process. That is, the signal deteriorates slowly, but the system treats it as a "sudden deterioration," completely failing to understand this change process. At the same time, existing solutions lack the ability to predict the duration of obstruction and cannot reasonably judge the timing of resource switching in conjunction with service latency constraints, easily triggering unnecessary link switching in short-term obstruction scenarios. Simply put, what could have been a short wait for recovery is instead rushed by the system to switch, resulting in even greater instability. On the other hand, when the obstruction lasts for a long time, the lack of a proactive judgment mechanism may cause the system to delay resource switching, increasing the risk of communication interruption; in other words, the switching may not be performed in time when it should, resulting in a genuine link break. In summary, the core problem with existing technologies is that they lack the ability to distinguish whether the current channel deterioration is a temporary phenomenon or a continuous deterioration, and are unable to make a reasonable decision between "continuing to maintain the current link" and "performing resource switching". Summary of the Invention

[0005] This application provides a method and system for optimizing the allocation of communication resources based on wireless network cooperation, which solves the problem that existing technologies lack the ability to distinguish whether the current channel degradation is a temporary phenomenon or a continuous deterioration.

[0006] To achieve the above objectives, the embodiments of this application disclose the following technical solutions:

[0007] On the one hand, this solution discloses a method for optimizing the allocation of communication resources based on wireless network cooperation, the steps of which include:

[0008] Step S1: Obtain the physical motion state information of the moving target within the communication area, as well as the channel state information, communication path spatial structure information, and service delay constraint information of the millimeter-wave link. The physical motion state information includes the spatial location, speed, and direction of movement of the moving target. The channel state information includes received signal strength change information and channel state change information. The channel state change information is used to characterize the channel degradation of the communication link.

[0009] Step S2: Based on the physical motion state information and the spatial structure information of the communication path, determine the spatial distribution of the communication path, and construct the corresponding first Fresnel zone spatial region based on the communication path. Construct an invasion state model of the first Fresnel zone spatial region of the communication path by the moving target, and calculate the invasion degree and invasion change trend. Based on the invasion degree and invasion change trend, characterize the occurrence state of occlusion events in the communication path, where the invasion degree is used to describe the occlusion depth, and the invasion change trend is used to describe the occlusion evolution direction and evolution speed.

[0010] Step S3: Based on the degree of occupancy and the trend of occupancy changes, and combined with the trend of channel state information changes, analyze the channel degradation of the communication link and predict the evolution stage and corresponding duration of the obstruction event.

[0011] Step S4: Based on the evolution stage and duration information of the occlusion event and the service latency constraint information, generate resource scheduling decision results. The resource scheduling decision results include scheduling strategy identifiers used to characterize the occlusion type.

[0012] Step S5: Based on the scheduling strategy identifier in the resource scheduling decision result, execute the matching hierarchical resource scheduling strategy to dynamically adjust the millimeter-wave link resources.

[0013] This method does not directly control based on instantaneous channel state. Instead, it introduces physical motion state information and communication path spatial structure information in step S1, transforming the approach from "channel observation" to "physical cause modeling." The technical motivation behind this is to avoid misjudgments caused by random fluctuations resulting from relying solely on changes in received signal strength. In step S2, by constructing a first Fresnel zone spatial region and establishing an encroachment state model for that region, the continuous spatial motion of the moving target is mapped to a quantifiable degree of encroachment and its changing trend. This transforms the obstruction process from a discrete event into a continuous evolutionary process. This mechanism leverages the high sensitivity of millimeter-wave propagation to spatial obstruction, providing input data for predicting the evolution stage and duration of the obstruction event in step S3. Step S3, through coupled analysis of the encroachment change trend and the channel state information change trend, achieves a causal explanation of channel degradation, rather than merely statistical prediction. This is technically necessary because a single channel parameter cannot distinguish between short-term disturbances and persistent obstruction. Step S4 further integrates duration information with business latency constraint information, introducing a constraint variable called "business tolerance time," enabling resource scheduling decisions to adapt to business needs. Finally, in step S5, a hierarchical resource scheduling strategy maps the decision results into specific execution actions, achieving a closed-loop feedback from "prediction" to "control." This closed-loop structure avoids the response lag problem caused by loose coupling of functional modules in traditional systems, thereby achieving a synergistic improvement in overall link stability and resource utilization efficiency.

[0014] Furthermore, the process of constructing the occupancy state model of the first Fresnel zone spatial region does not employ a binary judgment method for occlusion events. Instead, it discretizes the first Fresnel zone spatial region, mapping the continuous space into a computable discrete spatial grid. Combining the spatial position and direction of motion of the moving target, it calculates the occupancy state in each discrete spatial grid at each time step, thus forming a continuous sequence of occupancy ratio changes over time. Based on this, by sampling the occupancy ratio over time and performing sliding window processing, the rate of change and trend of change of the occupancy ratio are extracted. The degree of occupancy is used to characterize the occupancy depth, and the trend of occupancy change is used to characterize the occupancy evolution speed and direction. The technical mechanism of this processing method lies in using a combination of spatial discretization and time series analysis to transform the physical occupancy process into a predictable dynamic signal, thereby avoiding random errors caused by single-moment measurements and providing stable input for subsequent occupancy evolution stage division.

[0015] Furthermore, the process of predicting the evolution stages of an obstruction event does not directly divide the stages based on the degree of encroachment. Instead, it segments the encroachment change trend to construct an encroachment evolution stage sequence, and aligns this sequence with the received signal strength change information and channel state change information in the channel state information to form a channel change sequence. Further, by associating and matching the encroachment evolution stage sequence with the channel change sequence, a correspondence between encroachment evolution and channel degradation is established. This allows for the determination of the current obstruction evolution stage, and the duration information is extrapolated based on the encroachment change trend. The technical necessity of this process lies in the fact that relying solely on channel changes cannot distinguish the source of obstruction. By introducing the encroachment change trend, the formation mechanism of channel degradation can be explained from a physical layer, making the prediction results interpretable and generalizable.

[0016] Furthermore, the process of generating resource scheduling decisions does not simply rely on duration information for threshold judgment. Instead, it first determines the service tolerance time based on service latency constraints and compares it with the duration information of the obstruction event to generate a duration matching result. Subsequently, it classifies the current obstruction event by combining the trends of encroachment changes and channel state information changes to obtain an obstruction type identifier, and generates a resource scheduling decision based on this identifier. The technical mechanism of this decision-making process lies in introducing service-side constraints to transform environmental prediction results into executable control strategies, thereby avoiding unnecessary resource switching in short-term obstruction scenarios while ensuring communication continuity in continuous obstruction scenarios, and achieving adaptive adjustment of the system response.

[0017] Furthermore, the process of implementing the hierarchical resource scheduling strategy does not employ a single resource adjustment strategy. Instead, it executes a hierarchical resource scheduling strategy based on the occlusion type identifier: for short-term occlusion, the current millimeter-wave link is maintained through intra-link enhancement processing; for moderate occlusion, multi-link cooperative transmission is achieved through data substream partitioning; and for long-term occlusion, cross-link resource handover is performed. The technical motivation behind this hierarchical mechanism is to reduce the conflict between resource handover costs and link stability. Its associated effect is reflected in the ability to achieve gradual control from "local adjustment" to "global reconstruction" based on the duration of occlusion.

[0018] Furthermore, by introducing a communication path space model, direct path information and at least one reflected path information are incorporated into a unified modeling framework. When a direct path is detected to be blocked, the path propagation loss characterization parameters corresponding to each reflected path are calculated based on the propagation model. The reflected paths are then prioritized using channel state information, and a target reflected path is selected for resource scheduling. The technical necessity of this design lies in the fact that millimeter-wave communication is highly sensitive to path blockage, and reflected paths can serve as an effective compensation method. Through pre-modeling and dynamic selection, the continuity of the communication link can be guaranteed.

[0019] Furthermore, by acquiring historical occlusion event data and performing time-series modeling, a statistical model of occlusion duration is generated. The historical duration distribution information output by this model is then fused with the current encroachment trend to correct the duration information. The technical mechanism lies in using historical statistical patterns to correct real-time prediction results, reducing the uncertainty caused by a single observation and making the prediction results more consistent with the repetitive behavioral characteristics of actual operating scenarios.

[0020] Furthermore, the channel state information also includes channel phase change information and beam stability information. A phase change sequence is generated by performing phase differential processing on the channel phase change information, and a beam stability index is generated by performing statistical processing on the beam stability information. Finally, the phase change sequence is fused with the occupancy change trend to generate a channel degradation intensity index. The technical motivation behind this design is to enhance the perception of occupancy depth at the microscopic signal level, so that the division of occupancy evolution stages not only relies on spatial occupancy information but also incorporates fine-grained changes at the radio frequency layer, thereby improving the accuracy of the determination.

[0021] Furthermore, during resource scheduling, the current business data is prioritized to generate multiple business priority queues. Corresponding communication resource scheduling strategies are then allocated based on the priority information of each queue. The transmission status of each priority queue is further considered to calculate the resource allocation ratio for each business on different paths, generating resource allocation results that are dynamically updated. The technical necessity of this mechanism lies in the resource contention that occurs in multi-service concurrent scenarios. By introducing priority queues and dynamic resource allocation, the system can prioritize critical services under limited resource conditions, thereby improving the overall system service quality.

[0022] On the other hand, this solution discloses a communication resource optimization and allocation system based on wireless network collaboration. This system forms a closed-loop data pipeline structure through a data acquisition module, a modeling and processing module, a predictive analysis module, a decision generation module, and an execution control module. The data acquisition module provides multi-source input; the modeling and processing module maps physical motion state information to an occupancy state model of the first Fresnel zone; the predictive analysis module performs time-dimensional prediction based on occupancy level and channel state information; the decision generation module incorporates service delay constraints to complete strategy selection; and the execution control module translates the strategy into specific resource scheduling actions. The core of this system architecture lies in achieving cross-domain mapping from the physical world to communication control through a progressive processing link of "modeling—prediction—decision—execution," avoiding functional fragmentation between modules, thereby improving the overall system's adaptability and response efficiency to dynamic occupancy environments.

[0023] This application acquires the physical motion state information of the moving target, channel state information, communication path spatial structure information, and service delay constraint information, and constructs an encroachment state model of the first Fresnel zone spatial region of the communication path. It further calculates the degree of encroachment and its changing trend, enabling the communication link to no longer passively respond to channel changes based solely on instantaneous channel state information, but to continuously characterize the occurrence state of obstruction events. By combining the encroachment change trend with the channel state information change trend, it predicts the evolution stage and duration of obstruction events, thereby establishing a temporal structure awareness of channel degradation. Finally, it matches the duration information with service delay constraint information to generate resource scheduling decision results, executes a hierarchical resource scheduling strategy, and achieves dynamic adjustment of millimeter-wave link resources. The above technical solution essentially solves the problem that existing technologies cannot distinguish between short-term disturbances and continuous attenuation. In other words, the system no longer assumes the link is broken as soon as the signal deteriorates, but can determine whether the deterioration is temporary or will continue to worsen. At the same time, by predicting the evolution stage and duration of the blockage, the system can avoid unnecessary resource switching in the case of short-term blockage and perform resource scheduling in advance in the case of continuous blockage. This avoids the problems of "switching prematurely when recovery could have been delayed" and "failing to switch in time when it should have been switched," thereby improving the stability of the communication link, reducing system jitter, and improving the utilization efficiency of millimeter-wave link resources. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the overall method of an embodiment of the present invention;

[0025] Figure 2 This is a flowchart illustrating the construction of the invasion state model and the calculation of the invasion degree in an embodiment of the present invention.

[0026] Figure 3 This is a flowchart illustrating the evolution stages and duration prediction of occlusion events according to an embodiment of the present invention.

[0027] Figure 4 This is a flowchart illustrating the process of generating resource scheduling decision results according to an embodiment of the present invention.

[0028] Figure 5 This is a flowchart illustrating the execution of the hierarchical resource scheduling strategy according to an embodiment of the present invention.

[0029] Figure 6 This is a system module signal interaction sequence diagram according to an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In the following description, numerous specific details are set forth to provide a comprehensive understanding of the present invention. The present invention may be practiced without some or all of these specific details. In other instances, well-known processes have not been described in detail to avoid unnecessarily obscuring the present invention.

[0031] When used in conjunction with the terms "comprising," "method comprising," or similar language in this specification and appended claims, the singular forms "a," "some," and "the" include plural references unless the context clearly indicates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] In this embodiment, "communication path spatial structure information" refers to a structured data set used to describe the geometric relationship of the propagation path between the base station and the communication terminal. It includes the spatial coordinate relationship of the direct path, the path length, and the spatial location, reflective surface attributes, and path propagation direction information of at least one reflection path. Furthermore, at the engineering implementation level, this information can be obtained by three-dimensional modeling of the communication area or by using lidar, vision sensors, or environmental scanning equipment, and calibrated in conjunction with the distribution of fixed obstacles in the industrial scene, thereby forming calculable spatial structure description data.

[0033] The "communication path space model" is a spatial topology model constructed based on the spatial structure information of communication paths, used to describe the multipath structure of signal propagation. In its specific implementation, the model not only includes the geometric representation of direct and reflected paths, but also further includes the propagation loss characteristics and propagation delay characteristics of each path, thus providing basic support for subsequent path selection and resource scheduling.

[0034] The “first Fresnel zone” refers to the spatial region that plays a dominant role in signal propagation in millimeter-wave communication. Its spatial distribution is determined by the relative position between the communication terminal and the base station and the communication frequency.

[0035] The "First Fresnel Zone Spatial Region Encroachment Status Model" is a dynamic model describing the occupation of the first Fresnel zone spatial region by a moving target during the time evolution process. Its output includes the degree of encroachment and the trend of encroachment change. Among them, the "degree of encroachment" is the proportion of the first Fresnel zone spatial region occupied by the moving target, and the "trend of encroachment change" is the rate and direction of change of this proportion over time. In engineering implementation, it can be obtained through discrete spatial grid statistics and time series analysis.

[0036] "Obstruction Evolution Stage" refers to dividing the obstruction process into multiple stages with different channel degradation characteristics under the drive of the encroachment state model of the first Fresnel zone spatial region; "Duration Information" is the predicted result of the time length of the obstruction event from the current time to the end time; "Service Delay Constraint Information" is used to describe the maximum allowable delay range of service data during transmission; "Resource Scheduling Decision Result" is the control result generated based on the obstruction evolution stage, duration information and service delay constraint information.

[0037] Example 1

[0038] This scheme discloses a communication resource optimization allocation method based on wireless network cooperation. It transforms the traditional responsive resource scheduling mechanism, which is centered on channel state information, into a predictive scheduling mechanism centered on the "physical space encroachment evolution process." By constructing a mapping relationship between the encroachment state model of the first Fresnel zone and channel changes, it achieves temporal structure modeling of obstruction events and combines this with service delay constraints to make scheduling decisions. This enables the differentiation between short-term disturbances and long-term obstruction, improving the stability and resource utilization efficiency of millimeter-wave links in dynamic environments. The method includes the following steps:

[0039] Step S1: Obtain multi-source information within the communication area and establish a communication path space model:

[0040] In this embodiment, the physical motion state information of the moving target is acquired by multi-source sensing devices deployed in the communication area. This includes acquiring the spatial position, speed, and direction of movement of the moving target through lidar, ultra-wideband positioning system, or visual sensing devices; acquiring channel state information, including received signal strength change information and channel state change information, through the wireless measurement module between the communication terminal and the base station; acquiring communication path spatial structure information through a pre-established 3D environmental model or real-time scanning system, and performing spatial calibration in conjunction with the obstacle distribution in the industrial scene; and acquiring service delay constraint information through a service scheduling system.

[0041] In this embodiment, the moving target can be an industrial AGV, a logistics handling robot, an industrial forklift, a worker, or other target objects moving within the communication area that may obstruct the millimeter-wave link. The physical motion state information of the moving target is acquired through multi-source sensing devices deployed within the communication area, including obtaining the moving target's spatial location, speed, and direction of movement via lidar, ultra-wideband positioning systems, or visual sensing devices.

[0042] Furthermore, after obtaining the spatial structure information of the communication path, a spatial model of the communication path is constructed based on the spatial geometric relationship, including the spatial vector representation of the direct path and the spatial mapping relationship of the reflection path, and the propagation direction and reflection properties of each reflection path are determined by ray tracing.

[0043] To quantify the impact of complex environments on signal propagation within a communication area, a signal gain attenuation model under multipath propagation needs to be established. The system calculates the total path gain at specific spatial coordinates based on the spatial vector of the direct path and the geometric mapping relationship of each reflection path. Based on the superposition relationship between the energy of the direct path and the energy of each order of reflection paths, the multipath propagation gain calculation equation is constructed:

[0044] ;

[0045] in, Let p be the spatial path propagation gain characterization parameter. It is used to characterize the overall path loss level of the signal received at the target location under the constraint of the spatial structure information of the communication path. This parameter is obtained by logarithmic transformation of the power superposition result of the direct path and multiple reflection paths.

[0046] The direct path visibility factor is used to characterize whether the direct path between the base station and the communication terminal is blocked. This parameter is obtained by geometric intersection determination through the communication path space model. Specifically, it determines whether the moving target or environmental obstacle has spatial overlap with the direct path space segment. When there is no overlap, the preset value of 1 is taken. When there is obstruction, the preset value of 0 or the attenuation coefficient is taken.

[0047] The operating wavelength for millimeter waves is calculated based on the correspondence between wavelength and frequency according to the system's preset communication frequency parameters.

[0048] The direct distance between the base station and the communication terminal is obtained by calculating the Euclidean distance using spatial location coordinates.

[0049] n is the number of reflection paths, which is determined by the number of valid reflection paths identified in the communication path space model;

[0050] The total propagation distance of the i-th reflection path is calculated using the coordinates of the reflection point and the ray tracing algorithm.

[0051] The reflection coefficient of the i-th reflection path is obtained by looking up the material property table in the preset 3D environment model;

[0052] The deviation angle of the i-th reflection path relative to the main lobe of the receiving antenna is calculated based on the relationship between the antenna pattern and the spatial mapping.

[0053] Used to weight the effective energy of the reflection path in the receiving direction. When the reflection path is within the effective coverage direction of the main lobe of the receiving antenna, it participates in the energy contribution calculation. When the deviation angle exceeds the preset range, the corresponding energy contribution is recorded as zero.

[0054] By calculating the aforementioned spatial path propagation loss characterization parameters, the propagation loss level of each candidate propagation path under the current spatial state can be obtained. Based on this, the reflection paths can be prioritized and sorted. At the same time, this serves as input for generating resource scheduling decision results, which can be used to select target reflection paths or trigger intra-link parameter adjustments, spatial path compensation, or cross-link resource switching operations, thereby ensuring the feasibility and computability of the communication resource optimization allocation method in complex obstruction environments.

[0055] In this embodiment, the communication path space model includes direct path information and at least one reflection path information, wherein the reflection path information is used to characterize the non-direct propagation path formed by the environmental reflective surface, and participates in path selection and link compensation as a candidate transmission path in the subsequent occlusion prediction and resource scheduling process.

[0056] Step S2: Construct an encroachment status model for the first Fresnel zone and calculate the degree of encroachment and its changing trend:

[0057] In this embodiment, a first Fresnel spatial region model is constructed based on the relative spatial location information between the base station and the communication terminal and the communication frequency parameters, and spatial distribution data is generated. The spatial distribution data is further discretized into a grid structure, and at each moment, the occupancy status of the moving target in each grid is calculated based on its spatial location and direction of movement. The occupancy ratio of the first Fresnel spatial region is statistically analyzed based on the occupancy status, and an occupancy change sequence is formed through time series sampling. The occupancy change sequence is further processed through a sliding window to calculate the occupancy ratio change rate and trend.

[0058] In engineering implementation, a moving target can be approximated by a geometric envelope model, and the number of grids it occupies can be calculated in real time by edge computing nodes, thereby obtaining the degree of encroachment.

[0059] To finely describe the interference process of a moving target in the beam space, the system projects the first Fresnel zone onto a discrete spatial grid plane perpendicular to the central axis. By statistically analyzing the grid weights covered by the geometric envelope of the moving target in real time, an occupancy evaluation function is constructed. Subsequently, based on time-series sampled data of the occupancy ratio, the evolution rate is derived using a difference operator, thus obtaining the equation for calculating the occupancy change trend:

[0060] ;

[0061] Based on the above mapping relationship, physical displacement can be transformed into a continuous sequence of encroachment states.

[0062] in, for The degree of encroachment at any given time is the weighted proportion of the moving target's occupation of the first Fresnel zone spatial region, which is obtained by weighted statistical calculation of the occupation status of the discrete spatial grid.

[0063] It represents the trend of encroachment and is used to characterize the rate of change of the degree of encroachment in the time dimension. It is obtained by performing a difference operation on the time series of the degree of encroachment.

[0064] The first Fresnel spatial region is mapped to the total grid set. Based on the communication path spatial structure information and communication frequency parameters, the first Fresnel spatial region model is constructed, and the spatial region is divided into multiple discrete grid units according to the preset spatial resolution.

[0065] This represents the j-th spatial grid cell in the grid set G, whose spatial coordinates are determined by the discretization process;

[0066] The energy weight factor of the j-th grid reflects the importance of different spatial locations in the first Fresnel zone to signal propagation. Its value is based on the preset spatial energy distribution density function of the first Fresnel zone or calculated by the propagation model, so that the grids closer to the center of the main propagation path have higher weights.

[0067] For the occupancy indicator function, Used to determine whether a moving target occupies the j-th spatial grid cell at time t, when the physical geometric envelope of the moving target... With grid The value is 1 when there is an intersection in space, and 0 otherwise; this determination is obtained by geometrically intersecting the spatial position, size, and attitude information of the moving target with the grid coordinates.

[0068] The physical geometric envelope of the moving target at time t is defined. Based on the physical motion state information, a spatial geometric model of the moving target is constructed in real time. This physical motion state information is obtained by a positioning device or a visual sensing device and includes spatial position, motion speed and motion direction. The corresponding spatial occupancy area is generated by combining the preset target geometric template.

[0069] The sampling period for time series sampling is the system's preset measurement update frequency, which is derived from the system's sampling period settings for channel state information and physical motion state information.

[0070] By transforming spatial occupancy behavior into time-series data, continuous modeling of the occlusion process is achieved, enabling the system to quantify the occlusion evolution trend and thus improve the ability to characterize the dynamic changes of occlusion.

[0071] In this embodiment, a communication path space model is constructed based on the communication path space structure information, and a multipath propagation model is established in conjunction with channel state information. The reflection path information is used to characterize the non-direct propagation path formed by environmental reflective surfaces. Further, for each reflection path, its path propagation loss characterization parameter is calculated based on the propagation model. This parameter is determined by the path length, reflective surface material properties, and current channel state information. Subsequently, the system prioritizes the reflection paths based on their corresponding path propagation loss characterization parameters and real-time channel state information. From the ranking results, a set of reflection paths that meet preset path loss thresholds and link stability conditions is selected as a candidate path set. Based on this, the highest-priority reflection path is selected from the candidate path set as the target reflection path, which is then used for path compensation or link switching in subsequent resource scheduling processes.

[0072] Step S3: Predict the evolution stage and duration information of the occlusion event.

[0073] In this embodiment, the encroachment change trend is segmented to generate an encroachment evolution stage sequence; the received signal strength change information and channel state change information are time-aligned to form a channel change sequence; in this embodiment, the encroachment evolution stage is divided according to the direction of the encroachment change trend; when the encroachment change trend continues to increase, it corresponds to the encroachment growth stage; when the encroachment change trend remains basically stable, it corresponds to the encroachment stability stage; when the encroachment change trend continues to decrease, it corresponds to the encroachment weakening stage, thus forming an encroachment evolution stage sequence.

[0074] By associating and matching the encroachment evolution phase sequence with the channel change sequence, a correspondence between encroachment evolution and channel degradation is established. By performing time alignment processing on the encroachment evolution phase sequence and the channel change sequence, and by associating and matching the corresponding change relationship between the encroachment change trend and the channel state change trend, a correspondence between encroachment evolution and channel degradation is established.

[0075] Based on this correspondence, the current occlusion evolution stage is determined, and the occlusion duration is obtained by extrapolating the encroachment change trend. Based on the degree of encroachment and the encroachment change trend, the occlusion evolution stage is a stage representation obtained by dividing the occlusion process into segments based on the direction and magnitude of the encroachment change trend.

[0076] Furthermore, historical obstruction event data is acquired and time series modeling is performed to generate an obstruction duration statistical model. The output of this model is then fused with the current encroachment change trend to correct the duration information. The historical obstruction event data includes encroachment status information, channel status information, obstruction evolution stage information, and actual duration information corresponding to the historical obstruction process.

[0077] Simultaneously, phase difference processing is performed on channel phase change information to generate phase change sequence, and statistical processing is performed on beam stability information to generate beam stability index. This index is then fused with the encroachment change trend to generate channel degradation intensity index, which is used to further classify the obstruction evolution stage.

[0078] The system uses the ratio of the current saturation margin of the encroachment level to the rate of change to predict the physical travel duration, and incorporates historical statistical distribution for weighted correction. Based on the coupling relationship between the encroachment evolution stage sequence and historical duration distribution information, a corrected duration prediction equation is constructed:

[0079] ;

[0080] This enables accurate prediction of when the occlusion event will end.

[0081] To enhance the perception of obstruction depth at the microscopic signal feature level, the system performs multi-dimensional fusion of physical encroachment momentum and radio frequency phase jitter. Based on the nonlinear combination relationship between the phase change sequence and the beam stability index, a channel degradation intensity calculation equation is constructed:

[0082] ;

[0083] in, The corrected duration information is used to characterize the predicted duration of the current occlusion event from the current moment to the end moment;

[0084] To provide real-time prediction weight coefficients, the system adjusts the weight distribution between prediction results based on the current trend of encroachment and prediction results based on historical statistical models. It calculates the velocity variance based on the velocity sequence in the physical motion state information of the moving target and dynamically adjusts it based on the variance through preset mapping rules or functional relationships. This enhances the real-time prediction weight when the motion state is stable and enhances the historical statistical weight when the motion state fluctuates.

[0085] Let S represent the expected historical duration in the current scenario S, where S is the historical duration. τ represents the probability density distribution of the duration of historical occlusion events under scenario S. Historical samples matching the current communication path spatial structure information and business scenario are extracted from the historical occlusion event database, and the probability density distribution function is constructed through time series statistical modeling methods. τ represents the occlusion duration variable, which is the labeled duration data in the historical samples. This is a rate-of-change protection term, used to prevent the prediction result from being abnormally amplified when the rate of change approaches zero, resulting in a denominator of zero or an excessively small value; among which, To seize the absolute value of the changing trend, The preset minimum change threshold is a constant with a value greater than zero; when At that time, take As the denominator; when At that time, take As the denominator.

[0086] It is a channel degradation intensity index used to characterize the overall degradation degree of the current channel due to obstruction. It serves as an input parameter for the detailed division of obstruction evolution stages and resource scheduling decisions.

[0087] The variance of the phase change sequence represents the degree of fluctuation in the channel phase change. The phase change sequence Δϕ is obtained by performing phase difference processing on the channel phase change information in the channel state information, and the statistical variance of the sequence is calculated within a preset time window. Δϕ represents the phase change sequence, which is calculated by differentially analyzing the channel phase change information at consecutive time points.

[0088] The sensitivity adjustment factor is used to balance the contribution of channel phase change information and the occupancy state model output of the first Fresnel zone spatial region to the channel degradation intensity. It is preset according to the service delay constraint information or adjusted by mapping rules to increase the response weight of delay-sensitive services to channel phase changes.

[0089] It is used to characterize the impact of the physical encroachment process on channel degradation. It is derived from the encroachment degree and encroachment change trend output by the encroachment state model of the first Fresnel zone spatial region.

[0090] By establishing a mapping relationship between encroachment evolution and channel degradation, the system can identify the physical source of channel changes and predict the duration of obstruction through trend extrapolation and historical correction, thereby improving the ability to distinguish different types of obstruction.

[0091] Step S4: Generate resource scheduling decision results

[0092] In this embodiment, the service tolerance time is determined based on the service delay constraint information and compared with the obstruction duration information to generate a duration matching result; the obstruction event is classified and processed by combining the encroachment change trend and the channel state change trend to obtain the obstruction type identifier; the resource scheduling decision result is generated based on the obstruction type identifier, including the intra-link parameter adjustment strategy, the spatial path compensation strategy, or the cross-link resource switching strategy.

[0093] In engineering implementation, the resource scheduling decision results can be output to the base station control module and radio frequency processing module through the scheduling control unit to trigger specific resource scheduling behaviors.

[0094] When generating decision identifiers, the system needs to find the optimal balance between "switching overhead" and "communication benefits." Based on the difference logic between the corrected duration information and the service tolerance time, an occlusion type classification judgment equation is constructed:

[0095] ;

[0096] in, This is an occlusion type identifier used to characterize the category to which the current occlusion event belongs. Its value is a discrete identifier, where Type_A corresponds to short-term occlusion, Type_B corresponds to medium-term occlusion, and Type_C corresponds to long-term occlusion. This identifier is directly obtained from the output of the above interval determination logic.

[0097] The corrected duration information represents the predicted duration of the current occlusion event from the current moment to the end moment, which is calculated in step S3 based on the fusion of the encroachment change trend and the historical duration distribution information.

[0098] The service tolerance time represents the maximum allowable delay range during the transmission of the current service. This parameter is obtained by quantifying the service delay constraint information, specifically by parsing the delay requirements corresponding to the service type.

[0099] The link survival limit threshold is used to characterize the maximum tolerable obstruction time for maintaining the continuity of the millimeter-wave link under the current communication environment. It is preset based on the protocol stack reset delay caused by resource switching and can be configured by the system during the initialization phase based on the network protocol stack switching delay measurement results or empirical parameters.

[0100] By executing the above-mentioned judgment logic, the continuous duration prediction results are transformed into discrete occlusion type identifiers, thereby providing direct input for generating resource scheduling decision results in step S4. This enables the system to trigger differentiated communication resource scheduling strategies based on different occlusion types, thereby suppressing short-term disturbances and avoiding long-term occlusions in advance.

[0101] The scheduling strategy identifier in the resource scheduling decision result is used to uniquely indicate the corresponding hierarchical resource scheduling strategy type and serves as the basis for selecting the hierarchical resource scheduling strategy in step S5.

[0102] Step S5: Execute the hierarchical resource scheduling strategy

[0103] In this embodiment, a hierarchical resource scheduling strategy is executed based on the resource scheduling decision results. When the obstruction type is identified as short-term obstruction, intra-link enhancement processing is performed on the millimeter-wave link, including adjusting beam direction parameters, adjusting modulation and coding parameters, and performing data pre-buffering processing. When the obstruction type is identified as moderate obstruction, the service data is divided into multiple data sub-streams, and some data sub-streams are allocated to backup links for transmission. When the obstruction type is identified as long-term obstruction, cross-link resource handover processing is performed. The obstruction type identifier in the resource scheduling decision results is used to uniquely indicate the corresponding hierarchical resource scheduling strategy type and serves as the selection basis for executing the hierarchical resource scheduling strategy in step S5.

[0104] Furthermore, when a direct path is detected to be blocked, an unblocked reflection path is selected based on the communication path space model, and the reflection paths are prioritized based on the path propagation loss characterization parameters to determine the target reflection path. At the same time, the service data is prioritized according to the resource scheduling decision results, multiple service priority queues are generated, and differentiated resource allocation is performed according to the priority.

[0105] In multi-path parallel transmission or cross-link handover processes, to ensure deterministic transmission of high-priority services, the resource weights of each sub-stream need to be dynamically calculated. Based on the priority weights of each service priority queue and the instantaneous channel capacity of the target path, a resource allocation ratio calculation equation is constructed:

[0106]

[0107] This enables the optimal orchestration of millimeter-wave link resources across different spatial paths and service flows.

[0108] It is the resource allocation ratio of the i-th service priority queue on the k-th path, used to characterize the proportion of channel resources that the service can obtain on the corresponding path, and serves as the input for resource scheduling execution;

[0109] The priority weight factor for the i-th business queue is used to characterize the importance of different businesses. It is obtained based on the priority division of business data in step S5 and according to the preset priority mapping rules, such as being generated by mapping business type or service quality level.

[0110] The signal-to-noise ratio of the k-th target reflection path or backup link is used to characterize the instantaneous channel quality of the path. It is calculated and obtained through the received signal strength change information and channel state change information in the channel state information. Specifically, it can be obtained in real time by the link measurement module on the base station side or the communication terminal side.

[0111] This is the set of currently active business priority queues, used to represent the set of all businesses currently participating in resource allocation. It is dynamically maintained and obtained by the business scheduling system based on the real-time business request status.

[0112] It is a normalization factor used to uniformly scale the weights of each service and their contribution to channel capacity, thereby ensuring that the sum of the resource allocation ratios remains consistent with the total resource capacity.

[0113] The total available channel resource capacity of the system is used to characterize the total bandwidth or number of resource blocks that can be allocated to the millimeter-wave link in the current scheduling period. This parameter is obtained by pre-setting based on the available bandwidth configuration of the millimeter-wave band, the scheduling period length, and the system resource configuration parameters.

[0114] By employing a hierarchical resource scheduling strategy, the system can adopt differentiated processing methods under different occlusion conditions, thereby shifting from passive switching to proactive adjustment, which improves link stability and reduces resource switching overhead.

[0115] Example 2

[0116] In this embodiment, the wireless network-based collaborative communication resource optimization and allocation system consists of a data acquisition module, a modeling and processing module, a predictive analysis module, a decision generation module, and an execution control module. These modules are connected via a high-speed data bus and a shared memory mechanism, forming a closed-loop data processing link under unified timing scheduling control. The system front-end accesses external environmental data through sensor and wireless measurement interfaces, inputting the physical motion state information of the moving target and the channel state information of the millimeter-wave link to the data acquisition module. After internal data standardization processing, the data is transmitted to the modeling and processing module. The occupancy level and occupancy change trend output by the modeling and processing module are input as intermediate state variables to the predictive analysis module. The predictive analysis module generates the evolution stage and duration information of the occupancy event and transmits it to the decision generation module. The decision generation module combines service latency constraint information to generate resource scheduling decision results and sends them to the execution control module through the control interface. The execution control module performs actual scheduling of the millimeter-wave link resources, thereby completing a full closed-loop control process from perception to decision-making to execution.

[0117] Data Acquisition Module: In this embodiment, the data acquisition module is used to acquire the physical motion state information of the moving target and the channel state information of the millimeter-wave link. Internally, it includes a multi-source sensing interface unit, a wireless link measurement unit, and a data preprocessing subunit. Preferably, the multi-source sensing interface unit acquires the spatial position, speed, and direction of motion information of the moving target through a lidar interface, a visual acquisition interface, or a positioning signal receiving interface; the wireless link measurement unit acquires received signal strength change information and channel state change information through a base station side measurement interface or a communication terminal feedback interface; the data preprocessing subunit performs timestamp alignment and format unification processing on the acquired raw data to generate structured physical motion state information and channel state information. This module receives raw signals from the external environment and transmits the processed structured data to the modeling processing module through an internal bus, thereby providing a unified input data basis for subsequent modeling calculations. Through the preprocessing of this module, the inconsistency problem in sampling frequency and time reference of multi-source data is eliminated, thereby improving the stability of subsequent model calculations.

[0118] Modeling Processing Module: In this embodiment, the modeling processing module is used to construct an encroachment state model of the first Fresnel zone spatial region based on physical motion state information, and to calculate the encroachment degree and encroachment change trend. It includes a spatial modeling sub-unit, a grid mapping sub-unit, and an encroachment calculation sub-unit. Preferably, the spatial modeling sub-unit establishes a spatial region model of the first Fresnel zone spatial region based on the spatial structure information of the communication path; the grid mapping sub-unit discretizes the spatial region into multiple spatial grid units; and the encroachment calculation sub-unit calculates the occupancy state of each grid unit based on the spatial position and direction of movement of the moving target, and generates the encroachment degree and encroachment change trend based on time-series sampling. This module receives physical motion state information from the data acquisition module and outputs the encroachment degree and encroachment change trend to the prediction and analysis module. During data transmission, the conversion from spatial geometric data to time-series features is completed through a unified data interface. Through the modeling processing of this module, a quantitative expression of the physical occlusion process is achieved, thereby providing calculable dynamic input for subsequent prediction.

[0119] Predictive Analysis Module: In this embodiment, the predictive analysis module is used to predict the evolution stage and duration of obstruction events based on the degree of encroachment and channel state information. It includes a stage determination subunit, a time prediction subunit, and a data fusion subunit. Preferably, the stage determination subunit segments the encroachment change trend and performs correlation matching with channel state change information to determine the current obstruction evolution stage; the time prediction subunit extrapolates the duration information based on the encroachment change trend; and the data fusion subunit incorporates historical obstruction event data to correct the duration information. This module receives the encroachment degree and encroachment change trend from the modeling processing module, and indirectly obtains channel state information from the data acquisition module. It then outputs the generated obstruction event evolution stage and duration information to the decision generation module. Through the predictive analysis of this module, the current state is extended to the future time dimension, thereby providing a forward-looking basis for resource scheduling and improving the system's response capability to dynamic obstruction.

[0120] Decision Generation Module: In this embodiment, the decision generation module generates resource scheduling decision results based on duration information and service delay constraint information. It includes a constraint parsing subunit, a matching determination subunit, and a decision output subunit. Preferably, the constraint parsing subunit quantifies the service delay constraint information to generate a service tolerance time; the matching determination subunit compares the duration information with the service tolerance time and combines this with the occupancy change trend and channel state change trend to generate an obstruction type identifier; and the decision output subunit generates the resource scheduling decision result based on the obstruction type identifier. This module receives duration information from the prediction analysis module and obtains service delay constraint information from the service scheduling interface, ultimately outputting the resource scheduling decision result to the execution control module. Through the logical judgment of this module, a mapping from prediction results to control strategies is achieved, providing a clear decision basis for the resource scheduling process.

[0121] Execution Control Module: In this embodiment, the execution control module is used to execute a hierarchical resource scheduling strategy based on the resource scheduling decision results. Internally, it includes a link control subunit, a multi-path scheduling subunit, and a priority scheduling subunit. Preferably, the link control subunit adjusts intra-link parameters based on the resource scheduling decision results, including beam direction parameters and modulation / coding parameters; the multi-path scheduling subunit divides service data into substreams and allocates them to different communication paths based on the decision results; and the priority scheduling subunit performs differentiated resource allocation based on service priority queues. This module receives resource scheduling decision results from the decision generation module and acts on millimeter-wave link resources through the radio frequency control interface and scheduling control interface, while simultaneously feeding back the execution status to the system control unit to form a closed loop. Through the execution control of this module, real-time dynamic adjustment of communication resources is achieved, thereby improving the link stability and resource utilization efficiency of the system in complex obstruction environments.

[0122] In this embodiment, the encroachment state model of the first Fresnel zone is embedded in the data link between the modeling processing module and the predictive analysis module. By transforming spatial occupancy behavior into the degree of encroachment and the trend of encroachment changes, the predictive analysis module can determine the occlusion evolution stage based on continuous time series, thereby solving the problem that traditional systems cannot distinguish between short-term and long-term occlusion by relying solely on instantaneous channel states. Furthermore, the prediction mechanism for duration information and the fusion mechanism of historical data are embedded within the predictive analysis module. Through the data fusion subunit, historical occlusion event data is statistically modeled, making the duration information more stable and accurate, thereby improving the input reliability of the decision generation module.

[0123] In the decision generation module, by coupling duration information with service latency constraints, a joint decision-making logic between communication requirements and environmental changes is realized. This mechanism is directly embedded between the matching and decision output subunits, enabling the system to output differentiated resource scheduling decisions for different service types. The hierarchical resource scheduling strategy in the execution control module further concretizes the above decision results. Through the coordinated execution of link control, multi-path scheduling, and priority scheduling, abstract decisions are mapped to specific link control behaviors, thereby achieving closed-loop optimization of the entire process from environmental awareness to resource scheduling at the system level.

[0124] Example 3: Specific Application Scenarios

[0125] In an industrial manufacturing workshop environment, base stations are deployed on the top of the production line, and communication terminals are deployed on equipment or personnel terminals. Communication uses millimeter-wave links for high-bandwidth data transmission. During the production process, moving targets move between equipment, and when they enter the communication path area, they will block the communication link.

[0126] Without this solution, the system responds only to changes in received signal strength. When the signal drops, it immediately performs link switching, resulting in frequent switching to low-frequency links or other communication nodes, which causes communication jitter and resource waste.

[0127] After adopting this scheme, the system first identifies the encroachment process of the moving target on the first Fresnel zone space area through the communication path space model and the encroachment state model, and predicts the duration of the obstruction. When the prediction result shows that the obstruction duration is less than the service tolerance time, the system performs intra-link enhancement processing to maintain millimeter-wave link transmission. When the prediction result exceeds the service tolerance time, the system performs cross-link resource switching to ensure uninterrupted communication.

[0128] The above implementation method effectively distinguishes between short-term and long-term blockage, enabling the communication system to have higher stability and resource utilization efficiency in complex dynamic environments, while reducing the system overhead caused by resource switching.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation methods of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for optimizing the allocation of communication resources based on wireless network cooperation, characterized in that, Includes the following steps: Step S1: Obtain the physical motion state information of the moving target within the communication area, as well as the channel state information, communication path spatial structure information, and service delay constraint information of the millimeter-wave link. The physical motion state information includes the spatial location, speed, and direction of movement of the moving target. The channel state information includes received signal strength change information and channel state change information. The channel state change information is used to characterize the channel degradation of the communication link. Step S2: Based on the physical motion state information and the spatial structure information of the communication path, determine the distribution location of the communication path in space, and construct the corresponding first Fresnel zone spatial region based on the communication path. Construct an invasion state model of the first Fresnel zone spatial region of the communication path by the moving target, and calculate the degree of invasion and the trend of invasion change. Step S3: Based on the degree of occupancy and the trend of occupancy changes, and combined with the trend of channel state information changes, analyze the channel degradation of the communication link and predict the evolution stage and corresponding duration of the obstruction event. Step S4: Based on the evolution stage and duration information of the occlusion event and the service latency constraint information, generate resource scheduling decision results. The resource scheduling decision results include scheduling strategy identifiers used to characterize the occlusion type. Step S5: Based on the scheduling strategy identifier in the resource scheduling decision result, execute the matching hierarchical resource scheduling strategy to dynamically adjust the millimeter-wave link resources.

2. The method according to claim 1, characterized in that, The process of constructing the encroachment state model of the first Fresnel zone spatial region in step S2 includes: Based on the relative spatial location information between the base station and the communication terminal and the communication frequency parameters, a first Fresnel spatial region model is constructed, and corresponding spatial distribution data is generated. The spatial distribution data is then mapped to a discrete spatial grid. Calculate the occupancy status of the moving target in each discrete spatial grid based on the moving target's spatial location and direction of movement; The occupancy status is used to statistically determine the proportion of space occupied by moving targets in the first Fresnel zone, and the occupancy proportion is sampled over time to form an occupancy change sequence. A sliding window process is performed on the occupancy change sequence to calculate the rate of change and trend of occupancy ratio within each window, and the degree of encroachment and the trend of encroachment change are generated based on the rate of change and the trend of change.

3. The method according to claim 1, characterized in that, The process of predicting the evolution stage of occlusion events in step S3 includes: Based on the degree of encroachment and the trend of encroachment change, an occlusion evolution stage is constructed. The occlusion evolution stage is a stage representation obtained by dividing the occlusion process according to the direction and magnitude of the change in the encroachment change trend. The trend of encroachment is segmented to generate a sequence of encroachment evolution stages; The received signal strength change information and channel state change information in the channel state information are time-aligned to form a channel change sequence. The encroachment evolution phase sequence and the channel change sequence are correlated and matched to construct the correspondence between encroachment evolution and channel degradation. Based on the correspondence, the current occlusion evolution stage is determined. The occlusion evolution stage is divided according to the direction and magnitude of the encroachment change trend. The duration of the occlusion event is obtained by extrapolating the encroachment change trend.

4. The method according to claim 1, characterized in that, The process of generating resource scheduling decision results in step S4 includes: The service tolerance time is determined based on the service latency constraint information, and the service tolerance time is compared with the duration information of the occlusion event to generate a duration matching result; Based on the duration matching results, and combined with the trends of encroachment and channel state information, the current occlusion events are classified to obtain occlusion type identifiers. Based on the occlusion type identifier, a corresponding resource scheduling decision result is generated, and the corresponding communication resource scheduling strategy is executed based on the resource scheduling decision result, performing at least one of the following operations: intra-link parameter adjustment, spatial path compensation, or cross-link resource switching.

5. The method according to claim 1, characterized in that, The process of executing the hierarchical resource scheduling strategy in step S5 includes: When the occlusion type is identified as short-term occlusion, intra-link enhancement processing is performed on the current millimeter-wave link, including adjusting beam direction parameters, adjusting modulation and coding parameters, and performing data pre-buffering processing. When the occlusion type is identified as medium occlusion, the current service data is divided into multiple data sub-streams, and some of the data sub-streams are allocated to backup links for transmission; When the obstruction type is identified as a long-term obstruction type, cross-link resource switching is performed to migrate the current service data to other frequency bands or other communication nodes for transmission.

6. The method according to claim 1, characterized in that, Also includes: Before step S1, obtain the communication path space structure information and establish a communication path space model based on the communication path space structure information; The communication path space model includes direct path information and at least one reflection path information; In step S5, when it is detected that the direct path is blocked by a moving target, an unblocked reflection path is selected based on the communication path space model. A propagation model is constructed based on the spatial structure information of the communication path and the channel state information. The path propagation loss characterization parameters corresponding to each reflection path are calculated based on the propagation model. The reflection paths are prioritized based on the path propagation loss characterization parameters and channel state information, and the highest priority reflection path is selected as the target reflection path for resource scheduling.

7. The method according to claim 1, characterized in that, Also includes: Acquire historical occlusion event data, perform time series modeling on the historical occlusion event data, and generate a statistical model of occlusion duration; In step S3, the historical duration distribution information output by the occlusion duration statistical model is fused with the current encroachment trend. The duration information of the current occlusion event is corrected based on the result of the fusion processing to obtain the corrected duration information; The corrected duration information is used as input for generating the resource scheduling decision result in step S4.

8. The method according to claim 1, characterized in that, Channel state information also includes channel phase change information and beam stability information; In step S3, the channel phase change information is subjected to phase differential processing to generate a phase change sequence; Statistical processing is performed on beam stability information to generate beam stability indices; The phase change sequence and the encroachment change trend are fused together to generate a channel degradation intensity index; The occlusion evolution stages are further subdivided based on the channel degradation intensity index.

9. The method according to claim 1, characterized in that, Also includes: In step S5, based on the resource scheduling decision results, the current business data is prioritized and multiple business priority queues are generated. Based on the priority information corresponding to each service priority queue, a corresponding communication resource scheduling strategy is assigned to each service priority queue. During resource scheduling, link resources are allocated to high-priority service queues first, while low-priority service queues are subject to delayed scheduling or reduced transmission speed. Based on the transmission status of each service priority queue, the resource allocation ratio of each service on different paths is calculated, and the resource allocation result is generated according to the resource allocation ratio and dynamically updated.

10. A communication resource optimization and allocation system based on wireless network collaboration, characterized in that, include: The data acquisition module is used to acquire the physical motion state information of the moving target and the channel state information of the millimeter-wave link; The modeling and processing module is used to construct an encroachment state model of the first Fresnel zone spatial region based on physical motion state information, and to calculate the degree of encroachment and the trend of encroachment changes. The predictive analysis module is used to predict the evolution stage and duration of obstruction events based on the degree of intrusion and channel state information. The decision generation module is used to generate resource scheduling decision results based on duration information and business latency constraint information; The execution control module is used to execute hierarchical resource scheduling strategies based on the resource scheduling decision results.

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