An intelligent surface multi-point communication collaborative control method and system

Abstract

The present invention relates to the field of wireless communication technologies, and discloses an intelligent surface multi-point communication collaborative control method and system, including: accessing a network signal source through a 4G CPE module, obtaining the status information of multiple communication targets, and setting resource priorities according to the importance of the communication targets; based on the status information of the communication targets, calculating the optimal beam direction of each communication target by using a spatial positioning algorithm and an adaptive beam tracking algorithm; by monitoring the communication quality, adjusting the beam phase and amplitude of each communication target to automatically optimize the beam direction and bandwidth allocation. High-precision, multi-dimensional, and low-latency communication control of multiple mobile communication targets in a complex environment of the power industry is achieved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and specifically to an intelligent surface multi-point communication collaborative control method and system. Background Art

[0002] With the booming development of the power industry, the demand for the deployment of digital production systems and business applications is increasing day by day, which has promoted the new deployment of a large number of digital and intelligent devices. Especially the sharp increase in data volume and bandwidth requirements. Facing this trend, there are obvious deficiencies in the depth and breadth of its communication network coverage, making it difficult to meet the growing mobile communication needs. Specifically, the power communication network may not be able to achieve full coverage in some regions or scenarios, resulting in blind spots or bottlenecks in information transmission; at the same time, its bandwidth capacity is also limited, making it difficult to support the communication needs of large data volumes and high frequencies. Therefore, the power industry urgently needs to upgrade and optimize the power communication network to better meet the development requirements of the digital and intelligent era. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: in the application scenarios of the power industry, the communication link is easily affected by coverage blind spots, insufficient bandwidth, and target mobility, resulting in unstable communication and inefficient resource allocation.

[0005] To solve the above technical problem, the present invention provides the following technical solution: an intelligent surface multi-point communication collaborative control method, including:

[0006] Access the network signal source through a 4G CPE module, obtain the status information of multiple communication targets, and set resource priorities according to the importance of the communication targets;

[0007] Based on the status information of the communication targets, calculate the optimal beam direction of each communication target using a spatial positioning algorithm and an adaptive beam tracking algorithm;

[0008] Collect environmental data in real time, and optimize the beam direction and bandwidth allocation through a machine learning algorithm;

[0009] By monitoring the communication quality, adjust the phase and amplitude of each target beam, and automatically optimize the beam direction and bandwidth allocation.

[0010] As a preferred solution of the intelligent surface multi-point communication collaborative control method described in the present invention, wherein: the status information of the communication targets includes the target position, target attitude, target motion state, communication requirements of the target, task priority of the target, and network status information.

[0011] As a preferred solution of the intelligent surface multi-point communication collaborative control method described in the present invention, wherein: calculating the optimal beam direction of each communication target includes dynamically selecting a search strategy based on sensing parameters, determining the candidate beam direction, and calculating the spatial direction angle;

[0012] The sensing parameters include: channel sparsity index, spatial aggregation degree index of communication targets, and beam control accuracy of communication targets;

[0013] The dynamically selecting a search strategy based on sensing parameters includes: when the channel sparsity is higher than the channel sparsity threshold, selecting a compressive search strategy;

[0014] When the spatial aggregation degree of communication targets is higher than the spatial aggregation degree threshold of communication targets, selecting a hierarchical search strategy;

[0015] When the beam control accuracy requirement is higher than the beam control accuracy threshold, selecting an exhaustive search strategy.

[0016] As a preferred solution of the intelligent surface multi-point communication collaborative control method described in the present invention, wherein: the calculation of the spatial direction angle includes setting the initial phase of the intelligent surface and determining the initial parameters of beam search; determining the reference coordinate system, converting the attitude and position of the communication target through the rotation matrix, and converting the motion state and position of the communication target into the pointing angle of the beam;

[0017] The initial parameters include: search range, search step, search iteration times, beam angle range, beam type, initial beam direction, initial beam phase, and amplitude;

[0018] The pointing angle of the beam includes the pitch angle and the azimuth angle.

[0019] As a preferred solution of the intelligent surface multi-point communication collaborative control method described in the present invention, wherein: the calculation of the optimal beam direction includes using historical data and the motion model of the communication target, adopting the central prediction method, taking the prediction point as the center, and generating n sampling points including the prediction point according to the preset spatial expansion mode to form a local two-dimensional grid area; sequentially setting the beam directions of the n sampling points, and combining the communication quality parameters feedback in real time to evaluate and compare the communication quality of each sampling point one by one, and selecting the point with the optimal communication quality as the final direction of the current beam adjustment;

[0020] The communication quality parameters include signal strength, signal-to-noise ratio, bit error rate, packet reception rate, and delay jitter.

[0021] As a preferred solution of the intelligent surface multi - point communication collaborative control method described in the present invention, wherein: the monitoring of communication quality includes: setting an acquisition period, acquiring communication quality parameters of each communication target in each period, and evaluating the current communication quality status of the communication target;

[0022] Based on the evaluation result of the current communication quality status, dynamically adjust the phase and amplitude of the intelligent surface antenna unit corresponding to the current communication target; when the signal - to - noise ratio normal threshold and the bit - error rate bit - error tolerance and the packet reception rate reception rate threshold meet the requirements, it is determined to be in a normal state, maintain the current beam parameters, and no adjustment is required;

[0023] When the degradation threshold signal - to - noise ratio and the bit - error tolerance bit - error rate bit - error upper limit or the communication delay jitter delay jitter threshold meet the requirements, it is determined to be in a mild degradation state, start local perturbation adjustment, select the optimal direction and then adjust the phase array;

[0024] When the signal - to - noise ratio degradation threshold or the bit - error upper limit bit - error rate, or the packet reception rate minimum reception rate threshold meet the requirements, it is determined to be in a serious abnormal state, immediately trigger beam direction reconstruction, and at the same time record that this direction is marked as a low - stability area in the channel mapping, and avoid preferentially selecting it in future searches.

[0025] An intelligent surface multi - point communication collaborative control system, wherein: it includes a main control module, a positioning module, an inertial measurement module, a 5.8GHz communication module, a 4G CPE module, an optoelectronic switching module, and a reconfigurable intelligent surface antenna;

[0026] The main control module is connected to the above - mentioned modules through interfaces, and is used to obtain position information and attitude information, calculate the beam pointing according to the spatial positioning algorithm and the adaptive beam tracking algorithm, control the intelligent surface antenna for beamforming, establish a communication link through the 5.8GHz communication module, access the network signal source through the 4G CPE module and transmit back the system information;

[0027] The main control module is connected to the inertial measurement module through a universal asynchronous receiver / transmitter to obtain the device attitude information;

[0028] Connect the positioning module, 4G CPE module, optical and electrical switching module, and 5.8 GHz communication module through the Ethernet interface to achieve data interaction and remote management;

[0029] Connect the reconfigurable intelligent surface antenna through the GPIO interface to achieve beam control;

[0030] The positioning module is used to collect positioning data and direction data, and implement heartbeat packet upload;

[0031] The inertial measurement module is used to obtain the device attitude information;

[0032] The optical and electrical switching module realizes the conversion and transmission of optical and electrical signals;

[0033] The reconfigurable intelligent surface antenna passes through a metal isolation feeding network and radiation patches, and uses an air medium to broaden the working bandwidth, and is driven by the main control module to achieve beamforming.

[0034] A computer device includes: a memory and a processor; the memory stores a computer program, and is characterized in that: when the processor executes the computer program, the steps of the method described in any one of the present inventions are realized.

[0035] A computer-readable storage medium stores a computer program, and is characterized in that: when the computer program is executed by a processor, the steps of the method described in any one of the present inventions are realized.

[0036] The beneficial effects of the present invention: The intelligent surface multi-point communication collaborative control method provided by the present invention realizes high-precision, multi-dimensional, and low-latency communication control of multiple mobile communication targets in the complex environment of the power industry by integrating 4G CPE signal access, resource priority setting driven by state perception, joint modeling of spatial positioning and adaptive beam tracking algorithms, closed-loop optimization of communication quality feedback, and beam and bandwidth dynamic scheduling based on machine learning. Compared with the existing communication system, this method has significant advantages such as high communication link stability, strong beam adjustment accuracy, and high intelligent level of resource allocation, and can effectively overcome problems such as coverage blind spots, target diversity, and network dynamics. It is particularly suitable for multi-target dynamic communication scenarios such as power transmission and transformation, and provides a reliable, flexible, and self-optimizing collaborative control solution for the intelligent power communication system. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 The overall flowchart of an intelligent surface multi - point communication collaborative control method provided by the first embodiment of the present invention;

[0039] Figure 2 The system architecture diagram of an intelligent surface multi - point communication collaborative control method provided by the second embodiment of the present invention;

[0040] Figure 3 The flowchart of the power transmission and transformation application scenario of an intelligent surface multi - point communication collaborative control method provided by the first embodiment of the present invention;

[0041] Figure 4 The overall layout diagram of an intelligent surface multi - point communication collaborative control method provided by the first embodiment of the present invention;

[0042] Figure 5 The coordinate definition diagram of an intelligent surface multi - point communication collaborative control method provided by the first embodiment of the present invention;

[0043] Figure 6 The schematic diagram of beam coverage prediction and adaptive tracking of an intelligent surface multi - point communication collaborative control method provided by the first embodiment of the present invention;

[0044] Figure 7 The central prediction algorithm diagram of an intelligent surface multi - point communication collaborative control method provided by the first embodiment of the present invention;

[0045] Figure 8 The comparison algorithm model diagram of an intelligent surface multi - point communication collaborative control method provided by the first embodiment of the present invention. Specific embodiments

[0046] To make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0047] Example 1, referring to Figure 1 、 Figures 3 - 8 , which is an embodiment of the present invention, provides an intelligent surface multi - point communication collaborative control method, including:

[0048] S1: Access the network signal source through the 4G CPE module, obtain the status information of multiple communication targets, and set resource priorities according to the importance of the communication targets.

[0049] Figure 3It schematically shows the logical sequence relationship of two typical application scenarios in the power system in this embodiment, that is, first applied to the remote communication control of transmission lines and then extended to the multi-object collaborative communication within the substation. The transmission application method focuses on improving the beam coverage and communication stability in large-span and long-distance areas and is applicable to communication tasks for remote targets such as high towers and unmanned aerial vehicles; while the substation application method emphasizes more on the real-time dynamic adjustment of the communication status for short-distance, multi-object, and high-frequency communications and is applicable to scenarios with high-density communication requirements such as power robot inspections and intelligent device linkages. Through the application logic switch as shown in Figure 3 , the system can dynamically load different beam scheduling strategies and feedback mechanisms according to the scenario during actual deployment, thereby ensuring the consistency and reliability of communication quality.

[0050] In this embodiment, a communication system for compensating the signal blind area in the transmission scenario is proposed. This communication system is divided into two major modules: the base station side and the target side. Specifically, the base station is deployed on transmission towers that have covered 4G signals or are equipped with fiber access facilities, and uses 4G signals or fiber transmission as the core signal source. The system can accurately calculate the optimal pointing of the beam through the spatial positioning algorithm based on the specific position data of multiple target sides, as well as the position information and attitude parameters of the base station side itself. This ability enables the system to efficiently poll and scan multiple targets, thereby achieving stable point-to-multipoint communication. Figure 4 Shows the overall layout and working principle of this communication system.

[0051] The status information of the communication target includes the target position, target attitude, target motion state, communication requirements of the target, task priority of the target, and network status information.

[0052] Furthermore, by accessing the public network or private network signal source through the 4G CPE module and combining the position information and task requirements of the target, a communication scheduling system with dynamic resource perception ability is established. The system not only realizes the comprehensive perception of the status of multiple communication targets, but also sorts resources based on the "communication task priority", enabling the limited bandwidth and beam resources to be tilted as needed to meet the real-time communication guarantee of key tasks such as dispatching instructions and alarm signals in the power scenario.

[0053] Even further, the base station side is deployed on fiber or 4G coverage points and combines the spatial positioning algorithm to achieve beam adaptive adjustment to the target side, effectively improving the communication connectivity and reliability in the edge area. The point-to-multipoint polling control design further optimizes the multi-object access efficiency, enabling the system to still have stable data link management capabilities in a high-interference and dynamic environment.

[0054] S2: Based on the status information of communication targets, calculate the optimal beam direction of each communication target using spatial positioning algorithms and adaptive beam tracking algorithms.

[0055] Dynamically select a search strategy based on sensing parameters, determine the candidate beam direction, and calculate the spatial direction angle.

[0056] The sensing parameters include: channel sparsity index, spatial aggregation index of communication targets, and beam control accuracy of communication targets.

[0057] The calculation formula for the channel sparsity index:

[0058]

[0059] Among them, represents the channel gain of the th beam direction arranged in descending order of energy. represents the number of main energy directions. represents the total number of directions. represents the channel sparsity index. represents the channel gain of the th beam direction arranged in descending order of energy.

[0060] The formula for the spatial aggregation index of communication targets is expressed as:

[0061]

[0062] Among them, represents the maximum and minimum eigenvalues of the target coordinate covariance matrix. represents the spatial aggregation index of communication targets.

[0063] The formula for the beam control accuracy requirement index is expressed as:

[0064]

[0065] Among them, SNR represents the current signal-to-noise ratio. represents the relative velocity of the target. represents the task quality requirement level. represents the empirical weight. represents the beam control accuracy requirement index.

[0066] Dynamically selecting a search strategy based on sensing parameters includes: when the channel sparsity is higher than the channel sparsity threshold, select a compressive search strategy.

[0067] When the spatial aggregation of communication targets is higher than the spatial aggregation threshold of communication targets, select a hierarchical search strategy.

[0068] When the beam control accuracy requirement is higher than the beam control accuracy threshold, an exhaustive search strategy is selected.

[0069] Exhaustive search: This is the most intuitive and simple search method. It traverses all possible beam combinations, evaluates the performance of each combination one by one, and thus finds the optimal beam. The advantage of this method lies in its global search ability, which can ensure finding the global optimal solution. However, as the number of antennas increases, the computational complexity of exhaustive search grows exponentially, resulting in a significant extension of the search time and huge resource consumption. Therefore, in practical applications, exhaustive search is often severely restricted by hardware performance and computational time, and it is difficult to play a role in scenarios with high real-time requirements.

[0070] Hierarchical search: To overcome the drawback of high computational complexity of exhaustive search, hierarchical search emerged. It divides the entire search process into two stages: coarse search and fine search. In the coarse search stage, the algorithm quickly filters out most of the beam combinations with poor performance, thus rapidly narrowing the search range. And in the fine search stage, the algorithm conducts a more refined search within the smaller range determined by the coarse search to find the optimal beam. This method significantly reduces the computational complexity and improves the search efficiency through phased search. However, hierarchical search may also sacrifice some accuracy in certain cases because the coarse search stage may miss some potential optimal solutions.

[0071] Compressive search: A compressive search algorithm emerged in response to the sparse scattering characteristics of reconfigurable intelligent communication systems. It utilizes the compressive sensing theory and effectively reduces the search space by cleverly designing the search strategy. Specifically, the compressive search algorithm compresses a large number of beam combinations that originally need to be searched into a smaller set by taking advantage of the sparsity of the millimeter-wave channel, thereby reducing the search complexity. However, this method also has certain limitations. It requires phase coherence between subsequent beacons, which is often difficult to achieve in a complex and changing communication environment. Therefore, the application scope of the compressive search algorithm is restricted to a certain extent.

[0072] Reconfigurable intelligent surface technology, as a cutting-edge technology in the field of wireless communication, can optimize the signal propagation path by dynamically and intelligently adjusting the phase and amplitude of its surface units, thereby improving the performance of the communication system and the efficiency of beam search.

[0073] Figure 5 Shows the definition method of the elevation angle and azimuth angle adopted by the present invention in a three-dimensional space coordinate system, where the left figure is a three-dimensional space structure diagram and the right figure is a top view. The angle of the beam direction takes the electromagnetic surface as the origin and defines the spatial direction around the X, Y, and Z axes. represents the included angle of the beam relative to the Z axis. It represents the azimuth direction angle after the beam is projected around the Z-axis. This coordinate system provides a standardized mathematical reference for subsequent beam control direction calculation.

[0074] Determining the spatial direction angle for candidate beam direction calculation includes setting the initial phase of the intelligent surface and determining the initial parameters for beam search. Determine the reference coordinate system, and use the rotation matrix to transform the attitude and position of the communication target, and convert the motion state and position of the communication target into the pointing angle of the beam.

[0075] The initial parameters include: search range, search step size, search iteration times, beam angle range, beam type, initial beam pointing, initial beam phase, and amplitude.

[0076] The pointing angle of the beam includes the pitch angle and the azimuth angle.

[0077] The formula for transforming the attitude and position of the communication target by using the rotation matrix is expressed as:

[0078] HPB matrix calculation: Rotate in the order of Heading (Yaw), Pitch, Bank (Roll), that is, HPB order rotation.

[0079]

[0080]

[0081]

[0082] Among them, represents the Heading (heading angle), that is, the rotation angle around the Y-axis. represents the Pitch (pitch angle), that is, the rotation angle around the X-axis. represents the Bank (roll angle), that is, the rotation angle around the Z-axis. represents the rotation angle around the Y-axis of the rotation matrix, corresponding to . represents the rotation matrix of the rotation angle p around the X-axis, corresponding to . represents the rotation matrix of the rotation angle b around the Z-axis, corresponding to . represents cos(h), that is, the cosine value of the angle h. represents sin(h), that is, the sine value of the angle h. represents cos(p), that is, the cosine value of the angle p. represents sin(p), that is, the sine value of the angle p. represents cos(b), that is, the cosine value of the angle b. Denotes sin(b), i.e., the sine value of angle b.

[0083] Rotation matrix calculation:

[0084]

[0085]

[0086] Among them, R represents the final three-dimensional rotation matrix, which is composed of the results after rotation around the Y, X, and Z axes. H(h) represents the rotation matrix for rotating by angle h around the Y axis, also known as Heading. P(p) represents the rotation matrix for rotating by angle p around the X axis, also known as Pitch. B(b) represents the rotation matrix for rotating by angle b around the Z axis, also known as Bank. E represents the unit attitude reference matrix, that is, the rotation result when all rotation angles are 0.

[0087] Normalization calculation:

[0088]

[0089]

[0090]

[0091] Beam pointing θ calculation:

[0092] From , it can be deduced that

[0093] Beam pointing φ calculation:

[0094]

[0095] Among them, R represents the three-dimensional rotation matrix, which is composed of . Denotes the normalization process of the column vectors of matrix R, Denotes the target direction vector, Denotes the unit vector in the Y-axis direction, Denotes the attitude initial reference direction vector, and θ represents the pitch angle. Denotes the azimuth angle.

[0096] The communication system is innovatively applied to the substation scenario, focusing on achieving efficient mobile communication capabilities. The system is strategically deployed at a high position in the substation, ensuring that the entire station area is under the extensive coverage of the reconfigurable intelligent surface antenna. When the inspection robot executes the inspection task according to the preset customized route, the communication system can capture the accurate position information of the inspection robot in real time and automatically and dynamically calculate the best beam direction according to the adaptive tracking algorithm. By adopting beamforming technology, the system can intelligently track the moving trajectory of the inspection robot, thus establishing a stable and reliable mobile communication link. This innovative design not only improves communication efficiency but also greatly enhances communication flexibility and reliability in the substation scenario. As Figure 6 shown, it intuitively demonstrates the deployment and operation of the communication system in the substation.

[0097] The adaptive beam tracking algorithm is a key technology in the field of wireless communication. Its core goal is to dynamically and accurately adjust the beam direction according to the movement of the target and the change of channel conditions, so as to ensure a stable communication link with the target at all times. The implementation of this algorithm usually integrates a variety of advanced prediction and tracking technologies, such as Kalman filtering, particle filtering, etc., and is supplemented by an efficient feedback mechanism to achieve real-time adjustment of the beam direction.

[0098] Using historical data and the motion model of the communication target, the central prediction method is adopted. With the prediction point as the center, according to the preset spatial expansion mode, n sampling points including the prediction point are generated to form a local two-dimensional grid area. The beam directions are set for these n sampling points in turn, and the communication quality of each sampling point is evaluated and compared one by one in combination with the real-time feedback communication quality parameters, and the point with the best communication quality is selected as the final direction of the current beam adjustment.

[0099] The communication quality parameters include signal strength, signal-to-noise ratio, bit error rate, packet reception rate, and delay jitter.

[0100] Prediction stage: In the prediction stage of the adaptive beam tracking algorithm, the algorithm will make full use of historical data and the motion model of the target to accurately predict the future position of the target. Historical data may include the past position information, speed information, and acceleration information of the target, while the motion model is a mathematical description of the target's motion law. By combining the two, the algorithm can generate a reasonable estimate of the target's future position, providing a favorable basis for subsequent beam adjustment.

[0101] Adopt the central prediction algorithm: As Figure 7 shown, the center point is the prediction point, and the next point is taken out in turn according to the arrow order.

[0102] Tracking Phase: In the tracking phase, the algorithm quickly adjusts the beam direction based on the results obtained in the prediction phase to ensure that the beam accurately points to the target. During this process, the algorithm makes full use of the feedback mechanism to evaluate the effect of beam adjustment by real-time monitoring the quality of the communication link, such as signal strength, signal-to-noise ratio, etc. Once a prediction error or a decline in the communication link quality is detected, the algorithm immediately makes corrections by adjusting the beam direction or optimizing relevant parameters to restore and maintain stable communication with the target.

[0103] Among them, n takes the value of 9, and the 9-point comparison method is adopted: take the first 9 points of the center diffusion method, and the algorithm model is as Figure 8 shown in the box.

[0104] In addition, the adaptive beam tracking algorithm also has a high degree of flexibility and adaptability. In the face of complex and changing communication environments and target movements, the algorithm can quickly adapt and make corresponding adjustments to ensure the stability and reliability of the communication link. This adaptability not only improves the performance of the communication system but also reduces the risk of communication interruption caused by target movement and channel changes.

[0105] Furthermore, by introducing a dynamic search strategy selection mechanism driven by sensing parameters, the accuracy of beam pointing calculation and resource utilization efficiency are significantly improved. By setting three quantifiable indicators: channel sparsity, spatial aggregation degree, and beam control accuracy, and calculating the threshold conditions in formula form, intelligent switching among three strategies: compressed search, hierarchical search, and exhaustive search is realized. Thus, under different network structures, target distributions, and performance requirements, the most suitable beam search path is selected to avoid computational waste or insufficient accuracy problems caused by a one-size-fits-all fixed strategy. At the same time, in this stage, the pitch angle and azimuth angle are calculated by combining three-dimensional rotation transformation and spatial direction angle analysis, constructing a complete closed-loop logic from attitude sensing to spatial pointing, laying the foundation for precise beam control.

[0106] Further, an adaptive beam tracking algorithm constructs a tracking mechanism with "central prediction + multi-point comparison + real-time feedback" as the core. The algorithm first makes a central prediction of the target's future position based on historical trajectories and motion models, then generates 9 sampling points through local two-dimensional perturbations, quickly realizes fine adjustment under the condition of unchanged beam resources, and conducts point-by-point evaluation in combination with communication quality parameters (such as SNR, BER, PRR, etc.), dynamically selecting the optimal beam pointing. This mechanism not only enhances the system's tracking ability for moving targets but also maintains high link stability in complex scenarios, reducing the risk of communication interruption caused by mispointing or target deviation, fully demonstrating the outstanding advantages of the present invention in terms of real-time performance, robustness, and communication continuity.

[0107] S3: By monitoring the communication quality, adjust the beam phase and amplitude of each communication target to automatically optimize the beam pointing and bandwidth allocation.

[0108] Set the acquisition period, collect the communication quality parameters of each communication target within each period, and evaluate the current communication quality status of the communication target.

[0109] Based on the evaluation result of the communication quality status, the dynamic adjustment of the phase and amplitude of the intelligent surface antenna unit corresponding to the current communication target includes: when the signal-to-noise ratio normal threshold , and the bit error rate bit error tolerance , the packet reception rate reception rate threshold , it is determined to be in a normal state, maintain the current beam parameters, and no adjustment is required.

[0110] When the degradation threshold signal-to-noise ratio , and the bit error tolerance bit error rate bit error upper limit , or the communication delay jitter delay jitter threshold , it is determined to be in a mild degradation state, start local perturbation adjustment, and select the optimal direction and then adjust the phase array.

[0111] When the signal-to-noise ratio degradation threshold , or the bit error upper limit bit error rate, or the packet reception rate minimum reception rate threshold , it is determined to be in a severe abnormal state, immediately trigger beam direction reconstruction, and at the same time record that this direction is marked as a low-stability area in the channel mapping and avoid preferentially selecting it in future searches.

[0112] Among them, , , are set according to requirements and experience.

[0113] Furthermore, by setting the acquisition period, continuously monitor multi-dimensional communication quality parameters such as the signal-to-noise ratio, bit error rate, and packet reception rate of the communication target, construct a dynamic evaluation model, divide the communication state into three levels: normal, mild degradation, and severe abnormality, and implement a hierarchical response strategy accordingly. The system maintains the current beam configuration in the normal state to avoid wasting resources. In the mild degradation state, enable small-range perturbation scanning to adjust the beam direction at the lowest cost. In the severe abnormal state, quickly trigger beam reconstruction and introduce a direction shielding mechanism to avoid repeated attempts in the low-stability area, forming a closed-loop self-optimization process.

[0114] Furthermore, through this mechanism, the system can perceive the change of communication quality in real time and intelligently adjust the beam phase and amplitude accordingly, so as to realize the dynamic optimization of beam pointing and bandwidth allocation. This solution not only improves the stability and reliability of the communication link, but also significantly enhances the system's adaptability to environmental changes, target movement and channel fluctuations, and is especially suitable for the communication control requirements of multiple targets, high interference and strong dynamics in the power scenario, with high practicality and wide engineering promotion value.

[0115] Example 2, as Figure 2 shown, is an embodiment of the present invention, which provides an intelligent surface multi-point communication collaborative control system, including a main control module, a positioning module, an inertial measurement module, a 5.8GHz communication module, a 4G CPE module, an optoelectronic switching module, and a reconfigurable intelligent surface antenna.

[0116] The main control module is connected to the above-mentioned modules through interfaces, and is used to obtain position information and attitude information, calculate the beam pointing according to the spatial positioning algorithm and the adaptive beam tracking algorithm, control the intelligent surface antenna for beamforming, establish a communication link through the 5.8GHz communication module, access the network signal source through the 4G CPE module and transmit back the system information.

[0117] The main control module is connected to the inertial measurement module through a universal asynchronous receiver / transmitter to obtain the device attitude information.

[0118] It is connected to the positioning module, the 4G CPE module, the optoelectronic switching module and the 5.8GHz communication module through an Ethernet interface to realize data interaction and remote management.

[0119] It is connected to the reconfigurable intelligent surface antenna through a GPIO interface to realize beam control.

[0120] The positioning module is used to collect positioning data and direction data, and realize the upload of heartbeat packets.

[0121] The inertial measurement module is used to obtain the device attitude information.

[0122] The optoelectronic switching module realizes the conversion and transmission of optoelectronic signals.

[0123] The reconfigurable intelligent surface antenna passes through a metal isolation feeding network and radiation patches, and uses an air medium to broaden the working bandwidth, and is driven by the main control module to realize beamforming.

[0124] Example 3, an embodiment of the present invention, is different from the previous two embodiments in that:

[0125] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0126] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0127] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optic device, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as necessary, and then storing it in a computer memory.

[0128] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0129] Embodiment 4 is an embodiment of the present invention, which provides an intelligent surface multi-point communication collaborative control method and system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0130] In this embodiment, the test system is set up in a laboratory for a power transmission and transformation scenario. A 4G CPE module is used as the network signal source access device, and together with a dedicated positioning module, an inertial measurement module, and a 5.8 GHz communication module, a communication link is formed, constituting a complete intelligent surface multi-point communication collaborative control system. First, the 4G CPE module accesses the wireless network signals inside and outside the laboratory. After the system is started, the positioning module uses the built-in GPS and indoor assisted positioning technology to collect the position information, attitude data, and motion states of each communication target (such as inspection robots, power monitoring terminals, etc.) in real time. At the same time, the system collects the communication requirements, task priorities, and current network status information of the targets, and divides the resource priorities of each target according to pre-set rules. Next, the main control module inputs the collected status information into the spatial positioning algorithm, which adopts a dynamic search strategy selection mechanism based on sensing parameters. Specifically, the system calculates the channel sparsity index η, the target space aggregation index ξ, and the beam control accuracy requirement index δ, and compares them with the pre-set thresholds respectively to determine whether to adopt a compressive search, hierarchical search, or exhaustive search strategy in this beam search process. After determining the candidate beam directions, the system uses the rotation matrix to convert the motion state and position of the target into specific beam pointing angles, including the elevation angle θ and the azimuth angle φ. The initial parameters (search range, step size, number of iterations, beam type, initial beam pointing, phase, and amplitude) are all pre-set by the main control module. In addition, after obtaining the preliminary beam pointing, the system further adopts an adaptive beam tracking algorithm. This algorithm uses historical data and the target motion model to generate multiple sampling points in a local two-dimensional grid area centered on the prediction point through the central prediction method, and sequentially sets the beam directions. Then, combined with the real-time feedback communication quality parameters (such as signal strength, signal-to-noise ratio, bit error rate, packet reception rate, and delay jitter), they are evaluated one by one. Finally, the sampling point with the best communication quality is selected as the final pointing for the current beam adjustment. At the same time, the system sets a fixed acquisition period (such as 100.00 milliseconds) to collect the communication quality parameters of each target in real time, and evaluates the current communication state through the built-in feedback processing and anomaly judgment mechanism: when the SNR, BER, and PRR of the target all meet the "normal state" standard, the current beam configuration is maintained; if the target is in a mildly degraded state, local perturbation adjustment is started, and multiple candidate directions are generated by the 9-point perturbation method. After selecting the direction with the best quality, the phase and amplitude of the antenna array are updated; and when the target is in a severely abnormal state, beam reconstruction is immediately triggered, and this direction is marked as a low-stability area to avoid being preferentially selected in future searches. During the entire test process, each module operates in coordination, and high-speed data transmission and real-time feedback are achieved through the data interface to ensure the complete recording of experimental data. All software modules run on a customized embedded control system, and various key parameters are displayed in real time through a dedicated data acquisition software to ensure data accuracy and system response speed.

[0131] During the experiment, a total of three communication targets were selected, and their communication performance was recorded and analyzed in real time over two consecutive acquisition cycles.

[0132] For communication target 1, in the first acquisition cycle, its signal-to-noise ratio (SNR) was 21.35 dB, bit error rate (BER) was 0.06%, packet reception rate (PRR) was 98.75%, communication delay jitter was 18.20 milliseconds, and the initial beam pointing angle was 35.20° in elevation angle and 120.50° in azimuth angle. After beam adaptive adjustment, the angle was fine-tuned to 36.15° in elevation angle and 119.80° in azimuth angle, and the system automatically adjusted its bandwidth allocation to 4.50 MHz. In the second cycle, the SNR of target 1 was 20.80 dB, BER was 0.07%, PRR was 98.50%, jitter was 20.10 milliseconds, the adjusted beam was 36.00° in elevation angle and 119.60° in azimuth angle, and the bandwidth was adjusted to 4.55 MHz.

[0133] The SNR of communication target 2 in the first cycle was 18.45 dB, BER was 0.12%, PRR was 96.80%, delay jitter was 25.30 milliseconds, and the initial beam angle was 40.10° in elevation angle and 135.75° in azimuth angle. After beam adjustment, the direction changed to 42.50° in elevation angle and 134.20° in azimuth angle, and the bandwidth allocated by the system was 3.80 MHz. In the second cycle, the SNR was 17.90 dB, BER was 0.14%, PRR was 96.20%, jitter was 27.50 milliseconds, the beam was adjusted to 42.00° in elevation angle and 133.85° in azimuth angle, and the bandwidth was 3.75 MHz.

[0134] Communication target 3 was in an area with relatively poor channel conditions. Its SNR in the first cycle was 16.75 dB, BER was 0.20%, PRR was 94.30%, delay jitter was 32.10 milliseconds, the initial beam was 45.60° in elevation angle and 150.25° in azimuth angle, the adjusted direction was 47.80° in elevation angle and 149.00° in azimuth angle, and the bandwidth was 3.20 MHz. In the second cycle, the SNR dropped to 16.50 dB, BER was 0.22%, PRR was 93.95%, delay jitter was 33.40 milliseconds, the beam direction was adjusted to 47.50° in elevation angle and 148.75° in azimuth angle, and the bandwidth was 3.15 MHz.

[0135] For comparison, when the traditional fixed beam strategy is adopted in the same environment, the average SNR of Target 1 is 19.50 dB, the BER is 0.09%, the PRR is 97.20%, and the bandwidth is a fixed 4.20 MHz; the average SNR of Target 2 is 17.20 dB, the BER is 0.15%, the PRR is 95.50%, and the bandwidth is 3.60 MHz; the SNR of Target 3 is 15.50 dB, the BER is 0.25%, the PRR is 93.80%, and the bandwidth is 3.00 MHz. All fixed strategies do not perform beam tracking and bandwidth adaptive scheduling.

[0136] It can be seen that the intelligent surface multi-point communication collaborative control method implemented in the present invention has obvious advantages in beam adjustment and bandwidth allocation. First, for Target 1, in two consecutive cycles, by dynamically adjusting the beam phase and amplitude, its SNR remains above 20.80 dB, the BER is lower than 0.07%, the PRR is higher than 98.50%, and at the same time, the bandwidth allocation fluctuates slightly between 4.50 - 4.55 MHz, proving that the system can maintain a stable beam configuration when the communication state is normal. Compared with the traditional fixed beam strategy, the SNR of Target 1 is only 19.50 dB, the BER is 0.09%, the PRR only reaches 97.20%, and the bandwidth is fixed at 4.20 MHz, indicating that the adaptive adjustment of the present invention has significant advantages in improving signal quality, reducing the bit error rate, and increasing the data reception rate.

[0137] For Target 2, in the embodiment, through the adaptive beam tracking algorithm, after starting local perturbation adjustment in a slightly degraded state, the SNR of Target 2 slightly decreases from 18.45 dB to 17.90 dB, but the system successfully optimizes the beam direction, so that θ and φ after the final adjustment are adjusted from 40.10° and 135.75° to 42.50° and 134.20° respectively, and at the same time, the bandwidth allocation slightly decreases from 3.80 MHz to 3.75 MHz. Compared with the fixed beam strategy, when Target 2 is fixed, the average SNR is only 17.20 dB, the BER reaches 0.15%, the PRR is only 95.50%, and the bandwidth allocation is fixed at 3.60 MHz, indicating that the adaptive adjustment not only better compensates for beam errors in a dynamic environment, but also can maintain or even improve the signal-to-noise ratio and packet reception rate to a certain extent, thus ensuring the stability of the communication link.

[0138] For Target 3, when the system is in a severe abnormal state, it immediately triggers beam reconstruction and marks the problem direction as a low-stability area. The example data shows that the SNRs of Target 3 are 16.75 dB and 16.50 dB respectively, the BERs are 0.20% and 0.22% respectively, the PRRs are 94.30% and 93.95% respectively, and the bandwidth allocations are 3.20 MHz and 3.15 MHz respectively. Compared with the fixed-beam strategy, the fixed SNR of Target 3 is only 15.50 dB, the BER is 0.25%, the PRR is 93.80%, and the bandwidth is fixed at 3.00 MHz. Although Target 3 is in a relatively weak signal environment, through automatic beam reconstruction and local perturbation adjustment, the system effectively improves the overall quality of the communication link. Especially when the requirement for beam control accuracy is high, the dynamic adjustment ensures that in each acquisition cycle, the system can respond to the changes in the environment and target state in real time, thereby reducing the bit error rate, increasing the packet reception rate, and achieving intelligent optimization of bandwidth allocation.

[0139] Furthermore, the comparative experiment also shows that within 10 consecutive acquisition cycles, the average SNR of the system using the method of the present invention is about 1.25 - 1.50 dB higher than that of the fixed-beam strategy, the BER is reduced by about 0.03 - 0.04 percentage points, and the PRR is increased by about 1.50 - 2.00 percentage points; the bandwidth allocation shows a slight dynamic adjustment trend, and this flexible adjustment is a targeted optimization for the large target dynamics and complex channel environment in the power industry. Overall, the data fully proves that by introducing a beam phase and amplitude adjustment mechanism based on real-time feedback, the present invention can significantly improve the communication quality and link stability, while overcoming the defects of slow response and uneven resource allocation of the traditional fixed-beam strategy in a dynamic multi-target scenario, and has obvious innovation and practical value.

[0140] In addition, the algorithm operation results show that the adaptive beam tracking and dynamic resource scheduling algorithm of the present invention can achieve online update and optimization during system operation, support automatic switching of search strategies in different communication environments, so as to ensure rapid convergence to the optimal beam direction even when the target state changes violently. The experimental data and comparative results further illustrate the application effect of the algorithm in the actual power communication network, which not only improves the signal coverage rate but also reduces the instability of the communication link, demonstrating significant advantages and creative improvements compared with traditional technologies.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An intelligent surface multi-point communication collaborative control method, characterized in that Including: Access the network signal source through the 4G CPE module, obtain the status information of multiple communication targets, and set resource priorities according to the importance of the communication targets; Based on the status information of the communication targets, calculate the optimal beam pointing of each communication target using the spatial positioning algorithm and the adaptive beam tracking algorithm; By monitoring the communication quality, adjust the beam phase and amplitude of each communication target to automatically optimize the beam pointing and bandwidth allocation; The status information of the communication targets includes target position, target attitude, target motion state, communication requirements of the target, task priority of the target, and network status information; Calculating the optimal beam pointing of each communication target includes dynamically selecting a search strategy based on sensing parameters, determining the candidate beam direction, and calculating the spatial direction angle; The sensing parameters include: channel sparsity index, spatial aggregation index of communication targets, and beam control accuracy of communication targets; The dynamically selecting a search strategy based on sensing parameters includes: when the channel sparsity is higher than the channel sparsity threshold, select the compressed search strategy; When the spatial aggregation of communication targets is higher than the spatial aggregation threshold of communication targets, select the hierarchical search strategy; When the beam control accuracy requirement is higher than the beam control accuracy threshold, select the exhaustive search strategy; The calculation of the spatial direction angle includes setting the initial phase of the intelligent surface and determining the initial parameters of beam search; determining the reference coordinate system, converting the attitude and position of the communication target through the rotation matrix, and converting the motion state and position of the communication target into the pointing angle of the beam; The calculation of the optimal beam pointing includes using historical data and the motion model of the communication target, adopting the central prediction method, taking the prediction point as the center, generating n sampling points including the prediction point according to the preset spatial expansion mode to form a local two-dimensional grid area; sequentially setting the beam pointing for the n sampling points, and combining the communication quality parameters fed back in real time to evaluate and compare the communication quality of each sampling point one by one, and selecting the point with the optimal communication quality as the final pointing of the current beam adjustment.

2. The intelligent surface multi-point communication collaborative control method according to claim 1, characterized in that: The initial parameters include: search range, search step, search iteration times, beam angle range, beam type, initial beam pointing, initial beam phase, and amplitude; The pointing angle of the beam includes the elevation angle and the azimuth angle.

3. The intelligent surface multi-point communication collaborative control method according to claim 2, characterized in that: The communication quality parameters include signal strength, signal-to-noise ratio, bit error rate, packet reception rate, and delay jitter.

4. The intelligent surface multi-point communication collaborative control method according to claim 3, wherein: Monitoring the communication quality includes: setting the acquisition period, acquiring the communication quality parameters of each communication target in each period, and evaluating the current communication quality status of the communication target; Based on the evaluation result of the current communication quality status, dynamically adjust the phase and amplitude of the intelligent surface antenna unit corresponding to the current communication target; when the signal-to-noise ratio normal threshold , and the bit error rate bit error tolerance , the packet reception rate reception rate threshold , it is determined to be in a normal state, maintain the current beam parameters, and no adjustment is required; When the degradation threshold Signal-to-noise ratio , and the error tolerance Bit error rate Upper limit of bit error , or the communication delay jitter Delay jitter threshold When it is, it is determined as a mild degradation state, the local perturbation adjustment is started, and the phase array is adjusted after selecting the optimal direction; When the signal-to-noise ratio degradation threshold , or the upper limit of bit error bit error rate, or packet reception rate minimum reception rate threshold , it is determined as a serious abnormal state, and the beam direction reconstruction is immediately triggered. At the same time, this direction is marked as a low-stability area in the channel mapping, and future searches should avoid preferentially selecting it.

5. An intelligent surface multi-point communication collaboration control system adopting the method described in any one of claims 1-4, characterized in that: Including a main control module, a positioning module, an inertial measurement module, a 5.8GHz communication module, a 4G CPE module, an optical and electrical switching module, and a reconfigurable intelligent surface antenna; The main control module is connected to the above-mentioned modules through interfaces, used to obtain position information and attitude information, calculate the beam pointing according to the spatial positioning algorithm and the adaptive beam tracking algorithm, control the intelligent surface antenna to perform beam shaping, establish a communication link through the 5.8GHz communication module, access the network signal source through the 4G CPE module and transmit back the system information; The main control module is connected to the inertial measurement module through a universal asynchronous receiver / transmitter to obtain device attitude information; It is connected to the positioning module, 4G CPE module, optical electric switching module and 5.8GHz communication module through an Ethernet interface to achieve data interaction and remote management; It is connected to the reconfigurable intelligent surface antenna through a GPIO interface to achieve beam control; The positioning module is used to collect positioning data and direction data and implement heartbeat packet uploading; The inertial measurement module is used to obtain device attitude information; The optical electric switching module realizes the conversion and transmission of optical and electric signals; The reconfigurable intelligent surface antenna adopts a metal isolation feeding network and radiation patches, and uses an air medium to broaden the working bandwidth, and is driven by the main control module to achieve beamforming.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it realizes the steps of the intelligent surface multi-point communication cooperative control method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it realizes the steps of the intelligent surface multi-point communication cooperative control method described in any one of claims 1 to 4.

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