Intelligent surface multipoint communication cooperative control method and system

Abstract

The invention relates to the technical field of wireless communication, and discloses an intelligent surface multipoint communication cooperative control method and system, and the method comprises the steps: accessing a network signal source through a 4GCPE module, obtaining the state information of a plurality of communication targets, and setting a resource priority according to the importance of the communication targets; based on the state information of the communication targets, calculating the optimal beam pointing of each communication target by using a spatial positioning algorithm and an adaptive beam tracking algorithm; by monitoring the communication quality, the beam phase and amplitude of each communication target are adjusted so as to automatically optimize beam pointing and bandwidth allocation. And high-precision, multi-dimensional and low-delay communication control on a plurality of mobile communication targets in a complex environment of the power industry is realized.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly 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 both the depth and breadth of the coverage of its communication network, 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 areas 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 large data volumes and high-frequency communication requirements. 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] 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;

[0007] 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;

[0008] Real-time collecting environmental data, and optimizing the beam direction and bandwidth allocation through a machine learning algorithm;

[0009] By monitoring the communication quality, adjusting the phase and amplitude of each target beam, and automatically optimizing 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; The sensing parameters include: channel sparsity index, spatial aggregation degree 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, selecting a compressive search strategy; When the spatial aggregation degree of communication targets is higher than the spatial aggregation degree threshold of communication targets, selecting a hierarchical search strategy; When the beam control accuracy requirement is higher than the beam control accuracy threshold, selecting an exhaustive search strategy.

[0012] 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; The initial parameters include: search range, search step, search iteration times, beam angle range, beam type, initial beam direction, initial beam phase and amplitude; The pointing angle of the beam includes the elevation angle and the azimuth angle.

[0013] 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 direction 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 direction of the current beam adjustment; The communication quality parameters include signal strength, signal-to-noise ratio, bit error rate, packet reception rate, and delay jitter.

[0014] 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 the communication quality parameters of each communication target in each period, and evaluating the current communication quality state of the communication target; Based on the evaluation result of the current communication quality state, dynamically adjusting 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 data packet reception rate Reception rate threshold When the above conditions are met, it is determined to be in a normal state, and the current beam parameters are maintained without adjustment;

[0015] 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 When the above conditions are met, it is determined to be in a mild degradation state, and local perturbation adjustment is started. After selecting the optimal direction, the phase array is adjusted;

[0016] When the signal-to-noise ratio is lower than the degradation threshold or the bit error upper limit bit error rate, or the data packet reception rate is lower than the minimum reception rate threshold it is determined to be in a serious abnormal state, and beam direction reconstruction is immediately triggered. At the same time, this direction is marked as a low-stability area in the channel map, and future searches should avoid preferentially selecting it.

[0017] An intelligent surface multi-point communication collaborative control system, which includes: 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;

[0018] 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 beam shaping, establish a communication link through the 5.8GHz communication module, access the network signal source through the 4G CPE module and upload the system information;

[0019] The main control module is connected to the inertial measurement module through a universal asynchronous receiver / transmitter to obtain the device attitude information;

[0020] It is connected to the positioning module, 4G CPE module, optical and electrical switching module and 5.8GHz communication module through an Ethernet interface to realize data interaction and remote management;

[0021] It is connected to the reconfigurable intelligent surface antenna through a GPIO interface to realize beam control;

[0022] The positioning module is used to collect positioning data and direction data, and realize heartbeat packet upload;

[0023] The inertial measurement module is used to obtain the device attitude information;

[0024] The optoelectronic switching module realizes the conversion and transmission of optoelectronic signals;

[0025] 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 realize beamforming.

[0026] 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.

[0027] A computer-readable storage medium stores a computer program thereon, 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.

[0028] 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 a complex environment of the power industry through the integration of 4G CPE signal access, state perception-driven resource priority setting, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0030] Figure 1 It is the overall flowchart of an intelligent surface multi-point communication collaborative control method provided by the first embodiment of the present invention;

[0031] Figure 2 It is the system architecture diagram of an intelligent surface multi-point communication collaborative control method provided by the second embodiment of the present invention;

[0032] Figure 3Flow chart of power transmission and transformation application scenarios of a multi-point communication collaborative control method for intelligent surfaces provided in the first embodiment of the present invention;

[0033] Figure 4 Overall layout diagram of a multi-point communication collaborative control method for intelligent surfaces provided in the first embodiment of the present invention;

[0034] Figure 5 Coordinate definition diagram of a multi-point communication collaborative control method for intelligent surfaces provided in the first embodiment of the present invention;

[0035] Figure 6 Schematic diagram of beam coverage prediction and adaptive tracking of a multi-point communication collaborative control method for intelligent surfaces provided in the first embodiment of the present invention;

[0036] Figure 7 Central prediction algorithm diagram of a multi-point communication collaborative control method for intelligent surfaces provided in the first embodiment of the present invention;

[0037] Figure 8 Comparison algorithm model diagram of a multi-point communication collaborative control method for intelligent surfaces provided in the first embodiment of the present invention. Specific embodiments

[0038] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0039] Example 1, referring to Figure 1 、 Figures 3 - 8 , which is an embodiment of the present invention, provides a multi-point communication collaborative control method for intelligent surfaces, including: 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.

[0040] Figure 3 Illustrates the logical sequence relationship of two typical application scenarios in the power system in this embodiment case, that is, first applied to the remote communication control of transmission lines and then extended to the multi-target collaborative communication within the substation. The power transmission application method focuses on improving beam coverage and communication stability in large-span and long-distance areas, and is suitable for communication tasks of remote targets such as high towers and drones; while the substation application method emphasizes more on the real-time dynamic adjustment of the communication status of short-distance, multi-target, and high-frequency communications, and is suitable for scenarios with high-density communication requirements such as power robot inspections and intelligent device linkages. By such asFigure 3 For the application logic switching shown, the system can dynamically load different beam scheduling strategies and feedback mechanisms according to the scenario during actual deployment, so as to ensure the consistency and reliability of communication quality.

[0041] In this implementation case, a communication system for compensating signal blind spots in the power 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, thus achieving stable point-to-multipoint communication. Figure 4 Shows the overall layout and working principle of this communication system.

[0042] 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.

[0043] 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 states 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.

[0044] Even further, the base station side is deployed on optical fibers or 4G coverage points, and combines the spatial positioning algorithm to achieve adaptive beam adjustment for 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-target access efficiency, enabling the system to still have stable data link management ability in a high-interference and dynamic environment.

[0045] S2: Based on the status information of the communication target, use the spatial positioning algorithm and the adaptive beam tracking algorithm to calculate the best beam pointing for each communication target.

[0046] Dynamically select the search strategy based on the sensing parameters, and determine the candidate beam direction to calculate the spatial direction angle.

[0047] The sensing parameters include: the channel sparsity index, the spatial aggregation index of the communication target, and the beam control accuracy of the communication target.

[0048] The calculation formula of the channel sparsity index:

[0049]

[0050] 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.

[0051] The formula for the communication target space aggregation degree index is expressed as:

[0052]

[0053] Among them, represents the maximum and minimum eigenvalues of the target coordinate covariance matrix. represents the communication target space aggregation degree index.

[0054] The formula for the beam control accuracy requirement index is expressed as:

[0055]

[0056] Among them, SNR represents the current signal-to-noise ratio. represents the target relative velocity. represents the task quality requirement level. represents the empirical weight. represents the beam control accuracy requirement index.

[0057] Dynamically selecting a search strategy based on sensing parameters includes: when the channel sparsity is higher than the channel sparsity threshold, select the compressive search strategy.

[0058] When the communication target space aggregation degree is higher than the communication target space aggregation degree threshold, select the hierarchical search strategy.

[0059] When the beam control accuracy requirement is higher than the beam control accuracy threshold, select the exhaustive search strategy.

[0060] 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 best 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 the exhaustive search grows exponentially, resulting in a significant extension of the search time and huge resource consumption. Therefore, in practical applications, the exhaustive search is often severely limited by the hardware performance and computational time and is difficult to play a role in scenarios with high real-time requirements.

[0061] Hierarchical Search: To overcome the drawback of high computational complexity in exhaustive search, hierarchical search came into being. It cleverly 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 scope. 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 some potential optimal solutions may be missed in the coarse search stage.

[0062] Compressive Search: In response to the sparse scattering characteristics of reconfigurable intelligent communication systems, compressive search algorithms emerged. It utilizes the theory of compressive sensing 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 leveraging the sparsity of the millimeter-wave channel, thereby reducing the search complexity. However, this method also has certain limitations. It requires phase coherence to be maintained between subsequent beacons, which is often difficult to achieve in complex and changing communication environments. Therefore, the application scope of compressive search algorithms is somewhat restricted.

[0063] 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 enhancing the performance of the communication system and the efficiency of beam search.

[0064] 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 is defined with the electromagnetic surface as the origin, and the spatial direction is defined around the X, Y, and Z axes. represents the included angle of the beam relative to the Z axis. 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 calculations.

[0065] Determining the candidate beam direction to calculate the spatial direction angle includes setting the initial phase of the intelligent surface and determining the initial parameters of the beam search. Determine the reference coordinate system, and transform the attitude and position of the communication target through the rotation matrix, and convert the motion state and position of the communication target into the pointing angle of the beam.

[0066] 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.

[0067] The pointing angles of the beam include the pitch angle and the azimuth angle.

[0068] The formula for converting the attitude and position of a communication target through a rotation matrix is expressed as:

[0069] HPB matrix calculation: Rotate in the order of Heading (Yaw), Pitch, Bank (Roll), i.e., HPB order.

[0070]

[0071]

[0072]

[0073] Among them, represents the Heading (heading angle), i.e., the rotation angle around the Y-axis. represents the Pitch (pitch angle), i.e., the rotation angle around the X-axis. represents the Bank (roll angle), i.e., the rotation angle around the Z-axis. represents the rotation matrix of the rotation angle around the Y-axis 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), i.e., the cosine value of the angle h. represents sin(h), i.e., the sine value of the angle h. represents cos(p), i.e., the cosine value of the angle p. represents sin(p), i.e., the sine value of the angle p. represents cos(b), i.e., the cosine value of the angle b. represents sin(b), i.e., the sine value of the angle b.

[0074] Rotation matrix calculation:

[0075]

[0076]

[0077] Among them, R represents the final three-dimensional rotation matrix, which is composed of the results of rotations around the Y, X, and Z axes. H(h) represents the rotation matrix for rotating by an angle h around the Y axis, also known as Heading. P(p) represents the rotation matrix for rotating by an angle p around the X axis, also known as Pitch. B(b) represents the rotation matrix for rotating by an 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.

[0078] Normalization calculation:

[0079]

[0080]

[0081]

[0082] Beam pointing θ calculation:

[0083] From , it can be deduced that

[0084] Beam pointing φ calculation:

[0085]

[0086] Among them, R represents the three-dimensional rotation matrix, which is composed of constitute. represents normalizing the column vectors of matrix R, represents the target direction vector, represents the unit vector in the Y-axis direction, represents the initial attitude reference direction vector, and θ represents the pitch angle. represents the azimuth angle.

[0087] 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 yard to ensure that the entire yard 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 pointing according to the adaptive tracking algorithm. By adopting beamforming technology, the system can intelligently track the moving trajectory of the inspection robot, thereby establishing a stable and reliable mobile communication link. This innovative design not only improves the communication efficiency but also greatly enhances the 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 yard.

[0088] The adaptive beam tracking algorithm is a key technology in the field of wireless communication. Its core objective is to dynamically and precisely adjust the beam direction according to the movement of the target and the changes in 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 techniques, such as Kalman filtering, particle filtering, etc., and is supplemented by an efficient feedback mechanism to achieve real-time adjustment of the beam direction.

[0089] Using historical data and the motion model of the communication target, the central prediction method is adopted. With the predicted point as the center, according to the preset spatial expansion mode, n sampling points including the predicted point are generated to form a local two-dimensional grid area. The beam pointing is 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 pointing of the current beam adjustment.

[0090] The communication quality parameters include signal strength, signal-to-noise ratio, bit error rate, packet reception rate, and delay jitter.

[0091] 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, etc., and 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.

[0092] Adopt the central prediction algorithm: As Figure 7 shown, the center point is the predicted point, and the next point is taken out in turn according to the arrow order.

[0093] Tracking stage: In the tracking stage, the algorithm will quickly adjust the beam direction according to the results obtained in the prediction stage to ensure that the beam can accurately point to the target. In this process, the algorithm will make full use of the feedback mechanism to evaluate the effect of beam adjustment by real-time monitoring of the quality of the communication link, such as signal strength, signal-to-noise ratio, etc. Once a prediction error or a decrease in the quality of the communication link is found, the algorithm will immediately make corrections by adjusting the beam direction or optimizing relevant parameters to restore and maintain a stable communication with the target.

[0094] Among them, n takes the value of 9, and the 9-point comparison method is adopted: Take the first 9 points of the central diffusion method, and the algorithm model is as Figure 8 shown in the box.

[0095] In addition, the adaptive beam tracking algorithm also features high 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.

[0096] 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 achieved. Thus, under different network structures, target distributions, and performance requirements, the most suitable beam search path is selected, avoiding 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.

[0097] Further, an adaptive beam tracking algorithm constructs a tracking mechanism centered on "central prediction + multi-point comparison + real-time feedback". 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 without changing the 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.

[0098] 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.

[0099] Set the acquisition period, acquire the communication quality parameters of each communication target in each period, and evaluate the current communication quality status of the communication target.

[0100] Based on the evaluation result of the communication quality status, dynamically adjust the phase and amplitude of the intelligent surface antenna unit corresponding to the current communication target, including: when the signal-to-noise ratio normal threshold , and the bit error rate bit error tolerance , the packet reception rate reception rate threshold When it is in this state, it is determined to be in a normal state, and the current beam parameters are maintained without adjustment.

[0101] 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 this occurs, it is determined to be in a mild degradation state, and local perturbation adjustment is initiated. After selecting the optimal direction, the phase array is adjusted.

[0102] 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 When this occurs, it is determined to be in a severe abnormal state, and 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 are avoided from preferentially selecting it.

[0103] Among them, , , Are set according to requirements and experience.

[0104] Furthermore, by setting the acquisition period, continuously monitoring multi-dimensional communication quality parameters such as the signal-to-noise ratio, bit error rate, and packet reception rate of the communication target, constructing a dynamic evaluation model, dividing the communication state into three levels: normal, mild degradation, and severe abnormality, and implementing 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, a small-range perturbation scan is enabled to adjust the beam direction at the lowest cost. In the severe abnormal state, beam reconstruction is quickly triggered, and a direction shielding mechanism is introduced to avoid repeated attempts in the low-stability area, forming a closed-loop self-optimization process.

[0105] Even further, through this mechanism, the system can real-time sense the change in communication quality and accordingly intelligently adjust the beam phase and amplitude, thereby achieving 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 adaptive ability to environmental changes, target movement, and channel fluctuations. It is especially suitable for the communication control requirements of multiple targets, high interference, and strong dynamics in the power scenario, and has high practicality and wide engineering promotion value.

[0106] Embodiment 2, such as Figure 2As shown in the figure, an embodiment of the present invention provides an intelligent surface multi-point communication collaboration control system, 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.

[0107] 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 direction according to the spatial positioning algorithm and the adaptive beam tracking algorithm, control the intelligent surface antenna to perform 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.

[0108] The main control module is connected to the inertial measurement module through a universal asynchronous receiver / transmitter to obtain the device attitude information.

[0109] It is connected to the positioning module, the 4G CPE module, the optical and electrical switching module and the 5.8GHz communication module through an Ethernet interface to realize data interaction and remote management.

[0110] It is connected to the reconfigurable intelligent surface antenna through a GPIO interface to realize beam control.

[0111] The positioning module is used to collect positioning data and direction data, and realize the upload of heartbeat packets.

[0112] The inertial measurement module is used to obtain the device attitude information.

[0113] The optical and electrical switching module realizes the conversion and transmission of optical and electrical signals.

[0114] The reconfigurable intelligent surface antenna passes through a metal isolation feeding network and a radiation patch, and uses an air medium to broaden the working bandwidth, and is driven by the main control module to realize beamforming.

[0115] Embodiment 3, an embodiment of the present invention, is different from the previous two embodiments in that:

[0116] If the above-described functions are implemented in the form of software functional 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: 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, etc., which can store program codes of various kinds.

[0117] The logic and / or steps represented in the flowchart or described in other ways 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 used in combination with these instruction execution systems, apparatus, 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 connection with an instruction execution system, apparatus, or device.

[0118] 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), optical fiber 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 processing it in other suitable ways when necessary, and then storing it in a computer memory.

[0119] 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 of the following techniques well known in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0120] Embodiment 4, an embodiment of the present invention, 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.

[0121] 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 and collaboration control system. First, the 4G CPE module is used to access 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 resources 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 perception 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 compressed search, hierarchical search, or exhaustive search strategy in the current 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, and finally the sampling point with the best communication quality is selected as the final pointing of the current beam adjustment. At the same time, the system sets a fixed collection 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 slightly degraded state, local perturbation adjustment is started, and multiple candidate directions are generated through 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.

[0122] During the experiment, a total of three communication targets were selected, and their communication performance was recorded and analyzed in real time for two consecutive acquisition cycles.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 fixed at 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. No beam tracking and bandwidth adaptive scheduling are performed for all fixed strategies.

[0127] It can be seen that the intelligent surface multi-point communication cooperative control method implemented in the present invention has obvious advantages in beam adjustment and bandwidth allocation. First of all, 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.

[0128] For Target 2, in the embodiment, through the adaptive beam tracking algorithm, after starting local perturbation adjustment in the 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.

[0129] 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.

[0130] Furthermore, the comparative experiments also show 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.

[0131] 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 that it can still quickly converge to the optimal beam direction 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 over traditional technologies.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. 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 within the scope of the claims of the present invention.

Claims

1. An intelligent surface multi-point communication collaborative control method, characterized in that It includes: 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.

2. The intelligent surface multi-point communication collaborative control method according to claim 1, wherein: 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.

3. The intelligent surface multi-point communication collaborative control method according to claim 2, wherein: Calculating the optimal beam pointing of each communication target includes: Dynamically select a search strategy based on the sensing parameters, determine the candidate beam directions, and calculate the spatial direction angles; The sensing parameters include: channel sparsity index, spatial aggregation index of the communication targets, and beam control accuracy of the communication targets; The dynamically selecting a search strategy based on the sensing parameters includes: when the channel sparsity is higher than the channel sparsity threshold, select the compressive search strategy; When the spatial aggregation of the communication targets is higher than the spatial aggregation threshold of the 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.

4. The intelligent surface multi-point communication collaborative control method according to claim 3, characterized in that: The calculation of the spatial direction angles includes setting the initial phase of the intelligent surface and determining the initial parameters of the beam search; Determine the reference coordinate system, transform the attitude and position of the communication target through the rotation matrix, and convert the motion state and position of the communication target into the pointing angle of the beam; The initial parameters include: search range, search step size, number of search iterations, 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.

5. The intelligent surface multi-point communication collaborative control method according to claim 4, wherein: 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 set the beam pointing for the n sampling points, and combine the real-time feedback communication quality parameters to evaluate and compare the communication quality of each sampling point one by one, and select the point with the optimal communication quality as the final pointing of the current beam adjustment; The communication quality parameters include signal strength, signal-to-noise ratio, bit error rate, packet reception rate, and delay jitter.

6. The intelligent surface multi-point communication collaborative control method according to claim 5, characterized in that: 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 Normal threshold , and the error tolerance Bit error rate Upper limit of bit error , or the communication delay jitter Delay jitter threshold , 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 severe abnormal state, immediately triggering beam direction reconstruction, and at the same time recording that this direction is marked as a low-stability area in the channel map, and future searches should avoid preferentially selecting it.

7. An intelligent surface multi-point communication collaborative control system adopting the method according to any one of claims 1-6, characterized in that: It includes a main control module, a positioning module, an inertial measurement module, a 5.8 GHz communication module, a 4G CPE module, an optoelectronic 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 for beamforming, establish a communication link through the 5.8 GHz 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 electrical switching module and 5.8 GHz 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 electrical switching module realizes the conversion and transmission of optical and electrical 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.

8. 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 collaborative control method according to any one of claims 1 to 6.

9. 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 collaborative control method according to any one of claims 1 to 6.

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