CN-122028208-A - Emergent unmanned aerial vehicle communication base station

CN122028208ACN 122028208 ACN122028208 ACN 122028208ACN-122028208-A

Abstract

The invention relates to the field of emergency communication, in particular to an emergency unmanned aerial vehicle communication base station which comprises an acquisition analysis module, a parameter calculation module, a forming execution module, a networking negotiation module, a priority evaluation module, a scheduling execution module, a state monitoring module and a fault restoration module, wherein the system predicts an optimal spectrum allocation scheme by acquiring environmental spectrum data and calculates optimal beam parameters in real time by combining user equipment data to form a directional beam. And dynamically distributing communication tasks by the multi-machine cooperative networking, evaluating the communication priority according to the service type and the emergency rule, and carrying out resource scheduling. Meanwhile, the running state of each module is monitored, the abnormality is identified, a repair strategy is generated, the communication quality is ensured to be kept under high-speed maneuver and complex environments, the coverage gain and the system reliability are improved, and the network elasticity and the fault tolerance of emergency communication are enhanced.

Inventors

HU JIAWEI
SU HANG
ZHAO SHILIN

Assignees

福建省邮电规划设计院有限公司

Dates

Publication Date: 20260512
Application Date: 20260119

Claims (6)

1. The emergency unmanned aerial vehicle communication base station is characterized by comprising an acquisition analysis module, a parameter calculation module, a forming execution module, a networking negotiation module, a priority evaluation module, a scheduling execution module, a state monitoring module and a fault restoration module; The acquisition and analysis module is used for acquiring and analyzing the spectrum data in the corresponding area environment and predicting the current optimal spectrum allocation scheme; The parameter calculation module is used for acquiring multi-type data of each user equipment and calculating optimal beam parameters in real time by combining an optimal spectrum allocation scheme; the forming execution module forms directional beams of the corresponding area according to the calculated optimal beam parameters and by combining the working state of the current antenna unit; The networking negotiation module is used for constructing a multi-machine cooperative communication link, monitoring the communication load condition of each unmanned aerial vehicle in real time and distributing communication tasks; The priority evaluation module is used for receiving the service types transmitted by the user equipment and evaluating the priority of each communication requirement by combining with a preset emergency rule; the scheduling execution module allocates corresponding resources in real time based on the priority of the communication demands of each user; the state monitoring module is used for monitoring the running state of each module in real time and identifying corresponding abnormal information; The fault repairing module is used for receiving various abnormal information, analyzing the types of the abnormal information and generating corresponding repairing strategies.
2. The emergency unmanned aerial vehicle communication base station of claim 1, wherein the specific steps of predicting the current optimal spectrum allocation scheme by the acquisition and analysis module are as follows: S1.1, collecting spectrum signals of a corresponding area, removing noise in each spectrum signal through self-adaptive Gaussian filtering, uniformly converting spectrum data with different sampling rates into a matrix format with fixed dimensions, performing association matching on each processed spectrum data and similar scene data in a historical spectrum usage database, extracting spectrum change trend characteristics of each spectrum data within 30 minutes, and forming a combined input vector; S1.2, constructing and training a scheme prediction model, inputting a combined input vector into the trained scheme prediction model, extracting time domain features and frequency domain features in each frequency spectrum data by the scheme prediction model through a convolution layer, identifying corresponding interference signals, laminating the dimensions of all the time domain features and the frequency domain features through a pooling layer, comparing all the extracted features with various samples in a preset interference source feature library by a full connection layer, and outputting a corresponding interference source type judgment result; S1.3, based on the identification result of each interference source, marking the occupied frequency range, performing continuous scanning on the residual frequency range, analyzing the signal energy value of the residual frequency range, judging as idle candidate frequency ranges if the signal energy value is lower than a preset threshold, integrating all idle candidate frequency ranges to form an idle frequency range list, then acquiring and analyzing all signals in the current communication link, extracting the quality characteristics of all signals, and forming a signal quality evaluation matrix; S1.4, the model fuses the time domain characteristics and the frequency domain characteristics of each frequency spectrum, the judging result of each interference source type and the idle frequency band list, calculates each index under different frequency spectrum allocation combinations through a back propagation algorithm to generate a plurality of groups of candidate allocation schemes, and then applies a greedy algorithm to screen out schemes with comprehensive performance reaching preset performance from the plurality of groups of candidate allocation schemes to form an optimal frequency spectrum allocation scheme.
3. The communication base station of the emergency unmanned aerial vehicle of claim 1, wherein the specific steps of the parameter calculation module calculating the optimal beam parameters in real time are as follows: S2.1, collecting multi-type data of each user equipment, acquiring signal intensity data in various types of data, removing user equipment with signal intensity data lower than a preset range, dividing users with signal intensity within the preset range into the same communication group, distinguishing types of each user equipment according to equipment identification, acquiring interference source positions of each interference signal, marking as beam avoidance areas, combining corresponding unmanned aerial vehicle motion state data, predicting position and gesture change trend of each unmanned aerial vehicle within 2 seconds in the future through a Kalman filtering algorithm, generating a corresponding motion trail prediction curve, and then constructing a dynamic environment model; S2.2, taking maximized signal-to-interference ratio of a user group and minimized beam sidelobe leakage as targets, constructing an objective function, acquiring a central coordinate of the user group and taking the central coordinate as a beam main lobe pointing reference, taking the position of each interference source as a constraint item, taking motion prediction data of each unmanned aerial vehicle as a dynamic correction factor, simultaneously introducing a maximum output power threshold of an antenna array as a hardware constraint condition, and determining a variable boundary and a constraint range of the objective function; s2.3, solving an objective function by adopting a minimum mean square error algorithm, obtaining initial weight coefficients of each unit of the antenna array, taking the average signal intensity of a user group as a reference value, taking the error of an actual received signal and a reference signal as an iteration basis, updating the corresponding weight coefficient once per iteration, simultaneously calculating a beam pattern corresponding to the current weight, stopping calculation when the iteration error is smaller than a preset threshold value or reaches the preset iteration times, and outputting an optimal weight matrix; S2.4, converting the weight coefficient of each unit into corresponding beam space directional parameters according to an optimal weight matrix, calculating azimuth angles and pitch angles of main beam lobes, deriving phase differences and amplitude distribution ratios of each antenna unit from updated weight coefficients based on an antenna array theory, forming specific control parameters of each unit, substituting the calculated beam parameters into a dynamic environment model to simulate beam coverage, detecting whether the main beam directional interference sources or side lobes leak to an interference area, and if the interference risk exists, adjusting the azimuth angles of the beams and recalculating phase amplitude parameters.
4. The emergency unmanned aerial vehicle communication base station of claim 1, wherein the specific steps of forming the directional beam of the corresponding area are as follows: S3.1, extracting phase values, amplitude values and beam azimuth angles corresponding to all antenna units, classifying and storing according to antenna array numbers, establishing a corresponding mapping table, detecting the state of each antenna unit, resetting the initial phases of all antenna units to 0 radian, adjusting the amplitude to a preset power value, and synchronously calibrating the signal transmission time sequence of each antenna unit; S3.2, acquiring the actual output phase values of the phase shifters in the antenna units in real time, comparing the actual output phase values with the phase values corresponding to the antenna units, triggering adjustment until the phase values of all the antenna units meet the precision requirement if the error exceeds a preset threshold value, generating corresponding gain control signals according to the amplitude values corresponding to the antenna units, transmitting the gain control signals to the power amplifiers of the antenna units, and changing the amplitude of the corresponding output signals; And S3.3, acquiring the actual output power of each antenna unit in real time, comparing the actual output power with a corresponding amplitude value, dynamically adjusting the gain control signals of the corresponding amplifier, acquiring the communication signals of all the antenna units at the same time node, forming energy focusing in a preset direction by the space superposition effect of the antenna array by the unit signals, and finally synthesizing a directional beam pointing to the center of the user group.
5. The communication base station of the emergency unmanned aerial vehicle of claim 1, wherein the networking negotiation module allocates the communication tasks as follows: S4.1, collecting load indexes of all unmanned aerial vehicle nodes, synchronizing self load data to all network nodes through a load state broadcast message, establishing a network load state matrix, triggering a load balancing mechanism when any unmanned aerial vehicle node is monitored to exceed a preset threshold value, calculating the overload amount of the node according to the network load state matrix, and determining the type and the data amount of a transferable communication task by combining the priority of each task; S4.2, screening out nodes with load less than 50% from a load state matrix as target nodes, calculating link transmission cost between an overload node and each target node, selecting 1-2 target nodes with transmission cost lower than preset cost, sending a task migration request, feeding back an acceptance instruction after the target nodes confirm sufficient resources, synchronizing connection information and data cache of the migration task to the target nodes by the overload node, and notifying corresponding user equipment to switch a communication link to the target nodes after synchronization is completed; and S4.3, after the task migration is completed, the overload node and the target node update the local routing table and the load state respectively, the updated information is synchronized to the whole network through the broadcast message, each node rechecks the rationality of the routing path of the node according to the new routing and load information, and if the routing path exceeding the preset requirement exists, the routing renegotiation is triggered.
6. The communication base station of the emergency unmanned aerial vehicle of claim 1, wherein the specific steps of the scheduling execution module distributing corresponding resources in real time are as follows: S5.1, receiving service information uploaded by each user device, extracting device identification, service type field and additional information of each service type information, constructing a three-dimensional mapping table according to 'device type-service type-emergency degree' based on a preset rule base, marking basic priority corresponding to each type of combination in the three-dimensional mapping table, S5.2, substituting each analyzed equipment identifier and a corresponding service type field into a preset rule base, firstly matching the equipment identifier to determine a basic priority gear, refining the corresponding priority according to the service type field, simultaneously adjusting the corresponding priority by combining with an emergency degree keyword, and finally outputting a corresponding priority evaluation list; S5.3, determining corresponding resource allocation weights according to the priority levels of the communication demands of the user equipment, recording the upper limit of the resource demands of the communication demands of the user equipment, sequencing the priority levels of the communication demands from high to low, sequentially processing the resource supply of the communication demands from high to low according to the ranks, and then converting each resource allocation strategy into corresponding frequency spectrum control instructions, beam forming control instructions and routing scheduling instructions and issuing.

Description

Emergent unmanned aerial vehicle communication base station Technical Field The invention relates to the field of emergency communication, in particular to an emergency unmanned aerial vehicle communication base station. Background In sudden disaster scenes such as earthquakes, floods, forest fires and the like, large-area paralysis of ground communication infrastructures is extremely easy to occur due to factors such as building collapse, terrain damage, power supply interruption and the like, and the two-way communication links between the rescue command center and disaster areas and between first-line rescue workers are completely interrupted. The rescue command can not be issued in time, help seeking information of disaster-stricken personnel is difficult to report, key works such as medical emergency coordination, wounded transportation scheduling, accurate relief material throwing and the like can be trapped into stagnation, golden rescue time is seriously delayed, overall rescue efficiency is greatly reduced, risks of secondary disasters are further increased, and serious threat is formed to personal safety. The existing emergency unmanned aerial vehicle communication base station is difficult to adapt to the requirement of quick response under a sudden emergency scene, the communication stability is easily affected in a high-speed moving or complex electromagnetic environment, the overall communication performance is unstable, in addition, the service interruption probability of the existing emergency unmanned aerial vehicle communication base station is high, the continuity and reliability of emergency field communication are difficult to guarantee, the overall elasticity and fault tolerance of the network are weak, and therefore the emergency unmanned aerial vehicle communication base station is provided. Disclosure of Invention Therefore, an emergency unmanned aerial vehicle communication base station is needed to be provided to solve the problems of unstable communication performance and poor reliability. In order to achieve the above purpose, the inventor provides an emergency unmanned aerial vehicle communication base station, which comprises an acquisition analysis module, a parameter calculation module, a forming execution module, a networking negotiation module, a priority evaluation module, a scheduling execution module, a state monitoring module and a fault repair module; The acquisition and analysis module is used for acquiring and analyzing the spectrum data in the corresponding area environment and predicting the current optimal spectrum allocation scheme; The parameter calculation module is used for acquiring multi-type data of each user equipment and calculating optimal beam parameters in real time by combining an optimal spectrum allocation scheme; the forming execution module forms directional beams of the corresponding area according to the calculated optimal beam parameters and by combining the working state of the current antenna unit; The networking negotiation module is used for constructing a multi-machine cooperative communication link, monitoring the communication load condition of each unmanned aerial vehicle in real time and distributing communication tasks; The priority evaluation module is used for receiving the service types transmitted by the user equipment and evaluating the priority of each communication requirement by combining with a preset emergency rule; the scheduling execution module allocates corresponding resources in real time based on the priority of the communication demands of each user; the state monitoring module is used for monitoring the running state of each module in real time and identifying corresponding abnormal information; The fault repairing module is used for receiving various abnormal information, analyzing the types of the abnormal information and generating corresponding repairing strategies. Further, the specific steps of the acquisition and analysis module for predicting the current optimal spectrum allocation scheme are as follows: S1.1, collecting spectrum signals of a corresponding area, removing noise in each spectrum signal through self-adaptive Gaussian filtering, uniformly converting spectrum data with different sampling rates into a matrix format with fixed dimensions, performing association matching on each processed spectrum data and similar scene data in a historical spectrum usage database, extracting spectrum change trend characteristics of each spectrum data within 30 minutes, and forming a combined input vector; S1.2, constructing and training a scheme prediction model, inputting a combined input vector into the trained scheme prediction model, extracting time domain features and frequency domain features in each frequency spectrum data by the scheme prediction model through a convolution layer, identifying corresponding interference signals, laminating the dimensions of all the time domain features and the frequency doma