Emergency unmanned aerial vehicle communication base station

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 and 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 system predicts an optimal spectrum allocation scheme by collecting environment spectrum data, and calculates optimal beam parameters in real time in combination with user equipment data to form directional beams. And dynamically allocating communication tasks through multi-machine cooperative networking, evaluating communication priorities according to service types and emergency rules, and performing resource scheduling. And meanwhile, the operation state of each module is monitored, abnormity is identified, and a repair strategy is generated, so that the communication quality is kept in a high-speed maneuvering and complex environment, the coverage gain and the system reliability are improved, and the network elasticity and fault-tolerant capability of emergency communication are enhanced.

Description

Technical Field

[0001] This invention relates to the field of emergency communications, and more particularly to an emergency drone communication base station. Background Technology

[0002] In the event of sudden disasters such as earthquakes, floods, and forest fires, ground communication infrastructure is highly susceptible to widespread paralysis due to factors such as building collapses, terrain damage, and power outages. This directly leads to a complete disruption of two-way communication links between the rescue command center and the affected areas and frontline rescue personnel. This not only prevents timely issuance of rescue orders and the reporting of distress calls from affected individuals, but also brings critical tasks such as medical emergency coordination, casualty transport and dispatch, and precise delivery of relief supplies to a standstill. This severely delays crucial rescue time, significantly reduces overall rescue efficiency, and further exacerbates the risk of secondary disasters, posing a serious threat to personal safety.

[0003] Existing emergency drone communication base stations are ill-suited to the rapid response requirements of sudden emergency scenarios, and their communication stability is easily affected in high-speed movement or complex electromagnetic environments, resulting in unstable overall communication performance. Furthermore, existing emergency drone communication base stations have a high probability of service interruption, making it difficult to guarantee the continuity and reliability of communication at emergency sites, and their overall network resilience and fault tolerance are weak. To address these issues, we propose an emergency drone communication base station. Summary of the Invention

[0004] Therefore, it is necessary to provide an emergency drone communication base station to solve the problems of unstable communication performance and poor reliability.

[0005] To achieve the above objectives, the inventors provide an emergency drone communication base station, including a data acquisition and analysis module, a parameter calculation module, a data formation and execution module, a network negotiation module, a priority evaluation module, a scheduling and execution module, a status monitoring module, and a fault repair module;

[0006] The acquisition and analysis module is used to acquire and analyze spectrum data in the corresponding regional environment and predict the current optimal spectrum allocation scheme.

[0007] The parameter calculation module is used to acquire multiple types of data from each user device and, in conjunction with the optimal spectrum allocation scheme, calculate the optimal beam parameters in real time.

[0008] The shaping execution module forms a directional beam for the corresponding region based on the calculated optimal beam parameters and the current working state of the antenna unit.

[0009] The networking negotiation module is used to build a multi-drone collaborative communication link, monitor the communication load of each drone in real time, and allocate communication tasks.

[0010] The priority assessment module is used to receive the service types transmitted by each user equipment and, in conjunction with preset emergency rules, assess the priority of each communication requirement.

[0011] The scheduling and execution module allocates corresponding resources in real time based on the priority of each user's communication needs;

[0012] The status monitoring module is used to monitor the operating status of each module in real time and identify corresponding abnormal information.

[0013] The fault repair module is used to receive various abnormal information, analyze the type of each abnormal information, and generate corresponding repair strategies.

[0014] Furthermore, the specific steps of the acquisition and analysis module in predicting the current optimal spectrum allocation scheme are as follows:

[0015] S1.1: Collect the spectrum signal of the corresponding area, and remove the noise in each spectrum signal through adaptive Gaussian filtering. Then, convert the spectrum data with different sampling rates into a fixed-dimensional matrix format. After that, associate and match the processed spectrum data with the historical spectrum data in the database for similar scenarios, extract the spectrum change trend features of each spectrum data within 30 minutes, and form a combined input vector.

[0016] S1.2: Construct and train the scheme prediction model. Input the combined input vector into the trained scheme prediction model. The scheme prediction model extracts the time-domain and frequency-domain features from each spectrum data through convolutional layers and identifies the corresponding interference signals. Then, the pooling layer compresses the dimensions of all time-domain and frequency-domain features. The fully connected layer compares all extracted features with various samples in the preset interference source feature library and outputs the corresponding interference source type determination result.

[0017] S1.3: Based on the identification results of each interference source, mark the occupied frequency band range, perform continuous scanning on the remaining frequency band, and analyze the signal energy value of the remaining frequency band. If the signal energy value is lower than the preset threshold, it is determined to be an idle candidate frequency band. Integrate all idle candidate frequency bands to form an idle frequency band list. Then, acquire and analyze all signals in the current communication link, and extract the quality characteristics of all signals to form a signal quality evaluation matrix.

[0018] S1.4: The model integrates the time-domain and frequency-domain characteristics of each spectrum, the judgment results of each interference source type, and the list of idle frequency bands. It calculates various indicators under different spectrum allocation combinations through the backpropagation algorithm, generates multiple candidate allocation schemes, and then applies a greedy algorithm to select the scheme with the comprehensive performance that meets the preset performance from the multiple candidate allocation schemes, forming the optimal spectrum allocation scheme.

[0019] Furthermore, the specific steps of the parameter calculation module in calculating the optimal beam parameters in real time are as follows:

[0020] S2.1: Collect various types of data from each user device and obtain signal strength data from each type of data. Remove user devices whose signal strength data is below the preset range. Divide users whose signal strength is within the preset range into the same communication group. At the same time, distinguish the type of each user device according to the device identifier. Then, obtain the location of the interference source of each interference signal and mark it as a beam avoidance area. Combined with the corresponding UAV motion state data, use the Kalman filter algorithm to predict the position and attitude change trend of each UAV in the next 2 seconds, generate the corresponding motion trajectory prediction curve, and then build a dynamic environment model.

[0021] S2.2: With the goal of maximizing the signal-to-interference ratio of the user group and minimizing beam sidelobe leakage, an objective function is constructed. The center coordinates of the user group are obtained and used as the beam main lobe pointing reference. The positions of each interference source are used as constraints, and the motion prediction data of each UAV are used as dynamic correction factors. At the same time, the maximum output power threshold of the antenna array is introduced as a hardware constraint condition to determine the variable boundary and constraint range of the objective function.

[0022] S2.3: The objective function is solved using the minimum mean square error algorithm to obtain the initial weight coefficients of each element of the antenna array. The average signal strength of the user group is used as a reference value. The error between the actual received signal and the reference signal is used as the basis for iteration. The corresponding weight coefficients are updated in each iteration. At the same time, the beam pattern corresponding to the current weight is calculated. When the iteration error is less than the preset threshold or the preset number of iterations is reached, the calculation is stopped and the optimal weight matrix is ​​output.

[0023] S2.4: Based on the optimal weight matrix, the weight coefficients of each element are converted into corresponding beam spatial pointing parameters. The azimuth and elevation angles of the main lobe of the beam are calculated. At the same time, based on antenna array theory, the phase difference and amplitude distribution ratio of each antenna element are derived from the updated weight coefficients to form the specific control parameters of each element. The calculated beam parameters are substituted into the dynamic environment model to simulate the beam coverage range and detect whether there is a main lobe pointing to an interference source or side lobes leaking into the interference area. If there is an interference risk, the beam azimuth angle is adjusted and the phase and amplitude parameters are recalculated.

[0024] Furthermore, the specific steps for forming the directional beam in the corresponding region are as follows:

[0025] S3.1: Extract the phase value, amplitude value and beam azimuth angle corresponding to each antenna element, and store them according to the antenna array number. Establish the corresponding mapping table, and at the same time detect the status of each antenna element. Then reset the initial phase of all antenna elements to 0 radians, adjust the amplitude to the preset power value, and then synchronously calibrate the signal transmission timing of each antenna element.

[0026] S3.2: Real-time acquisition of the actual output phase value of the phase shifter in each antenna unit, and comparison with the corresponding phase value of each antenna unit. If the error exceeds the preset threshold, adjustment is triggered until the phase value of all antenna units meets the accuracy requirements. At the same time, a corresponding gain control signal is generated according to the amplitude value of each antenna unit and transmitted to the power amplifier of each antenna unit to change the amplitude of the corresponding output signal.

[0027] S3.3: Real-time acquisition of the actual output power of each antenna element and comparison with the corresponding amplitude value, dynamic adjustment of the gain control signal of the corresponding amplifier, and then acquisition of the communication signals of all antenna elements at the same time node. The signals of each element are focused in a preset direction through the spatial superposition effect of the antenna array, and finally synthesized into a directional beam pointing to the center of the user group.

[0028] Furthermore, the specific steps for the network negotiation module to allocate communication tasks are as follows:

[0029] S4.1: Collect the load indicators of each UAV node and synchronize its own load data to all nodes in the network through load status broadcast messages to establish a network load status matrix. When any UAV node is detected to exceed the preset threshold, the load balancing mechanism is triggered. Based on the network load status matrix, the overload of the node is calculated, and the migrationable communication task type and data volume are determined in combination with the priority of each task.

[0030] S4.2: Select nodes with a load of <50% from the load status matrix as target nodes, calculate the link transmission cost between the overloaded node and each target node, and select 1 to 2 target nodes with a transmission cost lower than the preset cost. Send a task migration request. After the target node confirms that the resources are sufficient, it will send back the acceptance instruction. The overloaded node will synchronize the connection information and data cache of the migration task to the target node. After the synchronization is completed, notify the corresponding user equipment to switch the communication link to the target node.

[0031] S4.3: After the task migration is completed, the overloaded node and the target node update their local routing tables and load status respectively, and synchronize the updated information to the entire network through broadcast messages. Each node re-verifies the rationality of its own routing path based on the new routing and load information. If there is a path that exceeds the preset requirements, route renegotiation is triggered.

[0032] Furthermore, the specific steps of the scheduling execution module in allocating the corresponding resources in real time are as follows:

[0033] S5.1: Accept service information uploaded by each user device, extract the device identifier, service type field, and additional information for each service type, and then construct a three-dimensional mapping table based on a preset rule base, according to "device type - service type - urgency level". Simultaneously, label the basic priority corresponding to each combination in the three-dimensional mapping table.

[0034] S5.2: Substitute the parsed device identifiers and corresponding business type fields into the preset rule base, first match the device identifiers to determine the basic priority level, then refine the corresponding priority based on the business type field, and adjust the corresponding priority in combination with urgency keywords, and finally output the corresponding priority evaluation list.

[0035] S5.3: Determine the corresponding resource allocation weight based on the priority level of each user equipment's communication needs, record the upper limit of resource requirements for each user equipment's communication needs, sort the priorities of each communication need from high to low, process the resource supply for each communication need in descending order of ranking, and then convert each resource allocation strategy into corresponding spectrum control commands, beamforming control commands, and routing scheduling commands, and issue them.

[0036] Unlike existing technologies, the above technical solution has the following advantages: This invention collects spectrum signals in a fixed area, denoises them using adaptive Gaussian filtering, and then converts different sampling rates into a fixed-dimensional matrix. Each spectrum signal is then matched with historical scenes to extract the changing trends of each spectrum signal within 30 minutes, forming a combined input vector. A scheme prediction model is trained and applied to extract the time-domain and frequency-domain features of the combined input vector and identify the corresponding interference sources. Simultaneously, a pooling layer and a fully connected layer are used to match the signal with a preset interference library to determine the corresponding interference source type. Based on the judgment results, the occupied frequency band range is marked, and the remaining frequency bands are continuously scanned. Idle candidate segments are then filtered according to energy thresholds. Simultaneously, the current link signal is acquired and its corresponding quality features are extracted to form a signal quality evaluation matrix. All features are fused and spectrum allocation is evaluated through backpropagation to generate candidate schemes. The optimal scheme is then selected using a greedy algorithm. Multiple types of data from each user device are collected, and weak devices are filtered out according to signal strength. These data are then clustered into communication group areas. Based on equipment type, the location of interference sources for each interference signal is obtained and avoidance zones are marked. Then, combined with UAV motion state data, Kalman filtering is used to predict the position and attitude change trends of each UAV within 2 seconds. Subsequently, a dynamic environment model is constructed with the goal of maximizing the signal-to-interference ratio and minimizing sidelobe leakage. An objective function is established under antenna power constraints. The weights of each antenna array element are solved and iteratively updated using minimum mean square error. The phase amplitude allocation corresponding to several years is derived from the updated weights, generating control parameters for each element. The coverage detection interference risk is simulated, and the azimuth and phase amplitude are adjusted. Then, the phase amplitude and azimuth mapping are stored according to the array element number, the initial phase synchronization timing is reset, and finally, the phase shifter phase and amplifier gain are calibrated in real time until the accuracy power is satisfied. The signals of each element are superimposed to form a directional beam pointing to the group center and output commands. This can reduce manual intervention, adapt to the rapid deployment needs of sudden emergency scenarios, ensure communication quality in high-speed maneuvering and complex environments, and improve coverage gain and overall communication performance.

[0037] This invention collects load metrics from each UAV node, broadcasts the load status to the entire network, and establishes a network-wide load status matrix. If any UAV node's load exceeds a preset threshold, a load balancing mechanism is triggered. The overload of the node is calculated, and the types and amounts of migrateable tasks are determined based on task priorities. Target nodes with loads <50% are then selected from the network-wide load status matrix, and the link transmission costs between the overloaded node and each target node are calculated. Simultaneously, 1-2 target nodes with costs lower than a preset layer cost are selected to send migration requests. After confirming sufficient resources, the target nodes synchronously cache the connection information and data of the migration tasks. Upon completion, they notify the corresponding user equipment to switch links. After migration, the overloaded node and target nodes update their local routing tables and load status, which are then synchronized to all network nodes. Each node verifies the route validity. The system is rational. If a path exceeds the preset demand, it triggers route renegotiation. Simultaneously, it receives service information uploaded by user devices within the range, extracts device identifiers, service types, and additional information, and evaluates the corresponding priority in a three-dimensional mapping table of "device type-service type-urgency level" based on a preset rule base. It also matches the device identifier to determine the basic level, refines it according to the service type, and adjusts it in conjunction with emergency keywords, outputting the corresponding priority list. Then, it determines the resource allocation weight and demand ceiling according to the priority level, sorts the communication demands by priority, and ensures the supply of resources in sequence. Finally, it converts the allocation strategy into spectrum control, beamforming, and route scheduling instructions, which can effectively reduce the probability of service interruption, ensure the continuity and reliability of emergency on-site communication, significantly improve the overall network resilience and fault tolerance, and enhance the controllability and practicality of emergency communication. Attached Figure Description

[0038] Figure 1 This is a system block diagram of an emergency drone communication base station proposed in this invention. Detailed Implementation

[0039] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0040] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0041] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0042] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0043] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0044] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0045] As understood in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0046] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0047] Unless otherwise expressly specified or limited, the terms "installation," "connection," "linking," "fixing," and "setting," as used in the description of the embodiments of this application, should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral arrangement; it can be a direct connection or an indirect connection through an intermediate medium; it can be a relationship of two components combined together, an interaction relationship between two components, or a connection within two structures. Those skilled in the art to which this application pertains can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0048] Please see Figure 1 Example 1 of this embodiment describes an emergency drone communication base station, which includes a data acquisition and analysis module, a parameter calculation module, a data formation and execution module, a network negotiation module, a priority evaluation module, a scheduling and execution module, a status monitoring module, and a fault repair module.

[0049] The acquisition and analysis module is used to collect and analyze spectrum data in the corresponding regional environment and predict the current optimal spectrum allocation scheme.

[0050] Specifically, the system collects spectral signals from the corresponding region and removes noise from each spectral signal using adaptive Gaussian filtering. Then, spectral data from different sampling rates are uniformly converted into a fixed-dimensional matrix format. The processed spectral data is then correlated and matched with historical spectral data from a database of similar scenarios to extract spectral change trend features within 30 minutes, forming a combined input vector. This vector is then used to construct and train a scheme prediction model. The combined input vector is fed into the trained scheme prediction model, which extracts temporal and frequency domain features from each spectral data point through convolutional layers and identifies corresponding interference signals. Pooling layers then compress the dimensionality of all temporal and frequency domain features. Finally, fully connected layers compare all extracted features with various samples in a pre-defined interference source feature library, outputting the corresponding... Based on the interference source type determination results, the occupied frequency band range is marked, the remaining frequency bands are continuously scanned, and the signal energy value of the remaining frequency bands is analyzed. If the signal energy value is lower than the preset threshold, it is determined to be an idle candidate frequency band. All idle candidate frequency bands are integrated to form an idle frequency band list. Then, all signals in the current communication link are acquired and analyzed, and the quality characteristics of all signals are extracted to form a signal quality evaluation matrix. The model integrates the time domain and frequency domain characteristics of each spectrum, the interference source type determination results, and the idle frequency band list. The backpropagation algorithm is used to calculate various indicators under different spectrum allocation combinations to generate multiple candidate allocation schemes. Then, a greedy algorithm is applied to select the scheme with the comprehensive performance that meets the preset performance from the multiple candidate allocation schemes to form the optimal spectrum allocation scheme.

[0051] The parameter calculation module is used to acquire various types of data from each user device and, in conjunction with the optimal spectrum allocation scheme, calculate the optimal beam parameters in real time.

[0052] Specifically, multiple types of data are collected from each user device, and signal strength data is obtained from each type of data. User devices with signal strength below a preset range are removed, and users with signal strength within the preset range are grouped into the same communication group. The type of each user device is also distinguished based on its device identifier. Then, the location of the interference source for each interference signal is obtained and marked as a beam avoidance area. Combined with the corresponding UAV motion state data, the position and attitude change trends of each UAV within the next 2 seconds are predicted using a Kalman filter algorithm, generating corresponding motion trajectory prediction curves. Subsequently, a dynamic environment model is constructed with the goal of maximizing the signal-to-interference ratio of the user group and minimizing beam sidelobe leakage. An objective function is constructed, the center coordinates of the user group are obtained and used as the beam main lobe pointing reference, the locations of each interference source are constraints, and the motion prediction data of each UAV is used as a dynamic correction factor. Simultaneously, the maximum output power threshold of the antenna array is introduced as a hardware constraint to determine the variable boundaries and constraint range of the objective function. The objective function is solved using the minimum mean square error algorithm to obtain the initial weight coefficients of each element in the antenna array. Using the average signal strength of the user group as a reference value, the error between the actual received signal and the reference signal is used as the basis for iteration. The corresponding weight coefficients are updated with each iteration, and the beam pattern corresponding to the current weight is calculated. The calculation stops when the iteration error is less than a preset threshold or when the preset number of iterations is reached, and the optimal weight matrix is ​​output. Based on the optimal weight matrix, the weight coefficients of each element are converted into corresponding beam spatial pointing parameters, and the azimuth and elevation angles of the main lobe are calculated. Simultaneously, based on antenna array theory, the phase difference and amplitude distribution ratio of each antenna element are derived from the updated weight coefficients, forming the specific control parameters for each element. The calculated beam parameters are substituted into a dynamic environment model to simulate the beam coverage area and detect whether the main lobe points to an interference source or whether side lobes leak into the interference area. If there is an interference risk, the beam azimuth angle is adjusted and the phase and amplitude parameters are recalculated.

[0053] The shaping execution module forms a directional beam for the corresponding region based on the calculated optimal beam parameters and the current working state of the antenna element.

[0054] Specifically, the phase value, amplitude value, and beam azimuth angle corresponding to each antenna element are extracted and stored according to the antenna array number, and a corresponding mapping table is established. At the same time, the status of each antenna element is detected, and the initial phase of all antenna elements is reset to 0 radians, and the amplitude is adjusted to the preset power value. Then, the signal transmission timing of each antenna element is calibrated synchronously. The actual output phase value of the phase shifter in each antenna element is collected in real time and compared with the corresponding phase value of each antenna element. If the error exceeds the preset threshold, adjustment is triggered until the phase value of all antenna elements meets the accuracy requirements. At the same time, the corresponding gain control signal is generated according to the amplitude value of each antenna element and transmitted to the power amplifier of each antenna element to change the amplitude of the corresponding output signal. The actual output power of each antenna element is collected in real time and compared with the corresponding amplitude value. The gain control signal of the corresponding amplifier is dynamically adjusted. Then, the communication signals of all antenna elements at the same time node are obtained. The signals of each element form energy focusing in the preset direction through the spatial superposition effect of the antenna array, and finally synthesize a directional beam pointing to the center of the user group.

[0055] Example 2, refer to Figure 1 An emergency drone communication base station includes a data acquisition and analysis module, a parameter calculation module, a data formation and execution module, a network negotiation module, a priority evaluation module, a scheduling and execution module, a status monitoring module, and a fault repair module.

[0056] The network negotiation module is used to build communication links for multi-drone collaboration, monitor the communication load of each drone in real time, and allocate communication tasks.

[0057] Specifically, the load metrics of each drone node are collected, and its own load data is synchronized to all nodes in the network via load status broadcast messages to establish a network-wide load status matrix. When any drone node is detected to exceed a preset threshold, a load balancing mechanism is triggered. Based on the network-wide load status matrix, the overload of the node is calculated, and the migrationable communication task types and data volumes are determined in conjunction with the priorities of each task. Nodes with a load of <50% are selected from the load status matrix as target nodes, and the link transmission cost between the overloaded node and each target node is calculated. At the same time, 1-2 target nodes with transmission costs lower than the preset cost are selected, and task migration requests are sent. After the target nodes confirm that there are sufficient resources, they respond with an acceptance instruction. The overloaded node synchronizes the connection information and data cache of the migration task to the target node. After synchronization, the corresponding user equipment is notified to switch the communication link to the target node. After the task migration is completed, the overloaded node and the target node update their local routing tables and load status respectively, and synchronize the updated information to the entire network via broadcast messages. Each node re-verifies the rationality of its own routing path based on the new routing and load information. If there is a path that exceeds the preset requirements, route renegotiation is triggered.

[0058] The priority assessment module is used to receive the service types transmitted by each user equipment and, in conjunction with preset emergency rules, assess the priority of each communication requirement.

[0059] The scheduling and execution module allocates corresponding resources in real time based on the priority of each user's communication needs.

[0060] Specifically, the system receives service information uploaded by each user device and extracts the device identifier, service type field, and additional information for each service type. Based on a preset rule base, a three-dimensional mapping table is constructed according to "device type - service type - urgency level." The system also labels the basic priority for each combination in the three-dimensional mapping table. The parsed device identifiers and corresponding service type fields are substituted into the preset rule base. First, the device identifiers are matched to determine the basic priority level. Then, the corresponding priorities are refined based on the service type fields. Simultaneously, the priorities are adjusted based on urgency level keywords. Finally, a corresponding priority evaluation list is output. Based on the priority level of each user device's communication needs, the corresponding resource allocation weights are determined, and the upper limit of resource requirements for each user device's communication needs is recorded. The priorities of each communication need are sorted from high to low, and resource allocation for each communication need is processed sequentially from high to low. Finally, each resource allocation strategy is converted into corresponding spectrum control commands, beamforming control commands, and routing scheduling commands, and then issued.

[0061] The status monitoring module is used to monitor the operating status of each module in real time and identify the corresponding abnormal information; the fault repair module is used to receive each abnormal information, analyze the type of each abnormal information, and generate corresponding repair strategies.

[0062] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. An emergency unmanned aerial vehicle (UAV) communication base station, characterized in that, It includes a data acquisition and analysis module, a parameter calculation module, a shaping and execution module, a network negotiation module, a priority evaluation module, a scheduling and execution module, a status monitoring module, and a fault repair module; The acquisition and analysis module is used to acquire and analyze spectrum data in the corresponding regional environment and predict the current optimal spectrum allocation scheme. The parameter calculation module is used to acquire multiple types of data from each user device and, in conjunction with the optimal spectrum allocation scheme, calculate the optimal beam parameters in real time. The shaping execution module forms a directional beam for the corresponding region based on the calculated optimal beam parameters and the current working state of the antenna unit. The networking negotiation module is used to build a multi-drone collaborative communication link, monitor the communication load of each drone in real time, and allocate communication tasks. The priority assessment module is used to receive the service types transmitted by each user equipment and, in conjunction with preset emergency rules, assess the priority of each communication requirement. The scheduling and execution module allocates corresponding resources in real time based on the priority of each user's communication needs; The status monitoring module is used to monitor the operating status of each module in real time and identify corresponding abnormal information. The fault repair module is used to receive various abnormal information, analyze the type of each abnormal information, and generate corresponding repair strategies.

2. The emergency drone communication base station according to claim 1, characterized in that: The specific steps of the acquisition and analysis module in predicting the current optimal spectrum allocation scheme are as follows: S1.1: Collect the spectrum signal of the corresponding area, and remove the noise in each spectrum signal through adaptive Gaussian filtering. Then, convert the spectrum data with different sampling rates into a fixed-dimensional matrix format. After that, associate and match the processed spectrum data with the historical spectrum data in the database for similar scenarios, extract the spectrum change trend features of each spectrum data within 30 minutes, and form a combined input vector. S1.2: Construct and train the scheme prediction model. Input the combined input vector into the trained scheme prediction model. The scheme prediction model extracts the time-domain and frequency-domain features from each spectrum data through convolutional layers and identifies the corresponding interference signals. Then, the pooling layer compresses the dimensions of all time-domain and frequency-domain features. The fully connected layer compares all extracted features with various samples in the preset interference source feature library and outputs the corresponding interference source type determination result. S1.3: Based on the identification results of each interference source, mark the occupied frequency band range, perform continuous scanning on the remaining frequency band, and analyze the signal energy value of the remaining frequency band. If the signal energy value is lower than the preset threshold, it is determined to be an idle candidate frequency band. Integrate all idle candidate frequency bands to form an idle frequency band list. Then, acquire and analyze all signals in the current communication link, and extract the quality characteristics of all signals to form a signal quality evaluation matrix. S1.4: The model integrates the time-domain and frequency-domain characteristics of each spectrum, the judgment results of each interference source type, and the list of idle frequency bands. It calculates various indicators under different spectrum allocation combinations through the backpropagation algorithm, generates multiple candidate allocation schemes, and then applies a greedy algorithm to select the scheme with the comprehensive performance that meets the preset performance from the multiple candidate allocation schemes, forming the optimal spectrum allocation scheme.

3. The emergency unmanned aerial vehicle (UAV) communication base station according to claim 1, characterized in that: The specific steps of the parameter calculation module in calculating the optimal beam parameters in real time are as follows: S2.1: Collect various types of data from each user device and obtain signal strength data from each type of data. Remove user devices whose signal strength data is below the preset range. Divide users whose signal strength is within the preset range into the same communication group. At the same time, distinguish the type of each user device according to the device identifier. Then, obtain the location of the interference source of each interference signal and mark it as a beam avoidance area. Combined with the corresponding UAV motion state data, use the Kalman filter algorithm to predict the position and attitude change trend of each UAV in the next 2 seconds, generate the corresponding motion trajectory prediction curve, and then build a dynamic environment model. S2.2: With the goal of maximizing the signal-to-interference ratio of the user group and minimizing beam sidelobe leakage, an objective function is constructed. The center coordinates of the user group are obtained and used as the beam main lobe pointing reference. The positions of each interference source are used as constraints, and the motion prediction data of each UAV are used as dynamic correction factors. At the same time, the maximum output power threshold of the antenna array is introduced as a hardware constraint condition to determine the variable boundary and constraint range of the objective function. S2.3: The objective function is solved using the minimum mean square error algorithm to obtain the initial weight coefficients of each element of the antenna array. The average signal strength of the user group is used as a reference value. The error between the actual received signal and the reference signal is used as the basis for iteration. The corresponding weight coefficients are updated in each iteration. At the same time, the beam pattern corresponding to the current weight is calculated. When the iteration error is less than the preset threshold or the preset number of iterations is reached, the calculation is stopped and the optimal weight matrix is ​​output. S2.4: Based on the optimal weight matrix, the weight coefficients of each element are converted into corresponding beam spatial pointing parameters. The azimuth and elevation angles of the main lobe of the beam are calculated. At the same time, based on antenna array theory, the phase difference and amplitude distribution ratio of each antenna element are derived from the updated weight coefficients to form the specific control parameters of each element. The calculated beam parameters are substituted into the dynamic environment model to simulate the beam coverage range and detect whether there is a main lobe pointing to an interference source or side lobes leaking into the interference area. If there is an interference risk, the beam azimuth angle is adjusted and the phase and amplitude parameters are recalculated.

4. The emergency drone communication base station according to claim 1, characterized in that: The specific steps for forming the directional beam in the corresponding region are as follows: S3.1: Extract the phase value, amplitude value and beam azimuth angle corresponding to each antenna element, and store them according to the antenna array number. Establish the corresponding mapping table, and at the same time detect the status of each antenna element. Then reset the initial phase of all antenna elements to 0 radians, adjust the amplitude to the preset power value, and then synchronously calibrate the signal transmission timing of each antenna element. S3.2: Real-time acquisition of the actual output phase value of the phase shifter in each antenna unit, and comparison with the corresponding phase value of each antenna unit. If the error exceeds the preset threshold, adjustment is triggered until the phase value of all antenna units meets the accuracy requirements. At the same time, a corresponding gain control signal is generated according to the amplitude value of each antenna unit and transmitted to the power amplifier of each antenna unit to change the amplitude of the corresponding output signal. S3.3: Real-time acquisition of the actual output power of each antenna element and comparison with the corresponding amplitude value, dynamic adjustment of the gain control signal of the corresponding amplifier, and then acquisition of the communication signals of all antenna elements at the same time node. The signals of each element are focused in a preset direction through the spatial superposition effect of the antenna array, and finally synthesized into a directional beam pointing to the center of the user group.

5. The emergency unmanned aerial vehicle (UAV) communication base station according to claim 1, characterized in that: The specific steps for the network negotiation module to allocate communication tasks are as follows: S4.1: Collect the load indicators of each UAV node and synchronize its own load data to all nodes in the network through load status broadcast messages to establish a network load status matrix. When any UAV node is detected to exceed the preset threshold, the load balancing mechanism is triggered. Based on the network load status matrix, the overload of the node is calculated, and the migrationable communication task type and data volume are determined in combination with the priority of each task. S4.2: Select nodes with a load of <50% from the load status matrix as target nodes, calculate the link transmission cost between the overloaded node and each target node, and select 1 to 2 target nodes with a transmission cost lower than the preset cost. Send a task migration request. After the target node confirms that the resources are sufficient, it will send back the acceptance instruction. The overloaded node will synchronize the connection information and data cache of the migration task to the target node. After the synchronization is completed, notify the corresponding user equipment to switch the communication link to the target node. S4.3: After the task migration is completed, the overloaded node and the target node update their local routing tables and load status respectively, and synchronize the updated information to the entire network through broadcast messages. Each node re-verifies the rationality of its own routing path based on the new routing and load information. If there is a path that exceeds the preset requirements, route renegotiation is triggered.

6. The emergency unmanned aerial vehicle (UAV) communication base station according to claim 1, characterized in that: The specific steps of the scheduling execution module in allocating the corresponding resources in real time are as follows: S5.1: Receive service information uploaded by each user device, extract the device identifier, service type field, and additional information for each service type, and then construct a three-dimensional mapping table based on a preset rule base, according to "device type - service type - urgency level". Simultaneously, label the basic priority corresponding to each combination in the three-dimensional mapping table. S5.2: Substitute the parsed device identifiers and corresponding business type fields into the preset rule library, first match the device identifiers to determine the basic priority level, then refine the corresponding priority based on the business type field, and adjust the corresponding priority in combination with urgency keywords, and finally output the corresponding priority evaluation list. S5.3: Determine the corresponding resource allocation weight based on the priority level of each user equipment's communication needs, record the upper limit of resource requirements for each user equipment's communication needs, sort the priorities of each communication need from high to low, process the resource supply for each communication need in descending order of ranking, and then convert each resource allocation strategy into corresponding spectrum control commands, beamforming control commands, and routing scheduling commands, and issue them.

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