An emergency dispatch system

Abstract

The application belongs to the technical field of wireless communication, and particularly relates to an emergency dispatching system, which comprises a data acquisition module, an orbit inversion prediction module, a beam pointing control module, a resource scheduling module and a load control module. Three-dimensional space-time data of user orbits are mapped into one-dimensional codes through Z-order space-time indexes, fast retrieval is realized, satellite position prediction errors are reduced by combining an atmospheric resistance model and a dynamic inversion algorithm, a visible window is calculated through spherical polygon intersection and union operation, the problem of insufficient prediction accuracy of traditional ephemeris is solved, a continuous preview path is generated by using spherical linear interpolation of quaternions, beam response delay is reduced, the problems of large traditional calculation amount and response delay are solved, and the system still maintains processing capacity when emergency communication demand occurs.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and specifically relates to an emergency dispatch system. Background Technology

[0002] Emergency communications are crucial for ensuring command, dispatch, and rescue coordination during sudden disasters and public safety incidents. Terrestrial communication networks are easily damaged or rendered ineffective by disasters, making satellite communications a core support due to their wide coverage. Current emergency systems based on satellite signal dispatch have integrated multiple satellite resources with terrestrial networks, achieving initial air-space-ground coordination. However, they still struggle to meet the highly dynamic and sudden core needs of emergency response areas.

[0003] Existing systems rely on digital beam pointing adjustments and mechanical structure drives of phased array antennas for large turning angles. However, the mechanical response speed is constrained by physical factors such as motor inertia and friction. The orbital speed of low-Earth orbit satellites limits visibility time in disaster areas, and communication targets such as rescue teams and disaster-stricken areas are often in motion, requiring the beam to maintain a certain update frequency to ensure continuous coverage. However, the physical limits of mechanical adjustment cause a time difference between beam pointing and target position, leading to signal coverage misses, a sharp drop in the signal-to-noise ratio received by user terminals, and an increased probability of signal interruption.

[0004] Moreover, the existing service area scheduling algorithm for low-Earth orbit constellations needs to simultaneously call and calculate multiple dimensions of parameters such as satellite orbital parameters, user distribution density, and beam load to determine the optimal service satellite. As the constellation scale expands, the amount of computation increases exponentially, resulting in significant computational delays. These delays directly lead to longer waiting times for user access links, delayed transmission of critical emergency data, and potential delays in rescue decisions. Summary of the Invention

[0005] The objective of this invention can be achieved through the following technical solutions: An emergency dispatch system includes: The data acquisition module is used to synchronize system time and acquire spatiotemporal data, business data, instruction data and meteorological data in real time. The spatiotemporal data includes the real-time latitude and longitude of the user terminal and the movement trajectory sequence. The three-dimensional data of longitude, latitude, and time are encoded and compressed using Z-order curve indexing; emergency requests within a set time window are filtered, high-frequency demand areas are identified, and they are correlated and integrated with scheduling instructions from the network management system. The orbit inversion and prediction module is used to obtain the initial ephemeris from the central control terminal, and correct the satellite position prediction value based on meteorological data and atmospheric drag model through dynamic orbit inversion algorithm; and based on the inverted satellite orbit parameters, it uses spherical polygon intersection and union operation to determine the satellite's visible window to the emergency area. The beam pointing control module is used to generate a beam pointing pre-aiming path by associating the satellite attitude in the orbital parameters retrieved when an emergency area is determined to exist, and combining it with spatiotemporal data, using a quaternion spherical linear interpolation algorithm. The quaternion path is then converted into a beam control code, and beam scanning is achieved through electronic phase control. The resource scheduling module is used to solve the optimal resource allocation scheme for satellites using particle swarm optimization, with the satellite-to-ground link quality and resource utilization rate as indicators. The optimal resource allocation scheme is then generated into resource scheduling instructions and sent to the corresponding satellites and ground terminals.

[0006] Preferably, it also includes a payload control module, used to calculate the energy entropy value of high-latitude regions based on the latitude, longitude and motion trajectory sequence obtained by the data acquisition module. The satellite data includes the real-time position and motion direction of the satellite. When the energy entropy value is greater than a preset entropy value threshold, it is determined to be a low-coverage area, and some non-core payloads in the low-coverage area are gradually shut down.

[0007] Preferably, the energy entropy value H is the entropy of the user demand intensity distribution. Entropy of satellite payload energy consumption distribution The weighted sum, the formula for calculating the weighted sum is: H = a and b are weight values.

[0008] Preferably, it also includes a data storage module for distributing and immutably storing scheduling data through the IPFS network and using content-addressed hashing; pushing data updates to each satellite and user terminal in real time, wherein the scheduling data includes three types: beam commands, time slot allocation, and payload status.

[0009] Preferably, the implementation process for achieving distributed and tamper-proof storage of scheduling data via the IPFS network includes: The three types of scheduling data are fragmented into 256KB pieces to obtain multiple data blocks. Calculate the SHA-256 hash of each data block, construct a Merkle directed acyclic graph, and use the root node to store a list of hashes for all data blocks; Upload the Merkle DAG to the IPFS network, and the node will then... Store the data, then calculate the SHA-256 hash of the root node to obtain the root hash; The Merkle DAG containing the hashes of all data blocks is uploaded to the IPFS network, and nodes store data using the root hash.

[0010] Preferably, the resource scheduling module further includes rapidly locating the user's position based on the Z-order index of the data acquisition module and correcting the DBF array element phase.

[0011] Preferably, the beam pointing control module further includes calculating the inertial torque and frictional resistance during the driving process of the mechanical steering antenna servo mechanism using the motor dynamics inversion model of the Lagrange equation, feeding it forward to the central control terminal and outputting a reverse compensation current.

[0012] Preferably, the process of calculating the reverse compensation current for beam pointing control includes: Calculate the system kinetic energy and system potential energy of the mechanical steering antenna, respectively. Constructing the Lagrange function: Using L=TV, the calculated system kinetic energy T and system potential energy V are substituted into the Lagrange function to derive the inverse model of motor dynamics; Substitute the beam pointing pre-aiming path into the motor dynamics inversion model to calculate the feedforward torque; The feedforward torque is converted into a feedforward drive current, which is the reverse compensation current.

[0013] Preferably, the orbit inversion prediction module includes integrating the satellite acceleration using the Runge-Kutta method to correct the predicted satellite position.

[0014] Preferably, the steps for the orbit inversion prediction module to determine the satellite's visual window for the emergency area include: The emergency area is projected onto the Earth's surface as a spherical polygon, and eight vertices of the area are sampled at equal angular intervals. For the satellite position at a certain time t, calculate the vertices of the visible region that satisfy the angle between the satellite, the region vertex, and the Earth's center. equal to minimum elevation angle Then determine the apex of the emergency zone. Within the satellite visualization area, the included angle is calculated using the spherical trigonometry formula. ; Calculate the intersection of the visible satellite polygon and the emergency dispatch area polygon. If the area of ​​the overlapping region is greater than zero, the satellite is visible at that moment. Traverse the satellite positions within the future time window and output the visible window time interval.

[0015] The beneficial effects of this invention are as follows: This invention maps the three-dimensional spatiotemporal data of user trajectories into one-dimensional codes using Z-order spatiotemporal indexing, enabling fast retrieval and solving the problem of slow time-series data querying in traditional cached databases. Furthermore, by combining atmospheric drag models with dynamic inversion algorithms, it reduces satellite position prediction errors. It calculates the visible window using spherical polygon intersection and union operations, addressing the issues of insufficient accuracy and coarse visible windows in traditional ephemeris predictions. Finally, it utilizes quaternion spherical linear interpolation to generate continuous pre-aiming paths, reducing beam response delay. Finally, it employs particle swarm optimization to fuse emergency request priorities, achieving a globally optimal balance between satellite-to-ground link quality and resource utilization, thus solving the problems of high computational load and response delay in traditional methods. Furthermore, by constructing a hash chain from the scheduling data, the stored data blocks are guaranteed not to be tampered with, thus further ensuring the security of scheduling instructions. Attached Figure Description

[0016] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 This is a system structure block diagram of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0019] Please see Figure 1 This embodiment provides an emergency dispatch system, which includes: The data acquisition module addresses the inefficiencies and delays in filtering emergency requests associated with traditional caching databases (such as Redis) when retrieving three-dimensional time-series data (latitude, longitude, and time). The module's functions include: The system synchronizes time via the BeiDou timing module or the NTPv4 protocol, and receives real-time data streams from the business platform, network management system, user terminals, and meteorological data sources. The real-time data streams include spatiotemporal data, business data, command data, and meteorological data. Spatiotemporal data includes real-time latitude and longitude of user terminals, movement trajectory sequences, and data generation timestamps. Business data includes user business types, including disaster data in emergency areas, rescue voice and general data, bandwidth requirements, emergency request frequency and priority (levels 1-5). Command data includes scheduling commands read by the network management system using the gRPC interface, beam commands in emergency areas, time slot allocation, and payload status. Meteorological data includes real-time atmospheric density, solar activity index, and geomagnetic index. InfluxDB time-series database is used to store real-time data streams; To address the issue of slow retrieval of highly dynamic user trajectories, a Z-order curve index is used to encode and compress the three-dimensional data of longitude, latitude, and time. InfluxDB's Continuous Queries (CQ) automatically filters emergency requests within a defined time window to identify high-frequency demand areas. This is then integrated with scheduling commands from the network management system. Specifically, InfluxDB's CQ automatically filters high-priority emergency requests, with configuration rules including execution every 100ms and calculation of emergency level L. cmd Data with a frequency of ≥4 requests per second and a request frequency of ≥5 times per second from the same user or region will be marked as priority 1 and pushed to multiple subsequent calculation modules in real time.

[0020] The construction of the Z-order curve index includes the following steps: Mapping 3D data to Within a one-dimensional integer range, a positioning accuracy of 20 is used to balance accuracy and storage, providing a foundation for subsequent bit-interleaved coding and avoiding index performance skew due to differences in the range of dimensional values. The normalization formula for the three dimensions is: ; in, These are the longitude and latitude of the user's location, respectively, and t is the data generation time. The current system time. This is the start time of the data in the last 3 seconds. and These are the normalized longitude, latitude, and time values, respectively. Perform Z-order bit interleaving encoding to divide the binary bits of the 3D normalized data into Z-order bits. Sequentially alternating concatenation generates one-dimensional Z-values. Bit interleaving compresses three-dimensional normalized data into one-dimensional Z-values, preserving spatiotemporal locality (adjacent spatiotemporal points are also adjacent in Z-values), thus supporting subsequent efficient index construction. ; The formal expression is as follows: Interleave is a bit-interleaving function that uses bitwise operations to alternately concatenate binary bits. A B+ tree index is constructed based on the Z-value to filter emergency requests within a set time window, identify high-frequency demand areas, and correlate and integrate them with scheduling instructions from the network management system; a specific emergency area is queried. When calculating user trajectories within the last 3 seconds, it is only necessary to calculate the Z-value interval [Z] corresponding to that region. min Z max By using B+ trees to locate data, retrieval latency is shortened.

[0021] By employing a progressive logic of normalization to unify the range, bit interleaving to compress dimensions, and efficient B+ tree retrieval, highly dynamic three-dimensional spatiotemporal data of longitude, latitude, and time is transformed into a one-dimensional index structure that can be quickly queried. This ultimately enables rapid retrieval of user trajectories, providing technical support for real-time data input (such as user location and movement trends) in emergency dispatch, and ensuring the timeliness and accuracy of dispatch decisions.

[0022] The orbit inversion prediction module addresses the issues of satellite position prediction errors caused by neglecting atmospheric drag in traditional ephemeris extrapolation and the coarse calculation of the visible window. The module includes acquiring initial ephemeris data from the central control unit, correcting the satellite position prediction value based on real-time atmospheric density data and an atmospheric drag model using a dynamic orbit inversion algorithm, and determining the satellite's visible window to the emergency area based on the inverted satellite orbit parameters using spherical polygon intersection and union operations. Specifically, the calculation process of the orbit inversion prediction module includes... S1: Obtain TLE dual-track orbit data of the low-Earth orbit satellite from the central control terminal, including orbital semi-major axis a, eccentricity e, orbital inclination i, right ascension of ascending node Ω, argument of perigee ω, and mean perigee M. Calculate the satellite's initial real-time position r0=(x0,y0,z0) based on the SGP4 model, which is the geocentric geofixed coordinate system ECEF. ECEF is a coordinate system fixed to the Earth, with the x-axis pointing to the intersection of the prime meridian and the equator, and the z-axis pointing to the North Pole, which can accurately describe the satellite's three-dimensional spatial position. ; ; Combining the orbital inclination angle i, the satellite's three-dimensional position in the ECEF coordinate system is finally obtained, where, This is the true anterior angle, which is solved by the approach anterior angle M using Newton's iteration method. The formula for calculating the approach anterior angle M is: Where E is the angle of near point; S2: Orbital perturbations of low-Earth orbit satellites (500-2000km) are mainly caused by atmospheric drag. The NRLMSIE-00 atmospheric model is used to calculate the real-time atmospheric density ρ. Input parameters include: solar activity index. Geomagnetic index Current UTC time , used to calculate solar declination and local time, as well as satellite orbital altitude h; The formula for calculating atmospheric density is: ,in, The atomic oxygen number density is set as the baseline value at 500 km. , =150km, atomic oxygen level The radius of the Earth; S3: Correct the predicted position of the satellite, including: Due to atmospheric drag, the satellite's acceleration is: ; in, The average drag coefficient for low-Earth orbit satellites is calibrated to 2.2. For satellite mass-to-surface ratio, The satellite's velocity relative to the atmosphere. = , This refers to the speed at which the atmosphere rotates with the Earth. The Runge-Kutta method (RK4) is used to integrate the acceleration, and the predicted satellite position for the next 3 seconds is corrected; the correction formula is as follows: ; in, =0.1s, Let t be the satellite position at time t; S4: Based on the inverted satellite orbit parameters, a spherical polygon intersection and union operation is used to determine the satellite's visibility window over the emergency area. To avoid terrain obstruction, the core condition for satellite visibility over the emergency area is: the angle between the satellite, the disaster area, and the geocentric region must be greater than or equal to the minimum elevation angle. (Minimum elevation angle set to 5) The implementation steps are as follows: The emergency zone (e.g., a circular area with a radius of 5km) is projected onto the Earth's surface as a spherical polygon, and eight vertices of the zone are sampled at equal angular intervals. ; For a satellite position r(t) at a certain time t, calculate the vertex U1 of the visible region. U8 satisfies the angle between the satellite, the regional vertex, and the Earth's center. Then determine the regional vertices of the emergency zone. This area falls within the satellite visualization region; The included angle is calculated using the spherical trigonometric cosine theorem. : ; The ratio of the latitude of the satellite to that of the regional vertex. The difference in longitude between the satellite's position and the visible area; The Sutherland-Hodgman algorithm is used to calculate the intersection of the satellite's visible polygon and the emergency dispatch area polygon. This algorithm uses an edge-by-edge clipping method, taking one polygon (the emergency area polygon) as a clipping window and clipping the other polygon (the satellite's visible polygon) to obtain the overlapping area. If the area of ​​the overlapping area is greater than zero, it is visible at that moment. The satellite position is traversed every 0.1 seconds within the next 3 seconds, that is, the visible area modeling in step 2 and the intersection area calculation in step 3 are repeated, and the visible window time interval is output. To address the issues of discontinuous pre-aiming paths in traditional segmented beams and significant delays (0.1-0.5 seconds) caused by inertia or friction during mechanical adjustments, this study utilizes spatial overlap quantization to combine the high dynamic characteristics of satellite orbits with the geographical extent of disaster areas. This allows for precise output of the time window when satellites can cover emergency areas, providing crucial time-dimensional decision-making support for beam pointing and resource allocation in emergency dispatch.

[0023] The beam pointing control module includes a phase control unit and a mechanical unit, wherein; The phased array unit is used to determine the presence of emergency domain scheduling, correlate the satellite attitude in the retrieved orbital parameters, and combine them with spatiotemporal data. For phased array antennas, a quaternion spherical linear interpolation algorithm is used to generate a beam pointing pre-aiming path. Since quaternions describe the rotational attitude of the beam pointing, they need to be converted into phase control commands (i.e., beam control codes) for each element of the phased array antenna to drive the antenna to achieve beam scanning. The specific implementation process includes: Calculate the quaternion: During the next 3 seconds of pre-aiming time, take 3 keyframes t0=0ms, t1=1500ms, t2=3000ms, and calculate the quaternion based on the satellite's predicted position. Calculate the total quaternion of the target beam pointing relative to the center point P of the emergency zone. ; In the ECEF coordinate system, the line-of-sight vector ; The formula for calculating quaternions is: , It is the Hamiltonian product; The beam pointing pre-aiming path is generated using a quaternion spherical linear interpolation algorithm, and the pre-aiming quaternion is generated at any time t. , ;in, These are the interpolation coefficients. , The angle between two quaternions. The quaternion is the difference step size between adjacent keyframes, which is set to 10ms.

[0024] To overcome the problems of slow response and low tracking accuracy of mechanical steering antennas, the mechanical unit calculates the inertial torque and frictional resistance during the drive process of the mechanical steering antenna servo mechanism using the motor dynamics inversion model of the Lagrange equation. This data is then fed forward to the central control unit and a reverse compensation current is output. This compensation current is superimposed on the motor drive current of the mechanical steering antenna for pointing tracking. The specific implementation process includes: Step 1: Construct a dynamic model of the mechanical system using the Lagrange equations: Convert the azimuth and elevation angles of the mechanical steering antenna into the generalized coordinates of the system; Calculate the kinetic energy T and potential energy V of the mechanical steering antenna system. T is the sum of the kinetic energies of the antenna reflector, support structure, motor rotor and other mechanisms of the mechanical steering antenna mechanism, and V is the antenna elevation angle and mass distribution. Calculate the gravitational potential energy. Constructing the Lagrange function: Using L=TV, the calculated system kinetic energy T and system potential energy V are substituted into the Lagrange function to derive the inverse model of motor dynamics; Since the parameters in the theoretical model (such as moment of inertia and coefficient of friction) are not accurate, a specific test current needs to be applied to the motor of the steering antenna to measure the actual motion response (position and velocity) of the antenna, and then the unknown parameters in the model are deduced by using system identification algorithms (such as the least squares method). Step 2: Perform calculations and inversion: The beam pointing control module receives a smooth and continuous beam pointing preview path generated by quaternion spherical linear interpolation and converts the beam pointing preview path into the desired angle, angular velocity, and angular acceleration trajectories on the azimuth and pitch axes. Substitute the beam pointing pre-aiming path into the dynamic model calculated in step 1 to calculate the feedforward torque. ; Converting the feedforward torque into feedforward drive current, assuming the motor torque constant matrix of the steering antenna's drive motor is... , Typically, it's a diagonal matrix, where the diagonal elements are the torque constants of each motor. The feedforward drive current... , ; Step 3: Perform compensation superposition and closed-loop control: Since the feedforward drive current is calculated to compensate for all resistances, it is calibrated as the reverse compensation current; The feedforward drive current (reverse compensation current) and the current output by the feedback controller are superimposed to form a total current command, which is then sent to the servo driver of the steering antenna motor. The driver drives the steering antenna motor, which in turn drives the mechanical steering antenna to perform pointing tracking.

[0025] By actively predicting rather than passively responding, tracking errors caused by inertia, gravity, and friction are eliminated, enabling high-speed and high-precision beam pointing, thereby effectively solving the problem of coverage misses.

[0026] The resource scheduling module uses satellite-to-ground link quality and resource utilization as indicators, introduces emergency request priority as a weighting factor, and employs particle swarm optimization (PSO) to solve for the optimal satellite resource allocation scheme. It then generates resource scheduling instructions based on the optimal allocation scheme and sends them to the corresponding satellites and ground terminals. The specific implementation process includes: Multiple emergency requests from multiple users in the coverage area are classified according to their urgency level, such as 1-5, with level 5 being the highest, and these are mapped as weighting factors. The link signal-to-noise ratio (SNR) and bit error rate (BER) of a user are monitored in real time by satellite payload, and combined into a link quality coefficient with a value of 0-1. The higher the value, the better the link. The total available satellite resources A include the total number of time slots and the total bandwidth, as well as the minimum resource requirement B (to ensure basic communication) for a given user and the maximum allocable resources C. Constructing a PSO (Particle Swarm Optimization) model: Each particle represents a resource allocation scheme, with dimensions equal to the total number of users n, and the particle's position vector. The amount of resources allocated to user i for particle k satisfies the following constraints: ; The fitness function, or optimization objective, is to maximize the fitness value using a weighted sum of link quality and resource utilization, and to gradually approach the optimal solution by initializing the particle swarm and iteratively updating particle positions and velocities, thus achieving the optimal satellite resource allocation scheme.

[0027] Furthermore, the resource scheduling module also includes functions such as quickly locating the user's position based on the Z-order index of the data acquisition module, correcting the DBF array element phase, and ensuring seamless switching. The specific implementation process includes: Enter the user ID to obtain the real-time location via Z-order index. ; Perform DBF element phase calculation. The DBF antenna contains 16 elements. The phase weighting value of the i-th element is... ,in, For the ka band wavelength, Set to 0.1m. For the spacing between array elements, The angle between the user and the array element normal. The payload control module is used to calculate the energy entropy value of high-latitude regions based on the latitude and longitude and motion trajectory sequence obtained in real time by the orbit inversion prediction module. Based on the latitude and longitude, it determines that the latitude is greater than 60° as the high-latitude region. The motion trajectory sequence obtains the satellite motion direction and calculates the satellite orbit tangent direction vector based on the satellite motion direction, which is used to predict the high-latitude region that the satellite will soon cover. When the energy entropy value is greater than a preset entropy threshold, it is determined to be a low-coverage area. The process involves gradually shutting down some non-core payloads in the low-coverage area, including: I. Define the energy entropy value H as the entropy of the user demand intensity distribution. Entropy of satellite payload energy consumption distribution The weighted sum, the formula for calculating the weighted sum is: H = To accommodate the priority requirements of urgent situations, the weight value a is set to 0.6, and the weight value b is set to 0.4. Among them, the entropy of user demand intensity distribution The acquisition process includes: Based on satellite motion direction and orbital characteristics, the high-latitude region is divided into 10 grid cells. Real-time demand data from these 10 grid cells is extracted using the Z-order index from the data acquisition module. This demand data includes demand intensity. j takes values ​​from 0 to 10. Calculate the demand probability for each grid cell using the following formula: ,if ,but Calculate using Shannon entropy formula The calculation formula is: A larger value indicates a more dispersed demand distribution, meaning lower resource utilization efficiency. Satellite payload energy distribution entropy The acquisition process includes: The satellite payload is divided into eight components: core payloads (beam control, emergency command reception, which cannot be shut down) and non-core payloads (secondary frequency band transmission and reception, general data processing). The real-time energy consumption of each non-core payload is statistically analyzed. k takes values ​​from 1 to 8, in units of W, and the energy consumption probability distribution for each non-core load is calculated: Calculate using Shannon entropy formula The calculation formula is: The larger the value, the more uneven the energy consumption distribution, and the higher the proportion of energy consumption of non-core loads. 2. When the energy entropy value exceeds the preset entropy threshold (H=0.7), it is determined to be a low-coverage area, triggering triangular membership function fuzzy control, and determining the shutdown ratio R based on the entropy value: The triangular membership function defines the membership degree of the input error and the rate of change of error, enabling the controller that controls shutdown to perform fuzzy inference based on the fuzzy rule base, thereby generating more flexible and accurate control signals. The input variable is to divide the energy entropy value into three fuzzy subsets, which include {medium entropy (0.5-0.7), high entropy (0.7-0.9), and extremely high entropy (0.9-1.0)}; The corresponding fuzzy subsets of the output variable shutdown ratio R for medium entropy, high entropy, and extremely high entropy are: {low (30%), medium (40%), and high (50%)}. Third, non-core payloads are sorted by energy consumption from high to low and shut down in three steps (500ms interval between each step) to achieve the target proportion, avoiding link fluctuations caused by a single shutdown. The payload control module quantifies the matching degree between user demand and satellite energy consumption through information entropy and combines it with fuzzy control to achieve gradient shutdown, optimizing energy consumption while ensuring demand.

[0028] The data storage module uses the Interplanetary File System (IPFS) to ensure immutable storage of scheduling data and time synchronization with distributed nodes, guaranteeing the reliable delivery of scheduling commands. It also uses content-addressed hashing. Furthermore, it pushes data updates to each satellite and user terminal in real time. The scheduling data includes three types: beam commands, time slot allocations, and payload status. Specifically, it includes: The large volume of scheduling data is split and a hash chain is constructed. The three types of scheduling data are divided into 256KB pieces to obtain multiple data blocks. , , The purpose of data sharding is to reduce the storage or transmission overhead of a single block of data, while supporting parallel processing. Calculate the SHA-256 hash of each data block , Construct a Merkle directed acyclic graph (DAG), with the root node used to store a hash list of all data blocks. Then calculate the SHA-256 hash of the root node to obtain the root hash. Root hash The calculation formula is: =SHA-256 (root node); This structure forms a hash chain, ensuring that all data blocks remain unchanged as long as the root hash remains the same; if any data block is modified, its hash will change. It will change, which in turn will affect the root hash. Changes are ensured through the hash chain mechanism of Merkle DAG, which guarantees that any data modification can be detected. Content-addressed storage is performed by uploading a Merkle DAG containing the hashes of all data blocks to the IPFS network, and nodes then access it via... (Content identifier) ​​stores data, not a traditional URL; if the data is tampered with, It will change, improving data security; Through the IPFS publish-subscribe (pub / sub) mechanism, real-time synchronization and data updates of scheduling data to satellite execution units and user terminals are achieved; After receiving the data, the subscribing node recalculates the data block hash and reconstructs the Merkle DAG to calculate the root hash. If the recalculated root hash is equal to the original root hash, the verification is successful; otherwise, the data is deemed to have been tampered with, and a retransmission is requested to further ensure the security of the scheduling instructions.

[0029] By mapping the three-dimensional spatiotemporal data of user trajectories to one-dimensional codes through Z-order spatiotemporal indexing, millisecond-level retrieval is achieved, solving the problem of slow time-series data query in traditional cache databases and providing fast data support for real-time scheduling; IPFS distributed storage realizes scheduling data synchronization through pub / sub mechanism, avoiding single point of failure and synchronization delay of centralized cache, and ensuring the efficiency of instruction delivery.

[0030] To address the issue of inaccurate satellite position prediction, this method uses an atmospheric drag model to invert satellite orbit perturbations, corrects the satellite position within the next second, generates a continuous beam pre-aiming path using quaternion interpolation, and outputs a dynamic compensation current using motor dynamics inversion. This solves the problems of traditional ephemeris extrapolation ignoring orbital disturbances and poor adaptability of segmented compensation, thereby reducing mechanical response delay and beam pointing error.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An emergency dispatch system, characterized in that: include: The data acquisition module is used to synchronize system time and acquire spatiotemporal data, business data, instruction data and meteorological data in real time. The spatiotemporal data includes the real-time latitude and longitude of the user terminal and the movement trajectory sequence. The three-dimensional data of longitude, latitude, and time are encoded and compressed using Z-order curve indexing; emergency requests within a set time window are filtered, high-frequency demand areas are identified, and they are correlated and integrated with scheduling instructions from the network management system. The orbit inversion and prediction module is used to acquire initial ephemeris data from the central control unit, and based on meteorological data and atmospheric drag models, corrects the predicted satellite position values ​​using a dynamic orbit inversion algorithm. Furthermore, based on the inverted satellite orbit parameters, it uses spherical polygon intersection operations to determine the satellite's visible window to the emergency area. The correction of the predicted satellite position values ​​using the dynamic orbit inversion algorithm includes the following processes: S1: Obtain TLE dual-track orbit data of low-orbit satellites from the central control terminal, calculate the initial real-time position of the satellites based on the SGP4 model, and calculate the three-dimensional position of the satellites in the ECEF coordinate system; S2: The NRLMSISE-00 atmospheric model is introduced to calculate real-time atmospheric density ρ. Input parameters include: solar activity index. Geomagnetic index Current UTC time , used to calculate solar declination and local time, as well as satellite orbital altitude h; S3: Correct the predicted position of the satellite, including: Due to atmospheric drag, the satellite's acceleration is: ； in, The average drag coefficient for low-Earth orbit satellites is calibrated to 2.

2. For satellite mass-to-surface ratio, The satellite's velocity relative to the atmosphere. = , This refers to the speed at which the atmosphere rotates with the Earth. The Runge-Kutta method is used to integrate the acceleration, and the predicted satellite position for the next 3 seconds is corrected; the correction formula is as follows: ； in, =0.1s, Let t be the satellite position at time t; The beam pointing control module, when an emergency area dispatch is detected, correlates the satellite attitude from the retrieved orbital parameters with spatiotemporal data, generates a beam pointing pre-aiming path using a quaternion spherical linear interpolation algorithm, converts the quaternion path into beam control codes, and achieves beam scanning through electronic phase control. The specific implementation process includes: Calculate the quaternion: During the next 3 seconds of pre-aiming time, take 3 keyframes t0=0ms, t1=1500ms, t2=3000ms, and calculate the quaternion based on the satellite's predicted position. Calculate the total quaternion of the target beam pointing relative to the center point P of the emergency zone. ; In the ECEF coordinate system, the line-of-sight vector ; ； The formula for calculating a total quaternion is: , It is the Hamiltonian product; The beam pointing pre-aiming path is generated using a quaternion spherical linear interpolation algorithm, and the pre-aiming quaternion is generated at any time t. , ;in, The difference coefficient, , The angle between two quaternions. , , The quaternion between adjacent keyframes is set to a step size of 10ms. Based on the beam pointing pre-aiming path, the beam pointing pre-aiming path is converted into phase control commands for each element of the phased array antenna; The resource scheduling module is used to solve the optimal resource allocation scheme for satellites using particle swarm optimization, with the satellite-to-ground link quality and resource utilization rate as indicators. The optimal resource allocation scheme is then used to generate resource scheduling instructions, which are then sent to the corresponding satellites and ground terminals.

2. The emergency dispatch system according to claim 1, characterized in that: It also includes a load control module, which is used to calculate the energy entropy value of high-latitude regions based on the latitude and longitude and motion trajectory sequence obtained by the orbit inversion prediction module. When the energy entropy value is greater than a preset entropy value threshold, it is determined to be a low-coverage area, and some non-core loads in the low-coverage area are gradually shut down.

3. An emergency dispatch system according to claim 2, characterized in that: The energy entropy value H represents the entropy of the distribution of user demand intensity. Entropy of satellite payload energy consumption distribution The weighted sum, the formula for calculating the weighted sum is: H = a and b are weight values.

4. An emergency dispatch system according to claim 2, characterized in that: It also includes a data storage module, which is used to realize distributed and tamper-proof storage of scheduling data through the IPFS network and to use content addressing hashing; and to push data updates to each satellite and user terminal in real time. The scheduling data includes three types: beam commands, time slot allocation, and payload status.

5. An emergency dispatch system according to claim 4, characterized in that: The implementation process for achieving distributed, tamper-proof storage of scheduling data via the IPFS network includes: The three types of scheduling data are divided into 256KB segments to obtain multiple data blocks; Calculate the SHA-256 hash of each data block, construct a Merkle directed acyclic graph, and use the root node to store a list of hashes for all data blocks; The Merkle DAG containing the hashes of all data blocks is uploaded to the IPFS network, and nodes store data using the root hash.

6. An emergency dispatch system according to claim 3, characterized in that: The resource scheduling module also includes the ability to quickly locate user positions based on the Z-order index of the data acquisition module and correct the phase of DBF array elements.

7. An emergency dispatch system according to claim 1, characterized in that: The beam pointing control module also includes calculating the inertial torque and frictional resistance during the driving process of the mechanical steering antenna servo mechanism using the motor dynamics inversion model of the Lagrange equation, feeding it forward to the central control terminal and outputting a reverse compensation current.

8. An emergency dispatch system according to claim 7, characterized in that: The beam pointing control module calculates the reverse compensation current, including: Calculate the system kinetic energy and system potential energy of the mechanical steering antenna, respectively. Construct a Lagrange function, and substitute the calculated kinetic energy T and potential energy V of the mechanical steering antenna system into the Lagrange function to obtain the motor dynamics inversion model. The specific steps include: Convert the azimuth and elevation angles of the mechanical steering antenna into the generalized coordinates of the system; Calculate the kinetic energy T and potential energy V of the mechanical steering antenna system. T is the sum of the kinetic energies of the antenna reflector, support structure, motor rotor and other mechanisms of the mechanical steering antenna mechanism, and V is the antenna elevation angle and mass distribution. Calculate the gravitational potential energy. Constructing the Lagrange function: Using L=TV, the calculated system kinetic energy T and system potential energy V are substituted into the Lagrange function to derive the inverse model of motor dynamics; Substituting the beam pointing pre-aiming path into the motor dynamics inversion model, the feedforward torque is calculated, specifically including the following process: The beam pointing control module receives a smooth and continuous beam pointing preview path generated by quaternion spherical linear interpolation and converts the beam pointing preview path into the desired angle, angular velocity, and angular acceleration trajectories on the azimuth and pitch axes. Substituting the beam pointing pre-aiming path into the calculated motor dynamics inversion model, the feedforward torque is calculated. ; Converting the feedforward torque into feedforward drive current, assuming the motor torque constant matrix of the steering antenna's drive motor is... , Typically, it's a diagonal matrix, where the diagonal elements are the torque constants of each motor. The feedforward drive current... , ; Step 3: Perform compensation superposition and closed-loop control: Since the feedforward drive current is calculated to compensate for all resistances, it is calibrated as the reverse compensation current; The feedforward torque is converted into a feedforward drive current, which is the reverse compensation current.

9. An emergency dispatch system according to claim 1, characterized in that: The orbit inversion prediction module includes integrating the satellite acceleration using the Runge-Kutta method to correct the predicted satellite position.

10. An emergency dispatch system according to claim 1, characterized in that: The steps for the orbit inversion prediction module to determine the satellite's visibility window for the emergency area include: The emergency area is projected onto the Earth's surface as a spherical polygon, and eight vertices of the area are sampled at equal angular intervals. For the satellite position at a certain time t, calculate the vertices of the visible region that satisfy the angle between the satellite, the region vertex, and the Earth's center. Greater than or equal to the minimum elevation angle Then determine the apex of the emergency zone. Within the satellite visualization area, the included angle is calculated using the spherical trigonometry formula. ; Calculate the intersection of the visible satellite polygon and the emergency dispatch area polygon. If the area of ​​the overlapping region is greater than zero, the satellite is visible at that moment. Traverse the satellite positions within the future time window and output the visible window time interval.

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