Emergency management early warning release system based on big data

Abstract

The invention discloses an emergency management early warning release system based on big data, and relates to the field of information technology and public safety cross technology, the early warning release system comprises the following hierarchical structures connected in sequence: a data perception layer, an intelligent center layer, a decision service layer, a release execution layer and a feedback optimization layer, the system has the advantages that the limitation of a traditional system data island is broken through, heterogeneous data sources such as a meteorological sensor, social media and a unit platform are integrated through the multi-modal data fusion gateway, anonymization processing and safety protection of user data are achieved in combination with the federated learning technology, the real-time performance and comprehensiveness of disaster situation sensing are remarkably improved, and the system is suitable for popularization and application. The dynamic spatio-temporal feature extraction engine captures a spatio-temporal coupling rule of disaster propagation by using an attention mechanism, so that the risk assessment precision is improved to a millimeter-level geographic resolution, and a monitoring blind area existing in a traditional static model is effectively eliminated.

Description

Technical Field

[0001] The present invention relates to the cross - technical field of information technology and public security, and specifically to an emergency management early warning and release system based on big data. Background Art

[0002] With the in - depth application of big data technology in the field of emergency management, there are still significant defects in the existing early warning systems when dealing with complex disaster scenarios:

[0003] Traditional systems rely on a single data source and lack a cross - departmental multi - modal data fusion mechanism, resulting in difficulty in aligning dynamic data of public information on social media with physical sensor data, forming data islands and evaluation blind spots. Traditional early warning models rely on a fixed threshold trigger mechanism and lack the ability to fuse spatio - temporal dynamic features, resulting in a false alarm rate of 15% - 20% and a response delay of more than 30 minutes. Information release relies on one - way channels such as television and radio, with insufficient coordination between mobile terminals and outdoor large screens, and the lack of intelligent guidance such as AR navigation. Emergency resource scheduling relies on manual experience and does not conduct multi - objective optimization by combining real - time traffic data and risk propagation models. These problems seriously restrict the accuracy and timeliness of disaster response;

[0004] Therefore, we propose an emergency management early warning and release system based on big data. Summary of the Invention

[0005] The purpose of the present invention is to provide an emergency management early warning and release system based on big data.

[0006] To achieve the above - mentioned purpose, the present invention provides the following technical solution: An emergency management early warning and release system based on big data, the early warning and release system includes the following hierarchical structures connected in sequence:

[0007] Data perception layer, including a multi - modal data fusion gateway deployed at the data source terminal, configured to collect heterogeneous data from meteorological sensors, social network platforms, and unit emergency management platforms;

[0008] Intelligent central layer, integrated with a streaming computing engine, a spatio - temporal feature extraction engine, and a dynamic early warning threshold generation module, generating a regional risk index through an attention mechanism that dynamically couples spatio - temporal features;

[0009] Decision - making service layer, constructing a knowledge graph containing disaster response plans, and configuring an emergency resource matching engine to implement material scheduling path planning;

[0010] Release execution layer, including a multi - screen linkage control device supporting augmented reality rendering, and an adaptive release strategy generator based on the audience portrait;

[0011] The feedback optimization layer connects the output ends of each module across levels and is configured with a channel efficiency evaluation matrix and a Bayesian network optimization engine.

[0012] As a further solution of the present invention: The data alignment process performed by the multi-modal data fusion gateway satisfies the following optimization equation:

[0013]

[0014] Where, represents finding the minimum value of the objective function with respect to the mapping matrix W. n is the upper limit of the summation symbol, representing the total amount of data samples. represents the cross-modal feature mapping matrix, and the training objective is to align heterogeneous data distributions. represents the structured data feature vector from the unit platform, and W T is the transpose of the matrix W, representing the social network data y i is the key parameter matrix that maps the social network data y from the d2-dimensional space to the unit data d1-dimensional space. represents the unstructured data feature vector from the social network. is the social data covariance matrix, which is updated in real time through a sliding window. λ is the federated learning regularization coefficient, which controls the balance between model complexity and data security protection. Tr(·) is the matrix trace operation, which calculates the sum of the main diagonal elements of the matrix. W T ΣW is the distribution consistency metric of the mapped feature space.

[0015] As a further solution of the present invention: The spatio-temporal feature extraction engine includes:

[0016] A three-dimensional geocoding unit that converts physical coordinates into Geohash codes containing elevation information;

[0017] A time series analysis unit that decomposes historical data trend terms and periodic terms using wavelet transforms;

[0018] A spatio-temporal coupling module that realizes feature fusion through the following attention mechanism:

[0019]

[0020] Where, ɑ ij ∈(0,1) is the spatio-temporal feature dynamic weight, which determines the contribution degree of each modality in disaster prediction. exp(·) represents the exponential function with the natural constant as the base. is the time feature vector. is the space feature vector. σ(·) is the LeakyReLU activation function. U ∈ is the trainable parameter vector, and u T is the transpose of the matrix u. || represents the vector concatenation operation, which extends the spatio-temporal vector to Q represents the dynamic time window length, with a value range of [1, 24] hours, which is a custom variable.

[0021] As a further solution of the present invention: The dynamic early warning threshold generation module includes components connected in series:

[0022] An anomaly detection unit that uses the Seasonal-Trend Decomposition using Loess (STL) algorithm to extract the data residual term;

[0023] A risk deduction unit that constructs a disaster propagation dynamics equation based on the SEIR model;

[0024] A threshold correction unit that configures a reinforcement learning strategy to adjust the early warning level according to historical disposal feedback.

[0025] As a further solution of the present invention: An emergency management early warning release system based on big data, characterized in that: The multi-objective optimization algorithm executed by the emergency resource matching engine is:

[0026]

[0027] where x = (x1,..., x p ) is the decision variable vector, representing the sequence of distribution path node numbers, is the feasible solution space, generated by the constraints of the road network adjacency matrix, is the objective weight coefficient, dynamically calculated by the Analytic Hierarchy Process, is the reference point, dynamically generated from the set of historical optimal solutions;

[0028] The objective function set f i includes: f1(x) the time cost of material distribution, f2(x) the path risk level, f3(x) the transportation economic cost.

[0029] As a further solution of the present invention: The adaptive release strategy generator includes:

[0030] An audience portrait unit that integrates mobile terminal signaling data and application usage records to generate group feature tags;

[0031] A channel optimization unit that uses a decision-making algorithm based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS);

[0032] An information generation unit that is configured with a rendering engine that supports generating augmented reality navigation routes.

[0033] As a further solution of the present invention: The multi-screen linkage control device includes:

[0034] A terminal adaptation module that converts the early warning information into display protocols for mobile phone pop-ups, outdoor LED screens, and in-vehicle terminals;

[0035] Multilingual conversion unit, integrating a dialect speech recognition engine and a neural machine translation model;

[0036] Emergency broadcast interface, conforming to the protocol stack of Digital Terrestrial Multimedia Broadcasting (DTMB);

[0037] Wireless transmission module: integrating the Beidou satellite communication short message protocol (RDSS frequency band 2491.75 MHz ± 4.08 MHz) and the 5G NR protocol stack, supporting the following functions:

[0038] (1) The 5G uplink channel allocation uses LDPC coding, with a subcarrier spacing of 30 kHz and a bandwidth of 100 MHz;

[0039] (2) Beidou short message communication congestion control algorithm, defining the packet retransmission interval as:

[0040] T retry =min(2 n ×T0, T max )

[0041] where n is the current retransmission count, T0 = 1 s, and T max =10 s.

[0042] As a further solution of the present invention: the feedback optimization layer includes:

[0043] Publication monitoring module, calculating the information reach rate and user response rate of each channel in real time;

[0044] Policy optimization engine, updating the channel weight parameters through the following Bayesian formula, and the specific process is as follows:

[0045] S801. Initialize the prior distribution P(θ);

[0046] S802. Calculate the posterior distribution P(θ|D) ∝ P(D|θ)P(θ) according to the observed data set D;

[0047] S803. Generate the channel weight parameters for the next cycle based on the posterior distribution;

[0048] where P(θ|D) is the likelihood function, representing the generation probability of the observed data under the parameter θ, θ = (θ1,..., θ k ) is the channel weight parameter vector, representing the priority of each publication channel, D is the observed data set, P(θ) is the prior distribution, and the channel weights are initialized based on historical data;

[0049] The observed data set D includes timeliness, coverage rate, and credibility score.

[0050] As a further solution of the present invention: The reliability guarantee of the system in a high-altitude environment is achieved through:

[0051] (1) The edge computing node adopts a wide-temperature chip and is configured with a heat dissipation device;

[0052] (2) The wireless transmission module integrates the Beidou satellite communication and 5G NR dual-mode protocol stacks, and the switching strategy is as follows:

[0053] When the 5G signal strength RSSI < -110 dBm, switch to Beidou short message communication;

[0054] When the 5G signal recovers and RSSI ≥ -95 dBm, automatically switch back to 5G NR;

[0055] (3) The service cluster deployed in a containerized manner is configured with a heartbeat monitoring mechanism, and when the service is detected to be unreachable, trigger replica reconstruction.

[0056] Adopting the above technical solutions, compared with the prior art, the beneficial effects of the present invention are as follows:

[0057] 1. The present invention breaks through the limitations of traditional system data islands. Through the multi-modal data fusion gateway, heterogeneous data sources such as meteorological sensors, social media, and unit platforms are integrated. Combining with federated learning technology, anonymization processing and security protection of user data are realized, significantly improving the real-time and comprehensiveness of disaster situation perception. The dynamic spatio-temporal feature extraction engine uses the attention mechanism to capture the spatio-temporal coupling law of disaster propagation, improving the risk assessment accuracy to millimeter-level geographical resolution and effectively eliminating the monitoring blind spots existing in traditional static models;

[0058] 2. The present invention innovatively applies the LSTM-GAN joint architecture to generate dynamic warning thresholds, combines with the SEIR disaster propagation model and the reinforcement learning feedback mechanism to realize minute-level adaptive adjustment of warning levels. The multi-objective optimization algorithm collaboratively optimizes the delivery time, path risk, and cost consumption in emergency resource scheduling, improving the material allocation efficiency compared with traditional manual decision-making and reducing the false alarm rate to the leading level in the industry;

[0059] 3. The present invention realizes the scenario-based presentation of warning information through a multi-screen linkage device supporting AR navigation, integrates dialect recognition and multi-language real-time conversion functions, significantly improving the information coverage ability of special groups. The feedback system based on the Bayesian network real-time evaluates the channel effectiveness and dynamically optimizes the release strategy, forming a closed-loop system of "monitoring - release - evaluation - iteration". The system response speed reaches the second level and still has excellent stability and continuous service capabilities in extreme environments. Description of the Drawings

[0060] Figure 1 It is the system hierarchical structure diagram in the embodiment of the present invention. Detailed Embodiments

[0061] The following further describes the specific implementation manners of the present invention in conjunction with the accompanying drawings. It should be noted here that the descriptions of these implementation manners are used to help understand the present invention, but do not limit the present invention.

[0062] In addition, the technical features involved in the various implementation manners of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] Please refer to the attached Figure 1 , an emergency management early warning release system based on big data of the present invention, the early warning release system includes the following hierarchical structures connected in sequence:

[0064] Data perception layer, including a multimodal data fusion gateway deployed at the data source terminal, configured to collect heterogeneous data from meteorological sensors, social network platforms, and unit emergency management platforms;

[0065] Intelligent central layer, integrated with a stream computing engine, a spatio-temporal feature extraction engine, and a dynamic early warning threshold generation module, generating a regional risk index through an attention mechanism that dynamically couples spatio-temporal features;

[0066] Decision service layer, constructing a knowledge graph including disaster response plans, and configuring an emergency resource matching engine to implement material scheduling path planning;

[0067] Release execution layer, including a multi-screen linkage control device supporting augmented reality rendering, and an adaptive release strategy generator based on the audience portrait;

[0068] Feedback optimization layer, cross-level connecting the output ends of each module, configured with a channel efficiency evaluation matrix and a Bayesian network optimization engine.

[0069] In an implementation manner of the present invention: the data alignment processing executed by the multimodal data fusion gateway satisfies the following optimization equation:

[0070]

[0071] Among them, represents finding the minimum value of the objective function for the mapping matrix W, n is the upper limit of the summation symbol, representing the total amount of data samples, represents the cross-modal feature mapping matrix, and the training objective is to align the heterogeneous data distributions, represents the structured data feature vector from the unit platform, W T is the transpose of the matrix W, representing the social network data y i is the key parameter matrix for mapping the social network data y from the d2-dimensional space to the unit data d1-dimensional space, represents the unstructured data feature vector from the social network, is the covariance matrix of social data, which is updated in real time through a sliding window. λ is the regularization coefficient of federated learning, which controls the balance between model complexity and data security protection. Tr(·) is the matrix trace operation, which calculates the sum of the main diagonal elements of the matrix, and W T ΣE is the distribution consistency metric of the mapped feature space.

[0072] In one embodiment of the present invention: The spatio-temporal feature extraction engine includes:

[0073] A three-dimensional geocoding unit that converts physical coordinates into Geohash codes containing altitude information;

[0074] A time series analysis unit that decomposes the historical data trend term and periodic term using wavelet transform;

[0075] A spatio-temporal coupling module that realizes feature fusion through the following attention mechanism:

[0076]

[0077] where ɑ ij ∈(0,1) is the spatio-temporal feature dynamic weight, which determines the contribution degree of each modality in disaster prediction. exp(·) represents the exponential function with the natural constant as the base, is the time feature vector, is the space feature vector, σ(·) is the LeakyReLU activation function, is the trainable parameter vector, u T is the transpose of matrix u, and || represents the vector concatenation operation, which extends the spatio-temporal vector to Q represents the dynamic time window length, and its value range is [1, 24] hours, which is a custom variable.

[0078] In one embodiment of the present invention: The dynamic early warning threshold generation module includes a series connection of:

[0079] An anomaly detection unit that extracts the data residual term using the seasonal-trend decomposition procedure based on loess (STL);

[0080] A risk deduction unit that constructs a disaster propagation dynamics equation based on the SEIR model;

[0081] A threshold correction unit that configures a reinforcement learning strategy to adjust the early warning level according to historical disposal feedback.

[0082] In one embodiment of the present invention: A big data-based emergency management early warning release system, characterized in that: The multi-objective optimization algorithm executed by the emergency resource matching engine is:

[0083]

[0084] Among them, x = (x1, …, x p ) is the decision variable vector, representing the sequence number of the distribution path nodes, is the feasible solution space, generated by the constraints of the road network adjacency matrix, is the target weight coefficient, dynamically calculated by the analytic hierarchy process, is the reference point, dynamically generated from the set of historical optimal solutions;

[0085] The objective function set f i includes: f1(x) material distribution time cost, f2(x) path risk level, f3(x) transportation economic cost.

[0086] In an embodiment of the present invention: The adaptive publishing strategy generator includes:

[0087] The audience portrait unit, integrating mobile terminal signaling data and application usage records to generate group characteristic tags;

[0088] The channel optimization unit, adopting a decision-making algorithm based on the technique for order preference by similarity to an ideal solution (TOPSIS);

[0089] The information generation unit, configured with a rendering engine supporting the generation of augmented reality navigation routes.

[0090] In an embodiment of the present invention: The multi-screen linkage control device includes:

[0091] The terminal adaptation module, converting the warning information into display protocols for mobile phone pop-up windows, outdoor LED screens, and in-vehicle terminals;

[0092] The multi-language conversion unit, integrating a dialect speech recognition engine and a neural machine translation model;

[0093] The emergency broadcast interface, conforming to the protocol stack of the digital television terrestrial broadcast standard (DTMB);

[0094] The wireless transmission module: integrating the Beidou satellite communication short message protocol (RDSS frequency band 2491.75 MHz ± 4.08 MHz) and the 5G NR protocol stack, supporting the following functions:

[0095] (1) The 5G uplink channel allocation uses LDPC coding, with a subcarrier spacing of 30 kHz and a bandwidth of 100 MHz;

[0096] (2) The Beidou short message communication congestion control algorithm, defining the packet retransmission interval as:

[0097] T retry = min(2 n × T0, T max )

[0098] where n is the current retransmission count, T0 = 1s, T max = 10s.

[0099] In an embodiment of the present invention: The feedback optimization layer includes:

[0100] A release monitoring module that calculates the information reach rate and user response rate of each channel in real time;

[0101] A strategy optimization engine that updates the channel weight parameters through the following Bayesian formula. The specific process is as follows:

[0102] S801. Initialize the prior distribution P(θ);

[0103] S802. Calculate the posterior distribution P(θ|D) ∝ P(D|θ)P(θ) according to the observed data set D;

[0104] S803. Generate the channel weight parameters for the next cycle based on the posterior distribution;

[0105] where P(θ|D) is the likelihood function, which represents the generation probability of the observed data under the parameter θ. θ = (θ1,..., θ k ) is the channel weight parameter vector, which represents the priority of each release channel. D is the observed data set, and P(θ) is the prior distribution, which initializes the channel weights based on historical data;

[0106] The observed data set D includes timeliness, coverage, and credibility scores.

[0107] In an embodiment of the present invention: The reliability guarantee of the system in a high-altitude environment is achieved through:

[0108] (1) The edge computing node adopts a wide-temperature chip and is configured with a heat dissipation device;

[0109] (2) The wireless transmission module integrates the Beidou satellite communication and 5G NR dual-mode protocol stack. The switching strategy is as follows:

[0110] When the 5G signal strength RSSI < -110dBm, switch to Beidou short message communication;

[0111] When the 5G signal recovers and RSSI ≥ -95dBm, automatically switch back to 5G NR;

[0112] (3) The service cluster deployed in a containerized manner is configured with a heartbeat monitoring mechanism, and when the service is detected to be unreachable, replica reconstruction is triggered.

[0113] Embodiment 1

[0114] This embodiment details the collaborative working mechanism between the data perception layer and the intelligent central layer:

[0115] As Figure 1 shown, the system is deployed in an A flood monitoring scenario, and the specific implementation steps are as follows:

[0116] 1. Multi-source data collection and alignment

[0117] The data perception layer collects structured data such as water level and flow velocity (sampling frequency 10Hz) in real time through edge computing nodes deployed at 12 hydrological stations, and synchronously accesses unstructured data (text, video) related to disasters on social media platforms;

[0118] The multi-modal data fusion gateway performs federated learning alignment processing:

[0119] Construct a cross-modal mapping matrix Map 128-dimensional social text features (encoded by BERT) to a 256-dimensional hydrological feature space;

[0120] Optimize the equation In, the regularization coefficient λ = 0.3 to balance data utility and data security protection;

[0121] Update the covariance matrix Σ through a 24-hour sliding window to eliminate the influence of seasonal data fluctuations;

[0122] 2. Spatio-temporal feature dynamic fusion

[0123] The spatio-temporal feature extraction engine in the intelligent central layer processes the aligned data stream:

[0124] Geocoding: Convert GPS coordinates to 12-bit Geo Hash codes and embed altitude information;

[0125] Time series decomposition: Use Haar wavelets to decompose historical water level data and extract the trend term T t 、period term S t and residual term R t ;

[0126] Attention fusion: The spatio-temporal coupling module calculates feature weights:

[0127]

[0128] Among them, the time feature (output by LSTM), the space feature (output by CNN), the parameter vector is trained by the Adam optimizer;

[0129] 3. The anomaly detection unit identifies the Z-score of the residual term R t and triggers risk deduction:

[0130] SEIR model parameter settings: The incubation period \(T = 48h\), and the infection rate \(\beta=0.32\) (fitted according to historical flood data);

[0131] Reinforcement learning feedback: When the warning level is misjudged, the policy network is updated through the TD error \(\delta = R+\gamma E(s',a') - E(s,a)\) (learning rate 0.001);

[0132] Among them, \(\delta\) represents the TD error, \(R\) represents the immediate reward, \(\gamma\) represents the discount factor, \(\gamma\in[0,1]\), \(E(s,a)\) represents the state-action value function, and \(E(s',a')\) represents the next state value;

[0133] Output the regional risk index RRI (0 - 100), and trigger a red warning when RRI>75.

[0134] Example 2

[0135] This example demonstrates the full-link operation of the decision service layer and the release and execution layer, taking the earthquake disaster emergency response as an example:

[0136] 1. Intelligent dispatching of emergency resources

[0137] After the decision service layer receives the epicenter coordinates (E103°34', N30°54') and the intensity 7 data:

[0138] Knowledge graph query: Match the disposal plan for "7.0-magnitude earthquake in Area A" in the historical case library to generate an initial dispatching plan;

[0139] Multi-objective optimization: Solved by the emergency resource matching engine:

[0140]

[0141] Among them:

[0142] \(f1\) is the arrival time of the rescue team (hours), reference point

[0143] \(f2\) is the path risk level (level 1 - 5), reference point

[0144] \(f3\) is the transportation cost (in ten thousand yuan), reference point

[0145] After 500 iterations of the NSGA-II algorithm, a Pareto optimal solution set is generated, and the balanced plan is selected: \(f1 = 9.2h\), \(f2 = 2.3\), \(f3 = 48.7\) ten thousand;

[0146] 2. Multimodal warning release

[0147] The release and execution layer performs precise delivery according to the audience portrait:

[0148] AR Navigation Generation: The rendering engine constructs a 3D escape path and overlays a real-time aftershock warning circle (radius 5 km);

[0149] Channel Optimization: The TOPSIS algorithm calculates the channel efficiency score:

[0150]

[0151]

[0152] Table 1

[0153] 3. Closed-loop Feedback Optimization

[0154] The feedback optimization layer monitors the release effect and adjusts dynamically:

[0155] Bayesian Network Update: Based on the data of the reach rate (87%) and response rate (63%) within 3 hours, calculate the posterior distribution:

[0156]

[0157] The updated weights θ = (0.41, 0.29, 0.30) (representing mobile phone / broadcast / vehicle terminal in sequence);

[0158] Container Self-healing: When the communication of the node at an altitude of 4500 meters is interrupted, the heartbeat monitoring mechanism (interval 5 seconds) triggers the reconstruction of the K8s replica, and the service recovery time < 30 seconds.

[0159] Example 3

[0160] This example elaborates in detail the reliability guarantee mechanism of the system in the extreme high-altitude environment, with the geological disaster warning in the Qinghai-Tibet Plateau as the application scenario:

[0161] 1. Hardware Environment Adaptation

[0162] Deploy edge computing nodes at the monitoring station on Mountain A at an altitude of 5000 meters:

[0163] Indicator 5G NR Mode Beidou Mode Average Transmission Delay 28 ms 6.5s Packet Loss Rate (24 hours) 0.8％ 2.3％ Maximum Transmission Radius 1.2 km (Base Station Coverage Range) Global Coverage

[0164] Table 2

[0165] Test Conditions:

[0166] 5G NR: n78 band (3.5 GHz), transmit power 23 dBm, MIMO 4×4;

[0167] Beidou: RDSS band, transmit power 10 W, using RS(128,64) forward error correction coding;

[0168] Container Service Self-healing Time: < 30 seconds (heartbeat detection interval 5 seconds).

[0169] Wide-temperature-range chipset: Adopts Xilinx UltraScale+ FPGA (operating temperature -40°C to 100°C), equipped with a liquid cooling device to ensure continuous operation in an environment with a 60°C day-night temperature difference;

[0170] Dual-mode communication module: Integrates Beidou RDSS short message (transmission frequency point 2491.75 MHz) and 5G NR n78 band (3.5 GHz). The automatic switching logic is as follows:

[0171] 1 When the signal strength RSSI < -110 dBm:

[0172] 2 Start Beidou message transmission (coding efficiency 1 / 3, information fragment length 560 bytes)

[0173] 3 Otherwise:

[0174] 4 Prefer to use 5G NR uplink (MCS20, TBS 992 bits)

[0175] Containerized service cluster: Deployed based on the Kubernetes architecture, configured with a Pod anti-affinity policy (anti-affinity) to ensure that the same service replicas are distributed across 3 physical nodes;

[0176] 2. Dynamic warning and self-healing mechanism

[0177] Heartbeat detection protocol:

[0178] Define the health check message format:

[0179] 1 struct Heartbeat{

[0180] 2 uint64 timestamp; / / UTC millisecond timestamp

[0181] 3 float32 temp; / / Chip temperature (°C)

[0182] 4 uint8 status; / / 0x01: normal 0x02: overload

[0183] 5}

[0184] Dynamic adjustment of detection interval:

[0185]

[0186] Among them, T check represents the dynamically adjusted health detection interval (unit: seconds), T augIt represents the historical average recovery time (initialized to 10 seconds). max(·) represents setting the lower limit of the detection interval to ensure the minimum detection frequency in extreme scenarios. The 5-second lower limit guarantees the minimum service availability in extreme environments. The constant 2 represents controlling the sensitivity of the detection interval to changes in system performance.

[0187] Example 4

[0188] This example demonstrates the collaborative application of multilingual conversion and closed-loop feedback optimization in a few specific regional groups. Taking the earthquake early warning in Area A as an example:

[0189] Multimodal information generation

[0190] Dialect recognition engine:

[0191] Build a language recognition model for specific regional groups (including 3 dialect variants in the east, south, and west):

[0192] Input: 16kHz sampled audio (mixed Chinese and Yi audio)

[0193] Model architecture: Conformer Encoder (12 layers, d model = 256) + CTC decoder

[0194] Accuracy of the test set: 92.7% for the eastern dialect, 88.4% for the southern dialect, and 85.6% for the western dialect

[0195] Neural machine translation:

[0196] Deploy a Transformer model (6 layers, d k = 64, d v = 64) to achieve real-time conversion from Chinese to Yi:

[0197]

[0198] Among them, Attention(·) represents dynamic weight allocation, represents the query matrix, n is the length of the input sequence, represents the key matrix, m is the length of the output sequence, represents the value matrix, d k = 64 represents the key vector dimension, is the scaling factor to prevent the dot product value from being too large and causing the gradient to disappear. softmax(·) represents the row-wise normalization function to generate attention weights (sum to 1), and K T is the transpose of matrix K;

[0199] It supports a vocabulary of 35,000 words, and the BLEU value reaches 41.2;

[0200] 2. Adaptive publishing strategy

[0201] Audience portrait construction:

[0202] Integrate signaling data to generate feature tags:

[0203] User ID Location Terminal Type Language Preference Network Status U1001 E103.67, N27.63 Android Eastern Yi 5G U1002 E103.71, N27.59 Feature Phone Chinese 2G

[0204] Table 3

[0205] AR navigation generation:

[0206] The rendering engine generates a 3D escape path based on the building BIM model:

[0207] Coordinate transformation: WGS84 → local coordinate system (EPSG:4547);

[0208] Path optimization: A* algorithm (heuristic function h(n) = Manhattan distance × 1.5);

[0209] 3. Closed-loop feedback optimization

[0210] Bayesian network update:

[0211] The observation dataset D contains:

[0212]

[0213]

[0214] Table 4

[0215] Posterior distribution calculation:

[0216]

[0217] The updated channel weight θ = (0.38, 0.29, 0.33) (representing mobile / broadcast / large screen in sequence);

[0218] Among them, θ = (θ1, θ2, θ3) is the channel weight parameter vector, representing the priority weights of three publishing channels: mobile, broadcast, and outdoor large screen. D is the observation dataset (including the channel performance indicators of two time windows). α = 10θ, β = 10(1 - θ) represent mapping the channel weight θ to the parameter space of the Beta distribution. · represents fusing the observation data of two time windows, and Beta(y t |α, β) represents the probability density function of the Beta distribution, Gamma(θ|k =

[0219] (2, λ = 0.5) represents the sparsity of the prior distribution to constrain the channel weights, preventing the channel weights from being overly concentrated on a single channel. k = 2 is the shape parameter that controls the skewness of the distribution, and λ = 0.5 is the rate parameter.

[0220] Although the present invention is disclosed above in a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, all modifications, equivalent changes, and decorations made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention fall within the protection scope defined by the claims of the present invention.

Claims

1. An emergency management early warning release system based on big data, characterized in that: The early warning release system includes the following hierarchical structures connected in sequence: The data perception layer includes a multi-modal data fusion gateway deployed at the data source terminal, configured to collect heterogeneous data from meteorological sensors, social network platforms, and unit emergency management platforms; The intelligent central layer integrates a streaming computing engine, a spatio-temporal feature extraction engine, and a dynamic early warning threshold generation module, and generates a regional risk index through an attention mechanism that dynamically couples spatio-temporal features; The decision-making service layer constructs a knowledge graph containing disaster response plans, and configures an emergency resource matching engine to implement material dispatching path planning; The release execution layer includes a multi-screen linkage control device supporting augmented reality rendering, and an adaptive release strategy generator based on the audience portrait; The feedback optimization layer is cross-level connected to the output ends of each module, and is configured with a channel efficiency evaluation matrix and a Bayesian network optimization engine.

2. The emergency management early warning release system based on big data according to claim 1, characterized in that: The data alignment process executed by the multi-modal data fusion gateway satisfies the following optimization equation: Among them, represents finding the minimum value of the objective function with respect to the mapping matrix W. n is the upper limit of the summation symbol, representing the total number of data samples. represents the cross-modal feature mapping matrix, and the training objective is to align heterogeneous data distributions. represents the structured data feature vector from the unit platform, and W T is the transpose of matrix W, representing the social network data y i is the key parameter matrix for mapping from the d2-dimensional space to the unit data d1-dimensional space. represents the unstructured data feature vector from the social network. is the social data covariance matrix, which is updated in real time through a sliding window. λ is the federated learning regularization coefficient, which controls the balance between model complexity and data security protection. Tr(·) is the matrix trace operation, which calculates the sum of the main diagonal elements of the matrix, and W T ΣW is the distribution consistency metric of the mapped feature space.

3. An emergency management early warning release system based on big data according to claim 1, characterized in that: The spatio-temporal feature extraction engine includes: A three-dimensional geocoding unit that converts physical coordinates into Geohash codes including altitude information; A time series analysis unit that decomposes historical data trend terms and periodic terms using wavelet transform; A spatio-temporal coupling module that realizes feature fusion through the following attention mechanism: Among them, α ij ∈(0,1) is the dynamic weight of spatio-temporal features, which determines the contribution degree of each modality in disaster prediction. exp(·) represents the exponential function with the natural constant as the base. is the time feature vector, is the space feature vector, σ(·) is the LeakyReLU activation function, is the vector of trainable parameters, u T is the transpose of matrix u, || represents the vector concatenation operation, which extends the spatio-temporal vector to Q represents the length of the dynamic time window, and its value range is [1, 24] hours, which is a user-defined variable.

4. An emergency management early warning and release system based on big data according to claim 1, characterized in that: The dynamic early warning threshold generation module includes a series connection of: An anomaly detection unit that extracts data residual terms using a seasonal time series decomposition algorithm; A risk deduction unit that constructs a disaster propagation dynamics equation based on the SEIR model; A threshold correction unit that configures a reinforcement learning strategy to adjust the early warning level according to historical disposal feedback.

5. The emergency management early warning and release system based on big data according to claim 1, wherein: The multi-objective optimization algorithm executed by the emergency resource matching engine is: where \(x=(x_1,\ldots,x p )\) is a decision variable vector representing the sequence of distribution path node numbers, is the feasible solution space, generated by the constraints of the road network adjacency matrix, is the target weight coefficient, dynamically calculated by the analytic hierarchy process, is the reference point, dynamically generated from the set of historical optimal solutions; Objective function set f i including: f1(x) material distribution time cost, f2(x) path risk level, f3(x) transportation economic cost.

6. The emergency management early warning release system based on big data according to claim 1, characterized in that: The adaptive release strategy generator includes: An audience portrait unit that integrates mobile terminal signaling data and application usage records to generate group feature tags; A channel optimization unit that uses a decision-making algorithm based on the technique for order preference by similarity to an ideal solution; An information generation unit that is configured with a rendering engine supporting the generation of augmented reality navigation routes.

7. An emergency management early warning and release system based on big data according to claim 1, characterized in that: The multi-screen linkage control device includes: A terminal adaptation module that converts early warning information into display protocols for mobile phone pop-ups, outdoor LED screens, and vehicle-mounted terminals; A multi-language conversion unit that integrates a dialect speech recognition engine and a neural machine translation model; An emergency broadcast interface that conforms to the protocol stack of digital television terrestrial broadcasting; A wireless transmission module: integrates the Beidou satellite communication short message protocol and the 5G NR protocol stack, and supports the following functions: (1) The 5G uplink channel allocation uses LDPC coding, with a subcarrier spacing of 30 kHz and a bandwidth of 100 MHz; (2) The Beidou short message communication congestion control algorithm defines the packet retransmission interval as: T retry = min(2 n × T0, T max ) where n is the current retransmission count, T0 = 1 s, T max = 10 s.

8. The emergency management early warning and release system based on big data according to claim 1, characterized in that: The feedback optimization layer includes: A release monitoring module that calculates the information reach rate and user response rate of each channel in real time; A strategy optimization engine that updates the channel weight parameters through the following Bayesian formula, and the specific process is: S801. Initialize the prior distribution P(θ); Calculate the posterior distribution according to the observation data set D S803. Generate the channel weight parameters for the next cycle based on the posterior distribution; Among them, P(θ|D) is the likelihood function, which represents the generation probability of the observed data under the parameter θ. θ = (θ1, …, θ k ) is the channel weight parameter vector, which represents the priority of each publishing channel. D is the observed data set, and P(θ) is the prior distribution, which initializes the channel weights based on historical data; The observation data set D includes timeliness, coverage, and credibility scores.

9. The emergency management early warning release system based on big data according to claim 1, characterized in that: The reliability guarantee of the system in a high-altitude environment is achieved through: (1) The edge computing node adopts a wide-temperature chip and is equipped with a heat dissipation device; (2) The wireless transmission module integrates the Beidou satellite communication and 5G NR dual-mode protocol stack; (3) The service cluster deployed in a containerized manner is configured with a heartbeat monitoring mechanism, which triggers replica reconstruction when the service is detected to be unreachable.

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