Geological disaster early warning information rapid publishing system and accurate pushing method based on priority

Abstract

The invention discloses a geological disaster early warning information rapid publishing system and a priority-based accurate pushing method, and relates to the technical field of geological disaster monitoring and early warning. According to the method, the deep learning model, the block chain technology and the multi-channel dynamic pushing strategy are integrated, so that high-precision prediction, safe storage and accurate distribution of geological disasters are realized, the convolutional neural network and the long-short-term memory network are utilized to process multi-source data, and the prediction accuracy is improved; the block chain is adopted to ensure that the information is not tampered and traceable; the information pushing priority is dynamically adjusted according to risk assessment and network conditions, rapid and accurate information distribution is achieved through multiple channels such as short messages, mobile applications and satellite communication, the response speed and reliability of the early warning system are improved, and powerful technical support is provided for effective disaster prevention and reduction.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring and early warning, and in particular to a rapid release system for geological disaster early warning information and a priority-based precise push method. Background Art

[0002] Geological hazard early warning and emergency response are core research areas in the field of disaster prevention and mitigation. Their importance lies in effectively reducing casualties and property losses, and ensuring social stability and sustainable development. Currently, geological hazard early warning systems have made some progress in data collection, analysis, and information delivery, but significant limitations remain. Traditional methods often rely on a single data source, such as ground sensors or satellite remote sensing, resulting in insufficient data fusion and low prediction accuracy. Warning information is often transmitted through a single channel, lacking specificity and making it difficult to cover complex terrain or areas with limited communication. Furthermore, information credibility and public response efficiency are not fully guaranteed, impacting emergency response effectiveness. These limitations stem from technical bottlenecks in the real-time fusion of multi-source data, the secure and traceable nature of warning information, and the accurate and efficient delivery of warning information.

[0003] Specifically, the core challenges are concentrated in the following aspects: First, the real-time collection and fusion analysis of multi-source data involves the heterogeneous integration of sensors, satellite remote sensing, and historical data, making it difficult to achieve high-precision disaster predictions. Second, the immutability and traceability of warning information. Traditional centralized storage is vulnerable to attacks and lacks transparency and trust mechanisms. Finally, dynamic and accurate push notifications for different groups and regions require comprehensive consideration of risk levels, network conditions, and multi-channel collaboration. Existing methods cannot achieve second-level responses and intuitive guidance. These technical difficulties directly restrict the timeliness and effectiveness of geological disaster warnings. Summary of the Invention

[0004] The purpose of this invention is to provide a rapid release system for geological disaster warning information and a priority-based precise push method. Through deep learning models, blockchain technology and multi-channel dynamic push strategies, high-precision prediction, secure storage and rapid and accurate distribution of geological disaster warning information are achieved.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] This application provides a rapid release system for geological disaster warning information, including:

[0007] The model building and evaluation module uses the collected multi-source data sets and a deep learning algorithm to build a high-precision geological disaster prediction model. By integrating convolutional neural networks and long-short-term memory networks, it outputs assessment results including the probability of geological disasters and risk levels.

[0008] The information storage and verification module determines whether the risk level assessment result exceeds the preset threshold. If it does, the warning information is stored in the distributed ledger through blockchain technology to obtain an unalterable and traceable warning information record. The hash check algorithm is then used to verify the integrity of the data, determine the credibility of the warning information, and output the warning information;

[0009] The dynamic priority strategy module uses a dynamic priority algorithm to calculate push priorities based on reliable early warning information, and determines a hierarchical push strategy based on risk level assessment results and the network status of the receiving terminal;

[0010] The multi-channel distribution module generates targeted push content based on credible warning information and layered push strategies, and distributes it via SMS, mobile applications, and satellite communications, achieving push results with a response time of seconds.

[0011] The feedback-driven optimization module collects user feedback on push results, uses feedback analysis algorithms to adjust the parameters of the dynamic priority algorithm, outputs the optimized push strategy, and updates the distribution process of subsequent warning information;

[0012] The performance monitoring adaptive module extracts network status data from high-priority areas from the optimized push strategy and uses an adaptive communication protocol to adjust the multi-channel collaborative distribution method to obtain push results covering complex terrain. It then uses a real-time monitoring algorithm to analyze the push arrival rate and response time, outputs the adjusted multi-source data collection frequency, and updates the initial multi-source data set generation process.

[0013] Furthermore, it also includes a data acquisition and integration module, which collects multi-source data from sensors, satellite remote sensing and historical databases through Internet of Things technology, and processes heterogeneous data through data standardization protocols and integrates them into a data set in a unified format.

[0014] Furthermore, in the information storage and verification module, a tamper-proof and traceable warning information record is obtained, specifically including:

[0015] When the risk assessment result exceeds the preset threshold, the warning information is encrypted using a preset encryption algorithm to generate encrypted warning data;

[0016] The encrypted warning data is uploaded to the distributed ledger through blockchain technology to generate an unalterable on-chain record. The hash value of the on-chain record is then obtained from the distributed ledger, and the integrity of the record is verified using a consistency check algorithm to ensure that the record has not been tampered with.

[0017] When the consistency check passes, the smart contract automatically triggers the audit process recorded on the chain, generates a traceable audit log, and then extracts historical warning data from the distributed ledger. The time series analysis algorithm is used to determine the trend changes of the warning information;

[0018] Based on the trend change results, the parameters of the preset threshold are updated to generate dynamically adjusted threshold data, which are then stored in the distributed ledger to generate new on-chain threshold records.

[0019] Furthermore, reliable warning information is output, including:

[0020] Obtain warning information records from the distributed ledger, parse the record content using a preset extraction protocol to obtain structured warning data, and then use a hash checksum algorithm to verify the structured warning data, generate a checksum result, and determine data integrity;

[0021] When the verification result shows that the data is complete, the credibility of the warning data is evaluated through the preset trust scoring model to obtain a credibility score. The credibility score is compared with the preset threshold. When the score is higher than the threshold, the warning data is marked as credible information.

[0022] Format the trusted information through the information processing pipeline to generate standardized trusted warning information, and then use encryption signature technology to sign the standardized trusted warning information to obtain encrypted and protected output data;

[0023] The encrypted and protected output data is transmitted to the target system through the preset output interface to complete the output of reliable warning information.

[0024] Furthermore, in the dynamic priority strategy module, a hierarchical push strategy is determined, specifically including:

[0025] Obtain credible warning information, extract key fields from the information source, and use preprocessing technology to clean the fields to obtain structured warning data;

[0026] A dynamic priority algorithm is used to calculate the initial push priority based on the key fields of the structured warning data to obtain a priority score. When the priority score is higher than the preset threshold, the push priority is adjusted based on the risk level assessment results to obtain an optimized priority sequence.

[0027] Based on the optimization priority sequence, network status data is obtained from the receiving terminal to determine the terminal's bandwidth and latency, and determine the terminal adaptation parameters. Then, a layered push technology is used to map the optimization priority sequence to the push level to obtain a layered push solution.

[0028] Through a layered push solution, a push strategy is generated, and message queue technology is used to schedule push tasks and determine the final push order. When changes in network conditions are detected during the push process, the terminal network data is re-acquired, the terminal adaptation parameters are updated, and the adjusted push strategy is obtained.

[0029] Furthermore, in the multi-channel distribution module, push results with a response time of seconds are obtained, including:

[0030] Obtain real-time data from trusted early warning information sources, use pre-established verification models to determine data credibility, and obtain filtered early warning information;

[0031] Extract key features from the filtered warning information, use a layered push strategy to generate targeted push content, determine the match between the content and the user group, and when the match is higher than the preset threshold, send the push content to the target user through the SMS distribution module and obtain the SMS sending status;

[0032] According to the SMS sending status, the mobile application distribution module is used to push content to users who have not successfully received SMS messages, and the mobile application push results are obtained. Then, the push content is distributed to users in remote areas through the satellite communication module to determine the transmission status of satellite communication;

[0033] The response time is extracted from the push results of SMS, mobile applications and satellite communications, and a multi-channel collaborative processing mechanism is used to optimize the push process to obtain push results with response times in seconds. When the response time does not reach the second level, the layered push strategy is adjusted and targeted push content is regenerated to obtain optimized push results.

[0034] Furthermore, in the feedback-driven optimization module, the optimized push strategy is output, specifically including:

[0035] Obtain user feedback data on pushed content, preset a feedback collection interface, extract feedback data from user interaction records, obtain a user feedback dataset, then process the user feedback dataset through an analysis algorithm, use a collaborative filtering algorithm to calculate user preference weights, and determine the feedback data feature vector;

[0036] When the preference weight of the feedback data feature vector exceeds the preset threshold, the dynamic priority algorithm is used to adjust the sorting parameters of the pushed content to obtain the optimized content sorting parameters. Then, based on the optimized content sorting parameters, the content distribution model is used to generate a push strategy to obtain a personalized push strategy.

[0037] Update the content and priority of warning information through personalized push strategies, distribute warning information through information distribution interfaces, obtain updated warning information streams, and then analyze the interaction data through data processing modules based on real-time user interaction data to obtain user interaction trends;

[0038] According to the user interaction trend, the input parameters of the collaborative filtering algorithm are adjusted to generate new user preference weights and obtain the updated feedback data feature vector.

[0039] Furthermore, in the performance monitoring adaptive module, push results covering complex terrain are obtained, including:

[0040] Obtain network status data from high-priority areas and use a preset parsing algorithm to extract signal strength, delay, and bandwidth characteristics to obtain regional network analysis results. Based on the regional network analysis results, an adaptive communication protocol is used to adjust transmission parameters based on signal strength and delay characteristics to determine the optimized communication protocol configuration.

[0041] Based on the optimized communication protocol configuration, the current status of multi-channel distribution is obtained. If the delay exceeds the preset threshold, the distribution weight is adjusted to obtain a collaborative distribution plan. The geographical data of complex terrain is then analyzed, and a path planning algorithm is used to generate a push path covering the complex terrain to obtain a push path set.

[0042] Based on the push path set, a load balancing algorithm is used to adjust the push task allocation of each channel and determine the multi-channel collaborative push task allocation plan;

[0043] By pushing the task allocation plan, a push result covering complex terrain is generated, and it is determined whether the push coverage rate reaches the preset threshold to obtain the final push result. Then, based on the final push result, the network status data of the high-priority area is updated, and feedback data for push strategy optimization is generated to obtain the push strategy update plan.

[0044] Furthermore, the adjusted multi-source data collection frequency is output, thereby updating the generation process of the initial multi-source data set, which specifically includes:

[0045] The push arrival rate and response time are obtained from the push results in complex terrain. Performance indicators are obtained through real-time monitoring algorithm analysis. When the performance indicators fall below the preset threshold, the adjustment optimization method is used to update the collection frequency and obtain the adjusted frequency parameters.

[0046] According to the adjusted frequency parameters, updated collected data is obtained from multiple data sources to generate a new data stream, which is then processed through the data generation process to obtain an optimized initial data set;

[0047] When the optimized initial dataset does not match the complex terrain features, the monitoring algorithm is used to reanalyze the push results, obtain updated performance indicators, and then generate the final initial dataset;

[0048] The final initial data set is verified through real-time monitoring algorithms to obtain push performance indicators that meet the needs of complex terrain.

[0049] This application provides a priority-based accurate push method for implementing accurate push of warning information in a geological disaster warning information rapid release system, including the following steps:

[0050] Use IoT technology to acquire multi-source data from sensors, satellite remote sensing, and historical databases, and use data standardization protocols to integrate heterogeneous data to obtain a multi-source data set in a unified format.

[0051] Based on multi-source data sets, a high-precision prediction model is constructed using a deep learning algorithm. This model integrates convolutional neural networks and long-short-term memory networks to output geological disaster probability and risk level assessment results. When the risk level assessment result exceeds the preset threshold, the warning information is stored in a distributed ledger through blockchain technology, resulting in an unalterable and traceable warning information record.

[0052] Obtain warning information records from the distributed ledger, use hash verification algorithms to verify data integrity, determine the credibility of the warning information, and output credible warning information;

[0053] Based on credible warning information, a dynamic priority algorithm is used to calculate the push priority. The layered push strategy is determined by combining the risk level assessment results with the network status of the receiving terminal.

[0054] Through multi-channel collaborative technology, targeted push content is generated from credible warning information and layered push strategies, and distributed through SMS, mobile applications, and satellite communications, achieving push results with a response time of seconds.

[0055] Based on the push results, obtain user feedback data, use the feedback analysis algorithm to adjust the parameters of the dynamic priority algorithm, output the optimized push strategy, and update the subsequent warning information distribution process;

[0056] The network status data of high-priority areas is extracted from the optimized push strategy, and the multi-channel collaborative distribution method is adjusted using an adaptive communication protocol to obtain push results covering complex terrain. The real-time monitoring algorithm is then used to analyze the push arrival rate and response time, output the adjusted multi-source data collection frequency, and update the initial multi-source data set generation process.

[0057] The beneficial effects of the present invention are:

[0058] The present invention achieves deep mining and fusion analysis of multi-source heterogeneous data by constructing a high-precision geological disaster prediction model. It uses deep learning algorithms, especially the combination of convolutional neural networks and long-short-term memory networks, to effectively process and analyze large amounts of data from sensors, satellite remote sensing, and historical databases. This not only improves the accuracy of geological disaster probability prediction, but also enhances the reliability of risk level assessment by integrating spatial and temporal features, significantly improving the predictive ability of the early warning system and the scientific basis of early warning information, providing more accurate data support for disaster prevention and mitigation.

[0059] The use of blockchain technology and hash check algorithm ensures the immutability and traceability of early warning information. By storing early warning information in a distributed ledger and using smart contracts to automatically trigger the audit process, the system generates a traceable audit log, which not only improves the transparency and trust of information, but also enables the system to adapt to the ever-changing environment and needs by dynamically adjusting the risk threshold, thereby enhancing the credibility of early warning information and the adaptability of the system, providing users with a safer and more reliable early warning service.

[0060] Through dynamic priority strategies and multi-channel distribution modules, accurate and efficient push of early warning information is achieved. Based on the risk level assessment results and the network status of the receiving terminal, a dynamic priority algorithm is used to calculate the push priority, and combined with multi-channel collaborative technologies such as SMS, mobile applications and satellite communications, it ensures that early warning information can reach target users quickly and accurately. In addition, by collecting user feedback and adjusting the push strategy using feedback analysis algorithms, the system can continuously optimize the push effect and improve user response rate and satisfaction. It not only achieves push results with a response in seconds, but also ensures the effective coverage of early warning information under complex terrain conditions through adaptive communication protocols and real-time monitoring algorithms, thereby significantly improving the emergency response capability and user interaction experience of the early warning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0062] Figure 1 A schematic diagram of the structure of the geological disaster warning information rapid release system provided in Example 1 of this application;

[0063] Figure 2 A schematic diagram of a process for obtaining a push result with a second-level response in the geological disaster warning information rapid release system provided in Example 1 of the present application;

[0064] Figure 3 A schematic diagram of the process of obtaining push results covering complex terrain in the rapid release system for geological disaster warning information provided in Example 1 of this application;

[0065] Figure 4 A flowchart of the priority-based precise push method provided in Example 2 of the present application. DETAILED DESCRIPTION

[0066] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and systems consistent with certain aspects of the present application, as detailed in the appended claims.

[0067] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0068] The following describes in detail the specific implementation methods, features and effects of the present invention in conjunction with the accompanying drawings and preferred embodiments.

[0069] Example 1

[0070] See also Figure 1-Figure 3 This embodiment provides a rapid release system for geological disaster warning information, including:

[0071] The data acquisition and integration module uses IoT technology to collect multi-source data from sensors, satellite remote sensing, and historical databases. It then processes heterogeneous data through data standardization protocols and integrates them into a unified data set, ensuring data consistency and availability, and providing a reliable foundation for subsequent analysis and prediction.

[0072] Specifically, IoT sensors such as displacement sensors, rain gauges, and soil moisture sensors are deployed at geological hazard risk points to collect data in real time. Satellite remote sensing technology is used to periodically obtain macro data such as surface deformation and vegetation coverage, and information such as geological structure and disaster occurrence records are retrieved from historical databases. For these heterogeneous data with a wide range of sources and different formats, a unified data standardization protocol is adopted to establish a data processing framework that includes modules such as data format conversion, metadata specifications, and data quality verification. Noise and outliers are removed through data cleaning, and data mapping and coding technologies are used to convert data in different formats into a unified standard. Finally, they are integrated into a consistent and usable data set to lay a solid data foundation for geological hazard analysis and prediction.

[0073] The model building and evaluation module uses the collected multi-source data sets and a deep learning algorithm to build a high-precision geological disaster prediction model. By integrating convolutional neural networks and long-short-term memory networks, the model improves the accuracy of predictions and outputs assessment results including the probability of geological disasters and risk levels, providing a scientific basis for the generation of early warning information.

[0074] Furthermore, in the model building and assessment module, the output includes the probability of geological disasters and risk level assessment results, specifically including:

[0075] Acquire geological data from multiple source datasets, integrate them into a unified dataset using data fusion technology to obtain a fused dataset, and then use feature extraction to separate spatial features from temporal features to obtain spatial feature sets and temporal feature sets;

[0076] When the spatial feature set contains high-dimensional image data, a convolutional neural network is used to process the spatial data to obtain a spatial feature vector. When there is no high-dimensional image data, the original spatial feature set is directly output.

[0077] Based on the time series feature set, a long short-term memory network is used to analyze the time series data to obtain the time series feature vector. Then, a deep learning model is constructed by fusing the spatial feature vector with the time series feature vector. The model training process is executed to obtain a trained deep learning model.

[0078] The trained deep learning model is used to predict the probability of geological disasters and obtain the probability value. Then, based on the probability value, the preset threshold is used to evaluate the risk level and determine the risk level.

[0079] Specifically, in the model construction and evaluation module, multi-source geological data are integrated, and spatial and temporal features are separated through feature extraction. For spatial features containing high-dimensional images, CNN is used to extract feature vectors; for temporal features, LSTM is used for analysis. After fusing these two types of feature vectors, a deep learning model is constructed and trained to predict the probability of geological disasters and assess the risk level. This process not only improves the accuracy of the prediction, but also provides a scientific basis for the rapid generation of reliable early warning information, thereby enabling more effective early warning and prevention of geological disasters.

[0080] The information storage and verification module determines whether the risk level assessment result exceeds the preset threshold. If it does, the warning information is stored in the distributed ledger through blockchain technology to obtain an unalterable and traceable warning information record. The hash check algorithm is then used to verify the integrity of the data, determine the credibility of the warning information, and output credible warning information;

[0081] Furthermore, in the information storage and verification module, a tamper-proof and traceable warning information record is obtained, specifically including:

[0082] When the risk assessment result exceeds the preset threshold, the warning information is encrypted using a preset encryption algorithm to generate encrypted warning data;

[0083] The encrypted warning data is uploaded to the distributed ledger through blockchain technology to generate an unalterable on-chain record. The hash value of the on-chain record is then obtained from the distributed ledger, and the integrity of the record is verified using a consistency check algorithm to ensure that the record has not been tampered with.

[0084] When the consistency check passes, the smart contract automatically triggers the audit process recorded on the chain, generates a traceable audit log, and then extracts historical warning data from the distributed ledger. The time series analysis algorithm is used to determine the trend changes of the warning information;

[0085] Based on the trend change results, the parameters of the preset threshold are updated to generate dynamically adjusted threshold data, which are then stored in the distributed ledger to generate new on-chain threshold records.

[0086] Furthermore, reliable warning information is output, including:

[0087] Obtain warning information records from the distributed ledger, parse the record content using a preset extraction protocol to obtain structured warning data, and then use a hash checksum algorithm to verify the structured warning data, generate a checksum result, and determine data integrity;

[0088] When the verification result shows that the data is complete, the credibility of the warning data is evaluated through the preset trust scoring model to obtain a credibility score. The credibility score is compared with the preset threshold. When the score is higher than the threshold, the warning data is marked as credible information.

[0089] Format the trusted information through the information processing pipeline to generate standardized trusted warning information, and then use encryption signature technology to sign the standardized trusted warning information to obtain encrypted and protected output data;

[0090] The encrypted and protected output data is transmitted to the target system through the preset output interface to complete the output of reliable warning information.

[0091] Specifically, through the information storage and verification module, the system achieves automated processing and verification of geological hazard risk assessment results. When the risk exceeds the preset threshold, the warning information is first encrypted and stored in the blockchain's distributed ledger, ensuring the security and immutability of the information. Subsequently, hash checksums and consistency algorithms are used to verify data integrity, and audits are automatically performed through smart contracts to generate traceable audit logs. In addition, the system can dynamically adjust risk thresholds based on trends in historical warning data, improving the adaptability of the warning system. Finally, the verified and formatted trusted warning information is encrypted and signed and securely transmitted to the target system, ensuring the accuracy, reliability, and timeliness of the warning information, providing solid information support for disaster prevention and emergency response.

[0092] The dynamic priority strategy module uses a dynamic priority algorithm to calculate push priorities based on credible warning information. It then determines a hierarchical push strategy based on risk level assessment results and the network status of the receiving terminal to ensure that warning information is pushed first based on its urgency and importance.

[0093] Furthermore, in the dynamic priority strategy module, a hierarchical push strategy is determined, specifically including:

[0094] Obtain credible warning information, extract key fields from the information source, and use preprocessing technology to clean the fields to obtain structured warning data;

[0095] A dynamic priority algorithm is used to calculate the initial push priority based on the key fields of the structured warning data to obtain a priority score. When the priority score is higher than the preset threshold, the push priority is adjusted based on the risk level assessment results to obtain an optimized priority sequence.

[0096] Based on the optimization priority sequence, network status data is obtained from the receiving terminal to determine the terminal's bandwidth and latency, and determine the terminal adaptation parameters. Then, a layered push technology is used to map the optimization priority sequence to the push level to obtain a layered push solution.

[0097] Through a layered push solution, a push strategy is generated, and message queue technology is used to schedule push tasks and determine the final push order. When changes in network conditions are detected during the push process, the terminal network data is re-acquired, the terminal adaptation parameters are updated, and the adjusted push strategy is obtained.

[0098] Specifically, the dynamic priority strategy module not only determines the hierarchical push strategy in the initial stage, but also continuously monitors the network status of the receiving terminal during the push process. Once the network bandwidth or delay changes, the module will immediately re-evaluate and update the terminal adaptation parameters to ensure that the warning information can be sent through the optimal channel and timing; in addition, the module works closely with the multi-channel distribution module to achieve rapid and accurate distribution of warning information through different channels such as SMS, mobile applications, and satellite communications based on the optimized push strategy. This dynamic adjustment and multi-channel collaboration capability enables the warning information release system to flexibly adapt to different environments and conditions, maximizing the coverage and arrival rate of warning information.

[0099] The multi-channel distribution module generates targeted push content based on credible warning information and layered push strategies, and distributes it through SMS, mobile applications and satellite communications. This multi-channel collaborative technology ensures that warning information can reach target users quickly and accurately, and obtain push results with a response within seconds.

[0100] Furthermore, in the multi-channel distribution module, push results with a response time of seconds are obtained, including:

[0101] S11. Obtain real-time data from a trusted early warning information source, use a pre-established verification model to determine the data's credibility, and obtain filtered early warning information;

[0102] S12. Extract key features from the screened warning information, use a layered push strategy to generate targeted push content, determine the match between the content and the user group, and when the match is higher than a preset threshold, send the push content to the target user through the SMS distribution module and obtain the SMS sending status;

[0103] S13. Based on the SMS sending status, use the mobile application distribution module to push content to users who have not successfully received the SMS, obtain the mobile application push result, and then distribute the push content to users in remote areas through the satellite communication module to determine the transmission status of the satellite communication;

[0104] S14. Extract the response time from the push results of SMS, mobile applications and satellite communications, and use a multi-channel collaborative processing mechanism to optimize the push process to obtain push results with a response time of seconds. When the response time does not reach the second level, adjust the layered push strategy, regenerate targeted push content, and obtain optimized push results.

[0105] Specifically, obtaining real-time data through trusted early warning information sources requires ensuring the reliability of the data source. In one possible implementation, trusted early warning information sources usually include weather warnings issued by meteorological departments, earthquake data from earthquake monitoring stations, or public safety notices from government departments. For example, the meteorological department collects rainfall, wind speed and other data in real time through sensor networks to generate heavy rain warnings. These data must be encrypted and transmitted to the processing center to avoid tampering. The verification model can judge the degree of abnormality of new data based on the characteristic distribution of historical data. For example, if the rainfall data in a certain area suddenly exceeds the historical average by 5 times, the model will mark it as an abnormality and eliminate it, thus obtaining reliable early warning information.

[0106] When extracting key features from the screened warning information, attention should be paid to the urgency and scope of impact of the information. Specifically, warning types such as heavy rain and earthquakes, and characteristic values such as an impact radius of 50 kilometers and a duration of 12 hours can be extracted. For example, the key features of a heavy rain warning include a rainfall intensity of 100 mm per hour and an affected area in the southern part of a city. The layered push strategy generates targeted push content based on the features, such as risk avoidance tips for residents and road condition warnings for transportation departments. The matching evaluation can be based on user group characteristics, such as geographic location and occupation. For example, if a user is located in the warning area and is a logistics driver with a matching degree higher than 90%, priority push is required. When pushing content through the SMS distribution module, a high arrival rate must be ensured.

[0107] In one embodiment, the SMS module can cooperate with the operator to send warnings using high-priority channels. For example, a rainstorm warning SMS is sent to 100,000 users within 1 minute, and the sending status shows 95% success. The mobile application distribution module pushes SMS to users who have not received the SMS and needs to be adapted to different devices. For example, the application can remind users to view the warning details through push notifications, and the push results show that 80% of users open the notification within 5 minutes. The satellite communication module is suitable for remote areas and needs to ensure transmission stability under low bandwidth. For example, a user in a mountainous area receives the warning text via satellite, and the transmission status shows a delay of less than 10 seconds. , extracting the response time from the multi-channel push results, it is necessary to pay attention to the efficiency of each channel. For example, the SMS response time is 1 second, the application push is 5 seconds, and the satellite communication is 8 seconds. The multi-channel collaborative processing mechanism can dynamically adjust the push priority. For example, if the SMS channel is congested, it will switch to the application push first to ensure a response in seconds. If the response time does not meet the standard, it is necessary to adjust the layered push strategy, such as increasing the push frequency or optimizing the content simplicity. For example, regenerate a shorter warning text, and the push results show that the response time is shortened to 2 seconds. This method significantly improves the push efficiency and coverage through multi-channel collaboration and dynamic adjustment.

[0108] The feedback-driven optimization module collects user feedback on push results, uses feedback analysis algorithms to adjust the parameters of the dynamic priority algorithm, outputs an optimized push strategy, and updates the subsequent warning information distribution process. This not only improves the relevance and effectiveness of warning information push, but also enhances the system's adaptability and user satisfaction. The optimized push strategy will be used to guide the subsequent warning information distribution process, ensuring that the information can reach the target users more accurately.

[0109] Furthermore, in the feedback-driven optimization module, the optimized push strategy is output, specifically including:

[0110] Obtain user feedback data on pushed content, preset a feedback collection interface, extract feedback data from user interaction records, obtain a user feedback dataset, then process the user feedback dataset through an analysis algorithm, use a collaborative filtering algorithm to calculate user preference weights, and determine the feedback data feature vector;

[0111] When the preference weight of the feedback data feature vector exceeds the preset threshold, the dynamic priority algorithm is used to adjust the sorting parameters of the pushed content to obtain the optimized content sorting parameters. Then, based on the optimized content sorting parameters, the content distribution model is used to generate a push strategy to obtain a personalized push strategy.

[0112] Update the content and priority of warning information through personalized push strategies, distribute warning information through information distribution interfaces, obtain updated warning information streams, and then analyze the interaction data through data processing modules based on real-time user interaction data to obtain user interaction trends;

[0113] According to the user interaction trend, the input parameters of the collaborative filtering algorithm are adjusted to generate new user preference weights and obtain the updated feedback data feature vector.

[0114] Specifically, the feedback-driven optimization module significantly improves the personalization and accuracy of warning information push. By analyzing user feedback and interaction trends, the system can dynamically adjust the push strategy to ensure that users receive the most relevant and interesting warning information. This optimization based on user preferences not only improves the relevance of information, but also enhances user participation and satisfaction, thereby improving the overall effectiveness of the warning system and user response rate.

[0115] The performance monitoring adaptive module extracts network status data for high-priority areas from the optimized push strategy and uses an adaptive communication protocol to adjust the multi-channel collaborative distribution method to obtain push results covering complex terrain. It then uses a real-time monitoring algorithm to analyze push arrival rates and response times, outputting an adjusted multi-source data collection frequency, and subsequently updating the initial multi-source dataset generation process. This ensures that the system can self-adjust and optimize based on actual push results and network conditions, thereby improving the adaptability and reliability of the early warning information release system.

[0116] Furthermore, in the performance monitoring adaptive module, push results covering complex terrain are obtained, including:

[0117] S21. Obtain network status data from the high-priority area, extract signal strength, delay, and bandwidth characteristics using a preset parsing algorithm, and obtain regional network analysis results. Based on the regional network analysis results, an adaptive communication protocol is used to adjust transmission parameters according to the signal strength and delay characteristics to determine an optimized communication protocol configuration.

[0118] S22. Based on the optimized communication protocol configuration, the current state of multi-channel distribution is obtained. If the delay exceeds a preset threshold, the distribution weight is adjusted to obtain a collaborative distribution plan. The geographic data of the complex terrain is then analyzed, and a path planning algorithm is used to generate a push path covering the complex terrain to obtain a push path set.

[0119] S23. Based on the push path set, a load balancing algorithm is used to adjust the push task allocation of each channel and determine a multi-channel collaborative push task allocation plan;

[0120] S24. Generate push results covering complex terrain by pushing task allocation plans, determine whether the push coverage rate reaches the preset threshold, and obtain the final push results. Then, based on the final push results, update the network status data of high-priority areas, generate feedback data for push strategy optimization, and obtain a push strategy update plan.

[0121] Specifically, in the process of obtaining network status data from high-priority areas, for example, signal strength, delay and bandwidth data can be collected in real time through sensors deployed at base stations. For example, in 5G base stations in the core area of a city, sensors record signal strength (such as -70dBm), delay (such as 10ms) and bandwidth (such as 500Mbps) once per second to form a time series data set. This method can accurately reflect the network status and provide a reliable basis for subsequent analysis.

[0122] In one possible implementation, when a preset parsing algorithm is used to extract signal strength, delay, and bandwidth features, feature extraction technology can be used to normalize the data. Specifically, the signal strength can be converted to a range of 0 to 1, the delay is normalized in milliseconds, and the bandwidth is proportionally mapped to a preset interval. For example, a signal strength of -70dBm may be normalized to 0.8, and a delay of 10ms may be normalized to 0.2. This method facilitates the unification of data of different dimensions and improves algorithm processing efficiency.

[0123] It should be noted that when adjusting transmission parameters based on regional network analysis results, the adaptive communication protocol will dynamically select the transmission mode according to signal strength and delay. For example, when the signal strength is lower than -80dBm and the delay is higher than 15ms, the protocol may switch to low-bandwidth mode to prioritize data integrity; when the signal strength is higher than -60dBm, the high-throughput mode is enabled. This adaptive adjustment can optimize communication efficiency.

[0124] Specifically, when obtaining the multi-channel distribution status and adjusting the distribution weight, dynamic allocation can be performed by monitoring the real-time delay data of each channel. For example, if the Wi-Fi channel delay is 20ms and the 5G channel delay is 5ms, more push tasks will be allocated to the 5G channel, and the weight may be increased from 0.5 to 0.7. This weight adjustment can effectively reduce the overall delay.

[0125] In one embodiment, when analyzing geographic data of complex terrain and generating push paths, the path planning algorithm may combine elevation maps and obstacle distribution. For example, in mountainous scenarios, the algorithm prioritizes valley paths with better signal coverage to avoid signal blind spots in high-altitude areas. The push path set may include three main paths, with coverage rates of 90%, 85%, and 80% for each path, respectively. This approach can ensure push coverage of complex terrain.

[0126] Preferably, when using a load balancing algorithm to adjust the push task distribution, tasks can be dynamically allocated according to the processing capacity of each channel. For example, if the processing capacity of the 5G channel is 1000 tasks / second and that of Wi-Fi is 500 tasks / second, tasks are allocated at a ratio of 2:1. This balanced distribution can make full use of resources and improve push efficiency. For example, when judging whether the push coverage rate reaches a preset threshold, the threshold can be set to 95%. If the current coverage rate is 90%, the distribution weight is recalculated through iterative optimization (such as increasing the 5G channel weight to 0.8) until the coverage rate meets the standard. This iterative method can continuously improve the push effect.

[0127] It is understandable that when updating the network status data of high-priority areas and generating a push policy update plan, the strategy can be adjusted through real-time feedback data. For example, if the feedback shows that the delay in a certain area has increased by 10%, the base station resource allocation in that area will be increased first. This closed-loop optimization can quickly respond to network changes and maintain the timeliness of the push strategy.

[0128] Furthermore, the adjusted multi-source data collection frequency is output, thereby updating the generation process of the initial multi-source data set, which specifically includes:

[0129] The push arrival rate and response time are obtained from the push results in complex terrain. Performance indicators are obtained through real-time monitoring algorithm analysis. When the performance indicators fall below the preset threshold, the adjustment optimization method is used to update the collection frequency and obtain the adjusted frequency parameters.

[0130] According to the adjusted frequency parameters, updated collected data is obtained from multiple data sources to generate a new data stream, which is then processed through the data generation process to obtain an optimized initial data set;

[0131] When the optimized initial dataset does not match the complex terrain features, the monitoring algorithm is used to reanalyze the push results, obtain updated performance indicators, and then generate the final initial dataset;

[0132] The final initial data set is verified through real-time monitoring algorithms to obtain push performance indicators that meet the needs of complex terrain.

[0133] Specifically, by dynamically adjusting the frequency of multi-source data collection, the system can optimize the initial data set to better adapt to the needs of complex terrain. This not only improves the efficiency and accuracy of warning information push, but also ensures a high degree of match between the data set and the actual terrain characteristics, thereby enhancing the reliability and effectiveness of the entire geological disaster warning system.

[0134] Example 2

[0135] See also Figure 4 This embodiment provides a priority-based accurate push method for realizing accurate push of warning information in a geological disaster warning information rapid release system, comprising the following steps:

[0136] S1. Use IoT technology to acquire multi-source data from sensors, satellite remote sensing, and historical databases, and use data standardization protocols to integrate heterogeneous data to obtain a multi-source data set in a unified format.

[0137] S2. Based on multi-source data sets, a high-precision prediction model is constructed using a deep learning algorithm. This model integrates convolutional neural networks and long-short-term memory networks to output geological hazard probability and risk level assessment results. When the risk level assessment result exceeds the preset threshold, the warning information is stored in a distributed ledger through blockchain technology, resulting in an unalterable and traceable warning information record.

[0138] S3. Obtain warning information records from the distributed ledger, use a hash algorithm to verify data integrity, determine the credibility of the warning information, and output credible warning information;

[0139] S4. Based on the credible warning information, a dynamic priority algorithm is used to calculate the push priority. The tiered push strategy is determined based on the risk level assessment results and the network status of the receiving terminal.

[0140] S5. Through multi-channel collaborative technology, targeted push content is generated from credible warning information and layered push strategies. SMS, mobile applications, and satellite communications are used for distribution, achieving push results with a response time of seconds.

[0141] S6. Based on the push results, obtain user feedback data, use the feedback analysis algorithm to adjust the parameters of the dynamic priority algorithm, output the optimized push strategy, and update the subsequent warning information distribution process;

[0142] S7. Extract network status data for high-priority areas from the optimized push strategy, use an adaptive communication protocol to adjust the multi-channel collaborative distribution method, obtain push results covering complex terrain, and then use a real-time monitoring algorithm to analyze the push arrival rate and response time, output the adjusted multi-source data collection frequency, and update the initial multi-source data set generation process.

[0143] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A geological disaster early warning information rapid release system, characterized by: include: The model building and evaluation module uses the collected multi-source data sets and a deep learning algorithm to build a high-precision geological disaster prediction model. By integrating convolutional neural networks and long-short-term memory networks, it outputs assessment results including the probability of geological disasters and risk levels. The information storage and verification module determines whether the risk level assessment result exceeds the preset threshold. If it does, the warning information is stored in the distributed ledger through blockchain technology to obtain an unalterable and traceable warning information record. The hash check algorithm is then used to verify the integrity of the data, determine the credibility of the warning information, and output the warning information; The dynamic priority strategy module uses a dynamic priority algorithm to calculate push priorities based on reliable early warning information, and determines a hierarchical push strategy based on risk level assessment results and the network status of the receiving terminal; The multi-channel distribution module generates targeted push content based on credible warning information and layered push strategies, and distributes it via SMS, mobile applications, and satellite communications, achieving push results with a response time of seconds. The feedback-driven optimization module collects user feedback on push results, uses feedback analysis algorithms to adjust the parameters of the dynamic priority algorithm, outputs the optimized push strategy, and updates the distribution process of subsequent warning information; The performance monitoring adaptive module extracts network status data from high-priority areas from the optimized push strategy and uses an adaptive communication protocol to adjust the multi-channel collaborative distribution method to obtain push results covering complex terrain. It then uses a real-time monitoring algorithm to analyze the push arrival rate and response time, outputs the adjusted multi-source data collection frequency, and updates the initial multi-source data set generation process.

2. The geological disaster early warning information rapid release system according to claim 1 is characterized by: It also includes a data acquisition and integration module, which collects multi-source data from sensors, satellite remote sensing and historical databases through Internet of Things technology, processes heterogeneous data through data standardization protocols, and integrates them into a data set in a unified format.

3. The geological disaster early warning information rapid release system according to claim 1 is characterized by: In the information storage and verification module, tamper-proof and traceable early warning information records are obtained, including: When the risk assessment result exceeds the preset threshold, the warning information is encrypted using a preset encryption algorithm to generate encrypted warning data; The encrypted warning data is uploaded to the distributed ledger through blockchain technology to generate an unalterable on-chain record. The hash value of the on-chain record is then obtained from the distributed ledger, and the integrity of the record is verified using a consistency check algorithm to ensure that the record has not been tampered with. When the consistency check passes, the smart contract automatically triggers the audit process recorded on the chain, generates a traceable audit log, and then extracts historical warning data from the distributed ledger. The time series analysis algorithm is used to determine the trend changes of the warning information; Based on the trend change results, the parameters of the preset threshold are updated to generate dynamically adjusted threshold data, which are then stored in the distributed ledger to generate new on-chain threshold records.

4. The geological disaster early warning information rapid release system according to claim 1 is characterized in that: Output credible warning information, including: Obtain warning information records from the distributed ledger, parse the record content using a preset extraction protocol to obtain structured warning data, and then use a hash checksum algorithm to verify the structured warning data, generate a checksum result, and determine data integrity; When the verification result shows that the data is complete, the credibility of the warning data is evaluated through the preset trust scoring model to obtain a credibility score. The credibility score is compared with the preset threshold. When the score is higher than the threshold, the warning data is marked as credible information. Format the trusted information through the information processing pipeline to generate standardized trusted warning information, and then use encryption signature technology to sign the standardized trusted warning information to obtain encrypted and protected output data; The encrypted and protected output data is transmitted to the target system through the preset output interface to complete the output of reliable warning information.

5. The geological disaster early warning information rapid release system according to claim 1 is characterized in that: In the dynamic priority strategy module, determine the hierarchical push strategy, including: Obtain credible warning information, extract key fields from the information source, and use preprocessing technology to clean the fields to obtain structured warning data; A dynamic priority algorithm is used to calculate the initial push priority based on the key fields of the structured warning data to obtain a priority score. When the priority score is higher than the preset threshold, the push priority is adjusted based on the risk level assessment results to obtain an optimized priority sequence. Based on the optimization priority sequence, network status data is obtained from the receiving terminal to determine the terminal's bandwidth and latency, and determine the terminal adaptation parameters. Then, a layered push technology is used to map the optimization priority sequence to the push level to obtain a layered push solution. Through a layered push solution, a push strategy is generated, and message queue technology is used to schedule push tasks and determine the final push order. When changes in network conditions are detected during the push process, the terminal network data is re-acquired, the terminal adaptation parameters are updated, and the adjusted push strategy is obtained.

6. The geological disaster early warning information rapid release system according to claim 1 is characterized in that: In the multi-channel distribution module, push results are responded to within seconds, including: Obtain real-time data from trusted early warning information sources, use pre-established verification models to determine data credibility, and obtain filtered early warning information; Extract key features from the filtered warning information, use a layered push strategy to generate targeted push content, determine the match between the content and the user group, and when the match is higher than the preset threshold, send the push content to the target user through the SMS distribution module and obtain the SMS sending status; According to the SMS sending status, the mobile application distribution module is used to push content to users who have not successfully received SMS messages, and the mobile application push results are obtained. Then, the push content is distributed to users in remote areas through the satellite communication module to determine the transmission status of satellite communication; The response time is extracted from the push results of SMS, mobile applications and satellite communications, and a multi-channel collaborative processing mechanism is used to optimize the push process to obtain push results with response times in seconds. When the response time does not reach the second level, the layered push strategy is adjusted and targeted push content is regenerated to obtain optimized push results.

7. The geological disaster early warning information rapid release system according to claim 1 is characterized in that: In the feedback-driven optimization module, the optimized push strategy is output, including: Obtain user feedback data on pushed content, preset a feedback collection interface, extract feedback data from user interaction records, obtain a user feedback dataset, then process the user feedback dataset through an analysis algorithm, use a collaborative filtering algorithm to calculate user preference weights, and determine the feedback data feature vector; When the preference weight of the feedback data feature vector exceeds the preset threshold, the dynamic priority algorithm is used to adjust the sorting parameters of the pushed content to obtain the optimized content sorting parameters. Then, based on the optimized content sorting parameters, the content distribution model is used to generate a push strategy to obtain a personalized push strategy. Update the content and priority of warning information through personalized push strategies, distribute warning information through information distribution interfaces, obtain updated warning information streams, and then analyze the interaction data through data processing modules based on real-time user interaction data to obtain user interaction trends; According to the user interaction trend, the input parameters of the collaborative filtering algorithm are adjusted to generate new user preference weights and obtain the updated feedback data feature vector.

8. The geological disaster early warning information rapid release system according to claim 1 is characterized by: In the performance monitoring adaptive module, push results covering complex terrain are obtained, including: Obtain network status data from high-priority areas and use a preset parsing algorithm to extract signal strength, delay, and bandwidth characteristics to obtain regional network analysis results. Based on the regional network analysis results, an adaptive communication protocol is used to adjust transmission parameters based on signal strength and delay characteristics to determine the optimized communication protocol configuration. Based on the optimized communication protocol configuration, the current status of multi-channel distribution is obtained. If the delay exceeds the preset threshold, the distribution weight is adjusted to obtain a collaborative distribution plan. The geographical data of complex terrain is then analyzed, and a path planning algorithm is used to generate a push path covering the complex terrain to obtain a push path set. Based on the push path set, a load balancing algorithm is used to adjust the push task allocation of each channel and determine the multi-channel collaborative push task allocation plan; By pushing the task allocation plan, a push result covering complex terrain is generated, and it is determined whether the push coverage rate reaches the preset threshold to obtain the final push result. Then, based on the final push result, the network status data of the high-priority area is updated, and feedback data for push strategy optimization is generated to obtain the push strategy update plan.

9. The geological disaster early warning information rapid release system according to claim 1 is characterized in that: Output the adjusted multi-source data collection frequency, and then update the generation process of the initial multi-source dataset, specifically including: The push arrival rate and response time are obtained from the push results in complex terrain. Performance indicators are obtained through real-time monitoring algorithm analysis. When the performance indicators fall below the preset threshold, the adjustment optimization method is used to update the collection frequency and obtain the adjusted frequency parameters. According to the adjusted frequency parameters, updated collected data is obtained from multiple data sources to generate a new data stream, which is then processed through the data generation process to obtain an optimized initial data set; When the optimized initial dataset does not match the complex terrain features, the monitoring algorithm is used to reanalyze the push results, obtain updated performance indicators, and then generate the final initial dataset; The final initial data set is verified through real-time monitoring algorithms to obtain push performance indicators that meet the needs of complex terrain.

10. A priority-based precise push method, applied to the geological disaster early warning information rapid release system according to any one of claims 1 to 9, characterized in that: It is used to realize the accurate push of early warning information in the geological disaster early warning information rapid release system, including the following steps: Use IoT technology to acquire multi-source data from sensors, satellite remote sensing, and historical databases, and use data standardization protocols to integrate heterogeneous data to obtain a multi-source data set in a unified format. Based on multi-source data sets, a high-precision prediction model is constructed using a deep learning algorithm. This model integrates convolutional neural networks and long-short-term memory networks to output geological disaster probability and risk level assessment results. When the risk level assessment result exceeds the preset threshold, the warning information is stored in a distributed ledger through blockchain technology, resulting in an unalterable and traceable warning information record. Obtain warning information records from the distributed ledger, use hash verification algorithms to verify data integrity, determine the credibility of the warning information, and output credible warning information; Based on credible warning information, a dynamic priority algorithm is used to calculate the push priority. The layered push strategy is determined by combining the risk level assessment results with the network status of the receiving terminal. Through multi-channel collaborative technology, targeted push content is generated from credible warning information and layered push strategies, and distributed through SMS, mobile applications, and satellite communications, achieving push results with a response time of seconds. Based on the push results, obtain user feedback data, use the feedback analysis algorithm to adjust the parameters of the dynamic priority algorithm, output the optimized push strategy, and update the subsequent warning information distribution process; The network status data of high-priority areas is extracted from the optimized push strategy, and the multi-channel collaborative distribution method is adjusted using an adaptive communication protocol to obtain push results covering complex terrain. The real-time monitoring algorithm is then used to analyze the push arrival rate and response time, output the adjusted multi-source data collection frequency, and update the initial multi-source data set generation process.

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