145 results about "Real time analytics" patented technology

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention relates to a data security processing method and system based on distributed storage, and relates to the technical field of computer information processing. The method comprises the following steps: cutting data into encryption fragments with a configurable number by adopting a dynamic fragmentation strategy, and generating a physically isolated dynamic check block in combination with a timestamp to realize tampering prevention; a dynamic threshold value is dynamically calculated based on the data sensitivity index and the node load, and the node is optimized through the reliability score for cooperative decryption; a database table is divided into independent marshalling storage according to main foreign key association, foreign key fields are encrypted by adopting cross keys, and cross-marshalling access needs to meet a multi-key threshold condition; an intelligent threat perception engine is constructed, access logs and threat intelligence are analyzed in real time, and key rotation, fragment replacement and joint defense response mechanisms are dynamically triggered. According to the invention, full life cycle protection of data is realized, and the problems of key leakage risk and cross-table association attack are effectively solved.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention discloses a multi-modal sentiment analysis method based on a gating circulation hierarchical fusion network, which comprises the following steps: S1, multi-modal data preprocessing: collecting text, audio and video data, carrying out preprocessing and time sequence alignment, and constructing an annotation data set; s2, constructing a gating circulation hierarchical fusion network: designing a three-level network architecture comprising a modal feature extraction layer, a gating fusion layer and an emotion recognition layer; s3, model training and optimization: adopting an Adam optimizer, taking a cross entropy loss function as a target, carrying out iterative training on the labeled data set, and inhibiting overfitting through Dropout regularization and learning rate attenuation; s4, emotion analysis: inputting data acquired in real time into the trained model, analyzing the emotion state of the user in real time through a feature extraction layer, a gating fusion layer and an emotion recognition layer, and outputting an emotion analysis result; according to the method, information of different modes can be fully utilized in the sentiment analysis task, and the sentiment recognition accuracy is improved.

The invention relates to the technical field of data processing, and discloses a heat supply network heat supply system control method based on a prediction model, and the method comprises the steps: S1, collecting historical operation data and real-time operation data of a heat supply network heat supply system, S2, carrying out the preprocessing of the historical operation data and the real-time operation data, and generating a standardized heat supply data set; by constructing a prediction model based on historical data and real-time data, the thermal load demand change in a future time period can be dynamically predicted, the problem of adjustment lag caused by dependence on a static model in a traditional control method is solved, the response ability of a heat supply system to extreme weather and user demand sudden change is improved, and the heat supply efficiency is improved. The stability and reliability of heat supply quality are guaranteed, the time hysteresis between a regulation and control instruction and the actual heat supply effect is reduced by analyzing the heat transmission characteristic and the delay effect of each node of the pipe network in real time, the phenomena of insufficient heat supply and energy waste caused by untimely regulation of the system are avoided, and the energy consumption is reduced. And the operation efficiency and the energy utilization rate of the heat supply system are improved.

The invention discloses an intelligent decision support system and method for grassroots social governance based on multi-modal fusion. The method comprises the following steps: S1, acquiring and preprocessing multi-modal data in the field of grassroots social governance; s2, constructing a heterogeneous graph structure according to the data type and the treatment subject category; s3, extracting single-modal features respectively and generating cross-modal dynamic association features; s4, performing collaborative optimization on the heterogeneous graph structure and the cross-modal fusion parameters by adopting a flying fox optimization algorithm; s5, analyzing data in real time according to the optimized heterogeneous graph, and generating potential risk early warning, event trend prediction and event association deduction information; s6, pushing an auxiliary decision-making scheme and a disposal strategy in real time; and S7, continuously optimizing the heterogeneous graph structure by using a feedback result. The decision-making efficiency and accuracy of grassroots social governance are effectively improved.

The invention discloses a real-time early warning system for analyzing abnormal behaviors of prison prisoners based on a behavior sequence, and relates to the field of real-time early warning, and the system comprises a data collection module which is used for collecting and obtaining structured data and unstructured data in a prison area; the behavior sequence extraction module generates a feature vector of a behavior atomic unit through three-dimensional convolution spatial-temporal feature extraction, and obtains a behavior vector through feature fusion; the behavior sequence modeling module constructs a behavior sequence through a double-flow LSTM architecture; the anomaly detection module calculates the KL divergence of the behavior codes and a hidden Markov model baseline by constructing the hidden Markov model baseline, and outputs anomaly probability distribution; and the grading early warning module performs grading early warning based on the output abnormal probability distribution. The method has the advantages that by fusing multi-source data and a deep learning technology, the behavior sequence of the prisoner is analyzed in real time, intelligent early warning is performed, the supervision efficiency and safety are remarkably improved, and conversion from passive monitoring to active intervention is realized.

The invention discloses a self-adaptive web application interaction method and system and intelligent equipment. The method comprises the following steps: acquiring multi-modal sensing data based on a multi-modal sensor; performing dual verification by combining the motion features and the physiological features of the user, and performing real-time analysis to obtain the current activity type of the user; counting operation behaviors of a user in the application interaction system, and performing analysis based on the operation behaviors to obtain a screen use hotspot; sensing a scene context by adopting an intelligent rendering engine, and automatically switching an interface normal form, so that the interface normal form is matched with an activity type; and dynamically optimizing the distribution density and the spatial position of interface elements by continuously learning a user interaction mode based on a screen hot area self-adaptive layout algorithm. According to the technical scheme, self-adaptive adjustment of the application interaction interface layout is achieved through double verification of user activity types and a screen hot area self-adaptive layout algorithm in combination with user interaction behavior habits, and interface personalization is achieved while the interaction experience feeling is improved.

The invention discloses a marketing strategy optimization method based on consumer group behavior analysis, and the method comprises the following steps: deeply integrating and analyzing multi-source heterogeneous data in real time, such as biological characteristic data, physical environment and spatial data, social contact and content interaction deep analysis data, and voice and dialogue analysis data; carrying out association modeling on the behavior data, psychological characteristics, physiological states and social environment factors, and constructing a dynamic holographic user portrait; a large language model and a multi-mode generation model are utilized, marketing content with high personalization and situation adaptation is generated in real time based on a user portrait and a real-time context, and an interactive co-creation mode of the user and AI is explored; and according to an evaluation result, performing online updating and optimization on each link by using a machine learning algorithm to form a continuously improved closed loop. According to the invention, a more accurate and personalized marketing strategy can be provided, the sense of participation of users is enhanced, and the marketing effect and enterprise competitiveness are improved.

The invention relates to the technical field of carbon emission, in particular to an on-chain enterprise carbon emission accounting method and system. The method comprises the following steps: acquiring energy consumption, process flow and supply chain collaborative data of an enterprise in links of raw material acquisition, production processing, logistics transportation, equipment operation and recovery processing through Internet of Things equipment, and generating a carbon emission data set; based on a life cycle evaluation theory, boundaries of direct emission, indirect energy emission and supply chain associated emission are divided, and enterprise-level, product-level and supply chain-level carbon emission accounting models are constructed. A machine learning algorithm is utilized to analyze production fluctuation data in real time, carbon emission factor weights and accounting boundary parameters are dynamically adjusted, and an accounting result is optimized. And outputting a result to a green purchase decision-making system, a carbon asset management system and a supply chain collaboration platform. The system integrates industrial Internet of Things equipment, block chain nodes and enterprise resource planning system interfaces, collects and verifies full-link data in real time, solves the problem of data collection, and improves the accounting reliability.

The invention relates to the technical field of prefabricated part construction, and discloses a prefabricated part construction safety management method based on information coding, and the method comprises the following steps: S1, building a prefabricated part information coding system; s2, performing identity binding on the prefabricated part through a radio frequency identification tag and a narrowband Internet of Things module; s3, deploying a multi-source sensor network; and S4, constructing a dynamic risk assessment model based on the Bayesian network. According to the prefabricated part construction safety management method based on information coding, a layered information coding system is used for accurately identifying prefabricated parts, and identity binding and real-time position data transmission of the prefabricated parts are achieved in combination with a radio frequency identification tag and a narrowband Internet of Things module; then, a multi-source sensor network is adopted to collect data such as component postures and environment, the data are input into the dynamic risk assessment model based on the Bayesian network, influences of various factors on risks such as falling and structural deformation are analyzed in real time, and potential risks can be predicted more accurately.

The invention relates to a distributed storage cache hotspot prediction method and system based on an access mode. The method comprises the following steps: acquiring an access log to obtain original data; the gradient change of the access frequency between the adjacent data blocks is analyzed in real time to divide the hot spot range and identify continuous hot spot data blocks; constructing a time sequence based on the historical access record, and pre-judging an access hotspot in a future time period by applying a time sequence trend prediction algorithm; constructing a multi-dimensional feature vector and inputting the multi-dimensional feature vector into a machine learning prediction model to output a hotspot probability of the corresponding data object in a future time period; according to the hotspot probability and a popularity threshold value, scheduling the data predicted as the hotspot into a cache, and allocating corresponding cache levels or storage paths for the data objects with different popularity at the same time; and periodically updating the prediction model and the popularity statistics to obtain dynamic prediction and cache optimization of the hotspot data. According to the invention, the prediction of the hotspot data is realized, and the hit rate and the system performance of the distributed cache are improved.

Systems and methods for compressing and querying data for real-time analytics. The system can receive log data and generate an intermediate representation by parsing a log template and variables from the log data into a columnar format. The method includes generating a compressed intermediate representation associated with an index type and storing the compressed intermediate representation in the columnar format based on the index type. The method includes receiving a search query and analyzing the search query to identify a user defined function. The method includes parsing the search query to convert the search query into one or more predicates that satisfy the search query. The method includes filtering the compressed log data based on the one or more predicates and providing a query result.

The invention discloses a block file space topology aggregation and cross-modal indexing method based on HDF5, and relates to the technical field of electric digital data processing. The method comprises the following steps of point cloud space division, space data separation and storage, multi-modal data fusion and multi-level index construction. The method comprises the following steps: performing space division on a point cloud space through an octree algorithm to obtain octree nodes, analyzing the relevance between the space division and a memory in real time to determine whether to perform octree division dynamic adjustment, then encoding each octree node, performing space block data separation storage, and simultaneously performing point cloud data access optimization. According to the method, the multi-level index system of the point cloud space is established, then spatial attribute aggregation is performed to establish the spatial diagram, multi-modal data integration fusion is performed, and finally the multi-level index system of the point cloud space is established, so that the efficiency of accessing and retrieving the image block file space is improved, and the problem of low cross-modal retrieval efficiency of the point cloud space divided based on an octree algorithm in the prior art is solved.

The invention discloses an intelligent work order dynamic scheduling processing method based on multi-source data fusion. The method comprises the following steps: collecting Internet of Things data, resource data and personnel data through a data fusion layer; the intelligent decision-making layer analyzes the Internet of Things data in real time, creates a pre-diagnosis work order, or manually creates a fault work order and submits the fault work order to the intelligent decision-making layer; after a work order enters the system, an intelligent decision-making layer calculates a comprehensive scheduling score for each maintenance personnel meeting the condition, automatically distributes the work order to the personnel with the highest score, and pushes all information to a communication terminal of the intelligent decision-making layer; during dispatching, warehouse inventory is automatically checked through the resource collaboration layer; after maintenance is completed, results and time are recorded back to the system, and a self-learning closed loop is formed. According to the method, the mode of finding things by people and finding objects by people is changed into the mode of finding people by things and preparing objects, the method is a fundamental change of an operation and maintenance mode, the per capita efficiency can be improved, the cost can be reduced, and excessive dependence on personal experience can be avoided.

The invention relates to the technical field of supply and demand data matching, and discloses a supply and demand data dynamic optimization system and method based on a full life cycle, and the system comprises a decision module, a data analysis module, a supply module and a storage and transportation module. Analyzing the supply portal information and the appeal portal information in real time to obtain layout analysis data appeal information; evaluating and sorting the layout analysis data according to the appeal information to obtain a plurality of supply sorting tables; constructing an initial supply and demand plan according to the supply sorting table and the demand information, and inputting the initial supply and demand plan into a supply portal for rationality analysis to obtain dynamic adjustment data; the initial supply and demand plan is updated according to the dynamic adjustment data, the supply portal constructs a production list according to the target supply and demand plan, and the appeal portal generates storage data and a transportation plan according to the production list. According to the method, all data of the supply and demand parties are matched and optimized by constructing a full-period supply and demand management architecture, so that the supply and demand matching efficiency is improved.

The invention provides a dynamic train operation planning method for analyzing passenger flow volume in real time based on a big data model, and belongs to the technical field of train operation planning. Data is collected and uploaded to a cloud according to pull-in data collection and the like, and then the data is processed and analyzed based on big data and a machine learning technology; generating a corresponding scheduling strategy according to the traffic data, generating a traffic map of routes and stations according to the traffic data, issuing the traffic map to a train scheduling center according to the scheduling strategy, dynamically adjusting a departure interval, implementing a non-stop strategy and the like, and realizing efficient operation and scheduling of the train. According to the method, the waiting time of passengers in the peak period is shortened by 20%-30%, the departure interval can be dynamically adjusted, the non-stop strategy can be implemented and the like according to the scheduling strategy, the running efficiency of the train is improved, and the time cost of the passengers is reduced.

The invention relates to the technical field of rail transit equipment safety, and discloses a rail transit vehicle domestic chip door controller fault diagnosis method based on deep learning, which comprises the following steps: step 1, through a vibration sensor, a temperature and humidity sensor and a door control system working state sensor which are deployed in a rail transit vehicle door control system, a fault diagnosis result is obtained; collecting operation data, environment data and external disturbance factor data of the door controller in real time; step 2, starting a data preprocessing module, performing cleaning and formatting operation on the data collected in real time in the step 1, and integrating the data from different sensors by adopting a multi-dimensional data fusion technology; according to the technical scheme, the deep learning model is adopted to analyze multi-source sensor data in real time, the technical effects of rapid fault recognition and early warning are achieved, compared with a traditional relay control mode applied in the prior art, the defects that response is slow and faults are not found in time are overcome, and the safety of passengers getting on and off the bus is improved.

The invention discloses an intelligent streaming media advertisement putting method and system, and particularly relates to the technical field of big data analysis, social media interaction data and watching behavior data of a user are collected in real time through a background of a streaming media platform, the collected data are analyzed in real time, a deep learning algorithm is used, the collected multi-dimensional data are combined, and the intelligent streaming media advertisement putting method and system are obtained. The method comprises the following steps: establishing a personalized interest model of a user, predicting a most relevant advertisement type, automatically screening out advertisement contents most relevant to the current interest, emotion and behavior of the user from an advertisement library according to a real-time data analysis result, carrying out dynamic adjustment, and carrying out advertisement insertion through an advertisement insertion technology of a streaming media platform. The advertisement content is seamlessly embedded into the video playing process, the advertisement insertion mode is adjusted according to the real-time user watching state, in the advertisement playing process, an advertisement effect report is generated according to the interactive feedback and watching behavior of the user, and future advertisement putting is optimized and adjusted through data analysis.

The invention belongs to the technical field of data mining, and provides a GUI proxy data mining method and system. The method comprises the following steps: based on a given initial user intention and an initial GUI (Graphical User Interface) state, constructing and maintaining an intention-trajectory tree through an improved MCTS (Monte Carlo Tree Search) algorithm; in the simulation stage of the MCTS algorithm, the MLLM is utilized to analyze the state of the current expansion node in real time, and a reward signal used for evaluating the value of the node is directly output; after the intention-trajectory tree is searched, all trajectories from a root node to a leaf node in the tree are extracted, and filtering is carried out based on a preset quality standard; for the filtered trajectory, automatically generating a corresponding natural language intention description, and forming an intention-trajectory pair; and performing iterative training on the MLLM used in the process by using the intention-trajectory pair subjected to intention recovery processing. According to the method, GUI proxy data mining of diversified high-quality data can be realized.

The invention discloses a system and a method for judging the health degree of manufacturing equipment. The system comprises a code book database, a historical data database, a modeling server and an edge operation server. The codebook database stores a plurality of monitoring parameters of the manufacturing equipment in each process. The historical data database stores real-time high-frequency data of all the parameters of the manufacturing equipment so as to provide historical high-frequency data of all the parameters of the manufacturing equipment. The modeling server is used for extracting the historical high-frequency data of the plurality of monitoring parameters from the historical database according to the codebook data of the codebook database, and extracting the characteristics of the historical high-frequency data of the plurality of monitoring parameters so as to establish a health degree model of the manufacturing equipment according to the characteristics of the historical high-frequency data of the plurality of monitoring parameters. The edge operation server analyzes real-time high-frequency data and a health degree model which are thrown upwards by the manufacturing equipment in the current station manufacturing process in real time, and then health index scores of the manufacturing equipment in the current station manufacturing process are generated.

The invention provides a method for on-line monitoring of hydrogen sulfide in a drilling process, and relates to the technical field of oil-gas field development, and the method comprises the following steps: preprocessing collected data from different sources to obtain original data; performing influence factor analysis processing on the original data to obtain the original data marked with influence factors; establishing a plurality of model combinations composed of machine learning models, and generating a strong classifier based on the result selection of the classifier; processing the original data marked with the influence factors by using a strong classifier to obtain a hydrogen sulfide pre-monitoring initial model; and performing parameter adjustment on the hydrogen sulfide pre-monitoring initial model by adopting a cross validation method to generate a hydrogen sulfide pre-monitoring final model. A data driving method is adopted, the hydrogen sulfide monitoring model is established by using data generated in the drilling process, hydrogen sulfide is analyzed and pre-monitored in real time, and the real-time performance of monitoring is improved.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system that may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor to execute code from a computing memory. The code may comprise an adaptor to push the received raw data to a database to processed data; an extractor to extract the processed data from the database; predictive analytics to receive the extracted processed data and apply thereto a predictive model comprised of target data for the at least one machine, and to provide feedback to the at least one machine to modify performance of the at least one machine based on the application of the predictive model; and a visualizer capable to provide at least a visualization of the feedback and the performance.

Systems and methods for automating and optimizing cross-institutional Request for Information (RFI) processing are described including generating and applying a RFI template, identity management, and multi-modal communication channels. A dynamic channel selection engine routes RFIs based on real-time analytics and compliance needs, while adaptive privacy controls protect sensitive data. Machine learning-driven workflow optimization predicts efficient processing steps and automates routine tasks. A plug-and-play integration layer enables seamless adoption with existing systems, and a unified audit framework ensures regulatory compliance.

The invention discloses a logistics warehouse scheduling system and method based on big data, and relates to the technical field of logistics warehouse management, and the scheduling system comprises a data collection and preprocessing module which is used for collecting various types of data in a warehouse and preprocessing the various types of data; the emergency recognition module is used for establishing a recognition model based on a BP neural network for recognizing emergencies; the scheduling decision module is used for formulating an optimal scheduling scheme after receiving the trigger signal of the emergency identification module; the execution monitoring module is used for monitoring the execution condition of the scheduling scheme formulated by the scheduling decision module and feeding back information in time; according to the logistics warehouse scheduling system and method based on the big data, the identification model based on the BP neural network is combined with the big data processing technology, warehouse data can be analyzed in real time, emergencies can be accurately and rapidly identified, and the hysteresis of manual identification of a traditional system is overcome.

The invention discloses an electric power intelligent warehouse management method and system based on a large model, and belongs to the technical field of intelligent warehousing, and the method comprises the steps: S1, obtaining the basic information and dynamic data of electric power materials in real time; s2, cleaning and integrating the basic information and the dynamic data to generate a structured database; s3, constructing a data analysis model based on the pre-trained large model, calling dynamic data and basic information, generating a demand prediction result and outputting a quality risk assessment result; s4, adjusting a preset inventory strategy according to the demand prediction result, and generating a processing suggestion according to the quality risk assessment result; and S5, feeding back the adjusted inventory strategy and the processing suggestion to a preset power intelligent warehousing operation system. The system comprises an acquisition module, a generation module, a prediction and evaluation module, an early warning module and a feedback module. According to the method, the future demand quantity of the electric power materials is predicted by constructing the data analysis model, and real-time data analysis and demand trend prediction are realized.

The invention discloses a unit cascade authentication method and system between application systems, and relates to the technical field of identity authentication, and the method comprises the steps: generating a quantum identity entropy identifier for each member entity by using a quantum random number generator according to a dynamic mutual trust threshold table, and periodically calculating the identity information entropy difference between a group cloud and each member entity, based on the identity information entropy difference between the group cloud and each member entity, incremental data synchronization is carried out through a block chain, an incremental synchronization data packet is obtained, the incremental synchronization data packet is input into a graph neural network model, network attack characteristics and a traffic mode are analyzed in real time, and a security routing decision matrix is generated; and selecting an optimal authentication path according to the secure routing decision matrix to perform an authentication request, and when the authentication request is performed, proving a compressed trust chain verification process by using a Merkle path, and outputting a cascade authentication result. According to the invention, the security, timeliness and resource efficiency of the cascade authentication process are improved through the identity authentication and trust establishment method.

The invention discloses an Internet of Things intelligent safety helmet sensing and early warning positioning method and system, and the method comprises the steps: continuously collecting the motion and environment data of a user through a built-in multi-sensor array of a helmet, employing a self-adaptive wireless communication technology, intelligently selecting a transmission mode according to a network condition, and guaranteeing the reliable transmission of the data to a cloud platform, the cloud end adopts a distributed time sequence database and a stream processing engine architecture to perform efficient storage and real-time analysis on mass data, performs matching through multi-time window feature extraction and a multi-level security rule base to realize accurate state evaluation and danger level judgment, and after a structured early warning message is generated, a high-availability message-oriented middleware cluster is utilized to perform real-time analysis on the mass data. Different service quality and pushing strategies are selected according to message priorities, it is ensured that early warning information is timely and reliably distributed to a monitoring terminal, and therefore an all-around safety protection system integrating real-time sensing, intelligent analysis, accurate early warning and closed-loop feedback is constructed.