111 results about "Dynamic database" patented technology

The invention relates to the technical field of power equipment state detection, in particular to an ultrahigh-frequency partial discharge online detection system, method and device and a medium, and the system comprises the steps: collecting an initial discharge signal in real time through an ultrahigh-frequency sensor array, and carrying out the preprocessing of the initial discharge signal, so as to obtain an ultrahigh-frequency discharge signal; carrying out peak detection on the ultrahigh-frequency discharge signal, and triggering a high-speed analog-to-digital converter to collect an original waveform when the amplitude exceeds a preset threshold value; performing multi-dimensional feature extraction on the original waveform by using a digital signal processor to obtain multiple groups of dimensional features; identifying and classifying the multiple groups of dimension features based on a random forest algorithm, generating discharge type labels and confidence coefficients, and storing the discharge type labels and the confidence coefficients in a dynamic database; carrying out spatial position calculation on the ultrahigh-frequency discharge signal by adopting a time difference method, and determining a three-dimensional coordinate of a discharge source; the dynamic database and the three-dimensional coordinates of the discharge source are subjected to space-time correlation and multi-dimensional analysis, a defect analysis result is generated, and high-precision online detection of the partial discharge defect of the high-voltage equipment is achieved.

The invention discloses a power grid energy storage capacity demand determination method and system based on multiple time scales, relates to the technical field of power grid energy storage capacity demand calculation, and aims to solve the problem of inaccurate energy storage capacity demand calculation. By generating multiple scenes, quantitatively screening key scenes and incorporating various uncertain factors, energy storage capacity calculation focuses on high-influence scenes, the coping capacity of the scheme to actual risks is enhanced, decision scientificity is improved, ultra-short-term to long-term multi-time scales are divided, core contradictions of all the scales are captured in a targeted mode, limitation of a single scale is avoided, and energy storage capacity calculation efficiency is improved. According to the method, capacity requirements and equipment distribution are integrated, operation rules are defined, visual documents are generated, multi-scale energy storage cooperative operation is achieved, the stability of a power grid is guaranteed, meanwhile, cost is reduced, scheme landing performance and operation efficiency are improved, multi-time-scale power grid state prediction is carried out based on a dynamic database, and historical rules and real-time data are combined, so that the power grid state prediction efficiency is improved. And the prediction coordination is ensured through multi-dimensional verification.

The invention provides an intelligent infusion system and an intelligent liquid control method based on database dynamic feedback regulation and control, and relates to the technical field of medical equipment intelligent control. The system comprises an infusion terminal execution module, a multi-parameter monitoring module, a data transmission module, a central control server module and a dynamic database module; patient information, an infusion scheme, a threshold range and multi-dimensional data of historical records are stored through the dynamic database module, the central control server module calls the data and compares the data with real-time physiological parameters and infusion state parameters collected by the multi-parameter monitoring module for analysis, and the target infusion speed is calculated. Early warning is carried out according to the target infusion speed and the real-time monitoring parameters, accurate monitoring of the regulation and control parameters and the individual condition of the patient is ensured, the infusion speed is always in the optimal range adaptive to the physiological state of the patient, and the infusion adverse reaction caused by parameter errors and exceeding the physiological range is avoided.

The invention relates to the field of ecological monitoring, in particular to a marine ecological anomaly detection method and system based on multi-source data. Comprising the following steps: acquiring multi-source dynamic data of a target sea area; constructing a dynamic quality evaluation system, inputting multi-source dynamic data, and outputting a quality control report; based on the multi-source dynamic data conforming to the preset quality threshold value, establishing a marine ecological dynamic database in combination with a spatio-temporal indexing mechanism; based on the marine ecological dynamic database, space-time anomaly features are extracted, and candidate anomaly regions are determined; and generating a marine ecology comprehensive abnormal index by adopting the dynamic weight calculation model, outputting a prediction result through a preset abnormal trend prediction model based on the marine ecology comprehensive abnormal index, and generating an early warning measure. Through a distributed crawler and sensor gateway collaborative architecture, comprehensive acquisition of macroscopic data and near-shore real-time data is realized, and high-quality basic data is provided for subsequent detection.

The invention belongs to the technical field of data processing, and discloses a multi-source heterogeneous data fused remote sensing map dynamic database construction method and system, and the method comprises the steps: firstly obtaining multi-source heterogeneous remote sensing data of a target area, extracting remote sensing time sequence data of a land parcel after preprocessing, and obtaining a remote sensing time sequence; constructing a time sequence semantic track containing a key semantic feature time sequence for each land parcel; identifying a semantic evolution trend by using a semantic drift detection model, and generating an abandoned suspicious score; the static factors, the dynamic factors and the prior knowledge of the land parcels are fused, a structure causal graph is constructed by means of a causal discovery algorithm, and abandoned causal credibility is reasoned based on a graph neural network model; and automatically generating abandoned land labels for the land parcels of which the double scores exceed the threshold value, and finally writing the land parcel information into a dynamic database. And the problems of construction lag and information incompleteness of the dynamic database are solved.

The invention relates to the technical field of intelligent warehousing, in particular to a double-extension-position stereoscopic warehouse scheduling method and system and a computer readable storage medium, and provides a double-extension-position stereoscopic warehouse scheduling method which comprises the steps that a to-be-executed task dynamic database is generated according to real-time to-be-executed tasks of a double-extension-position stereoscopic warehouse; establishing a storage location-task dynamic mapping relation based on a preset three-dimensional model of the double-extension-location stereoscopic warehouse and a to-be-executed task dynamic database; obtaining an initial path plan based on the storage location-task dynamic mapping relation; calculating a task conflict index according to the equipment parameters of the double-extension-position stereoscopic warehouse and the to-be-executed task dynamic database; and adjusting the initial path plan based on the task conflict index to obtain a scheduling scheme. According to the double-extension-position stereoscopic warehouse scheduling method, the technical problem of efficiency loss caused by insufficient dynamic scheduling capability of a double-extension-position stereoscopic warehouse scheduling system in related technologies is solved.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention discloses a self-adaptive control method and system for a mining drilling machine. The method comprises the steps that state parameter data in the drilling process of the drilling machine are collected in real time through a multi-source sensor; quickly matching the acquired data with a pre-constructed dynamic database, generating an initial control parameter, and inputting the initial control parameter into an optimization model to generate a self-adaptive control strategy; performing closed-loop control on a drilling machine executing mechanism according to the self-adaptive control strategy; and monitoring and evaluating the drilling performance index of the drilling machine in real time, and feeding back an evaluation result to the optimization model to realize online iterative optimization. Through three technical means of dynamic database construction, layered double closed-loop control and multi-parameter coupling optimization, adaptive sensing, intelligent decision making and accurate execution of the drilling machine under complex geological conditions are realized, the drilling efficiency is remarkably improved, the energy consumption is reduced, the operation safety is enhanced, and the engineering application value is extremely high.

The invention relates to the technical field of boiler operation management, in particular to a boiler soot blowing steam consumption multi-dimensional data analysis system which comprises a dynamic database construction module, a steam consumption calculation model construction module, an evaluation module and an optimization strategy generation module. The dynamic database construction module is used for cooperatively acquiring the ash scale thickness value of the heating surface partition of the boiler through an infrared thermal imager and a sound wave thickness measuring device, and realizing structured time sequence data fusion in combination with steam parameters and working condition parameters; the steam consumption calculation model building module is used for building a multi-dimensional consumption data set based on space, time and energy efficiency dimensions; the evaluation module outputs a unit ash scale removal steam consumption value, an invalid steam injection proportion value and a regional energy efficiency deviation coefficient value; the optimization strategy generation module generates a soot blowing pressure gradient adjustment strategy and a steam saturation temperature compensation strategy through index correlation analysis, fine evaluation and control optimization of soot blowing energy efficiency can be achieved, and the boiler operation efficiency and the steam utilization rate are improved.

The invention discloses an air travel plan and service customization system, and particularly relates to the technical field of big data, the air travel plan and service customization system comprises a user terminal module, an intelligent travel planning engine and a service integration platform, and the user terminal module is connected to the intelligent travel planning engine and the service integration platform through the Internet. A travel demand and a current position of a user are obtained through a travel preference input module and a positioning module of a user terminal module, a dynamic scheme is generated in combination with a flight dynamic database, a hotel resource pool and a scenic spot ticket API under an intelligent travel planning engine, and after the user confirms a travel, the travel demand and the current position of the user are obtained. The service integration platform synchronously books air tickets and hotels and generates electronic certificates, when flights are delayed, alternative traffic scheme calculation is automatically triggered, travel nodes are dynamically connected to map navigation service, and the travel state of a user is updated in real time.

The invention relates to the technical field of data processing, and discloses a low-code business data storage method and system based on an EAV model, and the method comprises the steps: carrying out the data model transformation of a common data type for the EAV model of each entity in a database, and carrying out the data model transformation when a user carries out the interaction of a target entity, according to the method, the corresponding data model can be called to realize rapid data query, the problem of low query efficiency caused by data format conversion is avoided, meanwhile, load limitation brought to system memory and CPU resources by performing data model transformation on each entity in each operation unit time period is considered as a constraint condition, and maximization of the query efficiency is taken as a target. The most reasonable data model transformation and updating of each entity are executed for each operation unit time period, the data model management efficiency of the database is improved, the data query efficiency is improved while excessive resources of the system are not occupied, and a dynamic database model capable of realizing low-code development flexibility and expandable service attributes is provided.

The invention relates to the technical field of bioinformatics, in particular to an analysis method of pathogen metagenome sequencing data, the host background is greatly reduced through two-stage host filtering (especially ML fine filtering), and the detection capacity of low-abundance pathogens is greatly improved and the false negative rate is reduced by combining a low-abundance enhancement module (UMI / denoising); the dynamic database integration ensures that the coverage is wide and newest; a priority ranking algorithm is combined with clinical metadata, so that the ranking of real pathogenic bacteria is effectively improved, and the interference of non-pathogenic bacteria / contaminating bacteria is inhibited; the confidence evaluation of the low-abundance module reduces false positive; dynamically updated databases and intelligent sequencing effectively cope with the problems of new pathogens and incomplete databases, and missing detection is reduced; false detection is reduced through strict denoising, confidence evaluation and interpretation based on clinical rules.

The invention relates to the technical field of production data processing, in particular to a printed circuit board production quality tracking method and system, and the method comprises the following steps: setting a quality detection point at each key production node of PCB production, carrying out the detection at each quality detection point, and collecting the quality data of a PCB in real time; analyzing the collected data by using a machine learning algorithm, and identifying the quality deviation of each key production node; a dynamic database is established, and production process data and quality detection results of each PCB are recorded in real time and used for tracking and analyzing the overall production quality; and judging whether the production node is an accidental fault or an actual fault based on a quality detection result in the dynamic database. According to the method, the quality problem in the production process can be quickly and accurately identified, and the uncertainty and risk of relying on later detection are greatly reduced.

The invention belongs to the field of big data, and particularly relates to a case detection optimization method based on big data. Acquiring case information for data preprocessing, and summarizing the data to a dynamic database in real time; identifying and associating data in the dynamic database, and establishing an index database with association logic; automatically constructing an evidence chain through an inference model and an index database; logic detection is carried out on the evidence chain, the evidence chain with strong logic association of time, figures and material evidences is reserved as a reference evidence chain, and abnormal points of the reference evidence chain are marked; and performing analysis and prediction based on abnormal point marks of the reference evidence chain, and giving at least one prediction direction to guide case detection. Data summarization is carried out through a big data technology, associated data is rapidly extracted through an index database, multiple evidence chains are automatically constructed for screening, investigation direction expansion is carried out through mark analysis and prediction of abnormal points, and the case solving efficiency is improved through data driving.

A distributed photovoltaic grid-connected point electric energy quality dynamic monitoring system relates to the field of distributed photovoltaic grid-connected points, and comprises a knowledge graph construction module used for constructing a dynamic knowledge graph based on a dynamic database and updating the dynamic knowledge graph by using real-time data and integrated dynamic knowledge; the causal analysis module is used for establishing a causal relationship model of the power quality problem based on the dynamic knowledge graph; and the backtracking and positioning module is used for performing causal chain backtracking based on the causal relationship model, positioning a root cause influencing the power quality and generating an analysis report. According to the method, the causal chain backtracking is carried out along the weighted directed acyclic graph by taking the abnormal index as a starting point based on the causal relationship model, so that the root cause node, such as a specific equipment fault or health deterioration, which causes the power quality problem can be quickly and automatically positioned, and the automation degree and the positioning precision of fault diagnosis are greatly improved.

The invention provides an unmanned cargo automatic handover system based on multi-mode biological recognition. The unmanned cargo automatic handover system comprises a biological recognition device, an identity verification module, a cargo handover control device and a cloud management platform. The biological recognition device integrates a fingerprint recognition module, a face recognition module and an iris recognition module, and high-accuracy identity recognition is ensured by using advanced sensors and algorithms. The identity verification module guarantees the safety of the verification process through real-time comparison and multi-factor verification in combination with dynamic database management. The cargo handover control device automatically controls unlocking and locking of a cargo cabin door, and records, encrypts and stores all handover operation data at the same time. According to the technical scheme, high safety and high efficiency of cargo handover of the logistics vehicle are achieved, the method is suitable for being widely applied to the modern logistics field, the safety and accuracy of the logistics vehicle in the cargo handover process are improved, and automation and traceability of operation are achieved.

The invention relates to the technical field of emergency command and dispatch, in particular to a dual-mode-based vehicle-mounted emergency dispatch method, system, device and medium, after a vehicle is started, a vehicle-mounted terminal carries out power-on self-test, after power-on self-test is passed, registration is carried out, vehicle data are collected, and the vehicle data are packaged and uploaded through dual-mode switching; the method comprises the steps of analyzing and processing packaged and uploaded vehicle data, storing the vehicle data in a dynamic database, detecting the vehicle data in the database, giving an alarm prompt for abnormal data, issuing an instruction according to the alarm prompt, executing a task according to the instruction after the instruction is confirmed, feeding back confirmation information, and meanwhile, carrying out continuous detection through dual-mode switching. The problem of failure of single-mode communication under the scene of base station damage or satellite bandwidth limitation is solved, the communication interruption rate is reduced to below 5%, high-speed transmission of large-capacity data is guaranteed, the Beidou guarantees key information basic communication, the bandwidth requirement and the global coverage are both considered, and the scheduling real-time performance and the environmental adaptability are enhanced.

The invention relates to the technical field of energy materials and fluid engineering, and provides a micro-nano particle morphology detection and slurrying performance prediction method, device and equipment, and the method comprises the steps: preprocessing a slurry fuel sample, collecting the micro-morphology image and particle size distribution data of particles in the slurry fuel sample, and forming an original data set; based on a regional convolutional neural network algorithm, carrying out segmentation and feature extraction on the micromorphological image in the original data set to identify particle morphological parameters in the micromorphological image; establishing a dynamic database containing particle morphology parameters and particle size distribution data; based on the data of the dynamic database, a grey correlation method is adopted to quantify the correlation degree between the particle morphology parameters and the corresponding pulping performance indexes, and linear fitting is carried out; and constructing a pulping performance prediction model to obtain a pulping performance prediction value. The method can be directly applied to quality control and process optimization in slurry fuel production, and assists in improving the production efficiency and the product quality consistency.

This invention provides a dynamic update method for single-server preprocessing of Private Information Retrieval (PIR), including an offline preprocessing stage, a dynamic database update stage, an online query stage, and an update stage. Based on the existing single-server preprocessing PIR retrieval scheme Piano, this invention adds support for dynamic database operations, supporting CRUD (Create, Read, Update) operations on the database. Unlike the cumbersome preprocessing work of comparable schemes, this invention introduces incremental preprocessing technology, allowing the original preprocessing to continue to be used after database changes, while incurring update costs proportional to the number of changes, rather than proportional to the size of the database. Compared to existing technologies, this invention reduces total communication costs, client computation time, and client storage space.

The invention relates to an autonomous exploration method based on hierarchical reinforcement learning target driving in a dense dynamic scene, and belongs to the field of robot autonomous navigation and environmental perception. The method comprises the steps of obtaining a pedestrian position vector based on a pedestrian frame in an RGBD image, and constructing a dynamic database in combination with radar scanning data; performing similarity calculation based on an RK radar descriptor of radar scanning data to obtain a high-level strategy switching result; a plurality of interest points are generated, and the interest point with the minimum composite cost function value is selected as a target point needing to be explored in the next step; according to a high-level strategy switching result, outputting high-level multi-modal fusion features by using the trained HRL model, and obtaining a final navigation skill in combination with a target point needing to be explored in the next step; and outputting a speed control instruction to realize autonomous exploration based on the bottom-layer multi-modal fusion features and final navigation skills output by the trained HRL model. The objective of the invention is to solve the technical problem that exploration efficiency and safety are difficult to balance in a complex environment in the prior art.

The invention relates to a multimodal transport highway-railway path dynamic planning method, which comprises the following steps of: acquiring multisource dynamic data of highways, railways and connection points in real time, predicting data in a specified time period in the future, and preprocessing the data to generate a dynamic database; a space-time coupling objective function with the shortest total transportation time, the lowest total transportation cost and the highest highway-railway connection efficiency as the cooperation objective is constructed; introducing a fitness dynamic calibration mechanism to correct a target function value; setting a decision window by taking the current moment as a starting point, solving a local optimal path in a rolling manner, and generating an initial scheme including a road real-time navigation path, railway dynamic shift matching and a connection point resource reservation time slice; setting a three-level early warning threshold based on the predicted value, and triggering a hierarchical adjustment mechanism to generate an adjustment scheme; and evaluating the time / cost change rate and the execution feasibility of the adjustment scheme, and executing after reaching the standard. The prospective performance, the dynamic adaptability and the landing reliability of path planning are effectively improved, the transportation cost and delay are effectively reduced, and the highway-railway combined transportation efficiency is remarkably improved.

The invention relates to a thermal power plant coal blending combustion optimization method based on multi-source data and an intelligent algorithm, and the method comprises the steps: obtaining the multi-source data from a fuel management and control platform, a coal yard digital system, a unit operation monitoring system, and an environmental protection monitoring system in real time, establishing a dynamic database including coal quality data, real-time inventory, unit operation constraints and environmental protection constraints; based on the dynamic database, taking the minimization of the total fire coal cost in the future T hours as a target function, and taking stable operation of a boiler, an environmental protection emission index and equipment processing capacity as constraint conditions, establishing a corresponding fire coal blending optimization model for each load period, and forming a multi-period optimization model; solving the multi-period optimization model by adopting an improved genetic algorithm to generate an optimal blending scheme sequence; through online monitoring, according to the optimal blending scheme, adopting a preset control strategy to realize closed-loop optimization of the combustion process; according to the invention, the comprehensive optimization of the fire coal cost, the combustion efficiency and the environment-friendly emission is realized.

The invention discloses a dynamic single-server client preprocessing private information retrieval method, and belongs to the technical field of private information retrieval. The method comprises a preprocessing stage: a client generates a private prompt and stores the private prompt in a local device; a query and response stage: the client accelerates query by using a private prompt; in the refreshing stage, the client repositions the used private prompt and updates the private prompt stored locally; and a dynamic updating stage: when the server updates or deletes the original data item or adds a new data item, the client directly updates the private prompt stored locally. According to the method, the client storage time and the server online calculation time are optimally balanced, and meanwhile, efficient updating of the dynamic database is supported. And when the database of the server dynamically changes, the private prompt stored in the local equipment of the client can be updated within the expected PRP calling time, so that the private prompt updating efficiency in a dynamic database scene is remarkably improved.

The embodiment of the application provides a reservoir capacity dynamic monitoring method and system based on a UAV, which comprises the following steps: obtaining a reservoir surface layer feature data set through a UAV, determining a water area boundary and scanning a reservoir bottom according to the data set, obtaining underwater topographic elevation and silt distribution data, and constructing a three-dimensional topographic model; determining topographic and silt change data based on dynamic change data of the three-dimensional topographic model of a continuous time sequence. Through change rate analysis, silt space distribution characteristics are obtained, and the amount of silt transport and the silt deposition space rule are determined in combination with water flow velocity. Key driving factors are screened through correlation analysis, a high-risk silt area is determined, silt change in the area is monitored, and thus the reservoir capacity change is mastered. The application can accurately and dynamically monitor reservoir siltation, clearly determine a high-risk area and a change mode, construct a dynamic database, update the capacity change in real time, and provide important technical support for reservoir management and flood control and disaster reduction.

The invention provides a track segmentation method, system and device based on space-time perception and adaptive learning, and belongs to the technical field of artificial intelligence and track data processing. The track segmentation method comprises the following steps: preprocessing original track data, and dividing an unlabeled data set and a labeled data set; the method comprises the following steps: constructing an encoder-decoder model based on Transform, and carrying out self-supervision pre-training through mask reconstruction and a contrast learning task; introducing a segmentation decision head, and performing supervised fine tuning on the pre-training model by using a small amount of labeled data; after model deployment, continuous adaptive learning is realized by constructing a dynamic database and regularly performing online mixing loss fine adjustment. The method effectively reduces the dependence of the model on labeled data, improves the generalization ability, long-term stability and decision interpretability in a complex scene, and is suitable for air traffic control, target intention recognition, trajectory prediction and other scenes.

This invention relates to the field of fire protection planning technology, specifically a method for fire station layout and fire equipment configuration based on GeoAI. The method includes: acquiring spatial data related to building identification and constructing a multi-source dataset; determining fire rescue demand points and types based on socio-semantic segmentation using a visual language model, and constructing a dynamic database of fire stations and fire equipment; establishing a comprehensive demand paradigm for fire rescue equipment and decomposing its equipment subsets; determining the optimal pairing of fire rescue demand points with multiple fire stations and evaluating the comprehensive weighted response time; identifying areas with weak fire rescue coverage and determining the initial locations of each candidate fire station; iteratively optimizing the locations of candidate stations to determine the proposed fire station layout scheme; optimizing the spatial layout and equipment configuration of fire stations in conjunction with fire rescue response capabilities; and outputting the fire station layout and fire equipment configuration scheme. This invention achieves differentiated and refined configuration of fire station site selection and equipment, providing a scientific basis for fire protection planning.

The invention provides a cloud warehouse supply chain enhanced design method based on drug catalog shared inventory. The method comprises the following steps: constructing a shared drug catalog interface; establishing a basic database and a dynamic database; designing a drug characteristic label library; constructing a multi-dimensional index system; establishing a mapping relationship between the cloud warehouse main identifier ID and the local code of each medical system through a code matching mapping mechanism; based on a dynamic database and a shared directory, real-time collaborative management and intelligent allocation of the inventory are realized; in combination with special attributes of drugs, fusion management of the shared directory and inventory data is realized; designing a multi-agent collaborative permission and security control mechanism; according to the invention, through the shared medicine catalog interface and unified database construction, the problems of non-uniform medicine catalog and information isolated island of each medical system in a traditional medicine supply chain are solved, and each medical system can share basic data and dynamic data of medicines in real time, so that more comprehensive and accurate information support is provided for decisions such as medicine purchase and inventory management.

The application discloses a zero offset compensation method of an inertial measurement unit of a vehicle, a vehicle and a computer readable storage medium. The method comprises: obtaining a target junction temperature of the inertial measurement unit when the vehicle is in a target working condition, and calculating a temperature change rate of the target junction temperature, wherein the target working condition is a static working condition or a uniform straight driving working condition; determining target zero offset data from a target database according to the target junction temperature and the temperature change rate, wherein the target database comprises a static database constructed when the vehicle is in the static working condition and a dynamic database constructed when the vehicle is in the uniform straight driving working condition; and performing zero offset compensation on the inertial measurement unit based on the target zero offset data. Thus, the application is suitable for a vehicle with an intelligent driving or intelligent auxiliary driving mode, and can retrieve an adaptive target database in combination with the target junction temperature and the temperature change rate when the vehicle is in the target working condition, and obtain target zero offset data, so that precise compensation of the IMU zero offset is realized.

Examples of the present disclosure describe systems and methods for sharing memory using a multi-ring shared, traversable and dynamic database. In aspects, the database may be synchronized and shared between multiple processes and / or operation mode protection rings of a system. The database may also be persisted to enable the management of information between hardware reboots and application sessions. The information stored in the database may be view independent, traversable, and resizable from various component views of the database. In some aspects, an event processor is additionally described. The event processor may use the database to allocate memory chunks of a shared heap to components / processes in one or more protection modes of the operating system.

The invention discloses a safety management method and system for an intelligent construction site, and belongs to the field of safety management.The safety management method for the intelligent construction site comprises the following steps that a risk feature database containing various illegal behaviors is constructed and serves as a comparison standard for identifying the illegal behaviors, and the risk feature database is a dynamic database and serves as a comparison standard for identifying the illegal behaviors; compared with the prior art, the method has the beneficial effects that by constructing the dynamic risk database, the accurate digital definition and continuous learning ability of various illegal behaviors are realized, the illegal behaviors can be automatically identified in an all-weather manner by means of real-time information acquisition and intelligent comparison, intervention is immediately performed through positioning voice warning, and the risk of the illegal behaviors can be effectively eliminated. The monitoring instantaneity and coverage range are greatly improved, a complete closed loop from warning sending to behavior correction (or upgrading reporting) is ensured through continuous tracking and state management, each violation event can be effectively tracked and processed, and the risk of safety accidents caused by untimely correction is effectively reduced.