622results about How to "Optimization" patented technology

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention discloses a new energy power generation equipment health management platform based on a large model, and relates to the technical field of power generation equipment health management. The system comprises a multivariate data acquisition and region identification module, a data preprocessing and region adaptation unit, a region perception large model analysis module, an intelligent operation and maintenance decision and scheduling module and a spare part inventory optimization sharing module. The multivariate data acquisition and area identification module deploys multiple types of sensors at key equipment parts of photovoltaic power generation stations in different areas, acquires equipment operation parameters and environment state data, endows the areas with unique codes and feature tags, and ensures that the data are traceable and are associated with area characteristics; according to the invention, the health management efficiency and accuracy of the new energy power generation equipment are significantly improved; the system not only realizes real-time monitoring and fault early warning of the equipment state, but also reduces the maintenance cost and improves the system operation efficiency by optimizing the operation and maintenance resource configuration and intelligent spare part inventory management.

The invention relates to a metropolitan area network traffic prediction application method based on a neural network, and belongs to the technical field of network traffic. Dividing a training set, a verification set and a test set; 70%, 20% and 10% of data are used for model training, optimization and evaluation; inputting the traffic data into the double-layer stacked LSTM network, obtaining hidden state information and extracting time sequence characteristics and dependency of network traffic; capturing a long-term and short-term dependency relationship in the flow by using an encoder; global dependency features are extracted through a self-attention mechanism; gradually predicting flow data of a plurality of time points in the future by adopting a decoder; an early stop strategy is adopted in model training to prevent overfitting; carrying out anti-standardization processing on a model prediction result, and recovering an original scale; and evaluating a model prediction result. According to the invention, training and optimization of the deep learning model are carried out on the historical data and the feature data of the metropolitan area network, so that the accuracy of IP network traffic prediction is remarkably improved.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention is suitable for the field of logistics transportation, and provides a hazardous chemical substance transportation path planning method and system, and the method comprises the steps: collecting road network information, road condition real-time data, weather data, population density distribution data, environment sensitive point distribution data, and hazardous chemical substance characteristic data; based on the collected data, establishing a risk assessment model, and performing risk assessment on each preset transportation path; constructing a multi-objective function, and distributing a corresponding weight for each objective; and searching and generating an optimal or suboptimal transportation path in the road network based on the multi-objective function, and when the change of the road condition data or the weather data exceeds a preset threshold value, performing risk assessment and path planning again, and regenerating the optimal or suboptimal transportation path. And the transportation data security is ensured. A path can be adjusted in real time to cope with road conditions and weather changes, the transportation risk is reduced, and the transportation cost, time and risk are balanced.

The invention provides a photovoltaic power station design system and method based on ecological collaborative optimization, and relates to the technical field of data processing. The system comprises an initial configuration module which is used for generating an initial layout structure of a photovoltaic module, performing energy-saving and consumption-reducing calculation and economic analysis according to generating capacity data, and obtaining initial energy-saving and consumption-reducing data and initial economic analysis data; the secondary optimization module is used for optimizing the parameters of the photovoltaic module based on the economical efficiency analysis data and determining energy-saving and consumption-reducing data and economical efficiency analysis data through the generating capacity optimization data; the ecological evaluation module is used for determining ecological benefit data of the photovoltaic power station according to the ecological influence parameters; and the collaborative optimization module is used for setting a power generation income weight and an ecological benefit weight so as to optimize the photovoltaic module layout structure and generate a photovoltaic power station design scheme based on ecological collaborative optimization. The method is used for realizing collaborative optimization of power generation benefits and ecological benefits of the photovoltaic power station, so that the power station gives consideration to ecological protection while improving the power generation efficiency.

The invention relates to task scheduling, in particular to an intelligent task optimization method based on a self-adaptive multi-dimensional dependency graph and dynamic scheduling, which comprises the following steps of: inputting task information and resource information; constructing a self-adaptive multi-dimensional dependency graph according to the task information; in the task execution process, the task state of each task is monitored in real time, and the self-adaptive multi-dimensional dependency graph is dynamically updated; calculating the effective priority of the current task, and updating the effective priority of the current task according to the updated adaptive multi-dimensional dependency graph; dynamically scheduling the current task by adopting a dynamic scheduling algorithm; monitoring a task execution state and a resource use condition of each task in real time; according to the task execution state and the resource use condition, whether dynamic scheduling needs to be adjusted or not is judged until all tasks are executed; according to the technical scheme, the defect that in the prior art, accurate and efficient task optimization scheduling is difficult to carry out under the multi-dimensional dependency relationship between the tasks can be effectively overcome.

The invention is suitable for the technical field of tumor treatment and protection, and particularly relates to an intelligent classification prediction method, system and equipment for tumor treatment and protection, and the method comprises the steps: obtaining tumor-related molecular marker detection values and clinical diagnosis and treatment features of all target patients, and comprehensively integrating the multi-dimensional medical information of the patients; according to the relevance of molecular markers and clinical features, a scientific and quantitative multi-dimensional prognosis layering index set is constructed, and an accurate layering evaluation framework is formed; inputting the medical data into a pre-trained multilayer neural network model, and generating a high-resolution tumor characterization map through a cross-modal feature fusion technology; based on the dynamic matching of the atlas and the hierarchical index set, the hierarchical contribution degree parameter and the prognosis risk score are calculated in combination with the random forest model, the precise quantitative evaluation of the condition of the patient is completed, a high-credibility quantitative basis is provided for clinical decision making, the over-treatment risk is effectively reduced, and the prognosis management effect of the patient is improved.

The invention discloses a power distribution network visual management and control method and device, a storage medium and a product, and the method comprises the steps: obtaining the multi-dimensional information of a power distribution network in a target region, carrying out the preprocessing of the multi-dimensional information, obtaining a standardized power distribution network data set, carrying out the recognition of a power distribution network problem according to the power distribution network data set, and obtaining a power distribution network fault. The method comprises the following steps: obtaining a structured problem list of a power distribution network, carrying out spatial positioning and attribute mapping processing on the structured problem list in a preset geographic information system to obtain a power distribution network problem spatial distribution representation, and carrying out multi-dimensional analysis and prediction on the operation condition of the power distribution network according to the power distribution network problem spatial distribution representation. And obtaining a management and control strategy of the power distribution network, carrying out resource allocation according to the management and control strategy to obtain a cooperative work flow, and visualizing the problem space distribution representation of the power distribution network and the cooperative work flow in a geographic information system to provide a visual management and control mode. And the management efficiency and safety of the power distribution network are improved.

The invention relates to the field of voltage regulation and control, in particular to a real-time voltage regulation method for a new energy automobile. Initializing a power distribution network voltage measurement module, and collecting voltage data of each node of the power distribution network; setting a voltage deviation threshold value, and judging whether the voltage is in a normal range or not; collected voltage is compared with a set voltage deviation threshold value, a result obtained after preliminary voltage regulation of an on-load voltage regulation transformer is compared with the set voltage deviation threshold value, and a result obtained after cooperative voltage regulation of an electric vehicle charging pile is compared with the set voltage deviation threshold value. According to a comparison result, tap adjustment of an on-load voltage regulation transformer, cooperative voltage regulation of an electric vehicle charging pile and voltage regulation participated by a distributed photovoltaic system are carried out in sequence; and formulating an optimization strategy based on historical data and results of three times of voltage regulation. By means of cooperative voltage regulation, optimal control, intelligent management and the like, the voltage stability is effectively improved, and the adaptability and flexibility of the system are enhanced.

The invention discloses an intelligent distributed photovoltaic power station optimal scheduling and operation and maintenance management method and system, and relates to the technical field of optimal scheduling. Historical power generation data, real-time meteorological data and future weather forecast of a photovoltaic power station are acquired; according to the historical power generation data, the real-time meteorological data and the future weather forecast, inputting the historical power generation data, the real-time meteorological data and the future weather forecast into a pre-trained power generation prediction model to obtain a photovoltaic power generation prediction value in a future time period; and obtaining the power grid demand power of the photovoltaic power station, and planning the charging and discharging strategy of the energy storage system according to the absolute value of the difference between the power grid demand power and the photovoltaic power generation prediction value. The difference between the power grid demand power and the photovoltaic generating capacity prediction value is analyzed, the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system are adjusted according to the difference, the completion degree of the charging and discharging strategy is judged through the charging and discharging time of the energy storage system, the adjusting function of the energy storage system is fully played, and the energy storage efficiency is improved. And the reliability and continuity of power supply are improved.

The invention discloses an automatic model tuning system based on an AI training intelligent workbench, and relates to the technical field of artificial intelligence, and the system comprises the steps: setting a data collection instrument and an environment monitoring point, collecting model real-time data in a model training process, and carrying out the preprocessing; performing model performance analysis according to the model real-time data and the model original data to obtain final parameter configuration; and performing model training by utilizing the final parameter configuration, dividing the model training process into different stages, dynamically adjusting an intelligent control strategy based on model performance requirements, and performing optimization on the different stages of the model. According to the method, through multi-dimensional performance evaluation, parameter configuration is optimized, and the robustness and generalization ability of the model are improved. Through dynamic adjustment of an intelligent control strategy, fine management of a training process is realized, efficiency and resource utilization are improved, model convergence is accelerated, and finally an efficient, stable and high-adaptability model training effect is achieved.

The invention discloses a molecular property prediction large language model construction method based on a knowledge graph. The method comprises a plurality of steps: firstly, acquiring pre-training corpora by using a high-throughput data acquisition technology, including related data such as molecular attributes and metabolic pathways; then, a knowledge graph about fourteen end points is constructed based on the data, and the knowledge graph represents the relation between the molecules and the attributes and the end points of the molecules in a triple form; thirdly, pre-training the large language model by using a LoRA technology, optimizing the model by adopting an incremental learning strategy, and forming an expert large model; then, a triple related to the target endpoint is retrieved from the knowledge graph through the RAG technology, and retrieval results are sorted; and filtering the retrieved triple through cue word design and an expert large model, and extracting key information. And finally, converting the filtered triple list into a natural language, inputting the natural language into an expert large model, generating a prediction result of molecular properties, and providing a reasoning basis. According to the method, the accuracy and efficiency of molecule and endpoint chemical reaction prediction are remarkably improved.

The invention provides a whole blood donor low hemoglobin delayed blood donation risk prediction method, which comprises a data normalization and abnormal value processing module, a classification feature coding module, a category balance and model fitting module, a hyper-parameter adjustment module and a model interpretation and deployment module. According to the prediction method provided by the invention, data normalization and targeted feature coding are carried out on blood donation records and hemoglobin detection data, an SMOTE oversampling algorithm is used for class balance and then fitting of a Light GBM model is carried out, and a Hyperband hyper-parameter adjustment method is used for adjusting and optimizing SMOTE and Light GBM parameters. And storing the tuning model for feature importance interpretation, model packaging and network deployment, thereby realizing the effects of optimizing the whole blood donor invitation strategy and enhancing the continuity and stability of blood supply.

The invention discloses a runoff prediction model based on multi-source information integration of an Informer model, and belongs to the technical field of hydropower station runoff prediction. The invention aims to solve the problem of poor accuracy of medium and long term runoff prediction, especially for runoff sequences with multi-source, non-linear and non-stationary characteristics. The monitoring power station range is selected, and historical runoff data of the hydropower station and hydrological information such as rainfall and temperature of a surrounding area are obtained; adopting a Pearson correlation analysis method to select strong correlation features as multi-source time sequence feature input of an Informer model; preprocessing the data by using an STL seasonal decomposition method, and extracting characteristics of a period term and a trend term; runoff prediction is carried out by adopting a sparse attention mechanism and a self-attention distillation mechanism of an Informer model; according to the method, the prediction speed and feature capture can be enhanced, the runoff prediction accuracy is improved, and powerful support is provided for water resource management and scheduling of the hydropower station.

The invention provides an advertisement delivery method and system, and the method comprises the steps: obtaining user behavior data, and carrying out the preprocessing, so as to convert the user behavior data into structural data; analyzing and processing the user behavior data based on a preset construction rule to extract user interest data; extracting user historical behavior data in a preset time period from the user behavior data, and analyzing and processing the user historical behavior data to obtain a user behavior prediction result; and pushing the advertisement in the advertisement library to the user according to the user interest data and the user behavior prediction result and based on a preset pushing strategy. The interest data of the user is determined by analyzing and processing the behavior data, the behavior trend of the user is predicted, and advertisement putting is performed on the user according to the interest data and the behavior trend of the user, so that the problem that the existing advertisement putting technology cannot accurately meet the personalized requirements of the user is solved, the advertisement putting accuracy and effect are improved, and the user experience is improved. And the user experience is improved.

The invention relates to the technical field of hierarchical storage, in particular to a photo-electromagnetic storage method and system based on hierarchical management, and the method comprises the following steps: collecting read-write speed, error rate, durability and cost data of optical storage and electromagnetic storage media, carrying out the sorting and grading processing of the data according to the read-write speed and the error rate, and carrying out the sorting and grading processing of the data; and performing score calculation on each storage medium to obtain a medium performance score. According to the method and the device, the data is effectively distributed to the most suitable storage medium by accurately scoring the performance of the storage medium and combining specific requirements of the to-be-stored data, so that the data access speed and efficiency are improved, the total storage cost is reduced, and particularly, when a large amount of data is processed, the data compression level and the encoding format are dynamically adjusted, and the data compression efficiency is improved. The storage efficiency of the data is further optimized, data redundancy is reduced, the overall performance of data storage and access is remarkably improved through optimization measures, and meanwhile long-term safety and integrity of the data are ensured.

The embodiment of the invention provides a self-adaptive detection and detection system for low-altitude flight inspection, and aims to improve the efficiency and safety of unmanned aerial vehicle inspection operation. Firstly, a plurality of unmanned aerial vehicle description information sets, unmanned aerial vehicle cluster description information and flight inspection task description information are acquired; the information covers performance parameters and task requirements of the unmanned aerial vehicles in different inspection scenes, then the information is loaded into a target adaptive inspection decision model for knowledge learning in advance, and target estimation confidence is generated by analyzing the information so as to evaluate the success probability of the unmanned aerial vehicle cluster in executing a specific inspection task. And when the target estimation confidence meets the set requirement, the corresponding unmanned aerial vehicle cluster is intelligently scheduled to execute the inspection task, so that efficient completion of the task is ensured, and the adaptability and flexibility of the unmanned aerial vehicle inspection operation are effectively improved through intelligent evaluation and dynamic scheduling.

The invention discloses a coal power multi-path transformation and new energy combination decision-making method under low-carbon transformation. The method comprises the following steps: firstly, constructing a system comprising first-level effect indexes such as environmental influence, economic benefit, energy safety and technical innovation and corresponding second-level indexes; then, alternative schemes of various coal power transformation technology and new energy project type combinations are generated; then quantitatively evaluating the effect index of each scheme, and calculating a comprehensive utility value by using multi-criterion decision analysis; identifying potential risk factors, and performing uncertainty analysis through Monte Carlo simulation; and finally, according to the low-carbon transformation target and policy evaluation scheme conformity, selecting a scheme which most conforms to the policy target, and continuously monitoring and adjusting. The coal-electricity multi-path transformation and new energy combination decision-making method under low-carbon transformation is beneficial to promoting energy low-carbon transformation, optimizing resource allocation, improving economic benefits, promoting technical innovation, enhancing risk management capability and ensuring that a project is consistent with policy orientation.

The invention discloses a waterlogging risk early warning threshold calculation method and system based on multi-source data analysis, and relates to the technical field of big data analysis and risk early warning, and the method comprises the steps: obtaining the multi-source data of a to-be-analyzed target disaster-bearing body; generating a target disaster-bearing body elevation model containing the building height information according to the building height information and the surrounding area elevation model data so as to analyze a surrounding runoff path of a target disaster-bearing body; correcting a surrounding runoff path according to the surrounding area satellite image to determine a corresponding disaster-bearing body catchment area and a disaster-bearing volume water area; calculating a rainfall ponding relation of the target disaster-bearing body; and acquiring a ponding height threshold value for the disaster-bearing volume water area, and calculating a rainfall early warning threshold value of the waterlogging risk corresponding to the target disaster-bearing body according to the ponding height threshold value and the rainfall ponding relationship. Therefore, personalized rainfall early warning thresholds for different disaster-bearing bodies are obtained through fine-grained analysis, and more accurate decision support is provided.

The invention belongs to the field of medical information systems, and provides a medical institution full-process doctor-seeing path planning method and system based on artificial intelligence, and the method comprises the steps: modeling each doctor-seeing link in a medical institution into a directed graph; the node features and the edge weights of the directed graph serve as input, the node features are updated through a graph neural network, and the weight of each node is dynamically generated; a queuing theory model is adopted for modeling according to the queuing condition of each node, and the average waiting time of each node is calculated; designing a multi-objective optimization function, and taking balance of the shortest path and the minimum waiting time as an optimization objective to generate an optimal doctor-seeing path scheme; the resource state of each node is dynamically monitored based on a real-time feedback mechanism of the Internet of Things equipment and a hospital information system, and when it is monitored that the resource state or waiting time of the node is changed, the graph neural network and the queuing theory model are recalled to update node features and priority weights.

The invention provides a knowledge graph-based scientific research talent and research and development task intelligent matching method and system, and the method comprises the steps: obtaining and analyzing a plurality of candidate research and development tasks and matching priorities thereof, and combining the research association knowledge feature data between scientific research talent nodes to construct a plurality of scientific research talent knowledge sub-graphs; furthermore, the scientific research talent nodes are subjected to priority ranking according to task matching priorities, so that the scientific research talent nodes in different sequences have different matching priority information in the same scientific research talent knowledge sub-graph. In the matching process, according to the matching priority information of the target talent knowledge entity, the target research and development task corresponding to the target talent knowledge entity is rapidly determined, and the target research and development task is associated to the corresponding talent knowledge entity, so that the matching efficiency of scientific research talents and the research and development task is improved, the matching accuracy and rationality are ensured, and the research and development efficiency is improved. And scientific research resource allocation can be optimized, and smooth proceeding of scientific research projects can be promoted.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention discloses a power transmission and transformation project cost intelligent review method based on cost fluctuation characteristics, and relates to the technical field of intelligent review, and the method comprises the following steps: collecting historical cost data, preprocessing the data based on NACEMD, and recognizing power transmission and transformation project cost influence factors; judging main influence factors influencing the cost by adopting correlation analysis, and carrying out clustering analysis on different project types to obtain different types of power transmission and transformation project sample sets; on the basis of the historical cost data and the transmission and transformation project sample set, cost intervals of different types of costs of the transmission and transformation project are calculated through a Cheff-Snow ratio inequality for different types of costs, and fluctuation intervals of different types of costs are judged; corresponding intelligent methods are set for different types of fees, and based on prediction analysis of the three types of fees, evaluation standard determination of the power transmission and transformation project cost is realized through input of related project parameters. And the scientificity and accuracy of the review process are ensured through model selection for different fluctuation types.

The invention provides a control method, device and equipment for a mine ventilation system and a storage medium, and the method comprises the steps: determining a first target branch based on a complete incidence matrix corresponding to a mine ventilation network diagram and the branch weight of each branch in the mine ventilation network diagram; acquiring target air volume data of the first target branch, and acquiring branch air volume data of each branch based on the target air volume data; obtaining a second target branch corresponding to an independent path in the mine ventilation network diagram based on the complete incidence matrix; obtaining target resistance data of the second target branch, and obtaining branch resistance data of each branch based on the target resistance data; based on the branch air volume data and the branch resistance data, wind resistance adjustment data and wind resistance data are obtained; obtaining actual wind resistance data based on the wind resistance data and the wind resistance adjustment data; and dynamically controlling the mine ventilation system based on the branch resistance, the air volume monitoring data and the actual air resistance data. And the ventilation system can be adjusted as required.

The invention discloses an electric power material supply management and control system and a method thereof. The electric power material supply management and control system comprises a master control center, a sub-control center, a material warehouse, a mobile control terminal and a data processing platform. The electric power material supply management and control method comprises the following steps: S1, material demand prediction; s2, intelligent scheduling; s3, performing real-time monitoring; s4, risk assessment and early warning; and S5, emergency response is carried out. The electric power material supply management and control system and the method thereof can improve the material supply efficiency, enhance the decision support capability, make the decision more accurate and efficient, help to optimize resource configuration, improve the overall operation efficiency, improve the transparency and visualization of a supply chain, enhance risk management, guarantee the safe and stable operation of a power grid, improve the emergency response speed, and improve the power supply efficiency. The resource allocation is optimized, the inventory cost is reduced, the fund utilization efficiency is improved, the optimal allocation of resources is realized, and the electric power material requirements of each region can be better met.

The embodiment of the invention provides a communication mode self-adaptive switching method, device and equipment based on an SDN (Software Defined Network), which are applied to the technical field of communication, and the method comprises the following steps: receiving a real-time monitoring result reported by a channel state monitoring unit, analyzing the monitoring result, and obtaining a current communication demand and channel state information; according to the current communication requirement and the channel state information, whether communication mode switching or transmission resource redistribution needs to be carried out is judged; if communication mode switching or transmission resource redistribution needs to be carried out, a corresponding instruction is generated and sent to the programmable network equipment; therefore, after the programmable network device receives the instruction, corresponding operation is executed, and communication mode switching or transmission resource redistribution is completed. In this way, the limitation of a single communication mode and fixed transmission resource allocation can be effectively overcome, and a more reliable, efficient and flexible communication solution is provided for the modern communication field.

The invention discloses a customized order receiving method and system for a designated driver based on a saas platform. The invention relates to the technical field of designated driving services. Receiving demand data of a driver, wherein the demand data comprises order receiving preference, a service area and service time; receiving personal information input by a driver, wherein the personal information comprises basic information and order receiving preference setting; wherein the demand data comprises order receiving preferences, service areas and service time of drivers and users; wherein the basic information comprises a name, a driving age and a vehicle type; according to the invention, the driver is allowed to set personalized order receiving conditions according to own order receiving preference, service area and time, so that the working flexibility of the driver is greatly improved. The driver can autonomously select the order suitable for himself / herself, so that the rejection rate of improper orders is reduced, and the working efficiency and the satisfaction degree are improved. And through an intelligent order receiving matching algorithm, the system can quickly match the most appropriate order for the driver, so that the empty driving time and waiting time of the driver are reduced, and the working efficiency and income are further improved.

The invention discloses an anesthesia assessment big data intelligent supervision system, which comprises a user interface module and a supervision system module, and is characterized in that the supervision system module comprises a multi-source data acquisition and integration module, a real-time data analysis and assessment module, an intelligent early warning and decision support module and a visual report and long-term tracking module; by integrating advanced information technology and medical knowledge, intelligentization and precision of anesthesia management are realized, all modules of the system cooperate with one another, data acquisition, processing, analysis and application are completed together, comprehensive, accurate and personalized anesthesia risk assessment and scheme suggestions are provided for users, and the user experience is improved. According to the method, doctors can be helped to master the condition of a patient more accurately, a more reasonable treatment scheme is formulated, risks and complications in the anesthesia process are reduced, the treatment effect and satisfaction degree of the patient are improved, configuration of medical resources can be optimized, the efficiency and quality of medical services are improved, and powerful support is provided for sustainable development of medical institutions.

The invention belongs to the technical field of the Internet of Things, and discloses a situation awareness method and system based on the Internet of Things, and the method comprises the steps: collecting environment awareness data, and extracting environment features and mineral features from the environment awareness data; storing the environment features and the mineral features in a block chain, and respectively judging whether the environment features and the mineral features meet preset standards or not; respectively predicting the environment situation and the mineral situation based on the judgment results of the environment characteristics and the mineral characteristics; and comprehensively evaluating the ore exploitability through a pre-established decision cluster on the basis of the prediction results of the environment situation and the mineral product situation. According to the system and the method, through a mechanism of sensing surrounding environment parameters and extracting specified mineral signs and other attributes, the environment parameters and the mineral signs can be accurately sensed and collected, and powerful data support is provided for accurate monitoring and resource management.