526 results about "Risk classification" patented technology

The invention discloses a network traffic threat analysis method based on operating system instruction hierarchy, which relates to the technical field of network security, and comprises the following steps: monitoring operating system instruction data in real time, constructing a dynamic behavior matrix and generating an instruction-level traffic diagram; analyzing the instruction level flow diagram, calculating first digit distribution of instruction data, comparing the first digit distribution with Benford's Law expected distribution, calculating an instruction behavior deviation degree through a statistical method, generating an abnormal score, performing risk classification in combination with a decision tree classification algorithm, and determining an abnormal instruction; and carrying out abnormal instruction classification by using a graph neural network, and identifying known attack behaviors and unknown abnormal behaviors. According to the method, more accurate attack behavior identification capability can be provided, a complete attack chain can be identified, more intelligent threat analysis is realized, false alarms are reduced, and the detection precision is improved, so that the behavior track of an attacker is accurately traced, and stronger safety protection capability is provided.

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction are improved.

The invention relates to the technical field of environmental pollution monitoring, and discloses an intelligent monitoring and tracing method and system for groundwater pollution, and the method comprises the steps: arranging a first monitoring well at the periphery of a target pollution source, obtaining data, calculating a comprehensive pollution index, dividing a risk area, and obtaining second monitoring data based on the optimized point distribution of the risk area, a four-dimensional space-time distribution model and a three-dimensional dynamic migration diffusion model are constructed, and pollution traceability analysis is carried out by combining the two models; according to the method, accurate monitoring of groundwater pollution is realized through a risk classification-driven differentiated point distribution strategy, and the temporal-spatial resolution and traceability precision of pollution plume migration simulation are remarkably improved through fusion of monitoring data and dynamic traceability analysis of a geologic model.

The invention belongs to the field of multi-modal artificial intelligence risk identification. The core comprises a multi-source heterogeneous data parallel acquisition module; a modal exclusive feature extraction module; a graph attention driven dynamic fusion module; and a risk classification module with an attention mechanism. A real-time fusion weight is generated through a cross-modal incidence matrix, adaptive feature weighting and hidden risk association mining are realized, and the recognition accuracy and interpretability of a complex scene are remarkably improved. The method is applied to the fields of financial risk control and industrial monitoring.

The invention discloses an AI-driven robot working environment safety sensing method, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the visual, laser radar, force sense and environment parameter data of a working environment in real time through a multi-mode sensor; performing feature extraction and semantic understanding on the acquired data by using a deep learning algorithm to generate multi-dimensional perception information of the environment; based on a preset security policy model, in combination with the multi-dimensional perception information, predicting and classifying potential risks; according to a risk classification result, a behavior strategy of the robot is dynamically adjusted, and active safety decision is realized; and through the continuous learning module, the deep learning model and the security policy model are optimized online by using newly collected data so as to adapt to environmental changes.

The invention discloses an adaptive neural network flood routing simulation and risk assessment method. The method comprises the following steps: step 1, generating multi-source hydrometeorological preprocessing data; 2, constructing a river network topological graph, generating node feature vectors and edge feature vectors, and forming time-space diagram input data; 3, inputting the liquid state time constant neural network to execute continuous time state updating, and generating a node hydrological state prediction vector set; step 4, injecting hydrodynamic physical consistency residual errors to generate a physical constraint hydrological state prediction vector set; 5, executing adaptive lag compensation, and generating a lag compensation hydrological state prediction vector set; 6, mapping to generate flow, water level and flood peak arrival time prediction data; and step 7, outputting a risk grading graph and early warning threshold list data. According to the invention, accurate prediction of the flood process and dynamic generation of the risk map are realized, and early warning precision and response efficiency are improved.

The invention relates to the technical field of fault diagnosis, in particular to a big data-based bus duct fault diagnosis system, which comprises a data synchronization module, an anomaly identification module, an amplitude judgment module, a traceability positioning module and a risk classification module. According to the invention, automatic synchronization of multi-node data is realized through a time correlation mode, layered discrimination is carried out on an abnormal working condition state by means of continuous comparison of data streams, dynamic identification of node parameter fluctuation is realized through interval adjustment and trend aggregation, and abnormal signals are positioned and traced in a link topology. The node operation risk is comprehensively researched and judged in combination with historical and real-time parameters, linkage analysis and active classification of multi-dimensional information are formed, efficient connection of links such as monitoring, recognition, tracing and classification is promoted, operation and maintenance decision and data full-process response are supported, node difference judgment and risk prompt in a complex scene are facilitated, and the method is suitable for popularization and application. And intelligent operation and maintenance of power supply system data driving are improved.

The invention relates to the technical field of financial risk control, and discloses a financial pre-opening account risk assessment system based on intelligent identification, and the system comprises a multi-dimensional situation feature collection module which is used for collecting multi-dimensional situation features in a pre-opening account process; the situation analysis processing module is used for converting the multi-dimensional situation features into situation feature vectors and carrying out hierarchical risk feature extraction on recognized similar situation cases; the risk assessment module is used for generating a risk score and a risk classification result; the knowledge graph updating module is used for performing quality evaluation on the newly extracted risk knowledge and dynamically updating the risk knowledge graph based on an evaluation result; the crowdsourcing knowledge collection module is used for forming a continuous optimization closed loop of risk assessment; according to the method, accurate identification of the financial account pre-opening risk is realized, the risk identification accuracy is improved, the risk knowledge updating period is shortened, the risk knowledge island problem is effectively solved, and a clear risk explanation path is provided.

The invention provides an environment detection method and system based on a sample target detection model. The method comprises the following steps: synchronously acquiring an environment image, a video stream and physical parameters by using a multi-mode sensor; decomposing the data into image features and environmental parameter components through a dual-time sequence control signal, and realizing space-time alignment by adopting a linear phase filter; constructing a foreground region template based on the depth information, and generating target recognition feature representation containing an abnormal blurred target; adversarial training is carried out on the lightweight target detection network in combination with a transfer learning strategy, the network integrates convolutional features and a Transform attention mechanism, and the weight is dynamically adjusted through environmental parameters; fusing a target result and sensor data in real-time detection, and inputting a decision tree model for risk grading; and after the early warning is triggered, reconstructing a false detection sample through an online learning mechanism and iteratively optimizing the model. The system correspondingly comprises a multi-modal data acquisition module, a data enhancement and annotation module, a model training module, a real-time detection and fusion module and an early warning and optimization module. According to the invention, through multi-source data fusion, dynamic data enhancement and an adaptive compensation mechanism, the small target detection precision, the environmental adaptability and the real-time early warning capability are significantly improved.

The invention relates to the technical field of aquatic product quality inspection and traceability, and discloses a block chain enabled aquatic product traceability and quality evaluation method. According to the method, complete cycle data of aquatic products is divided into a block chain storage layer, a circulation traceability layer and a quality inspection evaluation layer, the block chain storage layer comprises a plurality of distributed nodes, the circulation traceability layer records chain type circulation events from cultivation to sales, and the quality inspection evaluation layer integrates multiple types of quality inspection indexes. The method comprises the following steps: judging whether a block hash value in a block chain storage layer is abnormal or not, and if so, extracting a breeding environment parameter, a transportation temperature and humidity track and a sales inspection report in a circulation traceability layer; calculating a multi-source quality index fusion weight of the quality inspection evaluation layer, and analyzing the abnormal signal intensity of each detection node; and finally, performing quality risk classification by combining the information and a preset evaluation threshold, and generating a quality grade label. According to the method, credible traceability and scientific evaluation of complete-cycle data of aquatic products are realized, and an effective means is provided for quality control.

The embodiment of the invention relates to the field of artificial intelligence, and provides a model context protocol injection attack protection method and device based on dynamic semantic analysis, computer equipment and a storage medium, and the method comprises the steps: obtaining request data corresponding to a call request initiated by a user through a model context protocol, carrying out the preprocessing of the request data, obtaining the preprocessed request data; performing semantic vectorization on request text and context historical information in the request data through a lightweight bidirectional encoder representation model to output an initial risk score; correcting the initial risk score according to context historical information carried in a model context protocol to obtain a corrected final risk score; and performing hierarchical defense decision according to the final risk score, and determining risk grading information corresponding to each piece of request data so as to execute a protection action corresponding to each piece of risk grading information. By adopting the method, the accuracy of identifying the protocol injection attack can be improved.

The invention discloses a cold region tunnel freeze injury diagnosis, risk grading and control system and method, and relates to the technical field of tunnel engineering. The system comprises a basic data layer for storing disease, index and measure databases; the core analysis layer is used for diagnosing a frost heaving mechanism, identifying single-factor, double-factor and three-factor frost heaving types, calculating freezing depth and frost heaving force and analyzing sensitivity; the risk level evaluation layer is used for dividing a disease zone, a frost heaving level, a freezing injury risk and a freezing injury risk response level; and the intelligent decision-making layer constructs an intelligent comprehensive decision-making analysis platform to realize risk-measure accurate matching and closed-loop management and control. The method comprises the four steps of data acquisition and storage, frost heaving mechanism diagnosis and quantification, risk grade evaluation and intelligent decision measure matching, through multi-source data fusion, multi-factor coupling analysis and zoning and grading prevention and control, the whole-process accurate management and control of the cold region tunnel frost damage is realized, and the frost damage treatment efficiency and the tunnel operation safety are improved.

The embodiment of the invention relates to the technical field of bank risk control, and provides a bank network risk control optimization method and system based on flow behavior analysis, and the method comprises the steps: obtaining a bank network flow behavior data package set, and carrying out the flow behavior feature extraction processing of the bank network flow behavior data package set, obtaining a traffic behavior feature set of each traffic behavior data packet, and inputting the traffic behavior feature sets into a preset deep learning model for traffic behavior classification processing to generate traffic behavior classification results corresponding to the traffic behavior data packets; based on the traffic behavior classification result, calling a corresponding Bayesian classifier to carry out risk classification processing to generate a risk classification result corresponding to the traffic behavior data packet; and generating a bank network risk control optimization instruction set according to a risk classification result, and distributing the bank network risk control optimization instruction set to a corresponding bank network risk control execution node to trigger a risk control operation, thereby improving the accuracy and real-time level of bank network risk prevention and control.

The invention relates to a food safety risk analysis method and system based on big data. The method comprises the steps of collecting food full-life-cycle key data through Internet of Things equipment and a data platform, and completing data format standardization to form a structured data set; key risk factors are extracted through the structured data set, a risk factor weight matrix is constructed, and a primary risk value of the food sample is calculated; establishing a time sequence prediction model by using the primary risk value and historical time sequence data, calculating a risk change trend, and predicting a future risk fluctuation condition; calculating a food risk grade according to the primary risk value and the risk change trend, and generating a corresponding risk classification label; performing comparative analysis on the risk classification label and an actual supervision result, calculating a model deviation, and optimizing a risk calculation and trend prediction model based on a deviation result; the optimized analysis results are integrated, the risk level and the change trend are displayed through a graphical interface, and intelligent early warning prompts and intervention decision suggestions are provided.

The invention provides an enterprise production safety monitoring and checking system based on multi-source data fusion, and the system is characterized in that the system comprises a data collection module which collects production environment data, processes the data, and generates a time-space alignment data set; the feature screening module is used for screening features highly related to security from the space-time alignment data set to generate a feature matrix; the model construction module is used for constructing a risk assessment model, calculating a dynamic risk value according to the multi-dimensional features, and performing risk grading on the dynamic risk value; the response control module is used for starting a corresponding response strategy according to the risk classification and outputting an early warning instruction set and an equipment control signal; the monitoring calculation module is used for monitoring control, comparing risk value changes before and after treatment and calculating response efficiency; and the adjusting and optimizing module is used for carrying out dynamic adjustment according to the response efficiency and optimizing the response strategy. And the limitation of traditional single-dimensional monitoring is broken through, accurate correlation analysis of equipment, environment and personnel risks is realized, and the composite hidden danger recognition capability is remarkably improved.

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The invention provides a puree production line microbial pollution risk real-time monitoring and early warning method, which comprises the following steps: deploying multiple types of sensors, an industrial camera and an operation log interface, collecting environmental temperature and humidity, pH value, dissolved oxygen, image and operation behavior multi-modal data in a fermentation tank in real time, and carrying out edge calculation and standardized preprocessing to obtain the microbial pollution risk of a puree production line. Data missing, abnormity and time sequence difference are eliminated; multi-modal features and a dynamic knowledge graph constructed based on a production process and a pollution event are fused, and a deep learning model and a graph neural network are utilized to realize microbial pollution risk probability intelligent prediction; risk grading and a dynamic weighting algorithm are introduced, grading early warning signals and disposal suggestions are automatically generated, and a production control system is linked to execute response measures; according to the subsequent production state, continuous feedback is carried out, the model and the early warning threshold are adjusted in a self-adaptive mode, and the accuracy, response efficiency and production safety of pollution early warning are remarkably improved.

The invention discloses a road risk grading early warning method and system based on a Beidou satellite system, and the method comprises the steps: firstly carrying out the real-time positioning of a vehicle through Beidou dual-frequency signals, inertial navigation data, road side unit differential data and vehicle-mounted sensor data, and obtaining the precise position information; fusing the real-time position of the vehicle with meteorological data, vehicle-mounted OBD parameters and social media public opinion data to generate a dynamic risk factor matrix; a fuzzy rule base is constructed based on expert experience, the fuzzy weight of each risk factor in a matrix is obtained, meanwhile, the time sequence weight of each factor is predicted by means of an LSTM model, and a final road risk score is obtained through dynamic weighting. And performing graded early warning on the road risk in combination with the driver portrait, and feeding back and updating the fusion parameter, the fuzzy rule base or the LSTM model parameter according to the early warning effect to form a closed-loop optimization mechanism. Dynamic coupling analysis of multi-dimensional risk factors is realized, and the real-time performance and accuracy of road risk early warning are improved.

The invention relates to a terminal equipment risk grading method under a local area network in a hospital. Comprising the following steps: generating behavior fingerprints based on a communication rhythm, a protocol sequence and a started response sequence of equipment in a local area network; if a new online or abnormal behavior device cannot fit the historical rhythm, the new online or abnormal behavior device is marked as an identity-uncertain terminal; carrying out microscopic modeling on the access path, the request depth and the trigger resource change of the equipment before and after the risk trigger; constructing an equipment internal state change map and a network external stimulation event flow; analyzing a trigger cause behind the abnormality; modeling the continuity of the equipment risk state along with time evolution; a trend weighted curve mode is introduced, and the speed, stability and direction of risk increase are identified; the single risk is stabilized at a middle level and is not reduced for a long time; quickly raising the risk of single equipment; performing high-priority response; and adopting a strategy range convergence mechanism of local network stability: analyzing the states of other devices in the subnet / department local section where the device is located.

The invention relates to the technical field of robot path planning, and provides a walking robot path planning method and system, and the method comprises the steps: obtaining the environment parameters of a target region, constructing a multi-dimensional energy consumption map, carrying out the marching resistance analysis, obtaining a risk grading label, carrying out the path screening of the target region according to the risk grading label, and obtaining the path of the target region. After the path candidate set is obtained, map generation is carried out, and a path weight map is obtained; and acquiring electric energy stability distribution of the walking robot, performing path correction on the path weight map according to the electric energy stability distribution to obtain a path fitting map, and analyzing an optimal path output result according to the path fitting map and the path candidate set. Through the technical means of environment parameter acquisition, advancing resistance analysis, electric energy stability correction and the like, efficient optimization of walking robot path planning is successfully achieved, and the problem that in a complex environment, the walking robot path planning is prone to being affected by environment uncertainty, and consequently stability and reliability of walking robot path planning are poor is solved.

The invention relates to an elderly patient falling early warning method and system, and the method comprises the steps: collecting a motion signal, a physiological signal and an environment signal of a target patient, carrying out the preprocessing of the collected signal data, and generating a standardized multi-modal time series data flow and a data quality identifier; according to the standardized multi-modal time sequence data stream and the data quality identifier, constructing and storing an individualized baseline description of the target patient; outputting an abnormal tag and a corresponding evidence snapshot based on the individualized baseline description; according to the abnormal label, the evidence snapshot and the recent time sequence data of the target patient, generating a trigger factor description; according to the long-term behavior file and the short-term falling risk level, fusing to form a layered comprehensive risk file, and outputting a risk level, a priority and a suggested action list based on the comprehensive risk file; and implementing falling early warning and real-time closed-loop intervention on the target patient according to the priority and the suggested action list. According to the invention, the accuracy of early warning of falling of the elderly patient can be improved.

The invention relates to the technical field of image recognition, in particular to a burn scar hyperplasia risk assessment method combining semantic segmentation and texture feature analysis, which comprises the following steps: acquiring an image gray gradient, color jump and texture variance, calculating pixel mutation, extracting a mutation boundary, constructing a periodic direction field, and extracting a continuous offset region. According to the method, by extracting the pixel gray gradient, the color jump and the texture variance, calculating the boundary sudden change intensity and generating the layer, the structure change characteristics can be refined, the image anomaly perception precision can be enhanced, the semantic boundary can be identified based on the sudden change sequence, and the physical continuity is prevented from interfering the segmentation accuracy. A periodic evolution record is constructed through direction gradient, the dynamic trend of the structure is disclosed, an expansion area is locked in combination with direction continuous offset and change stability, a classification label is constructed through point location density, direction consistency and a gradient module value, and the interpretability and accuracy of risk identification are improved.

The invention relates to a user operation risk dynamic monitoring method and device based on data consanguinity and a medium, and the method comprises the following steps: obtaining operation behaviors of all users in real time, and extracting a segment of continuous operation behaviors of each user as a group of behavior fragments with semantic relevance; a plurality of continuous behavior segments of each user are matched with a preset behavior template, so that a plurality of intermediate behavior blocks are obtained through dimension reduction; according to a time sequence, traversing and extracting a predetermined number of intermediate behavior blocks from the plurality of intermediate behavior blocks, and respectively combining to generate a behavior fragment sequence; and respectively inputting the behavior fragment sequences into a pre-trained neural network model to obtain a risk classification result or a risk probability distribution result corresponding to each behavior fragment sequence. The method has the advantages that each operation behavior can be subjected to structured expression, the potential behavior chain can be reconstructed through the combination strategy, and the high-risk behavior path hidden in the data noise is recognized by means of the deep model.

The invention relates to the technical field of airport information processing, and provides an airport operation situation prediction method and system based on digital twinning, and the method comprises the steps: deploying a twinning space; wherein the twin space is used for representing a real airport geographic scene; loading at least one digital twin instance corresponding to the coordinate information and the entity object in the twin space; wherein the coordinate information is position information of the digital twin instance corresponding to the entity object; in response to a target scheduling instruction of the target digital twinning instance, executing hierarchical scheduling deduction of the target digital twinning instance in the twinning space to determine operation situation information of the target digital twinning instance in the real airport geographic scene; wherein the hierarchical scheduling deduction comprises a deduction space based on risk grading.

The invention discloses a factory soil and groundwater pollution risk management and control method. The method comprises the following steps: acquiring a factory data set at a current moment and a historical moment; establishing a risk prediction model based on the factory data set; dividing risk grade distribution in the factory based on the risk prediction model and repairing the risk grade distribution; for the high-risk area, in-situ chemical oxidation and a permeable reactive barrier are adopted for repairing; phytoremediation or microbial remediation is adopted for medium risk areas; a low-risk area does not need to be repaired; the method can accurately predict the risk grade distribution in the factory according to the geological parameters, pollutant monitoring data and environmental dynamics of the factory, realizes risk grading output by constructing a risk prediction model fusing a physical mechanism and machine learning advantages, improves the accuracy and repair efficiency of risk assessment, and improves the risk assessment efficiency. And the pertinence, the economical efficiency and the environmental benefits of pollution control are remarkably improved.

The invention discloses an environmental component detection data processing method and system in occupational health, and belongs to the technical field of environmental data processing, and the method comprises the steps: obtaining the original detection data of a multi-source detection device, obtaining the real-time position information of a worker, carrying out the matching, generating a structured monitoring matrix, and calculating a dynamic risk value; generating a real-time risk vector, identifying a combined pollution scene, calculating an enhancement effect, generating a superposition risk index, comparing the superposition risk index with an occupational contact limit value standard, generating a risk grading signal, carrying out short-term exposure trend analysis, and predicting future risk grade change. And generating an early warning decision instruction containing the current risk level and the predicted risk level to execute a double-path intervention strategy, and generating an equipment control signal for driving the physical equipment to act. According to the invention, multi-source monitoring data is dynamically associated with personnel positions, and multi-dimensional risk calculation, trend prediction and closed-loop control are carried out, so that accurate dynamic assessment and prospective early warning intervention of individual risks can be realized.

A soft rock roadway reinforced roof anchoring thickness collaborative design method comprises the steps that the proportion of grout filling cracks in a grouting reinforcement area is counted, a grouting dispersion uniformity index is calculated, and grouting effect evaluation is conducted; measuring the actual boundary depth of the grouting reinforcement area by adopting a method of combining drilling radar scanning and rock core sampling; mechanical parameters of the grouted rock mass are obtained, and a grouting enhancement coefficient is calculated; a stress transfer efficiency evaluation model is established, and the critical cooperative bearing surface depth is determined by analyzing the stress attenuation law at the boundary of the grouting reinforcement area; the critical anchoring layer thickness is calculated under the constraint condition that it is ensured that an anchoring system can effectively penetrate through a grouting strengthening area and go deep into a stable rock stratum; the cooperative work performance of a grouting reinforcement area and an anchoring system is quantitatively evaluated by calculating a system cooperation degree index, and a cooperative bearing efficiency evaluation system based on dynamic risk grading and control is established. According to the method, collaborative optimization of grouting and anchoring parameters can be achieved, and formation of a bolting-grouting integrated bearing structure can be ensured.

The invention provides an intelligent monitoring and risk early warning method for urban road settlement, and belongs to the technical field of urban management based on machine learning. The method comprises the following steps: firstly, collecting four types of multi-source space-time monitoring data, including urban road settlement data, underground environment data, pavement structure data and dynamic load and environment data; secondly, constructing a multi-source data space-time completion model, performing unsupervised completion on sparse monitoring area data, and generating space-time continuous settlement field data; thirdly, constructing a road settlement health index prediction model fusing multiple factors, inputting complementation data and original features, and outputting a health index of a 0-1 continuous interval; and finally, in combination with the health index and the road function level, four-level risk classification and dynamic early warning are realized. According to the method, the problems of space-time faults and data islands of a traditional method are solved, urban road global real-time monitoring is achieved, and early warning upgrading from qualitative judgment to quantitative grading is achieved.

The invention provides a self-adaptive electricity selling side spot auxiliary decision making method based on multi-model collaboration and risk dynamic grading, and is suitable for the technical field of electricity market transaction decision making. According to the method, historical and real-time market data are collected in real time, and the electricity price difference is predicted by using multiple prediction models. The mean square error of each model is calculated, the weight of the model is dynamically adjusted, and the prediction results of each model are integrated through a Stacking method, so that the prediction precision and robustness are remarkably improved. Based on the comprehensive prediction result and the real-time market index, the market risk is dynamically evaluated, and the arbitrage strategies are respectively formulated according to different risk levels: the aggressive arbitrage strategy is adopted under the low-risk condition, and the conservative strategy is adopted under the high-risk condition. According to the invention, through real-time transaction execution and closed-loop feedback, model parameters and strategy thresholds are continuously optimized, adaptive adjustment of spot transaction decisions is realized, and the profitability and risk control level of an electricity selling side are effectively improved.

The invention relates to a human body health state assessment system and assessment method based on multi-point acquisition, and the system comprises an event sensing module which is used for collecting an image data signal, a physiological signal and an environment data signal of a human body, and outputting an asynchronous pulse event flow when the signal change is detected; the pulse coding module is used for generating a space-time pulse sequence through a preset adaptive threshold coding technology; the neural network processing module is used for performing event driving processing by utilizing leakage integral distribution neurons in a preset pulse neural network according to the space-time pulse sequence to obtain a current health characteristic pulse mode; and the health state decoding module is used for obtaining a health state score and a risk classification result of the current human body through a preset pulse distribution rate analysis algorithm and a preset time sequence decoding technology according to the current health characteristic pulse mode. Therefore, the problems of high power consumption, high delay, low data transmission efficiency, low early pathological feature recognition accuracy and the like of a human health state evaluation system are solved.