911 results about "Behavioral analytics" patented technology

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

The invention provides a chemical industrial park illegal behavior intervention and tracing system based on edge calculation, and aims to solve the problems that an existing system is single in perception capability, insufficient in intelligent analysis, limited in edge collaboration, passive in intervention, weak in tracing capability and the like. According to the system, an improved ResNet-FPN and Transform architecture is adopted to realize multi-modal data fusion, and violation behavior analysis is carried out through a double-flow intention understanding framework and a graph neural network in deep combination with chemical process knowledge; a self-adaptive edge node networking and federated learning mechanism is adopted to realize dynamic scheduling of computing resources and knowledge migration; an intervention decision model based on risk entropy supports predictive intervention, and a multi-objective optimization algorithm ensures balanced safety and efficiency of an intervention strategy; a complete evidence chain is constructed by adopting a multi-modal trajectory reconstruction technology and a distributed evidence storage network, and credible tracing of violation behaviors is realized. The technical bottleneck of a traditional system in the aspects of professional scene understanding and real-time processing is broken through, and the intelligent level of safety management of the chemical industry park is remarkably improved.

The invention relates to the field of log fault analysis, in particular to a log aggregation fault diagnosis method and system based on artificial intelligence. The method comprises the following steps: collecting a multi-modal heterogeneous log, carrying out sliding time sequence slicing processing, carrying out time sequence association sequence reconstruction, and constructing a time sequence reconstruction log data stream; log event deep semantic analysis is carried out on the time sequence reconstruction log data stream, event semantic topological evolution is carried out, and a multi-dimensional event topological representation matrix is constructed; performing routine event behavior analysis and abnormal fault mode inference based on the multi-dimensional event topology representation matrix, and marking abnormal fault points; and the occurrence timestamp and the abnormal propagation rate of the abnormal fault point are calculated, fault space-time diffusion evolution is carried out, and a dynamic fault propagation path map is constructed. Through efficient and accurate fault traceability analysis, the fault diagnosis efficiency is greatly improved, and the stability and reliability of log data are improved.

The invention is an AI-driven blockchain platform for automated trade management, featuring smart contract customization, cross-chain interoperability, and quantum-resistant security. It tokenizes legal rights, obligations, and asset ownership as NFTs on the blockchain for transparency. The platform adapts contracts in real-time based on market data, compliance, and IoT-monitored trade conditions. It includes automated cryptocurrency payments with privacy controls, a decentralized dispute resolution system, and modules for modular automation and risk management. The platform supports dynamic compliance adjustments and ethical trade metrics, enhancing international trade operations through AI-driven matchmaking and behavioral analytics for partner vetting. By integrating advanced cryptographic techniques and IoT data, the platform ensures secure, efficient, and adaptable trade processes across multiple blockchain networks, optimizing workflows and safeguarding transactions against future technological threats.

The invention provides an advertisement recommendation method and system fused with user dynamic behavior modeling. The method comprises the following steps: acquiring a cross-device operation record of a user on multiple terminal devices, and synchronously acquiring a device unique identification code, high-frequency click area data and an operation timestamp; and constructing a space-time relation graph based on combined analysis of the equipment switching time interval and the click behavior, and generating a feature set reflecting the behavior correlation degree. The trajectory similarity is calculated at the edge calculation node through hardware acceleration, and migration correlation parameters are generated. And inputting the space-time relation graph into a behavior analysis model to extract coherent behavior characteristics, and forming a multi-dimensional scene portrait in combination with geographic position change and interface element distribution. And finally, through identifying a user cross-device interest migration mode, dynamically screening advertisement contents matched with the current scene, and realizing accurate pushing strategy optimization based on behavior continuity. According to the technical scheme provided by the invention, the accuracy of cross-terminal advertisement recommendation and the scene response speed are remarkably improved.

The invention relates to the technical field of server resource scheduling, in particular to a server resource scheduling system integrating AI and edge computing. The server resource scheduling system integrating the AI and the edge computing comprises a computing node module, a resource calling module, a task traversal module, a demand prediction module, a resource allocation module and a load scheduling module, the computing node module is used for monitoring the real-time running state of a server computing node and performing distributed node architecture fitting, and the load scheduling module is used for scheduling the load of the server computing node; constructing a distributed server node network; the resource calling module is used for acquiring a server running log; performing server resource calling behavior analysis on the server running log, performing resource calling time sequence characteristic evolution, and generating a server resource calling time sequence characteristic; and the task traversal module is used for carrying out unprocessed task traversal identification on the server running log and constructing an unprocessed task state mapping sequence. According to the invention, efficient and load-balanced server resource scheduling is realized.

The invention relates to the technical field of space behavior analysis, and discloses a user consumption behavior analysis method and system for different scenes in a commercial space, and the method comprises the steps: obtaining user behavior data through a wireless positioning device and a distributed environment sensor; performing space-time alignment on the multi-source heterogeneous data, and fusing commodity granularity data of a transaction system to construct a three-dimensional trajectory model; based on the three-dimensional track model, identifying residence characteristics and a moving mode of the user, and associating commodity distribution to generate a dynamic thermodynamic diagram; adjusting a guidance strategy and environmental parameters according to the density gradient of the thermodynamic diagram and the user attribute hierarchy; the payment feature data and the user behavior data are integrated to update the user tag, and a cross-regional association strategy is generated, the comprehensive, flexible and efficient commercial space user consumption behavior analysis method is provided, the problem that in the prior art, dynamic user behaviors are understood insufficiently is solved, and therefore the commercial intelligence process is promoted.

The invention relates to the technical field of user behavior analysis, and discloses a method for analyzing abnormal user behaviors based on logs and flow data, and the method comprises the steps: collecting user behavior data, including historical user behavior data and real-time user behavior data; based on historical user behavior data, constructing a group behavior baseline and an individual behavior baseline, and obtaining key behavior characteristics of the user; the deviation degree DEV of the key behavior characteristics of the user deviating from the group behavior baseline and the matching degree SimScore of the real-time behavior sequence of the user and the personal behavior baseline are calculated; according to the deviation degree DEV and the matching degree SimScore, whether the user behavior is abnormal or not is judged, and the risk level of the abnormal behavior is determined; triggering corresponding abnormal alarm and response measures according to the risk level; according to the method, the abnormal behavior of the user can be identified more accurately by fusing the log and the flow data and combining group and individual behavior baselines, and the accuracy and efficiency of anomaly detection are improved.

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

The invention provides a network security risk assessment system and method, and relates to the technical field of network security. The network security risk assessment system comprises a data acquisition layer, the data acquisition layer is connected with an analysis processing layer, the analysis processing layer is connected with an assessment decision-making layer, the assessment decision-making layer is connected with a display interaction layer, and the data acquisition layer acquires business scene data, network topology data, user behavior logs and social network conditions. According to the system, through highly integrated module design, 'information isolated island 'between traditional security tools is broken, comprehensive security situation awareness is realized, novel and unknown threats are dealt with by utilizing situational analysis and dynamic adjustment capability, false alarms and missing alarms are remarkably reduced, and the system is based on machine learning and behavior analysis. Intelligent priority ordering and automatic response can be realized, and the speed and accuracy of risk identification and response are improved.

The invention relates to the technical field of network security, in particular to a dynamic access blocking method based on zero trust. Comprising the following steps: step 1, collecting whole network flow data in real time in a bypass monitoring mode through a flow mirroring function of a network switch, performing deep packet inspection analysis on the collected original flow data, and extracting network flow characteristic parameters; 2, maintaining a dynamic identity information base; 3, performing real-time behavior analysis on each network session; 4, according to the risk assessment result and the real-time security context, generating a dynamic access control strategy based on a minimum permission principle; 5, implementing access control at the network execution point; and 6, continuously monitoring the network flow and the strategy execution effect, collecting feedback data, optimizing the risk assessment model and the strategy generation algorithm based on the feedback data, and forming closed-loop control. By dynamically updating the identity and asset information, the system can identify new assets or changes in real time, so that the adaptability and response capability of a network environment are improved.

The embodiment of the invention provides a smart shop unattended management method and device based on scene self-adaption, and the method comprises the steps: collecting shop data through a multi-path camera and a sensor, and extracting customer behavior features, commodity placement features and environment parameters; a scene recognition model and a behavior analysis model are innovatively constructed, and intelligent recognition of scene states and real-time monitoring of abnormal behaviors are achieved. A management strategy is dynamically matched based on a scene feature vector, and a refined management sub-strategy is formulated in combination with a time period, a passenger flow volume and an environment state. Meanwhile, operation data are analyzed through a self-adaptive optimization model, and dynamic updating and optimization of a management strategy are achieved. According to the method, the limitation of a traditional fixed management mode is broken through, and a comprehensive technical solution is provided for unmanned operation of the smart store.

The invention provides a traffic participant behavior analysis-based edge calculation signal lamp control method and system, and the method comprises the steps: obtaining video stream data of a target intersection, carrying out the frame-by-frame behavior state analysis of the video stream data through a spatial-temporal feature coding network, extracting the behavior state features of each traffic participant, and carrying out the analysis of the behavior state features of each traffic participant; and inputting the behavior state characteristics into a preset behavior matching model, generating a behavior triggering identifier of the traffic participant in the current time window, generating a signal lamp control instruction of an edge computing node according to the time sequence relevance between the behavior triggering identifier and the phase state of the current signal lamp, and sending the signal lamp control instruction to the edge computing node. And finally, a signal lamp phase switching time sequence of the target intersection is adjusted based on the signal lamp control instruction, so that the traffic participants meeting the traffic rule conflict condition obtain the traffic priority under the signal lamp phase switching time sequence. According to the method, the traffic accident risk is reduced, and meanwhile, the overall traffic efficiency of the intersection is optimized by dynamically adjusting the minimum response period and the phase duration parameters.

The invention discloses a construction site unsafe behavior detection method and system based on image recognition, and relates to the technical field of intelligent visual safety monitoring, and the method comprises the steps: collecting a construction image containing a constructor, carrying out the rectangular frame labeling of unsafe behaviors in the construction image, and generating a structured labeling data set; inputting the structured annotation data set into a YOLOv5 target detection model, performing training through Mosaic data enhancement and an adaptive anchor box generation algorithm, and outputting the trained YOLOv5 target detection model; based on the trained YOLOv5 target detection model, performing 128-dimensional feature vector extraction and track association matching on a detected target through a DeepSORT multi-target tracking algorithm, and outputting a track data table; and the recognition stability and behavior analysis depth of the system in a complex environment are enhanced, so that automation and intelligentization of safety management of a construction site are effectively supported.

The invention relates to the field of industrial internet intrusion prevention, in particular to a computer network big data security protection method and system, and the method comprises the following steps: monitoring network flow, collecting user identity, equipment security state, access time and network behavior data, and generating a network behavior record. In the invention, a dynamic security assessment system is constructed through network flow monitoring, identity verification and behavior analysis, and compared with static strategy or fixed rule matching, the dynamic security assessment system can adapt to a continuously changing network attack environment. Authority dynamic adjustment is performed based on the user identity verification state, flexible management and control of the access authority are ensured, and the problem of long-term high authority abuse is avoided. Threat detection not only depends on historical mode matching, but also is combined with real-time behavior analysis, and accurate isolation is executed for potential threats, so that protection measures are changed from passive response to active intervention, and the delay of manual intervention is reduced.

The present invention relates to the technical field of electricity applications. Disclosed are a supply-demand interaction game method based on electricity consumption behaviors of user groups, and a system. The method comprises: collecting and preprocessing historical electricity consumption data of smart terminals of user groups; using a clustering analysis method to perform classification on the types of electricity users; by means of the central-limit theorem and Monte-Carlo method, constructing a user electricity consumption behavior analysis model; creating an electricity user group evolutionary game model, so as to analyze long-term evolution patterns of electricity consumption strategies of the user groups; and power supply entities adjusting and formulating, on the basis of the analysis result, an electricity price incentive plan that maximizes benefits, and transmitting, via power lines, the updated plan to smart electricity consumption terminals of the users. By means of using electricity consumption data of user groups collected by a large number of smart terminal devices and combining the data with the evolutionary game theory, the present invention successfully performs mathematical modeling and analysis on electricity consumption behaviors of user groups, thus achieving mutual benefit, win-win outcomes and stable equilibrium between supply-demand parties in long-term supply-demand interaction.

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention provides an intelligent video monitoring system and method based on deep learning, and belongs to the technical field of video monitoring, and the system comprises a heterogeneous sensing array, a cognitive driving feature fusion module, a dynamic evolvable detection model, a causal reasoning tracking engine, a meta-knowledge enhancement behavior analysis module and a quantum-classical hybrid computing architecture. The heterogeneous sensing array comprises a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array and a millimeter wave radar, and is configured to generate multi-physical field sensing data; and the cognitive driving feature fusion module adopts a space-time-frequency spectrum joint coding technology, and integrates a three-dimensional convolutional network and a graph attention mechanism to realize cross-modal feature interaction. According to the invention, through multi-dimensional technical innovation, the intelligent video monitoring system with autonomous evolution capability is constructed, the perception capability, reasoning precision, resource efficiency and safety are obviously superior to those of a traditional scheme, and the high-standard requirements of smart cities, security and other scenes are met.

The invention relates to the field of enterprise safety assessment, in particular to an enterprise data safety capability assessment method and system. The method comprises the following steps of: obtaining an enterprise historical security log based on an enterprise information system, performing real-time data flow analysis and dynamic positioning of a storage position on the enterprise historical security log, and constructing an enterprise data flow mapping model; performing abnormal intrusion attack simulation on the enterprise data flow mapping model, performing enterprise security risk assessment, and marking potential risk nodes; performing historical normalized behavior analysis on the dynamic enterprise data flow model, and performing access behavior deviation detection on potential risk nodes to obtain abnormal intrusion behavior data of the enterprise system; and performing multi-time-window intrusion trajectory backstepping on abnormal intrusion behavior data of the enterprise system, and performing deep mining of abnormal attack features to obtain intrusion attack features. According to the invention, efficient and accurate enterprise data security assessment is realized, so that the enterprise is helped to make a targeted security protection decision.

The invention relates to the technical field of safety early warning, in particular to a safety early warning system based on big data, which comprises a sequence behavior analysis module, a frequency fluctuation identification module, an event number generation module, a sensitive grading judgment module and a notification content push module. According to the method, potential abnormal signals in access behaviors are identified through real-time dynamic analysis of the user access sequence and fusion of access interval change and category difference characteristics, risk behaviors are further screened based on a sudden change rule of access frequency, and accurate classification of abnormal events is realized by using unique identification and a mapping mechanism. According to the method, sensitive assets are finely graded by integrating multi-dimensional parameters, a hierarchical early warning basis is generated by adopting a weight quantification mode, push contents are directionally generated by combining different role permissions and identity information, a safety early warning process of data driving, parameter linkage and hierarchical perception is constructed, and the sensitivity of risk monitoring and the accuracy of hierarchical response are improved.

The invention discloses a platform and a monitoring method for monitoring the home safety of old people based on decision engine multi-device linkage, and the method comprises the steps: carrying out the data preprocessing according to the human body home data collected by an edge computing device; according to the human body home data, multi-modal data fusion is carried out to generate behavior identification data, and the multi-modal data fusion comprises one or more of time sequence alignment, modal completion and feature extraction; inputting the behavior recognition data into a local recognition model for real-time behavior analysis, and inputting the behavior recognition data into a cloud decision engine for multi-source data comprehensive deep analysis; and according to an output result of the local identification model and / or the cloud decision engine, performing operation of matching the smart home equipment and / or home behavior alarm processing. According to the invention, multi-device data integration and cooperative linkage measurement can be adopted to monitor the home safety of the elderly.

The invention relates to the technical field of corrosion detection, in particular to a grounding grid corrosion intelligent monitoring system which comprises an electrochemical data capture module, a corrosion behavior analysis module, a corrosion probability evaluation module and a monitoring strategy optimization module. According to the method, data errors and abnormal values are filtered and corrected, the overall quality of data is improved, the accuracy of analysis results is ensured, and in corrosion behavior analysis, the future trend of corrosion is effectively predicted through dynamic change detection of electrochemical properties and calculation of the corrosion rate; the time sequence analysis of the electrochemical impedance data not only enhances the prediction accuracy, but also improves the understanding depth of corrosion behaviors, the corrosion probability evaluation module adds consideration of random changes to the whole prediction model through the application of the Markov chain, so that the model is closer to the uncertainty of the real world, and through the optimization of the processing flow, the prediction accuracy is improved. The real-time performance and the early warning capability of the monitoring system are obviously improved, and the reliability and the operation accuracy of the system are enhanced.

The invention discloses a customer data mining exploration method and system based on big data, particularly relates to the technical field of data mining, and is used for solving the problems of feature conflicts and analysis distortion caused by semantic inconsistency of multi-source data in the prior art. A potential semantic conflict path is identified by constructing a cross-channel rule interaction entropy evaluation model, dynamic mapping and weight adjustment of conflict indexes are realized in combination with a domain knowledge base, and finally a customer behavior analysis result with consistent cross-channel semantics is generated. Multi-source behavior indexes are extracted based on business rule definition, a conflict source is accurately positioned through semantic similarity calculation and difference dimension analysis, and a weight fusion strategy is dynamically optimized according to real-time scene features, so that semantic ambiguity between channels is effectively eliminated, and the accuracy and decision support capability of customer portraits are improved.

The invention provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The invention discloses a smart park security and protection system and a control method thereof, and relates to the technical field of smart security and protection, the system comprises the following components: a diversified sensing module which integrates a video monitoring sensor, a face recognition sensor and a behavior analysis sensor and constructs an omnibearing sensing network. According to the technical scheme, all-dimensional and multi-angle monitoring and recognition of the park are achieved, the information acquisition capacity of the security and protection system is remarkably improved through the technological innovation, the comprehensiveness and accuracy of data are ensured, the diversified sensing module integrates multiple sensors of video monitoring, infrared detection, face recognition and behavior analysis, each sensor performs own functions and complements each other, and the monitoring and recognition efficiency is improved. According to the system, blind areas and errors possibly generated by a single sensor are effectively eliminated, the multi-angle sensing network enables the system to more accurately capture abnormal conditions in the park, and a solid foundation is provided for subsequent intelligent analysis and early warning.

The invention provides a retail supply chain optimization system and method based on big data analysis, and relates to the technical field of big data analysis. The system comprises a data acquisition module used for acquiring order data, logistics data and user behavior data and generating structured data; the data processing module is used for preprocessing the structured data and establishing a multi-dimensional association relationship among the data based on a preprocessing result; the inventory prediction module is used for predicting future order demands, calculating regional inventory demands and generating inventory distribution data; the inventory optimization module is used for generating replenishment data and an inventory adjustment strategy based on the inventory distribution data; the behavior analysis module is used for generating commodity recommendation data and promotion priorities based on the user behavior data in combination with the adjusted inventory distribution data; and the logistics allocation module is used for dynamically adjusting the logistics distribution path and the distribution priority. According to the technical scheme, the operation efficiency and flexibility of the retail supply chain can be improved.

The present invention relates to the field of data analysis technology, and specifically to a customer relationship management data analysis method and system based on artificial intelligence, comprising the following steps: step one, collecting customer data; step two, storing the data; step three, analyzing and mining the data; step four, building a customer portrait; step five, providing intelligent decision support for customer relationship management based on the customer portrait and data analysis results; the customer relationship management data analysis method and system based on artificial intelligence of the present invention can significantly improve the customer relationship management level of an enterprise. Through artificial intelligence deep learning, the system can automatically analyze a customer's massive conversation records, social media interactions, and transaction data, quickly identify customer needs and preferences, and then generate personalized customer portraits and behavior analysis reports, to help enterprises accurately locate potential customer groups, optimize service strategies and marketing plans, and thus improve customer satisfaction and loyalty.

The invention relates to the technical field of network advertisement promotion, in particular to a network advertisement promotion method and system based on the Internet, and the method comprises the following steps: obtaining user behavior data, analyzing a browsing track, a staying duration, a click frequency and an interactive behavior, calculating the activity of a user in real time, and recognizing the interest point of the user. And updating user tags in real time, classifying user groups, and generating a user tag data set. According to the method, the interest point of the user is identified, the user label is updated in real time, it is ensured that personalized recommendation of the advertisement conforms to the real-time behavior of the user, the active time period of the user is analyzed and predicted, resource waste caused by putting in a fixed time period is avoided, budget allocation is dynamically adjusted according to the adaptation degree between the behavior of the user and the platform, and the user experience is improved. The advertisement putting efficiency is improved, user behavior changes before and after advertisement display are compared, the advertisement putting effect is accurately evaluated, data support is provided for adjustment of advertisement strategies, and the advertisement putting effect and resource configuration are optimized.

The invention discloses an intelligent electric meter electricity utilization behavior interpretability analysis method based on a causal atlas. Through multi-source data fusion and a triple nested time window mechanism, a multi-level causal atlas is constructed; when abnormal power utilization is detected, identifying an affected area by adopting a local influence propagation algorithm, and correcting to realize dynamic map maintenance; a hierarchical lazy index updating mechanism is adopted, paths are divided into key, important and background levels according to importance, and only queried paths are verified; a causal path is quickly reconstructed through a multi-level degradation search strategy, and an interpretable analysis report is generated in combination with anti-factual reasoning. According to the method, the updating complexity and the index maintenance overhead are reduced, the query efficiency is improved, and an efficient interpretable technical scheme is provided for power consumption behavior analysis of the intelligent electric meter.

The invention provides a resource allocation method and system based on edge cloud, electronic equipment and a storage medium. The method comprises the steps that a data acquisition module acquires environment information in an edge cloud environment; wherein the environment information comprises application load information, equipment performance information and user behavior information; the data analysis module analyzes and processes the environment information to obtain an application load prediction result, an equipment capability evaluation result and a behavior analysis result, and determines an initial resource allocation strategy according to the application load prediction result, the equipment capability evaluation result and the behavior analysis result; optimizing the initial resource allocation strategy to obtain a target resource allocation strategy; the resource allocation module allocates the computing resources according to the target resource allocation strategy; and the monitoring feedback module monitors the use state of the computing resources in real time and feeds back the use state to the data analysis module, so that the data analysis module adjusts the target resource allocation strategy according to the use state.