851 results about "Predictive analytics" patented technology

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The present invention relates to a system and method for cross-domain predictive modeling using Bedrock-based foundation models and blockchain-anchored data. The invention integrates large-scale foundation model reasoning with distributed ledger-based data provenance to enable verifiable, secure, and explainable predictive analytics across heterogeneous domains such as finance, healthcare, logistics, and environmental systems. The system comprises a data ingestion unit for receiving and normalizing multi-domain datasets, a blockchain anchoring unit for generating cryptographic hashes and recording data provenance into a distributed ledger, a cross-domain harmonization processor for aligning heterogeneous feature representations into a unified latent space, a foundation model processor configured to execute Bedrock-based predictive inference with adaptive domain contextualization, a verification processor for validating predictions against blockchain-anchored ground truths, and a governance processor for maintaining immutable audit trails of model evolution.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

The invention relates to an intelligent power grid distribution line fault prediction analysis method, and the method comprises the steps: carrying out the synchronous collection and standardization processing of the real-time load, electrical parameters, environment information and component aging states of each sampling point of a distribution line through distributed sampling and precise space positioning; and based on multi-time scale dynamic feature and aging feature extraction, realizing automatic generation of a structured and layered feature library and scene labels. A label-driven historical model parameter migration and meta-learning mechanism is utilized to quickly adapt to new working conditions and finely adjust the weight of the model, multi-time scale features are fused to carry out contribution degree weighting, and finally the accuracy and robustness of fault prediction are improved. The system also continuously optimizes the model performance through real-time A / B comparison and automatic parameter switching, has the capabilities of data tracing and result interpretation, and significantly enhances the timeliness, reliability and intelligent level of power distribution network fault diagnosis.

The invention relates to the technical field of digital engineering, and particularly provides a BIM-based engineering construction dynamic three-dimensional visual management method and system, and the method comprises the steps: collecting construction multi-dimensional data, carrying out the correlation mapping with an initial BIM model component after preprocessing, and forming a structured construction element data set; dynamically reconstructing an initial BIM model based on the data set, generating a three-dimensional dynamic twinborn body, and dynamically displaying information such as superposition progress, quality, resources and environment; a prediction analysis model is called to generate progress prediction, quality early warning and resource optimization suggestions, and a final scheme is determined after visual simulation verification; and updating the data set and the twin according to an execution result, and iteratively optimizing the prediction model. According to the scheme, dynamic integration and visual management of construction total elements are achieved, the timeliness, accuracy and intelligent level of construction management are effectively improved through intelligent prediction and closed-loop optimization, the construction efficiency is improved, and the management cost is reduced.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention discloses a knowledge model data management system based on artificial intelligence, and relates to the field of data management, and the system comprises the following components: a data collection and storage module, an intelligent prediction and analysis module, an abnormal comprehensive processing module, a knowledge graph and traceability module and a man-machine interaction management module. According to the invention, the intelligent prediction analysis module is combined with a time sequence prediction model and a causal reasoning algorithm, abnormal fluctuation and causal association thereof in data can be accurately judged, the accuracy of anomaly detection is effectively improved, and meanwhile, the knowledge graph and traceability module uses the constructed knowledge graph and reinforcement learning algorithm to improve the accuracy of anomaly detection. According to the method, the relation chain can be quickly traced from the abnormal data, the problem source can be positioned, the abnormal traceability efficiency is remarkably improved, and the functions act together, so that the system can more quickly and accurately discover and process problems when facing a complex data environment, and the stability and reliability of data management are guaranteed.

The invention discloses a resource scheduling optimization method and system based on deep learning, and particularly relates to the technical field related to resource scheduling, real-time indexes such as CPU utilization rate, memory occupancy rate and network bandwidth are acquired through a lightweight monitoring agent, and resource demands in the future 3-10 minutes are predicted by using an improved LSTM (including a cross-cycle attention mechanism), so that resource scheduling optimization is realized. The heterogeneous resource matching degree is calculated in combination with a graph attention network, a hierarchical scheduling strategy and a dynamic fault-tolerant mechanism are implemented, and the scheduling effect is evaluated through a multi-objective optimization function. The system comprises a distributed sensing terminal, a predictive analysis engine, a decision center and other modules, and supports federated learning, elastic capacity expansion and contraction and visual evaluation. The resource utilization rate can be improved, delay and energy consumption are reduced, and the method is suitable for heterogeneous resource scheduling scenes such as cloud computing and edge computing.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant regulation and control method and system for realizing new energy consumption, and the system comprises a multi-source heterogeneous data collection module, an intelligent prediction analysis module, a resource aggregation modeling module, an optimization decision module, a block chain cooperation module, and a digital twinborn evaluation module. Multi-time-scale coupling prediction is carried out on new energy output and load demand through the deep space-time convolutional neural network, fluctuation and intermittency characteristics of new energy can be described, short-term and ultra-short-term prediction precision is improved, wind curtailment and light curtailment rate and load reduction risk are reduced, and the prediction efficiency is improved. According to the method, high matching between a virtual power plant scheduling plan and an actual operation condition is guaranteed, a flexible resource feature matrix is constructed, and distributed energy storage, interruptible load and electric vehicle multi-element resources are subjected to refined modeling and aggregation, so that a virtual unit capable of being efficiently scheduled can be formed, the resource utilization efficiency is improved, and the overall scheduling cost is reduced.

A system and method for predictive analysis, scenario simulation, and decision optimization is provided. The system includes a prediction management system executed on a distributed computing infrastructure, and a prediction engine configured to receive input data, including event parameters, user-defined constraints, real-time data feeds, and historical trends. The prediction engine generates predictive models using algorithms trained on historical event outcomes, assigns probability scores and confidence intervals to potential outcomes, and dynamically updates the models based on new input data. Actionable insights are generated and ranked according to predefined success criteria. A non-transitory computer-readable medium is used to store the predictive models, outcome probabilities, and actionable insights for subsequent analysis and reporting. This system facilitates enhanced decision-making by offering real-time insights and continuously refined predictions, thereby optimizing responses to complex events and scenarios.

A method for operating smart fixtures to enable artificial intelligence-based emergency detection, communication, and response within a building is disclosed. The method includes monitoring environmental conditions using sensors integrated into smart fixtures, such as power receptacles, light switches, and vents. Audio, image, and physiological motion data are captured and transmitted to a processor for analysis. The system extracts signal and motion-based features, identifies subjects, and calculates health parameters to assess distress levels. If an emergency is detected, an emergency communication signal is generated, and a communication session with a remote device is established. The system provides real-time situational updates and executes emergency response actions, such as adjusting power distribution and environmental controls. Predictive analytics modules enhance response efficiency by anticipating subject movements and optimizing emergency interventions. The smart fixtures facilitate intra-building communication and user interaction through voice-activated queries, enabling an adaptive and intelligent emergency management system.

The invention discloses an elastic resource beforehand early warning and scheduling method and system based on load prediction, and belongs to the technical field of cloud computing resource management and scheduling, and the method comprises the following steps: constructing a historical data collector, and collecting and preprocessing multi-dimensional time sequence load data of a business system in real time; constructing a prediction analysis engine, training a prediction model, accurately predicting future busy and idle time points and period trends of each service system, and generating a quantitative load prediction report; an elastic planner is constructed, and an elastic resource pre-expansion plan is automatically generated and executed before a business peak arrives based on a load prediction report and a preset strategy rule; constructing an intelligent scheduler, and executing an elastic capacity expansion action according to the elastic resource pre-capacity expansion plan; and outputting a complete elastic resource beforehand early warning and scheduling system based on load prediction. According to the invention, the response speed and service quality of the system are remarkably improved, and the method is suitable for resource management requirements in complex environments such as multi-cloud and mixed-cloud environments.

The invention discloses an intelligent planning production scheduling method and system based on a large model, a product and a medium, and relates to the field of production scheduling, and the method comprises the steps: inputting a text description record into a preset production large model for semantic analysis, extracting decision elements of a production expert in different scenes and a weight relation thereof, and obtaining a production scheduling knowledge base; inputting the scene description text into a preset large production model, and extracting core decision logic of experts in the associated scene; the method comprises the following steps: performing predictive analysis on real-time data of a production field based on a preset production large model to obtain a production state evolution trend in a preset time period, identifying productivity conflict points from the production state evolution trend, generating a plurality of alternative adjustment schemes based on the productivity conflict points, process constraint conditions and expert core decision logic, and inputting the plurality of alternative adjustment schemes into a preset target evaluation function for calculation, and selecting the alternative adjustment scheme with the highest comprehensive score as a planned production scheduling scheme. By implementing the method, the enterprise production plan adjustment efficiency can be improved.

An integrated document portfolio management and governance system and method are disclosed. The system includes a computing unit having an application interface adapted to present and / or formulate at least one input query. The system further includes aa central controller having a backend server communicably connected to the application interface of the computing unit. The backend server includes a data receiving component adapted to receive document dataset, each comprising a plurality of data elements, from a plurality of data sources in one or more formats. The backend server further includes a data ingestion module adapted to detect, normalize, and aggregate the plurality of data elements of the document dataset and subsequently store them within a central data repository. Furthermore, the backend server includes an ontology generator module adapted to create and maintain a dynamic ontology for the ingested datasets in real-time, wherein the plurality of data elements is categorized and contextualized in accordance with the dynamic ontology. Additionally, the backend server includes a governance module adapted to enforce & monitor data compliance policies and a data analysis module adapted to analyze the ingested data and generate actionable insights, wherein the actionable insights include one or more predictive analysis, data accuracy status, governance status, operational inefficiency, risk indicators, compliance gaps, and risk lineage and integrity. In operation, a user formulates an input query towards the central controller which in response is configured to automatically manage, govern & monitor the received data and subsequently visualize one or more actionable insights and / or compliance gaps onto the application interface of the computing unit.

The invention discloses a radar wave measurement method and system for the liquid level of a storage tank, and relates to the technical field of liquid level measurement. The radar wave measurement method for the liquid level of the storage tank comprises the following steps: acquiring radar echo signal data of the storage tank in a set measurement period, and preprocessing the radar echo signal data; based on a pre-trained liquid level identification deep learning model, performing prediction analysis on the radar echo signal data to obtain a liquid level feature set of the storage tank; and acquiring liquid level control parameters of the storage tank, analyzing liquid level control driving indexes of the storage tank in combination with the corresponding liquid level feature set, and controlling the start-stop state of a water pump of the storage tank based on the liquid level control driving indexes. Multi-dimensional modeling is carried out on radar echo signal data in a continuous time period, the limitation that a traditional liquid level measurement method depends on echo signals at a single moment to carry out height judgment is broken through, and the method is particularly suitable for industrial liquid level monitoring requirements with high disturbance and high precision requirements.

The invention discloses an ultra-short-term solar radiation prediction method, and the method comprises the steps: collecting meteorological observation data of a target radiation region, and the meteorological observation data comprise satellite cloud picture data, radar data and ground meteorological data; performing preprocessing and feature extraction on the meteorological observation data by using pre-constructed preprocessing multiple channels and feature extraction multiple channels, and fusing the extracted feature data to obtain meteorological fusion feature data; performing prediction analysis on the meteorological fusion feature data by using a pre-constructed target domain solar radiation predictor to obtain an ultra-short-term solar radiation prediction result; according to the method, the relation between meteorological fusion feature data and solar radiation can be analyzed more accurately, and the timeliness and accuracy of ultra-short-term solar radiation prediction are remarkably improved.

A platform which provides a system and method for intelligent document processing with anomaly detection and predictive analysis comprising a user interface which allows platform users to upload documents, a data acquisition engine that leverages one or more machine and / or deep learning algorithms to classify, validate, and enforce compliance of the uploaded documents, and an artificial intelligence engine that constructs and maintains the models developed from the machine and / or deep learning algorithms. The platform may utilize various bespoke APIs to integrate validated data with third-party systems when an authorized entity initiates the process. The platform can function as a system of record and central, secure repository for an applicant's documentation and information required for various application processes. In some embodiments, the platform utilizes a trained generative AI model to assist platform users and to provide predictive analysis responsive to user submitted queries.

A system and method for collaborative creation and management of intellectual property (IP) projects is disclosed. The system includes a server configured to dynamically assign roles and permissions to participants based on predefined criteria. A graphical user interface (GUI) facilitates real-time collaboration, editing, and version control, while a semantic analysis engine ensures compliance with intellectual property standards and assesses content novelty. Predictive analytics generate metrics, such as patentability scores and inventorship overlap, to guide project development. Contributions are electronically tracked and stored in a secure, non-transitory computer-readable medium alongside generated metrics. The system supports role-specific recommendations, automated notifications, and integration with external data sources for enhanced functionality. Participants can visualize contributions, resolve conflicts, and simulate commercialization outcomes, enabling efficient project management and innovation. This invention improves the collaborative development process, ensuring compliance, enhancing productivity, and maximizing the value of intellectual property assets.

The invention provides a distributed photovoltaic data acquisition method and device based on adaptive encryption communication and multi-link redundancy, and the method comprises the steps: collecting real-time power generation data, equipment state data and environment parameters of a photovoltaic system through a multi-source sensor, and forming original data; an MQTT over TLS adaptive encryption communication protocol is adopted to perform end-to-end encryption on original data, and dynamic key management is combined to ensure transmission security; based on a dynamic link selection algorithm, encrypted data is transmitted to a power master station through 4G and LoRaWAN double-link redundancy, so that the transmission reliability is improved; and the master station side decrypts the data and then integrates the data to a database to support power grid dispatching and predictive analysis. According to the invention, the problems of safety and reliability in distributed photovoltaic data acquisition are solved, and the method is suitable for a smart power grid monitoring scene with high reliability and high real-time performance requirements.

The invention relates to a clean coal calorific value prediction method, device and equipment, a medium and a product, and is applied to the technical field of computers. The method comprises the following steps: acquiring a dense medium density influence parameter of a target coal material; in the dense medium separation process of the target coal material, the dense medium density influence parameters are predicted and analyzed based on a preset clean coal heat value prediction model, and a clean coal heat value prediction result of the target coal material is obtained; the clean coal calorific value prediction model at least comprises a first sub-model, and the first sub-model is used for predicting a first prediction result corresponding to the target coal material based on the time sequence characteristics of the dense medium density influence parameters. Through dynamic change of dense medium density influence parameters, a set value of the dense medium density is adjusted in time, so that a clean coal calorific value prediction result can be reflected accurately in real time. The hysteresis problem caused by determining a calorific value result by chemical examination after dense medium separation is avoided, and the prediction in the separation process can dynamically adapt to complex working conditions. Therefore, the accuracy of clean coal calorific value prediction is improved.

The invention discloses a personnel behavior specification identification system based on a digital twinborn technology, and the system comprises a multi-source data collection and synchronization module which collects the multi-dimensional information of personnel behaviors through a plurality of sensors, and achieves the time synchronization and space alignment of data streams; the digital twinborn body construction module is used for completing high-precision three-dimensional reconstruction of human body postures and constructing an environment three-dimensional model; the behavior rule simulation verification module is used for constructing a rule base, loading and analyzing a behavior rule description file in real time, and carrying out high-precision physical simulation and consequence prediction analysis on illegal behaviors; and the real-time decision output module is used for constructing a multi-stage early warning mechanism, automatically triggering emergency disposal measures and generating an augmented reality superposition picture. Through triple capability upgrading of high precision, low delay and easy configuration, the system not only improves the accuracy and the real-time performance of behavior recognition, but also enhances the compatibility capability and the operation and maintenance efficiency for different industry application scenes, and has wide popularization value and feasibility.

There is described an artificial intelligence wearable ECG skin patch (400) to detect sudden cardiac arrest. The wearable ECG monitoring patch (400) with AI based predictive analytics and remote based cardiac monitoring (615) system that can detect cardiac arrhythmias automatically in real-time and make a diagnosis with AI models trained with acquired data. The wearable skin has a biocompatible polymer patch (400) which captures the electrical signal through a flexible printed electronic technology based conducting ink and a substrate. The microcontroller controls (201), store and transmit the data packets. The IoT connected signal transmission is capable of recording and transferring the data packets through wireless communication. The AI engine is capable of analysing, evaluating, testing and providing the data packets of sudden cardiac arrest through a peak detector algorithm. The ECG skin patch (400) to detect and measure the sudden cardiac arrest with the R-R interval time series to obtain heart rate variability.

The invention discloses a safety production standardized comprehensive management system and method, and relates to the technical field of safety production management, the system comprises the following components: a data acquisition module, a data preprocessing module, a model construction and training module, a prediction analysis module and a maintenance management module; according to the method, the time sequence data in the full life cycle of the equipment are continuously collected, the time sequence data comprise key parameters such as operation duration, start-stop times and maintenance records, the improved LSTM neural network is utilized to construct the equipment safety life prediction model, and the model can not only predict the overall life of the equipment, but also can predict the service life of the equipment. The method can accurately predict the residual safe use cycle of the easily-worn part, the accurate prediction capability enables an enterprise to plan a maintenance plan in advance, production interruption and safety accidents caused by sudden equipment faults are avoided, and the accuracy and foresight of equipment safety management are remarkably improved.

The embodiment of the invention provides an elastic fragment routing and cold and hot data optimization method based on multi-dimensional feature prediction and related equipment, and belongs to the technical field of distributed database storage optimization. The method comprises the following steps: receiving a data stream from Internet of Things equipment; performing analysis and feature extraction on the data stream to obtain multi-dimensional feature data; according to the multi-dimensional feature data and the time sequence prediction model, performing prediction analysis on a fragmentation strategy to obtain an optimal fragmentation strategy; analyzing the routing decision according to the data priority of the data stream and a dual-mode routing engine to obtain routing decision result data; generating a target index name and creating a target index according to the optimal fragmentation strategy and the routing decision result data; according to the target index name, the data stream is routed to the target index according to the fragmentation key for batch writing; the fragmentation keys are from multi-dimensional feature data. According to the embodiment of the invention, intelligent prediction of the fragment number and automatic hierarchical storage of data can be realized, and the problems of resource elastic scaling and cost efficiency optimization are fundamentally solved.

The invention discloses an intelligent manufacturing real-time decision-making method and system based on digital twinning, and relates to the technical field of intelligent manufacturing. Comprising the following steps: S1, collecting multi-source operation data in a physical production system in real time; s2, dynamically constructing and updating a digital twinborn model of the physical production system based on the multi-source operation data acquired in real time; and S3, based on the digital twin model, monitoring the operation state of the physical production system in real time. According to the method, the multi-source operation data of the physical production system are collected in real time, and the digital twin model is dynamically updated, so that no lag deviation between the model and the physical system is ensured; when a dynamic disturbance event is identified, event-driven real-time simulation and predictive analysis are started immediately, delay caused by traditional batch processing is avoided, and disturbance influence can be accurately evaluated in combination with historical data and expert experience; and then an optimal decision scheme is generated and screened in real time through a multi-objective optimization algorithm.

The invention provides a cooling tower floating water loss prediction analysis method, which comprises the following steps: acquiring and cleaning multi-dimensional operation data of a cooling tower, performing working condition label classification, feature normalization and derivative feature extension, and constructing a deep neural network model to realize floating water loss prediction after dynamically adjusting a feature weight based on a working condition classification clustering result. And in the online stage, real-time working condition labels and historical error feedback are combined, and the feature weights and model parameters are dynamically optimized, so that the generalization ability and prediction accuracy of the model under variable working conditions are improved, the engineering applicability and the online adaptive optimization ability are high, and water saving and intelligent operation management of the cooling tower system are effectively supported.

The invention relates to the technical field of pollution treatment of livestock and poultry breeding, in particular to a pollutant emission prediction and management system for a livestock and poultry farm, which comprises a data acquisition and storage unit, a data processing unit, a data processing unit and a data processing unit, and is characterized in that the data acquisition and storage unit is used for acquiring multi-source data of the farm in real time; the influence factor calculation unit obtains key influence factors influencing water quality change and malodorous gas based on production data and facility operation data of the farm; the model prediction analysis unit generates a predicted value of pollutant emission by using a dynamic multi-dimensional pollutant emission prediction model, and key influence factors are introduced in the prediction process for correction; and the management decision-making unit judges the early warning level and generates a control instruction according to the early warning level. According to the system, accurate prediction of emission of malodorous gas and water pollutants is realized by constructing a dynamic multi-dimensional pollutant emission prediction model, and meanwhile, the system not only prevents excessive emission of pollutants in advance, but also is deeply integrated with aquaculture production management to guide actual aquaculture operation.

The invention discloses a new energy electricity price accurate prediction method and system based on virtual power plant aggregation regulation and control, and relates to the technical field of industrial data processing. The method comprises the steps: S1, carrying out the preprocessing of meteorological environment data, output equipment data and battery power grid operation data; s2, constructing a variation dynamics analysis and mutation depth discrimination mechanism based on the meteorological environment data, and entering an output prediction and optimization process and a scheduling load response process in a layered manner; s3, executing energy storage collaborative optimization scheduling and parameter correction according to a meteorological output regression prediction analysis result; and S4, performing electricity price time sequence multi-source driving analysis, and reconstructing an electricity price fluctuation response strategy. The problems that new energy output violently fluctuates within a minute level due to sudden weather change, and electricity price prediction lags due to the fact that an existing prediction model depends on too low weather data updating frequency and cannot capture the rapid change in time are solved.