1611 results about "Decision system" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention relates to a network public opinion intelligent classification and emergency decision-making system based on multi-modal fusion and dynamic evolution, and belongs to the field of network public opinion monitoring and big data analysis and artificial intelligence. The system comprises a multi-source data acquisition and preprocessing module used for crawling multi-modal data, constructing a propagation path map after preprocessing, and identifying key propagation nodes; the multi-dimensional classification engine module is used for carrying out conflict intensity quantification on public opinion events and dynamically updating a rule word bank to keep the adaptability of a conflict intensity quantification model; the event graph construction and anomaly detection module is used for constructing a public opinion propagation path and public opinion event generality logic chain mode, monitoring public opinion propagation speed and giving an alarm; the stakeholder dynamic risk assessment module is used for finely classifying network public opinion participants, providing a basis for differential propagation intervention and simulating public opinion evolution to carry out risk simulation; and the intelligent decision-making and emergency response module executes different levels of emergency measures based on the risk index according to the hierarchical response strategy.

Systems and methods are disclosed for classifying objects using electro-optical and synthetic aperture radar images through multi-modal feature alignment and fusion. A computing system acquires and preprocesses image data, then aligns features across modalities using a multi-modal alignment engine. A cross-modal attention fusion network extracts and integrates complementary information using transformer-based attention mechanisms. A modality-specific feature extraction framework processes EO and SAR images through specialized branches, ensuring optimal feature representation. An adaptive fusion decision system dynamically determines the best fusion strategy based on image quality and confidence scores. A self-supervised consistency controller enforces alignment between EO and SAR features using contrastive learning. The fused representations are processed by a neural network to generate object classifications. This system improves accuracy and robustness in environments where one modality may be degraded or missing, enhancing applications such as remote sensing, surveillance, and autonomous navigation.

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention relates to the technical field of data center management, and discloses a data center digital twinborn simulation and decision-making system oriented to intelligent management. The system comprises a data center physical feature sensing module, a virtual space reconstruction module, an operation situation deduction engine, an abnormal behavior recognition module and a decision instruction generation module. Wherein the physical feature sensing module collects multi-dimensional operation parameters of the infrastructure in real time; the virtual space reconstruction module dynamically constructs a three-dimensional virtual model based on the collected parameters; running a situation deduction engine to simulate a resource scheduling and energy flow process; the abnormal behavior recognition module analyzes the analog data stream to detect an abnormal operation mode; and the decision instruction generation module integrates the abnormal information and generates an optimization regulation and control instruction for the physical equipment. According to the system, intelligent management of the data center is realized through real-time mapping, dynamic deduction and intelligent decision making of physical and virtual spaces, and the management precision and timeliness are improved.

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention discloses a nursing risk intervention decision-making system and method based on a knowledge graph, and relates to the technical field of medical informationization, and the method comprises the steps: building a dynamic medical nursing knowledge graph based on multi-source nursing data and a deep learning model, and carrying out the entity recognition through a bidirectional long-short term memory network and a conditional random field; capturing evolution characteristics of an entity relationship through a marginal attention mechanism and a time sequence diagram convolutional network, and optimizing a knowledge graph structure based on connectivity constraints and a relationship weight threshold; a knowledge structure is dynamically adjusted by using a strengthened learning driven graph optimization mechanism, the relationship weight between entities is optimized in real time, a multi-level risk representation model is constructed, and accurate recognition and early warning of nursing risks are realized through local risk feature extraction, global risk information aggregation and time sequence risk evolution analysis. According to the method, the complex association relationship between the medical entities and the nursing risk decision are deeply fused, and the multi-dimensional medical entity features are constructed into the association network between the entities, so that early risk identification is realized.

The invention relates to a coal mine safety production intelligent decision-making system based on digital twinning, and the system comprises a physical sensing layer which collects coal mine environment parameters, equipment states and personnel positioning data through the deployment of a multi-mode sensor network, and generates a structured data flow; the edge calculation layer is used for operating an incremental multi-objective evolutionary algorithm, quickly generating a cache strategy in combination with a strategy cache pool preloading mechanism, uploading the processed data to the digital twinborn layer, receiving a global instruction of the intelligent decision-making layer and decomposing the global instruction into a device-level control signal; the digital twinborn layer is used for receiving the real-time data uploaded by the edge calculation layer, updating the state of a digital twinborn body and feeding back an optimization demand to the intelligent decision-making layer; and the intelligent decision-making layer is used for generating a global strategy by means of digital twin-guided hybrid optimization and a special FPGA acceleration card for a coal mine, and fusing the cache strategy of the edge calculation layer and the global strategy of the intelligent decision-making layer to generate a global instruction.

The invention discloses an automatic driving system based on real-time holographic modeling and dynamic shielding compensation and a vehicle, and aims to solve the problem of complicated scene perception. The system comprises a real-time holographic environment modeling module for constructing a dynamic 3D environment model; the dynamic shielding compensation module outputs compensated environment data; the sensing data fusion module is used for integrating multi-source data; the high-precision positioning and mapping module is used for generating a centimeter-level environment map; the edge computing node is used for cooperatively processing fusion data; the AI self-adaptive decision making system generates a driving instruction, and the decision making efficiency is optimized and improved through a satellite auxiliary decision making unit; the V2Xcommunication module supports low-delay and high-security data interaction; the execution control unit is used for accurately controlling the vehicle; an FPGA + GPU parallel computing framework is adopted, and decision making and control are supported after modeling and compensation data are fused. According to the invention, optimal decision making and high-precision real-time control in a complex environment can be realized, and development of an automatic driving technology is promoted.

The invention discloses a distributed intelligent warehouse scheduling system based on artificial intelligence, and belongs to the technical field of warehouse scheduling. Comprising a multi-source environment sensing module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a man-machine interaction and visualization module. A warehouse digital twinborn model is constructed, immersive display of a storage state and a scheduling strategy is realized, an AR scene is superposed through a color coding path, a thermodynamic diagram and a particle flow form, a manager can intuitively master inventory distribution, task progress and an abnormal region, eye movement tracking and a gesture recognition technology support an interactive decision, and the workload of the manager is reduced. The AR marking function can mark an abnormal area and synchronize the abnormal area to a decision making system, and through combination of AR and AI, a brand new interaction normal form is provided for intelligence and humanization of warehouse management.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention relates to the technical field of intelligent early warning of digital twinning, and particularly discloses a slope multi-physics field fusion early warning decision-making system based on digital twinning, which is characterized in that physical monitoring data representing the macroscopic state of a slope and microscopic physical response signals reflecting internal damage evolution are synchronously acquired through a multi-modal data sensing module; space-time alignment, standardization and cross-modal fusion analysis are carried out through a damage eigenstate extraction module, and a unique eigendamage variable for quantitatively representing the real-time degradation degree of the material strength is interpreted; the twinborn self-evolution module takes the variable as a core observed quantity, and drives parameters and states of a slope mechanical model to be cooperatively and dynamically updated by adopting a data assimilation method, so that high-fidelity tracking of a digital model on physical reality is realized; and the prospective early warning decision module deduces a future spatio-temporal evolution path of the material strength parameters based on the calibrated model, and realizes graded early warning and intelligent decision support by combining Monte Carlo simulation and quantification of the instability risk probability.

The invention discloses a seabed tracked robot path planning method and system based on reinforcement learning, and the method comprises the steps: obtaining environment data in real time through multiple sensors, extracting submarine topography three-dimensional features, obstacle distribution and ocean current dynamic parameters in combination with a neural network, and carrying out the combined feature extraction; establishing a seabed environment simulation model, and synthesizing various landform training data by using an adversarial generation technology; a hierarchical reinforcement learning framework is designed, a global layer collaboratively optimizes a long-distance path through a distributed agent, a local layer designs a high-frequency control strategy, and the global and local strategies realize multi-dimensional collaborative optimization through a dynamic weight adjustment mechanism; model parameters in a dynamic environment are updated in real time through an online optimization module, and a simulation strategy is quickly deployed to an entity robot through transfer learning. According to the invention, a neural network feature extraction and multi-level reinforcement learning collaborative autonomous decision-making system is constructed, and a high-reliability and low-energy-consumption autonomous operation solution is provided for a deep sea operation scene.

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

The invention relates to the field of ring main units, in particular to an event-driven intelligent ring main unit and a control method, and the event-driven intelligent ring main unit comprises a cabinet body, a cabinet body built-in circuit breaker, an isolation grounding switch and a fusion sensing array. An array synchronously acquires mechanical and electrical quantities, an edge decision-making system takes a voltage zero crossing point as an absolute time mark nanosecond alignment event, a closed-loop control core generates a protection and topology reconstruction instruction in situ according to the event, and primary equipment is driven in a millisecond level; the intelligent communication interface dynamically schedules bandwidth according to the data priority, and key instructions are ensured to be issued in real time. According to the invention, the problems of fault misjudgment and local control delay caused by lack of a unified time sequence reference in a mechanical state and an electrical transient state of a traditional ring main unit are solved.

The invention relates to the technical field of abnormal luggage processing, and discloses an abnormal luggage processing method and system based on vector matching, and the method comprises the steps: constructing a mixed retrieval knowledge base, integrating multi-source heterogeneous data to generate a relational type and a vector knowledge base, and carrying out the index association; establishing a mixed retrieval rule engine, performing keyword analysis and semantic vectorization on the case description, generating a query instruction, and performing parallel retrieval to obtain a candidate scheme set; the development field enhancement decision system generates a structured processing suggestion through a multi-dimensional evidence chain and an RAG framework, and visually outputs a process specification and a compensation basis; the disposal system is constructed to integrate all the modules, and full-process automation is achieved. The system comprises a hybrid retrieval knowledge base construction module, a rule engine module, a domain enhancement decision module and an integration module. Vector matching and rule retrieval are combined, decision making is enhanced through a large model, the abnormal luggage processing efficiency, normalization and scientificity are improved, and the method is suitable for intelligent abnormal luggage processing in air transportation.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an interpretable automatic driving decision-making method based on causal knowledge. Comprising the following steps: step 1, discovering causals oriented to automatic decision knowledge; 2, constructing an interpretable automatic driving decision model; 3, evaluating the interpretability of the automatic driving decision-making system; and 4, evaluating the performance of the automatic driving decision-making system. The method comprises four steps of causal discovery for automatic driving decision-making knowledge, construction of an interpretable automatic driving decision-making model, evaluation of the interpretability of the automatic driving decision-making system, and evaluation of the performance of the automatic driving decision-making system. Based on a causal discovery model of decision observation data, key variables of a causal relationship influencing self-vehicle driving behaviors are found from the data, an interpretability standard and a decision performance evaluation index are defined, the interpretability and decision performance of an automatic driving decision system are evaluated, and the automatic driving decision system is continuously optimized and iterated.

The invention relates to the technical field of vehicle-mounted temperature control, and discloses a method and system for intelligently controlling the temperature and humidity of a vehicle-mounted cold chain and a storage medium. The method comprises the following steps: acquiring and preprocessing temperature and humidity data through a sensor; performing state evaluation based on a three-way decision model; constructing a temperature and humidity trend analysis model to predict a change trend; constructing a control decision system by adopting multi-agent reinforcement learning; executing a multi-mode control strategy; and adaptively updating the control system according to the execution effect. According to the application, the future temperature and humidity change trend is predicted according to the historical data and the real-time state, collaborative optimization control of temperature and humidity is realized, self-learning and continuous optimization capabilities are provided, and the accuracy, stability and energy utilization efficiency of cold chain temperature and humidity control are effectively improved.

The invention discloses a sky-ground integrated automatic driving intelligent decision-making system and method driven by a large language model, and the system comprises a satellite layer, a vehicle end layer and a cloud end layer, and achieves the precise, intelligent and efficient automatic driving decision-making through efficient data interaction and cooperative processing. The satellite layer comprises a low-orbit satellite constellation, transmits high-precision positioning information to a vehicle in real time and provides a space-time reference; the vehicle end layer is an executor, collects environment data in real time through a sensor, generates a preliminary driving decision by using high-precision positioning data and an end-to-end rapid decision model built in the vehicle end layer, and uploads driving data, environment sensing data and decision requirements to a cloud end; the cloud end carries out remote monitoring and management on the system, evaluates the vehicle state and carries out early warning, when the vehicle runs safely, the vehicle end is adopted for decision making, when potential risks or abnormal conditions exist, the large language model is adopted for decision making, optimization instructions are pushed to the vehicle end, model parameters or emergency disposal schemes are updated, and the vehicle end is assisted to correct the decision.

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention relates to the technical field of agricultural intellectualization, in particular to an artificial intelligence decision-making system for unmanned agricultural operation, which comprises an acquisition module, a construction module, a monitoring module, an AI decision-making module, a data fusion decision-making module and an execution control module which are in mutual signal connection, the semantic analysis module is used for collecting peasant household interview records and farm work log text data and carrying out semantic analysis on unstructured texts by adopting a natural language processing technology; the construction module is used for receiving the empirical feature vector and constructing a multi-dimensional associated knowledge graph by adopting a graph neural network; and the AI decision module is used for receiving the environment feature matrix, training by adopting a deep reinforcement learning model in combination with a general agricultural data set, and generating a decision scheme with a confidence score. According to the method, collaborative decision-making of regional implicit knowledge and multi-source environment data is realized by fusing a dynamic weighting mechanism of a peasant experience knowledge graph and deep reinforcement learning.

The invention relates to the technical field of intelligent decision making of production equipment, and discloses a decision making method and system based on a knowledge graph, and the decision making method based on the knowledge graph comprises the following steps: processing input data through a multi-granularity knowledge graph construction system, and obtaining a multi-scale knowledge graph; processing the real-time sensor data and the multi-scale knowledge graph through a time sequence knowledge dual representation learning framework to obtain a dynamic representation model; processing the dynamic representation model and the multi-scale knowledge graph through a time-varying causal propagation network decision system to obtain a decision analysis result; processing the decision analysis result and the multi-scale knowledge graph through a multi-hypothesis reasoning algorithm to obtain a root cause analysis report; a multi-scale knowledge graph is constructed by fusing multi-source heterogeneous data, and intelligent decision with high accuracy and strong interpretation is realized by combining time sequence knowledge dual representation learning and time-varying causal propagation network analysis.

The invention belongs to the technical field of decision optimization, and relates to an enterprise-level intelligent risk control decision system combined with a real-time data stream, and the system comprises a heterogeneous data distribution module which is used for obtaining multi-source heterogeneous data streams inside and outside an enterprise; the behavior time sequence splicing module is used for executing cross-system user ID association and time sequence recombination on the real-time data flow to generate a user behavior chain with continuous time stamps; the feature fusing calculation module is used for receiving the user behavior chain, performing feature extraction and outputting a real-time feature vector with a quality flag bit; the incremental model updating module is used for respectively generating a baseline risk score and a dynamic risk score; and the dynamic weight decision module is used for generating final decision parameters. And the risk control processing execution module responds to the final decision parameter to trigger a processing action, and configures a manual auditing arbitration channel and a feedback data generation unit. According to the method, the problems that a dual-check algorithm is not deeply coupled with a business index, and abnormal data which passes hash check but has logic violation flows into a real-time channel are solved.

The invention relates to an app automatic execution and test system based on multi-agent collaborative perception decision, and the system comprises three agents and an upper computer which is used for collecting information and carrying out interactive execution: an execution decision agent receives the output of a planning strategy recommendation agent and a perception agent, serves as input information, generates an instruction to control the upper computer, and carries out the execution of the decision. And interacting with the to-be-tested system to complete the execution task of the to-be-tested system. The three agents are an execution decision agent, a planning strategy recommendation agent and a perception agent, and the three agents jointly form a decision system of the app automatic execution system. After a task target is given, the system can interact with a system to be tested, text recognition, icon recognition, keyboard recognition, layout recognition and anomaly recognition are carried out on a UI page where the system is located through a sensing agent, and meanwhile, system information is stored in a knowledge base after a planning strategy recommendation agent carries out offline exploration; and the execution decision-making agent autonomously reasones out an execution action according to information input of the perception agent and the planning strategy recommendation agent so as to complete execution of a task target. According to the method, multiple agents with different capabilities are combined to work cooperatively while the reasoning capability of the large model is utilized, so that the accuracy and the stability of the model are greatly improved.

The invention discloses a financing guarantee business digital intelligence risk control method and system, and belongs to the field of financial science and technology, and the method comprises the steps: obtaining multi-source heterogeneous data of government affairs, supply chains, Internet and the like; calculating a data type comprehensive score D and judging a data type through a dynamic weight model based on a unified data parameter system comprising a source identifier, a structure specification, a time sequence, entity association and content characteristics; for different types of data, technologies such as block chain evidence storage, federated learning, a graph neural network and natural language processing are adopted to carry out differentiated risk assessment, guarantee limit output and risk early warning and other decisions. The system comprises a four-layer architecture of data acquisition, processing, modeling and decision support, and supports real-time access of multi-source data and automatic risk control. The method effectively deals with emerging scenes such as false trade and public opinion risks, meets the requirements of data security compliance, and provides an efficient and accurate digital intelligent risk control scheme for financing guarantee.

The invention discloses an energy optimization decision-making system based on cloud computing, and belongs to the technical field of energy optimization, and the method specifically comprises the steps: collecting energy consumption data and environmental parameter data of energy consumption equipment, carrying out the preprocessing, extracting space-time joint feature vectors of the energy consumption data and the environmental parameter data of the energy consumption equipment, and carrying out the calculation of the space-time joint feature vectors; constructing a space-time collaborative prediction model of dynamic heterogeneous graph fusion, predicting energy load in a future preset time period, establishing a multi-target optimization model including economic cost, carbon emission and equipment loss, and solving the multi-target optimization model to generate an optimal scheduling instruction; according to the method, millisecond-level data analysis and scheduling optimization are realized through the dynamic heterogeneous graph fused space-time collaborative prediction model and cloud computing, and the method is suitable for large-scale complex scenes and cross-domain collaborative scheduling.

The invention belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.