1399 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 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 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 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 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 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.

The invention relates to the technical field of digital manufacturing, and discloses a digital twin middle station and self-healing decision system for oil and gas equipment management, which comprises a multi-source sensing terminal, a twin optimization module, a fault topology diagnosis module and an early warning execution terminal, a multi-modal synchronous fusion mechanism is constructed, and when the state of oil and gas equipment is monitored, a heterogeneous data alignment standard is formulated, and a dynamic frequency normalization rule is set for different equipment types, so that the definiteness of multi-source sensing data fusion is ensured; meanwhile, vibration, temperature, pressure and sound wave data are synchronously collected in real time, millisecond-level detection can be achieved, the data time difference problem can be eliminated, the accuracy of equipment state feature extraction is guaranteed, and the feature fusion error is further reduced; and generating a self-adaptive correction coefficient in real time by calculating the dynamic deviation of the physical model and the data model.

The invention relates to the field of photovoltaic operation and maintenance, and discloses a photovoltaic operation and maintenance decision-making system and method based on data driving, and the method comprises the steps: carrying out the continuous mapping of a photovoltaic power station assembly cluster through an acquisition node, and generating a data priority chain table related to the shielding sensitivity; performing cross-domain synchronous tracking of a multi-scale variation sliding window on the data priority linked list, and constructing a multi-dimensional coupling feature tensor of photovoltaic array performance degradation; based on a multi-dimensional coupling feature tensor, a self-adaptive redundancy stripping model is utilized to extract a component attenuation evolution trajectory, and power generation efficiency clustering density mutation and power entropy migration inflection points are identified; component clusters and electrical coupling chains corresponding to the potential performance degradation candidate event set are calibrated as candidate intervention areas, and a local power generation income interference topological graph is constructed; and based on the local power generation income interference topological graph, dynamically reconstructing the sensing weight, the operation and maintenance window and the trigger threshold. The method has the advantage of improving the overall power generation income of the photovoltaic power station.

The invention relates to the technical field of logistics supply, and discloses a multi-source data fusion-based e-commerce supply chain anti-break decision-making system, which comprises a heterogeneous graph network construction and risk prediction module, a multi-source data fusion-based e-commerce supply chain anti-break decision-making module, a multi-source data fusion-based e-commerce supply chain anti-break decision-making module and a multi-source data fusion-based e-commerce supply chain anti-break decision-making module, a time sequence sensitive graph attention network is used to predict a goods shortage risk propagation path; the elastic recovery path generation module is used for modeling supply chain recovery into a sequence decision problem on a graph based on the constructed heterogeneous graph network, and generating a plurality of elastic recovery paths through a variational inference method; according to the method, the heterogeneous graph network is constructed and the time-sequence-sensitive graph attention network is realized, so that the system can accurately capture the cascade effect and the risk propagation path in the complex supply network, the prediction accuracy of the cargo shortage risk propagation path is improved, and false report and missing report are greatly reduced.

The invention provides a crop planting intelligent decision inference system and method based on multi-source data fusion, and relates to the technical field of agricultural planting, and the system comprises a multi-source data fusion module which is used for extracting key characteristics of target crop growth according to multi-source data; the intelligent sensing module is used for constructing an agricultural knowledge graph according to the multi-source heterogeneous data of crop planting; and the reasoning and decision-making module is used for performing decision-making reasoning on the multi-source data and the key features according to the agricultural knowledge graph, a preset reasoning model and a decision-making model. According to the crop planting decision-making system, the defects that the precision in the planting management process is not high due to the fact that a traditional crop planting decision-making system is insufficient in decision-making ability, and the generalization ability of the system is insufficient due to the fact that most decision-making systems are optimized in a single field or specific crops and lack a cross-field multi-source data cooperative processing mechanism are overcome.

The invention relates to the technical field of food processing, in particular to a system for predicting flavor formation and optimization decision in food processing based on machine learning, which comprises a sensing unit, a multi-source data acquisition and fusion module, a dynamic modeling module, an optimization decision module and an execution module which are in signal connection with one another, and the optimization decision module is used for receiving the flavor perception probability distribution data and the updated scoring reference data, solving a Pareto optimal solution set through a multi-target particle swarm optimization algorithm in combination with equipment physical constraint conditions, generating a candidate processing parameter scheme, inverting equipment control parameters for the candidate processing parameter scheme through a physical constraint neural network, and obtaining the flavor perception probability distribution data and the updated scoring reference data. And a final machining parameter adjusting instruction is generated and transmitted to the execution module. According to the method, through dynamic threshold modeling, a semantic-chemical attention mechanism and a time-space preference map dynamic correction technology, multi-source data and a multi-target optimization algorithm are fused, so that closed-loop accurate regulation and control of processing parameters are realized, and the flavor quality is improved.

The invention relates to a supply chain elastic optimization decision-making system based on big data. The system comprises a data acquisition module used for performing semantic alignment processing on multi-source heterogeneous environmental protection data; the ESG risk assessment module is used for constructing a cross-enterprise ESG risk assessment model and outputting a supplier environment toughness score; the optimization decision generation module is used for analyzing an environmental protection policy text, generating a compliance verification rule, and generating a supply chain path scheme set by constructing and solving a dual-objective optimization function; and the dynamic tuning module is used for monitoring supply chain execution data in real time, dynamically triggering a dynamic rerouting decision and updating the cross-enterprise ESG risk assessment model. The system is based on the technical means of data semantic alignment, rule embedding optimization, real-time dynamic tuning and the like, not only improves the decision robustness of the supply chain in a dynamic environment, but also improves the response speed and recovery capability of the supply chain in response to environmental changes and emergencies; and a powerful technical scheme is provided for an enterprise to construct a sustainable and high-elasticity supply chain system.