175 results about "Strategy execution" patented technology

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

The invention relates to the technical field of operation and maintenance safety compliance, and discloses an operation and maintenance service automation safety compliance detection system and method. The system comprises an operation and maintenance event acquisition unit, a strategy execution unit, a compliance analysis unit, a strategy optimization unit and a violation tracing unit. The operation and maintenance event acquisition unit captures operation and maintenance operation events and execution environment parameters in real time, packages the operation and maintenance operation events and the execution environment parameters into event data packets and transmits the event data packets to the strategy execution unit; the strategy execution unit extracts a reference strategy rule from the security strategy library, performs matching verification on the event and generates a result; the compliance analysis unit analyzes compliance fluctuation characteristics through a multi-dimensional compliance deviation model in combination with historical records; the strategy optimization unit dynamically adjusts the event capture frequency and corrects a strategy matching rule according to the fluctuation characteristics; the violation tracing unit counts the risk cumulant and generates a violation tracing instruction when the risk cumulant exceeds a preset capacity. According to the system, automation and dynamic adjustment of operation and maintenance safety compliance detection are realized, and a risk accumulation process can be effectively tracked.

The invention provides a real-time command and control strategy optimization method and device based on multi-agent reinforcement learning, equipment and a storage medium, and the method comprises the steps: deploying a pre-trained cooperative intention network on edge equipment of each combat unit, and coding local observation data and a historical sequence thereof into a low-dimensional local cooperative intention vector; replacing original high-dimensional data as inter-unit communication content; whether broadcasting is carried out or not is dynamically determined according to observation uncertainty and a channel state by combining a self-adaptive intention broadcasting mechanism, and communication resources are distributed according to needs; after each unit receives an adjacent collaborative intention vector, a lightweight space-time attention module in a pre-trained local strategy execution network carries out space weighting on adjacent intentions and fuses time features of own historical intentions, and a collaborative and consistent real-time command and control instruction is generated under the condition that global information convergence is not needed.

The invention discloses an airline dynamic price adjustment collaboration method and system, and the method is executed under the collaboration of a coordination server and a plurality of airline local servers, and comprises the steps: obtaining the historical flight ticket selling data of each airline local server through the airline local server, and carrying out the privacy protection processing of the data locally; each airline local server trains a global pricing model issued from the coordination server by using the local data subjected to privacy protection processing to obtain local model parameters and uploads the local model parameters to the coordination server; and the coordination server aggregates all the received local model parameters, updates the global pricing model, and issues the updated global pricing model parameters to the local server of each airline. According to the method, joint modeling and collaborative pricing are realized on the premise that the data are not out of the local, vicious price competition is avoided, the overall income of multiple airlines is improved, and the stability and fairness of strategy execution are guaranteed.

The invention relates to the technical field of data distributed storage, in particular to a data distributed secure storage system and method based on computer science and technology. The system comprises a data fragmentation and strategy binding module, a strategy declaration evidence storage and state tracking module, a strategy execution voucher generation module and a voucher verification and data assembly module. The system carries out fragmentation processing on original service data through a dynamic fragmentation algorithm to generate encrypted data fragments and a strategy binding statement; storing the strategy binding declaration to a distributed account book and maintaining a strategy state table; generating a policy execution voucher including an access qualification proof, a digital signature response, a policy hash value and a timestamp based on the system real-time state and the access request; and the distributed storage node implements decentralized data access control by verifying the policy execution credential. According to the method, the single-point fault and strategy tampering risk of centralized authority management are effectively solved, and a reliable data security sharing scheme is provided for a supply chain financial service scene.

The invention discloses an edge computing and distributed zero-trust architecture system and a data processing method. The system comprises an edge computing device cluster, a distributed zero-trust control center, an edge security gateway and a block chain identity infrastructure. The edge computing node collects hardware state, vulnerability and behavior data through the terminal security agent module; the block chain identity infrastructure provides distributed identity identification and verifiable credential service; the edge security gateway executes data packet filtering and single packet authentication; and the distributed zero-trust control center calculates a real-time security coefficient and generates a differentiation strategy, and the log is synchronized to the alliance chain for evidence storage. According to the data processing method, security access is realized through access request initiation, single packet authentication, data acquisition, trust evaluation, strategy execution and log evidence storage. According to the method, the edge device state and distributed zero trust are deeply fused, the problems of incomplete identity authentication and static permission control in an edge scene are solved, and the security and traceability of an edge computing network are improved.

The invention relates to the technical field of supply chain management and control, in particular to a supply chain intelligent management and control system and method based on multi-source data fusion, the system comprises a data acquisition module, a data fusion processing module, a digital twin deduction module, a multi-target decision module and a strategy execution and feedback module, and the method completes corresponding management and control steps according to the system. The method comprises the following steps: firstly collecting full-chain multi-source heterogeneous data of a supply chain, generating unified multi-dimensional state data through space-time alignment and multi-modal fusion, then constructing a digital twin model to carry out multi-scene deduction to output a candidate strategy, screening out a comprehensive optimal strategy through a multi-target decision, and finally analyzing and issuing the strategy and collecting execution effect feedback. And closed-loop iterative optimization is formed. According to the method, the problems of isolated data, subjective decision, no deduction system and lack of closed-loop optimization of traditional supply chain management and control are effectively solved, the management and control intelligence and automation level is improved, the supply chain toughness is enhanced, the operation efficiency is optimized, the operation cost is reduced, and full-chain collaborative management and control are realized.

The invention provides an intelligent caching method and device based on a self-adaptive caching strategy, and the method comprises the steps: collecting an access event flow of each caching key in real time through a sensing probe embedded in a caching client, the event flow comprising Key, operation type, timestamp, request source, hit state and response time; running a streaming processing engine at a client based on an access event stream, and calculating indexes such as instantaneous QPS, continuous miss times and short-term access frequency by adopting a sliding time window; a calculation result is input into a rule engine for online matching, a decision instruction for a single Key is generated, and the decision instruction comprises distributed mutual exclusion lock addition, local TTL dynamic adjustment and read-write mode switching; a decision instruction is directly executed in a client memory through a strategy executor, and nanosecond strategy effectiveness is achieved; and finally, collecting system feedback data after strategy execution, inputting the system feedback data into a machine learning module for online learning, and dynamically optimizing a rule threshold value or recommending a new strategy.

The invention relates to the technical field of computers, and belongs to a BIOS (Basic Input / Output System) and BMC (Baseboard Management Controller)-based GPU (Graphics Processing Unit) resource intelligent dynamic optimization method and system. A four-layer collaborative architecture of a firmware layer (BIOS), a management controller layer (BMC), a system layer (operating system kernel) and a hardware layer (GPU equipment) is adopted, and the architecture is a cross-layer closed-loop optimization architecture. An optimization decision function is decoupled from an operating system kernel and is deployed in an independent hardware management unit, namely a baseboard management controller (BMC). The system collects GPU operation data in real time through an operation system kernel space and sends the GPU operation data to a BMC; an intelligent optimization engine deployed in the BMC analyzes and calculates the data to generate an optimal GPU resource scheduling strategy; the strategy is returned to an operating system kernel and finally executed by a hardware layer, and intelligent dynamic optimization of GPU resources is achieved through cross-level interaction. Therefore, a continuous intelligent closed-loop optimization link of'deployment-data acquisition-intelligent decision-making-strategy execution 'is formed.

The invention belongs to the field of trusted data space, and provides a digital contract method and system for active driving execution in order to solve the problem that a digital contract system constructed in the prior art still has a trust gap and hidden dangers in an execution layer. The digital contract method comprises the following steps: the performance execution plug-in auditing module performs performance execution plug-in publishing and auditing, and compiles and blockchain evidence storage are performed on a plug-in source code passing the auditing; a data related party signs a digital contract through a digital contract arrangement engine module and stores the digital contract; the performance monitoring gateway monitors the digital contract strategy execution request, obtains the corresponding digital contract content and the evidence storage identifier from the digital contract arrangement engine module, and verifies the integrity and authenticity of the digital contract content; the performance monitoring gateway analyzes the contract content; and the performance monitoring gateway performs the performance execution strategy plug-in and stores a certificate, and judges whether the current strategy has a post strategy or not. According to the method, the hysteresis of signature and execution separation of the digital contract and performance auditing can be avoided.

The application discloses a kind of land space planning intelligent optimization method and system, it is related to land space planning technical field, the method is realized by the construction of linkage pressure index ORI, realizes the multidimensional coupling calculation between red line boundary ecological elasticity and internal degradation potential, comprehensively judges the linkage pressure intensity that space ecological system bears, and constructs dynamic response strategy in combination with linkage interval threshold value.When the linkage pressure index ORI value is higher than the second linkage interval threshold F2, automatic trigger repair response;It is between linkage interval threshold value, and boundary development is inhibited;Below the first linkage interval threshold F1, maintain the original circle layer unmoved.The mechanism not only improves the response efficiency and accuracy of planning management decision, but also provides intelligent adjustment basis with operability for land space governance, ensures that the whole chain closed-loop ecological regulation system effectively operates from data acquisition, model calculation to strategy execution.

The invention provides a self-adaptive collaborative security protection method based on attack path deduction, a medium and a system, and belongs to the technical field of network security. The method comprises the following steps: sampling possible paths from a current attack node to high-value assets based on an adaptive path sampling algorithm of Monte Carlo tree search to generate an attack path graph, calculating a protection effectiveness score and a service influence score according to key nodes and predicted attack actions, and generating an optimal protection instruction set based on a multi-target tradeoff function; an instruction is converted into a heterogeneous security device configuration command through a strategy execution cooperative controller, multi-device synchronous activation is realized by adopting a distributed transaction protocol based on a logic clock, and a protection effect is monitored and fed back to an artificial intelligence model for parameter optimization; the problem of protection strategy failure caused by asynchronous network security protection response and attack evolution is solved.

The invention discloses a service platform for completing bidding document production based on automation technology assistance, and belongs to the technical field of bidding services, and the service platform specifically comprises the steps that a user behavior collection module collects operation behavior data of a user in the bidding document production process; the decision preference analysis module processes the operation behavior data through an analysis model to generate user decision preference parameters; the strategy generation module generates a candidate bidding strategy set based on historical cases; the scene simulation module constructs a bidding scene simulation environment and records market competition data in a strategy execution process; the strategy evaluation module calculates a strategy competitiveness index according to the market competition data; the strategy execution module screens an optimal bidding strategy and converts the optimal bidding strategy into a specific bidding file making scheme; through intelligent analysis of user behaviors and dynamic simulation of a bidding environment, the accuracy and competitiveness of bidding document production are effectively improved.

The invention discloses a reinforcement learning-power hardware-in-the-loop training system and method for a power converter, and belongs to the technical field of power electronic control and artificial intelligence crossing. The system comprises a real-time digital simulator, a power amplifier, a controlled power converter and an AI accelerator. The method comprises the steps of S1, system initialization and environment configuration; s2, performing safe online training; and S3, strategy convergence and solidification. Through the power hardware-in-the-loop architecture, the risk of reinforcement learning exploration is completely avoided, and hardware damage caused by overvoltage and overcurrent is fundamentally eradicated in combination with a real-time hardware protection strategy. According to the method, the real object power converter controller is incorporated into a training closed loop, so that an intelligent agent strategy directly learns and adapts to the nonlinear dynamic state of real hardware, the gap from simulation to reality is thoroughly filled up, and it is ensured that a training result can be directly applied to an actual system. According to the invention, a full-real-time training system is constructed, and synchronous concurrent processing of data acquisition, strategy execution and model updating is realized.

The invention relates to the technical field of automatic driving, in particular to an open type automatic driving safety management system and method supporting multi-benefit-party strategy arbitration, and the system comprises an open type safety strategy interface module which is used for defining a standardized strategy description specification; the strategy plug-in management module is used for receiving and loading the strategy plug-ins conforming to the strategy description specifications; the perception and fusion module is used for collecting the vehicle state data, the environment perception data and the service context data and fusing the data to generate a unified context model; the multi-strategy evaluation module is used for screening a candidate strategy set according to a unified context model and a trigger condition when an abnormal event is detected, and evaluating and selecting an optimal strategy; and the strategy execution and monitoring module is used for executing the action template corresponding to the optimal strategy. According to the invention, the automatic driving vehicle can select adaptive degradation strategies according to different operation scenes and business requirements, and the adaptability of the system to diversified business scenes is improved.

The invention discloses a user dynamic access control method and system based on multi-dimensional data, and belongs to the technical field of software. The invention aims to solve the problems of user access control policy jitter, security vulnerability and difficulty in explaining compliance caused by static threshold, module independent decision and non-explicit modeling user experience cost and compliance risk cost in the prior art. Therefore, the invention provides a dynamic access control method, which comprises the following steps of: constructing a session-level multi-dimensional access context; dividing risk signal detection according to a visual angle; carrying out risk evidence combination and mode recognition; performing risk trajectory and strategy hysteresis control based on a state machine; unifying action arrangement and conflict resolution of the strategy center; strategy execution and session consistency control are carried out; and effect evaluation and strategy evolution based on multi-dimensional indexes are carried out. Compared with the prior art, multi-view evidence combination, session-level trajectory analysis, state machine and constraint solution and other means are introduced, multi-layer integration from a data layer, a logic layer, a time layer to a control layer is achieved, strategy jitter is effectively relieved, user experience is improved, service continuity is guaranteed, and compliance interpretability is enhanced.

The invention relates to a multi-factor dynamic correction investment benefit intelligent evaluation system, in particular to the field of investment evaluation, and aims to capture macroscopic event and project risk index impact in real time and dynamically generate factor impact coefficients through an event-driven factor correction module, so that the response speed and sensitivity of a model to project sudden change are remarkably improved, and the evaluation efficiency is improved. The self-adaptive weight optimization module utilizes a deep reinforcement learning framework to continuously optimize factor weight distribution based on revenue after risk adjustment in a rolling time window so as to realize self-adaptive adjustment of an investment strategy and optimization of a revenue-risk ratio, and the abnormal conduction early warning module constructs a dynamic directed graph and combines a graph neural network technology to realize early warning of the abnormal conduction. According to the method, abnormal factor conduction paths can be perspectively recognized, systematic engineering risks can be effectively warned, a strategy execution module fuses weight optimization results and risk warning signals, rebalance is executed under the condition that investment cost and risk control constraints are strictly met, and finally a project configuration scheme giving consideration to revenue, robustness and real-time performance is generated.

The invention discloses a cross-system data interaction security collaboration method, system, equipment and medium for a power management and control platform, and belongs to the technical field of power management and control, and the method comprises the steps: uniformly managing cross-system security policies through a security policy collaboration center, generating a global policy template, and eliminating policy conflicts through a conflict arbitration mechanism; dynamically adjusting the token validity period and the multi-factor authentication strength based on the risk level by combining a security authentication gateway; the data interaction bus integrates a multi-protocol adapter and an LZ77 compression algorithm to improve transmission efficiency, guarantees cross-system transaction atomicity through a three-stage protocol and a fusing mechanism of a transaction coordinator, and supports ten thousand-level concurrent interaction; and the risk perception module fuses the interaction frequency, the data sensitivity and the historical event quantitative risk level, links the dynamic strategy execution engine to trigger the authority falling, desensitization or current limiting measures, realizes the binding control of authority and transaction duration by adopting white list life cycle management, and effectively intercepts more than 8500,000 times of sensitive data leakage risk.

The invention provides a network boundary extension sensing method and system based on an AI model. The method comprises the following steps: acquiring multi-modal data such as physical fingerprints, protocol characteristics and flow modes of equipment through an edge sensing agent; generating a device-behavior joint representation vector through a feature decoupling encoder; constructing a dynamic time sequence topological graph sequence by using a space-time diagram neural network to map boundary evolution; threat reasoning is carried out by adopting three channels of rule matching, unsupervised anomaly detection and semi-supervised threat hunting; fusing decisions through a dynamic weighted voting mechanism and triggering an automatic response; and realizing closed-loop self-optimization based on incremental learning. The system correspondingly comprises an edge sensing layer, a central analysis layer, a strategy execution layer, a knowledge evolution module and a model credibility guarantee unit. According to the method, through cloud-edge-end collaborative reasoning and causal attribution analysis, the sensing and handling capability of unknown threats and APT attacks in a dynamic network boundary is remarkably improved.

The invention discloses a computer cluster security protection method and system. The method comprises the following steps: collecting multi-dimensional operation data in real time through an agent program deployed at each node; feature parameters are extracted from the data, and a safety evaluation model is adopted to calculate a safety score and map the safety score into a discrete safety level; gradient protection strategies including basic monitoring, enhanced protection and strict isolation strategies are automatically matched and loaded according to the security level; and monitoring a strategy execution effect, and performing closed-loop optimization on the model parameters based on feedback data. The system correspondingly comprises a data acquisition module, a safety evaluation module, a strategy management module, a strategy execution module and a feedback optimization module. According to the method, the problems of static strategy stiffness, inaccurate risk quantification, single response means and lack of self-learning are solved, dynamic self-adaptive protection is realized, the evaluation accuracy is improved, false alarms and missing alarms are reduced, and the cluster environment is ensured to be safe and reliable.

The invention provides a power distribution system source network load storage multi-time scale layering and grouping scheduling control method, and relates to the technical field of power distribution system scheduling control. The power distribution system source network load storage multi-time scale layering and grouping scheduling control method comprises the following steps of 1, source network load storage adjustable resource quantitative evaluation, 2, multi-time scale power prediction, 3, layering and grouping architecture construction, 4, multi-time scale scheduling strategy making, and 5, scheduling strategy execution and rolling updating. According to the invention, by constructing the quantitative model containing the algorithm formula and the hierarchical grouping architecture, the problems of power balance, scheduling economy and power grid safety under high-proportion new energy access are solved, the new energy consumption rate and the system flexibility are improved, the source-grid load-storage collaborative interaction is realized, and the new energy consumption capability and the power grid operation stability are improved.

The embodiment of the invention provides an agent-based resource processing method and device, and the method comprises the steps: carrying out the abnormal risk control decision of the abnormal resource data of an assisted user on a resource platform, obtaining at least one abnormal risk control strategy, and returning the abnormal risk control strategy to a client, the method comprises the steps of performing strategy execution verification on a resource platform based on a target abnormal risk control strategy uploaded by a client, performing bidirectional adaptation detection on a helped user and the resource platform according to resource request information submitted by the helped user through an agent entrance under the condition that the verification is not passed, and performing resource association recommendation to the client based on an adaptation detection result. Therefore, resource association recommendation of the helped user is realized on the basis of performing abnormal risk control on the abnormal resource data of the helped user.

The invention discloses a multi-site collaborative energy storage centralized control scheduling method, which relates to the technical field of multi-site collaborative energy storage, and comprises the following steps of: after an ambiguity condition of a scheduling strategy is identified, constructing a response mode vector based on a site historical scheduling behavior, and utilizing a response prediction model fusing a multi-head attention mechanism and a behavior trajectory transformation network to obtain a multi-site collaborative energy storage centralized control scheduling strategy; determining a site response behavior difference under the condition that the scheduling strategy has ambiguity; according to a site response behavior difference result, a strategy analysis prompt instruction is generated through a behavior deviation value mapping mode, the strategy analysis prompt instruction and an original scheduling strategy are combined to construct a structured scheduling strategy with a consistent analysis path, and unified analysis and constraint control of scheduling strategy execution logic are achieved. According to the method, the problem of multi-site response behavior difference caused by an ambiguous scheduling strategy is solved, unified analysis and closed-loop control of the scheduling strategy are realized, and the consistency and stability of cooperative scheduling are guaranteed.

This invention discloses a digital twin cold chain management method and system based on a multi-dimensional operation algorithm, belonging to the field of smart logistics and digital twin technology. By constructing a digital twin model of the cold chain, a physical foundation is provided for the digital twin simulation of cold chain operation, facilitating the simulation and generation of cold chain management and operation strategies. Furthermore, a cold chain operation target management model is constructed using a multi-dimensional operation algorithm, comprising multiple sub-models for cold chain operation target management. This allows for responses to different operational scenarios based on different sub-models, achieving adaptation and compatibility across multiple operational scenarios, resulting in more accurate and targeted operational strategies. In addition, the operational scenario judgment layer of the cold chain operation target management model can achieve autonomous scenario recognition and sub-model matching, greatly improving the adaptability of cold chain management. Through multiple rounds of simulation and target calculation, digital-level strategy execution is achieved, significantly improving the generation efficiency of cold chain management and operation strategies.

The invention discloses a factory intelligent management method and system based on the Internet of Things, and relates to the technical field of factory intelligent management. The invention discloses an intelligent factory management system based on the Internet of Things. The intelligent factory management system comprises a measuring point acquisition module, a semantic model module, an event encapsulation module, a strategy issuing module, a strategy execution module, a network disconnection autonomous module and a conservative strategy module. According to the invention, dynamic utility evaluation is carried out on strategy data based on a current context formed by an equipment semantic model and real-time event data, and comprehensive evaluation and conflict arbitration are carried out in combination with an industrial equipment set corresponding to a linkage action and respective utility evaluation results in strategy data with conflicts. The selection of the strategy to be executed does not only depend on a fixed version number and priority, but fully considers on-site working conditions, the necessity of the strategy and the conflict relationship between equipment, so that the occurrence probability of strategy conflict and invalid execution is reduced.

This invention discloses a power system for load forecasting and dynamic scheduling, relating to the field of power system technology. The system includes: constructing an initial simulation environment for the corresponding scheduling cycle in a digital twin model based on grid status and multi-dimensional load forecasting information from power grid, grid, load, and storage; generating a baseline scheduling plan by solving the solution; selecting candidate scheduling strategies with the highest comprehensive evaluation and associating them with grid status and multi-dimensional load forecasting information from power grid, grid, load, and storage, storing them together in a dynamic strategy library; executing the candidate scheduling strategies and collecting actual operating data to calculate the strategy execution deviation; and adaptively correcting the parameters of the digital twin model and the generation rules of the counterfactual scheduling strategy based on the strategy execution deviation. This invention continuously corrects the parameters of the digital twin model based on actual operating data and adaptively adjusts the disturbance strategy, resulting in higher prediction accuracy and simultaneous improvement in economic efficiency and safety margin of the scheduling strategy.

The invention relates to the technical field of software security governance, in particular to an open source component governance method and system based on multi-dimensional measurement and feedback optimization, and the method comprises the steps: collecting multi-source heterogeneous data of an open source component, carrying out the feature extraction, and constructing a basic measurement matrix containing security, compliance, efficiency and collaborative dimensions; respectively calculating a security dimension index and a compliance dimension index which represent a risk defense boundary, and an efficiency dimension index and a cooperation dimension index which represent business delivery kinetic energy, and generating a multi-dimensional efficiency state vector; mapping to a governance efficiency evaluation space, and generating an efficiency deviation characteristic value by calculating the coupling collaboration degree and restriction loss among the dimension indexes; and reversely deducing the adjustment gradient of the control strategy parameter to generate a dynamic correction instruction for the strategy execution component, issuing the dynamic correction instruction to the strategy execution component to update the control rule in real time, and executing a treatment operation on the open source component. According to the method, the treatment effect of the software can be effectively quantified, and the treatment strategy is guided to carry out targeted adjustment and continuous optimization.

This invention discloses an adaptive intelligent control system and control method for a ceramic dry granulation machine, belonging to the field of industrial automation and intelligent manufacturing technology. The method includes the following steps: S1, offline modeling and rule initialization; S2, real-time sensing and material state identification; S3, dynamic working condition judgment and strategy triggering; S4, adaptive adjustment strategy execution; S5, quality closed-loop verification and strategy self-optimization; S6, full-process linkage and safety protection. This invention is rationally designed, achieving high-precision and stable control of particle size distribution, morphology, and bulk density through multi-source sensor fusion, online material characteristic identification, and multi-variable collaborative adjustment. It is suitable for dry granulation scenarios involving no liquid phase participation, high friction, and easy material blockage.

This invention relates to the fields of supply chain collaborative management, risk warning, and AI scheduling technology, specifically a supply chain collaborative management and early warning system for PC board inkjet processing. It includes an external supply chain status perception unit for aggregating external status information and generating an external status vector; an internal logistics bottleneck monitoring unit for monitoring the inbound quality control (IQC) channel and generating IQC congestion alarm signals and internal material delay times; a collaborative vulnerability quantification unit for quantifying the collaborative vulnerability index and generating collaborative vulnerability status signals; a bimodal adaptive decision-making unit for generating scheduling strategy instructions; and a resilience strategy execution unit for executing material redundancy actions and resource redundancy actions to reduce the collaborative vulnerability index. This invention solves physical bottlenecks and proactively restores human-machine trust, ensuring the system automatically stabilizes after a crisis is resolved.

The invention belongs to the technical field of online education, and discloses an exercise recommendation system and method based on hierarchical reinforcement learning and multi-objective optimization, and the system comprises a multi-objective optimization index definition module which is used for defining exercise diversity, exercise novelty, recommendation accuracy, learner satisfaction, learner enthusiasm, learner score and system question setting time; a comprehensive objective function is formed through weighted summation; the three-layer reinforcement learning architecture module comprises a high-level strategy, a middle-level strategy and a low-level strategy which are respectively responsible for global optimization target planning, strategy execution and personalized exercise recommendation; the cognitive diagnosis model and Transform prediction model fusion module is used for evaluating the knowledge mastering condition of the learner and optimizing a recommendation strategy; and the improved Pareto optimization method module is used for processing uncertainty in multi-target optimization, solving target conflicts and improving the convergence speed of an algorithm through dynamic parameter adjustment. According to the invention, appropriate exercises can be accurately recommended according to the learning state of each student.