449 results about "Decision rule" patented technology

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

The invention provides a tunnel surrounding rock blasting excavation parameter setting method and system based on a neural network, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining geological characteristic data, blasting excavation historical records and excavation contour data of surrounding rock; sparse subspace clustering processing is carried out according to blasting excavation historical records to obtain a feature data set; performing stress state analysis according to the geological characteristic data, the excavation contour data and the characteristic data set to obtain a dynamic stress state and topological change data of the surrounding rock; performing association rule mining according to the blasting excavation historical record and the feature data set to obtain an optimized decision rule; performing nonlinear finite element analysis according to the dynamic stress state and the topological change data to obtain an equivalent material model of the surrounding rock; and according to the equivalent material model and the optimization decision rule, a blasting excavation parameter setting scheme is obtained. According to the invention, dynamic response analysis is carried out by using the neural network, and real-time monitoring of the surrounding rock under different blasting conditions is realized.

The invention relates to the technical field of intelligent equipment evaluation and fault prediction, and discloses a dynamic equipment quality evaluation method based on multi-agent collaborative reinforcement learning, and the method comprises the steps: obtaining original data from equipment and a sensor, and carrying out the preprocessing; analyzing the preprocessed data, and identifying key factors influencing the quality of the equipment; constructing a plurality of expert models, and predicting and evaluating an analysis result; fusing the weight of each expert model to obtain a comprehensive evaluation result; and performing risk assessment on the comprehensive assessment result, extracting key influence factors and decision rules, and generating an assessment result. Efficient fusion of multi-source data and automatic identification of key factors are realized, and the accuracy and reliability of equipment quality evaluation are remarkably improved; a model fusion result is optimized through dynamic weight distribution, so that the prediction performance is improved; the problems that in the prior art, data fusion is difficult, key factor identification depends on experience, and the interpretability of an evaluation result is insufficient are solved.

The invention provides a crop growth state evaluation method based on the agricultural Internet of Things, and the method is characterized in that the method specifically comprises the following steps: S1, collecting the multi-dimensional data of a crop growth environment in real time, and outputting an original data set; s2, aligning the multi-dimensional data according to geographic coordinates, extracting features, and constructing a multi-modal fusion feature vector; s3, automatically identifying the current growth stage of the crop and the confidence of the current growth stage according to the fused feature vector; s4, calculating a crop health degree score and grading according to the growth stage information and the feature data, and identifying a stress state detection result at the same time; s5, according to a quantitative evaluation result, predicting a growth trend and generating a suggested decision scheme; and S6, the decision is monitored, the decision execution effect is fed back to the system, and the model parameters and the decision rules are continuously optimized. A closed-loop mechanism of evaluation, decision, feedback and optimization is formed, the applicability and evaluation precision of the system are continuously improved, and agricultural production is promoted to be upgraded to precision and intelligence.

The invention discloses a multi-modal learning automatic complaint content analysis and classification method and system, and relates to the technical field of semantic analysis, and the method comprises the steps: collecting multi-modal complaint data, and carrying out the preprocessing of the collected multi-modal complaint data; extracting features of the preprocessed multi-modal complaint data, and fusing the features of different modal data; constructing a complaint content classification model by using the fused features, carrying out model training, and evaluating and optimizing the trained model; and deploying the optimized model for real-time classification. According to the multi-modal learning automatic complaint content analysis and classification method provided by the invention, the complaint content is quickly and accurately classified, and a targeted processing strategy is provided according to the main category, the emotional tendency and the specific theme of the complaint. And an emergency response mechanism is automatically triggered to ensure that problems are processed in real time and effective utilization of resources is ensured. Decision rules are continuously optimized from historical data learning, and the intelligent level of a processing strategy and the customer satisfaction degree are improved.

The invention discloses a multi-purpose optimized elevator intelligent scheduling method and system, and the method comprises the steps: achieving the prediction of an elevator passenger flow direction mode through the construction of a flow direction transfer tensor and an improved density peak clustering algorithm, and adjusting a clustering result when a passenger behavior suddenly changes; a direction inertia factor is introduced, a direction switching decision rule and a local direction stability adjusting mechanism are combined, and the elevator running direction is dynamically optimized; passenger priority factors are defined, comprehensive priority scores of passengers are calculated, a passenger gradient optimization scheduling model is constructed based on the comprehensive priority scores, and the priorities of the passengers are dynamically adjusted; a multi-elevator non-cooperative game model is constructed, and task allocation among multiple elevators is realized by defining an elevator revenue function and a strategy space and utilizing Nash equilibrium solution. According to the method, the scheduling strategy can be adaptively adjusted in a complex traffic mode, so that the response speed is higher, the resource utilization rate is better, and the passenger waiting time is shorter.

The invention belongs to the technical field of flight simulator maintenance, particularly relates to a flight simulator predictive maintenance method based on machine learning, and solves the problems that existing maintenance depends on regular inspection and passive maintenance, fault early warning lags behind, and the false and missing report rate is high. The method comprises the following steps: acquiring historical operation data, sensor time sequence data, fault records and environmental parameters of a flight simulator, and carrying out cleaning, labeling and feature fusion preprocessing on the historical operation data, the sensor time sequence data, the fault records and the environmental parameters; constructing a composite health feature set containing statistical features, dynamic health state values and aerial material reliability parameters; a mixed prediction model (random forest feature screening + LSTM time sequence prediction + adaptive correction reliability evaluation) is adopted to train a model, prediction result fusion analysis and multistage decision rule post-processing are combined, and a maintenance work order and a spare part demand plan are output. According to the method, the accuracy and timeliness of fault prediction are improved, the maintenance conversion from passive response to active pre-judgment is realized, and the maintenance cost and the non-planned shutdown risk are greatly reduced.

A self-adaptive dynamic stratum recognition method based on multi-source drilling parameters comprises the steps that firstly, the drilling rate, the rotating speed and oil pressure parameters are collected through a sensor set, a drilling depth-time curve is constructed, time sequence data are segmented through a sliding window, and filtering, standardization and abnormal value processing are carried out; calculating a mean value, a standard deviation and skewness statistical characteristic of each parameter in the window, and constructing a nine-dimensional characteristic vector in combination with an energy ratio and a covariant coefficient; updating a stratum feature library based on an incremental learning algorithm, and dynamically optimizing a clustering center to realize autonomous evolution; a confidence coefficient threshold value is set according to the statistical characteristics and the clustering distance, and stratum type dual discrimination is completed; by comparing a real-time judgment result with manual sampling data, the generalization of a learning rate parameter optimization model is dynamically adjusted; and outputting a stratum type corresponding to the current drilling depth and a confidence degree evaluation result based on the optimized clustering model. According to the invention, by improving a data processing mechanism and an adaptive decision rule, high-precision and strong-generalization real-time stratum identification is realized.

The invention relates to the technical field of tunnel engineering automation and intelligent control, and discloses a TBM tunneling parameter intelligent optimization decision-making system based on an LSTM network, and the system comprises a data collection and preprocessing module which obtains external data and outputs a tunneling parameter sequence; the probabilistic tunneling trend prediction module is used for outputting a prediction expected value and prediction uncertainty; the prospective geological precursor sensing module is used for matching and identifying known risks and outputting alarm events; and determining an optimal tunneling mode by the dynamic risk avoidance decision matrix. When the prediction uncertainty is too high, activating the prospective template driven by the uncertainty to excavate a new precursor template and update the template library; meanwhile, the decision-efficiency relevance evaluation and strategy self-optimization engine optimizes the decision rule according to the actual tunneling efficiency. According to the method, decision making is carried out through quantitative risk prediction and fusion of multi-source information, and a double learning closed loop of knowledge discovery and strategy optimization is established, so that the reliability, the adaptability and the long-term efficiency of system decision making are remarkably improved.

One embodiment provides a system which facilitates construction of an ensemble of machine learning models. During operation, the system determines a training set of data objects, wherein each data object is associated with one of a plurality of classes. The system divides the training set of data objects into a number of partitions. The system generates a respective machine learning model for each respective partition using a universal kernel function, which processes the data objects divided into a respective partition to obtain the ensemble of machine learning models. The system trains the machine learning models based on the data objects of the training set. The system predicts an outcome for a testing data object based on the ensemble of machine learning models and an ensemble decision rule.

The invention discloses an automatic control system based on full-automatic intelligent surface treatment equipment, which relates to the field of industrial production surface treatment and comprises a data acquisition module, a data analysis module, a process decision module, a control execution module, a fault monitoring module, a man-machine interaction module, a data storage and management module, a self-adaptive learning module, a remote monitoring and maintenance module and a green energy-saving module. An advanced sensor is combined with a micro-nano sensing technology, a deep reinforcement learning and transfer learning combined algorithm is applied, genetic optimization fuzzy reasoning is adopted, through magnetic suspension and compound control, faults are monitored based on deep learning, a VR technology is introduced to realize human-computer interaction, and a block chain technology is adopted to store data. And a meta-reinforcement learning algorithm is used to automatically adjust decision rules and parameters, and the remote collaborative robot carries out maintenance operation on the equipment. The system is accurate in data acquisition, scientific in analysis and decision, accurate in control, timely in fault diagnosis, convenient in man-machine interaction, efficient in data management, energy-saving and capable of comprehensively improving the production efficiency.

The invention discloses an elderly cognitive function adaptive training method and system based on multi-modal interaction feedback, and relates to the technical field of smart medical treatment. The method comprises the steps that basic information of a user is collected for initial cognitive ability evaluation, a user cognitive portrait is constructed according to an evaluation result, and an initial training task with the corresponding difficulty is allocated; collecting multi-modal interaction data in real time according to the initial training task; carrying out fusion analysis on the multi-modal interaction data by utilizing a machine learning model to obtain a quantized real-time state index; based on the real-time state index and the performance data of the current task, dynamically adjusting a subsequent training task through an adaptive decision rule engine; all-dimensional data of each training task is recorded, a visual cognitive competence development trend report is generated through longitudinal comparative analysis, and a machine learning model and a self-adaptive decision rule engine are continuously optimized and trained by utilizing accumulated user data to form an optimized training closed loop. The cognitive function training effect of the old people can be improved.

The invention relates to an unsteady-state crowd evacuation path planning method and device based on deep reinforcement learning. The method comprises the following steps of: simplifying an unsteady evacuation environment into a two-dimensional region by using a discrete grid method; modeling is performed on dangerous source dynamic diffusion, the fire source position and range are updated in real time, and dynamic fire development is simulated. A person is regarded as an intelligent agent and moves according to a decision rule guided by a potential energy field. And with maximization of evacuated people number and minimization of evacuation time as objective functions, constraint conditions are determined in combination with actual shopping malls and agent movement rules, and an unsteady crowd evacuation path planning model is constructed. An APF-MADDPG algorithm is adopted, path planning is converted into a Markov decision process, state features are designed and combined with a potential energy field to construct a deep reinforcement learning framework, and after a model is trained, unsteady crowd evacuation path planning is achieved. By adopting the method, the practicability and the real-time performance of crowd evacuation path planning can be improved.

The invention provides a greenhouse water, fertilizer and pesticide optimization control system and method based on environment-crop-input multi-element coupling, and the method comprises the steps: deploying a sensor network, collecting greenhouse data, processing the greenhouse data, generating a space-time continuous data set, and carrying out the optimization control of the greenhouse water, fertilizer and pesticide on the basis of the generated space-time continuous data set. And quantifying a dynamic causal chain of the environment, the crops and the input, generating a decision rule base, fusing the decision rule base with a real-time simulation result of the three-dimensional digital twinborn model of the root zone, and generating a water, pesticide and fertilizer regulation and control instruction. Space-time continuous acquisition and fusion processing of greenhouse environment-crop-input product data are realized through a multi-modal sensor network, a decision rule base containing a dynamic causal chain is constructed, real-time simulation of a root zone three-dimensional digital twinborn model and Bayesian causal reasoning are combined, an accurate water, fertilizer and pesticide regulation and control instruction is generated, and the accuracy of the water, fertilizer and pesticide regulation and control is improved. The nonlinear causal relationship among multiple elements is effectively quantified, and the decision scientificity is improved.

The invention discloses an emergency pre-examination grading system and method based on collaborative decision of a large language model and a tree model. The method comprises the following steps: constructing a data set according to patient information and screening data; training an initial random forest model based on the data set, generating a basic decision rule, mining and extracting high-frequency features based on association rules, and combining to obtain a candidate rule pool; constructing a cue word structure adaptive to the field, driving LLM to complete rule correction, and forming a correction rule set; performing multiple rounds of rule random division and rule combination generation based on the correction rule set, and then screening out an optimal rule; and on the basis of a specific scene, the expert rule and the corrected optimal rule are fused, matching is carried out on patients, and emergency pre-examination grading is realized. According to the method, the interpretability is improved while the grading accuracy is improved, and the problem of poor cross-courtyard generalization of a machine learning model is effectively solved. The generalization ability of the rule is remarkably improved, and the method is adaptive to a multi-center combined diagnosis and treatment scene.

The invention discloses a multivariable decision processing method and device, a storage medium and electronic device.The method comprises the steps that an initial multivariable decision rule system configured by a user side based on standard DSL grammar rule information is obtained, and the standard DSL grammar rule information is the DSL grammar rule information preset for a multivariable transaction decision development scene; determining all multivariable decision rule combinations corresponding to the initial multivariable decision rule system, determining a reference transaction job flow corresponding to the multivariable decision rule combinations, and constructing a multivariable combination instance library for the initial multivariable decision rule system, and performing engineering language conflict detection processing on the initial multivariable decision rule system based on the multivariable combination instance library to obtain a target multivariable decision rule system after conflict detection, and performing transaction operation process decision processing on the transaction data by adopting the target multivariable decision rule system.

The invention relates to the field of production line state monitoring, and particularly provides a production line state monitoring and MES integration notification method and system, and the method comprises the steps: collecting multi-modal data, and constructing a unified state context object; performing space-time alignment and dynamic weight fusion based on a process stage on the multi-source asynchronous data to obtain a feature vector; reasoning a semantic current state of the production line through a time sequence mode recognition model; generating an enhanced data set based on the state context and the current state of the production line, and outputting a structured state event through anomaly detection and root cause analysis; outputting an optimal response strategy through a predefined decision rule in combination with MES service information; and packaging the strategy into an operation instruction which can be identified by the MES and issuing the operation instruction, and driving the MES to execute work order creation, billboard updating or message notification. According to the method, semantic diagnosis, global decision and automatic response of the production line state are realized, so that the production transparency, the decision accuracy and the operation and maintenance automation level are improved.

The invention discloses a low-speed unmanned vehicle artificial intelligence decision and performance evaluation system, and belongs to the technical field of low-speed unmanned vehicle scheduling, and the system comprises a generation module which is used for generating a scene novelty index through monitoring the instantaneous offset of an environment parameter; the topology module is used for constructing a dynamic scene topology according to the scene novelty index, and the dynamic scene topology comprises nodes and edge weights generated based on the scene novelty index; the decision-making module is used for converting the edge weight into a decision-making rule according to the dynamic scene topology and generating a decision-making strategy; the performance calculation module is used for tracking the deviation between the vehicle motion parameter and the control instruction during the execution of the decision strategy to calculate a performance disturbance coefficient; and a topology calibration module. The method can effectively adapt to unrecorded edge scenes such as sudden congestion and severe weather superposition, improves the generalization ability of a system to complex scenes exceeding a pre-training range, and reduces decision errors caused by insufficient scene adaptation.

The invention provides an application method based on a privatized large model and an AI agent, and the method comprises the steps: deploying a pre-trained large language model on an internal server of an enterprise, carrying out the adjustment of the large language model through the own data of the enterprise, and carrying out the local deployment, and obtaining a privatized large language model; constructing an AI agent framework, and defining a task execution logic, a decision rule and an interactive interface with the privatized large language model of the AI agent; the AI agent receives a task request proposed by a user through a natural language, calls the privatized large language model to analyze the task request, and generates a task execution plan; and the AI agent executes a task according to a result output by the privatized large language model, and feeds back a task execution result to the user. According to the method, a privatized large language model is deployed in an enterprise, and the autonomous decision-making capability of an AI agent is combined, so that the data security is guaranteed, and meanwhile, efficient processing and intelligent decision-making support for complex tasks are realized.

The invention provides a flange assembly predictive maintenance method based on residual life distribution dynamic identification, which comprises the following steps: firstly, establishing a linear Wiener model of a flange assembly degradation process, and designing a Bayesian parameter dynamic updating mechanism based on normal-inverse gamma conjugate prior; secondly, deducing residual life complete probability distribution considering parameter uncertainty through a Monte Carlo sampling method, overcoming the limitation of point prediction, and providing a maintenance decision rule based on a time-probability threshold; and finally, establishing a decision parameter optimization model with the goal of minimizing the long-term average cost rate, and solving an optimal maintenance strategy through system simulation. Compared with the prior art, the residual life prediction accuracy is remarkably improved, the maintenance cost and the equipment reliability are effectively balanced through a probabilistic decision-making mechanism, the full-life-cycle maintenance cost is remarkably reduced while the flange sealing safety is ensured, and the method has important popularization value in engineering equipment predictive maintenance.

The invention discloses an aircraft control agent decision interpretation method and system, relates to the technical field of aircraft control and artificial intelligence crossing, and is used for solving the technical problem that an aircraft fault reason cannot be determined according to a decision result due to the fact that an existing model is poor in agent decision interpretation. The aircraft control agent decision interpretation method comprises the following steps: acquiring real-time working data of an aircraft; synchronizing and storing each kind of working data and then preprocessing; obtaining feature data influencing the decision of the aircraft based on a hypergraph theory; determining the type of a decision made by an intelligent agent, and dividing feature data obtained by a hypergraph theory into a training set and a test set; obtaining optimal division features in the training set according to an information gain rate criterion by using a decision tree algorithm, and determining parameters of a decision tree; selecting a root node and constructing a decision tree model; and performing evaluation and pruning optimization on the decision tree model, and performing visual display on a decision basis corresponding to the decision rule.

The invention provides a cloud intelligent gas leakage analysis and decision-making method based on lightweight discrimination, and the method comprises the steps: enabling a mobile inspection platform to movably collect the gas leakage monitoring information of each monitoring point in a monitoring region, and determining a lightweight discrimination index; the mobile inspection platform determines a hard threshold discrimination result by adopting a hard threshold decision rule based on the lightweight discrimination index; if it is judged that the risk level is high, local response is immediately carried out, and a preset decision scheme is matched; if the risk level is judged to be low, edge node region decision is started; an edge node calculates a gas concentration accumulated value, a concentration fluctuation intensity degree and a sudden increase leakage amplification early warning factor, the gas concentration accumulated value, the concentration fluctuation intensity degree and the sudden increase leakage amplification early warning factor are input into a LightGBM time sequence classification model together with a lightweight discrimination index, a leakage risk level is determined according to model output and serves as a soft threshold discrimination result, and a preset decision scheme is matched; and the cloud model optimizes and collects conflict samples to carry out incremental training on the model of the edge node. According to the invention, the missed alarm rate can be reduced, and the discrimination robustness is improved.

The invention discloses a power transmission line intelligent management and control method and system based on power distribution data feedback, and relates to the technical field of power transmission line management and control, and the method comprises the steps: integrating multi-source data, constructing a historical environment-electrical coupling feature matrix, extracting time sequence features through a long and short term memory network, and capturing a spatial distribution rule through combining a convolutional neural network; and establishing a dynamic line loss prediction model to simulate minimum line loss values in different environment scenes. And detecting an abnormal time period through an isolated forest algorithm, screening key damage factors in combination with Pearson correlation analysis and SHAP value analysis, dynamically generating and executing a loss reduction strategy priority event, and optimizing model parameters by using error back propagation and reinforcement learning. And finally, generating a typical environment scene loss reduction case library based on the optimization model, and simulating strategy effectiveness under an extreme condition through a digital twin technology to form a reusable decision rule set, thereby improving the operation efficiency of a power transmission line and the reliability of power supply.

The invention discloses a heterogeneity causal effect quantitative evaluation system and method for electric vehicle charging behaviors, and relates to the field of electric vehicle big data analysis, artificial intelligence and causal inference. The system comprises a data preparation and variable system construction module, a heterogeneity causal effect quantitative evaluation model construction module and a model application and result interpretation module. A dual machine learning framework is adopted, systematic deviation of high-dimensional hybrid factors is eliminated by calculating orthogonalization residual errors, a causal forest model is constructed on this basis, a conditional average processing effect is accurately and stably quantified based on a splitting criterion of maximizing effect heterogeneity, and a high-precision and high-precision effect is obtained. The problem of causal effect estimation deviation of a traditional method under the forms of high-dimensional data and complex functions is solved. In order to enhance the interpretability, an agent model based on a single decision tree is further constructed, and a complex causal forest conclusion is extracted into a group of visual and operable If-Then decision rules, so that a complete closed loop from data to robust and interpretable decisions is realized.

Various embodiments include a system that utilizes machine learning to detect and mitigate bot attacks. The system comprises processing circuitry. The processing circuitry obtains historical traffic data and attack traffic data in response to an attack notification. The attack traffic data characterizes traffic received during a bot attack and the historical traffic data characterizes other traffic received when the bot attack is not occurring. The processing circuitry extracts features from the historical traffic data and the attack traffic data. The processing circuitry trains a machine learning classifier to identify the features that correspond to attack traffic and the features that correspond to legitimate traffic. The processing circuitry forms decision rules based on an output from the machine learning classifier to block the attack traffic based on the features that correspond to the attack traffic. The processing circuitry generates one or more security policies based on the decision rules.

The invention relates to the technical field of data processing, in particular to a power grid probability equivalent model construction method and device based on multi-scene clustering, and the method comprises the steps: obtaining a power grid multi-dimensional data source, executing a spatial-temporal feature decoupling operation, and outputting a spatial-temporal feature tensor with a weight identifier; executing graph-driven enhanced clustering operation based on the tensor, constructing a topological risk graph structure, and generating a safety margin offset thermodynamic diagram through a deep reinforcement learning decision feature compression strategy; executing a double-algorithm optimization traceability operation, adopting a genetic algorithm to encode a feature cutting operation, taking a margin deviation value as a fitness function, optimizing a time window parameter by a butterfly algorithm, and outputting a feature operation traceability matrix; and feeding back the matrix to the enhanced clustering operation to update the decision rule. According to the method, the problem of dynamic security risk quantification distortion caused by scene clustering dimension reduction is solved under the constraint of limited computing resources through risk perception feature processing, enhanced resource allocation and a closed-loop architecture of double-algorithm traceability optimization.

The present application relates to the field of simulated emergency drills, and specifically discloses an emergency decision simulation system based on behavior trees and multi-resolution deaggregation. The emergency decision simulation system based on behavior trees and multi-resolution deaggregation includes a decision simulation system and a simulation deduction system; the decision simulation system includes a decision rule editor and a decision scheduling engine; the decision rule editor includes a mission layer, a task layer, and an action layer; the mission layer behavior tree, the task layer behavior tree, and the action layer behavior tree are linked through reference nodes; the mission layer behavior tree uses a prevention and control reference node, a search and rescue reference node, an emergency reference node, and a transfer reference node as the lowest level node of the mission layer behavior tree and the root node of the task layer behavior tree, and is provided with a situation processing node and a blackboard decoration node, so that the resolution gap and its impact on simulated emergency drills can be reduced, and the flexibility of simulated emergency drills and their coordination with real-time environments can be improved.

The invention relates to the technical field of forklift control, in particular to an efficient and energy-saving off-road forklift electric drive system and a control method. Multi-source data is collected through a multi-source sensor, and data preprocessing is carried out; outputting a control decision instruction according to a priority decision rule based on the preprocessed data in combination with a fuzzy logic algorithm; in combination with the working condition characteristics and the real-time data, dynamic parameter adjustment is performed on the rotating speed, the torque and the current parameters of the motor by utilizing a reinforcement learning framework, and a PWM duty ratio control instruction is generated; on the basis of a PWM duty ratio control instruction and a hydraulic pressure signal fed back by a hydraulic system, the same-frequency anti-phase compensation torque is calculated, a signal is injected to suppress fluctuation, and torque distribution and hydraulic flow are adjusted through a ramp function; and establishing a hierarchical health early warning mechanism according to the historical data, judging the current early warning level, and outputting a maintenance strategy instruction according to the early warning level. According to the scheme, equipment health management and stability are enhanced through reinforcement learning dynamic optimization and graded early warning.

The invention relates to the technical field of environment control, in particular to a cold and heat source environment control method and device with an air purification function, and the method comprises the steps: collecting various parameters through a sensor network, and carrying out the data fusion based on a decision rule base to select a dominant working mode; corresponding air treatment operation is executed, and a composite energy-saving strategy including precooling optimization and phase change latent heat assistance is triggered under the refrigeration working condition; fuzzy control and machine learning are applied to optimize system output and predict load, and a decision rule base and algorithm parameters are updated through self-learning; air supply and exhaust power is controlled to maintain the negative pressure gradient of the key area, and an air path distribution mechanism is adjusted to organize a one-way air flow line; the device comprises an intelligent control module, a sensor group, a fresh air module, a purification module, a cold and heat source module, a bypass pipeline and an air path distribution mechanism. The problems that an existing system is poor in function collaboration, low in energy efficiency and unreasonable in airflow organization are solved, and intelligent decision making, deep energy saving and accurate environment control are achieved.

A method for detecting faulty manufactured items based on natural language descriptions (NLDs), the method may include: (i) obtaining one or more neural networks (NNs) that are trained to detect objects that exhibit one or more textual features of a group of textual features; wherein members of the group are defined by NLDs; wherein the one or more NNs are trained on one or more training datasets, the training datasets are not based on sensed information units (SIUs) of the manufactured items; (ii) receiving a SIU that captures an evaluated manufactured item (EMI); (iii) processing the SIU of the EMI by the one or more NNs, to provide one or more NN processing results regarding one or more relationships between the EMI and the one or more textual features; and (iv) determining a status of the EMI, based on one or more decision rules and the one or more NN processing results.