533 results about "Genetic algorithm optimization" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention relates to the technical field of cable nondestructive testing, in particular to a cable defect detection system. The method mainly solves the problems that in the prior art, early-stage tiny defects in a cable are insufficient in recognition sensitivity, the omission ratio is high, and accurate classification cannot be achieved. According to the system, signals are synchronously collected through a multi-physics field composite sensing array, excitation parameters are optimized through a genetic algorithm, a three-dimensional defect probability graph is generated through fusion, a double-current Transform model is adopted to deeply analyze features and automatically recognize defect types, and finally, maintenance is guided through AR visualization, so that non-intrusive online accurate diagnosis and intelligent operation and maintenance of cable defects are achieved.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention discloses a life prediction method based on health index construction and neural network fusion, and belongs to the technical field of equipment state monitoring and predictive maintenance. According to the method, through multi-source degradation feature extraction, common dynamic principal component analysis (CDPCA) dimensionality reduction, health index construction and normalization, deep learning multi-model modeling, integrated learning fusion and Bayesian optimization hyper-parameter optimization, online health assessment and residual life prediction of the equipment part degradation process are realized. Specifically, the method comprises the following steps: firstly, extracting time domain, frequency domain and time-frequency domain features from a sensor acquisition signal, and performing dimension reduction through CDPCA to obtain effective degradation characterization; then, weighting the main features to construct a health index (HI) curve, optimizing the weight through a genetic algorithm, and then performing normalization; a plurality of neural network models such as CNN, Bi-GRU, Bi-RNN, Bi-LSTM and SRNN are constructed based on the normalized HI sequence, and degradation trend modeling is realized; inputting the output results of the neural networks into an integrated learning module for fusion optimization; and finally, carrying out automatic optimization on the key hyper-parameters of the model by utilizing Bayesian optimization. In the equipment operation process, a normalized HI curve can be calculated in real time and input into the fusion model, and the residual life estimation value of the part is dynamically output. According to the method, high-precision, high-robustness and online life prediction can be provided under complex working conditions, the safety and reliability of equipment operation and maintenance are improved, and the method has wide engineering application value.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention discloses a big language model generation content security test system and method in a black box scene. The system comprises a jailbreak prompt word library module used for storing jailbreak prompt words for performing security test on a big language model; the violation question and answer pair module is used for storing violation question and answer pairs covering different types; the response acquisition module is used for obtaining a data request packet according to query content formed by the jailbreak prompt word and the query request; the security analysis module is used for calculating the similarity between response data corresponding to the query request and an expected violation answer, taking the similarity as a security score, and inputting the security score into the adaptive optimization module; and the adaptive optimization module is used for optimizing the jailbreak prompt words output by the jailbreak prompt word bank module by using a genetic algorithm according to the security score output by the security analysis module. According to the method and the device, the security of the large language model generation content can be effectively tested.

The invention discloses an intelligent sealing test method and system for a wafer in a semiconductor chip, and relates to the technical field of semiconductor manufacturing, and the method comprises the steps: initializing a magneto-electric field cooperation parameter, optimizing the magneto-electric field cooperation parameter through a multi-target genetic algorithm, and driving core-shell nanoparticles to carry out the metallization filling of a high-precision through hole wafer, generating a wafer with a metalized through hole structure; based on the metalized through hole structure wafer, bonding parameters are generated through a laser Doppler frequency vibration spectrum analysis method, flip interconnection is achieved through a thermal ultrasonic virtual process, and a low-defect bonding wafer is generated; a finite element inversion algorithm is adopted, a stress distribution map of a bonding interface is established, an anti-thermal-stress packaging layer is constructed through an intelligent stress matching algorithm, and the low-defect bonding wafer is packaged; and through a multi-target genetic algorithm, magnetoelectric field cooperation parameters are optimized, core-shell nano-particles are driven to directionally deposit, and accurate control over the metallization filling process is achieved.

The invention discloses a die forming process parameter diagnosis method based on a genetic algorithm, and particularly relates to the field of data analysis. Comprising the steps of multi-source heterogeneous data fusion and enhancement processing, multi-target dynamic fitness function construction, constraint adaptive genetic algorithm optimization, process parameter-defect association mapping, online transfer learning optimization, dynamic environment online correction and man-machine collaborative decision verification. According to the method, collaborative optimization of the size, the energy consumption and the cycle time is realized through the multi-target dynamic fitness function, and the practicability and the flexibility of a parameter scheme are remarkably improved; a closed-loop learning system is constructed by utilizing transfer learning and a historical case library, so that the generalization ability and the result reliability are enhanced; temperature and humidity fluctuation is compensated in real time through an environment online correction function, the robustness and stability of the production process are greatly improved, and finally a comprehensive solution with intelligent optimization and manual intervention capabilities is formed.

The invention discloses a switch load balancing method based on fuzzy logic control, and the method comprises the following steps: S1, monitoring a network state in real time through quantum sensing equipment, and obtaining related parameters; s2, transmitting the parameters to a fuzzy logic controller for processing; s3, generating a load distribution strategy according to a predefined rule; s4, transmitting the preliminary strategy to each switch node in the block chain network, and recording related data; s5, verifying the data by using a distributed consensus mechanism of the block chain; s6, dynamically evaluating a node trust value based on an enhanced fuzzy trust model, and selecting nodes meeting requirements to participate in a load balancing game decision; s7, carrying out cooperative calculation on the selected nodes by utilizing a game theory cooperation mechanism, and generating a globally optimized load distribution strategy; and S8, optimizing a load distribution strategy through a quantum genetic algorithm, and performing automatic execution through an intelligent contract. According to the method, the advantages of fuzzy logic, the block chain and quantum computing are combined, and the intelligence and safety of load balancing are improved.

The invention relates to the technical field of cold chain warehouse logistics, and discloses a cold chain warehouse logistics intelligent distribution management system based on the Internet of Things technology. The system comprises a multi-source data processing module which collects storage environment and equipment operation data through a heterogeneous sensor, and preprocesses the data to generate a normalized matrix containing parameters such as temperature and humidity; the dynamic topology updating module is used for constructing an equipment association topology library, updating a relationship according to real-time data and generating a dynamic topological graph; the goods fresh-keeping reference configuration module inputs goods characteristics and equipment operation duration based on a topological graph mapping neural network model, and outputs a goods deterioration risk coefficient; an abnormal early warning decision module generates an early warning rule according to the data, and triggers delay verification in combination with real-time data; the scheduling decision optimization module generates an optimal scheduling scheme through a decision tree in combination with early warning and a resource state; the three-dimensional reservoir area mapping module constructs a three-dimensional grid model and maps environmental parameters; and the path optimization module optimizes the optimal path of the logistics equipment by using a genetic algorithm.

The invention relates to the technical field of wind power equipment detection, in particular to a wind turbine generator blade fracture damage detection method applying an inspection robot, which comprises the following steps of: S1, constructing a blade three-dimensional point cloud model and positioning the position of the robot in real time through a laser radar and a visual SLAM (Simultaneous Localization and Mapping) module carried on a robot body; s2, generating a spiral detection path covering the surface of the blade based on a path planning algorithm optimized by the genetic algorithm; s3, synchronously collecting blade surface data by adopting a multi-mode sensor array, wherein the blade surface data comprise pulse thermal imaging data, ultrasonic guided wave signals and high-resolution visible light images; s4, performing feature fusion on the acquired data through a deep convolutional neural network; through multi-modal sensor cooperative detection, deep learning feature fusion and digital twinborn evaluation technologies, full-process automatic detection from damage identification to life prediction is realized, the detection precision and efficiency are significantly improved, and reliable guarantee is provided for safe operation of the wind turbine generator.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention discloses a sewage treatment supervision method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: extracting a medium-time feature vector in multi-scale feature vectors, inputting a Transform model, predicting a chemical parameter, extracting a long-time feature vector, predicting a water quality parameter for a linear predictable segment through employing LSTM, and predicting a non-linear segment through employing TVF. Obtaining a comprehensive predicted value and a confidence interval, carrying out anomaly classification, defining an optimization function target, and optimizing process parameters by using a genetic algorithm; the prediction precision of nonlinear water quality parameters is improved by introducing subinterval division of spectral intensity data and multi-scale feature vector construction in combination with LSSVM, Transform and LSTM models, real-time dynamic adjustment of process parameters is realized through a BP neural network optimized by chaotic mapping and a genetic algorithm, and the intelligent level of a sewage treatment system is improved.

The invention relates to the technical field of Chinese trumpet creeper breeding, and discloses a Chinese trumpet creeper-based efficient breeding decision support system and method. The method comprises the following steps: acquiring multi-dimensional phenotypic data in a Chinese trumpet creeper breeding test, wherein the multi-dimensional phenotypic data comprises plant height, leaf area, flowering period and yield indexes; performing normalization processing on the multi-dimensional phenotypic data, eliminating influences of different dimensions, and calculating a variable coefficient of each phenotypic index; then screening out phenotypic indexes of which the stability is higher than a preset threshold value based on the variable coefficient, and constructing a Chinese trumpet creeper phenotypic feature matrix; carrying out dimensionality reduction on the Chinese trumpet creeper phenotypic feature matrix by adopting an adaptive weighting algorithm, and extracting key phenotypic features; inputting the key phenotypic features into a genetic algorithm optimization module, and calculating the contribution degree of each genetic locus in combination with the Chinese trumpet creeper genotype data; and constructing a Chinese trumpet creeper breeding decision model according to the contribution degree of the gene locus, and outputting an optimal breeding combination scheme. The system can optimize the Chinese trumpet creeper breeding process and provide effective support for Chinese trumpet creeper breeding work.

The invention discloses a five-axis machining tool path planning method for a complex curved surface, and relates to the technical field of numerical control machining. Subregions are divided based on the Gaussian curvature of a curved surface model, and a region equation is fitted; projecting the sub-regions and screening to obtain a boundary point set, fitting a curved surface equation by adopting cubic NURBS, and performing normal vector and curvature constraint optimization to splice an optimal curved surface equation; setting a section plane, calculating a section curve, calculating an arc length based on a Simpson formula, and planning a track point coordinate sequence according to the curvature; constructing a spiral equation of adjacent section planes, calculating a spiral step length based on a Simpson formula according to a position and tangent continuous constraint solving coefficient, and generating a cross-plane track point sequence; in the section plane, based on lossless and cutting direction constraints, an optimal angle sequence is obtained through optimization of a genetic algorithm, and a cross-plane angle sequence is obtained through cosine interpolation; and a machining pose sequence is generated through integration, and high-precision machining of the complex curved surface is achieved.

The invention discloses a hospital data integration and intelligent management system based on multi-source data, and particularly relates to the field of medical data management. Comprising a multi-source heterogeneous data acquisition layer, a self-adaptive data fusion engine, a privacy enhanced distributed storage layer, a multi-modal intelligent analysis center, a clinical decision enhancement module, a self-adaptive resource scheduling module and a full-process auditing and tracing module. Through cooperation of multi-modal model fusion, the clinical decision support accuracy is improved, the self-adaptive learning capability is achieved, the risk of data decryption leakage is avoided, 100% coverage of audit tracing and full-process operation of block chain evidence storage recording can be completed, the abnormal access detection rate is greatly improved, resource scheduling optimization is carried out, the equipment vacancy rate is reduced, and the method is suitable for large-scale popularization and application. A particle swarm optimization algorithm is combined with a digital twinborn technology, resource allocation is dynamically adjusted, scheduling is optimized through a genetic algorithm, invalid overtime is reduced, and the satisfaction degree of medical staff is improved.

The invention provides an intelligent optimization control method for stable operation of a hydraulic system in a complex environment, which comprises the following steps of: aiming at an abnormal mode, optimizing power configuration parameters by adopting a genetic algorithm, determining a standby pump activation threshold value and a main pump switching condition, and obtaining a power redundancy scheme; according to the slow response reason, pipeline interface data are obtained, a feedback control mechanism is adopted to adjust valve group cooperation parameters, and a rapid pressure adjustment sequence is obtained; acquiring real-time feedback data according to the final optimization instruction, and processing high-temperature and high-dust influence through an iterative updating process to obtain stable operation configuration; according to the stable operation configuration, the state of the whole system is judged, and if the state meets the preset standard, a cooperative control result is output and used for maintaining continuous operation of the hydraulic system in the complex environment.

The invention relates to an operation and maintenance detection method and system for a water affair facility based on big data, and the method comprises the following steps: collecting a four-dimensional core data set through a terminal layer device of a cloud-side-end three-stage collaborative architecture; analyzing the preprocessed data based on a preset hierarchical adaptive model, and generating a preliminary operation and maintenance strategy set for the fault; performing operation and maintenance strategy simulation deduction on each operation and maintenance strategy in the initial operation and maintenance strategy set by relying on a total-factor digital twinborn body of the water affair facility, and screening an optimal low-carbon operation and maintenance strategy; and starting a corresponding response mode according to an early warning grading standard corresponding to the regional operation and maintenance risk grade, optimizing operation and maintenance resource scheduling through an improved genetic algorithm, and executing low-carbon operation and maintenance operation. The method has the effect of effectively improving the operation and maintenance efficiency and reliability.

The invention relates to the technical field of data processing, in particular to an auxiliary driving device and system based on video image processing, and the device comprises a frame buffer queue with a multi-channel video collection module aligned with timestamps; a hierarchical calculation task decoupling module identifies task types in the frame buffer queue and marks priority labels, including a first priority label of a radar data analysis and distance superposition task, a second priority label of a vehicle profile generation task and a third priority label of a video fusion task, and outputs a data stream with priority labels; the calculation module generates a task processing result; the genetic algorithm optimization scheduling module generates a resource allocation strategy and controls the calculation module to execute task processing; and the output synchronous control module synthesizes a high-priority task result and performs frame rate synchronous output on a low-priority task. According to the method, the problem of real-time degradation caused by multi-path high-definition video stream processing is solved through task grading and dynamic resource scheduling, and the image output delay of the auxiliary driving scene is reduced.

The invention relates to an intelligent generation method, system and device for a passive design scheme of a green building and a medium. The method comprises the following steps: firstly, collecting site limitation data and climate data, inputting the data into a thermal performance simulation model, and outputting building heat loss and solar heat gain distribution; based on the result, a genetic algorithm is used for optimizing parameters such as the building orientation and the window-wall ratio, and a building design parameter combination is formed through energy consumption calculation, standard judgment and iterative optimization. Then, thermal and economic parameters are extracted from the design parameters to construct a data set, the parameters are screened to generate a building layout optimization instruction, and the layout is adjusted in combination with a dynamic adaptation standard to obtain an optimization scheme. And finally, associating the optimized layout with site parameters, performing energy model optimization to form a low-carbon scheme, and coupling environment adaptability data to generate a sustainable development scheme. By means of the method, the problems that traditional design parameter optimization is low in efficiency and multiple targets are difficult to balance can be solved, layout reasonability and energy consumption control precision are improved, and low carbon of the whole life cycle of the building is assisted.

The invention discloses a heat transfer performance prediction and optimization method of a multi-dimensional data weighted neural network, and belongs to the field of artificial intelligence and process equipment enhanced heat transfer. The prediction method comprises the following steps: acquiring multi-physical field heat transfer data, preprocessing the data, and dividing the data into a training set and a test set; performing multi-channel independent normalization processing on the preprocessed data; establishing a neural network prediction model; globally optimizing the weight and the bias parameter of the neural network by adopting a genetic algorithm; using the optimized parameters to initialize a neural network to carry out refined training; evaluating the model performance by using the test set and outputting a heat exchange performance prediction result; and carrying out heat exchange characteristic analysis and equipment optimization design based on a prediction result. According to the method, neural network hyper-parameters are optimized through the genetic algorithm, the problem that a traditional neural network is prone to falling into local optimum is solved, high-precision prediction of heat exchange performance parameters is achieved, and reliable technical support is provided for design and optimization of efficient heat exchange equipment.

The invention discloses a power grid material vehicle path planning method, and provides a space-time hierarchical modeling and cloud edge collaborative optimization architecture aiming at the problems of dynamic embargo, channel capacity fluctuation, multi-resource conflict, response lag and the like faced by power grid material dispatching in the prior art. By constructing a space-time coupled channel capacity-path planning hierarchical model, multi-commodity network flow pre-calculation and real-time vehicle path dynamic decoupling are carried out, and material distribution is optimized in combination with a space-time double-coding genetic algorithm; a multi-agent deep game decision-making mechanism is designed, channel priorities of different types of materials are dynamically balanced through reinforcement learning, and distributed conflict resolution is achieved; a block chain-based cross-domain scheduling instruction synchronization system is developed, low-delay path re-planning is realized by adopting a differential updating mechanism, and second-level updating of a scheduling scheme under an emergent road condition is supported; the on-chain evidence storage technology is introduced to guarantee instruction integrity and operation tracing, and the scheduling deadlock risk is reduced in combination with a key channel competition early warning model.

The invention relates to the technical field of control systems, in particular to a plate production control system with a heating function, which comprises a cross-domain data fusion module for acquiring deformation data, energy transfer data, electrical signal data, acoustic emission signals and stress distribution images through a distributed sensor network; generating a characteristic matrix containing a mapping relation of thermal resistance distribution, stress gradient and current density based on a physical field coupling model; the multi-parameter coupling control module synchronously outputs a temperature gradient adjustment signal, a pressure distribution control signal and a current density modulation signal according to the characteristic matrix; the heat flow coordinated regulation and control module combines conductive layer power data and temperature field characteristics; the digital twinning optimization module constructs a thermodynamic electric coupling virtual model, optimizes a weight coefficient through a genetic algorithm and returns the weight coefficient to the control module. Through cross-domain data association and multi-target cooperative control, heat conduction path stability and interface stress dynamic digestion are realized.

The invention relates to the technical field of ecological environment monitoring and resource scheduling, and discloses an ecological scheduling monitoring method and system based on multi-source remote sensing fusion. The method comprises the following steps: performing time-space consistency correction and fusion on multi-source data, and extracting initial ecological parameters; a feature enhancement parameter set and adaptive inversion parameters are obtained through optimization of a convolutional neural network and a genetic algorithm, key ecological indexes are inverted, and real-time ecological state description is generated; based on real-time ecological state adjustment model input, predicting an ecological change trend, and calculating to obtain an optimization decision variable; a water resource distribution scheme is generated, the distribution balance degree is evaluated, and final scheduling output is formed through iterative optimization; a final scheduling result is fed back to a data integration link, and updating and loop optimization of the preliminarily aligned data set are achieved. According to the invention, the integration of ecological monitoring and water resource scheduling under multi-source data fusion can be realized, and the balance of water resource distribution and the real-time performance of ecological system regulation and control are improved.

The invention discloses a production control method and system based on multi-source data fusion and an intelligent algorithm, and relates to the technical field of automatic control. Multi-dimensional production state information is collected through a sensor, multi-source heterogeneous data is integrated into a data set in a unified format through a data fusion technology, and the multi-dimensional production state information is obtained; a long short-term memory network is used for trend analysis and prediction, a multi-objective optimization algorithm is used for calculating an optimal control parameter, multi-loop cooperative adjustment is realized, an adjustment instruction is issued through a distributed control protocol, a priority ranking algorithm is introduced to ensure system stability, and a game theory negotiation algorithm is used for realizing dynamic resource allocation among production units. The production scheduling plan is optimized through the genetic algorithm, continuous monitoring and optimization can be achieved, control parameters can be adjusted in a self-adaptive mode, the production efficiency and the resource utilization rate are effectively improved, and intelligent automatic control under the complex production environment is achieved.

The invention discloses a cascade reservoir flood control water level combined application scheduling method and scheduling system, and relates to the technical field of cascade reservoir scheduling, and the method comprises the following steps: dividing into a plurality of sub-regions according to an administrative region, determining a sub-reservoir corresponding to each sub-region, and collecting the rainfall and historical flood discharge data; analyzing the water storage capacity of each sub-region at the moment corresponding to the historical data to form a training data set; constructing a flood season flood discharge prediction model, and obtaining the target flood discharge flowing into the sub-reservoir by using the prediction model; analyzing the water level change in the flood season according to the target flood discharge amount, and judging whether scheduling is needed; and if scheduling is needed, determining a drainage flow combination of each sub-reservoir, setting a constraint condition, and performing optimization through a multi-target genetic algorithm to obtain an optimal drainage flow scheme meeting constraints. According to the scheme, the scientificity, the accuracy and the intelligent level of combined dispatching of the cascade reservoirs are remarkably improved.

The invention relates to the technical field of control regulation, in particular to a supercritical unit variable load rate dynamic compensation control method, which comprises the following steps: acquiring unit operation data, performing credibility restoration, and outputting a credible parameter set; a time sequence mapping table is constructed based on the credible data, and boiler heat storage lag time, steam turbine control valve delay and a steam temperature interstage coupling coefficient are quantified; correcting the boiler water supply feedforward instruction by using the heat storage lag time to generate a compensated water supply instruction; according to the time sequence mapping table and the main steam pressure change rate, a multi-genetic algorithm is adopted to optimize a control valve constraint rule, and a dynamically constrained control valve action instruction is generated; an inter-stage coupling coefficient is called to generate a swing angle compensation amount, and water spraying temperature reduction disturbance is blocked; safety locking parameters are monitored to trigger buffering intervention in a grading mode, and compensation instructions are integrated to implement cooperative correction. The problem of out-of-step response of multiple execution units under the variable load working condition is solved, and the unit adjusting rate and the control stability are remarkably optimized.