1762 results about "Field data" patented technology

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

The invention discloses a process intelligent simulation method and system based on rational number fusion, and belongs to the technical field of high-end equipment manufacturing and artificial intelligence. The method comprises the following steps of: constructing a fusion database used for storing a mapping relationship between process parameters and organization characteristics by taking a mathematical model fusion normal form as a core, taking the mathematical model data as theoretical model data and taking the mathematical model data as industrial field data; constructing a multi-scale AI simulation agent model corrected by the industrial field data, and constructing an intelligent prediction model used for representing an association relationship between organization characteristics and service performance; and finally, receiving a target performance index, carrying out global optimization under double constraints of a physical boundary of a theory and a simulation environment which is corrected by a number by utilizing an AI reverse design engine, and carrying out reverse solution to obtain an optimal process parameter. Through a two-way closed-loop mechanism of a theoretic constraint number and a number correction theoretic, the fundamental problems of distortion of a theoretical model caused by a scale effect and lack of physical constraint with an AI model are solved.

The invention relates to the technical field of reservoir dam safety monitoring, and discloses a reservoir dam siltation dynamic monitoring and early warning system. Multi-dimensional sensing node arrays of the system are arranged at key positions of a dam body structure and a reservoir area, and sediment thickness distribution data, water flow velocity field data and sediment concentration gradient data are synchronously collected. And the edge computing node receives the original monitoring data, executes data cleaning and space-time alignment processing, and generates a standardized siltation feature data set. And the cloud analysis platform receives the data set, calculates a deposition evolution trend matrix through a space-time coupling prediction model, and outputs a reservoir area deposition risk level distribution map. And the dynamic visualization engine analyzes the risk level distribution map, generates a three-dimensional dynamic deposition situation model, and marks the space coordinates of the abnormal deposition area. And the early warning decision center generates a graded early warning instruction set according to the space coordinates of the abnormal region, and triggers a corresponding emergency response strategy.

The invention discloses a stamping die health state assessment method and system based on digital twinning, and relates to the technical field of die health state assessment, and the method comprises the following steps: a physical sensor network deployed on a stamping die collects die stamping process data in real time; constructing a finite element analysis simulation FEA model to simulate the working condition of the stamping die based on the geometric structure, the material attribute and the stamping process parameters of the die; according to the invention, the data of the die stamping process are collected in real time through the physical sensor network, and real-time calculation is carried out in combination with the finite element analysis simulation model, so that transient stress field, strain field and temperature field data of the die can be output in a short time, and real-time monitoring of the health state of the die is realized; by considering the degradation of the mold material performance along with the use time and the dynamic process of quantitative damage accumulation, the damage accumulation value is accurately calculated through the dynamic material performance database and the continuous damage mechanical model, and the accuracy of the evaluation result is improved.

The invention relates to the technical field of data processing, and discloses a thinking instruction data generation method and system, electronic equipment and a storage medium, and the method comprises the steps: integrating a multi-source heterogeneous nuclear industry data corpus, and forming a standardized data source; thinking chain instruction data are constructed through an instruction generation framework driven by a large language model, multi-level security constraint processing is performed on the thinking chain instruction data, and contents which do not conform to nuclear industry security specifications are identified and eliminated; executing multi-dimensional quality evaluation on the thinking chain instruction data passing the security constraint, and removing the thinking chain instruction data which does not reach a quality threshold value; and performing real-time updating and dynamic optimization on the nuclear industry data corpus and the thinking chain instruction data based on newly collected nuclear industry field data. The method can improve the flexibility, accuracy and timeliness of instruction generation.

The invention relates to a temperature forecast correction method based on a PSD-Net and a PSD-Net, and belongs to the technical field of weather forecast, and the method comprises the steps: obtaining a dynamic meteorological variable based on the PSD-Net, and loading topographic data and a report starting time observation truth value at the same time; preprocessing the dynamic meteorological variable, the topographic data and the report starting time observation truth value; inputting the preprocessed data into a temperature forecast correction model to obtain temperature correction field data; wherein the temperature forecast correction model is obtained by training a PSD-Net model based on a training data set, and the training data set comprises historical dynamic meteorological variables, topographic data and a report starting time observation true value. Compared with the traditional numerical mode and the original output of the archaeological model, the temperature forecast MAE corrected by the method is reduced by more than 37.6%, the accuracy rate within 2 DEG C is improved by 14%-19%, and the precision advantage is more prominent especially in complex terrain areas and extreme weather events.

The invention discloses a supervision method and device based on construction project quality detection and a medium, and relates to the technical field of project quality supervision, and the method comprises the steps: collecting construction site data, carrying out the preprocessing, and generating a site data set; performing real-time comparison and verification on the building information model and the field data set to obtain verification data, inputting the verification data into the building information model, performing construction consistency analysis and deviation identification, and generating a quality evaluation report; performing risk assessment and priority classification on the quality analysis data to obtain risk assessment data, and performing problem priority analysis and rectification scheme formulation on the risk assessment data to generate rectification execution data; and performing rectification effect analysis and overall quality evaluation according to the rectification execution data, and generating a project quality summary report. The real-time performance and the accuracy of construction quality monitoring are improved, the construction deviation is reduced to the maximum extent, the quality management is optimized, and the smooth proceeding of a project is guaranteed.

The invention discloses a data center machine room energy-saving optimization method and system based on thermal environment prediction. The method comprises two stages of offline modeling and online prediction optimization. In the off-line stage, a CFD simulation model is constructed based on a machine room physical structure, equipment layout and thermal load parameters, and high-precision temperature field data is generated; using the thermal environment prediction model to input equipment parameters and load change to output future space temperature distribution; meanwhile, an XGBoost hybrid energy consumption prediction model is constructed based on the wind speed ratio or the fan frequency, the number of running fans and related characteristics. In the online stage, the lowest energy consumption and the minimum temperature deviation serve as targets, and a Pareto optimal solution set is generated through an MOEA / D algorithm; the optimal wind speed ratio / frequency and equipment number combination is selected as required to control operation of the air conditioner; and dynamically updating model parameters by sliding a time window to realize long-term robust control of the system. According to the method, intelligent energy-saving control of the machine room is realized by fusing CFD simulation, time sequence prediction, energy consumption modeling and a multi-objective optimization algorithm.

The invention relates to the technical field of process optimization, in particular to a collaborative optimization analysis method for multi-station tower drum welding. In order to solve the problem that cooperative control of tower drum multi-station welding is difficult, firstly, multi-mode sensing data of welding seam vision, arc sensing, thermal field distribution and welding voiceprint are loaded, a fusion evaluation model is constructed to obtain a welding seam deviation value and a sensor confidence score, and the health state of a sensor is judged according to a threshold value; and a corresponding fault-tolerant reconstruction control mode is triggered to obtain welding tracking control parameters, then a multi-station welding heat source model is loaded, thermal interference is decoupled in combination with the control parameters and thermal field data, multiple stations are cooperatively regulated and controlled, a cooperative control instruction is output and executed, and welding process optimization is achieved.

The invention relates to the field of discrete manufacturing intelligence, in particular to an intelligent decision-making and business collaboration method based on an OAG ontology and an LLM large model, which comprises the following steps: collecting historical data and business documents, and performing directional fine tuning on a basic large language model to form the LLM large model; the method comprises the following steps: analyzing discrete manufacturing scene original data and business documents, and constructing an OAG ontology library; receiving field data in real time through the LLM large model, updating the OAG ontology library, and performing feedback optimization on the LLM large model to form bidirectional feedback; integrating real-time data and historical data, inputting the data into an LLM large model, converting the data into business knowledge through layering of an OAG ontology library, and generating a main and standby decision scheme; and establishing an agent federated center, issuing a decision scheme, collecting execution data and feeding back an LLM large model, and realizing cross-scene collaboration and full-process data closed loop. According to the invention, through a technical path of a whole-process data closed loop, a core pain point that an existing LLM does not understand a business is effectively solved, and whole-link value conversion of discrete manufacturing data is realized.

The invention discloses a multi-dimensional data-based temperature prediction method and system for a power integrated module, particularly relates to the technical field of temperature measurement, and is used for solving the problems of temperature field distortion and unreliable measurement result caused by thermal disturbance introduced by a sensor in temperature monitoring of an existing power integrated module. Multi-dimensional temperature data are formed by acquiring measurement data of a plurality of temperature sensors, a virtual heat flow path is reconstructed based on temperature gradient distribution, deviation degree analysis is carried out on the virtual heat flow path and an inherent structure heat flow path of a module so as to judge thermal disturbance influence, and reference isothermal surface topological structure features are acquired when disturbance occurs. A real-time isothermal surface is constructed, a thermal disturbance space position and an influence range are identified by detecting a curvature distribution sudden change area, a local thermal conductivity correction field is established to perform thermal field reconstruction on multi-dimensional temperature data to generate correction temperature field data, and finally a temperature change trend is analyzed according to the correction temperature field data to predict a future temperature state.

The invention discloses a wind field rapid prediction method and system based on optimized Latin hypercube sampling and POD-BPNN, and the method comprises the steps: carrying out Latin hypercube sampling to generate an initial sample point set, introducing a sensitivity weight, and carrying out the iterative optimization of sample distribution through a greedy strategy; assembling the CFD numerical simulation flow field data of all sample points in the sample point set into a flow field snapshot matrix, and determining a dynamic multi-target truncation order and a corresponding POD mode and coefficient; building and training a BPNN model, packaging the trained BPNN model, the POD modal matrix, the mean field and the flow field reconstruction logic into an FMU module, obtaining the FMU module which can be called in a cross-platform manner, and achieving the real-time prediction of a wind field. The invention relates to the technical field of wind field prediction, significantly improves the precision and calculation efficiency of wind field prediction, and provides an efficient and accurate solution for the rapid prediction of a wind field.

The invention provides a deep coal seam roadway surrounding rock stability intelligent early warning method, and relates to the technical field of coal mine dynamic disaster prevention and control. According to the intelligent early warning method for the stability of the surrounding rock of the deep coal seam roadway, a basic sample library is constructed through a dynamic and static combined loading rock test; synchronously collecting field surrounding rock static stress and micro-seismic data and carrying out time sequence polymerization; constructing an equivalent dynamic and static combined stress index based on a dynamic and static load superposition principle, and forming a seismic-force coupling feature vector; establishing an LSTM early warning model, adopting a transfer learning strategy, pre-training by using laboratory data, and then performing fine tuning by using field data; inputting the real-time feature vector into a model and outputting an instability probability; and verifying and correcting the probability based on actual deformation data of the surrounding rock, and applying a missing report sample to online incremental updating of the model. According to the method, the dynamic and static load coupling effect is quantified, the difficulty of small sample training is overcome, and accurate and self-adaptive early warning of the deep roadway surrounding rock instability risk is realized through a physical feedback closed-loop correction mechanism.

The invention discloses a data circulation risk identification method and system based on a multi-droop large model cognitive game, and belongs to the technical field of artificial intelligence. The method comprises the steps that a corpus containing multi-source data is constructed, and a vector knowledge base supporting mixed retrieval is established after processing; designing a compliance evaluation system fusing the general dimension and the dynamic scene dimension; training by utilizing domain data to obtain a vertical class large model of a data compliance domain; based on an intelligent body examination enhancement generation and thinking chain technology, driving a plurality of vertical class large models to perform a structured cognitive game and iterative convergence, and forming a consensus risk list; and finally, generating a risk identification report through case matching verification. According to the method, through multi-source knowledge fusion and a multi-vertical-class large model cognitive game mechanism, the randomness of large model output is inhibited, the problems of low manual auditing efficiency and insufficient professional property of a general large model are solved, and efficient and reliable data circulation risk identification is realized.

The invention discloses a large-scale regional sea wave rapid forecasting method and device based on data driving, and the method comprises the steps: obtaining multi-source marine physical environment data of a target sea area, and generating a standard physical field data flow with unified temporal-spatial resolution; constructing a multi-channel space-time input tensor containing wind field driving information, terrain boundary information and historical wave state information; the difference between the predicted wave height and the real wave height is minimized through a back propagation mechanism, so that a trained wave height prediction model is obtained; receiving latest wind speed field data output by a real-time observation or numerical forecasting mode, and outputting a sea wave significant wave height prediction field at a future target moment; the device is used for implementing the method. According to the technical scheme of the method provided by the invention, through a physical lag alignment mechanism, the time delay of transmitting wind energy to wave energy is accurately captured, and the modeling precision is improved; and meanwhile, rapid deduction of a large-scale sea wave field is realized based on deep learning, and high accuracy and high timeliness are achieved.

The invention discloses a PINN-based transformer convective heat transfer coefficient acquisition method, system and device and a medium, and relates to the technical field of transformers, and the method comprises the steps: building a three-dimensional thermal field simulation model based on transformer structure parameters, obtaining temperature field data through multi-physics field coupling simulation, building a sample set, training a physical information neural network model, and obtaining the convective heat transfer coefficient of a transformer. A loss function is defined, through weight adjustment and optimization algorithm training, loss change is dynamically monitored, convective heat transfer coefficient distribution is inversed, an inversion coefficient is substituted into a thermal model for forward calculation, iterative optimization is performed through comparison with independent measurement data, and accuracy and applicability are verified through actually measured temperature data. According to the method, the dependence on experimental data is reduced, the high-precision and high-efficiency acquisition of the convective heat transfer coefficient of the spatial change of the transformer is realized, and a reliable technical means is provided for thermal design and state evaluation of the transformer.

The invention relates to the field of data processing, in particular to a government affair field data query method and device based on multiple agents, and the method comprises the steps: receiving a user question, and carrying out intention recognition through a large language model to obtain a structured query intention; after intention recognition fails, a supplementary question request is initiated by using a clarification mechanism, and a structured query intention is acquired based on the supplementary question request; obtaining priori knowledge, converting the priori knowledge into priori knowledge embedded vectors, and summarizing the priori knowledge embedded vectors into a vector index; searching a plurality of pieces of reference data and database information corresponding to the question rewritten by the user according to the question rewritten by the user; querying the corresponding data table by using the large language model, and generating a complementation prompt word of the SQL query statement by using the prompt word template; and screening out a target SQL query statement according to the completion prompt word, obtaining a query result, and returning the query result to the user. According to the method and the device, the SQL query statement generation accuracy can be improved.

The invention relates to the technical field of construction safety, in particular to a trestle construction hazard source assessment method and system, and the method comprises the steps: constructing a hazard source data set corresponding to a trestle construction process, carrying out the entry recording of various potential hazard sources, and setting impact factors for representing risks; collecting field data of the construction process, and associating the field data with the influence factors to obtain risk characterization forming a hazard source; fusing the risk characterization with the environmental parameters, and constructing a space-time risk mapping layer; based on the space-time risk mapping layer, performing quantitative analysis on the hazard source through a multi-factor comprehensive model to obtain risk distribution characteristics in different level intervals; and according to the risk distribution characteristics, generating a space-marked trestle risk map. According to the invention, the problems of strong subjectivity and lack of systematic analysis of existing trestle construction hazard source assessment are effectively solved, the objectivity of risk assessment is enhanced, and the capability of accurately judging the risk state in a complex construction environment is improved.

The invention relates to the technical field of power supply services, in particular to a method and system for monitoring the business expansion process of a power supply enterprise in real time. The mobile terminal collects field data, and the edge data server generates a feature sequence to be subjected to compliance comparison; and the rule fusion container fuses the rule base and the historical problem base to generate compliance data. On the basis of field standardized mapping and version abstracts, existence, applicability, consistency and validity period verification is executed on the forced / recommended attributes, and compliance, element missing or non-compliance items are output; and generating positioning information for the non-conformity items, including an informatization problem identifier of a version and a rectification suggestion, converting the positioning information into a work order with a time limit, caching the work order in a weak network, and performing reconciliation and bidirectional verification with a central server after the network is recovered. And the nodes finish event triggering compliance record uploading, and the center summarizes and archives to form process-level compliance. According to the scheme, rule upgrading controllability, evidence chain traceability and process compliance closed loop are realized, and accuracy and efficiency are improved.

The invention discloses a lightweight optical flow neural network method for river velocity measurement, and the method comprises the steps: collecting a dynamic monitoring image of a water area surface flow field, calculating an approximate optical flow field through combining with a DeepFlow optical flow algorithm, and generating an optical flow label; an improved lightweight ConvFFN-Flow optical flow estimation model is built, and the model comprises a feature extraction module, a 4D related structure body module, a GRU optical flow residual error iteration module and an up-sampling module; by introducing a ConvFFN module, the model parameter scale and the calculation amount are reduced, and the optical flow prediction precision is kept; and based on pixel coordinates of front and rear frames of images of the optical flow field data, obtaining corresponding positions in a real three-dimensional space, calculating an actual displacement amount, and deducing to obtain a real flow velocity vector size and a flow direction of the water body surface. The method still has stable and high-precision optical flow estimation capability when being used for processing scenes such as fine river water surface texture and complex flow state change, and meanwhile, the calculation overhead and the storage demand are remarkably reduced.

The invention relates to the technical field of precision machining, in particular to a compensation method for thermal deformation of an aviation large-size workpiece, which comprises the following steps of: firstly, establishing a first mapping relation between the size change of a calibration piece at different temperatures and the temperature through a thermal deformation calibration experiment; then, by taking the experimental data as a reference, correcting a thermal expansion coefficient in the calibration piece simulation model through an optimization algorithm, and establishing a high-precision prediction model for deformation of the workpiece to be machined; in the actual machining process, real-time temperature field data are collected through a temperature sensor arranged in a key area of a machine tool, and the thermal deformation error of a workpiece to be machined and the thermal offset of the original point of a machining coordinate system are predicted through the prediction model; and finally, according to a prediction result, a machining track is compensated in real time by dynamically offsetting a machining coordinate system and modifying the scaling ratio of each axis of a numerical control system. According to the method, the problem that under the non-constant-temperature working condition, machining is out of tolerance due to thermal deformation of the aviation large-size workpiece is effectively solved, and the machining precision and the percent of pass are remarkably improved.

The invention discloses a rolling force prediction method of a cold tandem mill based on a Bayes-Light GBM algorithm, and belongs to the technical field of cold rolling, and the method comprises the following steps: obtaining rolling field data of the cold rolling mill, and determining characteristic parameters influencing the rolling force; on the basis of the characteristic parameters influencing the rolling force and the real rolling force of the cold tandem mill, characteristic parameter data sets are constructed; preprocessing the characteristic parameter data set; dividing based on the preprocessed feature parameter data set to obtain a training set and a test set; a Bayes-Light GBM model is constructed and used for predicting the rolling force of the cold tandem mill; on the basis of the training set data, the Bayes-Light GBM model is trained, and the trained Bayes-Light GBM model is obtained; and S7, inputting the data of the test set into the trained Bayes-LightGBM model to realize the prediction of the rolling force of the cold tandem mill.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention provides an intelligent monitoring and risk early warning method for urban road settlement, and belongs to the technical field of urban management based on machine learning. The method comprises the following steps: firstly, collecting four types of multi-source space-time monitoring data, including urban road settlement data, underground environment data, pavement structure data and dynamic load and environment data; secondly, constructing a multi-source data space-time completion model, performing unsupervised completion on sparse monitoring area data, and generating space-time continuous settlement field data; thirdly, constructing a road settlement health index prediction model fusing multiple factors, inputting complementation data and original features, and outputting a health index of a 0-1 continuous interval; and finally, in combination with the health index and the road function level, four-level risk classification and dynamic early warning are realized. According to the method, the problems of space-time faults and data islands of a traditional method are solved, urban road global real-time monitoring is achieved, and early warning upgrading from qualitative judgment to quantitative grading is achieved.

The invention discloses a GNN-PINN hybrid architecture-based propeller wake flow pressure field reconstruction device and method and a storage medium, and belongs to the field of flow field measurement. The method comprises the following steps: firstly, obtaining CFD simulation data of multi-model propellers under different working conditions and constructing a data set; adding noise to the velocity field data to simulate experimental noise; a hybrid architecture pressure reconstruction model based on deep fusion of a graph neural network (GNN) and a physical information neural network (PINN) is constructed, and the physical rationality of output is ensured through a physical constraint mechanism; after the network training is finished, PIV experimental data is used as a test set for verification, and the result shows that the GNN-PINN hybrid model can realize rapid and high-precision reconstruction of the wake flow pressure field. The method overcomes the defects of interference of traditional pressure measurement on a flow field and long consumed time of a CFD method, is suitable for wake flow pressure field reconstruction of propellers of various models and under various working conditions, and has high robustness and engineering application value.

The invention provides a cable fault diagnosis method and system based on multi-modal composite fusion. The method comprises the following steps: synchronously acquiring and performing time-space alignment on distributed acoustic, temperature and high-frequency pulse signals along a cable path; the acoustic signal is decoupled into an internal event channel and an external interference channel by time domain polarization analysis. And calculating the acoustic nonlinear index of the candidate event in the internal channel, dynamically correcting the judgment threshold value of the candidate event by using the real-time temperature signal, and performing cross-modal correlation verification with the high-frequency pulse signal so as to confirm that the candidate event is an electrical discharge event. And if the judgment result is true, an acoustic wave field data array of the event is extracted, and a passive synthetic aperture focusing algorithm is applied to reconstruct a two-dimensional morphological acoustic image of the fault source. And finally, integrating all information to generate a comprehensive diagnosis report. The technical problem that a traditional distributed sensing method can only provide one-dimensional fault positioning and cannot perform high-confidence qualitative and multi-dimensional morphological representation on the fault is solved.

The invention relates to the technical field of cold-chain logistics transportation and storage temperature control, in particular to an air compressor vehicle-mounted refrigeration and refrigeration house collaborative temperature control system based on AI scheduling, which comprises a multi-dimensional state sensing module used for collecting real-time temperature field data, environmental meteorological parameters, compressor operation states and goods category information in a refrigerator car and a refrigeration house, and sending the real-time temperature field data to a server; a structured multi-dimensional state vector is output; and the thermodynamic evolution prediction module is used for setting a prediction time domain with a finite length based on the multi-dimensional state vector, and calling and constructing a mathematical model of a heat balance relationship between the refrigerating capacity generated by the operation frequency of the compressor and the environment and the cargo heat load. According to the method, the comprehensive cost function fusing the energy consumption, the cargo quality loss and the equipment wear is constructed, and the hidden quality attenuation is converted into the dominant economic cost by using the Arrhenius model, so that the global operation benefit maximization of the system on the premise of ensuring the cargo safety is realized.

The invention relates to the technical field of numerical control, in particular to an engraving and milling machine machining track control method based on machine vision. Comprising the steps of receiving an operation task instruction, collecting a reflected light intensity matrix and processing thermal field data, and generating a source data packet; obtaining a fusion heat distribution matrix through the homography matrix; based on the friction thermal response coefficient, a physical tool nose contact area is judged, a dynamic feature mask is generated, the dynamic feature mask and the reflected light intensity matrix are operated, and a monitoring area is output; executing end coordinates are output through least square ellipse fitting, and sensor calibration parameters are inversely transformed into tool nose feedback coordinates; and reading interpolation instruction coordinates, calculating a differential vector, and driving a feed shaft motor to execute displacement compensation. According to the method, deep space-time coupling is performed on the physical cut-off characteristic of the infrared spectrum, the high-frequency texture characteristic of the visible light and the kinematics constraint of a numerical control system, so that a real tool nose hot spot and an optical virtual image are distinguished, and the uniqueness and authenticity of track identification are ensured.

The invention discloses a DTS and DAS fusion-based cable fault diagnosis and positioning method and system, and the method comprises the steps: synchronously collecting a distributed temperature sensing signal and a distributed sound wave sensing signal through a single sensing optical fiber which is laid together with a cable, and carrying out the time-space synchronization processing, obtaining temperature field data and phase disturbance field data which are aligned under the same space-time coordinate; calculating a cross-modal coherence spectrum of the temperature field data and the phase disturbance field data, quantifying physical coupling strength of vibration and temperature, and screening out potential fault candidate regions with coupling correlation; for the potential fault candidate region, adopting a dynamic attention mechanism of adaptive adjustment of a temperature change rate to perform nonlinear semantic fusion on the temperature feature and the vibration time-frequency feature to generate a fusion feature manifold; and calculating an entropy anomaly index reflecting a feature concentration ratio based on the generated fusion feature manifold, performing weighted calculation in combination with the coupling strength of the cross-modal coherence spectrum, and outputting an accurate spatial position and a fault type of a cable fault.