1142 results about "Benchmark data" patented technology

The invention discloses an intelligent financial risk early warning method and system based on a management decision, and relates to the technical field of data intelligence, and the method comprises a multi-source heterogeneous data collection module which obtains enterprise financial data, supply chain data, market public opinion data and industry reference data in real time through an API interface, risk keywords are extracted from news, social media and policy documents through a natural language processing technology according to the market public opinion data; the streaming data processing engine is constructed based on an Apache Flink framework, performs windowing processing on the real-time data stream, calculates the dynamic fluctuation ratio of financial indexes by sliding a time window, and compares the dynamic fluctuation ratio with a preset industry risk threshold value; and a risk decision fusion model, a dynamic threshold adaptive module and a man-machine collaborative early warning terminal. According to the method, millisecond-level financial index fluctuation monitoring is realized through a streaming computing framework, a knowledge graph and a natural language processing technology are fused, a risk entity and a causal chain are extracted from unstructured data, and a multi-dimensional risk portrait is constructed.

The invention relates to a cable online operation fault positioning, monitoring and early warning method and system based on a neural network algorithm, and the method comprises the steps: collecting the operation environment and state data of a cable through a multi-source sensor, and generating an original monitoring data set through noise reduction and time sequence alignment; aiming at the problem of periodic distortion caused by seasonal fluctuation of environment temperature and humidity, performing dynamic reference compensation to generate a reference data set without seasonal influence; aiming at a coupling effect of a dynamic load and temperature drift, calculating insulation performance evaluation data of temperature compensation through load-insulation correlation mapping; aiming at the problem of disconnection of waterproof sealing monitoring and fault positioning, a sealing degradation grade is generated based on regional high-humidity detection; aiming at the defect of a fixed threshold strategy, generating a fault coordinate and a risk probability in combination with spatial positioning and probability analysis; and finally, self-adaptive updating of the parameter library is realized through credibility verification.

The invention provides a self-adaptive adjustment Internet operation and maintenance strategy generation method and system, and relates to the technical field of Internet, and the method comprises the steps: 1, dynamically collecting the operation state data of a target operation and maintenance environment through a distributed sensor and an edge node, and generating a multi-dimensional time series data set; 2, performing space-time correlation analysis on the multi-dimensional time sequence data set, determining a reference data node, constructing a two-dimensional correlation structure, and generating a dynamic judgment interval; and step 3, respectively selecting monitoring sample sets in the inner domain and the outer domain of the dynamic judgment interval, generating a trajectory feature sequence according to the time evolution relationship of the sample sets, and calculating a dynamic correction coefficient based on the trajectory feature sequence. According to the method, the self-adaptive circulation control is formed by dynamically adjusting the threshold baseline, the judgment interval and the strategy generation rule, the accuracy and effectiveness of the internet operation and maintenance strategy are improved, and the stability of internet operation is enhanced.

The invention relates to the technical field of agricultural product storage management, in particular to an agricultural product storage intelligent management system based on big data, which comprises a multi-source data acquisition layer, an edge processing layer, a cloud processing layer and an execution control layer, compared with the prior art which only depends on manual recording of the picking date or simple visual sampling inspection for judging the freshness, the method has the advantages that the freshness key indexes such as the skin color degree and the texture structure of the agricultural product are synchronously and quantitatively extracted through the deep learning model in the warehousing link of the agricultural product; performing dual cross validation with the registration time to eliminate abnormal samples; the verified freshness reference data and parameters such as temperature, humidity, gas concentration and the like monitored in real time in a corresponding storage microenvironment are fused and input, and a self-adaptive shelf life prediction model based on a dynamic environmental stress factor is constructed; therefore, continuous and accurate quantification and early warning of decay processes of different types of agricultural products under various storage conditions are realized.

The invention relates to a fire prevention and control smart network method and system based on Beidou satellite navigation. The method comprises the following steps: firstly, acquiring real-time operation data of fire-fighting equipment in a building, and scanning a building structure through a three-dimensional imaging device to generate a three-dimensional digital model; secondly, fusing the real-time operation data, the three-dimensional digital model and preset environment parameters to form a fire prevention and control reference data set; thirdly, carrying out multi-dimensional data comparison on the reference data set based on a preset fire-fighting standard program, and generating an alarm signal with spatial positioning information; then, determining a fire-fighting facility failure area according to the alarm signal, and calculating a fire risk diffusion parameter; and finally, generating a grading early warning instruction according to the risk diffusion parameters, and constructing an electronic combat map containing the optimal rescue path. The method provides global and accurate basic data support for fire prevention and control, improves the timeliness and accuracy of early warning, optimizes the rescue path, and improves the fire disposal efficiency.

The invention discloses an ACM data analysis method and a data monitoring system, and belongs to the technical field of aircraft maintenance, and the method comprises the steps: constructing an ACM data set, setting a feature engineering processing method, carrying out coupling feature extraction through setting a dynamic window and calculating a dynamic correlation coefficient, dividing working conditions, eliminating fluctuation interference, and generating a structured time series data set; the method comprises the steps of obtaining ACM reference data of aircrafts of the same model, setting a reference fault judgment method, judging latest data in a structured time sequence data set, screening abnormal parameters, judging whether the ACM breaks down or not by combining an actual temperature adjusting effect, reducing single-parameter misjudgment, setting a coupling fault positioning method, diagnosing the faulty ACM, and distinguishing true faults and false anomalies. Meanwhile, a prediction scheduling method is set, fault prediction, health state prediction and service life evaluation are carried out on the fault-free ACM, the probability of sudden faults is reduced, and non-planned flight stopping is reduced.

The invention relates to the technical field of building simulation, and discloses a BIM-based special-shaped hyperboloid building site model generation method and system, and the method comprises the steps: collecting the multi-source heterogeneous point cloud data of a special-shaped hyperboloid building site to construct a three-dimensional feature tensor model, and recognizing the key curvature mutation point of the tensor model to construct a space reference topology network; based on the digital skeleton model, geometric contour features are analyzed, hyperboloid fitting is carried out, and an initial BIM model is obtained; performing multi-physics field coupling simulation on the initial model to obtain a target BIM model; extracting curvature distribution and structural stress parameters of the target BIM model to obtain basic model parameters, and constructing a building performance prediction model in combination with historical performance reference data; and inquiring a bearing capacity standard and a deformation threshold value, and analyzing the optimal curved surface parameter combination and a prestress applying strategy to obtain optimized simulation parameters, so as to simulate the special-shaped hyperboloid building by utilizing target model simulation. According to the method, the simulation precision of the special-shaped hyperboloid building can be improved.

The invention provides a distributed energy access-oriented intelligent electric meter multi-device collaborative optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the interpolation of a unified electric meter, an inverter and energy storage data granularity, building a joint time error model, obtaining calibration reference data, and completing the multi-device time synchronization calibration; based on the reference data, acquiring operation data of three devices, and using a joint energy consumption model to distinguish electric meter hardware faults and multi-device collaborative anomalies to complete fault diagnosis; calculating local line loss by combining an inverter-to-ammeter line and electric quantity data, verifying residual electricity metering deviation, and tracing and correcting settlement data to complete line loss-bidirectional metering linkage verification; associating the fault result with the corresponding relation between the long-term statistical fault type of the metering data and the deviation rate and the line loss to establish a model to predict deviation trend early warning; and determining a multi-device cooperative abnormal source and transformer area load interference relation, calculating an interference coefficient, feeding back a scheduling system, and marking a line reconstruction priority. By adopting the method, the distributed energy consumption efficiency and the operation reliability of the power distribution network are improved.

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

The invention discloses a metal grinding machine tool grinding wheel wear compensation type intelligent control system, and relates to the technical field of mechanical manufacturing automation, and the system comprises a contact type reference detection module which is used for obtaining the initial contour and reference wear loss of a grinding wheel through discrete point measurement in a non-machining state; the multi-parameter non-contact real-time detection module is used for collecting acoustic emission, vibration and spindle current signals in the grinding process through three types of sensors; the grinding environment sensing module collects the temperature of a grinding area and the cutting fluid concentration; the intelligent control unit receives reference data and an environment signal, performs deep learning on a wear loss prediction model after multi-source data fusion, performs periodic reference calibration, analyzes the dynamic wear loss, and synchronously transmits the data to the data storage and tracing module; the executing mechanism adjusts grinding wheel feed amount compensation according to the dynamic abrasion loss; and the data storage and tracing module stores full-process data in real time. According to the system, high-precision and real-time wear compensation is achieved, the machining precision and consistency are improved, and high-end manufacturing requirements are met.

The invention discloses a pipe liner stability analysis and health monitoring system for an urban water supply pipe network, and particularly relates to the field of water supply pipe network health monitoring, which comprises the following steps: a data acquisition module acquires three-axis vibration data and adds a timestamp and a topological label; the signal separation module extracts independent components of turbulence and structural vibration; the modal offset labeling module calculates an energy coupling ratio and identifies an offset modal; the stability attenuation evaluation module compares the reference data through the transfer function matrix to quantify the rigidity loss and converts the rigidity loss into a pipe liner stability attenuation rate; the synchronous association module analyzes the time sequence characteristics and marks synchronous association nodes; the pipe lining degradation traceability module constructs a traceability path tree and converts the traceability path tree into a decision result with geographic coordinates; the system can effectively distinguish fluid disturbance and structural abnormity, performs pipe lining degradation monitoring and traceability, realizes visual early warning, and ensures long-term stable operation of a water supply pipe network.

The invention provides an open vocabulary industrial defect detection method based on multi-modal prior prompt, which comprises the following steps: S1, collecting and sorting defect images of industrial products to be detected, and constructing a large industrial reference data set; s2, building a visual priori prompt pool, and injecting refined and fine-tuned priori into multi-scale industrial features; s3, designing a significance Gaussian distribution modeling mechanism, and capturing a complex spatial mode of position prior, so that fine-grained prior has better expression ability, and the generalization of prior is improved; s4, establishing a decoupling LoRA text encoder, and extracting an industrial semantic basis in the industrial text template through a hierarchical prompt template and a hierarchical decoupling LoRA mechanism; and S5, constructing a visual text fusion unit, and performing multi-modal fusion on text prompt embedding and visual features. According to the method, high-precision and strong-generalization defect detection is realized by fusing visual and text prior prompts, and the robustness and adaptability in a complex open environment are remarkably improved.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention relates to the technical field of image recognition and storage, and discloses a driving video recording method and system based on four-way monitoring. The method comprises the following steps: synchronously acquiring and processing the surrounding environment of a vehicle and the head posture of a driver through a video stream to obtain driving attention reference data; compressing and storing the sight blind area data according to the head posture features to obtain hierarchical video data; performing enhanced mapping on the graded video data to obtain an attention compensation image; performing threat identification and sorting on the compensation image to obtain threat level data; establishing a threat level index structure; and performing hierarchical storage on the data according to the attention coverage degree to obtain a storage data set. According to the method and the device, the technical problem that key scene data may be lost due to lack of consideration of driver attention distribution in a multi-channel video real-time recording process is solved.

The invention discloses a UKEY-based network submission method and system and a storage medium, and relates to the technical field of information security, and the method comprises the steps: receiving a login authentication request sent by a mechanism end; after verification, generating authentication information containing an organization number and a current timestamp; generating a session identifier, and integrating the session identifier, the organization number and the current timestamp to generate secret key negotiation reference data; encrypting the key negotiation reference data to obtain first encrypted data; receiving second encrypted data, and generating a symmetric encryption key according to the first encrypted data and the second encrypted data; receiving the encrypted data packet encrypted by the mechanism end by using the symmetric encryption key, and decrypting the encrypted data packet by using the symmetric encryption key to obtain submitted data; and after the validity of the submitted data is confirmed according to the session identifier, storing the submitted data into a preset database, and returning storage confirmation information to the mechanism end. By implementing the application, the security of network submission can be improved.

The invention discloses a construction method of a self-adaptive gated spectrum-space-graph collaborative fusion network. The construction method comprises the following steps: step 1, constructing a network overall architecture; step 2, spatial branching-hierarchical spatial feature modeling is carried out; step 3, spectrum branching-adaptive spectrum feature optimization; step 4, designing an adaptive gating fusion module AGFM; the invention provides a spectrum-space-graph collaborative fusion network, which is a double-flow architecture, and solves the problems of complex space-spectrum interactive modeling, spectrum redundancy and low calculation efficiency in hyperspectral image classification. According to the network, through integration of hierarchical spatial feature learning, adaptive spectrum optimization and a dynamic cross-modal fusion mechanism, complementary advantages of a graph attention mechanism, intelligent agent self-attention and data-driven spectrum modeling are effectively combined; experimental results on three reference data sets verify the advanced performance of the SGCFN, and ablation studies prove that each module has an irreplaceable effect on enhancing classification robustness.

The invention discloses a PCB laser drilling deviation detection method and system, and the method comprises the following steps: obtaining original image data corresponding to a PCB laser drilling region, standard hole site coordinate data in a drilling design file, and real-time operation parameter data corresponding to laser drilling equipment; performing preprocessing to generate preprocessed image data, a standard coordinate data set and an equipment operation reference data set; extracting edge contour feature data of a drilling area based on the preprocessed image data, and generating an initial deviation vector of an actual drilling contour and a design contour in combination with the standard coordinate data set; constructing a drilling deviation influence factor matrix based on the equipment operation reference data set in combination with the initial deviation vector, and generating a weight coefficient of each influence factor for drilling deviation; and correcting the initial deviation vector based on the weight coefficient, judging whether deviation exists in the drill hole or not, and generating a drill hole deviation detection result. According to the invention, the drilling deviation detection precision of the PCB can be improved.

The invention discloses a traffic video question and answer method based on inter-frame difference, and the method comprises the steps: innovatively constructing an inter-frame difference extraction module on the basis of an image pre-training model based on the local abrupt change characteristic of a traffic video: effectively capturing the abrupt change characteristic of a moving target through the pixel-level difference calculation of adjacent frames; and designing a gating feature fusion mechanism, dynamically aligning spatial distribution of differential features and visual features on the basis of fusing problem features, and realizing collaborative characterization of static scenes and dynamic changes. In order to enhance cross-modal feature alignment, a hierarchical comparison learning strategy is provided: differential-visual feature comparison is implemented at a bottom layer to enhance local dynamic perception, and text-visual comparison is performed at a high layer to establish global semantic association. Experimental results show that the classification performance is improved on the basis of the SUTD-TrafficQA reference data set.

The invention discloses a machine vision-based numerical control machine tool cutter wear identification method and system, and relates to the technical field of machine vision. A numerical control machine tool cutter two-dimensional image sequence is collected, high-precision point clouds are obtained through three-dimensional reconstruction and calibration, and high-precision cutter reference three-dimensional point clouds are obtained; constructing a functional region division rule, segmenting the high-precision tool reference three-dimensional point cloud to obtain a tool functional region, and generating tool region structured reference data through geometric shape quantitative description algorithm analysis; collecting regional structured data of the cutter in different machining periods, and obtaining a shape evolution track of the cutter through space-time registration; constructing a tool wear recognition model based on the time sequence convolutional network, inputting a tool morphology evolution trajectory, and outputting a tool wear type; and collecting tool historical case data to construct a wear mechanism knowledge base, performing matching mapping on a tool wear type and the knowledge base, and generating a tool wear identification result. And based on the wear identification result, realizing the identification of the wear of the numerical control machine tool cutter.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

The invention discloses a knowledge graph completion method based on combined prediction of large and small models, which comprises the following steps: 1, constructing and preprocessing a knowledge graph completion reference data set, and training by adopting a RotatE model to obtain candidate entities generated by the model and confidence scores; 2, constructing a related triad, an adjacent triad and entity long text description based on the query to form a context prompt, inputting the context prompt and the query into a large language model, and performing semantic reordering and scoring by the large language model; and 3, constructing a fine tuning data set, and performing fine tuning on the large language model to obtain the KGC task optimization-oriented large language model. And based on the KGC score, the LLM score and the dynamic weight, outputting a complementation result through joint prediction of a fusion result. According to the method, the structured reasoning ability of the small model and the deep semantic understanding of the large language are effectively combined, the prediction accuracy, robustness and specialty are remarkably improved, and the defects that a single model is weak in generalization ability and insufficient in semantic utilization are overcome.

The invention relates to the technical field of enterprise system construction, in particular to an enterprise standard system construction system based on dynamic classification and strategic adaptation, which comprises a dynamic classification module, a strategic adaptation verification module and a feedback optimization channel module, wherein strategic document semantic analysis and industry benchmark data are fused in real time through a dynamic classification module to generate a strategic weight coefficient, an environment sensitivity coefficient is calculated by combining compliance dynamics and a market risk model, and the fusion weight ratio of the strategic document semantic analysis and the industry benchmark data is dynamically adjusted based on threshold judgment, so that standard label weight distribution and hierarchical division are realized; the strategic adaptation verification module tracks the dynamic contribution degree of a standard node by using a node influence attenuation model of the association map, and detects strategic deviation by executing the deviation between an index and an expected value in real time; and after the feedback optimization channel module analyzes the deviation signal, the fusion weight is adjusted according to the deviation direction and amplitude, and standard level elastic lifting is triggered through sliding window monitoring, so that strategic suitability real-time verification and quantitative response to external environment change are realized.

The invention relates to the technical field of large-size part three-dimensional measurement, and discloses a large-size part three-dimensional size measurement method and device based on machine vision, and the method comprises the steps: obtaining a material reflection characteristic reference data set and original point cloud data; according to the reference data set, laser power and gain are adjusted in a self-adaptive mode, and optimized scanning parameters are obtained; and performing denoising, feature extraction, region segmentation and plane fitting according to the optimized parameters and the original point cloud to obtain a high-precision geometric dimensioning result. According to the invention, high-precision, high-stability and high-efficiency three-dimensional size measurement can be realized on a large-size part made of a complex material and having a curved surface, and the three-dimensional size measurement method is obviously superior to a traditional scheme.

The invention relates to the technical field of weather forecast, in particular to a weather downscaling method fusing numerical weather forecast and AI weather forecast, which comprises the following steps: acquiring numerical weather forecast data and satellite remote sensing observation data, and performing unified preprocessing, interpolation and mapping to generate refined forecast data and observation reference data. Then, local compensation based on a deep neural network and model-independent element learning are adopted to carry out regional adaptive compensation, a joint objective function is constructed to implement variational assimilation and joint optimization, and meanwhile, a generative adversarial network and a graph neural network are combined with reinforcement learning to realize error compensation and node-level adaptive correction; and finally, continuously updating parameters through closed-loop feedback iteration to generate updated forecast data. According to the method, the problems of insufficient local details and poor model adaptability in the downscaling process in the prior art are effectively solved, and the forecasting precision and robustness are improved.

The invention relates to the technical field of enterprise management optimization, in particular to an enterprise management optimization method and system based on big data matching, and the method comprises the following steps: S1, collecting enterprise operation data, industry reference data and supply chain dynamic data, and outputting standardized enterprise data; s2, matching the standardized enterprise data with a preset industry feature library to generate a multi-dimensional service label set; s3, constructing a dynamic priority evaluation model, and generating a priority ranking matrix; s4, establishing a resource characteristic spectrum database, and generating a strategy matching degree matrix; s5, generating a resource configuration scheme according to the strategy matching degree matrix; and S6, when the monitoring threshold changes, triggering a weight calibration mechanism. According to the invention, through big data matching and dynamic priority evaluation, enterprise resource configuration decisions are optimized, operation efficiency and market adaptability are improved, and risks and cost are reduced at the same time.

The invention relates to the technical field of large model algorithms, in particular to a large model dynamic compression optimization method and system based on sparse pruning, and the method comprises the steps: capturing original weight fluctuation data generated by resource fluctuation in reasoning, and obtaining sparse weight reference data through sparse processing; analyzing calculation complexity through model reasoning delay data, and separating reasoning delay amount caused by a model scale; dynamically controlling the model compression ratio within a preset performance range based on the delay amount and the sparse reference data, and collecting reasoning precision distribution data under different compression parameters; evaluating a model performance state under each parameter by means of a neural network simulation method, and generating a performance state simulation result; determining a model quality optimization compensation parameter based on a simulation result by combining resource fluctuation data acquired in real time in a compression process; and finally, the compression strategy is adaptively regulated and controlled through the compensation parameters, and collaborative optimization of model calculation complexity, reasoning precision and delay during dynamic change of hardware resources is realized.

A method, a system, and a computer program product for generating a benchmarking dataset. One or more queries for generation of one or more summaries of one or more electronic documents are received. The queries are modified using one or more parameters associated with the electronic documents to generate modified queries. The electronic documents are sent to a generative artificial intelligence (AI) model. The generative AI model generates summaries of the electronic documents based on at least one of: the initial queries and the modified queries. One or more labels for the electronic documents are generated using the summaries.

The invention discloses an aerospace equipment data-real fusion performance test method and device based on reinforcement learning, and the device comprises a digital twin model building module which is used for building an aerospace equipment high-fidelity mechanism model fusing multi-physical field features, a lightweight digital twinborn body adaptive to reinforcement learning training is generated through a model order reduction technology; the digital twinborn model correction module is used for carrying out consistency calibration of multi-dimensional features and key physical parameter correction on the reduced-order model based on object reference data; the performance test module based on reinforcement learning actively explores a coupling scene of an extreme working condition and multiple faults in an interaction environment by constructing an attacker agent, and autonomously explores a performance boundary and a limit envelope of equipment; and the number-real fusion verification module is used for carrying out a hardware-in-the-loop point compensation test by taking the performance limit obtained by the digital test as guidance so as to assist in decision making and optimization of a test scheme. According to the method, the coverage, the safety and the test efficiency of the limit performance test of the aerospace equipment are improved.

The invention discloses a real data generation method and system, a storage medium and a program product, and the method comprises the steps: carrying out the trend analysis of target data, and obtaining a trend coefficient of the change trend of the target data; constructing a global trend function according to the trend coefficient; obtaining a primary period value and a secondary period value of the target data; constructing a period superposition function according to the primary period value and the secondary period value; performing function synthesis on the global trend function and the periodic superposition function to obtain a time sequence characteristic function; acting the time sequence characteristic function on each time sampling point of the target time period to obtain a reference data sequence; performing random disturbance processing on the reference data sequence according to preset fluctuation parameters to obtain a target data sequence containing random fluctuation characteristics; and adjusting the target data sequence according to a preset data constraint condition, and generating real data meeting the data constraint condition. According to the method and the device, the accuracy of reflecting the time sequence evolution rule by real data is improved, and the capability of discovering defects in time sequence related function tests is further improved.

The invention discloses a high and low temperature environment test box thermal load verification method and system, and relates to the technical field of environment test, and the method comprises the steps: initializing a target temperature range, a cooling rate, a heating rate, a preset temperature fluctuation range and a thermal load demand, and carrying out the preliminary setting of a thermal load; adjusting a liquid nitrogen adjusting unit in real time by adopting a dynamic optimization algorithm, and adjusting internal thermal load parameters of the test box under the condition of no external thermal load to obtain reference data; based on a self-adaptive control algorithm, dynamically adjusting the matching relationship between the liquid nitrogen release rate and the internal thermal load of the test box, and verifying the operation state of the test box under different thermal load conditions; a self-adaptive thermal load adjusting algorithm is adopted, the relation between the liquid nitrogen release rate and thermal load change is adjusted in real time, and thermal load verification of the test box is carried out according to the states under different load conditions. According to the invention, through the dynamic heat exchange coefficient model and the high-order injection optimization algorithm, the thermal load verification precision of the high and low temperature environment test box is improved.