17136results about How to "Quality assurance" patented technology

The invention discloses an unmanned aerial vehicle routing inspection line adaptive obstacle detection method and system based on a monocular camera. The method comprises the steps of obtaining initial flight parameters, evaluating and optimizing the flight parameters based on scene complexity, collecting an original image sequence and carrying out quality evaluation and exposure compensation. Constructing a multi-resolution feature pyramid, extracting feature descriptors, and performing feature matching and verification; performing multi-constraint pose optimization and depth estimation based on a feature matching result, and reconstructing a three-dimensional point cloud; calculating direction enhancement response to extract a power line candidate area, performing multi-view line segment matching and three-dimensional reconstruction, and analyzing dynamic characteristics of the power line; and based on the power line three-dimensional model and the depth map, carrying out obstacle segmentation, calculating an obstacle distance, evaluating a risk level, and generating early warning information and a monitoring scheme. According to the invention, different scenes can be adaptively processed, the obstacle risk can be accurately identified and evaluated, and the inspection efficiency and reliability are improved.

The invention discloses a rolled metal surface defect automatic labeling method based on a multi-task self-adaptive model, which comprises the following steps: S1, collecting a rolled metal surface image, preprocessing and enhancing the rolled metal surface image, performing data labeling, and constructing a rolled metal surface defect data set; s2, constructing a multi-task adaptive model, and training the multi-task adaptive model by using the data set to obtain an automatic defect labeling model; and S3, inputting a to-be-labeled metal surface image into the defect automatic labeling model, and outputting a surface defect labeling result by executing the target detection task. According to the method, a multi-task learning framework is combined, joint training of target detection and instance segmentation tasks of metal surface defects in the rolling technology is achieved, the target detection task is focused in the reasoning stage, the labeling efficiency is improved, the labor cost is remarkably reduced, and the labeling efficiency is improved. The method can be widely applied to industrial defect detection and data automatic labeling scenes of rolling process metal, and is suitable for defect image automatic labeling tasks in a complex industrial environment.

The invention provides a robust multi-modal image segmentation method and system based on instance perception query, and the method comprises the steps: obtaining original image data and text description data, and generating training data through semantic analysis and causal graph construction; extracting image features and text features by adopting a multi-layer coding network, and establishing a feature dependency relationship through causal reasoning; generating instance clustering features based on attention calculation, and obtaining fusion features in combination with a multi-scale fusion strategy; and calculating query confidence and generating candidate segmentation masks, and outputting a final segmentation result through post-processing optimization. According to the method, by introducing causal reasoning and instance awareness query mechanisms, the cross-modal feature alignment precision and the robustness of the segmentation result are improved, and the method has better segmentation performance and generalization ability in a complex scene.

The invention provides an electromagnetic field composite powder feeding type plasma arc additive manufacturing formability regulation and control method and system, and relates to the technical field of plasma arc additive manufacturing. The flowing direction and speed of liquid metal in a molten pool in the deposition process are regulated and controlled through the target composite electromagnetic field, and the morphology and the internal organization structure of a deposition layer are controlled; in the process of regulating and controlling the liquid metal in the molten pool, process parameters and molten pool dynamic image data in the deposition process are collected in real time, and a pre-trained state recognition model is used for conducting self-adaptive adjustment on the process parameters of a subsequent deposition layer; and after deposition of each layer is completed, a laser scanning system is adopted for measuring the actual geometric size and surface appearance of the deposition layer, deposition manufacturing of the next layer is executed based on the optimized composite electromagnetic field control parameters, the corrected layered slicing data and the process path, and the execution is executed circularly till manufacturing of the whole workpiece is completed.

The invention discloses a black tea processing equipment supervision system, which relates to the technical field of food processing, and timely captures subtle environmental changes by monitoring a temperature fluctuation parameter Tw and a humidity fluctuation parameter Hw in a fermentation and drying process in real time and utilizing data of a temperature and humidity regulation and control analysis module and a sensor. Environment adjustment is carried out according to the fluctuation response speed coefficient Wrs and the temperature and humidity debugging coefficient Twhc, and the problem that the sensitivity of the system to parameter fluctuation is insufficient is solved; the durability Cnj, the offset Cpy and the response time Cxy of the sensor are evaluated in real time through the sensor detection unit, so that the precision and the stability of the sensor in a high-temperature and high-humidity environment are ensured, and the influence on quality monitoring due to sensor failure is avoided; meanwhile, the data processing module carries out noise filtering and standardization processing on environmental data, data accuracy is ensured, the problem that system data integration is insufficient is solved, data sharing among all procedures is smooth, and the overall production efficiency and the black tea quality stability are improved.

The invention discloses a combinatorial optimization automatic modeling-oriented training data generation method and system, and the method comprises the steps: receiving a problem description text, extracting key information through semantic analysis and knowledge reasoning, and constructing a structured optimization problem representation; reading the structured representation, extracting a target function component from the binary label matrix, extracting a constraint function component from the constraint relation adjacent matrix, extracting a variable definition component from the variable dependency matrix, and integrating the components to form a model component set; a value interval parameter is generated for the variable, a coefficient parameter is generated for the constraint matrix, a weight parameter is generated for the target function, and a parameterized model instance is obtained; and applying corresponding templates to scene elements, variables and constraint descriptions to generate a problem description text. Through the systematic information extraction and parameter generation method, automatic generation of high-quality training data is realized, and the diversity and practicability of the data are improved.

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention provides an unstructured data automatic processing method and system based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: obtaining unstructured data through an intelligent data collector, building a data feature fingerprint database, constructing a data quality evaluation model through an incremental learning algorithm, and carrying out the data standardization processing; extracting dominant features and recessive features by using a multi-layer cascade network structure and a self-attention mechanism, and constructing a feature association graph and a bidirectional reasoning channel; and performing rule matching and causal reasoning based on the domain knowledge base, and finally realizing automatic structuring of the unstructured data. According to the method, the automation degree and the accuracy of data processing are improved, the manual intervention cost is reduced, and the method has relatively high universality and expandability.

The invention discloses an energy storage lithium battery life degradation prediction method and system, and the method comprises the steps: obtaining and preprocessing lithium battery operation data, constructing a time sequence, carrying out the normalization processing, and dividing a training set and a test set; based on the normalized data, constructing a multi-head self-attention mechanism MSA module with masks, capturing multiple features in the input data through different time windows, and extracting an important feature set through a feature fusion method after screening; based on the important feature set, key hyper-parameters are preliminarily set, and an MSA-LSTM comprehensive prediction model is constructed and used for processing time sequence data and predicting the service life of the battery; an artificial bee colony algorithm is adopted to optimize hyper-parameters of the MSA-LSTM comprehensive prediction model; and training the optimized MSA-LSTM comprehensive prediction model by using the training set, verifying and evaluating the prediction performance by using the test set, predicting unknown data by using the MSA-LSTM comprehensive prediction model, and calculating the remaining service life RUL of the energy storage lithium battery. And safety accidents caused by battery faults are prevented.

The invention discloses a federal learning data processing method based on privacy protection, and the method comprises the following steps: S1, a client uses local data to train a generator of a generative adversarial network, and generates virtual data similar to the local data in distribution; s2, setting three privacy protection levels according to the privacy sensitivity of the local data; s3, performing model updating operation by using a homomorphic encryption technology; s4, utilizing a dynamic weight optimization mechanism based on a multi-arm bandit algorithm to obtain a priority processing data set; s5, preliminarily updating the global model, analyzing generated data through a reverse inference detection mechanism, and judging whether a privacy disclosure risk exists or not; s6, the server feeds back the updated global model to the client, and the client continues personalized model training in combination with local data; and S7, generating new virtual data, and carrying out the next round of global model training. According to the method, the generative adversarial network, differential privacy and homomorphic encryption are combined, so that efficient federated learning under privacy protection is realized.

The invention provides a mining intelligent watering dust fall method and system, electronic equipment and a storage medium, and relates to the field of watering dust fall. The method comprises the following steps: acquiring multi-source heterogeneous environment sensing data; preprocessing the multi-source heterogeneous environment sensing data to obtain preprocessed multi-source heterogeneous environment sensing data, and constructing a mining area holographic environment model based on the preprocessed multi-source heterogeneous environment sensing data; based on the holographic environment model of the mining area, extrapolating and estimating the dynamic change trend of the wind dust concentration by using a space-time sequence prediction algorithm to obtain a wind dust concentration distribution prediction result of each area of the mining area in a future time period; according to the technical scheme, dust suppression is taken as an optimization target, water mist parameters and a nozzle arrangement scheme are taken as decision variables, a mining area holographic environment model and a wind dust concentration distribution prediction result are taken as constraint conditions, an optimal spraying strategy meeting dust falling requirements is solved through a heuristic search algorithm, and the dust falling effect is improved and the water consumption is reduced through the technical scheme.

The invention provides a decoding and coding method, device and equipment, and the decoding method comprises the steps: decoding a first code stream of a current image block, and obtaining a coefficient hyper-parameter feature of the current image block; for each stage sub-block of the current image block, determining a probability distribution parameter of the stage sub-block based on the coefficient hyper-parameter feature, and decoding a second code stream of the current image block based on the probability distribution parameter to obtain a residual feature of the stage sub-block; based on a mean value prediction network, determining mean value features of the sub-blocks in the stage; determining a reconstruction feature of the stage sub-block based on the mean feature of the stage sub-block and the residual feature of the stage sub-block; performing feature aggregation on the reconstruction features of the sub-blocks in each stage to obtain the reconstruction features of the current image block; a reconstructed image block of the current image block is determined based on the reconstruction feature of the current image block. Through the scheme of the invention, the coding performance and the decoding performance are improved.

According to the technical scheme, the BIM model Web end lightweight display method comprises the following steps that S1, a project drawing is created; s2, modeling by using BIM software, and constructing a model hierarchical structure; s3, performing multi-level integration compression on the BIM model; s4, the lightweight BIM model is imported into Unity, and rendering is carried out; s5, UI interface element design is carried out by using a Unity UI; s6, a WebGL platform is set in the Unity, and WebGL items are packaged; and S7, deploying the file to a cloud server. The method has the beneficial effects that multi-level integration lightweight is adopted, and the integrity of geometric data and material texture information of the BIM model is guaranteed. The rendering detail level of the model is adjusted through Lod multi-level detail management built in Unity, and the rendering burden is reduced while the quality of the BIM model is ensured. And by utilizing a Unity asynchronous loading function, the transmission data volume is effectively reduced, and the loading speed and the rendering performance are improved.

The invention provides a distribution line fault waveform denoising analysis method based on an improved wavelet threshold function and computer equipment, and relates to the technical field of power system fault detection and signal processing. The method comprises the following steps: S1, obtaining a fault waveform of a distribution line through actual measurement or analogue simulation, and converting the fault waveform into a fault signal; s2, through denoising effect comparison, selecting an applicable wavelet basis function and a decomposition layer number, and carrying out wavelet transformation to decompose a fault signal; s3, carrying out wavelet decomposition coefficient threshold processing on the fault signal by adopting a self-adaptive adjustment mechanism of a threshold and an improved threshold function; and S4, performing discrete wavelet inverse transformation on the wavelet decomposition coefficient subjected to threshold processing to reconstruct a signal so as to obtain a denoised fault signal. According to the method, through the improved threshold function and the multi-scale analysis technology, the fault waveform denoising effect can be remarkably improved, the accuracy and the real-time performance of fault detection are ensured, and therefore the reliability and the safety of a distribution line are improved.

The invention provides a vegetation greening layout optimization method, and relates to the technical field of environmental engineering and garden design, and the method comprises the steps: carrying out the homologous retrieval through taking a target region plant growth influence factor as a constraint, and obtaining a plant type and a planting density; calculating a high-frequency planting density threshold value of the plant type; inputting the plant types into a type-attribute comparison table for matching to obtain time zone attributes, determining a plant combination and performing adaptive screening; carrying out vegetation layout design based on the adaptive plant combination and the high-frequency planting density threshold to generate an initial layout scheme; constructing a simulated growth environment to perform growth prediction and simulated viewing on the layout scheme; and performing evaluation and feasibility evaluation on the simulated landscape according to the landscape evaluation function, and determining an optimal layout scheme, thereby solving the technical problems that the vegetation configuration cannot be accurately performed and the vegetation growth effect and the landscape aesthetic property cannot be effectively predicted and evaluated due to lack of systematic analysis on the plant growth. The technical effect of improving the accuracy and predictability of the plant layout is achieved.

The invention relates to the technical field of protein enzymolysis, and discloses an intelligent control system and method for an animal protein enzymolysis production process, and the method comprises the steps: obtaining key attribute parameters of animal protein raw materials, building a raw material grading model, and obtaining initial grades of the raw materials; matching a preset grading corresponding relation library according to the initial grading to obtain an initial stage optimal production parameter range; collecting a first production parameter before enzymolysis to obtain an adjusted production parameter; starting enzymolysis according to the adjusted parameters, collecting process key parameters in real time, inputting an optimal production parameter prediction model, and predicting an optimal production parameter at the next moment; if the optimal production parameters exceed the safety range, correction is carried out; continuously performing enzymolysis according to the corrected parameters, and continuously predicting and correcting the parameters until the enzymolysis is finished; through raw material grading and parameter real-time optimization, the process parameters can be dynamically regulated and controlled according to different batches of raw materials, the production efficiency is improved to the maximum extent while the product quality is guaranteed, and remarkable intelligence and adaptability are achieved.

The invention aims to provide a method and a device for determining the peak area of chloride ions based on ion chromatography. The method for determining the peak area of chloride ions based on ion chromatography comprises the following steps: step 1, identifying a chromatographic peak: peak separation, peak qualitation, baseline correction, peak trailing treatment and peak overlapping treatment; and step 2, calculating the chromatographic peak area: peak definition, integration algorithm, calibration curve application, repeatability verification, impurity peak deduction and main peak area calculation. According to the method, a chromatographic peak identification and chromatographic peak area calculation mode is designed, so that the technical problem that other impurity ions may be introduced to influence chloride ion chromatographic peak identification and accurate area calculation in an existing mode of determining organic chlorine through ion chromatography is solved.

The invention discloses a visual network freight platform transportation transaction management system. The system comprises an order management module, a vehicle scheduling module, a transportation monitoring module, a transaction settlement module and a visual display module. Each module has innovative functions, such as order intelligent classification, vehicle optimization scheduling algorithm to minimize comprehensive cost, real-time monitoring and accurate settlement. The corresponding management method adopts advanced technologies to ensure efficient operation of each link, including block chain recording, intelligent contract settlement and the like. The whole system realizes efficient management, real-time monitoring and convenient visualization of transportation transaction of a freight platform, and improves the efficiency and quality of logistics transportation; according to the visual network freight platform management system, efficient and accurate order management is realized, vehicle scheduling is optimized, the cost is reduced, comprehensive and real-time transportation monitoring is guaranteed to be safe, safe and accurate transaction settlement is realized, convenient and visual display is convenient to make decisions, and the logistics transportation efficiency, quality and user experience are improved.

The invention relates to the technical field of data fusion, in particular to a multi-source data fusion law enforcement record generation system and method, and the system comprises a data collection module which normalizes and eliminates repeated data, an anomaly detection module which carries out the data verification and cleaning, and an information source evaluation module which carries out the quantitative scoring of a data source. And the weight adjustment module dynamically adjusts the weight based on the score, and the data fusion module executes weight fusion and field verification and outputs a collaborative law enforcement record. According to the method, the data quality is further ensured through the verification of the data value range and the calculation of the deviation value, and the reliability of the data set is enhanced by removing the abnormal data. And through quantitative scoring and dynamic weight adjustment, the quality evaluation of the data source and the fairness of input are ensured, so that the law enforcement record is more comprehensive and authoritative. The refined data processing not only improves the accuracy of law enforcement records, but also enhances the security and integrity of information, thereby significantly improving the response speed and decision quality of a law enforcement agency.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention provides a micro robot motion control method and system, and the method comprises the steps: carrying out the synchronization and calibration processing of environment state information, and generating a comprehensive perception image; analyzing the comprehensive perception image to obtain a classification result, and predicting the environment state change influencing the motion track of the micro robot in a period of time in the future based on the classification result; generating a candidate path by using a fast random tree, and optimizing the candidate path in combination with an AI search algorithm to obtain a target path; constructing a three-dimensional dynamic probability graph, continuously updating the three-dimensional dynamic probability graph by using a reinforcement learning algorithm, and generating an instant decision map; and estimating the position of the micro-robot by using a Kalman filter to obtain the position state of the micro-robot, adjusting the speed and steering angle of a driving motor of the micro-robot, and generating trajectory tracking of the micro-robot. According to the technical scheme provided by the invention, the flexibility and intelligence of the micro robot are improved.

The invention belongs to but not limited to the technical field of blind hole machining, and discloses a process method and system for laser composite efficient precision machining of blind holes, and the process method comprises a laser hole prefabricating stage and a laser fine hole repairing stage; a hole is preformed in the punching position of a workpiece through a large-pulse-energy laser beam, and fixed-point materials of the workpiece are rapidly removed; and then the hole is finely trimmed through ultra-short pulse width laser, the incident angle direction of a laser beam and the surface of the material is changed through a rotary cutting system, the hole wall and the hole bottom of the hole after the pre-hole stage are trimmed, fine trimming of the blind hole is completed, and the high-precision blind hole with the controllable taper is obtained. The method comprises the following steps: quickly removing a prefabricated blind hole in a workpiece material by using large pulse energy laser; adjusting process parameters, and quickly pre-processing a blind hole with a certain depth in a workpiece; and then the hole wall and the hole bottom of the blind hole are trimmed through high-speed rotating light beams and variable light beam incidence angles of the rotary cutting system, smooth hole wall, flat hole bottom and controllable taper of the blind hole are achieved, and the machining quality and efficiency of the blind hole of the difficult-to-machine material are improved.

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

The invention relates to the technical field of plant extraction, in particular to an intelligent plant extraction control method and system, and the method comprises the following steps: based on historic plant extraction data, including temperature, pressure and pH value during extraction, analyzing the long-term trend of parameters, predicting the requirements of plant extraction conditions by simulating parameter reactions under various conditions, and obtaining a plant extraction result; and obtaining a parameter prediction result. According to the method, long-term trend prediction of parameters such as temperature, pressure and pH value is realized through analysis of historically recorded plant extraction data, so that the parameters of extraction equipment are adjusted in advance, changes of different plant materials and environmental conditions are effectively adapted, the extraction efficiency is optimized, and by dynamically simulating the expression of the extraction efficiency in various time periods, the extraction efficiency is improved. According to the innovative scheme, an optimal extraction time window is mined, resource consumption and extraction time in the extraction process are reduced, and meanwhile the quality and consistency of extracts are kept.

The invention discloses an intelligent henhouse environment concentration monitoring system, and relates to the technical field of henhouse environment concentration monitoring. The influence of changes of environmental parameters in a henhouse in different seasons on chicken raising is solved. The system specifically comprises a sensor module, a data acquisition module, a data transmission module, a data processing module and a remote monitoring module, the sensor module acquires various environmental parameters in a henhouse and transmits the acquired data to the data acquisition module, and the data acquisition module receives the data transmitted by the sensor module, pre-processes the data and transmits the pre-processed data to the remote monitoring module. The preprocessed data is uploaded to the data processing module through the data transmission module, the data processing module analyzes and processes the data, and the collected original data is converted into useful information understood by a breeder. Parameters of the model can be obtained by fitting the model into historical data, and the parameters are used for predicting the influence of changes of environmental parameters in the henhouse on chicken raising in different seasons in the future.

The invention relates to the technical field of automatic control, in particular to a machine tool automatic adjustment control system which comprises a material analysis adjustment module, an environment adaptation adjustment module, a machining vibration compensation module and a machining energy efficiency management module. By monitoring and analyzing the running state of the machine tool in real time and adjusting the machine tool parameters based on historical machining data, the machine tool can automatically adjust the cutting speed and the feeding rate according to the physical characteristics of different materials, the machining efficiency is optimized, the machining quality of the workpiece surface is improved, the machine tool setting is adjusted according to the workshop environment change, and the machining efficiency is improved. The stability of the machining process and the quality of a workpiece are guaranteed, machining errors caused by vibration of the machine tool or the workpiece are reduced through real-time analysis and compensation of vibration data, the machining precision is kept, the energy utilization efficiency is improved through real-time monitoring and optimization of energy consumption of the machine tool, the production cost is reduced, and the production efficiency is improved. And the whole production process is more economical and environment-friendly.

The invention discloses a light guide plate film covering method, device and equipment, and the method comprises the steps: responding to the detected size, material and surface condition of a light guide plate, and judging whether the light guide plate meets a film covering standard or not; commanding the robot to grab the membrane material, accurately positioning the alignment operation of the light guide plate and the membrane material through a visual guidance system, and adjusting the fitting position; controlling the membrane material to be preliminarily attached to the light guide plate, and adjusting pressure and adsorption force in real time according to material characteristics; analyzing data of the visual detection system, commanding the intelligent fluid dynamics control device to process bubbles or wrinkles, and meanwhile, re-scanning the processed area to ensure that the defoaming effect is qualified; and controlling the curing time, the curing temperature or the UV light intensity according to the used curing mode. According to the technical scheme, the laminating quality is improved, and bubbles and flaws are reduced.

The embodiment of the invention discloses a data processing method and device, and relates to the technical field of computers, and the method comprises the steps: obtaining a target text, and obtaining P continuous frame texts through the target text; obtaining P noise images, obtaining a target frame text from the P continuous frame texts, and determining a target noise image from the P noise images; processing the target noise image through a target video generation model to obtain a target forward noise vector; and processing the business text, the business image and the target frame text through a target video generation model to obtain a fused coding feature, and performing denoising processing on the target noise image according to the target forward noise vector and the fused coding feature to obtain a target prediction image corresponding to the target frame text, and taking the target prediction image as a next frame image located in the business image in the video to be generated. By adopting the method and the device, the overall tone consistency of the text video can be improved, and the quality of the text video is ensured.

The invention discloses an automatic model tuning system based on an AI training intelligent workbench, and relates to the technical field of artificial intelligence, and the system comprises the steps: setting a data collection instrument and an environment monitoring point, collecting model real-time data in a model training process, and carrying out the preprocessing; performing model performance analysis according to the model real-time data and the model original data to obtain final parameter configuration; and performing model training by utilizing the final parameter configuration, dividing the model training process into different stages, dynamically adjusting an intelligent control strategy based on model performance requirements, and performing optimization on the different stages of the model. According to the method, through multi-dimensional performance evaluation, parameter configuration is optimized, and the robustness and generalization ability of the model are improved. Through dynamic adjustment of an intelligent control strategy, fine management of a training process is realized, efficiency and resource utilization are improved, model convergence is accelerated, and finally an efficient, stable and high-adaptability model training effect is achieved.

The invention relates to the technical field of video compression, in particular to a video monitoring data compression and transmission method and system, and the method comprises the following steps: analyzing the texture features of a video frame, calculating the gray gradient of each pixel, obtaining the texture change rate, detecting edge information, calculating the distribution density of edge pixels, and extracting a color histogram. And calculating the dispersion degree of color distribution. In the invention, a dynamic coding parameter adjustment mechanism based on content is introduced to analyze the video frame, a compression strategy is adjusted according to the content complexity, and the coding process can accurately meet the requirements of different video contents through comprehensive analysis on the texture change rate, the edge density and the color dispersion degree; according to the scheme, the coding parameters can be dynamically adjusted by monitoring the coded video quality in real time and comparing the coded video quality with the preset quality threshold value, and the storage and transmission of data are further optimized while the video quality is ensured.