2622results about How to "Improve automation" patented technology

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention discloses an electric power inspection system and method based on an unmanned aerial vehicle, which are used for solving the problem of how to realize autonomous path planning of the unmanned aerial vehicle in a complex three-dimensional environment so as to reduce redundant flight time and improve inspection efficiency. The electric power inspection system comprises a path planning unit, an obstacle avoidance positioning unit and a dynamic obstacle avoidance unit which are connected in sequence. Three-dimensional map data and real-time environment information of a to-be-inspected power line are obtained through a path planning unit, real-time path planning is carried out based on the three-dimensional map data and the real-time environment information, and an optimal flight path of the unmanned aerial vehicle is dynamically generated; obstacle avoidance positioning is carried out on the power equipment on the optimal flight path and the surrounding environment thereof through an obstacle avoidance positioning unit, and real-time positioning information of an obstacle is generated; and a dynamic obstacle avoidance unit constructs a dynamic obstacle map based on the real-time positioning information, and automatically plans an obstacle avoidance strategy according to the dynamic obstacle map, so that the unmanned aerial vehicle realizes dynamic obstacle avoidance based on the obstacle avoidance strategy.

The invention provides a penetration test agent system driven by a large language model and a test method, and the system comprises a memory unit which is composed of a penetration test knowledge base, comprises data elements including vulnerability information, a penetration mode and tool information, and employs a knowledge graph to establish a vulnerability knowledge representation model; the intelligent unit is realized by a large model and is used for generating a tool calling instruction, a penetration test load and a penetration test strategy based on the memory unit; and the planning unit is used for splitting the penetration test task into a task planning state machine consisting of controllable sub-tasks so as to guide the large language model to execute a specific task. Through introduction of the large model, the knowledge base and the task planning unit, the automation and intelligence level of the penetration test task is improved; the integrity of knowledge retrieval is improved, and the interpretability and consistency of large model output are ensured; for a complex scene of a penetration test process, a highly customized proxy behavior is realized, and a penetration test task execution process is guaranteed.

The invention provides a community management method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of community management. The method comprises the steps of obtaining an initial inspection task of an unmanned aerial vehicle cluster and planning an inspection route, and controlling the unmanned aerial vehicle cluster to execute inspection operation to obtain multi-source inspection data. And constructing a community portrait based on the inspection data, identifying community management hidden dangers through a classification reasoning model, and generating an early warning event and a processing task. A processing task is decomposed into hierarchical sub-tasks by utilizing an ontology reasoning framework, a task execution main body is selected, and a task set is generated. And a multi-Agent reinforcement learning framework is adopted to carry out space-time collaborative arrangement on the task set to form an overall collaborative scheme, the overall collaborative scheme is converted into an interpretable scheduling decision instruction, and finally the unmanned aerial vehicle is controlled to execute the task. According to the technical scheme, the emergency response speed of security and protection and the capability of coping with complex conditions are improved.

The invention discloses a control method and device of a building cold and heat source system self-adaptive to load requirements. The control method comprises the following steps: acquiring historical operation data, physical characteristics and equipment system data of a building, and establishing a building load prediction model; generating the hourly load curve, and predicting the cooling or heating demand of each time period in the future according to the curve; dynamically adjusting the refrigerating or heating load demand, and generating a corresponding cooling or heating curve; and constructing a machine learning algorithm based on an equipment mechanism model, optimizing a control strategy of the cold and heat source system based on the historical operation data of the equipment, and outputting control parameters of the equipment. According to the technical scheme, the load of the building cold and heat source system is intelligently predicted and adjusted, the energy efficiency of the system is improved, invalid energy consumption is reduced, energy utilization is optimized, control parameter optimization is achieved through a machine learning algorithm, efficient operation is ensured, indoor comfort is kept, and the purpose of saving energy is achieved.

The invention provides a plastic product quality detection method and system based on machine vision, and relates to the technical field of optical intelligent detection.The method comprises the steps that information extraction is conducted on an image data set through a contour recognition channel, and the contour of an identification component is determined; performing fitting filling of the point cloud data stream by taking the contour of the identification component as a constraint, generating an identification component point cloud, and fusing and building a plastic grid model; comparing based on the plastic grid model, and positioning size deviation information; and performing defect extraction on the plastic grid model based on the defect identification elements, determining abnormal defect information, and generating a quality detection report. Through the method, the technical problems of poor detection precision, comprehensiveness and detection efficiency caused by insufficient precision, accuracy and integrity of surface defect and dimensional deviation detection and low automation degree of an existing method can be solved, the automation of quality detection can be improved, and the detection efficiency is improved. And the effects of improving the detection accuracy, comprehensiveness and detection efficiency are achieved.

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention relates to the technical field of real-time prediction of three-dimensional forms, in particular to a real-time dynamic prediction system and method for the three-dimensional form of a high-pressure jet grouting pile. The method comprises the following steps: carrying out stratum characteristic identification on a construction site to obtain stratum distribution data; key monitoring point positions are determined according to the stratum distribution data, and a multi-source sensor monitoring network is established; carrying out anti-interference optimization processing on the multi-source sensor monitoring network, and carrying out real-time acquisition on high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; and spatio-temporal evolution processing based on injection pressure, lifting speed and rotating speed is conducted on the real-time high-pressure jet grouting data, and parameter change characteristic data are generated. According to the method, the construction stratum distribution and the high-pressure jet grouting parameters are obtained and analyzed in real time, the parameter-stratum response relation model is established, grout diffusion and pile body forming dynamic prediction are conducted on different stratum characteristics in a targeted mode, and the precise construction capacity under the complex stratum condition is greatly improved.

The invention provides an intelligent dosing method and system, and the method comprises the following steps: building a multi-source water quality data collection and preprocessing unit, and achieving high-quality data acquisition through employing a self-adaptive sampling frequency adjustment and data complementation algorithm; constructing a parameter feature extraction and correlation analysis system, and establishing a parameter coupling network; an intelligent dosing control strategy optimization system is constructed, and accurate control over the dosing process is achieved through deep reinforcement learning and a multi-objective optimization algorithm; a time-frequency domain dual evaluation system and a space-time two-dimensional feature extraction framework are established, and control effect evaluation and system characteristic analysis are achieved. According to the invention, the accuracy and economical efficiency of dosing control are obviously improved, the standard reaching rate of the effluent quality is improved to 99.5% or above, the use efficiency of chemicals is improved by 40-50%, and the operation cost is reduced by 35-45%.

The invention relates to a pipeline detection robot defect identification method based on image processing, which comprises the following steps: carrying out continuous image acquisition on the inner wall of a pipeline through image acquisition equipment on a pipeline robot to obtain image data of the inner wall of the pipeline; performing image preprocessing on the pipeline inner wall image data; pipeline defects in the pipeline inner wall image data after image preprocessing are detected, and image segmentation is carried out on a defect area; and carrying out feature extraction on the defect area, carrying out defect classification, and outputting an identification classification result. According to the invention, based on algorithms of image processing, artificial intelligence and machine learning, defects of cracks, corrosion, leakage and the like in the pipeline can be automatically identified, the limitation of manual detection is solved, automatic and intelligent detection is realized, the technical effects are mainly embodied in the aspects of improving the detection precision and efficiency and the ability to adapt to different pipeline environments, and the method is suitable for popularization and application. High application and popularization values are realized.

The invention discloses a 3D printing process intelligent control method and system based on real-time feedback, and relates to the technical field of 3D printing, a data acquisition module collects a printing layer surface image and printing data in real time, constructs an image data set and a printing data set, and performs cleaning and standardization processing on the data in a preprocessing module; the image analysis module analyzes surface defects of a printing layer by using a convolutional neural network, generates a defect score D, compares the defect score D with a preset defect score threshold D1, evaluates the state of the printing layer and triggers early warning, and the comprehensive evaluation module calculates a crack generation probability Z by combining printing data and the defect score; and the intelligent control module compares the crack generation probability Z with a preset crack generation probability threshold value Zl, evaluates the printing state and feeds back and controls printing parameters according to a result, so that crack and structure defects in the printing process are effectively prevented and reduced, and the printing precision and the finished product quality are improved.

The invention relates to a shield tunneling machine tunneling error compensation control method and system, and relates to the field of tunneling operation, and the method comprises the steps: carrying out the time domain alignment and cutting region mapping alignment of a predicted soil feature distribution sequence and a predicted tool wear distribution sequence, and carrying out the integration to obtain a predicted operation state data sequence; and performing iterative optimization analysis on tunneling control parameters of the shield tunneling machine on the basis of the predicted operation state data sequence by taking the minimum overall tunneling operation error of the cutterhead as an expected target until a preset optimization scale is met, outputting an optimal tunneling parameter sequence, and controlling the shield tunneling machine to execute tunnel construction in a future time zone. According to the method, the technical problems of insufficient tunneling precision and low operation efficiency caused by the fact that a traditional method lacks a dynamic adjustment mechanism and cannot adjust tunneling control parameters in a targeted manner according to soil texture changes and cutter wear conditions can be solved; by effectively reducing the tunneling operation error, the accuracy and the operation efficiency of the shield tunneling machine in the tunneling process are remarkably improved.

The invention relates to the technical field of audio testing, and discloses an intelligent audio testing system frequency response analysis method, device and equipment, and the method comprises the steps: carrying out the audio parameter initialization of an intelligent audio testing system, and obtaining a testing parameter matrix and an excitation signal; performing dual-channel acquisition and time-frequency feature extraction to obtain a time-frequency feature data set; executing double-branch processing of time domain noise reduction and frequency domain enhancement to obtain a purified feature data set and a feature weighting matrix; performing segmented recursive calculation and feature aggregation to obtain a frequency response feature vector; decomposing the frequency response feature vector according to a test frequency band, and performing environment compensation and nonlinear calibration to obtain a standard frequency response data set; and inputting the standard frequency response data set into the deep neural network for feature recognition and mode classification to obtain a frequency response curve and a phase characteristic curve, thereby improving the test precision, the anti-interference capability, the automation degree and the adaptability of the intelligent audio test system.

The invention relates to the technical field of mine potential safety hazard identification, in particular to a mine potential safety hazard identification method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a global image of a mining area by using an unmanned aerial vehicle camera, and constructing a three-dimensional model; thirdly, performing rock-soil landslide probability estimation through weathering erosion mechanism reduction in combination with the three-dimensional model and the global image, and calculating landslide coercing force potential energy increment; then, according to the landslide probability and the coercing force incremental data, carrying out risk grade endowing on the local area of the mining area; and finally, constructing a mine potential safety hazard identification optimization model based on the risk level, and sending the model to a terminal for potential hazard identification. According to the invention, the mine potential safety hazard identification technology is optimized, so that the mine potential safety hazard identification technology is more perfect.

The invention discloses a wireless screen projection management system and method based on the Internet of Things technology, and belongs to the technical field of the Internet of Things. In the system, equipment is registered through an Internet of Things platform, the Internet of Things is used for automatic equipment discovery and automatic equipment broadcasting, and a control center identifies and manages newly-added equipment; a user initiates a wireless screen projection request, and the control center authorizes and manages the request based on a preset strategy; a data compression algorithm is adopted to reduce transmission data volume; analyzing the network condition in real time by using a deep Q network algorithm, and dynamically adjusting a data transmission strategy; recording screen projection quality parameters, analyzing historical screen projection data by using an artificial intelligence algorithm, predicting potential problems, providing optimization suggestions, and dynamically allocating and adjusting network bandwidth and equipment resources; collaboration among the equipment is realized by using the Internet of Things; faults occurring in the screen projection process are monitored in real time, a decision tree algorithm is used for analyzing fault causes, and recovery measures are automatically taken.

The invention relates to the field of voltage regulation and control, in particular to a real-time voltage regulation method for a new energy automobile. Initializing a power distribution network voltage measurement module, and collecting voltage data of each node of the power distribution network; setting a voltage deviation threshold value, and judging whether the voltage is in a normal range or not; collected voltage is compared with a set voltage deviation threshold value, a result obtained after preliminary voltage regulation of an on-load voltage regulation transformer is compared with the set voltage deviation threshold value, and a result obtained after cooperative voltage regulation of an electric vehicle charging pile is compared with the set voltage deviation threshold value. According to a comparison result, tap adjustment of an on-load voltage regulation transformer, cooperative voltage regulation of an electric vehicle charging pile and voltage regulation participated by a distributed photovoltaic system are carried out in sequence; and formulating an optimization strategy based on historical data and results of three times of voltage regulation. By means of cooperative voltage regulation, optimal control, intelligent management and the like, the voltage stability is effectively improved, and the adaptability and flexibility of the system are enhanced.

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.

The invention discloses an intelligent interactive Agent system based on Langchain and an implementation method, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps of 1, establishing an intelligent interactive Agent system by using an interface and an integrated tool provided by a Langchain framework, 2, managing calling of various tool modules and services through a central scheduling module, and dynamically registering and unbinding the tool modules by using the interface provided by the Langchain framework, and 3, processing user input by using a large language model through a process management module, and establishing an intelligent interactive Agent system. 4, a series of tool modules are integrated through the integrated tool management module, the tool modules comprise a text search tool, a weather query tool and a video information acquisition tool, each tool module works independently or is in linkage with other tool modules, and the tool modules are connected with the integrated tool management module. And 5, managing a user interaction interface through a front-end management module, supporting a user to query and control in a natural language, displaying a processing progress and a processing result in real time, and providing feedback and help information.

The intelligent factory data acquisition platform comprises a data sensing layer, a multi-protocol transmission layer, a data fusion layer and an application service layer, the data sensing layer is used for collecting equipment state and environment parameter multi-source heterogeneous data through various sensors, preprocessing the collected data and transmitting the preprocessed data to the multi-protocol transmission layer; the multi-protocol transmission layer is used for receiving the preprocessed data transmitted by the data sensing layer, packaging the data through a multi-protocol data transmission unit, then carrying out multi-protocol transmission, and outputting the transmitted data to the data fusion layer; the data fusion layer is used for receiving the multi-source heterogeneous data transmitted by the multi-protocol transmission layer, performing feature extraction and fusion on the heterogeneous data by adopting a self-correcting convolutional recurrent neural network, and outputting fused feature representation to the application service layer; and the application service layer is used for receiving the fusion features output by the data fusion layer, and performing equipment health diagnosis, process parameter optimization and energy consumption statistical analysis intelligent application services based on the fusion features.

The invention provides a credential platform data migration verification method and system, and relates to the technical field of computers.The method comprises the steps that all to-be-migrated source data in a credential platform database are read, basic information of the source data is obtained, and a data label is set for each piece of data; reading fields and contents in the migration data, and constructing a data similarity verification model; selecting a migration path and a target database according to the migration demand, and dynamically evaluating the similarity between the source data and the target data through a data similarity verification model; verifying the migrated source data based on the target database, and performing incremental verification on data of different fields in the table; the data table of the to-be-migrated source data is verified in real time among different migration nodes, data changes on each node are verified step by step through an increment verification mechanism, if data missing or verification inconsistency occurs in the verification process, an alarm mechanism is triggered, and the problem of data missing or inconsistency can be automatically recognized and corrected.

The invention belongs to the technical field of automobile information safety fuzzy testing, discloses an automobile information safety fuzzy testing method based on a large model, and develops optimization application research of LLMs in an automobile software fuzzy testing scene by utilizing the advantages of an artificial intelligence technology in the aspects of data processing, natural language interaction, generalization ability and the like. The concealment and effectiveness of the test set generated by the fuzz test are optimized, and the fuzz test process efficiency is improved. Through the implementation of the invention, related key technologies are applied to the fuzz test of the automobile, and other related products and enterprises in China are radiated by writing technical standards, so that the fuzz test system can be used as an application demonstration and sample plate of the fuzz test of the intelligent driving automobile. According to market analysis, fuzzy test calculation is carried out on 100 thousand automobiles every year according to the method, and the data safety and the system safety are improved.

The invention discloses a hard rock stratum continuous excavation automatic cutting depth control method based on deep learning. The control method comprises the steps of establishment and training of a deep neural network model, pre-drilling fracturing operation and rock stratum strength parameter prediction, determination of a reasonable cutting depth control value and monitoring and control of continuous excavation operation. The cutting performance of the hard rock stratum is improved through pre-drilling fracturing operation, meanwhile, the strength parameter of the fractured rock stratum is predicted according to a deep learning algorithm, then a reasonable cutting depth value is determined, the excavation efficiency of continuous excavation equipment in the hard rock stratum environment can be improved, and the consumption of cutting tools is reduced; and then, according to real-time monitoring data and the trained deep neural network model, a current continuous mining equipment operation strategy is calculated in real time, so that real-time monitoring and control of the cutting depth, the cutting mechanism power and the mining working face flatness are realized, and improvement of the control precision, safety and automation of the continuous mining equipment is facilitated.

The invention relates to the technical field of automatic welding seam grinding, in particular to a welding seam grinding method, system and device based on laser vision stirring friction, and the method comprises the steps that a laser image of a welding seam is obtained, gray threshold segmentation is conducted on the laser image, and a laser stripe image is obtained; extracting sub-pixel feature points of the laser stripe image to obtain a laser stripe center line, and converting a two-dimensional point set of the laser stripe center line into a three-dimensional space to form a laser stripe three-dimensional point cloud; performing feature extraction on the laser stripe three-dimensional point cloud to obtain feature data of a flash area; and based on the feature data of the flash area, a polishing path is planned, and the flash area of the welding seam is polished and machined along the polishing path through a polishing tool. According to the invention, automatic identification and accurate polishing of the weld flash are realized, the polishing efficiency and quality are improved, and manual intervention is reduced.

The invention discloses a deep convolutional neural network-based pineapple maturity nondestructive testing method and system, and relates to the technical field of vegetable and fruit detection. Comprising the following steps: acquiring a pineapple appearance image with known surface texture feature parameters and color feature parameters, and preprocessing the pineapple appearance image to form a sample image; and establishing a convolutional neural network prediction model based on the sample image, inputting a pineapple appearance image and outputting corresponding feature parameters. And collecting an appearance image of a pineapple to be detected, preprocessing the appearance image, inputting the preprocessed appearance image into the prediction model to obtain characteristic parameters, and calculating a maturity judgment coefficient. And detecting the ethylene and carbon dioxide concentrations of the pineapple to be detected, and correcting the maturity judgment coefficient to obtain an accurate judgment coefficient. According to the method, the judgment threshold value is dynamically corrected, the accurate judgment coefficient is compared with the dynamic threshold value, different maturity judgment results are sent out, more accurate and comprehensive maturity evaluation is provided, and more efficient and accurate decision support is provided.

The invention relates to an intelligent control method for a high-speed aircraft, and the method comprises the steps: building an aircraft control architecture, carrying out the decision-making of a neural network model, training a decision-making neural network, and carrying out the real-time compensation of a control instruction according to the flight state of the aircraft, so as to achieve the control of a variable-configuration high-speed aircraft. An aircraft controller adopts a model + learning double-layer architecture, a decision neural network is designed to be of a Double Network structure, overload errors of pitching, yawing and rolling channels are considered in a reward function design, training of a decision neural network model is based on a double-delay deterministic strategy gradient algorithm, a training process is optimized by adopting a prior experience playback technology and a target strategy smoothing mechanism, and a decision neural network model is obtained. The control system can achieve real-time intelligent adjustment of control instructions, overload quantities and attitude angles of three channels of the aircraft and corresponding rudder deflection instructions output by the controller at the last moment are obtained from the environment, the intelligent body outputs control instructions of pitching, yawing and rolling channels, and therefore intelligent control over the variable-configuration high-speed aircraft is achieved.

The invention relates to the technical field of color correction, and discloses a publication printing color correction method and system based on deep learning, and the method achieves the precise correction and compensation of the color of a publication printed matter through a series of steps of cosine distance calculation, color-environment matching model, color deviation calculation, operation parameter adjustment and the like. The core of the method lies in that the accuracy of the printing color is effectively improved and the color quality of the printed matter is ensured to meet the expected standard through careful analysis and processing of the printing color by using the deep learning technology. The correction method provided by the invention has obvious advantages, not only can realize automatic color correction, reduce human intervention and improve the stability and consistency of correction, but also can optimize the operation parameters of the printing equipment, so that the printing equipment can keep a good working state in different printing environments, and the adaptability of the printing process is enhanced.

The invention provides a pre-cleaning and disinfecting process for empty barrels of barreled water, which comprises the following specific steps: identifying the smudginess and damage conditions of the empty barrels through online detection equipment for the appearance of the empty barrels, transmitting data to a control system, and removing the damaged barrels by the control system according to a detection result and conveying the smudginess barrels into cleaning equipment. And the cleaning equipment carries out deep cleaning and disinfection by adjusting parameters such as flushing pressure, time, water quantity, concentration of alkali wash, concentration of disinfectant, number of rotation turns of an empty barrel and the like according to an instruction of the control system. And the system dynamically adjusts the parameters to adapt to empty barrels with different pollution degrees. And after cleaning is completed, the cleaning condition of the barrel is checked again through another appearance detection device, and the empty barrel which is not cleaned can be separated and sent back to be cleaned again. According to the process, a flexible operation mode is provided, the cleaning and disinfection requirements of different occasions can be met, and the cleaning and disinfection effect of the inner wall of the barrel is effectively improved.

The invention relates to an EVA high-concentration color master batch detection method based on multispectral imaging, which comprises the following steps: S1, arranging an adjustable multicolor LED light source on a production line, and acquiring a multispectral image of a color master batch by using a multispectral imager to obtain a spectral data set; s2, extracting spectrums from the spectral data set based on a color master batch component identification model, and identifying spectral characteristics of different components or impurities; s3, constructing a color master batch color difference detection model and a defect identification model, and respectively carrying out color difference detection and defect identification based on the spectral characteristics; s4, based on real-time image color difference detection and defect recognition results, feeding back and controlling a control system of a production line based on an intelligent agent, and realizing automatic adjustment; and S5, visualization is carried out based on color difference detection and defect identification results, so that an operator can obtain a real-time color master batch detection result, and alarm processing is carried out on unqualified products. According to the scheme, high-efficiency and accurate monitoring on the EVA high-concentration color master batch is realized, and production intelligence and high product quality are ensured.

The invention discloses a contract risk dynamic scoring method and a multi-level approval device, and belongs to the technical field of contract management, the method comprises the steps that risk fields in a contract form are extracted to form a to-be-verified list, and the risk fields are fields needing risk management and control in the contract form; extracting risk fields meeting trigger conditions in the to-be-verified list to form a target list, wherein the trigger conditions have corresponding weights; calculating the sum value of the weights corresponding to the risk fields in the target list to obtain a risk score; and matching a corresponding risk level according to the risk score. According to the invention, the risk assessment of the contract is realized through the preset triggering condition, and the efficiency and accuracy of contract management are improved.