114results about How to "Improve decision-making efficiency" patented technology

The invention discloses a rice seedling leaf age intelligent identification system based on a depth camera, and belongs to the technical field of agricultural intelligent equipment and machine vision. Comprising a monitoring point gridding path planning module, a mechanical arm cooperative positioning data acquisition module, an RGB-D data preprocessing module, a dynamic foreground seedling accurate extraction module, a leaf age recognition confidence evaluation module, a leaf age data visualization presentation module and an integrated control strategy feedback module. RGB color features and connected domain analysis are combined to optimize a mask, a front single seedling is accurately segmented, background interference is eliminated, the confidence coefficient is dynamically calibrated through a deep learning model and image quality features, texture definition and edge continuity features are combined to generate calibrated confidence coefficient, confidence coefficient evaluation is directly associated with an agricultural decision, and the accuracy of the agricultural decision is improved. The leaf age result is generated only based on reliable data, the personnel rechecking demand is reduced, and the decision-making efficiency is improved.

The invention discloses a scene perception information fusion decision-making method for industrial field multi-source heterogeneous data, which comprises an edge layer, a cloud layer and an interaction layer which form a three-level collaborative architecture, and belongs to the technical field of industrial field data processing. The method comprises the specific steps that S1, an edge layer collects industrial field multi-source heterogeneous original data and carries out self-adaptive preprocessing to obtain cleaned multi-source feature data; s2, the edge layer processes the multi-source feature data through a lightweight scene perception model, and outputs a preliminary scene judgment result; s3, the cloud layer constructs and updates a causal relationship graph based on industrial business knowledge and incremental data of the edge layer, processes data uploaded by the edge layer through a causal reasoning fusion model, outputs a decision result and issues the decision result through the interaction layer; and S4, the cloud layer optimizes parameters of the causal reasoning fusion model and the lightweight scene perception model based on a decision execution result fed back by the edge layer, and issues the optimized parameters to the edge layer through the interaction layer to realize dynamic iteration.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.

The invention discloses a special first-aid medicine and article storage and distribution construction system and method. The system comprises an Internet of Things module, a multi-subject cooperation module, an inter-hospital cooperation module, a cloud computing platform module, a block chain module, an intelligent scheduling center module, a storage network module and an emergency response module. The method comprises the steps of 1, demand integration and joint purchase; step 2, storage network construction and inventory optimization; step 3, intelligent scheduling and path planning; 4, emergency response and cross-mechanism deployment are carried out; 5, whole-process tracing and data uploading are carried out; step 6, performance monitoring and continuous optimization; according to the invention, a special first-aid medicine and article storage and distribution construction system is designed, and standardized data interaction and close cooperation among medical institutions, suppliers and logistics enterprises are realized by establishing a multi-subject cooperation mechanism such as joint purchase, inventory sharing, emergency allocation and performance monitoring. The joint purchasing module gathers the purchasing demands of the medical institutions in the region.

The invention discloses a remote data management platform for security operation and maintenance service, and belongs to the technical field of computer network security and data operation and maintenance management. The platform comprises a data acquisition module, an access control module, a remote operation and maintenance control module, a data isolation and encryption module, a security auditing and tracing module, an intelligent risk identification module, a strategy decision engine and a visual management interface module. The system adopts a layered deployment architecture, and realizes multi-layer protection, dynamic strategy adjustment and traceable auditing of the whole remote operation and maintenance process through encryption transmission, block chain type log chain, deep learning risk identification and knowledge graph strategy optimization. The platform has the functions of multi-factor authentication, log chain tampering prevention, intelligent risk early warning and strategy self-learning, can effectively prevent unauthorized access and data leakage, improves the safety, intelligence and verifiability of remote operation and maintenance, and is suitable for multi-scene safety operation and maintenance management of medical treatment, energy, government affairs, industrial control and the like.

The invention provides a transmission optical path routing planning method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an optical path planning request; based on the endpoint information and pre-stored optical network resource data, generating a plurality of candidate optical paths meeting connectivity requirements among service endpoints; constructing a multi-target fitness function according to an optimization target in the routing strategy, and determining a fitness value of each candidate light path based on the multi-target fitness function; and selecting a target light path from the plurality of candidate light paths. According to the method, the multi-target fitness function is constructed based on the optimization targets in the routing strategy, and the fitness value of each candidate light path is determined based on the multi-target fitness function, so that the comprehensive performance of the plurality of candidate light paths under the plurality of mutually restricted optimization targets is objectively evaluated; therefore, the optimal target light path can be selected from the plurality of candidate light paths based on the fitness value, and the automation level and decision-making efficiency of light path planning are improved.

The present application relates to the technical field of system performance optimization and resource scheduling, in particular to a system performance recovery method based on equipment adjustment and a strategy support platform thereof. The method comprises: inputting system information to be recovered; sequentially performing system reorganization feasibility evaluation and optimization, function replacement and spare parts commissioning evaluation, external allocation feasibility evaluation and optimization, and generating a recovery scheme; finally integrating the scheme and encoding output by using a preset encoding rule. The equipment adjustment operation is summarized as four basic types of system reorganization, function replacement, spare parts commissioning and external allocation. The strategy support platform comprises a hardware layer and a software layer, and through the internal scheme optimization program, the method process is automatically executed, the system information is received and the encoded optimal performance recovery strategy is output. The present application realizes the automatic generation and rapid decision of the system performance recovery strategy through the structured recovery process and the unified encoding system.

This invention relates to the field of decorative engineering technology, and discloses a method and system for generating virtual renderings of decorative engineering projects. The method includes: S1, parsing CAD drawings; S2, identifying professional intent; S3, defining and configuring key aesthetic elements; S4, attributing and correcting model parameters; S5, conflict detection; S6, adaptive fusion and correction; S7, material mapping and lighting calculation; and S8, outputting the virtual rendering. This invention solves the problem that the complexity of CAD drawings leads to incomplete or inaccurate geometric structures in 3D models, which in turn affects material mapping and lighting calculation, thus impacting the visual realism of virtual renderings.

The invention relates to the technical field of industrial cloud computing, and discloses a new energy station oriented data-drive intelligent diagnosis system for wind power equipment. The system comprises a data receiving and processing module, a diagnosis context construction module, a correlation graph generation module, a running state slicing module and a diagnosis report generation module. The invention aims to solve the problems of low efficiency and poor conclusion consistency caused by the fact that an existing fault diagnosis method depends on artificial experience to construct an analysis scene, associated information is difficult to search, and an analysis process is fragmented. According to the method, the diagnosis context with the optimized signal-to-noise ratio is automatically constructed, multi-dimensional data association is intelligently mined in the diagnosis context, the diagnosis path efficiency is evaluated, meanwhile, the focus is converged and analyzed in combination with the running state slices, and finally a structured diagnosis report is automatically generated; according to the method, progressive and navigation type intelligent analysis from abnormity to root is realized, and the accuracy, the automation level and the decision-making efficiency of fault diagnosis of the wind power equipment are remarkably improved.

The present application relates to gear detection and nanomaterial application technical field, especially two-dimensional nanomaterial repair agent gear protection film endoscope quantitative detection method, including obtaining heavy load tooth surface space distance point cloud data, and combining the local curvature normal vector matrix of involute analytical model fitting, accordingly adjusting the output space adaptive light field of light emitting array;Under the adaptive light field, a plurality of polarization state initial image sequences are collected, and the Stokes parameter matrix including total light intensity and circular polarization difference information is solved;A binary isolation mask matrix is constructed using the linear polarization degree matrix and the preset threshold value, the metal reflection background data is stripped, and the phase delay matrix is converted into a texture image matrix.The present application realizes the visualization, non-destructive quantitative detection of two-dimensional nanomaterial composite protective film, and effectively solves the microfeature extraction problem under strong reflection interference.

The invention relates to the technical field of open caisson construction, in particular to an active control type assembly type mechanized open caisson construction visualization system and method, and the method comprises the following steps: obtaining construction data of open caisson construction equipment in real time; based on construction data, obtained in real time, of the open caisson construction equipment, the corresponding robot three-dimensional model and the open caisson three-dimensional model are driven to synchronously move and are displayed; and in the intelligent control mode, the intelligent control module is in communication connection with the open caisson construction equipment, receives an operation instruction of a user and issues the received operation instruction to the open caisson construction equipment for execution so as to realize intelligent control. According to the method, the digital twinborn model is constructed, visualization and whole-process analog simulation of the whole operation process of the underwater robot and visualization of the open caisson posture and construction information are achieved, and an operator is helped to complete settlement construction work more accurately and efficiently.

The invention relates to the technical field of three-dimensional geographic modeling, in particular to a land utilization planning auxiliary method and system based on a 3D terrain model, and the method comprises the following steps: obtaining point cloud and elevation information, converting the information into a terrain curved surface, discretizing the information into a voxel grid, analyzing a planning index, converting the planning index into a geometric constraint boundary, and traversing to construct a planning voxel field. Calculating earthwork digging and filling requirements and sunshine hours to generate an evaluation matrix, correcting the building voxel distribution and finely adjusting the terrain elevation. According to the method, terrain refined expression is realized by using an octree segmentation algorithm, indexes such as a plot ratio are converted into three-dimensional space geometric constraints, site suitability is evaluated through connectivity analysis, and building layout and earthwork balance are automatically optimized by adopting morphological operation, so that the problems of lack of space intuition and error accumulation in traditional two-dimensional planning are solved; the planning calculation precision and decision scientificity under the complex terrain are remarkably improved, and dynamic adjustment and visual expression of the scheme are achieved.

The present application relates to the technical field of design work display, and specifically relates to an environmental design work display system, which comprises a model extraction module, a three-dimensional laser scanner is used to scan a design work model, point cloud data is obtained, and a digital twin model is generated; an intelligent detection module is used to detect the digital twin model and identify design defect areas; a dynamic optimization module generates a plurality of parameter adjustment schemes for each defect area; and an interactive display module synchronously displays an original design, optimization schemes and key index comparison data in a side-by-side view, and supports a user to select and apply an optimization scheme in real time. The present application generates a digital twin model through three-dimensional laser scanning, automatically detects key parameters such as fire passage width and energy-saving heat transfer coefficient in combination with a specification database, and calculates defect severity scores, so as to realize the precision of problem positioning and the priority of sorting.

The invention relates to the technical field of intelligent security and protection monitoring, in particular to an intelligent prevention and control system and method for an on-duty perimeter electronic sentry based on AI vision, and the system comprises a full-dimensional sensing module which collects visible light, thermal imaging video streams and radar detection data of a perimeter area; the intelligent analysis module is used for carrying out behavior feature extraction on the video stream based on a deep learning model and generating a three-dimensional track vector of an intrusion target; the comprehensive management and control module maps the real-time track of the invasion target to a virtual electronic defense area for visual presentation; and the linkage processing module is used for scheduling the sound optical driver isolation equipment and the unmanned aerial vehicle countering unit according to the threat level logic resolving result. According to the invention, by fusing AI vision and a multi-dimensional perception technology, accurate identification and all-weather automatic tracking of perimeter intrusion behaviors are realized, the problems of dependence on manpower, existence of blind areas and response lag in traditional security and protection are solved, and the three-dimensional prevention and control level and emergency response speed of an on-duty area are greatly improved.

The present application relates to the technical field of smart grid, disclose a kind of multi-factor associated return home degree winter power load pre-control method and system, method includes: the historical power consumption data obtained in advance is transformed to obtain power load data, and power load data is identified to obtain power consumption problem;The influencing factor is obtained by analyzing power consumption problem, and the strong correlation factor data matrix is obtained by analyzing influencing factor, and the correlation influence coefficient is obtained by analyzing strong correlation factor data matrix;The trained power load prediction model is obtained by iterative training to power load prediction model, and the current power consumption data obtained in advance is input into trained power load prediction model to obtain load prediction data;The red yellow green classification area is obtained by dividing power grid;Power load pre-control scheme is formulated, so as to carry out power consumption prevention and control management.The present application can solve the problem that power grid overload is difficult to prevent and control during return home degree winter and power consumption prediction is not accurate enough.

The invention relates to a cognitive flashing system and method based on neural symbols, and the system comprises a multi-modal sensing module which is used for obtaining multi-modal data of a vehicle, and the multi-modal data comprises state data, software feature data and context data; the neural symbol fusion module is used for outputting a first flashing task schedule and a second flashing task schedule through a symbol inference engine and a neural network based on the multi-modal data; the flash task scheduling comprises flash target and flash resource allocation; the fusion module is used for fusing the first flashing task scheduling and the second flashing task scheduling through an attention mechanism and outputting a third flashing task scheduling; and the decision generation module is used for executing the flashing operation based on the third flashing task scheduling. By fusing symbolic reasoning and deep learning, the problems of stiffness and black box of a traditional flashing decision system are solved, and the adaptivity, safety and decision efficiency of vehicle software OTA flashing are remarkably improved.

This invention proposes a dynamic routing decision-making method, specifically a TSSDN dynamic routing decision-making method based on the DDPG deep reinforcement learning algorithm. The aim is to design dynamic routing decisions using deep reinforcement learning algorithms based on network dynamic awareness prediction results, thereby reducing the transmission latency of low-priority flows in time-sensitive networks. This invention uses a deep learning algorithm to predict switch queue lengths in real time, and then makes next-hop routing decisions based on the prediction results. Higher prediction accuracy leads to better dynamic routing performance and improved decision-making efficiency. The implementation steps are: 1) Constructing the TSSDN network node architecture; 2) Constructing the network topology; 3) Constructing a topology feature extraction method based on PCA; 4) Constructing a prediction model based on a deep learning algorithm; 5) Constructing a routing decision-making model based on a deep reinforcement learning algorithm; 6) Iteratively training the deep reinforcement learning-based routing decision-making model based on the prediction results. This invention can be applied to scenarios such as telemedicine.

The invention discloses an equestrian competition performance prediction system based on multi-modal monitoring, and relates to the technical field of equestrian sport monitoring and intelligent prediction. The system comprises an acquisition module, a processing module, a prediction module, a display module and a data closed-loop optimization mechanism. The acquisition module is used for acquiring horse video and physiological and environmental multi-modal data through multiple types of equipment; the processing module is used for realizing data fusion processing, gait behavior recognition and facial expression emotion evaluation; the prediction module outputs a competitive performance quantitative score and risk early warning based on a machine learning model; the display module provides a visual interaction interface; and the data closed-loop optimization mechanism realizes model iterative optimization through competition result return. According to the method, the problems of single monitoring dimension, data splitting, lack of prediction capability and the like in the prior art are solved, comprehensive perception of horse states and accurate prediction of competitive performance are realized, the scientific level of training management is improved, and the method has wide practical prospects and industrialization values.

The application provides a pre-tailings discarding mode distinguishing method based on useful mineral dissemination characteristics, and relates to the field of mineral processing. The pre-tailings discarding mode distinguishing method based on useful mineral dissemination characteristics comprises the following steps: preparing a plurality of optical sections from an ore sample; analyzing the optical sections by using an optical microscope and an image analysis technology, and classifying the dissemination characteristics of useful minerals in the optical sections; calculating the average occupancy of local enrichment type in all optical sections, the average occupancy of vein type in all optical sections, and calculating a pre-enrichment proportion Pvalid; comparing the pre-enrichment proportion with a preset threshold value, and if Pvalid is greater than or equal to the preset threshold value, it is determined that the ore sample is suitable for pre-enrichment by using a pre-tailings discarding process. The prediction conclusion of the method of the application is highly consistent with actual industrial test and production data, has the advantage of high accuracy, solves the problem that the pre-discarding effect can only be detected by test, and is beneficial to improving work efficiency and reducing cost.

This invention discloses a BIM-based architectural engineering design method and system, relating to the field of architectural engineering design technology. The method includes: extracting the geometric features of each component of the target building, including bounding box coordinates, center point, dimensions, and principal direction vectors; calculating the average bounding box side length based on the geometric features; dividing the building space into grid cells and marking dense grid cells; offsetting components within the dense grid cells along three-dimensional directions to generate extended feature vectors; performing principal component rotation and multiple orthogonal projection on the extended feature vectors to generate a two-dimensional hash index; mapping components to index buckets and neighboring buckets; combining components pairwise within the index buckets; analyzing three-dimensional overlapping intervals to identify conflicting component pairs; statistically analyzing the overlapping volume of conflicting component pairs and their coefficient of variation; and marking component pairs with abnormal volumes to form conflict marker objects for BIM-driven component position adjustment. This solves the problem of missed detection of conflict candidate pairs in highly overlapping areas of the building space.

The invention discloses a river regulation data analysis management system and method based on artificial intelligence, and relates to the field of intelligent management, and the system comprises a main control module which is used for the general control of the data stream editing and instruction transmission of each function module; the water layer division module is used for defining and dividing a water layer of a target water area, dividing the water area into a plurality of independent water layers according to hydrological characteristics, and distributing a unique identifier for each water layer; the silt collection module is used for acquiring obstruction accumulation data in a specified water layer through a sensor network; the floating acquisition module is used for acquiring floating object coverage data in a specified water layer; fusion analysis is carried out on multi-dimensional and real-time collected data by using an artificial intelligence deep learning algorithm, and the deterioration risk probability of a specific period in the future can be output, so that a management department can identify a high-risk area and intervene in advance before a water quality or sedimentation problem actually occurs or deteriorates, and then the logic of river regulation is assisted to be changed.

The invention discloses a mine robot emergency risk avoiding intelligent decision-making system, and the system comprises a disaster risk prediction module which is used for carrying out the time sequence prediction based on the data of a multi-source sensor, and outputting a disaster occurrence probability and a risk comprehensive index; the dynamic risk avoiding path planning module is used for constructing a dynamic risk map and performing path planning by adopting an improved A * algorithm; according to the system, advanced early warning, dynamic path planning, hierarchical response and multi-agent coordination in a mine catastrophe environment are realized by constructing an intelligent closed-loop architecture of perception, prediction, decision making, execution and coordination. Multi-source sensor data are fused, an LSTM time sequence prediction model is adopted to carry out disaster probability prediction, an A * algorithm is improved to realize risk sensitive path planning, a hierarchical response strategy is realized based on a finite-state machine, and robot group collaborative risk avoiding is realized through a publishing and subscribing mechanism. According to the method, the survivability, the decision-making efficiency and the collaborative operation level of the mine robot in the catastrophe environment are improved.

The application discloses an industrial decision support system and method based on a large model and artificial intelligence, and belongs to the technical field of industrial intelligent decision-making.The system comprises the following steps: collecting industrial field data streams in real time and preprocessing to generate a structured time series dataset; using an industrial state analysis large model to extract a characteristic vector sequence, combining a sliding time window division and a neural Granger causality test algorithm to generate a causality knowledge rule set; using a neural differential equation framework to predict equipment state data prediction values; performing risk benefit assessment based on the prediction values to generate a structured decision instruction; after executing the instruction, comparing the prediction values and measured value deviations by using a sliding window cumulative error statistical method to dynamically optimize the model; and through the degenerative modeling and dynamic prediction of the causality rule constraint, and the closed-loop verification of the sliding window cumulative error statistical method, high-precision prediction of the equipment state, risk tracing and model self-adaptive optimization are realized.

The invention relates to a portable gas inspection and concentration distribution visualization method, system and equipment, and the method comprises the steps: carrying out pumping type sampling in a process that a portable gas detector moves in a to-be-detected area, and obtaining gas concentration data sets of different space sampling points in real time; synchronously recording real-time position information corresponding to each sampling point through a positioning unit integrated in the gas detector to form a spatio-temporal data set associated with the gas concentration data set; constructing a three-dimensional concentration field model reflecting continuous spatial distribution of gas concentration in the to-be-detected area by adopting a spatial interpolation algorithm; and performing fusion rendering on the three-dimensional concentration field model and the two-dimensional map or the three-dimensional real scene model of the to-be-detected area to generate a gas concentration distribution visual map, and visually displaying the concentration level and the diffusion trend of each point in the space in a gradient color manner. Therefore, the purposes of visual and accurate analysis and positioning of the gas space distribution, the diffusion trend and the risk area are achieved.

The application discloses an interactive method of a double-tracing safety production supervision system, relates to the technical field of safety production supervision systems, and discloses the interactive method of the double-tracing safety production supervision system, which integrates an AI identification and an artificial reporting double-source hidden danger data collection mechanism, cross-verify double-source hidden danger data, constructs a double-binding system of data tracing credentials and operation process tracing records, and combines visual interaction of a GIS map and multi-dimensional safety performance evaluation, effectively solves the technical problems of incomplete hidden danger coverage, difficult responsibility tracing, missing quantitative evaluation and insufficient terminal cooperation in a traditional supervision system, can realize comprehensive collection of double-source hidden danger data, cross-checking and purification of double-source hidden danger data, complete construction of a double-tracing mechanism, quantitative evaluation of safety performance and closed-loop management of a supervision process, and effectively improves the coverage range, tracing capability and decision-making efficiency of safety production supervision.

The application discloses a kind of brain-like reinforcement learning method and system based on layered experience playback, it is related to reinforcement learning and brain-like computing technical field.The method comprises the following steps: S1, collecting observation data and pre-processing;S2, initialize experience buffer pool and actor network, critic network and corresponding target network, parameter initialization;S3, initialize exploration noise and select action from actor network according to current state and execute, store the obtained experience sample to experience buffer pool;S4, obtain new sample from experience buffer pool, carry out short-term memory pool update;S5, use attention discrimination module to determine whether part of experience in short-term memory experience pool is transferred to long-term memory experience pool;S6, the parameters of actor network, critic network and corresponding target network are updated.The application uses the above method, improves the experience utilization rate of intelligent agent, improves the performance of reinforcement learning, and has wide application potential in many fields.

The application discloses a micro-meteorological perception and multi-agent collaborative decision-making method and system for low-altitude scenes, and belongs to the technical field of low-altitude emergency command and unmanned aerial vehicle cluster scheduling. The system comprises a perception layer, a decision-making layer and an execution layer. The perception layer generates a micro-scale three-dimensional wind field through cascading downscaling, and constructs a voxelized risk cost field based on the wind resistance of unmanned aerial vehicles. The decision-making layer adopts a heterogeneous multi-agent collaborative decision-making engine, realizes the binding of a large language model and a domain calculation model through function interface reasoning double channels, combines a four-stage specialized workflow arrangement, and introduces an unoverlappable meteorological veto mechanism. The execution layer generates simulation verification files and supports dynamic strategy correction. The application solves the problems of insufficient micro-scale meteorological perception accuracy of existing systems, lack of meteorological hard constraints in multi-agent collaboration, and low flight safety resilience, and can be widely applied to low-altitude emergency scenes such as medical material delivery and disaster rescue.

The invention relates to the technical field of industrial process control and data acquisition, and particularly discloses a transformer copper strip production process data real-time monitoring and wireless transmission system which comprises a data acquisition and edge processing layer, a deterministic wireless transmission layer and a centralized analysis and display layer. The data acquisition and edge processing layer acquires and preprocesses production data in real time; the deterministic wireless transmission layer constructs a low-delay and high-reliability wireless transmission channel based on a time division multiple access and frequency division multiplexing mechanism; and the centralized analysis and display layer stores, analyzes and visualizes the data. By means of the scheme, millisecond-level and deterministic wireless transmission and omnibearing intelligent monitoring of data in the production process are achieved, and the production reliability and the intelligent level are improved.

The application discloses a coal gangue mechanical grinding enhancement activation method, comprising the following steps: obtaining multi-dimensional characterization data of coal gangue, performing inhomogeneous modeling on coal gangue particles through a patch algorithm based on the multi-dimensional characterization data, calculating the crushing energy consumption of each patch according to the grinding parameters of the mineral phase, learning the feature mapping of the grinding parameters and the lattice state through a double-branch attention network for high-activity patches, combining the grinding parameters as a strategy set, solving the optimization objective through a non-dominated sorting genetic algorithm using the strategy set, obtaining an optimal solution set of the grinding parameters, screening a target grinding parameter based on the optimal solution set through the mineral phase of an application scenario, and performing targeted grinding on the coal gangue to obtain activated and enhanced coal gangue grinding particles. The method performs inhomogeneous modeling on the multi-dimensional characterization data of the coal gangue, and screens a target grinding parameter according to an application scenario, thereby improving the pertinence of the coal gangue grinding effect, and reducing the grinding energy consumption and processing cost.

The present application relates to the technical field of machine learning data reproducibility, and more particularly to a data reproducible version pipeline management method based on machine learning, which submits target data to a versioned data processing pipeline, performs data analysis and generates a versioned execution record, collects a feature health degree parameter of the target data in the versioned execution record and analyzes a feature health degree index, determines whether the matching degree of feature extraction of the target data meets a standard based on the feature health degree index, collects a feature fidelity parameter of a target data reproduction result in the versioned execution record and analyzes a feature fidelity index in response to the matching degree meeting the standard, determines whether the reliability of the target data reproduction result is abnormal based on the feature fidelity index, calculates a reproduction abnormality index in response to the reliability being abnormal, and determines the cause of the reliability abnormality and corresponding processing measures based on the reproduction abnormality index. The present application improves the reliability of data traceability in the reproduction link.