256 results about "Objective data" patented technology

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention discloses a psychological support strategy generation engine based on multi-modal data fusion, and relates to the technical field of psychological health, the engine comprises a multi-modal data acquisition module, a multi-modal data fusion processing module, a dynamic recovery trajectory modeling module, a recovery process prediction and strategy generation module and a strategy evaluation and feedback adjustment module; multi-modal data such as physiology, behaviors and voice are deeply combined with core theories such as cognitive behavior therapy and positive psychology through an innovative mechanism, data processing is performed according to theoretical constraint cleaning, and data compliance is ensured; fusing the matching degree of the mid-quantized data and the theoretical dimension, and associating objective data with theoretical indexes; dynamic modeling strengthens sensitive features such as a critical period of wound recovery, so that evaluation reflects physiological and behavior changes and embodies intrinsic features such as psychological toughness, a real psychological state can be accurately captured, a recovery risk can be recognized in advance, a scientific basis is provided for accurate intervention, and pertinence and foresight of psychological support are effectively improved.

The invention relates to the field of Internet of Things technology, data analysis and insurance claim settlement, in particular to a security Internet of Things and insurance claim settlement intelligent evaluation integrated system, which comprises a security state data acquisition module, a dynamic security portrait module, an accident traceability and responsibility intelligent division module and an automatic claim settlement evaluation module. According to the method, deep analysis of multi-source heterogeneous environment data and accurate representation of the safety state are achieved, the dynamic safety portrait module can capture the essential topological structure relation of the data by constructing the functions of topological feature extraction, state representation, multi-scale anomaly detection, risk prediction and the like, and high-precision recognition and risk prediction of gradient anomaly are achieved. After an accident occurs, a complete accident data chain can be constructed, the responsibility proportion of each party can be intelligently divided based on objective data, and the compensation amount can be automatically calculated, so that full-process automation from safety monitoring to insurance claim settlement is realized, the anomaly detection accuracy is greatly improved, and potential risks can be found in advance.

The invention provides a collateral dredging instrument parameter optimization method based on subjective and objective data fusion, and relates to the technical field of collateral dredging instruments, and the method comprises the following steps: generating an initial electrical stimulation parameter according to an initial electrophysiological signal of the collateral dredging instrument for detecting a target acupoint, and starting the output; in an initial stage of starting output, receiving a subjective trigger signal which is fed back by a user and represents a tolerable critical point, and recording a stimulation intensity value corresponding to the subjective trigger signal as a current personal comfort threshold value of the user; performing continuous stimulation by taking the current personal comfort threshold as basic intensity, and simultaneously executing the following parallel steps: acquiring a continuous or discrete real-time subjective comfort feedback signal input by a user through an interactive interface in real time; monitoring a dynamic electrophysiological signal of the target acupuncture point in real time, and analyzing the change trend of the dynamic electrophysiological signal to generate an objective adjustment suggestion signal; and performing fusion analysis on the real-time subjective comfort feedback signal and the objective adjustment suggestion signal, and performing real-time dynamic adjustment on the current electrical stimulation parameter according to the fusion analysis.

A computer-implemented system and method for dynamic, explainable, and adaptive prioritization of cybersecurity controls is disclosed. The system ingests unstructured threat reports and employs a natural language processing (NLP) module to automatically extract adversary tactics, techniques, and procedures (TTPs). A scoring module applies a mathematical time-decay function to the extracted intelligence. A novel hybrid prioritization engine provides explainability-by-design by computationally integrating these objective, data-driven scores with organization-specific context within a transparent multi-criteria decision analysis (MCDA) model. Critically, the system establishes a self-optimizing closed feedback loop; it receives real-world control effectiveness metrics from the operational environment and uses this data as new ground-truth labels to continuously and automatically retrain internal machine learning models. This adaptive mechanism improves the computer's own predictive accuracy and resource allocation efficiency over time, representing a tangible technical improvement.

The invention relates to the technical field of computer data processing and mode recognition, and discloses an intelligent handwriting record analysis system supporting translation process traceability and capability assessment, which comprises the following steps: collecting multi-dimensional handwriting data generated when a user translates by using an intelligent pen; the Hidden Markov model decodes the handwriting into translation behavior primitive sequences such as smooth writing, hesitant pause and the like; automatically dividing semantic regions such as a main translation region and a draft region by a density clustering algorithm; performing space-time fusion attribution to generate a space-time attribution log for accurately recording behaviors, time and positions; and establishing a multi-dimensional capability index system containing translation efficiency, quality and strategies based on the log, and carrying out quantitative calculation and generating a visual user capability portrait. According to the method, the invisible translation cognition process is converted into traceable and quantifiable objective data, deep insight and comprehensive evaluation of the translation process are achieved, and a scientific and efficient analysis tool is provided for translation teaching and interpreter ability evaluation.

The invention relates to the technical field of dam safety detection, and discloses a dam hidden danger troubleshooting method and system.The dam hidden danger troubleshooting method comprises the steps that a visual dam image collected in a multi-mode mode is obtained, a target detection model is used for processing the visual dam image, information of at least one bounding box with a hidden danger target is output, and then the bounding box is cut; the method comprises the steps of obtaining a local image where a hidden danger target is located, processing the local image by using a semantic segmentation model, outputting a pixel-level segmentation mask corresponding to the hidden danger target, and calculating quantitative geometric parameters of the hidden danger target based on the pixel-level segmentation mask, thereby providing objective data support for subsequent risk study and judgment of dam hidden dangers. According to the method, subjective judgment errors of manual detection are avoided, automatic dam hidden danger investigation is achieved, manpower and cost of manual inspection are greatly reduced, automatic collection, detection and quantitative analysis of dam images are achieved, dam hidden danger can be found and rectified in time, and the risk of dam safety accidents is reduced.

The invention discloses a multi-objective optimization method based on a Black-Litterman model for an ore grinding grading process, a multi-objective optimization device based on the Black-Litterman model for the ore grinding grading process, multi-objective optimization equipment and a computer readable storage medium. According to the method, the experience viewpoint of a process expert and historical data are organically fused by adopting the Black-Litterman model; the limitation that a traditional optimization method only depends on data driving or only depends on expert experience is overcome, and collaborative complementation of subjective knowledge and objective data is achieved. The deep learning technology is introduced to extract time series data features, and the convolutional neural network is combined to fuse expert viewpoints, so that the optimization model can fully excavate complex dynamic characteristics and inherent rules in the ore grinding classification process. Practical experience is fused into a prediction framework, so that the result is more suitable for practical operation, and the accuracy of ore grinding classification multi-objective optimization is improved.

The invention discloses a multi-agent-based college entrance examination consultation method and system, and belongs to the technical field of large language model application. The problems that in the prior art, a traditional college entrance examination consultation system based on a large language model is limited in complex problem processing capacity, insufficient in personalized service capacity and lack of expert knowledge are solved. The method comprises the following steps: S1, constructing a local knowledge base containing objective data and expert knowledge extracted through a specific process; s2, designing a knowledge retrieval tool for accessing a network and a local knowledge base; s3, introducing a long-term memory mechanism to store, apply and update personalized information of the user in a cross-session manner; and S4, constructing a multi-agent collaborative architecture, analyzing user intentions according to user personalized information, scheduling expert agents, and integrating results to realize college entrance examination consultation. According to the method, the accuracy and comprehensiveness of answering are effectively improved, continuous personalized services are provided, and the method can be applied to online college entrance examination consultation.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

Systems, methods, and computer-readable media for and improvement rehabilitation infrastructure. For example, a method may include using, inter alia, objective data to optimize treatment plans to support user progression during rehabilitation for the purpose of assisting in determining AI-driven interventions by using machine learning to generate at least one data signature. In some embodiments, a method may perform AI-driven interventions via optimization of treatment plans in order to support user progression during rehabilitation by performing, based on data signatures related to medically-associated events, selective data injection. Some other embodiments use evidence-based research to enable AI and machine learning to select and modify treatment plans in real-time at scale. Further, the improved rehabilitation infrastructure enables, via pain management, user compliance with treatment plans.

The invention discloses a mattress fabric comfort evaluation method, device and equipment and a storage medium. The method comprises the following steps: collecting objective data from a mattress fabric sample according to a plurality of indexes; performing range standardization processing on the objective data to obtain a range standardization value; performing weighted comprehensive calculation on the mattress fabric sample according to the range standardization value to obtain a weighted comprehensive score; performing clustering analysis on the objective data by adopting a clustering algorithm to obtain an optimal k value; dividing the mattress fabric sample into k levels according to the weighted comprehensive score, setting k comfort evaluation score ranges based on the k levels, and constructing a comfort evaluation model; and evaluating by using the comfort evaluation model. According to the method, the influence of a plurality of comfort level related indexes on the comfort level of the mattress fabric and the weight of the influence are comprehensively considered, the objective data are converted into the comfort level score of the mattress fabric, and the relation between the objective data and the comfort level of the mattress fabric is established, so that the comfort level evaluation effect of the mattress fabric can be improved.

The invention discloses an online response system for online text editing, relates to the technical field of online response, and solves the technical problems of insufficient context understanding depth and subjective result integration decision. And establishing a scene-keyword mapping library and a three-dimensional sorting retrieval mechanism, optimizing a retrieval keyword by fusing context information in an associated scene so as to enable response to meet real-time requirements of a user, and avoiding a non-response condition through scene positioning and fuzzy association in a non-associated scene, so that the user experience is improved. A matching degree score, a knowledge base source authority degree, an interaction preference matching degree and a historical behavior tendency degree are incorporated into a scoring model, subjective rules are replaced by objective data, decision-making is dynamically performed based on score difference, decision-making deviation caused by a single index is avoided, the user recognition degree of a conflict processing result is improved, and the user experience is improved. And meanwhile, the credibility of the result is enhanced by processing the process traceability label.

The invention provides a construction interruption risk reasoning and plan generation method and device. The method comprises the following steps: acquiring multiple types of risk factors influencing construction interruption and a historical data set; taking a Bayesian information criterion as a scoring function, and constructing a directed acyclic graph structure based on the scoring function, a historical data set and a constraint set determined by domain knowledge; the constraint set is used for limiting whether edges between nodes exist or not in the construction process of the directed acyclic graph structure; based on the directed acyclic graph structure and the historical data set, a conditional probability table of the directed acyclic graph structure is determined, and the directed acyclic graph structure and the conditional probability table form a Bayesian network model; determining the risk probability of the target construction interruption event based on a Bayesian network model; and optimizing a preset construction plan combination based on the risk probability, and determining an optimal plan combination. According to the directed acyclic graph structure, objective data and domain knowledge are considered, and the accuracy of subsequent risk reasoning and plan generation is improved.

Provided is an information processing method, etc. that assists a user in interpreting behavior of a generated machine learning model. In the information processing method, a computer executes processing of recording a plurality of sets of an explanatory data vector xn input to an existing machine learning model (21) and an objective data vector yn output from the machine learning model (21) in association with each other, calculating an interpretation matrix A_dagger which is a vector product of an explanatory matrix X in which a plurality of sets of the explanatory data vector xn is arranged and a generalized inverse matrix of an objective matrix Y in which the objective data vector yn is arranged in an order corresponding to the explanatory data vector X, and outputting a chart (41, 42, and 43) related to the interpretation matrix A_dagger.

The invention discloses a saline-alkali soil tillage suitability grading method and system, and belongs to the technical field of agricultural land resource evaluation. The method comprises the following steps: firstly, collecting multi-dimensional data including a basic soil index, an environmental condition index and a crop adaptation index; secondly, index data are processed through an extreme value standardization method, and the dimensional influence is eliminated; an AHP-entropy weight combination weighting method is adopted to determine index weights, and subjective experience and objective data features are considered; calculating a suitability comprehensive index based on a weighted summation method; and finally, dividing the tillage suitability of the saline-alkali soil into four grades of high suitability, moderate suitability, mild suitability and unsuitability in combination with a comprehensive index and a dynamic threshold adjustment mechanism. The method solves the problems that an existing evaluation method is single in index, fixed in standard and low in efficiency, can provide an accurate and scientific basis for saline-alkali soil improvement and utilization and crop planting planning, and has remarkable economic and ecological benefits.

The invention relates to the technical field of buildings, in particular to a style and feature fusion design method for a building roof photovoltaic system. The method aims at overcoming the defects that in the design process of an existing roof photovoltaic system, multi-dimensional requirements such as style, appearance, economy and safety cannot be quantified and unified, and the design period is long and the landing difficulty is large due to the fact that design and modeling links are disjointed and separated. According to the method, traditional fuzzy requirements of building style and appearance coordination, visual influence, landscape sensitivity and the like depending on subjective experience are converted into calculable parameters based on objective data, a scientific and unified evaluation criterion is provided for a design scheme, dependence on artificial experience is reduced, and standardization and efficiency of an approval process are improved; the invention aims to shorten the design period and guarantee the technical feasibility, economic rationality and style compliance of an existing building roof photovoltaic installation project in a complex urban and rural environment, especially in a style sensitive area.

The application discloses a kind of humanoid robot linear joint permanent magnet motor design optimization method, this method includes: based on the mechanical characteristics of human gait cycle to establish knee joint connecting rod mechanism dynamics model, extract characteristic working condition point and establish screw-motor electromechanical matching criterion, using Latin hypercube sampling to obtain design sample point, establish permanent magnet motor multi-objective optimization model with limit torque, torque ripple, efficiency as target, then introduce evolutionary algorithm for global optimization, obtain Pareto non-inferior solution set;Finally, based on CRITIC-TOPSIS evaluation method, fusion objective data characteristics and subjective working condition demand, determine the optimal design scheme from Pareto non-inferior solution set.The design optimization method of the application can define the design requirements of permanent magnet motor, and can also scientifically distribute the target weight, ultimately can obtain the permanent magnet motor scheme highly matched with specific joint working condition, effectively improves the power performance and operating stability of humanoid robot linear joint.

The embodiment of the invention relates to the technical field of sorting, and discloses a sorting management method based on artificial intelligence, and the method comprises the steps: obtaining the real-time operation data of a physical logistics center; inputting the real-time operation data into an operation and maintenance large model, and generating a first operation and maintenance instruction by the operation and maintenance large model based on semantic understanding of the operation data; in the digital twin, executing a simulation operation based on the first operation and maintenance instruction to generate simulation operation and maintenance information; calculating systematic deviation based on the simulated operation and maintenance information and actual operation and maintenance information acquired from a physical logistics center; and generating and outputting a second operation and maintenance instruction subjected to deviation correction to the physical logistics center by the target large model so as to carry out sorting management operation. According to the method provided by the embodiment of the invention, the decision-making process is processed by the operation and maintenance large model and the target large model. The models perform semantic understanding and decision generation based on mass data and complex algorithms, so that sorting management is based on objective data analysis and model deduction, and the decision is more scientific, stable and quantifiable.

The invention discloses a commercial vehicle door closing sound quality problem identification and optimization method and system and electronic equipment, and relates to the field of quality improvement, and the method comprises the steps: A1, obtaining a door closing use scene of a user; acquiring hotspot items concerned by a user side based on the door closing use scene of the user; setting a subjective data sampling scene based on the door closing use scene of the user and the hotspot items concerned by the user side; a2, on the basis of a set subjective data sampling scene, a simulation user side feels channel data sampling; according to simulation user side feeling channel data sampling, obtaining evaluation data of a user side corresponding concerned hotspot item; step A3, setting an objective data sampling object based on corresponding concerned hotspot items and evaluation data of the user side; correspondingly setting an objective data sampling object, and carrying out objective data sampling; sampling corresponding objective data, and obtaining design data associated with a door closing use scene in vehicle design; the design data comprises vehicle design items and corresponding item parameters.

The invention discloses an upper limb robot rehabilitation training control method based on electromyographic signals, and relates to the technical field of rehabilitation medical treatment, and the method comprises the following steps: collecting electromyographic signals of a human body upper limb target muscle group in different action modes, and constructing an electromyographic signal feature library, screening an electromyographic signal matched with the rehabilitation training action from an electromyographic signal feature library and marking the electromyographic signal as a training trigger signal; extracting a signal effective value, a spectrum feature and an action correlation degree of the training trigger signals, judging whether feature enhancement processing needs to be performed on the at least two groups of training trigger signals according to the signal effective value and the spectrum feature, and marking the corresponding training trigger signals as control instruction generation signals; acquiring an upper limb movement track parameter corresponding to the control instruction generation signal; the method has the advantages that quantitative analysis of the electromyographic signals can reflect the neuromuscular function state of a patient in real time, objective data support is provided for training scheme adjustment, and training scientificity and repeatability are improved.

This invention discloses a method, device, electronic equipment, and medium for assessing the risk of pulmonary embolism. It relies entirely on collected non-invasive objective data and eliminates subjective judgments based on medical history or physician experience, effectively avoiding risk misjudgments caused by subjective variables in the PESI / sPESI model. This ensures the consistency and reliability of the assessment results. The ratio of Qa to Qb is used as the core assessment indicator. This ratio is a continuous variable, eliminating the need for empirical segmentation of parameters and avoiding significant differences in assessment results among patients with similar conditions. The risk level is assessed based on the ratio of Qa to Qb obtained through hemodynamic simulation. The results perfectly match clinical understanding of the severity of pulmonary embolism, improving assessment accuracy. Simultaneously, it reduces patient examination trauma and avoids high equipment and operational costs.

This invention proposes an efficient system and method for processing objective data from helicopter flight simulators. The system includes a data preprocessing module for preprocessing acquired complete flight data, including data fusion and time-series correction; a data segmentation module for segmenting the raw simulator objective data based on helicopter configuration, control variable changes, and flight status to obtain segmented data; a batch processing module for providing a batch processing entry point for multiple sets of segmented data, simultaneously importing multiple sets of segmented data and multiple data parameters into one or more other processing modules for processing; an outlier removal module for identifying and removing outliers from the batch-processed segmented data; a curve fitting module for correcting the data after outlier removal; and a filtering module for recovering the true information as much as possible from the segmented data containing interference.

This invention relates to the field of physical and mental health monitoring technology, specifically to methods for assessing physical and mental health, methods for recommending exercise, and systems for supporting physical and mental growth. When assessing an individual's physical and mental health, this invention considers not only psychological health levels and physical indicators but also the individual's physical activity status. Since physical activity status is objective data, and existing research has shown a close correlation between physical activity and physical and mental health, this invention uses quantifiable and health-related objective data on physical activity status to assess physical and mental health, thereby improving the accuracy of the assessment results. Furthermore, this invention recommends exercise programs based on the assessment results of physical and mental health, ensuring that the recommended exercises can improve the individual's physical and mental health.

The present disclosure provides an object recommendation method, an object recommendation device, an electronic device and a storage medium, which can be applied to the technical field of big data. The object recommendation method comprises: determining M first objective indexes, N second objective indexes and a target variable relationship matched with an application scene, the target variable relationship being used to determine a to-be-recommended object, the first objective indexes being used to represent transaction characteristics of a user, the second objective indexes being used to represent social relationship characteristics of the user, M being greater than or equal to 1, and N being greater than or equal to 1; obtaining a recommendation data set of a target user, the recommendation data set comprising first objective data corresponding to the first objective indexes and second objective data corresponding to the second objective indexes; and determining the to-be-recommended object according to the M first objective data, the N second objective data and the target variable relationship.

The application provides an L3 level automatic driving takeover process safety evaluation method based on IAHP-EWM-LDM, which comprehensively considers the driver visual characteristics, the driver steering behavior and the vehicle running state in the whole takeover process, proposes a takeover process safety representation parameter, screens the parameter through a variation coefficient method and a Spearman correlation discrimination method to obtain an evaluation index, obtains an index subjective weight by using an improved analytic hierarchy process, obtains an index objective weight by using an entropy weight method, and obtains an index comprehensive weight by using a maximum level difference method to combine the subjective weight and the objective weight, so as to propose an index weight obtaining method combining the improved analytic hierarchy process, the entropy weight method and the maximum level difference method. Therefore, a takeover process safety evaluation model is constructed to evaluate the takeover process. Compared with the prior art, the application analyzes the whole process of the driver taking over the vehicle, and increases the index of risk perception. On the basis of analyzing the objective data to obtain information, the subjective expert experience is combined to realize the comprehensive evaluation of the takeover process safety.

This invention relates to the intersection of traditional Chinese medicine modernization and intelligent healthcare, specifically falling under the category of pre-chronic disease screening and health management technology. More particularly, it relates to a method for diagnosing and treating pre-type 2 diabetes based on traditional Chinese medicine state theory, along with electronic devices and computer-readable storage media, constructing a complete, human-machine collaborative closed-loop diagnostic and treatment process. This method achieves objective data collection through portable devices using the four diagnostic methods of traditional Chinese medicine, utilizes a multi-algorithm fusion model to accurately identify the "potential pathological state" of pre-T2DM, and intelligently generates personalized intervention prescriptions based on this. Finally, it quantifies the effects and dynamically optimizes the treatment plan through data re-collection and model re-evaluation. This solves the problems of strong subjectivity in traditional Chinese medicine diagnosis and treatment, disconnection from modern technology, and lack of quantitative feedback and continuous adjustment after intervention. It achieves full-cycle, intelligent, and personalized health management for pre-T2DM, from identification and intervention to follow-up management.

The invention relates to the technical field of clinical medical instruments and operation, and discloses a safe and high-success-rate operation method and system for sternum marrow puncture. The invention provides a closed-loop solution based on multi-modal information fusion and intelligent control in order to solve the pain points that traditional sternum paracentesis highly depends on experience of doctors, the complication risk is high and the success rate is unstable. According to the method, a patient personalized three-dimensional model is constructed through images such as preoperative CT or intraoperative ultrasound, risk early warning is conducted through a machine learning model, and an optimal puncture track is generated through a path planning algorithm. The core innovation of the method is as follows: in the puncture process, real-time image navigation and needle point pressure feedback are dynamically combined, and the subjective'falling sense 'is quantified into objective data for the first time so as to accurately control the depth; a set of intelligent safety braking system is integrated, the puncture needle can be automatically locked when it is monitored that thrust is abnormal, and the fatal risk that the heart and great blood vessels are damaged due to too deep puncture is actively avoided.

The application discloses a food full-link quality deterioration tracing method and system, a medium and a product, and relates to the technical field of food quality tracing.The method is implemented, and a sensory description label in consumer feedback information is extracted by a tracing system; with the aid of a preset food quality knowledge graph, accurate mapping of subjective sensory information and objective physical and chemical indexes is realized, and the problem that subjective feedback and objective data are disconnected in a traditional tracing system is solved.The tracing system carries out dynamic deduction on food full-link monitoring data through a preset quality deterioration dynamics model, and upgrades food quality deterioration from static threshold judgment to dynamic numerical evolution track analysis, so that the cumulative deterioration effect of each circulation link can be quantified.The tracing system screens food quality deterioration responsibility links based on average deterioration rate and link deterioration increment, traces deterioration inducement of the responsibility links in combination with the preset food quality knowledge graph, accurately defines the deterioration responsibility and traces the reasons, and improves the tracing efficiency and accuracy.

The present application relates to the technical field of computer vision and intelligent mine, and particularly relates to a step blasting large block rate control method based on AI visual identification. The content includes: collecting and preprocessing original image data, combining with known size reference, constructing local scale field; based on the local scale field, identifying the blast pile effective area of the preprocessed image, calculating the blast pile effective area probability, and obtaining the region range of the blast pile main body; in the region range of the blast pile main body, combining with the local scale field, constructing the crack separation energy field to obtain the independent block region; combining with the crack separation energy field, calculating the equivalent block degree of the independent block region, and combining with the block space distribution and the blast pile effective area probability, quantitatively evaluating different control strategies. The problems that the traditional method cannot effectively distinguish the adherent block, is easily disturbed by environmental factors such as shadow, lacks a quantitative evaluation mechanism driven by objective data, and is difficult to stably and reliably perform closed-loop optimization control are solved.