12results about How to "Overcome one-sidedness" patented technology

The invention relates to the technical field of agricultural information and seed and seedling cultivation, and discloses a peanut planting method for relieving continuous cropping obstacles of peanuts. The method comprises the following steps: constructing initial digital twins of a land parcel, and synchronously constructing a disease-resistant peanut variety screening and seedling pretreatment system; historical continuous cropping information, soil physicochemical and microorganism data, meteorological time sequence data and seedling cultivation parameters are fused, the system is driven to evolve into operation state digital twins, and seedling-soil-environment collaborative simulation is achieved. Evaluating the continuous cropping obstacle risk and the seedling adaptability based on the twinborn body, and generating a comprehensive diagnosis report containing a nutrient imbalance index, pathogenic bacteria abundance prediction, a root exudates cumulative effect and a seedling disease-resistant adaptation coefficient; according to the method, differentiated seed and seedling optimization, soil improvement and cultivation strategies are implemented, and management measures are dynamically optimized through real-time data feedback in a growth cycle. Through cooperative driving of digital twinning and seed and seedling cultivation, source prevention, control and intervention of continuous cropping obstacles are achieved.

The application provides a big data-based intelligent manufacturing supply chain management system and method. A cloud big data engine is used to jointly model supplier capacity, logistics timeliness and historical abnormal events based on multi-source data flow of a manufacturing supply chain, and generate a risk probability matrix of material shortage in multiple supply chain nodes. When the risk probability of material shortage of a supply chain node exceeds a material shortage threshold, a risk decision is made on the risk probability matrix and the work-in-process cache amount as a joint state space, with the goal of minimizing total interruption cost and maximizing delivery rate, and a material shortage scheduling scheme of the manufacturing supply chain is output. The material shortage scheduling scheme is distributed to corresponding suppliers and logistics parties through a blockchain smart contract, and order splitting and payment binding are automatically executed to complete the material shortage scheduling of the supply chain. Based on the above scheme, multi-objective autonomous scheduling of material shortage risk of the manufacturing supply chain can be realized, thereby improving the anti-disturbance capability of the manufacturing supply chain under interruption environment.

This invention discloses a method and system for alleviating livestock and poultry stress based on olfactory-tactile synergy, belonging to the field of smart farming technology. The method includes: collecting livestock and poultry behavior, sound, physiological, and thermal imaging data through multimodal sensors to extract multidimensional feature vectors; calculating a comprehensive stress score and determining the stress level using a multivariate state estimation model; generating an olfactory-tactile synergistic intervention strategy based on a large language model combined with current state and historical data; evaluating the effect in real time after intervention using a temporal deep learning model, and dynamically adjusting the strategy based on feedback until stress is alleviated. This invention solves the problems of existing stress intervention methods being singular and having limited effectiveness, achieving multimodal stress identification, intelligent collaborative decision-making, and closed-loop adaptive intervention, significantly improving the accuracy, timeliness, and animal welfare level of stress relief.

The application provides a highway slope meteorological disaster early warning model training method, an early warning method and a system, positive and negative samples are respectively constructed based on historical disaster record and non-disaster record; based on sample slope position and collection time, time series data of multiple meteorological elements before disaster is extracted from meteorological grid point data through spatial interpolation matching to generate meteorological statistical characteristics; initial training sample set is constructed by fusing slope static attributes and meteorological statistical characteristics, adding disaster type and risk level labels; the initial model is pre-trained based on the sample set, the feature importance is evaluated through multi-index, and the feature subset is formed by screening; the features outside the subset are removed from the initial training sample set to form the target training sample set, and multiple candidate models are trained based on this, and the target disaster type recognition model and the target risk level recognition model are determined according to the evaluation index. Through multi-source feature fusion, automatic feature screening and multi-model optimization, the accuracy and reliability of the early warning are improved.

The invention provides a capability assessment method and device for group discussion participants, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining text data of group discussion; inputting the text data into a pre-trained personal general ability assessment model and a pre-trained team leader assessment model, and outputting a personal general ability assessment result and a team leader assessment result; wherein the personal universal ability evaluation model is a large language model which is trained by performing supervision and fine tuning through a training data set comprising an information node, a decision node and a utility node and combining a low-rank self-adaptive method; the team leader ability evaluation model is used for evaluating the thinking excitation ability, conflict coordination ability and discussion field control ability of the participants based on an event sequence containing participant identifiers, speaking keywords and speaking timestamps. Through the method provided by the invention, the personal general ability can be effectively evaluated, and the team leader ability can be quantitatively evaluated.

The present application relates to the technical field of industrial automation and artificial intelligence, and particularly relates to a work behavior intelligent identification and guidance method and system. The method comprises: collecting multi-modal perception information of a work environment; identifying a current work behavior of an operator based on the multi-modal perception information; performing matching analysis on the current work behavior and a predefined target work behavior sequence to generate behavior difference information containing time sequence and quality deviation; and generating and outputting guidance instructions for guiding the operator based on the behavior difference information, the instructions being capable of controlling action parameters of an auxiliary execution device or output rhythm of interface prompts. Through multi-modal fusion perception, fine behavior difference analysis, dynamic personalized guidance and adaptive learning, the present application realizes precise, intelligent and flexible real-time guidance and collaborative control of the work process, effectively improving work quality, efficiency and human-machine collaboration level.

The invention discloses a pavement engineering construction data processing and construction quality control method and system, and belongs to the technical field of pavement engineering construction. The method comprises the following steps: collecting multi-source construction data in real time through sensors arranged on materials, equipment and a site; predicting a future trend value of each quality influence factor by using a gray model; determining the comprehensive weight of each factor by adopting an analytic hierarchy process; the correlation degree between the factors is calculated through a grey correlation analysis method; constructing a multi-level grey comprehensive evaluation system based on the predicted value, the weight and the correlation degree; according to the prediction result, generating a plurality of construction parameter combination schemes in a preset step length to form a candidate scheme set; sorting the candidate schemes by adopting the comprehensive evaluation system, and selecting an optimal construction scheme; and finally, the parameters of the optimal scheme are issued to corresponding construction equipment through a control interface, the equipment operation parameters are dynamically adjusted, and real-time closed-loop control over the construction quality is achieved. According to the invention, accurate, dynamic and intelligent control of the construction process is realized.

The invention relates to the technical field of session data analysis, and discloses a time axis fusion analysis-based multi-modal session data analysis method and system, and the method comprises the steps: obtaining multi-modal session data, and enabling each modal session data in the multi-modal session data to have a corresponding modal session time axis; according to a preset anti-interference fitting algorithm, all modal session time axes are calibrated, and a unified wall clock time axis of the multi-modal session data is generated; identifying key information nodes in the multi-modal session data based on a unified wall clock time axis, and calculating an emotion vector corresponding to each moment in the multi-modal session data to obtain an emotion intensity curve of the multi-modal session data; and according to all the key information nodes and the emotional intensity curve, generating an interactive emotional map of the multi-modal session data. Therefore, by implementing the multi-modal session data analysis method, the traceability of the analysis result can be improved while the analysis accuracy and objectivity of the multi-modal session data are improved.

The invention discloses a transaction data automatic labeling method and system based on multi-modal learning, and relates to the technical field of data processing. The method comprises the steps of obtaining target transaction data containing transaction text data and transaction behavior time sequence data, and performing vectorization mapping on the target transaction data to obtain a first modal feature vector and a second modal feature vector; inputting the two groups of feature vectors into a multi-modal labeling model, and generating an initial prediction label through feature fusion; calculating the contribution degree weight of each modal feature vector to determine a dominant feature vector, and generating a feature suppression mask to perform shielding processing on the dominant feature vector to obtain a disturbance feature vector; inputting the disturbance feature vector and other unshielded modal feature vectors into the model again to execute secondary reasoning, and obtaining a verification prediction label; and calculating a semantic offset degree between the initial prediction label and the verification prediction label, calibrating the confidence degree according to the semantic offset degree, and outputting a final labeling result. According to the invention, the robustness and accuracy of transaction data labeling are improved.

The invention relates to the field of safety, and discloses a coal seam working face long and short hole multi-dimensional hydraulic fracturing effect analysis and evaluation method, which comprises the following steps: firstly, constructing a space detection matrix consisting of long drill holes and short drill holes, and acquiring a background fracture image and an initial water outlet state before fracturing by utilizing a panoramic peeping system; when hydraulic fracturing is implemented, pressure flow time sequence data of fracturing holes and water outlet response time of adjacent holes are synchronously collected; after fracturing, scanning again to obtain a fracture image, and constructing a multi-source database containing spatio-temporal information; based on the time difference between the sudden pressure drop point and the water outlet response time, the crack propagation apparent velocity is calculated in combination with the spatial distance, and the dominant direction is determined; and finally, selecting drill holes in a dominant direction to compare fracture forms, fusing an image analysis item and a hydraulic response item to calculate a fracture network development index, and evaluating a fracture grade. According to the method, the quantitative characterization of the crack propagation dynamic characteristics is realized, the subjectivity problem of a single evaluation means is solved, and the scientificity of an evaluation result is improved.

The invention relates to the technical field of civil engineering, and discloses a digital twinborn modeling and risk prevention method for infrastructure tunnel construction, which comprises the following steps: dynamically fusing real-time physical field data acquired by a multi-modal sensing network into a three-dimensional numerical model through a data assimilation method to generate a four-dimensional digital twinborn body synchronously evolved with a physical entity; extracting risk precursor characteristic indexes such as stress concentration and damage localization from the four-dimensional digital twinborn body, constructing a comprehensive risk index and performing dynamic evaluation; and when the risk state exceeds a preset threshold value, performing deduction based on the four-dimensional digital twinborn body. The problem that traditional risk management lags behind and depends on experience is solved, accurate sensing of the surrounding rock state, advanced recognition of risks and closed-loop adaptive optimization of construction parameters are achieved by constructing a real-time synchronous dynamic model, and the safety and the intelligent level of tunnel construction are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle position prediction method and device, equipment and a storage medium, and the method comprises the steps: continuously obtaining the state data of a first unmanned aerial vehicle, and enabling the state data to comprise the position and the flight speed of the first unmanned aerial vehicle; according to historical state data of the first unmanned aerial vehicle, the flight mode of the first unmanned aerial vehicle is identified, and the historical state data is state data obtained at historical moments; according to the historical state data, the flight mode and the current state data, the position of the first unmanned aerial vehicle at the first moment is predicted, the predicted position is obtained, the current state data is the state data obtained at the current moment, and the first moment is the future moment away from the current moment by the preset duration delta t. In this way, the position of the unmanned aerial vehicle can be accurately predicted.