10results about How to "Clear guidance" patented technology

The invention relates to the technical field of electrical operation safety management, in particular to an artificial intelligence-based electrical operation misoperation prediction and correction system, which comprises a data acquisition module, a data processing module, a misoperation prediction module, a correction suggestion generation module, a real-time monitoring and feedback module, a self-learning module and a data storage module, operation data and environment data are collected in real time through a sensor network, the data are preprocessed, feature vectors are generated, a misoperation prediction model is constructed based on a long short-term memory network algorithm, the misoperation occurrence probability is predicted, a decision tree algorithm is adopted to generate correction suggestions, and prediction results and the correction suggestions are presented through a human-computer interaction interface. The self-learning module is used for dynamically optimizing the prediction model and the suggestion generation model; the data storage module is used for classifying and storing the operation data, the environment data and the prediction result; according to the invention, the misoperation is accurately predicted in real time, the intelligent correction suggestion is generated, and the safety and efficiency of electrical operation are remarkably improved.

The invention discloses a product full life cycle carbon emission tracking method, which is embedded in a product carbon footprint management service platform, and specifically comprises the following steps: S1, formulating a product definition boundary and a truncation standard based on a carbon emission calculation formula; s2, data acquisition and accounting are completed according to local conditions, and real-time monitoring and data acquisition are carried out on all links in the product production process. According to the method, the total emission amount of greenhouse gases directly or indirectly generated by a product in the whole life cycle of the product is calculated, including the emission in the extraction, production, transportation, use and waste processes of raw materials, so that the calculation and result traceability of the carbon footprint in the whole life cycle of the product are realized. Accurate carbon emission data are provided for enterprises, the green level of the production process and technical equipment is improved, and the transformation of the enterprises and the society to low-carbon economy is promoted.

The invention provides a method for judging the inactivation temperature and the inactivation characteristic of a hydroformylation catalyst. The method comprises the following steps: S1, preparing a hydroformylation reaction solution; s2, measuring the reaction liquid, filling the reaction liquid into an adiabatic test instrument to simulate a hydroformylation reaction, and when the temperature is increased to T0, stopping heating and carrying out an adiabatic reaction; recording the temperature change, and obtaining the highest temperature Tm; s3, cooling the test ball, taking out half of a reaction material, adding a hydroformylation reaction raw material, heating to simulate a hydroformylation reaction, stopping heating when the temperature is increased to T1, carrying out an adiabatic reaction, and recording the highest temperature Tmf; s4, comparing T1, Tm and Tmf; if Tmf is equal to T1, the inactivation temperature of the catalyst is Tm, and irreversible inactivation is carried out; if Tmf is equal to Tm, the inactivation temperature of the catalyst is Tm, and reversible inactivation is carried out; if Tmfgt; and Tm, taking out the material in the test ball, and repeating the steps S2-S4. The judgment method disclosed by the invention is short in test period and high in safety, does not need separation and component analysis on a test sample, and has guiding significance on catalyst operation upper limit and homogeneous catalyst recovery temperature selection research.

The invention discloses a coal mine fault control degree evaluation method and system, and relates to the technical field of fault identification reliability evaluation. The method comprises the following steps: fusing a mining area feature set with a label set, and establishing a training set and a to-be-predicted set; constructing a support vector machine dichotomy model and training the support vector machine dichotomy model; inputting the test set into the trained model, outputting a posterior probability value P1 of each sample point, and determining a dynamic threshold according to the posterior probability value; mapping the posterior probability into the fault point category by using the dynamically determined threshold value, and evaluating the quality of the fault point; and evaluating the fault risk grade of an area with densely distributed fault points in the mining area through the proportions of the breakpoints with different qualities. According to the method, objectivity and high efficiency of AI can be maintained, and reliability quantitative evaluation capability of artificial experience can be recovered and even exceeded.

This invention provides a method for virtual digital lofting and real-time calibration of installation pose of a guide rail, comprising: providing at least two positioning fixtures, each fixture having three collinear optical markers symmetrically arranged on both sides relative to the center marker; fixing the two positioning fixtures to the upper and lower ends of the guide rail respectively, so that the positioning fixtures are in close contact with the two working surfaces of the guide rail; acquiring the global coordinates of all optical markers in real time through a motion capture camera array, and independently calculating the six degrees of freedom pose deviation of the guide rail based on a coordinate comparison scheme corresponding to the layout of the positioning fixtures: calculating the horizontal translation deviation of the upper end of the guide rail; calculating the tilt angle deviation of the guide rail in the vertical plane; calculating the rotation angle deviation of the guide rail about its own length axis by using the coordinate difference of the two markers on both sides of the same positioning fixture on a single coordinate axis; and making targeted adjustments to the installation state of the guide rail according to the calculated pose deviations of each degree of freedom, thereby completing real-time calibration.

The invention discloses a parallel tailgating steering traffic guidance method, system, device and equipment, which are used for improving the traffic efficiency of tailgating steering. The method comprises the following steps: acquiring a tailgating steering signal state; according to the tailgating steering signal state, guiding equipment corresponding to the opened first tailgating steering preset parking space is controlled, and the use state of the opened first tailgating steering preset parking space is guided; the first tailgating steering preset parking space is arranged on a steering lane adjacent to the tailgating steering lane, and the first tailgating steering preset parking space is communicated with the tailgating steering lane; the position or size of the first tailgating steering preset parking space is determined according to the vehicle steering motion space.

The present invention discloses a design method for a business system virtual conference assistant based on artificial intelligence, which relates to the field of artificial intelligence technology, including: inputting user voice information into a conference system in real time, converting the voice information into text and performing fuzzy matching processing; building a large language model, using the processed information as the first input of the large language model; reasoning the first output of the large language model and integrating it as the second input of the large language model; the virtual assistant operates according to the second output of the large language model and executes corresponding system instructions. This method aims to solve technical problems such as insufficient multi-step reasoning capabilities, imperfect real-time data integration, and inaccurate personalized strategy recommendations in the current emergency response system.

The application discloses a nuclear power plant safety function success channel implementation method and device, computer equipment and a storage medium, and the method comprises the following steps: according to the overall design criteria and the functional structure design features of the nuclear power plant, a hierarchical function coupling structure model of the target tree-success tree-state tree of the nuclear power plant is established; further, according to the key safety parameter supervision feedback and the alarm grading system, the complete or damaged state of the safety function state of the nuclear power plant is determined, the corresponding emergency response task target is formulated, the key safety function relief and recovery structured success channel is generated through the hierarchical function task association structure model deduction analysis, the safety level division of the safety function implementation front system is referred to, the safety function success channel set optimization sorting and visual operation instruction are realized. The method provided by the application can quickly guide the operator to perform safety function recovery and relief, and reduce the consequence harm influence of the accident.

The invention relates to the field of unmanned system cooperative control and intelligent decision making, in particular to a double-layer priority playback and self-supervision-assisted unmanned ship cooperative hunting method, which comprises the following steps of: constructing a sea area environment model for cooperative hunting of multiple unmanned ships; establishing a cooperative control strategy network, wherein the cooperative control strategy network comprises a centralized training stage and a distributed execution stage; a double-layer priority experience playback mechanism is introduced in the centralized training stage; a self-supervision auxiliary task module is introduced into the cooperative control strategy network; the method comprises the following steps: constructing a multi-dimensional reward function, carrying out comprehensive evaluation on a target approaching behavior, a cooperative reward state and a safety constraint of a reward unmanned ship, defining a reward success condition, calculating a reward result, outputting a control instruction of each reward unmanned ship according to the reward result, and carrying out cooperative reward on a target unmanned ship. According to the invention, through double-layer priority experience playback and self-supervision auxiliary tasks, the cooperative hunting efficiency, learning stability and strategy adaptability of multiple unmanned ships in a complex sea area are significantly improved.

The present invention discloses an order optimization allocation method based on the reliability evaluation of riders on an intelligent food delivery platform, including: S1: constructing a multi-objective food delivery order optimization allocation model with the goals of minimizing the total delivery cost of the food delivery platform, maximizing the rider reliability, and maximizing the customer satisfaction; S2: based on the linear weighted sum method, integrating the multi-objective food delivery order optimization allocation model to obtain a single objective function model; S3: constructing an order optimization allocation strategy based on an adaptive large neighborhood search algorithm combined with a simulated annealing mechanism; and obtaining an order optimization allocation scheme that optimizes the single objective function model based on the order optimization allocation strategy. The present invention solves the problem that the traditional manual allocation method is not only inefficient, but also difficult to ensure the optimality of order allocation, especially in the face of uneven rider reliability and complex and changeable delivery environment, and cannot effectively optimize order allocation.