8results about How to "Guaranteed objectivity" patented technology

This invention discloses a method and system for dynamically determining the impact of construction projects on hydrological monitoring, relating to the field of hydrological monitoring technology. The specific steps are as follows: constructing an evaluation index system that couples static and dynamic indicators; assigning static weights to static indicators using the analytic hierarchy process (AHP) and dynamic weights to dynamic indicators using the entropy weight method; calculating the total weight based on the static and dynamic weights; constructing and training a machine learning model; inputting the static, dynamic, and total weight data into the trained machine learning model; and outputting the impact level; automatically matching response measures based on the impact level and trend prediction results. This invention proposes a dynamic weight correction algorithm based on project stage and hydrological conditions, applying machine learning algorithms to determine the impact level of construction projects on hydrological monitoring, achieving a closed-loop linkage from level determination to measure matching, and solving the problems of singular and disconnected evaluation in existing technologies.

The application provides a mine intelligent alarm method and system based on multi-dimensional perception information, relates to the technical field of intelligent alarm, extracts abnormal driving criteria corresponding to mine abnormal events from all mine safety state information, associates and judges the abnormal driving criteria with the protection response limit value of the partition where the monitoring terminal is located, obtains the alarm situation index of the region where the mine abnormal event is located, when the abnormal driving criteria trigger the alarm condition, generates dynamic alarm information within the period from the occurrence of the abnormality to the cancellation through the alarm judgment rule, classifies and disposes the dynamic alarm information, obtains the alarm marker value corresponding to each abnormality level, determines the risk warning level in the mine according to the alarm situation index and all alarm marker values, and performs linkage alarm according to the risk warning level. The application can construct a full-link collaborative alarm mechanism for global linkage disposal of mine abnormal events, so as to improve the accuracy of mine underground safety risk prevention and control.

The invention discloses a coal reservoir permeability test optimization method and system, and the method comprises the steps: carrying out the clustering analysis of coal samples of different coal orders through a K-means clustering and random forest algorithm, and constructing a permeability test database containing the permeability of different coal orders, the fracture pressure, the pore characteristics and other parameters; on the basis of a CT scanning result of the coal sample, physical characteristics such as a pore structure and fracture distribution of the lump coal are analyzed, a representative position is selected for sample preparation, and a fracture pressure interval of the sample is pre-judged according to a database; intelligently adjusting a pressurization strategy according to the pre-judgment interval, and switching to slow gradient pressurization when the pressure is close to the fracture pressure of the coal sample; and according to the measured data points, abnormal points and invalid data points in a fracture area are automatically identified and eliminated based on a random forest algorithm, representative data points are optimally selected in a permeability test stable section and a characteristic inflection point interval, and finally data constraint and fitting correction are performed through a P50 probability method. The method can improve the efficiency and accuracy of the laboratory coal permeability test.

This invention discloses an improved INS-MARCOS-based method for assessing the construction safety risks of water conservancy projects, comprising the following steps: First, constructing a construction safety risk assessment index system; second, evaluating expert qualifications (BWM method) and extracting the information entropy of the expert semantic evaluation matrix to verify the scoring validity (AEWM method), generating expert combination weights through combined weighting; subsequently, converting the qualitative evaluations of multiple experts into interval intelligence numbers (INS), and using the interval intelligence weighted average operator (INWA) with expert weights as the index to aggregate multi-source risk information; finally, introducing an improved MARCOS model to construct dual benchmarks of absolute safety and accident limits, calculating the comprehensive utility measure value of each risk indicator and arranging them in descending order of hazard. This invention effectively overcomes the problems of uneven distribution of expert discourse power and information ambiguity in group decision-making, realizing the identification of core risk indicators at water conservancy construction sites, and providing reliable quantitative support for disaster prevention decision-making.

The application discloses a martial arts free combat action recognition training method and system based on deep vision, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: acquiring time sequence three-dimensional skeleton coordinate data sequence of a trainer; generating a starting trend data stream and a trajectory continuity data stream through parallel calculation, performing correlation analysis to generate a behavior starting judgment data stream, and performing joint detection of direction and speed on the trajectory continuity data stream to generate an action interruption data stream; based on logical operation of the behavior starting and action interruption data streams, a behavior authenticity judgment data stream is generated; according to a real attack judgment result, an attack end frame is positioned, posture structure features are extracted as end posture feature data, and a posture reconstruction time length from the attack end to the recovery of defense is calculated to input a pre-trained behavior stage judgment model to output a continuous attack and defense behavior stage data stream. The application realizes automatic and refined recognition of tactical intention and continuous attack and defense stages in martial arts free combat confrontation.

This invention relates to the field of electric drive system design and development for new energy vehicles, specifically to a method for testing and analyzing the dynamic stiffness of electric drive assembly mounts and the accuracy of simulation. The method involves standardized frequency response testing of the electric drive assembly mounts, identification of minimum dynamic stiffness, and the construction of a dynamic simulation model consistent with the test boundaries to extract the minimum dynamic stiffness on the simulation side. Then, it sequentially performs frequency and amplitude error analysis of the minimum dynamic stiffness points in the tests and simulations, as well as average dynamic stiffness error analysis. Finally, it comprehensively calculates accuracy performance coefficients based on multi-dimensional error coefficients and establishes a scoring system to achieve a quantitative evaluation of the test and simulation accuracy. This method solves the technical problems in existing technologies where evaluating the dynamic stiffness of electric drive assembly mounts requires multiple physical impact tests, which are resource-intensive, time-consuming, and lack a systematic and quantitative analysis method for testing and quantifying the accuracy of mount dynamic stiffness, thus failing to utilize the preliminary analysis results to guide subsequent mount design and development.

This invention relates to the field of data processing technology for industrial equipment condition monitoring, and discloses a data quality monitoring and storage system based on equipment electrical condition assessment. The system includes: a physical correlation verification engine that uses a baseline library linking electrical condition and vibration characteristics to assess and label the diagnostic effectiveness of vibration data streams; and a baseline adaptive calibration module that can dynamically and adaptively calibrate the baseline library based on high-reliability data output by the engine itself. This invention solves the problem of long-term reliability decay caused by cognitive aging in static diagnostic models by constructing a self-evolving closed loop for the core knowledge base of the diagnostic system, enabling it to iterate synchronously with the long-term drift of equipment physical characteristics. This ensures the continuous accuracy of data quality assessment throughout the entire equipment lifecycle.

The application discloses a substation inspection image-oriented equipment identification and quality grade joint evaluation method, which comprises inputting collected original substation images into an image identification and quality grade joint evaluation model, wherein the model comprises a detection perception image enhancement module, a target detection module, an ROI extraction module and an image quality evaluation module connected in sequence; the original substation images are subjected to enhancement processing by using the detection perception image enhancement module to obtain enhanced images; the enhanced images are subjected to target detection by using the target detection module to obtain power equipment category labels and ROI frame coordinate information; the corresponding equipment ROI images are obtained by cutting the original substation images according to the ROI frame coordinate information by using the ROI extraction module; and the equipment ROI images are subjected to no-reference image quality evaluation by using the image quality evaluation module to obtain quality grade labels; and the application realizes the improvement of equipment identification accuracy and the reliable output of image quality grades.