6results about How to "Improve judgment efficiency" 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 discloses a power electronic distribution network transformer propagation model optimal adaptation method and system, and the method comprises the following steps: calculating transformer winding leakage inductance and winding resistance, and transformer excitation inductance and excitation resistance; estimating the equivalent value of each link distributed capacitance; deducing the propagation coefficient of the high-frequency transformer model and the low-frequency transformer model at any frequency under the excitation source; calculating the relative deviation amount of the corresponding propagation coefficient of the high-frequency transformer model and the low-frequency transformer model at different frequencies in the required frequency band of the simulation scene; obtaining the maximum value of the relative deviation amount of the propagation coefficient under all conditions in the required frequency band of the simulation scene; when all the maximum values of the relative deviation amount of the propagation coefficient are less than the set relative deviation limit value, the low-frequency transformer model is selected for the simulation scene, otherwise the high-frequency transformer model is selected; the application has the advantages that the optimal selection of the transformer model is performed under the consideration of the simulation efficiency and accuracy requirements.

This invention relates to a method for determining soil compaction degree using balers based on real-time machine parameters. Specifically, it relates to a method for determining soil compaction degree in the agricultural field. The purpose of this invention is to address the problems of existing soil compaction degree determination methods, which rely on laboratory testing, consider only a single parameter, have low accuracy, and poor real-time performance. The process is as follows: Acquire the real-time driving speed of the tractor, the tractor's own weight, the tractor's load weight, the tractor's tire pressure, the number of tractor trips, the mass of straw collected per unit time, and the baler's straw compaction density; obtain standardized real-time driving speed of the tractor, standardized tractor's own weight, standardized tractor's load weight, standardized tractor's tire pressure, standardized number of tractor trips, standardized mass of straw collected per unit time, and standardized baler's straw compaction density; calculate the soil compaction degree using a matrix method; and classify the soil compaction degree into grades.

The application discloses a large model protection method and related device, and relates to the field of computers, which comprises the following steps: firstly, the attack risk of a business request is determined by using a ban list, keyword matching and a pre-configured large model; then, whether the request is an attack request is determined by combining user credibility when the request is determined as a high-risk request; when the user credibility is insufficient and the request is determined as an attack request, the business request is intercepted, so that the large model built in a business system does not process the request. The application improves the determination efficiency under the premise of ensuring the determination accuracy by using the risk determination mode of ban list, keyword and large model in sequence; the misjudgment rate of attacks is further reduced by combining the user credibility, and finally, the large model security protection task with high accuracy, low misjudgment rate and high real-time performance is realized.

The application discloses a method and device for mining data relationship of a point of interest, an equipment and a medium, and relates to the technical field of geographic information. The method comprises the following steps: acquiring position information of a point of interest; determining a road loop matched with the point of interest based on the position information; the road loop is a closed loop surrounded by external roads in road network data, and there is no road chain in the internal part of the road loop to divide the road loop into two parts; the road chain is composed of one external road or connected by two or more external roads; the external road is a road located outside the point of interest; determining a search range of the point of interest based on the road loop; and mining data relationship of the point of interest based on the search range. The application has high efficiency and recall rate when performing data mining on the point of interest.

This invention belongs to the field of power line fault detection technology and discloses a method, system, equipment, and medium for diagnosing power distribution line faults. The method includes: acquiring initial fault signal data corresponding to the power distribution line, preprocessing the initial fault signal data, and outputting fault signal data; decomposing the fault signal data using joint decomposition technology to obtain intrinsic mode components (IMCs); introducing information mining technology to extract the distribution change feature vector of the fault signal and fusing it with the IMCs to obtain a change image; extracting local features of the change image through a convolutional neural network and combining it with a strategy function optimized by a Huffman tree for fault classification, and outputting the fault diagnosis result. This invention applies the Softmax strategy of Huffman trees, improving the efficiency and accuracy of judgment, enabling early identification and warning of fault signals in power distribution lines, and improving the efficiency of power distribution line fault diagnosis.