16results about How to "Avoid subjective bias" patented technology

The invention relates to the technical field of nucleic acid detection, and discloses a method and device for detecting and distinguishing radix trichosanthis, poria cocos and radix puerariae and terminal device.The method comprises the steps that genome DNA of a to-be-detected sample is extracted, a specific DNA fragment is amplified through a universal amplification primer and a specific amplification primer, and an amplification product is obtained; the method comprises the following steps: respectively constructing independent detection modules aiming at radix trichosanthis, poria cocos and radix puerariae, wherein each independent detection module comprises Cas12a protein, a test probe and specific crRNA; the specific crRNA is obtained by screening according to a PAM sequence and a detection contrast ratio. According to the invention, genome DNA of a to-be-detected sample is extracted, a universal amplification primer and a specific amplification primer are utilized, then an amplification product is added into an independent detection module, and specific crRNA is utilized to carry out specific recognition on the amplification product, so that a molecular detection system aiming at radix trichosanthis, poria cocos and radix puerariae is established, and effective distinguishing of different traditional Chinese medicinal material components is realized.

The invention belongs to the technical field of power grid disaster prevention and reduction, and particularly relates to a risk assessment method and device for deicing jump of a power transmission line, electronic equipment and a computer readable storage medium. The method comprises the following steps: determining risk influence factors influencing the deicing jump of the power transmission line, and constructing an original parameter data set containing a plurality of deicing working conditions; establishing a conductor deicing model under a plurality of deicing working conditions and carrying out analogue simulation to obtain a risk consequence index; constructing a standardized decision matrix, respectively calculating the comparison intensity and conflict of each risk influence factor, and calculating the comprehensive information amount and objective weight of each risk influence factor to obtain an objective weight vector; and identifying the key dominant factors based on the objective weight vector and carrying out working condition optimization in combination with the risk consequence indexes under each deicing working condition to obtain the optimal deicing working condition with the lowest comprehensive risk. According to the method, the CRITIC method is applied to power transmission line deicing jump risk assessment, and the deicing working condition with small risk can be selected.

The application provides a detection method for the anti-pulverization performance of white corundum, and belongs to the technical field of material performance detection. The method comprises seven logically progressive steps, i.e. sample standardization preparation, multi-mode working condition simulation loading, multi-layer screening detection after pulverization, multi-dimensional pulverization characteristic extraction, stress pulverization mathematical model construction based on an exponential-power law mixed model, and comprehensive evaluation based on a sensitivity weighting method. The exponential-power law mixed model accurately depicts the exponential growth characteristics of the pulverization rate near the critical stress, and the sensitivity weighting method makes the determination of the weight coefficient have a clear physical meaning and objectivity, thereby avoiding subjective deviation caused by artificial weighting. Compared with the prior art, the application can more scientifically, comprehensively and objectively evaluate the anti-pulverization performance of white corundum, and provides a reliable basis for quality control and application selection of the white corundum.

The invention relates to a multi-level community identification method with a priority core node and a similar structure in a population flow network, and belongs to the technical field of community detection and complex network processing. The method comprises the following steps: quantizing the significance of a flow edge weight through an exceeding probability, and repeatedly extracting core node pairs to combine and form a microscopic community; identifying a core node by adopting a C-index centrality measurement index, and performing label alignment on the microscopic community and the core node; based on the node destination travel distribution sequence, calculating structural similarity by using a dynamic programming algorithm; and carrying out label propagation by combining with structure similar neighbors to generate a final macroscopic community. According to the method, the diversity of flow directions, the relative importance of flow edges and the distribution characteristics of travel structures in a flow network are comprehensively considered. Compared with a traditional algorithm, the method can more accurately describe the central distribution and the multi-layer structure of the population flow network, and can provide a scientific basis for urban agglomeration evolution analysis, traffic planning and sustainable city development.

This application relates to the technical field of environmental monitoring and data analysis, and particularly to a screening method for priority-controlled OPEs based on surface water concentration data. According to the organophosphate ester flame retardants (OPEs) exposure database, the monitoring data of each OPE compound within a specified time period is obtained. For each OPE compound, based on the monitoring data, the quantitative data of environmental persistence, bioaccumulation, and toxicity effects are respectively obtained, and the exposure potential index and hazard potential index are obtained through principal component analysis; the exposure potential index and hazard potential index are processed to obtain the priority control index; based on the priority control index, sorting is carried out to screen OPE compounds that meet the preset conditions, and the screening results are determined as the priority control targets. This application can simultaneously identify key pollutants with high exposure risks and high hazard risks, reveal the main risk drivers of different OPEs, and effectively support the screening and priority control decision-making of emerging pollutants.

The invention discloses a snake wound classification and recognition model construction method based on serum marker electrochemical detection and deep learning, and belongs to the technical field of biosensing and intelligent diagnosis. The method comprises the following steps: firstly, screening based on proteomics to obtain multiple serum protein biomarkers related to snake bite types, constructing a multi-electrode electrochemical sensing platform, and collecting electrochemical signals through cyclic voltammetry (CV) to obtain electrochemical fingerprint characteristics of a serum sample; and after the obtained signal is subjected to normalization and data enhancement processing, the signal is input into a one-dimensional convolutional neural network (1D-CNN) model for feature learning and classification identification. Through combination of multi-index detection of biomarkers and an end-to-end deep learning algorithm, the method realizes rapid and accurate identification of three different snake wound types of agkistrodon acutus, cobra and trimeresurus remeresurus. The invention constructs a high-sensitivity and rapid diagnosis method for snake wounds, and the method has good popularization and clinical transformation potential.

The invention relates to the technical field of image recognition, and discloses a multi-modal AI image recognition method and system applied to arrival goods acceptance of power equipment, and the method comprises the steps: obtaining multi-modal data of target equipment, and carrying out the standardization of the multi-modal data, and obtaining the standardized data; according to a preset acceptance standard, abnormal contours and thermal anomaly features in the data are extracted; performing cross-modal correlation analysis on the two features to obtain composite defect features; the composite defect feature confidence is evaluated, and a high-confidence evaluation report is generated; and combining acceptance standard matching report compliance to form a structured acceptance report. According to the scheme, the acceptance accuracy and compliance are improved, and the safety risk is reduced. According to the scheme, the problems of incomplete single-mode detection, unassociated multi-mode data, no confidence evaluation of results, difficulty in compliance matching of reports, easiness in missing judgment and existence of potential safety hazards in existing acceptance check are solved.

This invention provides a method and system for predicting the risk of aortic remodeling after aortic dissection, comprising the following steps: receiving CTA images of the patient's chest and abdomen at multiple time points, such as preoperative and short or mid-operative postoperatively, and obtaining aortic segmentation results based on the CTA images; obtaining the displacement field of each voxel point between the two time points based on the aortic segmentation results, and obtaining vascular deformation mapping results based on the displacement field; extracting multi-dimensional features from the three-dimensional aortic segmentation model and the vascular deformation mapping results respectively; and predicting the risk of adverse aortic remodeling after surgery based on the multi-dimensional features. The beneficial effects of this invention are: by comprehensively utilizing multi-dimensional, dynamic, and static features, the prediction model constructed by this invention theoretically has higher discrimination and calibration than existing single-index models. Therefore, this invention can detect early, local abnormal deformations that cannot be detected by traditional diameter measurement, significantly advancing the risk identification time point.

The invention discloses a coastal zone ecological environment quality high-precision remote sensing evaluation method based on ecological subareas, and relates to the technical field of ecological environment remote sensing, and the method comprises the following steps: dividing a research area into three ecological areas of land, wetland and water body based on multi-source land utilization and cover data; constructing a remote sensing index system of partition adaptability based on a partition adaptation strategy; and forming an ecological environment quality map covering a complete coastal zone based on PCA and a spatial integration strategy. According to the coastal zone ecological environment quality high-precision remote sensing evaluation method based on the ecological partition, evaluation blind areas and misjudgment caused by unified modeling of traditional remote sensing ecological indexes can be effectively solved; the stable representation capability, the stress response capability and the ecological interpretation capability of spatial differentiation of the ecological environment quality of the coastal zone compound ecological system are improved, misjudgment is reduced, and a more reliable tool is provided for coastal zone ecological environment monitoring and management work.

The present application relates to the technical field of elevator image processing, and in particular to an elevator image text generation method, system, device and medium based on semantic recognition and large image segmentation model, the method comprising: obtaining an original image of an elevator detection scene and preprocessing; processing the preprocessed original image based on a pre-trained semantic segmentation model to generate a segmentation mask image of elevator components; inputting the original image and the segmentation mask image into an image encoder respectively for feature extraction, and fusing the extracted features to generate cross-modal fusion features; based on the cross-modal fusion features, using a text generation model to analyze elevator component correlation information to generate an initial text description; using a multi-source calibration mechanism to optimize the initial text description to generate a standardized elevator state document containing structured metadata. The purpose is to realize automatic semantic analysis and structured report generation of elevator images.

The application discloses an eight-station parallel resistance calibration method and device based on a high-precision resistance network, and relates to the technical field of intelligent sensors.The application comprises the following steps: acquiring resistance range configuration information of each handheld digital multimeter, determining standard resistance values corresponding to each range, and placing the multimeters in eight calibration stations; synchronously outputting the standard resistance values corresponding to each station to each calibration station through a high-precision resistance network; reading resistance measurement values displayed by each multimeter through an image sensor; determining error correction coefficients corresponding to each station through data expansion and comprehensive evaluation according to historical calibration data, correcting the resistance measurement values of each station, comparing the corrected measurement values with the corresponding standard resistance values, and determining resistance calibration results of each multimeter according to the comparison results.The application solves the technical problems that the existing resistance calibration method for handheld digital multimeters relies on manual operation, which leads to low efficiency, and each station cannot be synchronously and parallelly calibrated, which leads to poor accuracy of the calibration results.

The present application relates to the technical field of remote sensing image processing, and particularly relates to a remote sensing identification method for gordon euryale seed traditional Chinese medicinal materials based on deep learning, which comprises the steps of data acquisition, feature extraction, construction of an optimized feature set, construction of a convolutional neural network model and model training. The method can distinguish crops with high spectral similarity with high precision, and is fully automated. No manual intervention is required in the processes of phenological feature extraction, feature optimization and intelligent interpretation, subjective bias caused by manual experience is avoided, the overall processing efficiency is improved compared with the manual auxiliary method, the method can be quickly popularized to automatic interpretation in a large area; meanwhile, the method is compatible with optical and radar multi-source data, and can still maintain high classification accuracy in the case that optical images are disturbed by clouds and rain or crop plots are severely fragmented, thereby significantly improving the applicability in complex scenes.

The invention relates to the technical field of nondestructive testing of power equipment, and discloses an X-ray nondestructive testing method for crimping quality of a strain clamp of a power transmission line. The method comprises the following steps: fusing a topological structure, operation and maintenance records and meteorological data to generate an intelligent detection task list; the pose of the X-ray device is automatically calibrated, and multi-angle transmission imaging is carried out; performing standardization processing on the image, and inputting the image into a pre-trained multi-scale convolutional neural network to realize pixel-level defect segmentation; and calculating a crimping quality comprehensive evaluation index in combination with the three-dimensional defect depth and the service degradation factor, and generating a graded early warning signal to be pushed to an operation and maintenance platform. According to the method, through full-process closed-loop management, the detection precision, efficiency and response speed are remarkably improved, and safe and reliable operation of the power transmission line is guaranteed.

The invention provides a sea moving target trajectory prediction method and system based on intention guidance and environment constraint, and belongs to the technical field of sea traffic control and trajectory prediction. The method comprises the following steps: extracting a marine traffic channel based on AIS trajectory data; environment characteristics of navigation channel nodes and edges are counted; representing a historical track as a set of channel nodes and edges; constructing a behavior imitation learning model, and solving through a neural network to obtain a global reward function; the intention discrimination model is trained, and the behavior probability of short-time channel nodes and long-time ports or anchorage grounds is output; and based on an observation trajectory and an intention discrimination result, decomposing a speed sequence by using wavelet transform under intention guidance, predicting speed change, and interpolating to generate future trajectory coordinates. According to the method, the channel network is constructed in a data driving mode, subjective deviation of human rules is avoided, an intention guiding mechanism is introduced to inhibit long-time-sequence prediction error accumulation, and adaptability to complex ocean scenes is enhanced in combination with environment feature statistics.

The invention relates to the technical field of industrial research, and discloses an industrial research-oriented natural language query and automatic visualization generation method, which comprises the following steps of: acquiring a natural language query statement of a user, adapting to different accent and speech speed through a speech recognition module, and converting the natural language query statement into a text; a natural language analysis model with sentiment analysis and intention mining capabilities is used for extracting key information such as analysis indexes, time ranges and industry categories, and emphasis is extracted by combining historical records and context error correction clarification and adjustment; on the basis of the key information and a multi-industry data feature library containing an intelligent query optimization algorithm, query scripts adaptive to different industry data structures are automatically generated and executed; and generating a matching chart according to a query result in combination with user preferences and an analysis scene, associating a traceability identifier, and labeling a credibility level. According to the method, professional SQL writing and visual configuration capability are not needed, analysis is directly driven by a natural language, and the operation threshold is reduced; and intelligent chart adaptation avoids subjective deviation.

The application discloses a kind of based on multi-factor fusion's electric bicycle battery swap cabinet site selection suitability evaluation method, belong to urban traffic supporting facilities site selection technical field.For the problem that existing battery swap cabinet site selection lacks quantitative analysis, low degree of automation, the application fuses analytic hierarchy process AHP, geographic information system GIS, preference order structure evaluation method PROMETHEE and Python-ArcPy technology, first constructs construction, traffic, ecological three-dimensional index system, and determines the index weight by AHP;Then realize data cleaning, differential normalization and weighted superposition automatic calculation using Python-ArcPy, output comprehensive score;Again, extract high suitable area as candidate point by GIS;Finally, calculate net flow value ranking using PROMETHEE method, combined with service radius and field research screening optimal point.The application realizes site selection whole process automation and spatial visualization, significantly improves evaluation efficiency and accuracy, and can be popularized to similar new energy supporting facilities planning.