337results about How to "Avoid subjectivity" patented technology

The invention discloses a vehicle illegal parking detection system based on unmanned aerial vehicle inspection shooting, and relates to the technical field of vehicle illegal parking inspection. The vehicle illegal parking detection platform is in communication connection with an unmanned aerial vehicle image acquisition module, an image preprocessing module, a vehicle detection analysis module, an illegal parking judgment module, a license plate recognition module and an early warning notification module, and all the modules are in electric signal connection. According to the method, a large area can be rapidly covered through unmanned aerial vehicle inspection shooting, real-time monitoring and detection of vehicle illegal parking behaviors are achieved, compared with a traditional manual inspection mode, unmanned aerial vehicle inspection greatly shortens the detection period, the detection accuracy is remarkably improved, and the detection efficiency is improved through a high-precision image recognition algorithm. The vehicle illegal parking detection system can accurately recognize the license plate number, the vehicle type and the illegal parking position, and subjectivity and errors of manual judgment are effectively avoided.

The invention provides a traditional Chinese medicine abnormal state recognition method, system and device and a storage medium, and relates to the field of abnormal recognition. The method comprises the following steps: acquiring product information of a to-be-detected traditional Chinese medicine, and determining a data acquisition standard of the to-be-detected traditional Chinese medicine based on the product information of the to-be-detected traditional Chinese medicine and a preset detection rule; based on a data acquisition standard of the traditional Chinese medicine to be detected, inputting the image data into a preset image anomaly detection model to obtain a preliminary image anomaly region; performing feature fusion on the visual feature data and the spectral feature data to obtain an abnormal feature vector; and inputting the abnormal feature vector into a preset abnormal state judgment model, outputting abnormal state data, and performing abnormal state judgment based on the abnormal state data and a preset abnormal state judgment threshold to obtain an abnormal state detection result of the to-be-detected traditional Chinese medicine. Through the scheme, the abnormal state of the traditional Chinese medicine can be identified timely and accurately.

The invention belongs to the field of tunnel engineering, and particularly discloses a tunnel surrounding rock multi-mode intelligent fine grading method and system, and the method comprises the steps: 1, constructing a sample library which comprises parameters while drilling of a tunnel face, a digital image and a corresponding tunnel face surrounding rock grade; 2, constructing a while-drilling parameter refined grading characteristic system; 3, constructing a surrounding rock intelligent grading model based on multi-modal data fusion; 4, dividing the rock mass in front of the tunnel face into a plurality of three-dimensional space unit bodies according to the tunnel contour and blast hole distribution characteristics; and 5, based on the surrounding rock intelligent grading model, finely and intelligently judging the grade of the tunnel face surrounding rock. According to the method, the intelligent surrounding rock grading model based on the multi-modal data fusion theory is constructed, the refined intelligent judgment of the tunnel face surrounding rock grade is realized, the precision and robustness of the refined intelligent judgment of the surrounding rock grade of the model are improved, the surrounding rock grading level in the construction stage is improved, more comprehensive reference is provided for a subsequent construction scheme, and the construction efficiency is improved. And tunnel intelligent construction is guided.

The invention relates to a soil health evaluation method and device based on soil multifunction, and the method comprises the steps: collecting a soil health index value of a to-be-evaluated region according to a preset collection index; performing function division on the collection indexes to obtain a plurality of soil function categories; respectively calculating soil function scores corresponding to a plurality of soil function categories according to the soil health index values; performing clustering analysis on the plurality of soil function categories of the to-be-evaluated region to obtain weight distribution of the soil function categories; according to the weight distribution and the soil function score, calculating to obtain a soil multifunctional index of the to-be-evaluated region; and performing soil health evaluation on the to-be-evaluated area according to the soil multifunctional index to obtain an evaluation grade. According to the invention, the performance of the soil in different functional aspects can be reflected more accurately, and the soil health condition can be evaluated accurately.

The invention relates to the technical field of printing and silk-screen identification, and particularly discloses an OCR (Optical Character Recognition) method and system for PCB (Printed Circuit Board) component printing and silk-screen. PCB images under visible light, near-infrared and ultraviolet light wavebands are acquired by utilizing a multispectral imaging technology, the visibility of characters is improved, and the interference of a high-reflection or shadow region is inhibited; the quality coefficient of the image is obtained by calculating the structural similarity of the image on different scales, and the quality coefficient is compared with a preset threshold value, so that a high-quality image is automatically screened out; the method comprises the following steps of: comprehensively analyzing each region by extracting contrast, brightness and sharpness data of a high-quality image, and determining and marking effective character regions; a dynamic time warping algorithm is adopted to calculate a deviation coefficient between an actual silk-screen characteristic and a design file characteristic, and whether a printing error exists in silk-screen or not is accurately recognized by comparing the deviation coefficient with a preset threshold value.

The invention belongs to the technical field of task scheduling, and provides an artificial intelligence task scheduling method based on deep learning, which comprises the following steps: analyzing and processing a task by using a deep learning algorithm, performing deep learning and processing on the task based on performance data to obtain a task evaluation value, grading the task according to the task evaluation value, and performing task scheduling. The method comprises the following steps: acquiring task data based on comparison of a task evaluation value and a task evaluation standard value, acquiring a task completion value and an advanced task completion value based on the task data, processing to obtain an advanced performance value, comparing the advanced performance value with an advanced performance threshold value, marking a worker according to a comparison result, and marking the worker according to the comparison of the advanced performance value. And comparing the number of the high-level tasks with the number of the high-level personnel to obtain an optimized scheduling value, carrying out combined analysis on the optimized scheduling value YH and the high-level performance value BX to obtain an optimized scheduling coefficient, redistributing the high-level tasks according to the optimized scheduling coefficient, and establishing a redistribution task library.

The invention provides a multi-task-based power transmission line stockbridge damper defect detection method, which comprises the following steps: acquiring an original power transmission line stockbridge damper image, and generating a data sample close to a real fault sample; a multi-task detection network model is established, a classification loss function and a segmentation loss function are constructed for the multi-task detection network model, and the established multi-task detection network model comprises a shared feature layer, classification branches and segmentation branches; based on a data sample, training the multi-task detection network model by adopting an alternate training mode to obtain a trained multi-task detection network model, and in the training process, adaptively adjusting weight parameters by adopting an adaptive dynamic weighting strategy; and inputting a to-be-detected image of the vibration damper of the power transmission line into the trained multi-task detection network model to obtain segmentation positioning and defect detection results of the vibration damper of the power transmission line. According to the invention, the problem of poor robustness and detection performance in the prior art can be solved.

The invention relates to the technical field of tunnel water inrush safety assessment, in particular to a tunnel water inrush disaster prediction method and device and a storage medium, and the method comprises the steps: S1, building a data set based on tunnel water inrush disaster cases, analyzing and selecting disaster-inducing factors, and determining disaster grading standards; s2, dividing the data set into a training set and a test set according to a set proportional relation; s3, performing data preprocessing on a training set and a test set in the data set by taking the disaster-causing factor as a characteristic variable; compared with the prior art, the tunnel water inrush disaster prediction method has the beneficial effects that the tunnel water inrush disaster is intelligently predicted by using the machine learning method, the problems of strong subjectivity, poor applicability and the like of a traditional prediction method can be avoided, meanwhile, disaster misjudgment and missed judgment caused by technicians are avoided, and the model performance and the prediction precision are improved.

The invention belongs to the technical field of stratum recognition, and particularly relates to a shield parameter geological recognition calculation method and system based on an unsupervised clustering algorithm, and the method comprises the steps: selecting a plurality of groups of data containing factors affecting stratum category recognition; pCA converts a group of complex related variables into fewer and irrelevant variables, and the new variables are called as main components and can capture most of information in original data; determining the clustering number of K-means + + by adopting an elbow method; determining the clustering number of a hierarchical clustering method by adopting a hierarchical tree; k-means + + and hierarchical clustering are respectively used for clustering the data, and evaluation indexes such as identification precision or contour coefficients of the data and the data are calculated, so that potential modes in the data are analyzed and mined more comprehensively. The invention aims to deeply explore the internal relevance between shield tunnel parameters and geological types, provides an innovative and robust solution for real-time detection of geological features in shield construction by overcoming the limitation of the prior art, and has wide application potential.

The invention relates to the technical field of three-dimensional scanning, in particular to a method for determining a three-dimensional scanning distance and range under a given precision condition, which determines the precision of a three-dimensional scanner within a specified range under different distances through a calibration test and detailed data analysis of a system, provides reliable scanning parameter selection, and improves the accuracy of the three-dimensional scanning distance and range. The method is widely applied to the fields of building measurement, industrial detection, cultural heritage protection and the like; according to the method, subjectivity and blindness of scanning parameter selection are avoided through a scientific parameter selection method, meanwhile, the working efficiency of scanning operation is improved, accidental errors are reduced through multiple tests and data averaging processing, comprehensive data are provided, influence factors and rules of scanning precision are clarified, the scanning process is optimized, and the scanning accuracy is improved. The efficiency and the result precision are improved, and the problem that the precision reliability of an existing three-dimensional scanning system in practical application is limited is solved.

The embodiment of the invention discloses an ear canal CT image segmentation method based on improved nn-UNet, and the method comprises the steps: collecting and marking the ear canal CT data of a plurality of patients, and making a data set; preprocessing the data; training on a training set by using a model improved based on nn-UNet, dividing a data set for multiple times by using a five-fold cross validation method, then training and verifying each fold, and storing a training model after training is completed; reasoning and testing the trained model on the test set data which are not seen; post-processing is carried out, and an output result of the model is optimized; and formulating an evaluation scheme to evaluate the prediction result. According to the method, the segmentation efficiency and accuracy can be greatly improved, and subjective deviation and human resource waste possibly generated in the manual segmentation process can be effectively avoided.

The present invention discloses a method and system for automatically detecting surface defects of a flexible touch screen, comprising the following steps: S1: using the Retinex method to compensate for the illumination of the surface image of the flexible touch screen, and converting it to the HSV color space to obtain the converted image; S2: calculating the directional feature map based on the converted image; S3: constructing a Gaussian mixture model based on the directional feature map, and clustering the features using the expectation maximization algorithm to obtain the initial defect area; S4: optimizing the initial defect area using morphological processing to obtain the optimized defect area; S5: splicing the converted image and the optimized defect area to obtain an input image, and performing defect detection on the input image based on a multi-scale feature fusion network of deep learning. The present invention can realize fast and accurate detection of surface defects of a flexible touch screen.

The invention discloses a medicine packaging box pattern printing defect detection method, and relates to the technical field of printing quality detection, and the main scheme is as follows: collecting to-be-detected printing data and standard printing packaging box template data, and then calculating a character deviation index, a pattern position coordinate index and a to-be-detected color index according to the data; wherein the character deviation index is obtained by the pixel and format similarity index of the character area; the pattern position coordinate index is calculated based on the standard position coordinate system number and the data to be measured; a color index to be detected is calculated by a deviation color coefficient and the like, then text, position and color index thresholds are set, whether a text area, a pattern position and a pattern color are qualified or not is judged by comparing each index with a corresponding threshold, the printing defect is judged to exist as long as one of the indexes is unqualified, and the method can effectively detect the printing defect.

The invention belongs to the technical field of artificial intelligence, and relates to a question and answer method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a target question input by a user, employing a text embedding model to carry out vectorization processing on the target question, and obtaining a question vector; obtaining a fact entry vector set extracted in advance based on the first language model and the text embedding model, and calculating the similarity between the problem vector and each fact entry vector in the fact entry vector set; obtaining at least one fact entry vector with the highest similarity with the problem vector, and determining a fact entry corresponding to the fact entry vector as a target fact entry; and adopting a second language model to generate reply content for the target question according to the target question and the target fact entry. In addition, the invention also relates to a block chain technology, and data such as a target problem and a fact entry vector set can be stored in a block chain. The method can reduce the burden of manually extracting question-answer pairs, and improves the question-answer efficiency and accuracy.

The invention relates to the technical field of logistics management, in particular to a cross-border logistics demand prediction method and system, which can provide accurate and reliable prediction results and decision support for cross-border logistics enterprises. The method comprises the following steps: acquiring a destination cargo demand influence parameter set and a delivery place cargo supply influence parameter set; respectively performing feature analysis on the destination cargo demand influence parameter set and the shipping place cargo supply influence parameter set to obtain a destination cargo demand feature index and a shipping place cargo supply feature index; comparing the destination cargo demand characteristic index with the shipping place cargo supply characteristic index, extracting a minimum characteristic index, and marking the minimum characteristic index as a logistics demand characteristic index; obtaining the real-time cross-border transportation cost and the historical same-period cargo transportation characteristic quantity corresponding to the logistics demand characteristic index; and inputting the logistics demand characteristic index and the real-time cross-border transportation cost into a pre-constructed cross-border logistics demand influence analysis model as input.

The invention relates to the technical field of industrial clusters, and discloses an industrial cluster industrial chain construction method and device, equipment and a medium, and the method comprises the steps: obtaining supply associated data and property right associated data of an enterprise of an industry category to which a target industrial cluster belongs; constructing a directed contact network based on the supply association data; constructing an undirected contact network based on the property right associated data; calculating property right connection degrees among the nodes in the undirected connection network, and accumulating the property right connection degrees to paths with corresponding node relations in the directed connection network according to a preset weight to generate a multi-source data enhanced network; and traversing all node paths in the multi-source data enhanced network, calculating the accumulated connection strength of each node path, and determining the path with the highest accumulated connection strength as the industrial cluster core industrial chain. According to the method, the universality, professional interpretability and objective accuracy of the constructed industrial chain are improved.

The invention discloses a photovoltaic project natural disaster risk knowledge graph construction method, and relates to the field of key infrastructure risk assessment, and the method comprises the steps: S1, constructing a photovoltaic project natural disaster geographic information database based on text big data; s2, extracting a natural disaster risk causal relationship of the photovoltaic project based on machine learning; and S3, drawing a photovoltaic project natural disaster risk knowledge graph based on the photovoltaic project natural disaster geographic information base and the photovoltaic project natural disaster risk causal relationship. According to the method, the limitation that an existing method depends on traditional statistical data and is based on single disaster case analysis is solved, the data bottleneck is broken through from the source, the advantages of disaster text big data are fully played, and a big sample database for photovoltaic project natural disasters is built through natural language processing and a geographic information system platform; therefore, the natural disaster knowledge graph constructed by the method has higher universality and integrity.

The invention discloses an illness state early warning system based on mobile nursing, and relates to the technical field of medical nursing, the illness state early warning system comprises a data acquisition module, a cloud memory, an illness state analysis module and a mobile nursing platform; and the data acquisition module is used for acquiring patient information. According to the illness state early warning system based on mobile nursing, the system obtains comprehensive patient information through the data acquisition module, and the cloud memory is used for caching, so that the integrity and traceability of data are ensured; a disease state analysis module adopts a disease diagnosis model combining a support vector machine and a neural network to compare and analyze vital sign features and image features of the patient with standard data and a typical model in a medical knowledge base, so that subjectivity caused by manual judgment only depending on doctors is avoided, the diagnosis accuracy is improved, and the diagnosis efficiency is improved. Therefore, the problems that disease diagnosis in an existing medical system is high in subjectivity, complex change trends are difficult to find, and timeliness is insufficient are solved.

The invention relates to the technical field of road network maintenance management, and discloses an urban road network intelligent maintenance management method and system, and the system comprises an image obtaining module which obtains the real-time image of each road section in a to-be-maintained region; the information acquisition module acquires data information of each road section. The model establishing module is used for establishing a road condition model. And the evaluation module is used for determining the state score of each road section according to the state data and the relationship between the deformation data and the preset state data in the preset time period. And the analysis module obtains feature data of each lesion type in the lesion data, determines labels of the road sections according to matching between the state data and the feature data, and determines maintenance grades of the road sections according to a relationship among the daily average traffic flow, the labels of the road sections and state scores. And the message sending module sends the maintenance level of each road section to the road network maintenance platform. According to the invention, through cooperation of all the modules, the maintenance level of each road section is determined, and the accuracy of urban road network maintenance management is effectively improved.

The invention discloses a concrete slump detection method based on deep learning and a stirring pump truck, and the method comprises the following steps: S1, collecting historical concrete slump data and slump process data, and carrying out preprocessing; s2, extracting raw material features and mix proportion features, calculating identification feature vectors, and constructing a historical concrete slump database; s3, calculating a concrete slump sequence in the pre-processed slump process data; s4, extracting initial concrete slump and concrete slump sequence characteristics, and calculating first concrete slump; and S5, collecting the raw material data of the concrete slump detection, calculating the identification feature vector of the raw material data, and performing second correction on the first concrete slump by using the identification feature vector in the historical concrete slump database to obtain a second concrete slump. The problems that a traditional concrete detection method is high in uncertainty and difficult to deal with the influence of raw material fluctuation on the concrete slump can be solved.

The invention discloses an industrial multi-dimensional data fusion system and method, and relates to the technical field of electronic bill multi-dimensional monitoring, and the method comprises the following steps: building an electronic bill service platform, building an enterprise file for an internal enterprise of the platform based on MySQL, collecting the electronic bill information of the internal enterprise entering the platform, including a draft bill, a sales bill and a purchase bill, and storing the collected information in the platform. Recording the data in a related enterprise file; on the basis of electronic bill data in enterprises, information extraction is performed on bills, and bill data under archives of the enterprises of the two parties are retrieved for the enterprises entering the platform. According to the industrial multi-dimensional data fusion system and method, by fusing multi-dimensional data such as enterprise archives, electronic bill data and business range portraits, suspicious bill judgment is carried out on electronic bills, the business range portraits are dynamically generated based on actual business data of enterprises, the accuracy of determination of suspicious bills is enhanced, and the efficiency is improved. The workload of manual bill checking is reduced, and the practicability and functionality are enhanced.

The invention discloses an intelligent college entrance examination voluntary adaptation method and system, relates to the technical field of education examination assistance, and aims to solve the problems of information asymmetry, individual difference of examinees and voluntary filling efficiency and accuracy during voluntary filling of the examinees. According to the method, advanced technologies such as big data analysis and an artificial intelligence algorithm are integrated, a personalized and accurate volunteer filling scheme is provided for examinees, and the scientificity and rationality of volunteer selection are remarkably improved. The system automatically processes mass data, relieves the burden of examinees, updates information in real time, and ensures the accuracy of decision basis. And meanwhile, the system ensures fairness and transparency, the educational resource distribution is optimized, and the admission satisfaction is improved. Generally speaking, the patent not only renovates a traditional volunteer filling mode and improves efficiency and satisfaction, but also promotes educational fairness through a scientific method and realizes effective adaptation between examinees and colleges.

The invention relates to a coal slime flotation froth three-dimensional model classification method based on a Pointnet network, and aims to solve the technical problem that the current two-dimensional froth image cannot process the states of three-dimensional froth space fluctuation and the like and the internal relationship of the space, and the technical scheme is as follows: expanding a data set; designing a foam characteristic network architecture; extracting a high-dimensional feature lambda 1 by using a point cloud feature extractor f theta; projecting the high-dimensional feature lambda 1 into a low-dimensional space by using a feature projection head # imgabs0 # to obtain a low-dimensional feature lambda 2 of the single-groove foam point cloud; splicing and fusing the single-groove foam point cloud features by using the feature fusion function yf to obtain an overall feature gamma; classifying the overall feature gamma through a multi-layer perceptron (MLP) and a Softmax function, and outputting a classification result; evaluating and analyzing. According to the method, the foam three-dimensional point cloud models of a plurality of flotation cells are used as input, the limitation of a single-cell data source is made up, the generalization ability and stability of the model are improved by using the flotation machine overall feature training network, and the performance of the model is optimized.

The invention discloses a locomotive brake detection performance determination method, and the method comprises the steps: employing a vehicle-mounted device on a locomotive to collect dynamic data generated in a brake test process in real time, and the dynamic data comprise air cylinder pressure, pipeline pressure, a brake handle position signal and other operation related parameters; the collected dynamic data are preprocessed, a test standard curve set is constructed in combination with historical test data, and the standard curve set is classified and stored according to the locomotive model and the brake model. According to the invention, a standard test data set is constructed based on a large amount of test data of locomotives and brakes of different models, feature extraction and fitting analysis of dynamic data are realized in combination with a feature sampling algorithm, a least square fitting algorithm and a multiple linear regression model, and through comparison of a fitting curve and a standard test curve, a dynamic test result is obtained. The system automatically judges the normalization and integrity of the operation, and subjective errors and omissions of manual analysis are avoided.

The invention relates to the technical field of coal mill powder pipes, in particular to a powder leakage defect automatic identification method based on digital twinning and AI video analysis, and the method comprises the steps: obtaining operation parameters and environmental parameters of a coal mill powder pipe, calculating a powder leakage risk characterization coefficient, and judging whether there is a potential risk of powder leakage; image data of the surface and the surrounding area of the powder pipe in the initial starting period are collected through an AI video analysis module, powder leakage characteristic parameters are extracted, a characteristic change curve segment is drawn, similarity comparison is conducted on the characteristic change curve segment and the reference characteristic curve segment, and whether a suspected powder leakage area exists or not is judged; actual operation parameters and model prediction parameters of the powder pipe in a stable operation period are obtained based on a digital twin model, the fluctuation degree of parameter deviation is calculated, and whether the powder leakage defect exists or not is comprehensively judged by combining a suspected powder leakage area judgment result; the early-stage accurate identification of the powder leakage defect is realized, the detection efficiency and precision are improved, and the operation and maintenance cost and the safety risk are reduced.

The invention discloses a wind turbine generator hydraulic station degradation trend identification method and device, and the method comprises the steps: obtaining monitoring data of a plurality of influence factors in a preset time period, and forming time series data; determining an abnormal frequency of each influence factor serving as an evaluation index based on the time sequence data; normalizing each evaluation index to obtain an initial score of each evaluation index; determining a comprehensive score according to the initial score and the fusion weight; taking the time sequence data and the comprehensive score as input and expected output respectively, and training a comprehensive score model; and monitoring the hydraulic station in real time by using the comprehensive scoring model so as to identify a degradation trend and perform early warning according to a preset threshold value. According to the technical scheme, the health state of the hydraulic station of the wind turbine generator can be efficiently reflected at low cost, the degradation trend is recognized, and early warning is conducted.

The invention provides a power system anomaly detection method and system, and the method comprises the steps: carrying out the prediction of a photovoltaic panel assembly based on real-time environment parameters collected in real time and a constructed power prediction model, and obtaining theoretical power generation power data; comparing the obtained real-time power generation power data of the photovoltaic panel assembly with the theoretical power generation power data to obtain a comparison result, and determining an abnormal photovoltaic panel assembly; and performing analysis based on the image data of the abnormal photovoltaic panel assembly acquired in real time to obtain an abnormal result. According to the method, by comparing the real-time power generation power and the theoretical power generation power of the photovoltaic panel assembly, the photovoltaic panel assembly with the abnormal power generation efficiency can be rapidly identified, potential problems can be directly, effectively and timely found, energy waste and potential safety hazards caused by reduction of the power generation efficiency are avoided, and the power generation efficiency of the photovoltaic panel assembly is improved. By acquiring and analyzing the image data of the photovoltaic panel assembly in real time, the actual condition of the photovoltaic panel can be known more visually, and the accuracy of anomaly detection is improved.

The invention relates to the field of privacy protection, in particular to a vehicle position privacy protection method and system based on local differential privacy, and the method comprises the steps: dividing a target region where a vehicle runs to obtain at least one large region, and dividing each large region to obtain a plurality of small regions; obtaining a vehicle number estimation total variance of each small area in the large area based on a disturbance overturning probability of epsilon-local differential privacy, and adding the vehicle number estimation total variances of all the small areas in the large area to obtain a comprehensive total variance; obtaining an optimization function based on the comprehensive total variance, and solving the optimization function to obtain an optimal disturbance flipping probability; and the optimal disturbance overturning probability is used as the disturbance overturning probability of epsilon-local differential privacy to disturb the position information of the vehicle, and privacy protection of the vehicle position is realized. According to the invention, the optimal balance between privacy protection and data availability can be realized, and the evaluation accuracy of vehicle distribution is improved.

The invention discloses an NFC testing method based on machine learning, and relates to the technical field of wireless communication testing, and the testing method comprises the specific steps: S100, data collection and preprocessing: carrying out the basic testing of an NFC signal under different environment conditions, collecting the multi-dimensional data of signal intensity, transmission speed and error rate, and carrying out the data collection and preprocessing; by introducing deep fusion of the electromagnetic field theory and the machine learning technology, dimensions and methods of NFC testing are greatly enriched, the electromagnetic field theory serves as an important branch of physics, an accurate mathematical model and an experimental verification method of the electromagnetic field theory provide a solid theoretical basis for propagation characteristics of NFC signals in a complex environment, and the electromagnetic field theory and the machine learning technology are combined in advance. In combination with the strong data processing and prediction capability of a machine learning algorithm, the attenuation, reflection and diffraction phenomena of NFC signals in different media can be predicted theoretically, the model can be continuously corrected and perfected through actual test data, and the accuracy and reliability of a prediction result are ensured.

The invention discloses a word document-based knowledge graph generation method and apparatus, and an electronic device, and relates to the field of data processing. The method comprises the following steps of: analyzing a target document to obtain a corresponding xml (Extensible Markup Language) file; the xml file is analyzed to obtain a first data structure, and the first data structure comprises a plurality of elements; in the first data structure, sorting the plurality of elements according to a preset sorting rule to obtain a second data structure; segmenting the second data structure by adopting a preset recursive function to obtain a plurality of entities and a relationship among the plurality of entities; and taking the plurality of entities as nodes of a preset knowledge graph, and taking the relationship among the plurality of entities as edges of the preset knowledge graph to obtain a target knowledge graph. According to the technical scheme provided by the invention, the document content is converted into the XML format for recognition and extraction, so that the accuracy of converting the document into the knowledge graph is improved.