1097 results about "Rapid processing" patented technology

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

The invention relates to the technical field of electric power government affair management, and provides a cross-department government affair big data business co-processing method based on a computing power platform and data fusion, comprising the following steps: S1, deploying a heterogeneous computing power cluster, and establishing a computing power resource dynamic scheduling data processing platform comprising a CPU, a GPU, an FPGA and an edge computing node; s2, acquiring multi-dimensional data of power grid equipment data, power grid load data, industry power consumption data and government policy data; and S3, designing a cross-department data access system based on the zero-trust architecture, and developing a multi-dimensional model of a power grid risk prediction model, an industry energy consumption analysis model and a carbon emission accounting model in power management. The space-time joint probability prediction and conflict priority ranking algorithm is applied to power-government affair emergency disposal, intellectualization and precision of cross-department business collaboration are achieved, power faults with serious consequences are rapidly handled according to the priority level, and the harmfulness and unpredictability of the power faults can be reduced.

The invention discloses a knowledge question and answer rapid processing system based on artificial intelligence. The system comprises a knowledge graph construction module, a semantic entity analysis module, a user demand analysis module, a question and answer generation and interaction module, a back-end service interface module, a user feedback and optimization module and a security and privacy protection module. The knowledge graph construction module is used for constructing a knowledge graph; the semantic entity analysis module can identify semantic entities in questions of a user; the user demand analysis module can analyze the questioning demand of the user; the question and answer generation and interaction module is used for generating answers according to the recognized semantic entities and user requirements; the back-end service interface module provides an interface to realize load balancing and automatic capacity expansion and contraction; the user feedback and optimization module collects user evaluation and optimizes the model according to feedback; and the security and privacy protection module is used for protecting the privacy of user data and the security of the system. The user intention and entity information can be accurately recognized, and the response speed of the question answering system is remarkably improved.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The invention provides a dynamic monitoring method for a soil mass seepage field of a power transmission tower slope. According to the method, related data of the slope are acquired through multi-source data acquisition and technologies of pressure sensing and static pressure measurement, displacement monitoring, multi-sensor fusion, Beidou / GPS positioning, resistivity method exploration, soil moisture content monitoring and the like; fusing the collected multi-source data, analyzing the dynamic change of the seepage field by using a risk assessment model and a machine learning algorithm, and predicting the evolution trend of the seepage field; a lightweight model is deployed at a monitoring terminal to realize local rapid processing of data, alarm information is immediately sent out once an abnormal state is monitored, and scientific and reasonable decision support is provided for protection and treatment of a transmission tower slope according to early warning information in combination with a pre-made decision scheme; according to the invention, the accuracy and timeliness of transmission tower slope soil seepage field monitoring can be effectively improved, and the threat of landslide and other disasters to a transmission line is reduced.

The invention discloses a large-breadth cross-scale adhesive film defect detection method and a large-breadth cross-scale adhesive film defect detection device. The method comprises the following steps: carrying out coarse focusing on a camera and capturing a glue film image, carrying out coarse scanning on the glue film image and extracting information of a printed image-text in the glue film image by using an image difference method, inputting an output difference image into an anchor frame algorithm, inputting an output fine scanning target area coordinate into a genetic algorithm, and carrying out fine scanning on the glue film image. Outputting an optimized fine scanning path in combination with S curve planning characteristics of the motor; the motion platform is driven to complete path execution by combining double-axis linkage control with the fine scanning path, camera focusing is completed in the path execution process, and a high-resolution image of a defect area is collected and registered; and carrying out image splicing and fusion on the registered images by combining an optimal suture line algorithm with an improved Laplacian pyramid fusion method, and carrying out defect detection on the fused complete adhesive film image. The method has the advantages of high splicing precision and fast processing efficiency, and can effectively retain more detail information.

The invention relates to a data cleaning method and device based on a Drools rule engine. The method comprises the following steps: acquiring original data from various data sources through a data acquisition module; converting the collected original data into a data format which can be processed by a Drools rule engine, and inserting the data format into a working memory; the data cleaning module carries out cleaning processing on the data according to a predefined cleaning rule, wherein the cleaning processing comprises operations such as duplicate removal, missing value filling and format correction; outputting the cleaned data to a target system through a data output module; and recording a cleaning log through the monitoring module, counting a cleaning result, and providing visual display. According to the scheme, a Drools rule engine is adopted, dynamic loading and updating rules are supported, and the dynamic loading and updating rules can take effect without restarting a system; the flexibility is high, and updating of various data sources and dynamic rules is supported; the cleaning efficiency is high, and large-scale data can be rapidly processed through the rule engine; the monitoring function is complete, real-time log recording and visual display are provided, and the cleaning process is convenient to manage and optimize.

The invention provides an HY-2A ocean water vapor inversion method based on machine learning, and the method comprises the steps: constructing a high-quality HY-2A scanning microwave radiometer data set through the data preprocessing, matching, feature extraction and normalization processing of HY-2A satellite scanning microwave radiometer data and ERA5 reanalysis data. A plurality of machine learning models are used for training and testing, Bayesian optimization is used for realizing hyper-parameter automatic adjustment, an optimal solution in finite time is obtained, an SHAP method is used for explaining the models, contribution of each characteristic variable is clear, interpretability of the used machine learning models is improved, and finally high-precision inversion of the water vapor over the sea is realized. The method has efficient calculation performance, can realize rapid processing of large-scale marine meteorological data, and improves the interpretability of a machine learning model. The method has important application value in numerical weather forecast, ocean extreme weather forecast, climate monitoring and other geophysical and meteorological fields.

The invention relates to the technical field of big data and data security, in particular to a cloud-edge collaboration and AI fusion data classification and grading method and system and a storage medium, cloud computing and edge computing are combined, the AI technology is used for data classification and grading, and rapid processing and efficient analysis of data are achieved; for structured data and unstructured data, different recognition and processing methods are adopted, including regular expression, keyword matching, dictionary matching, neural network, natural language processing and other algorithm models; particularly, a neural network and a natural language processing technology are combined with an unsupervised learning method and a semi-supervised learning method to realize an automatic classification and grading method of unstructured data in stages, rapid data processing and response can be carried out on an edge layer through a cloud edge collaboration mechanism, and the efficiency is improved. And deep analysis and model training are carried out by using the powerful computing power of the cloud layer.

The invention discloses an equipment operation and maintenance agent implementation method based on a small-parameter large language model, and the method comprises the steps: constructing an equipment fault diagnosis knowledge base, and providing a large-parameter-magnitude large language model capable of completing an Agent complex task; abnormal information of the historical data is collected and processed; analyzing abnormal information in the historical data, identifying and extracting trend characteristics of the measuring points; the method comprises the following steps: constructing an Agent Prompt template library, and generating a plurality of Agent Prompts; constructing an Agent fine tuning training data set based on a plurality of Agent Prompts; and performing Agent fine tuning on the small-parameter large-language model to form a fine-tuned small-parameter large-language model. A small-parameter large language model is deployed in a localized manner, so that the privacy and security of data are ensured; by migrating the Agent capability to the small-parameter large language model, a fault diagnosis task can be quickly processed in a localized environment, and the diagnosis efficiency is improved.

The invention discloses a network flow measurement method and device based on a data packet interval sketch data structure and a medium. Each arriving data packet is mapped to each corresponding bucket in a sketch data structure through a plurality of independent hash functions, the sketch data structure is a two-dimensional array form structure comprising multi-row and multi-column buckets, and empty bucket processing, identical flow updating or conflict flow replacement judgment is executed according to the conditions of the data packets and the buckets. According to the method, a brand-new interval value sketch is designed, and efficient and accurate large flow detection is realized on the premise of high processing speed and low memory occupation by comprehensively utilizing two pieces of multi-dimensional statistical information of the flow estimation value and the data packet interval. Meanwhile, the large flow and the small flow in the network environment can be well distinguished, so that the method can be better suitable for network measurement tasks in different scenes such as large flow detection and abrupt flow detection.

The invention provides a processor based on an RISC-V architecture and a data processing system and method. The processor comprises an instruction obtaining unit, an instruction decoding unit, a register file, a distribution unit, an execution unit, an expansion processing unit and a data access interface. Decoding the program instruction to obtain an instruction type and an application field type; identifying an application field type, distributing the program instruction to an execution unit and an expansion processing unit according to a preset distribution rule under the condition that the application field type accords with a preset field type, and identifying an instruction type under the condition that the application field type does not accord with the preset field type; when the instruction type of the program instruction is an RISC-V instruction set instruction, the RISC-V instruction set instruction is distributed to the execution unit, and when the instruction type of the program instruction is an extension instruction, the extension instruction is distributed to the extension processing unit; the instruction can be quickly processed for different application fields, and the instruction processing efficiency is improved.

The invention discloses a primary and secondary collaborative planning method based on a novel power distribution system, and relates to the technical field of power system optimization, and the method comprises the following specific steps: constructing a power distribution network model and fault feature analysis, designing an adaptive protection mechanism, realizing a rapid fault processing mechanism, and performing primary and secondary system collaborative planning. And implementing and verifying: implementing a planning method in the power distribution network, installing and debugging related equipment, carrying out field test, and verifying the effectiveness of an adaptive protection mechanism and a fault rapid processing mechanism, the running state of the power distribution network is monitored in real time, and the protection constant value is automatically adjusted according to the monitored fault characteristic parameters, so that the reliability of the power distribution network is improved. Therefore, the adaptive capacity of the system to a complex and changeable operation environment is remarkably improved, the dynamic adjustment mechanism can quickly respond to dynamic factors of new energy output fluctuation and load change, it is ensured that a protection strategy is always matched with the operation state of the system, and the problem of protection maloperation or operation refusal caused by rigid setting of a fixed value is effectively prevented.

The invention discloses a power distribution cabinet cable monitoring method, system and device based on edge calculation, and relates to the technical field of power system monitoring and early warning, and the method comprises the steps: arranging a sensor in a power distribution cabinet, and collecting the displacement and deformation data of a cable; carrying out preprocessing and preliminary analysis on the collected data by utilizing edge computing equipment; the method comprises the following steps: determining whether cable displacement deformation is in an abnormal state or not according to a preset judgment condition through edge computing equipment, and uploading abnormal data to a cloud server if the cable displacement deformation is judged to be abnormal; and the cloud server receives the abnormal data and carries out deep analysis in combination with historical data. Through rapid processing of the edge computing device, once the cable is abnormal, the system can timely perceive and upload abnormal data, the real-time performance of monitoring is remarkably improved, it is ensured that the state change of the cable can be rapidly captured, meanwhile, a deep learning algorithm is introduced, more accurate anomaly detection is achieved, and the accuracy and reliability of early warning are improved.

The invention discloses a real-time imaging detection system based on a shell ring welding seam, and relates to the technical field of shell ring welding seam detection, the system comprises an imaging acquisition module, and a high-resolution imaging device is used to carry out multi-dimensional and omnibearing image acquisition on the shell ring welding seam. According to the invention, clear, accurate and complete welding seam image information can be obtained through the imaging acquisition module by adopting high-resolution imaging equipment, the accuracy and comprehensiveness of detection are remarkably improved, the data transmission module can quickly transmit a large amount of image data to the image processing module for quick processing, the detection period is greatly shortened, and the detection efficiency is improved. According to the method, the detection efficiency is remarkably improved, the rapid detection requirement in large-scale production is met, through the machine learning algorithm module and through learning and training of a large amount of training data, welding seam features can be automatically and accurately extracted, defects can be automatically and accurately recognized, dependence on artificial experience is reduced, and the detection reliability and stability are improved.

The invention provides a correction method and system for anisotropic reflection of GF6-WFV data, and belongs to the technical field of spatial information.The correction method comprises the steps that the GF6-WFV data is subjected to block processing, data of a block center is compared with data extracted from satellite navigation file data, atmospheric correction is carried out on the block data one by one, and the correction efficiency is improved; the processing efficiency and the correction precision are remarkably improved, image data are divided into small blocks with 2000 * 2000 pixels through blocking processing, the pixel amount calculated each time is reduced, the method is more suitable for rapid processing of large-data-amount and high-resolution images, the blocking coverage range is large, local and overall consistency can be taken into account, and secondly, the processing efficiency and the correction precision are greatly improved. And the zenith angle and observation angle parameters of each block are standardized by the angle normalization model, so that the angle difference between different areas is reduced, and the accuracy of a correction result is improved. And finally, in combination with a lookup table technology, computing resource consumption can be remarkably reduced, and the method is convenient to operate in practical application of radiation correction and has the advantages of high efficiency and convenience.

The invention discloses a computer vision target detection method and system, and relates to the technical field of vision detection, the computer vision target detection system comprises a camera and a data processing terminal, and the computer vision target detection system further comprises the following modules: a data acquisition module acquires image and video data through the camera, preprocesses the acquired data, and sends the preprocessed data to a display module; according to the technical scheme of the present invention, the target detection module dynamically selects a lightweight, medium-complexity or high-complexity sub-network for detection based on the complexity score by means of the dynamic weight and the adaptive threshold value, so that the complexity of the target detection module is improved, and the resolution of the image is improved by means of the image enhancement algorithm, and the noise reduction processing is performed on the video data by means of the filtering algorithm. The calculation amount of the model is adjusted in a self-adaptive manner, when a simple image is faced, the lightweight sub-network can be selected for rapid processing, the detection efficiency is improved, the high-complexity sub-network is adopted for a complex image, the detection precision is ensured, the calculation resources and the detection effect are balanced, and the adaptability of the model in different scenes is enhanced.

Embodiments of the present disclosure provide a data processing method and apparatus, an electronic device and a storage medium. The data processing method comprises: displaying table data, the table data comprising at least one data object and at least one piece of basic statistical information corresponding to the data object; in response to a trigger operation for a preset control, presenting a dialogue component window on a page of the table data; and in response to receiving, by means of the dialogue component window, a first message comprising a target demand, displaying a reply message in the dialogue component window, the reply message comprising conclusion content and / or a recommended demand template for the target demand, wherein the conclusion content comprises at least one of a text, a table and a chart. Once the table data is displayed, the table data is analyzed on the basis of the target demand and a language model to obtain the reply message and display same, thereby realizing rapid processing and analysis of the table data.

The invention provides a heavy-load unmanned aerial vehicle cooperative hoisting method, device and equipment and a medium, and relates to the technical field of edge calculation, and the method comprises the steps that a virtual pilot unmanned aerial vehicle in an unmanned aerial vehicle cluster calculates total load data of loads hoisted by a plurality of edge nodes based on load distribution data of each edge node; based on the total load data and the node position, determining an overall motion track of the unmanned aerial vehicle cluster, and sending the overall motion track to the edge node, so that the edge node finds a reference tension value needing to be provided and a node motion track based on an ant colony algorithm; if it is determined that the lifting rope of the first unmanned aerial vehicle in the multiple unmanned aerial vehicles is broken in the flight process, according to a preset load redistribution scheme, synchronous tension is distributed to each second unmanned aerial vehicle; and setting the first unmanned aerial vehicle as a proxy center node, and receiving an updated movement track of the proxy center node for the unmanned aerial vehicle cluster. According to the invention, the sudden fault in the cooperative lifting process of the multiple unmanned aerial vehicles can be quickly processed.

The invention provides an image real-time filter processing method based on edge calculation, and the method comprises the steps: obtaining the local texture feature distribution of an input image, calculating the gradient variation of each pixel point in the horizontal and vertical directions, and calculating a quantitative index value reflecting the image texture complexity based on the statistical distribution feature of the gradient variation; executing a parallelized filter effect rendering operation based on the optimized processing task allocation scheme, and applying corresponding filter parameters to each image block through a multi-thread cooperative processing mechanism to form filter rendering configuration information suitable for the performance of the current mobile device; and carrying out fusion reconstruction on brightness, saturation and hue component data after filter processing, generating a final filter processing image through a reverse conversion process from HSV to RGB, and recording parameter configuration information in the processing process at the same time for rapid processing of subsequent similar images.

The invention discloses a heart failure combined left beam branch conduction block classification method based on feature fusion, and the method comprises the steps: extracting radiomics features and deep learning features through a heart magnetic resonance image, carrying out the feature fusion and selection, constructing a classification model through a machine learning algorithm, and carrying out the classification of the heart failure combined left beam branch conduction block. According to the method, the advantages of deep learning in the aspect of image processing and the high efficiency of machine learning in a classification task are combined, and complex information of a heart image can be reflected more comprehensively and accurately by combining radiomics features and deep learning features; an automatic and intelligent feature extraction and classification method is utilized, a large amount of medical image data can be rapidly processed and analyzed, accurate classification of the heart failure combined left bundle branch conduction block is achieved, the method has higher classification accuracy and higher robustness, and in the field of medical image classification, the method has the advantages that the classification accuracy is improved, and the classification efficiency is improved. And the efficiency and the quality of medical services can be improved.

The invention relates to the technical field of data processing, in particular to an LLM-based multi-source heterogeneous data intelligent fusion and self-adaptive processing method, which comprises the following steps of: receiving multi-source heterogeneous data, matching character sequences of mailbox fields and identity card number fields in records one by one by adopting a preset regular expression model, according to a preset key value pair mapping dictionary, items are uniformly converted, data records which cannot pass format verification, type conversion and value domain mapping are marked as doubt, and data records which cannot be processed or are marked as suspicious are separated. According to the method, a hierarchical processing flow is constructed, and deterministic format verification, type conversion and value domain mapping are arranged at the front end of a processing link, so that most simple data quality problems can be quickly processed with low calculation overhead. Then, numeric statistics and character string comparison are used to carry out fuzzy correction on the data, and spelling errors and numeric abnormities which cannot be covered by rules are further solved.

A deep learning-based rock microcrack identification and detection method belongs to the technical field of rock microcrack detection and identification in geological resources and geological engineering, and greatly improves the detection precision of rock microcracks by integrating a multi-scale feature pre-extraction module (MFP), an encoder space cascade coding path (ESCE) and a decoder input SE module. The conditions of false detection and missing detection are reduced, and the precision and the stability of micro-crack detection in the rock are remarkably improved. According to the method, through a reasonably designed network structure, the calculation amount of the network is reduced, the detection process is made to be more lightweight, and the method is suitable for rapid processing and real-time application of a large-scale data set. According to the method, the requirement for manual participation is greatly reduced, automatic detection and classification of the microcracks in the rock are achieved through the deep learning algorithm, the reliability and efficiency of rock integrity and mechanical property evaluation are improved, and the method has wide geological engineering application prospects.

The invention relates to an image registration method and a real-time tracking method, and the method comprises the steps: dividing a reference frame and a subsequent frame into a plurality of regions according to the same rule, carrying out the registration of the reference frame and the subsequent frame, and obtaining a region combination (a reference frame region and a subsequent frame region) where each pair of registration points are located; screening out matching point pairs according to the distribution condition of the registration point pairs in all region combinations; and calculating a transformation matrix according to the matching point pairs. As the change of the images between the continuous frames is not large, the method calculates the transformation matrix for registration by using the most representative local area, can avoid interference information brought by the influence of the environment and noise on the partial area in the image, can ensure that the screened matching point pair for registration has high reliability, and can improve the registration accuracy. In addition, calculation is simple and convenient, the rapid processing requirement of real-time registration can be met, and the method has wide application scenes and value.

The invention provides a method and device for rapidly processing distributed optical fiber monitoring data based on an AI large model, a terminal and a medium, and belongs to the technical field of monitoring data processing. The method comprises the following steps: acquiring monitoring data of each monitoring point in a target area; wherein the target area is divided into a plurality of sub-areas, each sub-area comprises a plurality of monitoring points, and each monitoring point is a distributed optical fiber sensor; for each sub-region, fusing the monitoring data of each monitoring point in the sub-region in a spatial dimension based on a first AI large model to obtain a time sequence vector of the sub-region, and performing preliminary anomaly judgment on time change characteristics of the time sequence vector; and if the data matrixes of all the sub-regions pass the preliminary anomaly judgment, fusing the time sequence vectors of all the sub-regions in the time dimension based on a second AI large model to obtain a spatial vector of the target region, and performing secondary anomaly judgment on the spatial change characteristics of the spatial vector. According to the invention, distributed optical fiber monitoring data can be processed efficiently and accurately.

The invention discloses a memory fault processing method and device, electronic equipment and a readable storage medium, and relates to the technical field of computers.The method comprises the steps that when a memory line fault exists in a memory of a target system, an isolation operation is triggered for a target physical address, the target physical address at least comprises the physical address of the fault line with the memory line fault in the memory, the technical problem of system downtime caused by the fact that the memory line fault cannot be rapidly processed in the related technology is solved, the isolation process is started for the target physical address in time when the memory line fault is detected, and the system downtime is reduced. Performance reduction and data error accumulation caused by the fact that the fault line continues to participate in data reading and writing are avoided, and then the technical effect of reducing the risk of system downtime is achieved.

The invention provides a shale total organic carbon content logging prediction method and device based on a random forest algorithm. The method comprises the steps that logging curve data are collected and processed; constructing a TOC prediction model according to the collected logging curve data by using a random forest algorithm; training the prediction model by using a training data set of TOC values; and predicting the shale TOC by using the trained prediction model. According to the method, the mud shale TOC prediction precision is improved, the influence of human factors is eliminated, rapid processing and comprehensive analysis of a large amount of logging data can be achieved, key information related to TOC is extracted, the defects of a linear or nonlinear model and a single machine learning algorithm model are overcome, the mud shale TOC prediction precision is effectively improved, and the prediction accuracy of the mud shale TOC is improved. Powerful support is provided for exploration and development of shale oil and gas.

The invention discloses a document information comparison method and device, equipment and a medium. In the method, firstly, after a word document set with a plurality of to-be-compared word documents is obtained, text contents of paragraphs in the plurality of to-be-compared word documents are extracted; carrying out literal difference detection on text contents of paragraphs in a standard word document in the word document to be compared and text contents of paragraphs in comparison word documents except the standard word document by using a character comparison algorithm, and identifying a difference part in each paragraph; and finally, performing semantic difference detection on the text content corresponding to the difference part by using a semantic difference model, and identifying the difference type of the difference part. In the process, due to the fact that the model is used for conducting semantic difference detection on the difference part, the difference type of the specific difference part is detected, the semantic difference of the text can be accurately recognized, the whole difference detection process is efficient, and large-scale documents can be rapidly processed.