298 results about "Web crawler" patented technology

The invention discloses a multi-modal data fusion method and system based on a large-model intelligent agent, belongs to the technical field of artificial intelligence, big data processing and intelligent agents, and aims to solve the technical problem of how to effectively integrate data of different modalities such as texts, images and voices by using the large-model intelligent agent. According to the technical scheme, the method comprises the steps that data collection and preprocessing are conducted, specifically, various modal data of texts, images and voice are collected through a web crawler, an API interface, a camera and microphone equipment, the collected data are preprocessed, the preprocessed multi-modal data are obtained, and the data quality is ensured; feature extraction and mapping: extracting corresponding modal features from the preprocessed multi-modal data through CNN and Transform models, mapping the different modal features to the same space, and combining the aligned features to form comprehensive feature representation; carrying out multi-modal fusion processing; and performing intelligent decision and feedback.

The invention discloses an electronic product personalized marketing method based on a big data accurate portrait, and relates to the technical field of big data processing, and the method comprises the following steps: multi-source data collection and cross-domain fusion processing: obtaining a multi-source data set related to an electronic product from each platform of the Internet by using a web crawler and a streaming processing engine, the multi-source data set is subjected to cleaning classification and alignment fusion processing to generate multi-modal data, and then the multi-modal data is updated in real time to dynamically reflect data changes, so that a foundation is laid for subsequent data analysis; according to the method, multi-source data is collected by using a web crawler and a streaming processing engine, quantitative analysis is performed on the data by using a Hofscode culture dimension model and a Maslow demand level theory, a user portrait is constructed by using a distributed data flow engine Apache Flink to dynamically update a label, and a GAN (GAN Adversarial Generative Network) anomaly detection and evaluation analysis method is applied, so that the user portrait is obtained. And comprehensive and accurate data acquisition and processing are realized.

The invention discloses an automatic financial data collection system based on a web crawler technology. The system comprises an intelligent crawler collection module, a financial data processing module and a data collection module. The intelligent crawler acquisition module comprises a dynamic scheduling crawler cluster unit, a multi-format capture engine and a metadata marking unit; the dynamic scheduling crawler cluster configures a routing algorithm based on reinforcement learning, and dynamically adjusts an IP proxy strategy through an anti-crawling feature matrix; the multi-format capture engine is integrated with a PDF / HTML / JSON heterogeneous data analysis channel; the metadata marking unit generates a data source label by adopting multi-layer coding conversion; and the financial data processing module comprises a cross-format analyzer, a data cleaning pipeline and a structured storage unit. According to the scheme, through innovative designs such as multi-modal data analysis, self-adaptive anti-creeping confrontation, dynamic resource scheduling and intelligent association analysis, the core problems of low heterogeneous data integration efficiency, weak dynamic anti-creeping confrontation capability and the like are solved.

The invention discloses a multi-modal data management method based on a large model technology, and relates to the technical field of combination of artificial intelligence and a large data technology, and the method comprises the following steps: collecting multi-modal data of a target body through a web crawler, processing the collected multi-modal data, extracting features in the processed multi-modal data, and storing the features in a database; a multi-modal feature vector is formed through combination of an attention mechanism and a weighted summation method, association between different modal features is learned by using a multi-modal auto-encoder and a multi-modal Transform model, a multi-modal feature space is constructed, and a multi-modal feature vector is obtained. According to the method, a web crawler acquisition technology, a multi-modal feature extraction and fusion technology and a modern information technology are closely combined, multi-modal data such as texts, images, audios and videos are accurately captured, and high-quality multi-modal feature vectors and a unified multi-modal feature space are obtained; and real-time and comprehensive monitoring of the whole process from acquisition to processing and storage of the multi-modal data is achieved.

The invention provides a Web automatic penetration test method and device, electronic equipment and a storage medium, and belongs to the technical field of website vulnerability tests.The method comprises the steps that a web crawler collects target Web information to form a basic set; detecting vulnerability acquisition information; matching the adaptive script, and generating an intelligent load to verify vulnerability availability; constructing a knowledge graph to determine vulnerability association, and evaluating success rate and risk; planning an optimal attack route by using reinforcement learning, and determining attack depth and range, namely attack path risk features; and evaluating the risk by the dynamic scoring model, and generating a visual report containing detailed information, results and repair and improvement suggestions. According to the automatic penetration testing method, manual intervention is reduced in the whole process, dependence on professional experience is reduced, efficiency is improved, and the problems that traditional testing is tedious and low in efficiency are solved.

The invention discloses a personalized customized vi design method and system, and relates to the technical field of visual identification design, the design method comprises the following specific steps: S100, data acquisition: docking with an enterprise internal management system to obtain core data, using a web crawler tool to collect Internet related information, and using the web crawler tool to store the core data; according to the method, data in multiple aspects of industry attributes, brand positioning and color preference of target audiences of an enterprise are deeply analyzed by utilizing an artificial intelligence technology, highly intelligent color analysis and recommendation are realized, and the color matching degree of the industry, the color preference of the target audiences and multiple dimensions of color coordination correction factors are comprehensively considered. According to the method, the color scheme meeting the enterprise image and the market demand can be accurately recommended to the user, the intelligent color recommendation not only improves the design efficiency, but also ensures that the color scheme is highly matched with the enterprise positioning and audience preference, and the visual impact and brand recognition degree of the design are enhanced.

The invention relates to an intelligent visual crawler configuration system and method based on a large model, and the system comprises a data input module, an AI feature analysis module, a rule generation module, and a visual configuration module, the data input module is responsible for receiving a target website or webpage content, and obtaining an HTML source code or a dynamically rendered complete webpage structure; the AI feature analysis module is used for automatically analyzing structural features and semantic features of a webpage by utilizing a pre-trained AI large model; the rule generation module is used for automatically generating a crawling rule based on an analysis result, wherein the crawling rule comprises a page turning path, list area positioning and field extraction logic; and the visual configuration module is used for providing a visual user interface, displaying an analysis result and allowing a user to manually adjust the generated rule. The problems that an existing web crawler technology is low in efficiency and poor in adaptability in the aspect of webpage rule configuration are solved, the rule generation efficiency is remarkably improved through the system and method, the system and method are suitable for more unknown types of webpages, and the dynamic adaptation and continuous optimization capacity is achieved.

A wearable electronic medical device that stores initial baseline biometric data in memory and determines patient-specific thresholds for monitored biometric parameters. Biometric sensors detect changes in these parameters in response to therapeutic treatment or disease progression, and exceeded thresholds activate actions such as patient notifications, changes in treatment, and transmission of data. Trusted receivers receive suggested diagnoses and treatment options determined by an AI-powered web crawler. The treatment includes electrocuetical and pharmaceutical treatment, and the physiological change includes an indication of a change in the cardiovascular condition. The device determines patient-specific thresholds by applying statistical weighting to monitored biometric parameters and determining an acceptable data set.

The invention relates to the technical field of agriculture, in particular to a large-model-driven agricultural knowledge graph analysis method and system, and aims to realize standardized processing of multi-source heterogeneous data through a three-stage preprocessing process and combine with a field adaptive large-model training technology so as to realize the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis system. A special system containing 2000 + entity types of crops / diseases / farming operation and the like is constructed. In the entity extraction link, the large model zero sample learning ability is utilized, novel agricultural entities can be automatically recognized, the entity recognition accuracy is improved by 35% compared with a traditional method, and particularly in cross-modal alignment of pest and disease damage images and text description, feature vector Euclidean distance minimization is achieved through a ResNet50-BERT fusion model, and the alignment precision reaches 92% or above. The dynamic updating mechanism captures three core periodicals and policy documents in real time on the basis of web crawlers, the monthly updating frequency of the knowledge graph is improved to four times in combination with an incremental updating algorithm, the timeliness and integrity of agricultural knowledge are ensured, and technical guarantee is provided for precise agricultural data management.

The invention relates to the technical field of information management, and discloses a project cloud platform data management method based on a process market, and the method comprises the steps: obtaining process demand information inputted by an enterprise user, and obtaining an enterprise name of the enterprise user; crawling enterprise process information through a web crawler tool, and performing multi-level classification to construct a process market platform; obtaining the business scope and the enterprise scale of the enterprise user according to the enterprise name, and determining the enterprise type of the enterprise user according to the business scope; performing keyword extraction on the process demand information input by the enterprise user to determine a target keyword of the process demand information; and matching a demand flow list in a flow market platform according to the enterprise type, the enterprise scale and the target keyword, and pushing the demand flow list to the enterprise user. According to the method, an intelligent process market platform is constructed, keyword extraction and analysis are carried out on demands of enterprise users, automatic aggregation and accurate matching of process resources are realized, and the process adaptation accuracy is remarkably improved.

The invention belongs to the technical field of hotel operation virtual simulation analysis, and particularly discloses a hotel operation virtual simulation analysis method and system and a storage medium, and the method comprises the steps: integrating construction data, historical operation logs and other multi-source data, and carrying out the multi-dimensional correlation analysis through the simulation initial data; the problem of decision deviation caused by the fact that a traditional method depends on artificial experience is solved, and operation bottleneck problems such as a service efficiency threshold value and resource allocation conflicts can be accurately recognized through a three-dimensional simulation model, so that high-precision data support is provided for optimization decision. Competition pricing data are collected in real time through a web crawler tool, competition influence factors are monitored, the check-in waiting loss probability is analyzed, in combination with environment monitoring data, competition opponent strategy change and market change data can be synchronized in real time, dynamic pricing weight parameters are loaded in a three-dimensional simulation model, the customer waiting duration and the loss rate are reduced, and the user experience is improved. And the technical efficiency of service resource scheduling is improved.

The invention relates to a data processing method and system, in particular to a vocational education multi-source data federation treatment method and system, and belongs to the technical field of vocational education informatization and big data. Then constructing a knowledge graph in a triple form and mapping the knowledge graph to a multi-dimensional tensor space; secondly, multi-source data compatibility is analyzed and evaluated through topological features, and feature fusion maintaining topological invariants is executed; finally, knowledge reasoning and service are provided based on a quantum probability field model, the complex relation expression ability is improved through tensor representation, the heterogeneous data fusion quality is guaranteed through topology durability, the uncertainty in knowledge service is processed by introducing the quantum probability theory, and the reliability of the knowledge reasoning and service is improved. The problems of multi-source heterogeneous, complex relation, insufficient service accuracy and the like of vocational education data are effectively solved, and a comprehensive data federation governance solution is provided for the vocational education field.

The invention provides a personalized safety risk early warning method and system integrated with rapid help calling and a medium, and relates to the technical field of safety early warning, and the method comprises the steps: collecting and obtaining an information platform data set through employing a crawler technology, and carrying out the safety risk recognition, and obtaining a risk region feature set and a risk region position set; calculating to obtain a real-time distance information set of the risk area position set and the current position information of the target user; performing multi-dimensional risk assessment on the risk area feature set and the real-time distance information set to obtain risk area hidden danger factors; and constructing a dynamic safety early warning strategy library, carrying out strategy analysis on the hidden danger factors of the risk area, determining a target safety risk early warning strategy, and carrying out dynamic safety early warning on the target user. The technical problems of incomplete safety risk identification and insufficient early warning accuracy caused by limited data sources in the prior art are solved, and the technical effects of improving the real-time performance and accuracy of risk early warning and the flexibility of emergency response are achieved.

The invention discloses an automatic ransomware collection and analysis method and system based on a large language model in the technical field of network security, and the method comprises the steps: constructing a target data source, collecting a first sample of a ransomware, and capturing the first sample which is actually triggered in an enterprise-level network environment and context behavior data of the first sample from the first sample; an automatic crawler system is established by adopting an open source web crawler framework to establish real-time connection with a malicious code platform, the popularity of a first sample on the malicious code platform is analyzed, and an optimization task is set. According to the method, the natural language processing capacity and the automation technology of a large language model are combined, efficient capture, accurate analysis and quick response of the samples are achieved, an anti-virtualization detection mechanism of the blackmail virus is effectively coped with by implementing an anti-virtualization detection confrontation strategy, and an intelligent protection chain is constructed for network security.

The invention provides a real estate data cleaning method based on the Internet, and belongs to the technical field of data cleaning, and the method comprises the steps: collecting real estate data from a plurality of Internet data sources by using a web crawler technology, monitoring the collection process based on a monitoring tool to generate monitoring data, and setting a transmission set for each piece of real estate information under each data source; respectively counting the type distribution of each house type; performing anomaly identification on the real estate information, and when the anomaly type is related to transmission anomaly, analyzing the update type of the corresponding transmission set to perform anomaly update on the corresponding real estate information; when the exception type is irrelevant to the transmission exception, analyzing an input behavior corresponding to the internet data source, searching and identifying similar information by using an identification field corresponding to exception information, and performing error updating; based on distribution of all types and authority of each internet data source, all house types are arranged in sequence, and format conversion and classified storage are carried out. And the quality and availability of the real estate data are effectively improved.

The invention provides a method and a system for constructing a time sequence knowledge graph in the water conservancy field based on a T-ATLOP model, which are applied to the water conservancy field, mainly comprise two key levels, namely a data layer and a knowledge extraction layer, and realize semi-automatic time sequence knowledge graph construction. The method comprises the following steps: carrying out batch document processing on water conservancy census data by utilizing a large language model through a prompt project, crawling water conservancy consultation of water conservancy halls of all provinces and cities by a web crawler, constructing a time sequence knowledge graph data set HE-DOCU through two-step method labeling of large language model assistance and heuristic strategy labeling, and carrying out a knowledge extraction layer part on the basis of an ATLOP model. A new model T-ATLOP is provided for knowledge extraction. And finally, storing the knowledge tetrad of the knowledge extraction layer by using a graph database to form a water conservancy field time sequence knowledge graph. According to the method, the more perfect water conservancy field time sequence knowledge graph is constructed by fully utilizing the water conservancy information, so that the accuracy and the intelligent level of querying related knowledge by hydrological employees can be improved.

The invention relates to the technical field of safety production management and intelligent analysis, in particular to a production safety accident key cause link identification method based on a complex network, which comprises the following steps of: acquiring structured production safety accident report data from a safety production supervision platform by utilizing a web crawler technology; a TF-IDF algorithm is combined with an industrial standard term library to optimize Jieba word segmentation, and a chi-square statistical method is adopted to calculate the correlation between each risk keyword and an accident category. By fusing text mining, statistical analysis and complex network theories and combining field dictionary optimization word segmentation, multi-accident model adaptation and triple centrality index analysis, the problems that a traditional method is single in analysis dimension, depends on subjective experience and is insufficient in technology integration are solved; efficient utilization of unstructured data, scientific layering of risk factors and accurate identification of key cause links are realized, and objectivity and effectiveness of safety management of a complex production system are effectively improved.

The present application relates to a data processing method and system, in particular to a vocational education multi-source data federal governance method and system, belonging to the field of vocational education informatization and big data technology, which first acquires and preprocesses vocational education multi-source data through network crawler and OCR technology; then constructs a knowledge graph in the form of triplets and maps it to a multi-dimensional tensor space; then evaluates the compatibility of multi-source data through topological feature analysis, and performs feature fusion that maintains topological invariants; finally, based on a quantum probability field model, knowledge reasoning and services are provided, the present application uses tensor representation to improve the expression ability of complex relationships, ensures the quality of heterogeneous data fusion through topological persistence, and introduces quantum probability theory to handle uncertainty in knowledge services, effectively solving the problems of multi-source heterogeneous vocational education data, complex relationships and insufficient service accuracy, and providing a comprehensive data federal governance solution for the vocational education field.

Disclosed in the present disclosure is a fast evaluation method of site seismic liquefaction hazard based on artificial intelligence algorithm: establishing a historical seismic and site information database, the database including a demand input module, a web crawler module, a data processing module, and a database module connected in sequence; a neural network model performs prediction to acquire a post-earthquake site dominant frequency; and, on the basis of the post-earthquake site dominant frequency, acquiring a site earthquake damage degree and seismic performance parameters. The present disclosure solves the problem of fast evaluating post-earthquake site earthquake damage and site seismic performance parameters, and can rapidly evaluate the site liquefaction or softening earthquake damage degree and site seismic performance parameters in given earthquake conditions.

The invention discloses a multi-dimensional hotspot extraction method based on data analysis, relates to the technical field of data processing, and solves the problem that features and hotspot feature sets of text information data, image video data and voice information data are difficult to extract respectively after data is automatically captured in real time and preprocessed by using a web crawler technology. The hot events are difficult to identify by using a clustering algorithm, and analysis and evaluation of time dimension, space dimension and emotion dimension are performed on the hot events; and the development trend of the hot event is difficult to predict. According to the method, text information, image video and voice information data are automatically captured through a web crawler, multi-modal fusion is carried out after preprocessing and hot spot feature extraction, a hot spot event is identified by utilizing a clustering algorithm, and multi-dimensional analysis, evaluation and prediction are carried out on the hot spot event.

The invention discloses a digital financial management system based on big data, and relates to the technical field of financial management. Comprising a data acquisition module for acquiring financial related data through a web crawler unit and a data interface unit and storing the financial related data in an original database; the data preprocessing module is used for performing cleaning, word segmentation and labeling operation on the original data to obtain text data; and the topic analysis module is used for analyzing the preprocessed text data, identifying hot topics, judging sources of the hot topics and adding feature tags. The system covers a plurality of modules of data acquisition, preprocessing, topic analysis, trend analysis, matching comparison, scoring mechanism, investment suggestion generation and the like, and a complete digital financial management ecology is formed. The system can identify hot topics, judge sources of the hot topics and provide personalized investment suggestions according to risk preferences and market conditions of investors; and the satisfaction degree and the investment success rate of investors can be improved.

The invention discloses a data standardization multi-level intelligent mapping method and system based on deep learning, and the method comprises the following steps: dynamically constructing a multi-source standard library, collecting latest standards through an industry standard collection module based on a web crawler, and processing conflicts among different standards through a conflict detection algorithm; performing layer-by-layer verification on the input data from a field level, a structure level and a semantic level in sequence by adopting a three-level intelligent mapping decision model; analyzing semantic ambiguity through the finely-adjusted DeepSeek model, and generating a mapping suggestion based on a confidence coefficient dynamic threshold value; manually corrected data is fed back to the model for incremental training, and the mapping precision is optimized. By means of the collection module based on the web crawler and the enterprise standard analysis module, the accuracy of the standard library is maintained through the conflict detection algorithm, the problem that updating of a traditional standard library lags behind is solved, accurate data mapping can be guaranteed, errors caused by standard lagging are avoided, and the accuracy and timeliness of data processing are guaranteed.

The invention relates to a knowledge graph generation method and system fusing multi-source science and technology data visualization results. The invention provides a systematic solution for solving the problems that in the prior art, multi-source heterogeneous data fusion is difficult, preprocessing intellectualization is insufficient, implicit relation mining is insufficient, and the visualization effect is poor. The method comprises the steps that a data import module obtains data through local and web crawlers, and a keyword is expanded through a GPT model; the preprocessing module is used for cleaning, synonym merging and field standardization; the information processing module generates one-dimensional graph data and a two-dimensional triple, wherein an implicit relationship is predicted by a BERT model and a graph neural network; the graph structure planning module adopts force-oriented layout and dimensionality reduction optimization visualization; and the co-imported network graph is divided into communities through a difference filter and a Louvain algorithm. According to the method, multi-source data is effectively integrated, noise is automatically processed, semantic association is mined, the high-readability chart is generated, and the construction efficiency and the visualization effect of the knowledge graph are remarkably improved.

The invention discloses a network information security protection method based on big data, and relates to the technical field of network information security, and the protection method comprises the following specific steps: S100, data collection: through a web crawler technology and a data interface, carrying out data collection; network data of texts, user behaviors and network traffic are collected in real time from a social media platform, a network service provider and a user terminal, and accurate recognition and early warning of network information security threats are achieved through social media emotion mining and user behavior emotion association analysis. By applying a natural language processing technology and constructing an exclusive emotion dictionary, potential threat speech which contains negative emotions and is related to network security can be accurately recognized, meanwhile, user behavior data are combined, a user behavior emotion association model is established, user behaviors are monitored in real time, the threat recognition accuracy is improved, and the threat recognition efficiency is improved. And powerful support is provided for taking safety protection measures in time.

A system for dynamic cybersecurity scoring using traffic fingerprinting and score improvement, that uses a web crawler that sends message prompts to external hosts and receives responses from external hosts, a time-series data store that produces time-series data from the message responses, and a directed computational graph module that analyzes the time-series data to produce a weighted score representing the overall cybersecurity state of an organization.

The invention discloses a self-adaptive multi-dimensional evaluation high-quality scientific and technological information screening method and system, and relates to the technical field of information screening, and the method comprises the following steps: collecting data from a database by using an API interface and a web crawler technology, and establishing a multi-source data set; establishing a preprocessing data set; performing feature extraction on the preprocessed data set, and establishing a multi-dimensional feature set after standardization processing; carrying out dimension evaluation under a six-dimensional evaluation channel on the multi-dimensional feature set, and establishing a dimension score; and after the dynamic weight mapped by the dimension score is adaptively configured, weighted calculation is executed, and a scientific and technological information screening result is generated. The technical problems that in the prior art, due to the fact that the scientific and technological information screening dimension is single, the evaluation weight is fixed and different scenes are difficult to adapt, the screening result is insufficient in accuracy and comprehensiveness are solved, and the purposes of achieving self-adaptive multi-dimensional evaluation and high-quality screening of the scientific and technological information and improving the screening efficiency are achieved. And the accuracy and comprehensiveness of scientific and technological information screening are improved.

The invention relates to the technical field of asset assessment. The asset evaluation intelligent system based on the AI technology is provided, and the system comprises the steps that web crawler capture processing is conducted on a data source of a public network channel, and a first type of heterogeneous data is obtained; aPI interface docking processing is carried out on the data source of the enterprise internal system, and second-class heterogeneous data is obtained; performing logic splicing processing on the first type of heterogeneous data and the second type of heterogeneous data to generate multi-source heterogeneous asset evaluation data; performing model matching processing on the asset value characteristic matrix based on the asset type, and selecting an estimation model architecture; performing interpretability analysis processing on the dynamic evaluation parameters to generate a key factor contribution degree and an interpretable evaluation conclusion; and performing document processing on the contribution degree of the key factor and the interpretable evaluation conclusion to generate an interactive evaluation document so as to improve the multi-source data integration capability, enhance the risk dynamic evaluation adaptability and strengthen the real-time relevance and interpretability of the evaluation document.

Embodiments of the technology described programmatically decrease the number of spam Uniform Resource locators (URLs) that are accessed from untrusted domains when the subdomain prefix is above a threshold probability of having been randomly generated. In this regard, prior to adding a discovered set of URLs to a crawl queue of a web crawler, the URLs are filtered into URLs from trusted domains and untrusted domains determined by a statistical model. The trusted domain URLs are added to the crawl queue, and the remaining URLs are sandboxed to filter out spam URLs. The subdomain prefixes of the sandboxed URLs are applied to a neural network to determine the probability that the subdomain prefixes are randomly generated. When a subdomain prefix is above a threshold probability of having been randomly generated, the subdomain is determined to be a spam subdomain and can be blocked.

The invention discloses a talent data and post matching method based on weight adjustment and related equipment, which can effectively improve the matching accuracy, help recruiters to quickly position adaptive candidates, reduce the manual screening cost and improve the recruitment efficiency, thereby meeting the requirements of quick recruitment of enterprises and efficient job hunting of talents. The method comprises the following steps: capturing multi-source data by utilizing a web crawler, and carrying out natural language processing on the multi-source data to obtain structured data; post time sequence characteristics of the structured post data in the structured data are extracted; when it is determined that the post time sequence features meet post requirements, performing multi-dimensional analysis on the post time sequence features to correct deviations and fuse weights, and then generating target post time sequence features; extracting talent features of the structured talent data in the structured data; and converting the target post time sequence features and the talent features into target vectors, and inputting the target vectors into a deep learning model to obtain a prediction matching degree of talent data and posts.

A vulnerability documentation system detects vulnerabilities having outdated or undocumented formatted descriptions for corresponding remediation actions. A web crawler crawls the Internet for configuration data for software / firmware affected by the detected vulnerabilities and descriptive content for the remediation actions. The vulnerability documentation system prompts and LLM with a prompt for each detected vulnerability comprising instructions to generate a formatted description for remediation actions using the crawled configuration data / descriptive content. The vulnerability documentation system then populates natural language descriptions of remediation actions from the formatted descriptions and pushes the natural language descriptions to affected devices.