25results about How to "Improve regulatory efficiency" patented technology

The invention discloses a cross-platform network content intelligent supervision method and system based on multi-modal AI, relates to the technical field of network content supervision, realizes unified acquisition and management of cross-platform content by establishing a multi-platform data acquisition adapter, and solves the technical problem that an existing system cannot realize cross-platform supervision. Secondly, deep fusion analysis of texts, images and videos is realized by constructing a multi-modal AI analysis engine, and the accuracy of content understanding and risk identification is greatly improved; accurate risk classification is realized by establishing a grading risk label system, and a scientific basis is provided for supervision decision making; and finally, by generating an intelligent supervision report and establishing a real-time monitoring early warning mechanism, a comprehensive and visual supervision analysis tool is provided for supervisors, and the supervision efficiency and effect are remarkably improved. Meanwhile, by adopting a ReAct reasoning framework, the system can perform multi-step reasoning analysis and perform comprehensive judgment in combination with analysis results of texts, images and videos, so that the limitation of single-modal analysis is avoided, and the accuracy and reliability of risk identification are improved.

In order to solve the problem of whether the construction worker wears a reflective vest in the prior art, the application provides a real-time high-precision reflective vest wearing state recognition method and device. The method first acquires a construction site image, then inputs the construction site image into a target detection network structure model, and finally obtains a target detection prediction result image of the construction worker and the reflective vest category. Whether the construction worker wears the reflective vest is determined by calculating the coincidence degree of the anchor frame of the reflective vest and the construction worker, so as to realize the identification of whether the construction worker of high-altitude operation or heavy equipment wears the reflective vest. The application is not only suitable for reflective vest detection in multiple scenes, but also has strong robustness. In addition, it can be applied to low-cost and lightweight operation equipment in the construction site with small parameter operation amount, so as to promote the realization of the intelligent construction site.

The utility model belongs to the technical field of safety valve calibration detection, specifically relates to a kind of safety valve digitalization supervision system, including data acquisition unit, electronic identification unit, hand-held terminal unit and data processing supervision unit, data acquisition unit includes pressure sensor, inspection data acquisition platform, data collector and calibration platform controller, electronic identification unit includes explosion-proof fixture and RFID radio frequency tag, RFID radio frequency tag is fixed on safety valve ontology by explosion-proof fixture, hand-held terminal unit includes hand-held PDA, and hand-held PDA is connected with RFID radio frequency tag by radio frequency signal, and data processing supervision unit includes cloud server and system terminal, and cloud server is connected with electronic identification unit, hand-held terminal unit and data acquisition unit respectively by wireless network.The utility model can realize the management supervision of safety valve full life cycle.

This invention discloses a method and system for risk assessment and graded early warning of offshore wind farm vessels. The method involves collecting and fusing multi-source sensing data; performing spatiotemporal alignment and fusion processing on the fused multi-source sensing data to generate a bird's-eye view feature representation containing spatiotemporal characteristic information of the vessel; based on the bird's-eye view feature representation, employing a fusion prediction model combining a vessel motion physics model and a time series prediction algorithm, calculating the motion reachability set of the target vessel within the predicted time period; conducting a risk assessment based on the spatial relationship between the motion reachability set and a preset safe area of ​​the wind farm, and triggering graded early warnings based on the assessment results. This invention solves the problems of blind spots in single-source monitoring and high false alarm rates in traditional early warning systems, achieving accurate and dynamic assessment and proactive early warning of vessel collision risks in offshore wind farm waters, significantly improving regulatory safety and efficiency.

The invention relates to the technical field of medical informatics, in particular to an internal medicine chronic disease medication compliance safety monitoring system fused with a block chain, and is used for solving the problems that an existing system is lack of medicine traceability, poor in data safety, inaccurate in compliance judgment and insufficient in cross-mechanism collaboration. The system comprises a multi-modal intelligent medication terminal, an edge-federal cooperative processing node, a block chain main and side chain cluster, a fusion type dynamic evaluation platform and a self-adaptive hierarchical intervention engine, and each module encrypts link interaction through a national secret SM9 algorithm. The terminal captures the medicine taking action in a non-contact mode, collects physiological indexes and verifies the legality of the medicine in a double-code mode; the edge-federated node preprocesses data and performs federated learning to protect privacy; the main chain and the side chain of the block chain store traceability core data and real-time information; the evaluation platform outputs a four-level compliance result; the intervention engine dynamically adjusts the policy. The system realizes drug and data dual safety, improves the monitoring accuracy and intervention effect, adapts to multiple scenes and special groups, and assists cross-mechanism collaborative management and control.

The application discloses an intelligent supervision system and method for electromechanical equipment based on artificial intelligence, relates to the technical field of electromechanical equipment supervision, and comprises an attribute combination set generation module, a key matching rule output module, a necessary optimization index analysis module, a comprehensive service influence degree analysis module, a rule updating module and a secondary verification response module; the attribute combination set generation module is used for generating an equipment attribute combination set; the key matching rule output module is used for extracting a key matching rule of an electromechanical equipment monitoring event record; the necessary optimization index analysis module is used for matching a necessary optimization index of a rule; the comprehensive service influence degree analysis module is used for analyzing a comprehensive service influence degree of a gray scale rule in which equipment attributes of a to-be-analyzed equipment group are located; the rule updating module is used for updating a key matching rule of the to-be-analyzed equipment group under a corresponding attribute combination; and the secondary verification response module is used for performing secondary verification on a target electromechanical equipment group in which the key matching rule is replaced.

The invention relates to the technical field of two-dimensional code traceability, and particularly discloses a product full-cycle information traceability system and traceability method based on a two-dimensional code, and aims to reduce potential safety hazards of traceability data and reduce anti-counterfeiting risks. Comprising a data acquisition module used for acquiring key nodes of a product in a full life cycle, and automatically acquiring process data, environment data and operation subject information associated with the key nodes through deployed sensors and an automatic identification device; the data processing module is used for receiving, cleaning and integrating the process data, the environment data and the operation subject information associated with the key nodes to generate a product unique identifier, and constructing a structured full-life-cycle digital file through the product unique identifier; the dynamic encryption and traceability generation module is used for dynamically encrypting corresponding key nodes in the full-life-cycle digital archive and generating real-time two-dimensional codes; and the query verification module is used for scanning according to the real-time two-dimensional code to generate tracing information.

The invention discloses an aquatic product processing security management and control system based on blockchain traceability, and relates to the technical field of network security and data traceability, and the system comprises the steps that a multi-source data collection module collects processing whole-link parameters and related information, encrypts and uploads the parameters and related information, and associates an identifier; the data preprocessing and encryption module cleans standardized data, encrypts sensitive information and generates a unique identifier; the block chain traceability core module constructs an alliance chain to realize data chaining and non-tampering traceability; the intelligent safety management and control module evaluates compliance in real time and links equipment to dispose risks; the distributed data storage module adopts a hybrid architecture to guarantee data reliability; and the visual interaction and early warning module presents information and pushes early warning. According to the invention, full-chain data credible traceability is realized based on the block chain, risks are intelligently controlled and accurately identified and disposed, multi-agent collaboration is efficient, and data security is guaranteed; the method adapts to multi-scene requirements, improves processing compliance and emergency response capability, and assists in industrial safety management and control upgrading.

The application discloses a kind of supervision method and device, equipment, program product, storage medium, the method comprises: obtaining the first video set of first camera set;Wherein, the video in the first video set corresponds with the camera in the first camera set;According to the first video set determines target interest content;Obtain the first information corresponding to the target interest content;The first information includes the first sub-information of the target interest content and the second sub-information of target camera;The target camera is the camera corresponding to the video where the target interest content is located;According to the first information determines data association result, the data association result is the result of the data association of the target camera and first system;The association result is used to the supervision of the target interest content.

The application discloses a kind of intelligent management system and method of product oil transaction fusing knowledge graph, it is related to the technical field of intelligent management, including the following steps, step S100, based on nonlinear fork type bifurcation model, the stable boundary of entity association is obtained and entity is filtered, step S200, construct heterogeneous graph convolution network, based on graph query language and heterogeneous graph convolution network, obtain knowledge spectrum diagram, step S300, based on knowledge spectrum diagram identification current abnormal node and trace back first transaction path.The application is by introducing nonlinear dynamics method to process the stability screening of transaction entity, eliminate risk entity from the root, realize the abnormal tracing based on graph reasoning, provide the complete transaction path of explainable, upgrade knowledge graph from static knowledge base to dynamic analysis engine, promote product oil transaction management from experience-driven to data intelligent-driven, have significant technical advantages and application value in improving regulatory efficiency, preventing and controlling transaction risk, supporting tax compliance and the like.

This invention discloses an intelligent detection method for smoking behavior in no-smoking areas based on target detection and image segmentation. The method involves scaling up the image / video frames of the no-smoking area to be detected, and then preprocessing the scaled images using a standardized approach. A YOLO11 target detection model is then constructed to detect smoking-related targets in the preprocessed no-smoking area images, generating detection images. Rectangular regions containing suspected cigarettes and related human hands are cropped from the detection images and input into a SAM image segmentation model for optimization. Simultaneously, the center coordinates of the suspected cigarettes are also input into the model, achieving pixel-level precise segmentation of the cigarettes, hands, and smoke, extracting accurate target contour features. Finally, by combining spatial relationships and feature judgment rules, the final identification and determination of smoking behavior is completed.

According to the supervision-oriented AI robot system for auditing the network audio-visual content, a simulation terminal module generates a simulation operation signal by simulating a real user operation behavior, and the simulation operation signal comprises a dynamic event flow; the supervision strategy driving search module generates a content search signal according to the simulation operation signal; the multi-modal collaborative analysis module receives the multi-modal content data signal, and generates an illegal content identification signal through collaborative processing of a multi-modal large model and a professional small model; the risk early warning module generates a risk early warning signal in real time according to the illegal content identification signal, and the risk early warning signal triggers automatic notification and illegal content marking operation on a supervisor; the evidence obtaining and storing module receives an evidence storing trigger signal and an associated multi-mode content data signal. According to the supervision-oriented AI robot system for auditing the network audio-visual content, the problems that a traditional auditing mode is low in efficiency and different in standard in an environment of massive network audio-visual content can be solved.

Embodiments of the present application disclose a kind of based on video stream's agricultural land biological behavior monitoring and analysis method, device and equipment.The based on video stream's agricultural land biological behavior monitoring and analysis method includes: obtaining agricultural land monitoring video stream data;According to agricultural land monitoring video stream data, the category and behavior trajectory of each target organism in video stream are determined;Based on the category and behavior trajectory of each target organism, whether each target organism has harmfulness to agricultural production is determined;If target organism has harmfulness to agricultural production, then the corresponding alarm information is generated and sent to user confirmation and storage.Through the method, the following can be realized: using agricultural land monitoring video stream data as data source, the category and behavior trajectory of target organism in field of view range are identified and analyzed, to judge the harmfulness of target organism to agricultural production, if certain harm is generated, alarm information is generated to notify relevant user.To realize the intelligent supervision of agricultural land, reduce labor cost, improve the efficiency of supervision.

This invention provides a method and system for dynamic monitoring of anthropogenically disturbed soil and water conservation patches based on a knowledge graph. The method specifically includes: constructing a dynamic knowledge representation framework: dividing soil and water conservation knowledge into static and dynamic knowledge, and establishing semantic associations between static and dynamic knowledge; constructing an ontology: defining entity categories, defining relationship types between entities based on the dynamic knowledge representation framework, and defining entity attributes; constructing an ontology structure containing entity categories, relationship types, and attribute constraints, and generating relationship annotation templates; knowledge extraction and fusion: extracting entities and relationships from remote sensing image data based on the relationship annotation templates, and fusing the extracted knowledge; constructing entity-relationship-attribute triples to build a knowledge graph; and assessing the risk of soil erosion in dynamically disturbed soil and water conservation patches based on the constructed knowledge graph. This invention provides a scientific basis for decision-making by regulatory authorities and has broad engineering application value.

This invention discloses a self-governing method for VOCs quality control, belonging to the field of VOCs governance technology. The method includes: setting up a VOCs information model for users; improving the VOCs monitoring system based on the VOCs information model; conducting real-time monitoring through the VOCs monitoring system to obtain VOCs monitoring data; matching applicable measures from a preset measure library based on the VOCs information model; performing governance simulation based on the applicable measures and the VOCs information model to obtain simulated governance expected data; displaying the simulated governance expected data in the VOCs information model; acquiring governance needs in real time; analyzing the governance needs based on the VOCs information model; and determining the recommended governance method to meet the governance needs. This invention not only solves the self-governing VOCs quality control problem of a single enterprise, but also achieves data interoperability, resource collaboration, and intelligent decision-making for multiple users by constructing a regional / industry-level comprehensive VOCs governance platform.

The invention relates to the technical field of security house intelligent supervision, and discloses a security house subleasing and lending interception method based on person and certificate verification and behavior analysis, and the method comprises the following steps: S1, collecting the biological characteristic information and identity card information of a tenant when the tenant checks in, performing real-time verification and strong binding on the biological characteristic information and the identity card information through a management platform, and setting the successfully bound biological characteristic information as the primary door opening authority of the tenant; according to the method, biological characteristics are strongly bound with people and certificates, so that masquerading and permission lending are completely eradicated; data are collected by the intelligent door lock, the management platform analyzes abnormity, automatic all-weather supervision is achieved, and cost reduction and efficiency improvement are achieved; early warning is carried out through behavior analysis, authority is dynamically adjusted, and beforehand prevention and in-event interception are achieved; multiple key links are linked to construct a closed-loop system, rent payment reminding is enhanced, the house resource utilization rate is improved, and house management refinement and compliance are comprehensively improved and guaranteed.

The application discloses a propolis component intelligent identification and tracing system and method, relates to the technical field of computer vision and pattern recognition, and comprises the following steps: a data acquisition module is used for collecting spectral data, chromatographic data and traceability information such as origin and processing; a data preprocessing unit is used for filtering, baseline correction and denoising, eliminating overlapping peaks, normalizing data and synchronizing time stamps; a component feature extraction module is used for screening 10 spectral characteristic peaks, calculating chromatographic characteristics, and reducing the dimensionality to 32 dimensions after splicing through principal component analysis; an intelligent identification module is used for determining purity grades and confidence levels by using a CNN-LSTM model; a traceability management module is used for storing full-link information in a block chain and managing and querying permissions in a distributed database; and a result output module is used for visually displaying results and triggering an alarm when the confidence level or the traceability integrity is not up to standard. The application improves identification accuracy through multi-source fusion, the model can be updated online to adapt to new scenarios, and the quality of propolis and the market order are efficiently guaranteed.

The application discloses a kind of school canteen special fund use compliance intelligent verification method and system, it is related to financial audit and computer vision technical field.The method includes: obtaining the financial accounting data and business field image data of canteen;Through image recognition technology, the category and component of the dishes distributed by the special fund corresponding window are determined;Combined with the pre-stored recipe database, the theoretical food material consumption data is calculated;Theoretical consumption data is associated and compared with the actual delivery cost recorded in financial accounts;According to the comparison result, compliance verification report is automatically generated.The system includes data acquisition interface, visual recognition unit, recipe database, verification engine and report generation unit for realizing the above method.The application realizes the automation, traceable verification of financial compliance requirements such as "special purpose fund" by fusing multi-modal data and intelligent analysis, changes traditional post-event manual audit into in-process intelligent prevention and control, effectively improves the transparency, accuracy and efficiency of fund supervision.

The invention discloses a sludge state monitoring method and device, electronic equipment and a storage medium. According to the sludge state monitoring method and device, due to the fact that sludge multi-dimensional state parameters are integrated and spatial-temporal characteristic analysis is carried out, dynamic association among the parameters is mined to identify a sludge state change rule and predict a trend; therefore, the technical problem that in an existing sludge monitoring method, due to the fact that a single parameter threshold value is adopted for judgment or simple PID control is adopted, and an inter-parameter dynamic correlation model is not established, the abnormal recognition misjudgment rate is too high can be solved. The technical effects of reducing the sludge state abnormity identification misjudgment rate, improving the sludge monitoring precision and foresight, optimizing the regulation and control efficiency of the sewage treatment equipment and guaranteeing the stable operation of the sewage treatment process are achieved.

The invention discloses a method, a device and a system for realizing data access control based on a digital contract, relates to the field of data security, solves the problem of insufficient security of existing data access, and comprises the following steps: S1, acquiring an electronic contract, generating a digital contract element list, and checking; s2, generating a contract according to a consistency verification result to obtain a digital contract, and deploying the digital contract in combination with a data flow alliance chain; s3, extracting a data connection node to verify and judge the data request, and controlling the data according to a verification and judgment result; s4, performing authority control on the data user, performing data output on the data user based on the authority control, and collecting and uploading full-flow information of data output to an alliance chain for evidence storage; s5, monitoring the digital contract, and controlling the digital contract in real time; according to the invention, the security and stability of data access can be effectively improved.

The present application provides an EOBD-based pollution source online monitoring equipment operation quality intelligent diagnosis method and system, to solve the technical problems that the existing technology highly depends on manual experience, the troubleshooting efficiency is low and the problems are easy to be missed, and the specific content comprises: S1. Constructing an online monitoring equipment operation quality intelligent diagnosis model and loading it to a mobile terminal, completing the parameter initialization of the online monitoring equipment operation quality intelligent diagnosis model; the online monitoring equipment operation quality intelligent diagnosis model comprises a plurality of digital sub-models generated based on expert checking logic conversion; the present scheme breaks the high dependence of traditional on-site inspection on manual experience, shortens the time consumption of single-point inspection from days to ten minutes, effectively avoids the omission and subjective bias of manual troubleshooting; at the same time, without deploying high-cost hardware on the enterprise side, the low-cost full-process closed-loop management of on-site non-human intervention type rapid inspection and problem rectification is realized, and the precision and supervision efficiency of environmental protection law enforcement are significantly improved.

The embodiment of the invention provides a power grid equipment supervision method and device and a related product. The method comprises the following steps: acquiring operation data of power grid equipment; the operation data comprises at least one of the following items: a processor occupancy rate, a memory occupancy rate, a storage occupancy rate, firmware version information and a system log; according to the operation data, determining whether the power grid equipment meets an upgrading condition, and upgrading the power grid equipment under the condition of determining that the power grid equipment meets the upgrading condition; the upgrading condition comprises at least one of the following items: the performance of the power grid equipment is reduced, the firmware version of the power grid equipment is not a first version, or the power grid equipment has vulnerabilities; and performing first detection processing on the upgraded power grid equipment, and sending a first detection result corresponding to the first detection processing to the target platform. According to the embodiment of the invention, the power grid equipment can be supervised in real time, and the efficiency of supervising the power grid equipment is improved.

The invention relates to the technical field of novel organic silicon materials and fine chemical engineering, in particular to redox response type cross-linked silicon paste containing fullerene and ferrocene groups as well as a preparation method and application of the redox response type cross-linked silicon paste. The preparation method of the cross-linked silicon paste comprises the following steps: adding a vinyl modified filler, a fullerene cross-linking agent and a resin cross-linking agent into carrier oil under a stirring condition, mixing, adding a catalyst, and carrying out a hydrosilylation reaction to form primary cross-linked silicon paste particles with an interpenetrating network structure; and adding a functional end-capping reagent and a catalyst, continuously reacting, and adding carrier oil after the reaction is finished. The cross-linking density is controllable, the mechanical strength is high, the surface hydrophilic and hydrophobic intelligent switching function is achieved, and intelligent conversion of hydrophilic easy emulsification during processing and hydrophobic strong foam inhibition during use is achieved. The organic silicon defoaming agent is applied to a defoaming agent, hydrophilic in an oxidation state assists emulsification, hydrophobic in a reduction state assists foam inhibition, and the contradiction that the emulsification stability and the foam inhibition durability of a traditional organic silicon defoaming agent are difficult to consider at the same time is solved.

The embodiment of the invention provides a nursing monitoring method and device, equipment and a storage medium, and particularly discloses the steps that nursing image data generated in the nursing process are acquired, and the nursing image data comprise nursing video data and nursing audio data; performing frame extraction processing on the nursing video data to obtain multiple frames of images arranged according to a time sequence, inputting the multiple frames of images into a pre-trained multi-modal large model, and receiving a first recognition result output by the multi-modal large model; converting the nursing audio data into text data, inputting the text data into a pre-trained large language model, and receiving a second recognition result output by the large language model; the second recognition result is obtained after the large language model performs text analysis on the text data according to a preset nursing specification; and obtaining a nursing monitoring result corresponding to the nursing image data according to the first identification result and the second identification result.

This disclosure relates to a method and apparatus for detecting anomalies in goods. The method includes: acquiring declaration data and preprocessing the declaration data; generating time series features based on the preprocessed declaration data; generating category embedding features based on the preprocessed declaration data; generating intelligence features based on the preprocessed declaration data; fusing the time series features, category embedding features, and intelligence features to generate fused features; inputting the fused features into a time series model to generate a predicted import quantity; and comparing the predicted import quantity with the declared import quantity in the declaration data to determine whether there are anomalies in the declaration data.