4269results about How to "Improve integrity" patented technology

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

The invention relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention discloses an object labeling method and system based on laser point cloud, particularly relates to the technical field of laser point cloud processing, and is used for solving the problem of inaccurate labeling result caused by a complex reflection phenomenon caused by a non-Lambertian surface in the prior art. The method comprises the steps of obtaining original point cloud data collected by a laser radar and performing preprocessing; identifying a non-Lambertian reflection region based on the reflection intensity information and the geometric topology feature, and carrying out filtering correction; correcting false mirror image points through multi-layer iteration correction, and reconstructing boundaries to generate corrected point cloud data; grouping and classifying target objects in the corrected point cloud data based on a clustering algorithm combining global features and local features; finely dividing the boundary of the preliminary segmentation result by adopting an attention mechanism network, and evaluating the uncertainty of a boundary region through a local mutual information entropy; and adjusting a boundary division strategy according to an uncertainty evaluation result, and generating an optimized segmentation result.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention provides a distributed power data asset management privacy protection encryption architecture design method, which comprises the steps of generating a watermark with robustness and imperceptibility by adopting a content-based digital watermarking algorithm according to statistical characteristics of power data assets, and storing a watermark embedding process and an extraction result through a blockchain; a graph-based data model is adopted, the electric power data assets carrying the watermarks serve as nodes, the data flow relation serves as edges, and an electric power data asset consanguinity graph is constructed; for distributed storage of the blood relationship map, a P2P network topology is constructed by adopting a block chain and a distributed hash table, and blood relationship map data is stored in each node of an alliance chain in a distributed manner; and verifying the digital watermark information embedded in all circulation path nodes through which the power data assets pass in the blood relationship map, calculating the similarity between the extracted watermark and the original watermark, calculating the distortion rate, and judging whether the power data assets are tampered or not.

The invention discloses a large-scale point cloud registration method, system and device based on a power grid power transmission line and a storage medium, and the method comprises the steps: collecting point cloud data of the power transmission line under different visual angles, and forming a source point cloud set and a target point cloud set; down-sampling point cloud data in the source point cloud set and the target point cloud set, dynamically adjusting the sampling granularity of different density regions, and obtaining representative point cloud data from the source point cloud set and the target point cloud set; extracting point cloud feature points, accelerating neighborhood search through a KD-Tree structure, and adaptively adjusting a neighborhood search radius; constructing a geometric topology model; performing coarse registration based on an improved RANSAC algorithm, selecting non-collinear feature points, and optimizing a transformation model; an improved ICP algorithm is adopted to carry out fine registration, and normal vector features are utilized to introduce weighting factors to optimize registration precision. According to the invention, large-scale power transmission line point cloud data can be efficiently and accurately processed, and the method is especially suitable for power inspection, three-dimensional modeling and maintenance scenes.

The invention discloses a power grid data management system and method based on artificial intelligence data analysis in the technical field of power grid anomaly detection. The method comprises the steps of obtaining operation data of nodes in a power grid system; calculating an exception comprehensive score of the node based on the operation data by combining an exception detection model deployed by edge computing equipment; determining an abnormal response level of the node based on the abnormal comprehensive score; and triggering a corresponding response strategy based on the exception response level of the node, and performing exception processing on the node. According to the method, the lightweight anomaly detection model is deployed on the edge computing equipment, the power grid operation data is monitored in real time, the abnormal condition is quickly identified, the reliability and safety of power grid operation are improved, and the problems of response delay, network bandwidth limitation, single-point fault and the like of a traditional centralized anomaly detection system are solved.

The invention discloses a block chain-based digital right encryption management method and system, and relates to the technical field of digital right security management. According to the method, a unique identifier is generated for digital asset information through a Hash algorithm, data privacy is ensured by using asymmetric encryption, and asset uniqueness identification and information encryption protection are realized; according to the method, encrypted asset information is subjected to uplink evidence storage through an intelligent contract, node verification is carried out by adopting a consensus algorithm, tampering resistance, authenticity and transparency of the asset information are analyzed, authenticity of digital assets and safety and reliability of storage are guaranteed, legality of ownership transfer is verified through a consensus mechanism when the digital assets are transacted or transferred, and security and reliability of the digital assets are guaranteed. It is ensured that the transaction meets the preset compliance standard, the asset state on the block chain is updated in real time, and the security and compliance of the transaction are improved; the asset ownership history and the transaction path are tracked and recorded through the traceability mechanism of the block chain, and the traceability and transparency of the asset circulation process are realized.

The invention discloses an enterprise-level knowledge base construction method based on a large model. The enterprise-level knowledge base construction method comprises the following specific steps: acquiring original data of an enterprise and preprocessing the original data to obtain first target data; performing knowledge extraction and representation based on the first target data to obtain second target data; based on the random-degree large model and the second target data, performing knowledge graph construction and storage to obtain an enterprise-level knowledge base; performing knowledge retrieval and recommendation on the enterprise-level knowledge base by using a modular retrieval enhancement generation method; and periodically updating and maintaining the enterprise-level knowledge base. According to the method, keywords and concepts in various knowledge carriers such as internal documents, reports and communication records of enterprises can be automatically identified based on the strong analysis capability of the random-degree large model, and automatic association among knowledge elements is realized; the enterprise-level knowledge base is quickly constructed, the knowledge base is periodically updated and maintained in time, the content of the enterprise-level knowledge base is enriched, and a foundation is laid for subsequent enterprise knowledge search.

The invention belongs to the technical field of unmanned aerial vehicle inspection, and particularly provides a photovoltaic power station inspection method and system based on an unmanned aerial vehicle and a GIS, and the method comprises the steps: obtaining initial geographic information data of a photovoltaic power station, enabling the unmanned aerial vehicle to execute initial surveying and mapping flight, collecting high-resolution image data, and constructing a three-dimensional model of the photovoltaic power station; fusing the high-resolution image data with the initial geographic information data to generate a multi-factor initial intelligent inspection path; the unmanned aerial vehicle flies along the initial intelligent inspection path, collects multi-modal data of the photovoltaic module, and transmits the multi-modal data to a GIS for data fusion and updating to obtain fused data; generating a fault distribution thermodynamic diagram and a maintenance priority map; the inspection path is dynamically adjusted, and the three-dimensional model of the photovoltaic power station is updated. According to the method and the system, comprehensive inspection can be carried out on the photovoltaic power station, the inspection accuracy and efficiency are improved, the inspection path can be dynamically adjusted, unnecessary repeated inspection is reduced, and the inspection cost is effectively reduced.

The invention discloses a reservoir bank slope geological disaster risk identification method based on an unmanned aerial vehicle laser radar, and relates to the technical field of reservoir bank slope prevention and control, an unmanned aerial vehicle is used for carrying laser radar equipment, flight scanning is carried out on a reservoir bank slope area, and three-dimensional point cloud data is obtained. According to the reservoir bank slope identification method based on the unmanned aerial vehicle laser radar detection data, high-precision terrain and geological information of the reservoir bank slope is rapidly acquired in a non-contact manner by using the three-dimensional imaging capability of the unmanned aerial vehicle laser radar technology, so that the data acquisition efficiency and safety are greatly improved; the method also remarkably improves the precision and integrity of data, accurately captures key topographic factors such as topographic relief, gradient, slope direction and the like through laser radar scanning, and provides a solid foundation for the accurate recognition of bank slope types in combination with geological factors such as geological structures, lithology and the like, so that the prediction and evaluation of bank slope disasters are more accurate. Preventive measures can be taken in advance, and disaster risks are reduced.

The invention relates to the technical field of supply chain management, in particular to a block chain-based culture product tracing method and system, and the method comprises the following steps: collecting the type, producing area and growth cycle data of culture products, distributing an identifier for each data item, coding the data and the associated identifiers in an upload format, and storing the data and the associated identifiers in a database; and uploading the data to a block chain network through an encryption channel, and generating a coded data record. According to the method, the timestamps and the data records are bound, each record is processed by using the hash function, and the security of each supply chain node is verified, so that not only is the integrity of data processing improved, but also an effective verification means is provided for consumers, and the security of the consumers is improved by decrypting and verifying the encrypted data. The security and privacy protection of data in storage and transmission are ensured, the production and circulation process of products is tracked through a block chain query function, the food security is improved, the supervision compliance is enhanced, and a powerful tool is provided for solving fraud and pollution problems in a supply chain.

The invention provides an unstructured data automatic processing method and system based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: obtaining unstructured data through an intelligent data collector, building a data feature fingerprint database, constructing a data quality evaluation model through an incremental learning algorithm, and carrying out the data standardization processing; extracting dominant features and recessive features by using a multi-layer cascade network structure and a self-attention mechanism, and constructing a feature association graph and a bidirectional reasoning channel; and performing rule matching and causal reasoning based on the domain knowledge base, and finally realizing automatic structuring of the unstructured data. According to the method, the automation degree and the accuracy of data processing are improved, the manual intervention cost is reduced, and the method has relatively high universality and expandability.

The invention provides an unmanned aerial vehicle route layout method and device for complex terrains, equipment and a medium, and relates to the technical field of unmanned aerial vehicle aerial photogrammetry. The method comprises the following steps: carrying out preliminary region division on a terrain region to be surveyed, and determining a plurality of region units; clustering the area units according to topographic observation data to obtain survey sub-areas; determining a survey priority corresponding to each survey sub-region, and matching a basic flight route corresponding to the unmanned aerial vehicle according to the survey priority; and splicing the different types of basic flight routes corresponding to the survey sub-regions, and performing optimization adjustment on the spliced basic flight routes to obtain a flight route of the target unmanned aerial vehicle so as to perform topographic survey on the topographic region to be surveyed. According to the technical scheme, the route planning of the unmanned aerial vehicle can adapt to various types of landform features, and the safety and aerial survey efficiency of the unmanned aerial vehicle are ensured while the integrity and accuracy of aerial survey data are improved.

The invention relates to the technical field of data processing, and discloses a multi-granularity extraction and enhancement method, device and equipment for multi-source heterogeneous data. The method comprises the following steps: performing multi-level feature extraction on a multi-modal data set and original feature parameters to obtain a multi-modal feature set; performing correlation analysis on the multi-modal feature set to obtain a feature correlation matrix and optimized feature representation; performing cross-modal knowledge migration processing on the feature incidence matrix and the optimized feature representation to obtain migration feature parameters; performing dynamic fusion processing on the migration feature parameters to obtain target fusion features; and performing multi-task segmentation processing according to the target fusion features to obtain a plurality of sub-task sequences, performing time sequence enhancement and multi-task fusion analysis on each sub-task sequence to obtain target extraction data, and performing visual display on the target extraction data. According to the method and the device, the efficiency and the accuracy of multi-granularity extraction and enhancement for multi-source heterogeneous data are improved.

The invention discloses an emotion-enhanced digital human driving and presenting system and method, and belongs to the technical field of computers.The system comprises an audio decoupling module, a potential diffusion model module, a human motion prior module, a training module and a rendered image generation module; predicting and reconstructing the facial expression and gesture action sequence to obtain a final action sequence; and generating a neural rendering image based on the final action sequence. According to the method, through voice input, emotion and style features in voice are captured, a cross attention feature fusion mechanism is introduced to enhance expression of emotion and personal style features, a voice-driven gesture generation task is expanded to a facial expression and gesture action synchronous generation task, the integrity of generated digital human actions is realized, and the user experience is improved. The emotional expressive force of facial expressions and gestures of the digital human is greatly improved.

The invention belongs to the technical field of perovskite solar cells, and particularly discloses a self-assembly monomolecular film hole transport material based on diphosphoric acid and a preparation method and application thereof.The diphosphoric acid serves as an anchoring group, the chelation between the material and a conductive base can be effectively enhanced, the self-assembly characteristic of the material on the surface of a substrate is improved, and the hole transport efficiency is improved. The compact and smooth self-assembled monomolecular film is formed. Meanwhile, the electron and space effects of alkyl chains or benzene ring connecting units are regulated and controlled, so that the molecular stereo configuration and energy level matching are optimized. The material disclosed by the invention is simple in synthesis steps and low in preparation cost, and industrial synthesis of the material can be realized. When the material is applied to an inverted perovskite solar cell as a hole transport layer, photoelectric conversion efficiency exceeding 24.7% and excellent device stability performance can be obtained, and huge commercial prospects are shown.

The invention discloses a building operation and maintenance method and system based on digital twinning, and relates to the technical field of building operation and maintenance, and the method comprises the steps: collecting key equipment data of a building, and constructing a three-dimensional digital twinning model; extracting feature data of the collected data, identifying an abnormal detection frame by using a target detection model, and combining to generate an equipment state vector; calculating the transition probability of a state component according to the equipment state vector, determining the fault probability of the equipment, and carrying out the residual life prediction through an LSTM model; a criticality score and a usage intensity score are calculated. According to the method, vibration signal power distribution is extracted, quantitative indexes are provided for frequency domain analysis of the operation state of key equipment, follow-up state evaluation is facilitated, dynamic changes of the regional environment are reflected by calculating the change rate of the temperature and humidity values, data support is provided for analyzing the influence of the external environment on the operation state of the equipment, and the reliability of the equipment is improved. Multi-dimensional information of the equipment operation state and the external environment is included through the fusion vector.

The invention provides a lightweight underwater image enhancement method based on adaptive feature fusion, and the method comprises the steps: decomposing an underwater image enhancement task into a strong prior enhancement task and a fine-grained enhancement task through employing a lightweight underwater image enhancement network based on adaptive feature fusion, so as to make full use of the multi-scale information in an image; the strong prior enhancement task is used for extracting global and low-layer features of the image; the fine-grained enhancement task is used for improving local details and color recovery of the image so as to improve the visual quality; the lightweight underwater image enhancement network comprises a multi-scale feature aggregation module and a global channel attention module, the multi-scale feature aggregation module is composed of an adaptive feature fusion module and a segmented convolution feed-forward network, and the lightweight underwater image enhancement network is used for combining global and local information of features and adaptively adjusting importance of each channel through information association between channels.

The invention relates to the technical field of tea leaf detection, and discloses a tea leaf quality grade detection method and system.The method comprises the steps that a tea leaf sample is collected and pretreated; extracting chemical component indexes and physical properties of the pretreated tea leaves, and performing fusion and analysis; and establishing a tea quality grade classification model, inputting characteristics of a tea sample to be detected, outputting a detection result, and generating a tea quality grade report. Through combination of the support vector machine, the gradient boosting decision tree and the improved logistic regression algorithm, the precision of the tea quality classification model is optimized. The dynamic threshold function and the segmented excitation function are introduced to further solve the boundary fuzzy problem of the high-grade tea leaves and the low-grade tea leaves, and accurate judgment of tea leaf grade classification is achieved. And data restoration is carried out through weighted compensation correction and a recheck process, so that the output tea grade report is ensured to have high accuracy and reliability.

The invention relates to an activity scene arrangement scheme generation method and system, and the method comprises the following steps: obtaining activity scene data, and carrying out the analysis to obtain an activity theme and the characteristics of participants; objective condition parameters of the target site are obtained, and the candidate arrangement elements are screened; constructing a multi-dimensional attribute vector according to the screened arrangement elements; clustering the multi-dimensional attribute vectors, and performing spatial layout planning on layout elements; performing combined optimization on the generated spatial layout plan, color scheme and light effect setting; the time period of the activity is extracted, and the recommended arrangement scheme is adjusted according to the relevance; and performing visual rendering on the adjusted recommended arrangement scheme, and optimizing and determining an activity scene arrangement scheme. Intelligentization and individuation of activity scene arrangement are achieved, integrity, creativity and coordination of the arrangement effect are improved, meanwhile, influences of actual site conditions and activity time are considered, and a more optimized scene arrangement solution is provided for a user.

The invention discloses a block chain multi-node secure communication method for an industrial internet, and the method comprises the steps: constructing a multi-chain cooperation mechanism, and obtaining an inter-chain interaction path table; determining a task partition optimization scheme and inter-chain interaction path update data based on the inter-chain interaction path table; matching an adaptive node resource for each task according to a task partition optimization scheme; determining an encryption mechanism between a cross-chain protocol and a block chain according to the security requirement of the task; constructing a multi-level block chain node hierarchical architecture and an intra-chain and inter-chain communication architecture, and formulating a parallel communication scheduling scheme; formulating a dynamic load balancing strategy based on the node resource use condition and the task execution state; and obtaining the task execution progress and the resource use condition of each block chain node, and adjusting the task scheduling and cross-chain communication path. Through the multi-dimensional collaborative design, the execution efficiency, the resource utilization rate and the communication security of the industrial internet are remarkably improved, and the stability and the reliability in a complex multi-chain collaborative scene are ensured.

The invention discloses a fiber foamed concrete and a preparation method thereof. The fiber foamed concrete comprises the following raw materials in parts by weight: 5290 parts of cement, 4965 parts of water, 1.361 part of foaming solution, 1.2964 part of fly ash and 10 parts of fiber. The preparation method comprises the following steps: proportionally weighing the components; sequentially adding the cement and fly ash into a stirrer, proportionally weighing the water, uniformly stirring the mixture by the stirrer, and adding the fiber; preparing and adding the foaming solution; gradually adding the foam into the mixture, and continuously stirring until all the components are uniformly mixed; pouring the mixture into a mold on which lubricating oil is brushed, and curing in a curing chamber; after 24 hours, removing the mold; and respectively curing for 3 days, 7 days and 28 days for experimental detection. The fiber foamed concrete has the advantages of favorable thermal insulation property, favorable compression resistance and favorable bending resistance, and is applicable to a railway bed strengthening layer.

The invention relates to the technical field of fan blade detection, and provides a wind power plant fan blade detection method and system based on three-dimensional point clouds, and the method comprises the following steps: carrying out the multi-angle scanning of a fan blade through a laser scanner, and obtaining the three-dimensional point cloud data of the fan blade; performing registration processing on the three-dimensional point cloud data, and unifying the three-dimensional point cloud data into the same coordinate system to obtain registered point cloud data; performing block acceleration processing on the registered point cloud data by adopting an octree partitioning algorithm to obtain block acceleration point cloud data; performing feature extraction and feature segmentation on the block acceleration point cloud data to obtain a key feature region of the fan blade; performing defect identification analysis on the key feature region based on a point cloud network enhancement model to obtain a defect detection result, the defect detection result comprising a defect position and a defect type; and generating a detection report containing the defect position and the defect type, and visually displaying the detection report. According to the invention, the integrity of data acquisition and the precision of defect identification are improved, and the efficiency of fan blade detection is improved.

The invention provides an optical remote sensing image change detection method and system based on change perception and semantic guidance, a storage medium and electronic equipment, and the method comprises the following steps: firstly, carrying out the preprocessing of a dual-tense image, and carrying out the labeling of a change region, and generating a change label graph; and cutting the image and the label into small samples, and inputting the small samples as training data into the CASGNet model. Multi-scale features are extracted through an encoder, change perception is carried out through a space-time difference fusion module, cross-time change information is effectively captured, pseudo change noise is restrained, and reliable multi-scale difference features are obtained. Thirdly, the semantic guiding module decodes the multi-scale difference features, semantic masks are generated through the semantic aggregation module, fusion of the multi-scale features is guided, interference is eliminated, and edge details of a change area are complemented; and finally, in a detection stage, the preprocessed sample to be detected is input into the model, a complete and accurate change detection result is obtained, and the method is suitable for a detection task of a complex scene and a high-resolution image.