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The invention, which relates to the technical field of image identification processing, discloses an AI-based image identification detection system comprising the following components: an optical physical model processing module, an environmental factor perception and analysis module, a color deviation correction module, a micro-feature enhancement module, an AI image identification module and a data storage and dynamic update module. By combining the optical physical model and the micro-feature enhancement algorithm based on the physical principle, the accuracy and reliability of object recognition under complex weather and illumination conditions are remarkably improved, and by introducing the optical physical model, the interaction between light and the object surface under different environments is accurately simulated, so that the object recognition accuracy and reliability are improved. And meanwhile, the micro-feature enhancement algorithm is optimally designed for specific environments such as low illumination and strong wind, so that the signal strength of the micro-feature region is effectively enhanced, and micro-feature deformation caused by environmental factors is recovered.

The invention discloses a large-scale sample storage management method and system based on a multi-dimensional label system. The method comprises the following steps: acquiring large-scale sample data input, and establishing a multi-dimensional label system for each piece of sample data based on a sample source, sample quality and sample feature dimensions; performing automatic auxiliary identification and classification and quality evaluation on the sample data, generating a multi-dimensional label for the sample according to a multi-dimensional label system, selecting a proper storage layer according to the multi-dimensional label of the sample, and storing the sample in a specified storage layer after the storage space is optimized; and returning a retrieval result according to a retrieval condition input by a user, and distributing the retrieved sample to a required task or analysis scene according to a requirement. According to the method, by establishing the multi-dimensional metadata labels, retrieval, positioning and management of the samples can be completed quickly and efficiently. Meanwhile, automatic auxiliary classification and recognition of the samples are achieved, and the data management efficiency is improved.

The invention provides a student growth archive management method, which comprises the following steps of: performing structured processing on multi-source heterogeneous data of paper archives, electronic documents and audio and video data by adopting a data acquisition and conversion technology according to heterogeneous data characteristics of student growth archive management, performing data cleaning, conversion and integration operation to obtain unified structured digital information, and storing the structured digital information into a database; a foundation is laid for subsequent big data analysis; based on a result of machine learning analysis, a student portrait and a knowledge graph are constructed, a comprehensive and dynamic student personal file is formed through multi-dimensional description of learning tracks, ability characteristics and psychological characteristics of students, entities such as students, courses, teachers and the like and relationships thereof are visualized by utilizing a knowledge graph technology, and a knowledge graph is created. The association relationship and influence factors among the students are mined, and a basis is provided for personalized education and accurate teaching.

The invention discloses a multi-modal medical data fusion and analysis platform, and particularly relates to the technical field of medical assistance, and the platform comprises a data collection and preprocessing module which is used for integrating, cleaning and standardizing multi-source data, and is used for efficiently fusing and safely storing the multi-modal data; the data fusion and storage module supports rapid retrieval and management; and the data analysis and modeling module is used for carrying out feature extraction and pattern recognition by utilizing a machine learning algorithm and providing deep prediction analysis. The system integrates, cleans and standardizes medical data from multiple sources through the data acquisition and preprocessing module, ensures data quality and consistency, then efficiently fuses data of different modalities into a single data set through the data fusion and storage module, supports rapid retrieval and management, and finally utilizes the data analysis and modeling module to analyze and model the medical data. And deep prediction and risk assessment of diseases are provided through feature extraction, model training and prediction analysis.

The invention provides a multi-modal process data trusted storage method and system combined with a block chain, and belongs to the technical field of data storage, and the method comprises the following steps: obtaining multi-modal process data; constructing a metadata minimum set covering the full life cycle of the multi-modal process data; performing under-chain storage on the multi-modal process data and the metadata minimum set, and verifying the multi-modal process data to obtain verification process data; recording the digital signature evidence of the verification process data on a block chain to obtain an on-chain shared signature evidence of the verification process data; based on the on-chain shared signature evidence, establishing a mapping relationship between the hash value of the on-chain shared signature evidence and the under-chain verification process data; and storing the verification process data and the mapping relationship in a distributed process database. According to the method, the block chain and the distributed storage are combined to construct a multi-modal process data trusted storage system with coordination of on-chain and off-chain, so that the trusted storage of the multi-modal process data in the whole life cycle is realized.

The invention discloses an enterprise micro-marketing auxiliary method and system based on a large language model. The efficiency and effect of marketing are improved by using the large language model. According to the method, by integrating a vector database, an embedded model and a context learning technology, the capabilities of real-time understanding of user intentions, personalized reply and quick service change adaptation are realized. The method has the core advantages of accurate user demand analysis, personalized service provision and efficient knowledge retrieval system. By analyzing user behaviors and dialogue records, product recommendation and marketing strategies according with user preferences can be provided. In addition, the method also has a real-time verbal skill updating capability, so that the timeliness and accuracy of information are ensured; the automatic order information extraction technology improves the information processing efficiency, and reduces the time and cost of manual operation. According to the invention, the marketing efficiency is improved, the interactive experience between the user and the customer service is enhanced, and competitive advantages are provided for enterprises in the market with intense competition.

The invention relates to the technical field of logistics distribution data fusion, in particular to a data fusion method and system based on a logistics distribution knowledge graph. The method comprises the following steps: extracting original data and carrying out independent representation learning and unified representation processing; extracting uniformly expressed context information and carrying out dynamic perception embedding to obtain semantic enhancement features; fusing time-space and dependency features and physical characteristic features based on semantic enhancement features, performing message passing and relation inference by using a graph neural network, and finally determining a multi-mode relation set; global entity clustering and local feature fine matching are carried out based on semantic enhancement features, hierarchical entity alignment is carried out in combination with the multi-mode relation set, and an aligned entity set is obtained; and constructing an entity filling graph according to the aligned entity set, supplementing multi-dimensional relation attributes, and finally generating a logistics distribution knowledge graph. According to the invention, through a logistics distribution data fusion technology, a more comprehensive and more intelligent logistics distribution knowledge graph is realized.

The invention belongs to but is not limited to the technical field of software development, and particularly relates to a multi-round reasoning and feedback optimization method and system for industrial software codes, and the method comprises the steps: S1, vector code library establishment: the establishment of a vector code library is composed of three parts of feedback establishment, vector database establishment and format summarization; s2, code reasoning optimization: the code reasoning optimization mainly comprises four aspects: control flow graph analysis, multi-query generation and reciprocal ranking fusion retrieval, reasoning generation subtask and code chain submodule generation, and code combination completion; and S3, feedback optimization: the feedback optimization is composed of score feedback optimization and log feedback optimization. According to the method, an automatic verification mechanism is introduced, logic consistency check and performance evaluation are carried out on the generated and optimized codes, and the correctness and execution efficiency of the codes are ensured. The verification mechanism can find and correct errors in real time in each stage of generation, so that the cost of manual verification is reduced, and the development efficiency is improved.

The invention discloses a well industrial and mining unmanned driving simulation test method based on cluster hardware-in-the-loop, and relates to the field of unmanned driving simulation, and the method comprises the steps: constructing a simulation environment of an operation map; building a cluster hardware-in-the-loop simulation system on the constructed operation map simulation environment, and simulating an ore drawing machine, a draw shaft, a transport vehicle and a carry-scraper on the simulation system; when a plurality of loading points or unloading points exist in the constructed operation map simulation environment, calculating an optimal loading point, an optimal unloading point and an optimal driving route of each transport vehicle to realize transport vehicle scheduling simulation; the cluster hardware-in-the-loop simulation system adopts a distributed calculation architecture to perform parallel simulation calculation on a plurality of ore drawing machines, draw shafts, carry-scrapers and transport vehicles. Aiming at the low simulation test efficiency of the underground unmanned vehicle in the prior art, the underground simulation test efficiency is improved.

The invention relates to the technical field of legal text generation, in particular to an intelligent case generation method based on a judicial knowledge domain atlas, which comprises the following steps: S1, constructing the judicial knowledge domain atlas; s2, extracting case facts and judgment reason parts of the document; s3, classifying and splitting case facts of the document; s4, carrying out deep learning to generate an abstract; s5, correlation comparison of the content is carried out, and integrity and accuracy are verified; s6, optimizing a judgment key through a convolutional neural network model; s7, automatically generating correlation indexes with other historical cases according to correlation analysis in the judicial knowledge domain graph; s8, summarizing the results, and splicing the results into a complete case document; according to the method, by combining the judicial knowledge domain graph and the deep learning technology, the legal document is automatically generated, the integrity of document content and the accuracy of legal logic are ensured, historical cases are efficiently associated, and the efficiency and quality of judgment document generation are remarkably improved.

The embodiment of the invention discloses a network security alarm processing method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a network security alarm; retrieving a target text vector associated with the network security alarm in a network security domain knowledge base; the network security domain knowledge base is a knowledge base constructed after vectorization processing is carried out on network security corpora; based on the target text vector, an alarm vector corresponding to the network security alarm, a preset tool library and a large language model, obtaining a research and judgment processing flow of the network security alarm; executing a research and judgment processing flow; and obtaining a research and judgment result of the network security alarm based on an execution result of the research and judgment processing flow.

The invention discloses an advertisement compliance knowledge question-answering method, device and equipment for foreign enterprises, and the method comprises the steps: obtaining advertisement compliance data of a global data source, carrying out the document structuring and knowledge extraction of the advertisement compliance data, and constructing an advertisement compliance knowledge graph; converting text data in the advertisement compliance knowledge graph into vectors to obtain vector data; semantic analysis is conducted on a query question input by a user, construction is conducted in combination with the vector data, prompt words are obtained, the prompt words are input into a preset large language model, and target answers are obtained; and outputting the target answer based on the advertisement compliance knowledge graph, and visually displaying the target answer in a corresponding answer query graph. And comprehensive and efficient compliance information query and answer services can be provided, so that compliance review requirements of foreign enterprises on advertisements in a multi-language and cross-regional environment can be met.

The invention relates to a packaging machine two-dimensional code dynamic tracing method, device and equipment. The method comprises the following steps: inputting registration identification information of a field management unit into a central control system for identity verification and clock synchronization calculation, and generating a global timestamp sequence; generating a trigger time sequence control signal; acquiring a first two-dimensional code image of the inner-layer packaging unit and a second two-dimensional code image of the outer-layer packaging unit to obtain a first coded data stream and a second coded data stream; performing hierarchical correlation operation to generate a hierarchical rejection signal; generating an associated data packet from the first coded data stream and the second coded data stream which pass the verification, and generating an associated backup table; and establishing multi-level traceability indexes based on the associated data packet, and sorting the multi-level traceability indexes according to the global timestamp sequence to generate a chained traceability data structure. The real-time performance and the accuracy of the two-dimensional code data of the packaging machine are guaranteed, and an effective data exception handling mechanism and an effective backup recovery strategy are achieved.

The invention relates to the technical field of medicine cold-chain logistics, and discloses a medicine cold-chain logistics dynamic data cloud platform and a route analysis method.The cloud platform comprises a data acquisition unit, a preprocessing unit, a data transmission unit, a storage management unit and a route analysis unit. Key data such as temperature, humidity and position are collected in real time through multiple sensors and are subjected to standardized preprocessing, so that the data are comprehensive, real-time and reliable, and a foundation is laid for subsequent analysis. In the aspect of storage management, partition storage is carried out based on time and space dimensions, and the data retrieval and management efficiency is greatly improved; in the aspect of route planning, factors such as time, temperature and humidity, congestion and the like are comprehensively considered, a scientific route cost evaluation function is constructed, costs such as transportation duration, temperature and humidity stability, road conditions and the like are covered, and in combination with a greedy algorithm, the medicine transportation quality can be guaranteed, the cost can be effectively reduced, and the logistics efficiency can be improved.

The invention discloses an intelligent question and answer method and system based on a mine safety large model. The method comprises the following steps: constructing a vector database based on a mine document data set; obtaining an input user question, and generating a corresponding similar question based on the user question; after the similar questions and the user questions are combined, vectorization processing is carried out, and user question vectors are generated; performing correlation retrieval of the text blocks in a vector database based on the user question vector to obtain a corresponding preliminarily screened text block set; reordering the preliminarily screened text block set to obtain a finely arranged text block set; and inputting the user question and the reordered finely-arranged text block set into a large model for correlation analysis, outputting the text blocks most relevant to the user question, and sorting the text blocks according to similarity to form a data set as a retrieval result. According to the method, related questions are generated on the basis of user questions to cover more possible query intentions and expression modes, so that the accuracy and efficiency of mine risk hidden danger question answering are improved.

The invention discloses a multi-source water environment data processing method and system based on intelligent Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: carrying out the standardization processing of obtained multi-source heterogeneous water environment monitoring data, obtaining the standardized data, carrying out the multi-scale decomposition, and obtaining low-dimensional data; a distributed storage architecture is adopted for low-dimensional data, the data are stored on a plurality of nodes in a scattered mode, a data index and metadata management mechanism is established, and data retrieval and access are conducted; cleaning, aggregating and analyzing the low-dimensional data by adopting a stream data processing method, extracting key indexes and events, setting a water quality parameter threshold value and a change rate threshold value, constructing a water quality abnormity early warning rule in combination with a decision tree method, and judging whether water quality abnormity exists or not; and constructing a water quality prediction model through a support vector machine and a random forest machine learning method, and predicting a water quality trend. According to the invention, powerful technical support is provided for water environment monitoring and management.

The invention discloses a method for generating a three-dimensional building GLTF file based on index compression. The method comprises the following steps: S1, collecting three-dimensional coordinate data of a building; s2, grouping and classifying the data according to floors to form a classification index structure based on a hash table; s3, selecting a reference model and recording Mesh data and vertex coordinates of the reference model; s4, calculating a translation matrix T of the polyhedral structure, and performing coordinate transformation; s5, generating Mesh data of other polyhedrons based on index compression, and reducing redundant surface data by adopting a topology preserving surface optimization algorithm; s6, independently processing the geometric data of each floor, and generating corresponding Mesh data and Node nodes; and S7, combining all floor data to generate a complete GLTF file, and performing compression by using a dynamic Huffman coding algorithm. Through the improved index compression and dynamic Huffman coding technology, the file size is remarkably reduced, the transmission efficiency is improved, and the geometric and topological integrity of the model is ensured.

The invention discloses a method for designing a tree data storage mode in a relational database by using preorder traversal, which comprises the following steps: S1, basic node data of a tree structure is constructed in the relational database, each node comprises id, name, lvalue and rvalue attributes, and the lvalue and the rvalue are used for identifying the position relationship of the nodes in a tree through an interval representation method; s2, setting a query condition according to a node ID needing to be queried, and determining a descendant node range of the node through lvalue and rvalue attributes of the node; s3, the query results are subjected to ascending sort of the left value lvalue, and a sort list (sort List) is constructed; and S4, by traversing the sortedList, in combination with the temporary set level List and the cache level NodeCache, calculating and setting a father node ID (pid) of each node and whether the father node ID (pid) is a leaf node isLeaf attribute or not according to the logic of pre-order traversal, thereby completing the rapid construction of the tree structure.

The invention discloses a blockchain-based intelligent video monitoring data storage security sharing method for an iron and steel plant. The method comprises the following steps of: generating a public key PKS and a master key TMK based on an attribute encryption algorithm; distributing shares of the shared key S by constructing a matrix and defining a function, wherein the shares are distributed in different rows of the matrix; re-encrypting the data by using a hybrid proxy re-encryption algorithm based on a ciphertext strategy; secure data storage and access control are realized based on a secure storage mode of revocable attribute-based encryption RABE and a block chain; and in combination with a block chain technology, a consensus algorithm is adopted to verify transactions, so that the security and robustness of the system are enhanced. According to the invention, the utilization rate of the data is improved, the cooperation efficiency is enhanced, the security and transparency of the video monitoring data are improved, and the cost of information transmission and storage is reduced. The distributed storage and access control of the data are realized, the confidentiality and integrity of the cloud center data are ensured, and the security and efficiency of the steel plant video monitoring system in the storage and transmission process are enhanced.

The invention belongs to the field of artificial intelligence, and relates to a material retrieval method which comprises the following steps: acquiring a material sample set, and performing multi-modal feature extraction on each material picture of the material sample set to obtain text features and image features; splicing the text feature and the image feature of each material picture to obtain a plurality of multi-modal fusion features; performing Hash coding on the multi-modal fusion features to obtain material Hash codes of all the multi-modal fusion features, and constructing a retrieval database based on all the material Hash codes; obtaining a multi-modal fusion feature of the to-be-detected image to obtain a query vector, and calculating a hash value of the query vector to obtain a query hash value; and matching a material hash code most similar to the query hash value from a retrieval database to obtain a target hash code, and taking a material corresponding to the target hash code as a retrieval result. The invention further provides a material retrieval device, computer equipment and a storage medium. According to the method, quick retrieval of the material library is realized, and the accuracy of material retrieval is improved.

The invention discloses an integrated energy system planning optimization method and computing equipment, and the method comprises the steps: obtaining various equipment parameters, load demand data and energy prices in an integrated energy system, taking the lowest annual total cost of the integrated energy system as a first target, and taking the highest integrated energy efficiency as a second target; the total annual cost comprises the annual investment cost, the energy purchase cost, the annual operation and maintenance cost, the stepped carbon transaction cost and the wind curtailment and light curtailment penalty cost, and the comprehensive energy efficiency is used for representing the ratio of the total energy supply amount to the total energy consumption amount of each type of equipment in the comprehensive energy system; and solving the planning optimization model by using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution, and determining the optimal capacity configuration of each type of equipment in the integrated energy system based on the optimal solution. According to the method, the optimal capacity configuration of each type of equipment in the integrated energy system can be reasonably planned on the premise of meeting the load requirement of the system.

The invention discloses a fault prediction method and device for an intelligent operation and maintenance system, and relates to the technical field of fault prediction. The method comprises the following steps: receiving a prediction request sent by a user side, and determining first equipment according to the prediction request; determining preset first monitoring data according to a first model corresponding to the first equipment; judging whether the first monitoring data is consistent with preset first monitoring data or not; when the first monitoring data is inconsistent with preset first monitoring data, determining that the first equipment has a first fault; obtaining a target association degree between the first device and the second device; judging whether the target association degree is greater than or equal to a preset association degree; and if the target association degree is greater than or equal to the preset association degree, determining that the first equipment and the second equipment have fault states, and sending the fault states to the user side, so that a user corresponding to the user side checks the fault states. By implementing the technical scheme provided by the invention, the operation and maintenance personnel have enough time to repair, and unnecessary loss caused by fault expansion is avoided.

An astronavigation material database system belongs to the technical field of astronavigation material databases and comprises a basic platform layer, an application service layer and a presentation layer. The public service layer comprises a public service plate; the application function layer comprises but is not limited to a material management plate, a material data application plate and a material big data analysis plate. The invention discloses an astronavigation material data system, which comprises a word standard specification, establishes an astronavigation material database, can perform big data analysis according to a computer software technology, a network technology and a digital technology, and can realize intelligent recommendation and intelligent knowledge management of astronavigation materials.

The invention provides a plant seed felt management method and system based on a block chain, and relates to the technical field of agricultural management, and the method comprises the steps: obtaining growth parameter information and identification information of a plant seed felt, uploading the growth parameter information and the identification information to a block chain network, and generating a growth file of the plant seed felt; extracting a smart contract through visual features in a block chain network, analyzing the growth image and the growth video, obtaining growth features, identifying growth abnormality conditions of the plant seed felt based on an abnormality identification smart contract, and triggering a quality tracing process when the growth abnormality conditions occur. Carrying out early warning prompt on other plant seed felts in the same batch; and generating a three-dimensional digital twinborn model of the plant seed felt based on the digital twinborn smart contract in the block chain network, simulating the growth state of the plant seed felt based on different growth parameter information, generating a simulated growth result, and dynamically adjusting the growth parameters of the plant seed felt based on the simulated growth result.

The invention discloses an enterprise supply chain side evaluation method and system based on big data, and provides an innovative solution for the problems of low data processing efficiency, weak analysis and prediction ability, insufficient causal relationship analysis, decision support lag and the like in supply chain management. The method comprises the steps of integrating and processing real-time data, deeply analyzing the data by using an advanced analysis and prediction module in combination with a mathematical model and a machine learning algorithm, deeply understanding a causal relationship in a supply chain through a causal relationship analysis and optimization module, and finally accelerating a decision process through a visualization and decision support module. According to the system, through modular design and an advanced algorithm, the supply chain management efficiency and scientificity are remarkably improved, and comprehensive evaluation and optimization of the supply chain are achieved. The invention aims to solve the problem of improving the efficiency, accuracy and scientificity of enterprise supply chain management under the background of big data so as to adapt to rapidly changing market demands and a complex supply chain network.

The invention discloses an electric energy meter data acquisition and analysis method based on an infrared communication technology, and relates to the technical field of intelligent power grids and data analysis, and the method comprises the steps: collecting a data signal transmitted by an infrared port of an electric energy meter, receiving an infrared data signal through an infrared receiving module, and converting the infrared data signal into an electric signal; performing filtering and noise reduction processing on the converted electric signal to obtain filtered electric energy meter data; storing the filtered electric energy meter data into a data cache region according to a time sequence, and grouping and classifying the electric energy meter data in the data cache region according to the number of the electric energy meter; performing feature extraction on the grouped electric energy meter data, and generating a power consumption feature curve in combination with the power consumption; and establishing a power consumption behavior model based on the power consumption characteristic curve, and identifying an abnormal power consumption mode. According to the invention, stable data acquisition is realized through the infrared receiving module, analysis of electricity consumption abnormity is realized in combination with a support vector machine algorithm, and the performance index of an electric energy meter data acquisition and analysis system is comprehensively improved.

The invention discloses a big data hypergraph network modeling method and device, computer equipment and a product, aims to solve the problem that efficiency and accuracy are difficult to consider when large-scale data of intellectual property is processed in the prior art, and aims at the characteristics of high increment, high timeliness and multiple modes of the intellectual property data. The method comprises the following steps: analyzing and concluding passed intellectual property files, classifying according to corresponding professions, and establishing a corresponding intellectual property text library; constructing a text corpus based on intellectual property by using a BERT algorithm model, performing capsule graph neural network processing on the image information of the corresponding category, obtaining a correlation between image objects and text attributes thereof, and constructing an image database; capturing complex correlation characteristics in the text by utilizing the characteristics of the graph convolutional neural network; and constructing a hypergraph neural network model. The invention provides a big data hypergraph network modeling method capable of processing high-increment, high-timeliness and multi-modal data.

The invention relates to the technical field of deep learning, and discloses a potential hot content identification method based on a mainstream value droop domain knowledge base, which comprises the following steps: constructing the mainstream value droop domain knowledge base, carrying out topic modeling through a hidden Dirichlet distribution model and extracting potential topic words, an event triple is extracted through a RoBERTa-CRF (Conditional Random Field) model, and an event atlas is constructed; nodes related to the hot spots are retrieved from the event atlas through an atlas retrieval engine, sub-graphs formed by the related nodes are converted into vector representation through a graph embedding technology, and a hot spot mode is formed; and by calculating Jaccard similarity, matching content features similar to the hotspot mode in the fusion graph representation network, and identifying potential hotspot content. According to the method, the forward enhanced information content is accurately identified and screened through thematic modeling, event extraction, atlas retrieval, similarity calculation and other technologies, and the real-time monitoring and prediction capability on the social hotspot trend can be enhanced.

The invention provides a link tracking method and system for a service cluster based on log burying points, and belongs to the technical field of distributed monitoring. According to the method, a link tracking ID is generated through a snowflake algorithm at a starting point of service calling, and service IDs are distributed to micro-service nodes participating in service calling; setting log burying points at the entrance, the exit and the key logic processing points of each service according to preset burying point logic; asynchronously recording link tracking information contained in the service node through the log burying point, and uploading the information to a storage system for storage; according to the link tracking ID and the service ID in the link tracking information, the link tracking information is connected in series, a complete calling link called by the service is restored, and meanwhile a service calling topological graph is generated. According to the method, real link information can be quickly and accurately tracked, and quick retrieval and analysis of a large amount of real-time link data can be supported.

The invention discloses a text log management method and device based on an embedded system, equipment and a medium. According to the text log management method based on the embedded system, a storage medium is provided, the storage medium is divided into storage areas, and the storage areas are divided according to partitions; the management method comprises a writing method, a quick retrieval method and a data reading method. The method can be conveniently applied to an embedded system with relatively few hardware resources, the contradiction between embedded system development and large-capacity log data storage requirements is solved, meanwhile, the requirement that field maintenance is simple and easy to use can be met, and the method can be suitable for various storage media, occupies few system hardware resources, can be rapidly deployed, and has high practicability. And the method also has the advantages of quick retrieval, sequential writing, data reading, automatic coverage, few occupied resources and the like.