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The invention discloses a thrombus excision system. An embolus is extracted through a mechanical embolus extraction device, a blood vessel is dredged, and revascularization of the blood vessel is achieved. A saccule guide catheter is put at the near end of the embolus part when the embolus is extracted, blood flow is temporarily blocked, and thus a fine crushed embolus generated in the embolus extraction process is prevented from being flushed to the far end of the blood vessel; meanwhile, for a relatively large embolus, a part of thrombus can also be sucked by virtue of the saccule guide catheter, and then the residual thrombus can be extracted by virtue of a thrombus exsector. The thrombus exsector, the saccule guide catheter and a delivery microcatheter are arranged on the thrombus excision system disclosed by the invention, wherein the thrombus exsector is used as a main component, an excision spring is arranged, and the release state of the excision spring is a conical spiral state. Compared with the prior art, the thrombus excision system has the advantages that the thrombus exsector is very soft, the minimal through size is 0.014' (0.36mm), the thrombus exsector can easily enter a cerebral vessel, a near-end protection technology is adopted in the operation process, and thrombus suction is combined with mechanical embolus extraction, so that the success rate of the operation is improved, and the complications are reduced.

The invention belongs to the technical field of artificial intelligence, and relates to a power document intelligent question and answer method and system based on a large language model. The method comprises the steps of obtaining document data according to a preset period; preprocessing the document data to obtain an adaptive document; constructing a vector retrieval library through an embedded model according to the adaptive document; obtaining a user query question, inputting the user query question into a vector retrieval library, performing retrieval based on a vector similarity retrieval method and a keyword retrieval method to obtain related text blocks, and screening out the most related text block from the related text blocks through a two-stage rearrangement technology; a user queries the question and the most relevant text block and inputs the question and the most relevant text block into the large language model to obtain preliminary intelligent answer information; and correcting the preliminary intelligent answer information based on a preset self-provincial inversion mechanism to obtain final intelligent answer information. Multiple rounds of questions and answers and context understanding are supported based on a correction mechanism, context consistency can be kept in continuous query of complex technical questions, and the accuracy of multiple rounds of questions and responses is ensured.

The invention discloses an intelligent finance and tax question answering method based on an artificial intelligence question answering system, and the method comprises the steps: S1, generating a structured semantic representation through a natural language processing technology, and generating an intention tag through a machine learning algorithm; s2, constructing and maintaining a knowledge graph containing finance and tax field related information, storing the information in the knowledge graph in a structured manner, and reasoning by using a logical reasoning algorithm to generate a preliminary answer; s3, designing a document retrieval and indexing mechanism, and verifying and supplementing the preliminary answer; s4, training the system based on the finance and tax related data and question and answer pairs, and continuously updating the model; s5, identifying common finance and taxation question modes by using a mode identification technology, and providing standardized answers; and S6, supporting multiple rounds of dialogues with the user, and generating more detailed and accurate answers by gradually clarifying the questions of the user. The method has the advantages of high accuracy, high response speed and capability of continuously learning and adapting to new problems.

The invention belongs to the field of safety research of advanced persistent threat detection, and particularly relates to an APT detection method based on semantic enhancement and an attention mechanism. The method comprises the following steps: collecting and analyzing a system log, and constructing a traceability graph according to the analyzed system log; obtaining a summary sentence of each node according to attributes of the nodes of the traceability graph and types of edges, inputting the summary sentences into a BERT model, and extracting attribute semantic feature vector representation of the nodes in the traceability graph; generating a sub-graph of each node according to the nodes in the traceability graph, extracting a feature matrix of each sub-graph by using the self-attention graph convolutional network, and taking the feature matrix as a node feature vector representation of each sub-graph; and inputting the node feature vector representation of the to-be-detected node into the lightweight classifier, and carrying out anomaly detection. According to the method, the semantic information of the log is deeply mined and understood, the coding structure and causal information of the traceability graph are deeply mined, and the accuracy and efficiency of APT attack detection are improved.

The invention provides a smart agriculture fusion system based on Internet of Things and digital twinning, and relates to the technical field of data acquisition, and the system comprises an Internet of Things sensing network which is used for collecting data of soil humidity, soil oxygen, environmental parameters, meteorological information and crop growth states; the big data processing unit is used for receiving the data acquired by the sensor and feeding back the processed data to the digital twinborn model; establishing a digital twinborn model, and constructing the digital twinborn model of the farmland by using the real-time data and historical data of the farmland through a computer learning method and a deep learning algorithm; the control and decision-making unit is used for making farmland management strategies and decisions based on the analysis and prediction results of the digital twinborn model; a user remotely monitors and manages the operation state of the farmland through a terminal, and through the Internet of Things and the digital twinning technology, informatization and intelligentization of the whole chain of agricultural production are achieved, and the efficiency and quality of agricultural production are improved.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention provides a content publishing method, a content interaction method, a content interaction system, a content interaction device, equipment, a storage medium and a program product. The content publishing method comprises the following steps: in response to a triggering operation on a shooting control in a shooting interface, generating target content; displaying an editing interface comprising a publishing control for publishing the target content and a label adding control; in response to a triggering operation on the tag adding control, adding a content tag to the target content; and in response to a trigger operation on the release control, releasing the target content including the content tag. The content interaction method comprises the steps of displaying a content display interface comprising a second position identification control, and displaying associated published content or a map interface in response to a triggering operation on the second position identification control. According to the method provided by the invention, the content including the content label can be published at the publishing end, lightweight publishing is realized, the publishing efficiency is improved, the associated publishing content or the map interface is displayed at the display end, the limitation of a content interaction mode is avoided, and the use experience is improved.

The invention discloses hydraulic machine equipment suitable for die forging and blank making of various flanges and a forging method, and belongs to the technical field of flange production equipment.The hydraulic machine equipment suitable for die forging and blank making of various flanges comprises a base and further comprises a top plate, the top plate is arranged on the upper side of the base, and a guide column is arranged between the top plate and the base; the first hydraulic oil cylinder is fixedly arranged on the top plate, and a piston rod of the first hydraulic oil cylinder is connected with a pressure head; all positions of the forge piece are sequentially arranged on the lower side of the pressing head through the swing mechanism, all positions of the forge piece are conveniently and evenly forged, workers do not need to shake the forge piece back and forth by utilizing a clamp or a clamp on the side, and when the pressing head moves upwards every time, the die holder is automatically separated and is matched with the air spraying opening to blow away waste chips generated in the die holder during forging and pressing of the forge piece; and excessive scraps are prevented from being accumulated in the die holder and being matched with the forge piece gradually filling the die holder again, the follow-up processing time of the flange forge piece is shortened, and the flange processing efficiency is improved.

The invention provides a multi-agent cooperation system, which comprises a multi-agent cooperation engine module, a task decomposition and distribution module and a tool calling normal form module, and is characterized in that the task decomposition and distribution module is used for decomposing tasks and distributing decomposed sub-tasks to agents; the multi-agent cooperation engine module is used for managing cooperation and communication between agents; and the tool calling module enables the intelligent agent to dynamically call different tools. According to the multi-agent collaboration system, the collaboration efficiency among the agents, the flexibility of task decomposition, the adaptivity of tool calling and the intelligent level of user interaction are remarkably improved, and remarkable technical advantages and effects are shown in complex task processing and dynamic environment processing.

The invention relates to the technical field of intelligent agriculture, in particular to a mulberry field monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring environment and soil data of a mulberry field through a sensor network to obtain original sensor data; performing time sequence data integration on the original sensor data to obtain an environment parameter data set; the method comprises the following steps: performing multispectral image acquisition on mulberries in a mulberry field through a multispectral camera, and performing image preprocessing to obtain preprocessed image data; estimating the chlorophyll content according to the preprocessed image data to obtain chlorophyll content data; and performing growth health parameter analysis according to the preprocessed image data and the chlorophyll content data to obtain growth health parameters. By combining the sensor technology, the multispectral imaging technology and the intelligent algorithm, comprehensive, accurate and real-time monitoring of the mulberry field is achieved, and an effective technical means is provided for fine management of the mulberry field.

The invention discloses a cardiothoracic surgery postoperative complication assessment method and system based on data analysis, and relates to the technical field of medical treatment, and the main scheme is as follows: collecting basic operation data, preoperative examination data, basic information data of a patient and real-time postoperative recovery data of the patient, calculating the complexity of the operation according to the basic operation data, and determining the postoperative complication of the patient according to the calculated complexity; the method comprises the steps of calculating preoperative complication indexes through preoperative examination data, calculating basic information indexes through patient information data, calculating vital sign comprehensive indexes through real-time postoperative recovery data, jointly calculating complication risk scores through the indexes, comparing the complication risk scores with a complication risk score threshold set, and then judging risk levels. And formulating a management strategy and generating an evaluation report. According to the system, the accuracy and systematicness of postoperative complication evaluation are improved, and powerful support is provided for clinical management.

The invention relates to the technical field of computer television image communication, in particular to a computer television all-in-one machine control method, system and device. The method comprises the following steps: through multi-view image acquisition and processing, generating a three-dimensional scene grid and carrying out scene element analysis to form a scene element set, then chasing limbs of a user, generating a user limb image set, restoring an environment basic tone, obtaining display content of the all-in-one machine and carrying out content semantic analysis, and finally obtaining a user limb image set. Calculating interaction intensity to obtain content interaction intensity data, speculating a user body language and performing user intention prediction in combination with the interaction intensity data, adjusting display content of the all-in-one machine based on the predicted user operation intention, generating simulation adjustment content, performing composition optimization through a scene element set, and generating updated display content. The method for controlling the computer and television all-in-one machine is more accurate and more efficient.

The invention discloses a method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity, and belongs to the technical field of water environment and water ecology monitoring. Acquiring a water body hyperspectral near-sensing reflectivity spectral data set, a satellite remote sensing surface reflectance data set and a water environment actual measurement data set, and performing preprocessing; a synchronous data set containing # imgabs0 # and water quality and a synchronous data set containing # imgabs1 # and water quality are constructed; based on the synchronous data of the # imgabs2 # and the water quality, synchronous data of the # imgabs3 # and the water quality are generated, and synchronous data of the # imgabs4 # and the # imgabs5 # are obtained; the # imgabs6 # is normalized to obtain # imgabs7 #, and the quantitative conversion relation between the # imgabs7 # and the # imgabs8 # is constructed band by band to eliminate errors; generating a fused satellite-ground data set, and constructing a training water quality parameter inversion model; and updating corresponding methods and data sets in real time by repeating specified steps, and optimizing the model. According to the method, the inversion result is displayed and downloaded according to the query instruction, the satellite spectrum precision and the satellite-ground synchronous data set scale are improved, and the method is used for high-precision water environment monitoring.

The invention discloses a water and soil loss telemetry data analysis method and system based on deep learning, and relates to the technical field of data analysis, and the method comprises the steps: extracting soil loss data, surface runoff data and topographic data; fusing the soil loss data, the surface runoff data and the topographic data, and obtaining water and soil loss risk index data and soil erosion rate data through a feature extraction technology; calculating weight values of the water and soil loss risk index data and the soil erosion rate data by using a feature evaluation technology; and based on the water and soil loss risk index data, the soil erosion rate data and the weight value, constructing a water and soil loss prediction model by using a deep learning framework and a space-time analysis algorithm. According to the invention, through telemetry data collection and feature extraction, key water and soil loss information can be rapidly obtained, the data processing flow is simplified, and the accuracy and reliability of data are ensured.

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention relates to the technical field of behavior recognition, in particular to a machine learning-based surgical personnel video behavior recognition method and system. The method comprises the following steps: acquiring a video frame sequence and a point cloud frame sequence from a hand washing area of an operator through an RGB camera and laser ranging equipment to obtain the video frame sequence and the point cloud frame sequence; performing hand point cloud segmentation on the point cloud frame sequence to obtain hand point cloud data; performing action original data generation according to the video frame sequence and the hand point cloud data to obtain action original data; and extracting position and attitude information of hand key points from the original motion data to obtain video key point feature data. Visual information is captured through the RGB camera, three-dimensional depth information is obtained through the laser ranging device, fine analysis and evaluation are carried out on actions in combination with the deep learning network, and finally higher recognition precision, robustness and automation level are achieved.

The invention discloses a remote sensing data processing method and system, a product and a readable medium, and relates to the field of remote sensing data processing, and the method comprises the steps: carrying out the registration and correction of remote sensing images in different time periods, and obtaining a remote sensing image spatial-temporal index; calculating shadow ranges generated by each building in the target area at different times based on the building height data and the remote sensing image spatio-temporal index, and superposing the shadow ranges corresponding to the buildings to obtain a shadow coverage frequency map; calculating a shielding degree score of an area of each time phase in the shadow coverage frequency diagram based on a remote sensing image spatio-temporal index, and selecting an optimal time phase with the lowest shielding degree as an optimal observation time phase according to the shielding degree scores; based on the optimal observation time phase, dividing a region in the shadow coverage frequency diagram into a plurality of target sub-regions according to a shielding degree score; and repairing each target sub-region by adopting a preset repairing strategy according to the shielding degree score. By implementing the method, the accuracy of restoring the shadow-shielded region in the remote sensing image can be improved.

The invention relates to an Internet of Things equipment intelligent management platform based on edge computing, and aims to realize efficient and intelligent management of Internet of Things equipment through an edge computing technology. According to the platform, the edge nodes are deployed near the data source, so that local processing and storage of the data are realized, the data transmission delay is remarkably reduced, and the response speed of the system is improved. And the platform is provided with an intelligent management module, so that the equipment state can be monitored in real time, faults can be predicted, repair operation can be automatically executed, and the stability and reliability of the system are enhanced. And a self-adaptive data stream processing algorithm is adopted, and the data stream is dynamically adjusted according to a real-time network state and equipment requirements, so that the high efficiency of data processing is ensured. The secure transmission module protects the data transmission process through an end-to-end encryption technology, and prevents data leakage and tampering. The cloud-side collaborative resource scheduling system realizes optimal allocation of resources, and a user-defined rule engine provides high flexibility and personalized management strategies to meet different user requirements.

The invention relates to the technical field of wastewater treatment, in particular to a generation method for a chemical production wastewater treatment system control model. The method comprises the following steps: collecting chemical wastewater data by using an online monitoring instrument, wherein the chemical wastewater data comprises tin-containing wastewater data and etching liquid waste liquid data; determining a wastewater recovery control stage according to the chemical wastewater data; constructing a wastewater control model by utilizing chemical engineering simulation software based on the wastewater recovery control stage; inputting the chemical wastewater data into a wastewater control model, simulating a wastewater recovery control process by using simulation software to obtain wastewater recovery control data, and performing control abnormality judgment on the wastewater recovery control data to obtain abnormal wastewater data; the high-definition industrial camera is used for collecting images of the waste water sedimentation tank, and a centrifugal pump image is recognized and obtained from the images of the waste water sedimentation tank. According to the method, wastewater recovery control is optimized based on a wastewater treatment technology, and the wastewater recovery efficiency and the anomaly detection rate are remarkably improved.

The invention relates to the technical field of data storage management, in particular to a data storage method based on a large model. The method comprises the following steps: performing input class cluster fragmentation optimization on a target pre-training large model to obtain storage modulus fragmentation data; constructing an intelligent fragmentation storage strategy by using the storage modulus fragmentation data; obtaining target model training data; performing intelligent fragmentation storage on the target model training data by using an intelligent fragmentation storage strategy, and performing redundancy compression optimization to obtain dynamic redundancy storage index data; and training data storage space optimization is carried out according to the dynamic redundancy storage index data, and an optimized data storage strategy is obtained. According to the method, intelligent fragmentation storage and refined data redundancy compression are performed on the data, so that efficient and stable model training data access is realized, the problem of low data storage and access efficiency in a large model training process is solved, and the model training efficiency and reasoning real-time performance are remarkably improved.

The invention relates to the technical field of bolt mounting equipment, in particular to an angle steel tower bolt perforation mounting method. By means of autonomous navigation of the installation robot and flexible operation of the mechanical arm, rapid positioning and taking-out of bolts and nuts are achieved, and the risk of manual climbing and operation is reduced. Accurate insertion and preliminary positioning of the bolt are ensured through accurate control of the mechanical arm, then the fastening effect is enhanced by combining impact tightening with the intermittent hammering technology, the fit clearance between the angle steel is effectively eliminated, and the bolt is prevented from loosening. The whole process is highly automatic, personal errors are reduced, and the mounting quality and the working safety are improved.

The invention relates to the technical field of intelligent data processing and artificial intelligence in the electric power industry, and provides an electric power semantic large model adaptive data query and dynamic response method and system.The method comprises the steps that electric power industry natural language query and related images are obtained, and after feature extraction is conducted through a large language model and a deep learning model, the images are obtained; the knowledge graph and the visual question and answer graph neural network are utilized, features of different modes are unified into the same representation in a bidirectional fusion mode, fused features are obtained, after intelligent error correction and natural language query optimization are conducted through the hypergraph neural network and multiple rounds of interaction, an extended query statement is obtained through hypergraph reasoning, and a query result is obtained. Converting the extended query statement into a structured query statement through an autoregression model to obtain a query result; and performing dynamic permission adaptation, sensitive data protection and compliance and security check on a query result to realize dynamic response. And the query accuracy and efficiency of the power industry are greatly improved.

The invention discloses a method for automatically generating a BIM model based on point cloud data. The method comprises the following steps: step 1, preprocessing original point cloud data; 2, carrying out the semantic segmentation of the point cloud of the building construction; step 3, segmenting point cloud instances of the building components; 4, performing IFC construction reverse Revit modeling based on the point cloud data and the point cloud instance segmentation result, and converting the point cloud data into a three-dimensional BIM model of the building component; 5, after reverse modeling of the building components is completed, all the building components are recombined and integrated based on the coordinate data, a complete three-dimensional BIM building model is formed, accurate positions and relations of all the building components in the global coordinate system are ensured, and overall optimization of the BIM building model is conducted. The invention relates to the technical field of information, and can solve the problems that in the prior art, modeling based on point cloud data depends on manual stroking and mold turning, the efficiency is low, and errors are large.

The invention provides a sewage treatment operation cloud platform based on Internet of Things and GIS, and the platform is characterized in that a data collection layer is used for obtaining user water consumption data and sewage pipe network data, and distributing all the data to each edge calculation node; the data storage layer is used for temporarily storing the data preprocessed by the edge computing node into a high-frequency storage chain, aggregating to obtain sewage pipe network comprehensive data, and permanently storing the sewage pipe network comprehensive data into a low-frequency storage chain; the data analysis layer is used for extracting sewage pipe network comprehensive data in the low-frequency storage chain, constructing a multi-dimensional dynamic GIS model by utilizing a GIS system, analyzing and predicting the multi-dimensional dynamic GIS model through a sewage treatment rectification analysis model, and generating a sewage treatment rectification plan of the sewage pipe network according to an analysis and prediction result; and the application service layer is used for acquiring all the sewage treatment and rectification plans and distributing all the sewage treatment and rectification plans to a management client held by a sewage pipe network manager. The sewage treatment device has the effect of efficiently and intelligently realizing sewage treatment.

The invention discloses an intelligent data information rapid checking and question-answering system, which relates to the technical field of intelligent data information and comprises a document import module, a vectorization analysis module, an NLP processing module, a question-answering matching module and a result output module. The document import module is used for receiving and importing documents including but not limited to PDF, DOC, TXT and PPT, analyzing the documents in different formats by adopting corresponding libraries and tools, identifying and classifying the files by identifying title formats and question number formats when processing the files in test questions and test paper formats, and performing preliminary analysis on the contents of the documents by utilizing a machine learning algorithm; according to the invention, the system not only can realize the rapid checking and question-answering functions of documents, but also can be applied to the field of educational training, realizes the online editing, management and test paper formation of test questions, reduces the manpower consumption in basic training business, and integrally improves the working quality and efficiency; meanwhile, the system can help enterprises to carry out internal training and examination, and improves the practicability and application range of the system.

The invention discloses a coal mine fireproof early warning method based on intelligent monitoring, and relates to the technical field of coal mine fireproof early warning, and the method comprises the following steps: obtaining coal mine environment information data parameters in real time through a coal mine fireproof early warning system, including humidity dynamic parameter information and air pressure parameter information; the environment is intelligently evaluated through the generated environment safety change index, when the environment is judged to be a high-risk environment, the system dynamically adjusts the sampling frequency based on the preset sampling frequency, fine changes such as methane diffusion or air static pressure fluctuation in the early stage of a fire disaster are captured in time, rapid early warning is ensured, and the safety of the fire disaster is improved. The accuracy and timeliness of signal capture are improved, and early intervention time is won for managers. And in a low-risk environment, the system collects data at a fixed sampling frequency, so that unnecessary calculation tasks and energy consumption are reduced, and long-term monitoring and trend analysis are supported. Through the dual regulation and control mechanism, the system not only ensures the data integrity, but also optimizes the resource utilization and operation efficiency.

The invention discloses a standardized electrical switch cabinet detection method based on the Internet of Things, and relates to the technical field of electrical switch cabinet detection, and the method specifically comprises the following steps: obtaining the core information of all abnormal data generated by all sensors in an electrical switch cabinet in real time, and carrying out the analysis after obtaining, evaluating the priority of each abnormal data in the electrical switch cabinet with the high current leakage phenomenon, and dividing each abnormal data into low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data according to an evaluation result; and according to the division result of each abnormal data, constructing an abnormal data hierarchical sorting mechanism, and respectively adopting different sorting and processing measures for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data. According to the method, the problem of unreasonable priority processing of the abnormal data in a high-current leakage scene is solved, quick response and accurate detection are realized, and the fault processing efficiency and the system reliability are improved.

According to the home assistant system based on the large language model agent, a voice interaction module receives voice input of a user and converts the voice input into a text, multi-language recognition and voice synthesis are achieved through natural language processing, and natural and smooth two-way voice interaction is supported. The WeChat interaction module receives and processes text or voice information sent by a user through WeChat, and remote instruction input and information feedback are achieved. The core of the system is a plurality of functional Agent modules. The task analysis Agent analyzes semantics input by a user based on a large language model and dispatches a corresponding functional Agent to execute tasks, and the functions of information query, smart home control, information transmission, mobile navigation, monitoring and the like are covered. Personalized learning and optimization can be carried out according to a user-defined information base, use habits and historical records, the task execution precision and efficiency are improved, and the method is suitable for multi-scene application in the field of smart home.

The invention discloses an AI-based marketing commodity short video generation method and device, a medium and a product, and relates to the field of short video generation. The method comprises the following steps: constructing a short video industry knowledge base; obtaining question information input by a user; searching answers from a short video industry knowledge base according to the question information; obtaining a video selected from the answers by the user as a reference short video, and obtaining commodity information uploaded by the user; the commodity information comprises commodity selling points and commodity materials; retrieving a reference copywriting from the short video industry knowledge base according to the commodity selling point, taking the reference copywriting as knowledge supplementary information, generating a script copywriting according to the reference short video and the knowledge supplementary information, and generating picture information according to the script copywriting and the commodity material; and editing the picture information to obtain a short video of the commodity information. The output efficiency of the running amount materials can be improved.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.