78results about How to "Realize automatic extraction" patented technology

The invention discloses a financial data automatic filling method and device, equipment and a storage medium, and belongs to the technical field of big data. The method comprises the following steps: determining a data source corresponding to original financial data by using a pre-trained data classification model; acquiring a data processing rule engine and a target data filling template matched with the data source; performing data cleaning on the original financial data based on a data cleaning rule; performing data analysis on the standardized data by using a data analysis rule; performing association relation extraction on the target data source field and the target filling field by using a data mapping rule, and performing data field mapping according to the field association relation; and according to a data field mapping result, filling the target data source field into the target data filling template to generate a financial data report. The invention further relates to the technical field of block chains, and financial data are stored on the block chain nodes. According to the method and the device, the applicability and the operation convenience of automatic filling and reporting of financial data in a multi-source data scene are improved.

The invention relates to the technical field of color correction, and discloses a publication printing color correction method and system based on deep learning, and the method achieves the precise correction and compensation of the color of a publication printed matter through a series of steps of cosine distance calculation, color-environment matching model, color deviation calculation, operation parameter adjustment and the like. The core of the method lies in that the accuracy of the printing color is effectively improved and the color quality of the printed matter is ensured to meet the expected standard through careful analysis and processing of the printing color by using the deep learning technology. The correction method provided by the invention has obvious advantages, not only can realize automatic color correction, reduce human intervention and improve the stability and consistency of correction, but also can optimize the operation parameters of the printing equipment, so that the printing equipment can keep a good working state in different printing environments, and the adaptability of the printing process is enhanced.

The invention belongs to the technical field of ground unmanned platforms, and particularly relates to a sky-air-ground integrated cross-country environment high-precision map construction method. Comprising the following steps: 1, acquiring a cross-country environment high-precision map; and 2, making a cross-country environment high-precision map. According to the invention, automatic extraction of elements is realized by combining an automatic extraction technology; in the aspect of map making, making specifications are provided, and the running efficiency of a high-precision geographic information system is improved while it is guaranteed that the cross-country maneuvering requirement of a ground unmanned platform in the cross-country environment is met; incremental data updating is carried out on the high-precision map based on a real-time mobile environment perception result of the ground unmanned platform so as to ensure the timeliness of the high-precision map. Through the method, the blank of a high-precision map technology in the field of automatic driving in the cross-country environment is filled up, and support is provided for improving the cross-country maneuverability of a ground unmanned platform.

The invention belongs to the technical field of computer system security, and particularly relates to a binary component vulnerability recognition method based on a large language model. The method solves the problems of various equipment types, various instruction set architectures, different compilers, different compiling optimization levels and the like in the current Internet of Things binary component vulnerability recognition, and comprises the following steps: S100: collecting various vulnerability description information of an Internet of Things component, extracting data from the vulnerability description information, and constructing a text information data set; s200, performing text segmentation processing on a long text in the text information data set; s300, inputting the constructed text information data set into a large language model for training and optimization; and S400, detecting the binary component by using the trained vulnerability recognition large model to obtain a binary component vulnerability detection result report.

The invention discloses a social network public opinion monitoring method and system based on artificial intelligence. The method comprises the following steps of obtaining public opinion data from a social network platform and performing data preprocessing; hot events are automatically extracted from the public opinion data, and a density peak clustering algorithm is utilized to identify a high-density area and determine the popularity of the hot events. A BERT model is adopted to carry out sentiment classification on a text, context features are extracted in combination with bidirectional LSTM, and sentiment tendency is output through a Softmax classifier. For a specific target event, a multi-head attention mechanism and a convolutional neural network are used for specific target emotion classification. And performing noise reduction and decomposition on the time sequence data, constructing a plurality of basic prediction models, generating a combined prediction model in combination with weighted average, and monitoring and predicting the public opinion development trend in real time. Accurate public opinion analysis and decision support are provided for governments and enterprises, public opinion risks are reduced, social stability is maintained, and social construction is promoted.

The invention relates to a lightweight electrical equipment multi-target detection method and system, and the method comprises the steps: collecting multi-angle images of different types of electrical equipment, obtaining an experiment data set, constructing a Ghostnet lightweight network structure, introducing a CBAM attention mechanism and an SPPFCSPC convolution structure to improve a YOLOv8 model, and obtaining a multi-target detection model based on the improved YOLOv8 model. And training, verifying and evaluating the multi-target detection model based on the improved YOLOv8 model to obtain a multi-target detection optimal model for electrical equipment multi-target detection. According to the method and the system, the model complexity and the calculation amount can be reduced while the detection precision is ensured, and an effective solution is provided for multi-target identification detection of lightweight electrical equipment.

The invention discloses a river channel desilting device for water conservancy projects, and belongs to the technical field of water conservancy projects, the river channel desilting device comprises a collecting box used for storing sludge, a sludge pump is fixedly mounted at the top of the collecting box, and a feeding pipe and a discharging pipe are respectively communicated in a feeding port and a discharging port of the sludge pump; the extraction mechanism is used for extracting sludge, and the extraction mechanism comprises a mounting seat, an extraction pipe and a connecting pipe; the dewatering mechanism is used for dewatering sludge and comprises a collecting hopper and a dewatering cylinder, a conveying shaft is rotatably mounted in the dewatering cylinder, a spiral auger is fixedly mounted on the outer side of the conveying shaft, and the collecting hopper is slidably connected to the upper portion of the dewatering cylinder. Through the cooperation of the extraction mechanism and the dehydration mechanism, automatic extraction and dehydration of river silt can be achieved, the crushing mechanism is matched to achieve cutting and scattering of the silt, the scattering effect can be adjusted according to the flowing degree of the silt, the fluidity of the silt is improved, silt blockage is avoided, and the desilting efficiency is improved.

The invention discloses a landing typhoon residual vortex detection method based on YOLOv5, and the method comprises the steps: employing ERA5 data and landing typhoon optimal path data, screening a convolutional neural network YOLOv5 model, carrying out the screening of initial data based on a potential height field, taking the screened data as a model sample set, building a typhoon residual vortex detection model, and achieving the detection of weak vortexes. In the life stage after the landing typhoon is weakened into tropical low-voltage shutdown, the existence and development conditions of the landing typhoon residual vortex are accurately detected and monitored, accurate reference is provided for meteorological early warning and disaster management, and the accuracy rate of a test set is as high as 70.08%.

The invention belongs to the technical field of fault diagnosis, and particularly relates to a gearbox fault diagnosis method and system based on cyclic pulse characteristic decomposition, and the method comprises the steps: constructing a new dictionary matrix, namely a cyclic pulse dictionary matrix, to accurately estimate a pulse cycle period contained in a signal formed by mixing a plurality of periodic pulse signals; a Goertzel algorithm is adopted to obtain corresponding frequency amplitudes according to the magnitude sequence of the energy values of the pulse cycle periods; and separating and reconstructing the CPCC corresponding to the pulse cycle period, and decomposing the signal into a plurality of CPCCs. According to the invention, the cyclic pulse dictionary matrix is constructed to carry out sparse representation on the pulse cycle period of the periodic pulse signal, and the cycle period of the fault pulse characteristic in the gear vibration signal is accurately estimated; the Goertzel algorithm is adopted to decompose and reconstruct the CPCC in the gear vibration signal according to the pulse cycle period, and the decomposed CPCC contains abundant periodic pulse characteristic information.

The utility model belongs to the technical field of medical instruments, and discloses a bone marrow collection device capable of quantitatively sucking, which comprises a storage bin, a suction tube, a suction tube, a suction tube, a suction tube and a suction tube, and is characterized in that the storage bin takes the shape of a hollow cylindrical shell and is used for storing collected bone marrow; the needle head and the storage bin are coaxially arranged, and the end, away from the needle tip, of the needle head extends into the storage cavity; the piston is slidably connected into the storage bin, the peripheral wall of the piston is tightly attached to the inner wall of the storage bin, a pair of push rods is fixed to the end, away from the needle head, of the piston, the other ends of the push rods penetrate through the storage bin and are slidably connected with the storage bin, and the ends, away from the piston, of the push rods are fixed to the push plate; the distance between the pull-back push plate, the push rod and the piston during bone marrow extraction is controlled by controlling and adjusting the distance between the pressing plate and the storage bin, then the amount of the extracted bone marrow is controlled, the purpose of quantitatively collecting the bone marrow is achieved, and the quantitative collection amount can be adjusted.

The invention discloses an automatic fracture scanning electron microscope image recognition method based on a convolutional neural network, and the method comprises the steps: collecting a fracture scanning electron microscope image, and manually calibrating the type of the fracture scanning electron microscope image; secondly, performing image preprocessing to form a data set; then, based on a deep learning convolutional neural network, an improved CNN network model is adopted, and based on a plurality of convolution kernels, a maximum pooling layer and a global average pooling algorithm, the metal fracture scanning image is analyzed, and the type of the metal fracture image is determined; and finally, inputting a to-be-recognized image, and performing statistics on a classification model of the image by using a global average pooling algorithm. According to the method, the risk that the accuracy is reduced due to experience or long-time work of personnel can be avoided, the time cost of analysis work is reduced, the type of metal fracture analysis is obtained, and the analysis result is reliable and efficient.

The invention relates to the technical field of automobile seat massage devices, in particular to an automobile seat massager mechanism which comprises an automobile seat massager composed of a first mounting frame, a second mounting frame, a massage mechanism, a driving mechanism and an abutting plate. The massage mechanism comprises a sleeve arranged on the first mounting frame, a first piston arranged in the sleeve in a reciprocating manner, an air bag fixedly mounted at one end of the sleeve, and a push rod which is fixedly connected to the outer surface of the first piston and penetrates through the sleeve and the interior of the first mounting frame; the connecting block is welded to the other end of the push rod; the reset spring is fixedly mounted on one side, close to the push rod, of the connecting block; a lubricating mechanism used for lubricating the pushing arm is arranged on the outer surface of the second mounting frame. According to the seat massage device, various technologies such as mechanical transmission, electric adjustment and automatic lubrication are combined, accurate adjustment of the massage effect and long-term stable operation of equipment are achieved, the practicability and comfort of the seat massage device are improved, and time and energy of a user are saved.

The present invention discloses a grinding device for processing a brake disc, which relates to the technical field of brake disc processing, and comprises a box body and a brake disc body. A placing table is rotatably installed on the top of the box body, and the brake disc body is installed on the top of the placing table. A fixed frame is fixedly installed on one side of the box body, and a mobile frame is arranged on the fixed frame. A first cross bar is arranged on one side of the mobile frame, and a grinding component for grinding the upper and lower surfaces of the brake disc body is arranged on the first cross bar. The grinding device for processing a brake disc realizes reciprocating and uniform grinding of the brake disc body by a surface grinding piece through a series of mechanical linkage structures driven by a driving motor. This mechanized grinding method is more accurate and stable than manual operation, and effectively improves the grinding efficiency and quality.

The invention relates to the field of safety supervision, in particular to a food safety supervision system based on a cloud platform, and the system comprises a collection module which collects a contour diagram of a package and a morphological diagram of target food; the fitting degree analysis module is used for determining the contour coincidence degree and the edge distance according to the contour map and the form map, and determining whether the fitting degree of the target food and the package is qualified or not based on the contour coincidence degree and the edge distance; the shake risk analysis module is used for detecting the package with qualified fitting degree to obtain the deformation degree and the distribution condition of the package folds in the transportation process so as to determine whether the shake risk is qualified or not; the packaging material evaluation module is used for carrying out migration test on the package with the qualified shaking risk so as to evaluate the material safety of the package according to the migration condition of substances in the packaging material to the food; and the safety determination module determines that the target food in the package is safe food according to the fitting degree, the shaking risk and the material safety. And comprehensive guarantee is provided for food safety.

The invention discloses a sample data marking method for an image detection model. The method comprises the following steps: S1, determining a non-abnormal image as a basic image; s2, an image to be detected is obtained and compared with the basic image, and the image to be detected and the basic image are subjected to inverse color superposition or inverse color duplicate removal to screen out abnormal images in the image to be detected; s3, independently extracting and splitting each abnormal position in the abnormal image through contour detection to form a plurality of independent graphs; s4, determining a minimum bounding rectangular frame according to the edge pixel position of each independent graph; and S5, digitizing the determined minimum bounding rectangular frame, and taking the digitized minimum bounding rectangular frame as sample data for image detection model training.

The invention relates to the field of image processing, and discloses a method for extracting the miss distance of a projectile drop point of a staring type visible light photoelectric theodolite. According to the implementation scheme, the method comprises the following steps: 1, determining a staring view field, acquiring a visible light image in real time by a photoelectric theodolite, and caching a background frame; 2, differentiating the current frame image obtained in real time and the background frame to obtain a differential image; 3, performing differential image target extraction based on multi-feature joint filtering on the differential image; 4, calculating coordinates of the center of mass and the lowest point of the target extracted in the current frame; and 5, judging whether the target centroid ordinate of the frame is the minimum value or not, and outputting the target lowest point miss distance when the target centroid ordinate is the minimum value. The target detection method based on multi-feature combined filtering is adopted, interference of strong light, noisy points, large-area background cloud and false targets is effectively removed, and the detection capacity of target drop points of visible light in a target range is improved.

The invention is suitable for the technical field of Internet, and provides an OCR (Optical Character Recognition)-based financial statement automatic proofreading method and device. In the embodiment, after a financial department obtains an initial financial statement file, text information in the initial financial statement file is automatically recognized and extracted through the OCR technology, then financial subject name matching is conducted on the text information through a preset keyword library, and therefore the subject amount of each financial subject in the text information is obtained; and after confidence evaluation is carried out on the financial statement file, a subject amount lower than a preset threshold value is determined to be checked and modified, and finally a checked target financial statement file is obtained. According to the method and the device, the financial department can realize automatic extraction and matching of financial report information through the OCR and the corresponding algorithm rule, and performs final verification through the financial rule, so that the extraction efficiency and the analysis efficiency of financial data are greatly improved, and the manpower expenditure is reduced.

The application relates to a target feature point extraction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed image; acquiring a template corresponding to the to-be-processed image and acquiring the position relationship between the template and standard feature points; the template is an image generated based on a sample image, and the standard feature points are located in the template; registering the template to the to-be-processed image; according to the position relationship between the template and the standard feature points and the position relationship between the registered template and the to-be-processed image, determining target feature points corresponding to the standard feature points in the to-be-processed image. The method can realize automatic extraction of target feature points.

The invention discloses a configured interface docking and analysis method, device and equipment and a medium, and the method comprises the steps: obtaining an interface document of a third-party platform, and combining with preset configuration items according to the interface document to obtain configuration information; the configuration information and the channel identifier are associated and stored in a preset database; in response to a service request for a third-party platform, obtaining configuration information from a preset database; the service request carries a channel identifier and request service data; assembling based on the request configuration and the request business data to obtain a request body, and sending the request body to a third-party platform according to the service address and the request format; and extracting response service data obtained by the third-party platform in response to the request body based on the request response configuration. Through the configuration mode, the tedious development process is avoided, automatic data extraction is achieved through the configuration information corresponding to the third-party platform, rapid docking is completed, the docking efficiency is improved, the research and development period is shortened, and the method can be widely applied to the technical field of data processing.

The invention discloses a water environment situation big data research, judgment and treatment integrated system. The system comprises a plurality of big data of different sources, space unit division, a data warehouse module, an index system module, a deep learning model and a water environment situation early warning and prediction module. On the basis of a big data platform, through a source-sink relationship of a drainage basin water environment, big data feature values and label values of the drainage basin water environment and influence factors of the drainage basin water environment are identified and extracted, and on-line real-time prediction of the drainage basin water environment based on deep learning is carried out under a big data condition, so that a water environment situation research and judgment intelligent decision support system is constructed. According to the method, intelligent decision analysis and situation study and judgment of the water environment in the whole watershed scale are realized, the method is practically applied to watershed water environment management, and a new method and a new model are provided for intelligent decision of the watershed water environment. In addition, a watershed water environment intelligent decision informatization big data platform is constructed, and powerful support is provided for intelligent research and judgment and intelligent management of watershed water environment postures.

The embodiment of the present invention discloses a method, device, equipment and storage medium for extracting text labels. The method comprises: obtaining each text of the label to be extracted, and vectorizing each of the texts to obtain a text vector corresponding to the corresponding text; clustering each of the text vectors to obtain at least one text clustering result; extracting keywords from each of the text clustering results to obtain each label candidate word corresponding to each of the text clustering results; and determining the text label of each of the texts according to each of the label candidate words corresponding to each of the text clustering results. Through the above technical solution, the automatic extraction of text labels is realized, and the accuracy and comprehensiveness of text label extraction and the scalability of the label extraction method are improved.

The invention discloses a gear fault diagnosis method based on linear discriminant analysis and random forest, and the method comprises the steps: building a fault simulation test bench, collecting a vibration signal, and constructing a fault sample data set; carrying out CEEMDAN-combined improved wavelet threshold noise reduction on the obtained vibration signal, and carrying out signal preprocessing; based on a random forest classifier, in combination with a linear discriminant analysis method, establishing an LDA-RFA gear fault diagnosis model; performing parameter optimization on the constructed gear fault diagnosis model to obtain optimal parameters of the model; and performing model training by using the gear fault public data set to complete gear fault diagnosis work. The fault information can be accurately extracted, and the accuracy of gear fault diagnosis is improved.

The invention discloses a rock internal crack detection method and system based on fault enhancement, and the method comprises the steps: collecting an original image of a rock internal structure, and carrying out the multi-scale decomposition, and obtaining a low-frequency component and a high-frequency component; performing denoising processing according to the low-frequency component and the high-frequency component to obtain a denoised image; using an edge detection algorithm to extract crack boundary features in the de-noised image to obtain a boundary enhanced image; and obtaining a final crack detection result based on the boundary enhanced image. According to the method, the overall structure and detail features of the rock image are effectively separated through multi-scale decomposition, and the precision and integrity of crack recognition are remarkably improved in combination with targeted denoising and boundary enhancement processing; furthermore, on the basis of image segmentation and spatial analysis, automatic extraction and distribution rule quantification of the crack region are realized, so that a reliable and efficient technical means is provided for accurate evaluation of the internal structure of the rock.

The invention discloses a cardboard extraction device and a cardboard extraction method, and relates to the field of carton production equipment. A frame, a cardboard conveying assembly, a driving device, a guide plate and a guide strip are arranged in a carton production line. The driving assembly can drive the guide plate to move or swing, so that the guide plate switches between two states of contact with the cardboard and non-contact with the cardboard. When the carton production line is working normally, the guide plate is in a state of non-contact with the cardboard. When the cardboard needs to be extracted, the driving assembly drives the guide plate to protrude on the conveying plane of the cardboard conveying assembly, and then the cardboard passing through the cardboard conveying assembly contacts the guide plate and is guided to the guide strip by the guide plate, so that the cardboard is automatically extracted, which is convenient and simple and will not cause a decrease in production efficiency. When the bottom surface of the cardboard conveying assembly is used as the conveying plane, a conveying belt structure is arranged below the cardboard conveying assembly, so that after the extracted cardboard falls on the conveying belt structure, it is conveyed to the outside of the frame by the conveying belt structure, so that it is convenient to take.

The method and system for automatically extracting key frames of blast furnace material surface video provided by the present invention collects material surface video of blast furnace smelting process, locates the significant area of ​​material surface image based on boundary prior and image feature space, extracts accurate feature points of significant area of ​​image, and calculates pixel displacement of two adjacent frames of image according to optical flow method; then proposes a method of fusing local density maximum and GMM model clustering to identify material surface state; finally, divides the video collected by high-temperature industrial endoscope into different periods according to the change of density of image feature points, and extracts key frames of each material distribution period based on material surface state recognition result. The present invention can accurately identify material surface state and eliminate redundant information of material surface video, and automatically extract material surface video key frames with stable central airflow and obvious image features from blast furnace material surface video. At the same time, the present invention is suitable for automatic extraction of key frames of other videos with periodic changes and uneven image quality.

This invention relates to a rapid, non-destructive testing method and system for lipid content and deterioration degree in red pine nuts based on hyperspectral imaging and deep learning. The invention belongs to the field of non-destructive testing technology for agricultural and forestry product quality and safety. It addresses the problems of existing methods for detecting lipid content and oxidation degree in red pine nut kernels, which generally suffer from complex operation procedures, high costs for large-scale testing, long processing times, and difficulty in achieving full-process testing during storage and transportation. Key technical points: The original near-infrared spectral data of pine nut samples are obtained through hyperspectral acquisition; the true values ​​of lipid reference content and reference degree of oxidation deterioration of pine nut samples at different sampling times are determined, and a database is established based on these values. The original near-infrared spectral data of red pine nuts are preprocessed, and the data from different batches of red pine nut samples are divided into training and validation sets. An improved one-dimensional dilated convolutional network based on a dynamic weight allocation module is designed to construct a deep learning model suitable for the spectral feature analysis of red pine nut kernels. The constructed model is trained using a backpropagation algorithm, and the network weight parameters are updated in reverse by minimizing the loss function. When the constructed model meets the requirements for rapid detection, it can be used for efficient and non-destructive simultaneous detection of lipid content and oxidative rancidity in red pine nuts.

The present application relates to the technical field of corpus collection control, and particularly relates to a corpus automatic collection and quality control method and system based on an intelligent algorithm, which constructs state representation by acquiring corpus attribute information and generates an adversarial collection strategy, extracts blind area corpus after execution, selects boundary corpus and collects, constructs feature mapping to determine associated corpus, transmits quality mark value to associated corpus to obtain a corrected quality value, transmits utility measurement value to boundary corpus to obtain a predicted utility value, and finally fuses quality and utility values to execute corpus quality control, thereby improving the comprehensiveness of corpus collection and the accuracy of corpus quality control.

The application belongs to the technical field of remote sensing earth observation, and particularly relates to a large-format remote sensing image multi-target extraction method and system, which comprises S1 large-format overlapping sliding window segmentation; S2 cross-modal space mapping and geometric prior prompt generation; S3 adaptive segmentation target function based on spatial prior constraint; S4 affine restoration of local pixel coordinates to global coordinates; and S5 overlapping area mask confidence weighted fusion and final result generation. A visual language multi-modal large model is used as a semantic analysis front end, and a segmentation large model is used as a pixel-level decoding back end, the two are connected through a spatial prior prompt, and the whole process is driven by a natural language multi-round dialogue, so that end-to-end high-precision automatic extraction of ground objects is realized.

The invention discloses an information summary information extraction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining conference text information of target conference information, the conference text information comprising speaking text information of at least one conference speaking object; segmenting the conference text information according to conference speaking objects to obtain speaking text segments of the conference speaking objects; performing key information extraction on the speaking text segment of each conference speaking object to obtain key point information of each conference speaking object; based on a preset content category and the key point information of each conference speaking object, generating category summary information corresponding to each preset content category; and based on the category summary information corresponding to each preset content category, generating information summary information corresponding to the target conference information. The information summary information in the conference text information is automatically extracted, and the extraction efficiency of the information summary information is improved.

The invention relates to the technical field of sports health monitoring, and particularly discloses a real-time running fatigue monitoring method and system, electronic equipment and a storage medium, and the method comprises the following steps: receiving real-time heart rate data in a running process; extracting a plurality of heart rate variability characteristics from the real-time heart rate data; inputting the plurality of heart rate variability characteristics into a fatigue monitoring model; wherein the fatigue monitoring model is obtained in advance based on indoor running data training, and is obtained by carrying out cross-scene optimization by adopting transfer learning and incremental training strategies and utilizing outdoor running data; the multiple heart rate variability characteristics are processed through the fatigue monitoring model, and a real-time fatigue quantification result is generated; and outputting fatigue state information according to the real-time fatigue quantification result. The technical problems that subjective evaluation is poor in real-time performance, the application of an existing sensing technology is limited, and the dynamic adaptability of a model is insufficient are solved, and the running fatigue state real-time monitoring capacity and the cross-scene adaptability are improved.