185results about How to "Realize automatic identification" patented technology

The invention discloses a self-evolution network security defense strategy generation and dynamic deployment method, which comprises the following steps of: acquiring multi-dimensional data such as original network flow data, and generating an optimized feature matrix through adaptive interval division and symbolization conversion; constructing a feature graph structure based on the optimized feature matrix, extracting threat features by using a graph neural network and an attention mechanism, and generating a threat mode set; analyzing threat propagation characteristics according to the threat evaluation index set and the threat incidence matrix, and generating a threat propagation graph; and generating an initial defense strategy set based on a threat assessment result, and generating an optimized defense strategy set through dependency analysis and conflict elimination. According to the invention, through adaptive feature extraction and dynamic strategy optimization, the intelligent level and protection effect of network security defense are improved.

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 discloses a judicial field semantic abstracting method, system and device based on a large model and a storage medium. The method comprises the steps that voice recognition is conducted on a court trial video, voice is converted into a court trial text, the court trial text is labeled, and a labeled text set is obtained to serve as a training set; training the large model by using the labeled text set, and finely adjusting the large model after each round of training; performing voice recognition on the actual court trial video, converting the voice into an actual court trial text, and extracting a timestamp; utilizing the trained large model to identify the court hearing process of the actual court hearing text, and reasoning a court hearing process text; matching the reasoned court hearing process text to timestamps by using a matching algorithm, and generating video anchor points when the timestamps at the starting positions of all the processes in the court hearing process text correspond to the anchor points of the processes of the court hearing video; abstracting the reasoned court hearing process text; and combining the abstract text with the video anchor points to generate structured court hearing information. According to the invention, the court trial video processing efficiency is improved.

The invention provides a battery cell tab cutting and inspecting machine, and relates to the technical field of battery cell tab cutting and inspecting machines, the battery cell tab cutting and inspecting machine comprises a transfer device, a turntable device, a cutting device, a shaping device, a CCD detection device, an OCV / IR detection device and a feeding device, the transfer device comprises a feeding linear module supporting seat, a first bottom plate, a discharging conveying supporting seat and a discharging conveying assembly, the two sides of the surface of the first bottom plate are fixedly connected with a discharging conveying supporting seat and a feeding linear module supporting seat correspondingly, a discharging conveying assembly is installed on the surface of the upper end of the discharging conveying supporting seat, and a discharging linear module is arranged on one side of the discharging conveying assembly. According to the invention, the problem that the battery cell is inconvenient to cut due to the fact that the position of the battery cell is easy to deviate in the cutting process of the battery cell tab and the welding process of the protection plate when the battery cell tab needs to be cut and the protection plate needs to be welded on the battery cell in the packaging process of the battery cell is solved.

The invention relates to the technical field of risk early warning, and provides an operation risk early warning method, device and system for substation operation and maintenance personnel and a medium. According to the implementation scheme, the method comprises the steps of training a behavior recognition model based on multi-modal historical data to obtain a target recognition model; based on the current operation data of the operation and maintenance personnel, performing risk identification on the current operation behavior represented by the current operation data through a target identification model to obtain an identification result; if the identification result is that the current operation behavior belongs to the dangerous behavior, triggering a multi-dimensional risk assessment mechanism to perform risk assessment on the current operation behavior to obtain a risk assessment result; and generating risk early warning information based on the risk assessment result. According to the embodiment of the invention, the intelligent early warning of the operation risk of the operation and maintenance personnel of the transformer substation can be realized, and the safety guarantee level of the operation and maintenance personnel during operation is improved.

The invention discloses a photomask defect identification method, and belongs to the technical field of photomask detection. The method comprises the following steps: S1, acquiring a surface defect image; s2, preprocessing the surface defect image based on the imaging characteristics of the defect; s3, performing filtering algorithm correction on the preprocessed surface defect image; s4, performing colorimetric analysis on the surface defect image corrected by the filtering algorithm to obtain a particle region; s5, on the basis of morphological characteristics of defects, segmenting the particle region to obtain defect islands; and S6, performing morphological analysis on the defective island to obtain a defect type and a defect size. The photomask defect identification method provided by the invention combines morphological and colorimetric analysis methods to automatically process the surface defect image and extract the defect island, improves the accuracy of defect identification, gets rid of the constraint of the difference of different equipment imaging systems, realizes automatic batch processing, and saves the manpower and material investment in the mass production link.

The invention belongs to the field of artificial intelligence and finance, and relates to a risk identification factor mining method, which comprises the following steps of: setting different types of operators according to an insurance risk identification target through acquired data of different data types, constructing a genetic programming model according to the different types of operators, and obtaining a risk identification factor by initializing a formula population. And iteratively calculating the risk interpretation degree, continuously optimizing the formula tree through a genetic algorithm to obtain a final formula tree species group, selecting the formula tree with the highest risk interpretation degree from the final formula tree species group as a risk identification factor, and performing test optimization on the risk identification factor to obtain an optimal risk identification model. The invention further provides a risk identification factor mining device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the text data and the numeric data can be stored in a block chain. According to the invention, the automatic identification of the insurance risk can be better realized, and the accuracy of risk identification is improved.

The invention discloses an operation risk management and control method and device based on image recognition, electronic equipment and a storage medium. The method comprises the steps that an operation site video of a current operation site is acquired; inputting the operation site video into a preset operation risk type identification model, so that the operation risk type identification model extracts image frames of the operation site video, sequentially performing image feature extraction on the image frames, and performing identification on the extracted image features according to the identification result of the operation site video; comparing the operation risk characteristics with operation risk characteristics of different operation risk types in a preset image characteristic library one by one, and when the operation risk characteristics consistent with the image characteristics are detected, taking the corresponding operation risk type as the operation risk type of the current operation site and outputting the operation risk type; and matching a corresponding operation risk management and control scheme according to the output operation risk type, and then performing risk management and control on the current operation site according to the operation risk management and control scheme. According to the invention, the operation risk management and control efficiency can be improved.

The invention relates to the technical field of soft package carrying, and discloses a high-speed intelligent feeding system and method.The high-speed intelligent feeding system comprises a 3D visual perception module, a 2D image recognition and positioning unit, a control decision unit, an execution mechanism unit, a bag breaking and shaking processing module, a material bag recycling module, a data processing and analyzing unit and a system integration and management unit; according to the invention, by combining 3D photographing and 2D photographing technologies, efficient and accurate material identification and positioning are realized, in the 2D photographing process, the system can determine the center point coordinates of N bags at the same time, and only one-time 3D and 2D photographing is needed in the same plane, so that the process period is greatly saved, the whole feeding process is quicker, and the production efficiency is improved. The 3D photographing technology provides three-dimensional information of materials, the 2D photographing technology provides two-dimensional image information of the materials, through combination of 3D data, the system can achieve accurate positioning of the materials, and the system can adapt to materials of different shapes, sizes and numbers and can flexibly process various complex material conditions.

The invention provides a user interface similar element identification and analysis method and system, a storage medium and electronic equipment. The method comprises the following steps of obtaining a rectangular region where a to-be-identified similar element is located in a user interface image; preprocessing the user interface image to obtain a user interface grayscale image; for different adjustment parameters, performing gray adjustment on each pixel in the user interface gray image to obtain a gray adjustment image; constructing a similar element rectangular region group for each gray level adjustment image; for each similar element rectangular region group, screening the contained similar element rectangular regions to obtain an effective similar element rectangular region group; and calculating a score value of the effective similar element rectangular region group. According to the user interface similar element identification analysis method and system, the storage medium and the electronic equipment, automatic identification of the similar elements of the user interface can be realized only by the screenshot, and the accuracy is high.

The invention relates to the technical field of direct-current GIS fault detection, in particular to a partial discharge identification method and system suitable for a direct-current GIS, and the method comprises the steps: collecting a partial discharge signal of an unknown defect on the direct-current GIS, and obtaining a pulse sequence representation diagram according to the relation between the discharge time and the discharge capacity in the partial discharge signal of the unknown defect; inputting the pulse sequence representation diagram into the trained GIS partial discharge mode recognition model, and performing defect recognition to obtain a recognition result; and comparing the known defect with an unknown defect to make a manual judgment according to the manually observed partial discharge spectrum characteristics of the known defect, and combining a manual judgment result with an identification result to obtain a judgment result of the unknown defect. According to the method, the partial discharge phenomenon occurring in the operation process of the direct-current GIS can be quickly classified, the specific defect type can be identified, and stable operation of the direct-current GIS is facilitated.

The invention discloses a primary and secondary synergistic flexible stop valve with a self-recovery valve rod, relates to the technical field of valves, and aims to solve the problems of how to find a breakage fault of the valve rod in time and how to recover the function of the valve rod automatically. The stop valve comprises a main valve and a secondary valve, wherein the main valve mainly comprises a main valve body, a main valve cover, a main valve rod, a main valve core, a valve rod fracture self-sensing mechanism and a redundant valve rod mechanism; through the arranged valve rod breakage self-sensing mechanism, automatic identification can be achieved when the main valve rod is broken and the main valve element falls to the valve seat; through the arranged redundant valve rod mechanism, after the valve rod fracture self-sensing mechanism recognizes that the main valve rod is fractured, the redundant valve rod can be controlled to replace the main valve rod to be connected with the main valve element, self-recovery of the function of the valve rod is achieved, and the toughness of the structural layer of the stop valve is guaranteed; the secondary valve can replace the main valve to carry out overflowing control after the main valve rod is broken and before self-repairing, so that the stop valve keeps the original function without losing, and the toughness of the functional level of the stop valve is ensured.

The invention discloses an adapter card identification method and device, electronic equipment and a computer readable storage medium, the method and device are applied to a substrate management controller, the substrate management controller and a basic input and output system are both in communication connection with a preset storage medium, and the method comprises the steps that an adapter card physical link information table is traversed, and a target adapter card is determined; determining a target serial number corresponding to the target riser card in the riser card white list; determining a target storage medium channel corresponding to the target serial number in the riser card static information table; reading through the target storage medium channel to obtain a real serial number of the target riser card; if the real serial number is consistent with the target serial number, target static information corresponding to the target serial number in the riser card static information table is written into a preset storage medium to be read by the basic input and output system, and the target riser card is recognized by the basic input and output system. According to the technical scheme, the automatic identification of the adapter card by the basic input / output system in the server is realized through the baseboard management controller.

The invention relates to an electric power construction site target identification method and system based on a deep learning network. The method comprises the following steps: collecting photos of an electric power engineering construction site; the collected photos are input into a pre-constructed and trained image recognition network, target recognition is carried out on the electric power engineering construction site, a target recognition result is obtained, and the target recognition result comprises a bounding box, a confidence probability and a target category; and according to the identified bounding box, confidence probability and target category, performing target construction state judgment, and comparing with a pre-established construction safety knowledge graph to judge whether illegal operation or potential risk exists. Compared with the prior art, the real-time supervision capability of the construction site can be effectively improved, automatic identification of construction operation is realized, various conditions of the construction site are comprehensively evaluated and dealt with, the burden of manual supervision is relieved, and meanwhile, the objectivity and accuracy of judgment are improved.

The invention belongs to the technical field of aviation machinery control, and relates to a method for automatically identifying an external hanging object, which comprises the following steps of: 1, defining an identification position for a hole position on an external hanging object connector; 2, defining a hole site on the external hanging object connector to put an external hanging object; 3, the external store is hung on the airplane through the hanging frame, the external store connector is in butt joint with the hanging frame connector, and the hook connector is in butt joint with the hanging frame connector; 4, the hook outputs a ground signal to the airborne task system through a hook connector, meanwhile, an identification signal of the external store is output to the airborne task system through a connector of the external store, the airborne task system collects the ground signal of the hook and the identification signal of the external store at the same time through a collection circuit, meanwhile, the airplane is in a ground state, and the hook is connected with the external store. The airborne task system completes automatic identification of the external store; and 5, reporting the type of the external store to a display control system through a bus after the identification is completed.

The invention discloses a fault detection method and device for relay protection equipment and a medium, and the method comprises the steps: collecting the operation data of the relay protection equipment through a mobile operation and maintenance tester, and constructing a relay protection topological structure based on the relay protection equipment, the automatic identification of the incoming and outgoing line connection relation of each topological node in the relay protection topological structure is realized; on the basis of a relay protection topological structure, current protection information of a transformer outgoing line is collected based on multiple agents, and whether faults occur on all feeder lines or not is judged; according to the relay protection topological structure, JSON data is generated for the collection information of each topological node for automatic testing and sent to a relay protection tester, the test result is compared with the feedback result of whether a fault occurs on each feeder line for verification, and relay protection fault detection is completed. According to the method, automatic identification of the incoming and outgoing line connection relation of each topological node can be realized as soon as possible, the method is suitable for standardized automatic test of relay protection equipment, and information fusion based on a relay protection test result and mobile operation and maintenance inspection is realized.

The utility model discloses a portable intelligent substation microstation equipment inspection device, including front end equipment, back end equipment and cloud equipment. Front end equipment is handheld inspection terminal, and integrated main control board, camera, display screen, fingerprint identification, communication interface etc. parts are used for information collection and interaction, and cloud equipment includes equipment information database, fault log record library, solution library, and is connected with front and back end equipment through information interaction module, realizes data storage and scheme calling, and back end equipment includes video processing, state monitoring, fault prediction, personnel scheduling and information transmission etc. module, forms the automation inspection and fault processing process. The device has the advantages of flexible deployment, accurate identification, efficient response, and can improve the intelligent degree of inspection and the substation operation and maintenance efficiency.

The application discloses a kind of pulse Doppler radar weather cloud clutter automatic filtering methods, belong to radar data processing technical field.The method utilizes the statistical characteristics of weather cloud clutter on radar echo spectrum diagram, and the spatial position and motion characteristics of radar track in monitoring area, realizes the automatic identification and effective elimination of weather cloud clutter, reduces false track, and improves the detection performance of radar to "low, slow and small" target.

The invention relates to the technical field of power system fault detection, in particular to a factory power distribution system fault interval section determining method and system and terminal device.The factory power distribution system fault interval section determining method comprises the steps that when a power failure fault of a power distribution system is monitored, voltage and current data within a preset duration before the fault is obtained; comparing the phase current amplitude with a rated current threshold value, judging that the phase current amplitude is an external fault if the phase current amplitude does not exceed the limit, calculating a zero-sequence current if the phase current amplitude exceeds the limit, and comparing the zero-sequence current with a zero-sequence threshold value to distinguish a ground fault or an inter-phase fault; adaptively calling a phase difference analysis process according to an identification result, calculating a phase difference between a zero-sequence current and a zero-sequence voltage during a grounding fault, calculating a phase difference between a fault phase voltage and a fault phase current during an inter-phase fault, and judging whether a fault interval is internal or external according to a phase difference interval; and a structured message is automatically generated by integrating fault types, interval judgment and triggering criterion paths, so that quick and accurate fault positioning and handling are realized.

The application provides a model training method, a geological type identification method, a device and an electronic device. The model training method comprises: obtaining first training data, wherein the first training data comprises first working parameters of a tunneling device and a geological type corresponding to the first working parameters; inputting the first training data into a pre-trained feature extraction model to obtain second training data output by the feature extraction model, wherein the second training data comprises first target working parameters and a geological type corresponding to the first target working parameters, and the first target working parameters are parameters obtained by performing feature extraction on the first working parameters by the feature extraction model; and inputting the second training data into a pre-constructed network model to perform training, thereby obtaining a geological type identification model. The technical solution can at least alleviate the problem of low accuracy of a judgment result in the prior art when a geological type in front of a TBM tunnel face is judged.

This invention discloses a master-slave task synchronization method, device, controller, and rail train, relating to the field of rail transit technology. The method includes: the current controller identifying the master-slave presence status on the rail train's chassis; determining the system master control status corresponding to the current controller based on the master-slave presence status; if the system master control status is in master control mode, generating an interrupt request at a first preset time interval, performing synchronization task processing according to the interrupt request, and sending the interrupt request to other present controllers. This invention achieves automatic master-slave controller identification by determining the system master control status corresponding to the current controller based on the identified master-slave presence status; by having one controller acting as the system master controller generate and send an interrupt request to other controllers acting as system slave controllers, all controllers can perform strict master-slave task synchronization according to the interrupt request of the system master controller, thus improving the accuracy of master-slave task synchronization.

The invention discloses an underwater CSK-OFDM modulation and detection method based on LED linear constraint, and the method comprises the steps: enabling a transmitter to map a data bit stream of a constellation diagram into a complex symbol stream, converting the complex symbol stream into a real number time domain sequence through an ACO-OFDM module, carrying out the LED amplitude limiting of the time domain sequence, and obtaining a time domain clipping sequence; transmitting the time domain clipping sequence in a UOWC channel through a corresponding LED, introducing symbol timing deviation and amplitude limiting noise into a frequency domain by a receiver, and processing the time domain clipping sequence by using an ACO-OFDM module again to obtain a received signal; under the maximum expectation framework, the probability from each alternative constellation diagram is calculated, symbol timing deviation and additive white Gaussian noise variance are estimated, a globally optimal solution is obtained, and a constellation diagram recognition result is output through maximum likelihood demodulation. According to the method, the linear range constraint of the LED and the complex environment under the UOWC scene are comprehensively considered.

The application discloses an ultrasonic image processing method, device, equipment and storage medium, and the method comprises the steps of detecting a selection instruction for indicating selection of a target object in an ultrasonic image, taking a coordinate indicated by the selection instruction as a starting point coordinate; and performing boundary diffusion identification around the starting point coordinate to identify a boundary contour of the target object from the ultrasonic image. The application takes any point in the target object as the starting point coordinate, and performs boundary diffusion around the starting point coordinate to identify the boundary contour of the target object. The application automatically identifies the boundary of the target object, reduces human intervention, improves the efficiency and accuracy of boundary identification. The boundary contour is identified based on the change of energy coefficients between pixel points, and the change of the energy coefficients conforms to the change of texture features at the internal and boundary parts of the target object, so that the boundary contour can be accurately identified. The whole boundary diffusion process can be superimposed and displayed in the ultrasonic image, and the visualization of the whole boundary diffusion process is realized.

The application relates to the field of data processing and discloses a structured data automatic extraction and output method and device for business bills. The method comprises the following steps: providing a plurality of pre-created and published template snapshots and data structure constraint rules; acquiring a bill image; automatically determining a target template type matched by the bill image, loading corresponding template snapshots and data structure constraint rules according to the target template type; performing optical character (OCR) recognition on the bill image to obtain OCR text; and extracting and outputting corresponding structured business data from the OCR text through an information extraction engine according to extraction prompt words and data structure constraint rules. The embodiment of the application can self-define a template, automatically identify a bill type, perform data extraction through context semantic understanding to break through the difference barrier of field naming, and directly output structured business data, so as to meet the automation, standardization and traceability requirements of enterprise-level bill processing.

The application discloses a quick finite element modeling method of a five-axis machining center driven by G code instructions, and belongs to the technical field of machine tool simulation and structural dynamics. Step 1: independent meshing of parts and pre-computation of element matrices; step 2: establishment of a machine tool kinematics model and construction of a joint surface node index table; step 3: G code instruction analysis and node coordinate updating based on inverse kinematics solution; step 4: automatic pairing of joint surface nodes based on the K nearest neighbor algorithm; step 5: master-slave node freedom degree deduplication and preprocessing based on a spherical hinge connection pair; step 6: overall matrix assembly based on matrix reuse and rotation transformation; and step 7: multiple constraint application and system matrix condensation processing. The application can realize dynamic updating of a machine tool finite element model, reflect time-varying structural characteristics driven by G code instructions in a machining process, provide a more accurate mechanical model for dynamic simulation, and support forward design iteration and machining process optimization of a machine tool structure.

The present application relates to a kind of based on multi-target patch type ultrasonic hemodynamic state quantification and classification method, comprising: obtaining the ultrasonic echo signal of multiple channels and carrying out filtering and gain compensation preprocessing;Extract the hemodynamic physical parameter of each channel;The hemodynamic physical parameter of each channel is carried out multi-scale coarse-grained processing to construct multi-scale fuzzy entropy spectrum, and hemodynamic physical parameter is mapped as the local entropy grade score of the channel based on multi-scale fuzzy entropy spectrum;According to the signal quality evaluation result of each channel, determine dynamic weight, based on dynamic weight, the local entropy grade score of each channel is weighted and summed to obtain comprehensive quantification index;The time sequence of the comprehensive quantification index of multiple continuous moments is analyzed and output stability label by recursion characteristic;The local entropy grade score of each channel, comprehensive quantification index and stability label constitute multidimensional feature vector, input preset classifier, and output hemodynamic state classification result.

This invention discloses a coal spill identification method based on information entropy, relating to the field of detection technology. The method includes the following steps: Step 1: For each frame image of the monitored area, convert it into a grayscale image and represent it using matrix A; Step 2: Perform edge detection on matrix A, and record the result as matrix C; Step 3: Calculate the information entropy of matrix C to obtain the frame entropy H of the grayscale image; Step 4: Process each frame of the real-time frame images generated sequentially in the monitored area through steps 1 to 3 to obtain a frame entropy sequence; Step 5: Calculate the frame entropy mean μ and variance σ of the frame entropy sequence. 2 Determine the threshold H for frame entropy alarm. gate Step 6: When the frame entropy of several consecutive frames exceeds the threshold H gate If a coal spill occurs, a shutdown command is sent. This invention utilizes a pure algorithm, avoiding the time-consuming and labor-intensive cleanup caused by coal blockage, and providing a reliable technical guarantee.

The application discloses a code spraying recognition method applied to three-dimensional intelligent detection of steel plates and relates to the code spraying recognition technical field.The method comprises the following steps: acquiring image data of a product with engraved code spraying; pre-processing the image data to separate and obtain image data of a code spraying character region; performing character cutting on the image data of the code spraying character region to obtain image data of a single code spraying character; identifying the image data of the single code spraying character through a machine learning algorithm; and arranging and combining the identified characters to form a complete code spraying section.The method not only realizes automatic identification of code spraying, improves the accuracy and stability of code spraying recognition, simplifies the code spraying recognition process and greatly reduces the labor intensity of operators, but also is not affected by external use environments and is not limited by hardware platforms, so that the portability is high.

This application relates to the field of violation recognition technology, and in particular to a method for identifying violation video information based on a convolutional neural network model. The method acquires preprocessed monitoring video of a power operation site; divides the preprocessed video into M segments, extracts representative frames from each segment to obtain a representative frame sequence; inputs the representative frame sequence into a residual convolutional network, and after the first convolutional layer of each residual block, a channel attention module calculates the channel weight vector, then performs selective temporal shift guided by the channel weight vector, and obtains frame feature vectors after complete forward propagation; temporal aggregation and multi-class fusion are performed on all frame feature vectors to output the violation behavior recognition result. This application integrates spatial features and temporal information, realizing end-to-end automatic identification of power operation violations, improving the timeliness and accuracy of violation detection.

This invention provides an automatic identification method for sandstone and conglomerate based on imaging logging and combined logging data, comprising: Step 1, preprocessing imaging logging data; Step 2, constructing sandstone and conglomerate lithology sample data; Step 3, constructing an automatic identification network structure for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data; Step 4, training and validating the automatic identification network for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data; Step 5, conducting application tests of the automatic identification method for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data. This automatic identification method for sandstone and conglomerate based on imaging logging and combined logging data takes one-dimensional logging data and two-dimensional imaging logging data as input, and utilizes the constructed combined identification network model to effectively fuse multi-dimensional logging data, achieving high-precision automatic identification of sandstone and conglomerate lithofacies and lithology.