46results about How to "Realize intelligent identification" patented technology

The invention provides a community management method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of community management. The method comprises the steps of obtaining an initial inspection task of an unmanned aerial vehicle cluster and planning an inspection route, and controlling the unmanned aerial vehicle cluster to execute inspection operation to obtain multi-source inspection data. And constructing a community portrait based on the inspection data, identifying community management hidden dangers through a classification reasoning model, and generating an early warning event and a processing task. A processing task is decomposed into hierarchical sub-tasks by utilizing an ontology reasoning framework, a task execution main body is selected, and a task set is generated. And a multi-Agent reinforcement learning framework is adopted to carry out space-time collaborative arrangement on the task set to form an overall collaborative scheme, the overall collaborative scheme is converted into an interpretable scheduling decision instruction, and finally the unmanned aerial vehicle is controlled to execute the task. According to the technical scheme, the emergency response speed of security and protection and the capability of coping with complex conditions are improved.

The invention discloses an optical cable fault diagnosis system and method based on a vibration filtering algorithm, relates to the technical field of optical cable fault diagnosis, and solves the problems of low efficiency, long response time and the like in the prior art and the technical problem that the health condition of an optical cable is difficult to comprehensively assess through single optical power data. The quality data of the optical cable are acquired; training an artificial intelligence model based on the historical vibration signal data to obtain an optical cable fault identification model; recognizing the vibration data based on an optical cable fault recognition model to obtain a recognition result; calculating an optical cable aging evaluation coefficient based on the vibration signal data and the optical power loss data; constructing an optical cable aging evaluation coefficient correction model based on the environment data to correct the optical cable aging evaluation coefficient obtained through calculation, and obtaining a target optical cable aging evaluation coefficient; and judging whether to carry out fault early warning based on the identification result and the target optical cable aging evaluation coefficient. The technical problem is solved.

The invention relates to a visual analysis method and device for multiple internal corrosion failure types of a pipeline. The method comprises the following steps: inputting internal corrosion failure picture data to be recognized into an internal corrosion failure type recognition model to obtain a predicted internal corrosion failure type and a feature map of the last convolutional layer; analyzing the predicted internal corrosion failure type and the feature map of the last convolutional layer by using Grad-CAM to obtain a feature region thermodynamic diagram; based on the predicted internal corrosion failure type and the characteristic region thermodynamic diagram, obtaining the contribution degree of the characteristic of each characteristic region to an identification result; wherein the internal corrosion failure type identification model is obtained by the following steps: obtaining a pipeline internal corrosion failure data set; inputting the internal corrosion failure data set into an initial internal corrosion failure type identification model for training to obtain a trained internal corrosion failure type identification model; wherein the initial internal corrosion failure type identification model is constructed by using a Resnet18 convolutional neural network.

The invention relates to the field of bridge detection, in particular to a bridge pavement layer damage detection method and system based on a ground detection radar. The method comprises the following steps: scanning a bridge pavement layer by using a ground detection radar, and collecting radar echo data; performing denoising and filtering processing to obtain a clear radar signal; judging whether a damage sign exists or not, if so, determining the radar signal as the damage sign, and entering the next step, otherwise, returning to the first step; waveform coding feature extraction is carried out on the detected damage signs, and damage features are identified; carrying out comparative analysis on the extracted damage features and a database in a normal state, and determining existence and types of the damage; according to an analysis result, utilizing an adaptive frequency modulation technology to accurately detect the damage depth and range; predicting a damage development trend; and obtaining a detection report according to the analysis result and the prediction result. The method improves the damage detection precision and detection efficiency, and achieves the accurate prediction of the damage development trend.

The invention provides an ocean internal wave data acquisition method, medium and system, and belongs to the technical field of ocean hydrological observation.The ocean internal wave data acquisition method comprises the steps that an acoustic Doppler flow velocity profiler is used for acquiring water body movement data, a temperature-salinity-depth profiler is used for acquiring seawater parameters, and meanwhile a satellite altimeter and drifting buoy data are combined. Performing time-frequency analysis on the water body motion data, and obtaining internal wave candidate data by using band-pass filtering; and constructing an internal wave mathematical equation set comprising a hydromechanics basic equation, buoyancy frequency, a frequency dispersion relation and the like, and verifying and screening out effective data. And inputting the effective data into a pre-trained classification model to determine an internal wave type, finally extracting a key waveform segment through a preset extraction equation set, and generating and outputting internal wave characteristic waveform data. According to the method, multi-source observation data and a mathematical physical model are combined, identification, classification and feature extraction of the ocean internal waves are realized, and the problem that a data acquisition method capable of effectively extracting effective signals of the ocean internal waves and accurately identifying the features of the internal waves is lacked at present is solved.

The invention relates to the technical field of medical informatization, and particularly discloses a blood disease intelligent diagnosis and treatment data processing system which adopts an image processing technology based on deep learning to perform cell detection on a bone marrow morphological image of a patient object so as to focus on a cell target in the image. The method comprises the following steps: acquiring a cell target, performing multi-level feature extraction on the cell target to capture superficial detail features and trunk features of the cell, and performing significant sparse interactive coding on the superficial detail features and the trunk features to obtain cell feature representation with higher distinction degree, thereby realizing intelligent identification of abnormal cells. Through the mode, the cell characteristics in the blood sample can be automatically identified and analyzed, and rapid detection and diagnosis of abnormal cells are realized, so that a doctor can be assisted to carry out faster and more accurate blood disease diagnosis, and the workload of the doctor is relieved.

The invention relates to an overseas website access encrypted traffic detection method based on multi-scale information entropy modeling, and belongs to the technical field of communication. The method comprises the following steps: collecting and preprocessing network traffic, and extracting a data packet byte sequence, a length sequence and a time interval sequence; calculating a multi-scale information entropy matrix of the traffic sequence based on different time scales and packet window scales; inputting the information entropy matrix into a convolutional neural network and attention mechanism fusion method for feature extraction; a long-time dependency relationship is modeled through a self-attention mechanism by using a Transform model, so that the accuracy of flow analysis is further improved; model training optimization is carried out through a cross entropy loss function and an Adam optimization algorithm, and the generalization ability is improved by adopting regularization and data enhancement methods. According to the invention, intelligent identification of Chinese websites and non-Chinese websites is realized in an encrypted traffic environment.

The invention relates to the technical field of automobile part machining control, and discloses an automobile part production monitoring method and system, and the system comprises an automobile part machining welding spot feature recognition module, an automobile part machining welding spot state analysis module, and an automobile part spot welding process parameter adjustment module. According to actual coordinate parameters and theoretical coordinate parameters of the body-in-white part machining welding spots, coordinate error parameters of the body-in-white part machining welding spots and theoretical position adjusting coordinate parameters of the body-in-white part machining welding spots are accurately measured by combining numerical analysis; digital metering of spot welding machining position errors and spot welding machining theory adjusting coordinates of automobile body-in-white parts is achieved; and the automobile part generation monitoring end performs automobile part machining welding spot position parameter adjustment operation in a self-adaptive manner based on the body-in-white part machining welding spot theoretical position adjustment coordinate parameters in combination with the industrial robot control end and the spot welding machining equipment.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method based on the 5G positioning technology, and the method comprises the steps: obtaining the geographic position information of a target region, and carrying out the displacement monitoring, underground space monitoring, underground water monitoring and meteorological monitoring of the target region based on the geographic position information, acquiring real-time displacement data, underground space detection data, underground water monitoring data and meteorological data; processing and analyzing the real-time displacement data, the underground space detection data, the underground water monitoring data and the meteorological data to obtain a geological disaster prediction result; and based on the geological disaster prediction result, giving an optimal treatment scheme and sending out early warning information. According to the invention, intelligent identification, early warning and treatment of hidden danger of geological disasters can be realized, and the safety of a monitoring area is effectively guaranteed.

The present disclosure relates to a method and device for identifying tectonic coal based on borehole gamma data. The method comprises: acquiring gamma curve data and mechanical drilling speed data collected in real time during the process of logging while drilling; preprocessing the gamma curve data and the mechanical drilling speed data to obtain depth sequence data; performing segmentation processing on the depth sequence data to obtain a plurality of data sample units; for each data sample unit, extracting a plurality of sample features for representing the response mode of tectonic coal from the data sample unit, fusing the plurality of sample features to obtain a multi-parameter fusion feature vector; and inputting the multi-parameter fusion feature vector into a trained machine learning classification model to obtain a classification result output by the machine learning classification model. The present scheme improves the accuracy and efficiency of tectonic coal identification.

The application discloses a novel end device of a surgical robot system, which comprises a parent control module and a child tool module detachably installed on the parent control module; a plurality of positioning holes are arranged on the end face of the parent control module towards the child tool module; a plurality of positioning pillars for identifying the model of a surgical tool are arranged on the end face of the child tool module towards the parent control module; when the plurality of positioning pillars on the child tool module are inserted into the plurality of positioning holes on the parent control module, the parent control module identifies the model of the surgical tool of the child tool module by judging the layout style and / or length of the inserted positioning pillars. The end device of the application can be installed on the mechanical arm of the surgical robot to realize intelligent identification.

This invention provides a deep learning-based intelligent analysis method for cardiovascular medical images, comprising the following steps: S1, heterogeneous data fusion and acquisition of multimodal medical images and physiological signals; S2, intelligent extraction and quantitative analysis of vascular structural features based on deep learning; S3, hemodynamic parameter modeling and risk region segmentation based on fused structural features; S4, texture feature mining and intelligent identification of lesion types in high-risk areas; S5, cardiovascular disease risk stratification prediction based on multi-dimensional feature fusion; S6, federated learning optimization and closed-loop update of the model. This invention's deep learning-based intelligent analysis method for cardiovascular medical images achieves, for the first time, heterogeneous fusion of medical images, millimeter-wave radar-PPG physiological signals, and clinical molecular data, and achieves precise spatiotemporal alignment through GPS-controlled crystal oscillators, overcoming the limitations of single image analysis and providing a more comprehensive feature foundation for subsequent feature extraction and risk prediction.

This invention discloses a neural network-based anti-disassembly identification system and method for data center equipment, belonging to the field of neural network technology. Based on the data center equipment management backend, it obtains equipment structural assembly information, constructs a set of detachable components and corresponding structural nodes and their connections, introduces connection attribute parameters, establishes a structural connection constraint matrix for detachable components, constructs a baseline state model of equipment structural connections, calculates structural differences, and generates component-level abnormal state identifiers. The structural differences and abnormal states of each detachable component are used as structural state feature vectors input to the disassembly behavior identification neural network model, outputting a disassembly risk score to determine whether illegal disassembly of the data center equipment exists. When illegal disassembly is detected, the system generates corresponding alarm information and triggers warning and handling operations, achieving intelligent identification and risk assessment of data center equipment disassembly behavior, improving the accuracy and real-time performance of equipment structural safety supervision.

The invention relates to the technical field of gas leakage early warning, in particular to a gas leakage early warning method and system based on Internet of Things pressure monitoring, and the method comprises the steps: carrying out the node detection of a target pipeline, obtaining a distributed monitoring network, carrying out the monitoring data collection of the target pipeline, obtaining a monitoring information set, and carrying out the monitoring data analysis of the monitoring information set. A monitoring feature vector set is obtained, leakage judgment is carried out, a leakage judgment result is obtained, if the leakage judgment result is that no leakage event exists, a monitoring report is constructed according to the leakage judgment result and the monitoring feature vector set, and if the leakage judgment result is that the leakage event exists, leakage positioning analysis is carried out on the target pipeline, and a leakage position set is obtained; and obtaining a monitoring report and an early warning instruction according to the leakage judgment result, the monitoring feature vector set and the leakage position set. According to the invention, the problems of low early warning real-time performance and weak leakage position positioning capability in the current gas leakage monitoring process can be solved.

The application discloses a fallen pineapple righting device and a harvesting method, which are arranged on a harvesting vehicle and comprise a righting frame, a clamping mechanism arranged on the righting frame and used for clamping the pineapple, a vertical driving mechanism used for driving the clamping mechanism to move vertically, a horizontal driving mechanism used for driving the clamping mechanism to move horizontally, and a visual recognition positioning module used for recognizing and positioning the fallen pineapple. The clamping mechanism comprises two oppositely arranged clamping claws and a clamping driving mechanism used for driving the two clamping claws to open and close. The clamping mechanism is connected with the driving end of the vertical driving mechanism, the vertical driving mechanism is connected with the driving end of the horizontal driving mechanism, and the driving direction of the horizontal driving mechanism is perpendicular to the direction in which the harvesting vehicle advances to harvest. The righting device and the harvesting method can intelligently recognize the posture of the fallen pineapple, accurately clamp the crown bud and right the fallen pineapple.

The application discloses a kind of energy-saving control method of frequency conversion speed regulation and energy recovery cooperation rig plunger pump, and relates to industrial control technical field.The method includes: monitoring the current working power of rig plunger pump at first working frequency, when power exceeds preset first working power interval, first working power time sequence is collected;According to the time sequence data, the geological adaptability coefficient is calculated using the pre-trained geological adaptability analyzer;Determine whether the working frequency needs to be adjusted, and determine the second working frequency and the corresponding second working power interval accordingly;Based on the second working frequency, frequency converter, energy storage device and energy recovery device are cooperatively controlled.The application realizes intelligent perception and self-adaptive adjustment to formation change, and significantly improves the energy efficiency and operation economy of rig plunger pump under complex geological conditions through the collaborative management of energy.

The invention discloses a risk control method and device, electronic equipment and a storage medium. According to the specific implementation scheme, the method comprises the steps of obtaining to-be-audited data information uploaded by a user; identifying the to-be-audited data information according to a medical risk rule and a financial risk rule of a preset risk identification rule base to obtain a customer risk level of the to-be-audited data information; and determining service limitation conditions of the user according to the customer risk level. According to the embodiment of the invention, comprehensive intelligent risk identification and effective management are realized, accurate prediction of the risk level is ensured, the risk identification efficiency is improved, the risk leakage is reduced, and the user experience is improved.

This invention relates to the field of expense reimbursement management technology, and in particular to a centralized management system and method for expense reimbursement documents based on financial sharing. The management system includes a financial sharing module, an interaction management module, and an expense reimbursement document interaction module. The financial sharing module pushes expense reimbursement document data to the interaction management module in real time. The interaction management module controls and monitors the entire interaction process of expense reimbursement documents. The expense reimbursement document interaction module implements the entire interaction process of expense reimbursement documents. A first machine learning model is established in the financial sharing module as its generator network; a second machine learning model is established in the interaction management module as its generator network; a discriminant network for the interaction management module is established in the financial sharing module; and a discriminant network for the financial sharing module is established in the interaction management module. This management system and method effectively solve the problems of risk and time-consuming / labor-intensive processes in expense reimbursement document interaction.

The application discloses a sintering machine tail state monitoring method based on machine vision. The method comprises the following steps: S1, preprocessing of the input sintering machine tail image; S2, color gamut conversion of the original image to extract the combustion area in the image; S3, image processing after extracting the combustion area and extraction of the machine tail area; S4, feature value calculation of the extracted machine tail area; and S5, sintering state judgment and monitoring according to the feature value. The application constructs a sintering machine tail state monitoring system through machine vision and image processing related technologies, realizes real-time monitoring of the sintering machine, gives a prompt and a warning to the abnormal part, further realizes intelligent production of the sintering process section, improves the production efficiency of the steel enterprise, reduces the labor cost, and further promotes the intelligent manufacturing transformation and upgrading and intelligent development of the steel metallurgy field in China.

The invention provides a method for online measurement of laser ablation morphology and recognition of ablation features, relates to the field of image processing, and solves the problems that the existing measurement method cannot adapt to the interference of tangential airflow, the strong contrast between strong and weak light leads to measurement distortion, and the ablation features cannot be obtained online. The method comprises: a fill light measurement process: using a high-speed camera to shoot a laser ablation morphology image, using a fill light source to irradiate the ablation area, suppressing the strong self-luminescence of the ablation area, and measuring the laser ablation morphology online; an image processing process: obtaining the calibration parameters of the high-speed camera, preprocessing the laser ablation morphology image, and correcting the image by perspective transformation, using background difference to segment the image, detecting the ablation area, measuring the size of the detected ablation area, and completing ablation feature recognition. The invention can effectively realize the online measurement of ablation morphology and the intelligent recognition of ablation morphology features.

The invention relates to the technical field of wireless network communication, in particular to a network abnormal transmission identification blocking method and system based on data mining, and the method comprises the steps: carrying out the preprocessing of collected real-time network flow data, and generating real-time network flow feature data; performing real-time network abnormal traffic type analysis processing on the data and the established different network abnormal traffic type feature data to generate real-time network traffic analysis data, and if the real-time network traffic analysis data is normal, ending the network traffic identification operation; if the real-time network traffic analysis data is abnormal, generating real-time network abnormal traffic type feature data, and performing blocking node spatial position search processing on the real-time network abnormal traffic type feature data and the established network abnormal traffic type blocking node spatial position data to obtain real-time network abnormal traffic blocking node spatial position data; and finally, constructing real-time network abnormal traffic identification data, and executing real-time network abnormal traffic blocking operation to realize intelligent identification and accurate blocking of the network abnormal traffic.

This invention belongs to the field of natural language processing and engineering cost estimation technology, specifically involving an automatic process segmentation method for engineering bills of quantities that integrates semantic understanding and sequence labeling. It achieves automatic bill of quantities segmentation by deeply integrating engineering domain knowledge and deep learning. Through atomized process segmentation and BIOA sequence labeling, process-level granularity is achieved, accurately separating different processes and providing a favorable premise for subsequent cost item matching. By introducing the BERT+CRF sequence labeling model, the semantic logic of construction is understood, and through the B-I tagging mechanism, the main process and its supplementary descriptions are automatically merged, maintaining the semantic integrity of the process. This overcomes the dual defects of existing technologies, namely, "illusion in overall prediction" and "unintelligent mechanical segmentation."

The present invention discloses a mechanical model intelligent simplification analysis system based on human-computer interaction, including a simplifiable structure intelligent interactive analysis module, a structure simplification validity intelligent identification module, a symmetric structure intelligent variable parameter module, a structural symmetry intelligent identification module and a load symmetry intelligent identification module. The present invention is suitable for the teaching of engineering mechanics courses, and can perform intelligent interactive simplification analysis on symmetrical structures or local statically determinate structures, and perform simplified validity identification on simplified mechanical models obtained after simplification analysis. At the same time, it can realize intelligent variable parameters of simplifiable mechanical models of symmetrical structures and intelligent identification of the symmetry of mechanical models after parameter variation, and can assist students in further recognizing and understanding the internal force, deformation and stiffness characteristics of the original model in the process of performing intelligent interactive simplification analysis of mechanical models, thereby improving students' learning efficiency when studying the "Engineering Mechanics" system course.

The application discloses a scheme for automatically discovering the similarity between different table data of a relational database based on a Bloom filter algorithm, reads all table information from the relational database, forms a table information dictionary and stores the table information dictionary; a filter is established based on a unique value set of N by using the Bloom filter algorithm; the established Bloom filter is transmitted to a unique value set of M, the value is filtered, and a return result set {R1, R2, R3, … Rn: R epsilon (True, False)} is counted; a probability W is counted: ∑R{true} / (R{true|false}); when W>ɑ (ɑ is a given threshold value), it is determined that M and N are similar, otherwise, M and N are not similar. The application can analyze all tables and all fields, and judge the data similarity between tables without relying on any artificial rules, artificial judgment or predefined instructions, and can be used for main data discovery, data reference relationship generation and data table relationship construction in a data management process, so that the traditional data management process is changed from manual to intelligent, and the management accuracy and management efficiency are effectively improved.

The application discloses a kind of reciprocating compressor fault early warning method, system, equipment and storage medium, the method includes: to the pre-processing of multi-source sensor data, obtain standardized data frame sequence set;Standardized data frame sequence set is based on time domain feature extraction, frequency domain feature extraction, angle domain order feature extraction and displacement feature extraction;To the multi-source feature fusion normalization of extracted time domain feature, frequency domain feature, angle domain order feature and displacement feature;Fusion normalized multi-source feature vector is input into fault diagnosis model, and outputs fault diagnosis report, carries out fault early warning to reciprocating compressor, and fault diagnosis model is based on mechanism rule engine and CNN-LSTM attention network combination construction.The application effectively integrates the vibration, displacement, key phase and other multi-source sensor data of reciprocating compressor, overcomes the technical bottleneck of single sensor information deficiency and poor adaptability of traditional diagnostic method, and improves the accuracy of fault diagnosis.

The invention provides a self-attention website fingerprint identification method based on position target guidance, and the method comprises the steps: generating a fingerprint feature vector of a to-be-classified website through a neural network according to the network flow generated by a network user and based on an attention mechanism; for a traffic data set to be classified, firstly, the traffic is preprocessed by using a two-dimensional convolution model, then extracted features are recombined into a feature matrix with the position as the target orientation, and in combination with processing of convolution calculation and a self-attention mechanism, obtained new website fingerprint feature vectors are input into a classifier to be classified. In the processing process based on the attention mechanism, the generated fingerprint feature vector simultaneously has features in time and space, and the two features have relevance, so that the classifier also improves the classification accuracy. According to the method, intelligent identification of website traffic can be realized, and the method is of great significance to monitoring and management of anonymous network website access.

This utility model relates to the field of food production technology and discloses an automatic food carton sealing machine, including an operating table. A loading platform and a unloading platform are respectively provided at both ends of the operating table. A pressing component is fixedly installed on the loading platform, and a carton body is placed on the loading platform. A transmission component and a sealing component are provided on the operating table, and an adhesive tape body is provided on the top of the sealing component. In this utility model, by setting up the pressing component, the two short tongues of the carton body are flattened and the two long tongues are pre-shaped, while the height of the carton body can be measured. After receiving the height data, the remote control center adjusts the overall height of the sealing component. By setting up the transmission component to clamp and transmit the carton body, the width of the carton body can also be measured. After receiving the height data, the remote control center adjusts the auxiliary clamping and feeding distance of the sealing component, thereby realizing intelligent identification of carton data.

The application discloses a rural land consolidation potential intelligent identification method and device, obtains a rural land object to be consolidated through spatial range vector data and high-resolution remote sensing image data of the rural land to be consolidated, extracts image features of the rural land object to be consolidated, establishes a rural land consolidation potential identification model, realizes automatic identification of the rural land consolidation potential, and corrects the identified potential land block through a participatory survey. The application fully utilizes the advantages of rich and diverse ground object information of high-resolution remote sensing images, realizes intelligent identification of the rural land consolidation potential land block in a manner that random forest classification identification is combined with participatory survey, and makes the identified consolidation potential land block more accurate in scale, more accurate in spatial position and higher in available degree.

The present invention relates to the fields of optical fiber sensing and deep learning, and specifically to a low-frequency underwater acoustic signal detection method and device based on a bionic optical fiber ciliary array; the advantages of the optical F-P cavity structure being simple, high sensitivity, and easy multiplexing are fully utilized, and the advantages of the ciliary array being extremely high sensitivity and narrowband filtering are combined to control the length of each ciliary inside the array, so that the cavity length of the F-P cavity formed by the end face of each ciliary and the reflector is different, and the corresponding resonant frequency is also inconsistent, so that the ciliary array can detect sound waves of different frequencies, and the frequency composition of the incoming wave can be directly obtained without complex Fourier transform and filtering operations, thereby realizing intelligent and autonomous recognition of target signals. Compared with the traditional hydrophone system, the device is more sensitive, compact, and efficient, and has the advantages of narrowband filtering, real-time spectrum extraction, and autonomous target recognition, and has broad application prospects.

This invention discloses a naming entity recognition method for aviation equipment models that integrates multi-source knowledge, comprising the following steps: S1: constructing a corpus for recognizing flat and nested entities of aviation equipment models; S2: establishing a naming entity recognition model integrating multi-source knowledge based on the corpus constructed in step S1; S3: using the naming entity recognition model established in step S2 to recognize the named entities of aviation equipment models. The naming entity recognition model integrating multi-source knowledge generally adopts an MRC model, integrating naming rule information into the query information in the input text, and combining RoBERTa and domain dictionary enhancement methods to extract semantic information during text encoding. The recognition method in this invention achieves F1 scores of 89.26% and 85.53% for flat entities and nested entities, respectively, realizing intelligent recognition of naming entities for aviation equipment models.