27results about How to "Comprehensive judgment" patented technology

The invention relates to the technical field of mold management, in particular to a mold abnormity management system and method based on machine vision, and the system comprises a mold abnormity detection module, a fault prediction and health management module, an intelligent repair and maintenance scheduling module, and a data analysis and visualization module. The mold anomaly detection module collects data by using a high-definition camera and a thermal infrared imager, and identifies surface defects through a convolutional neural network; the fault prediction and health management module predicts a fault risk and performs early warning by means of time sequence analysis and a machine learning technology; the intelligent repair and maintenance scheduling module optimizes a repair strategy and task allocation by using deep reinforcement learning and a genetic algorithm; and the data analysis and visualization module displays data by means of Grafana and Kibana and realizes intelligent alarm and decision support. According to the invention, accurate detection of mold abnormity, effective prediction of faults, optimization of maintenance scheduling and efficient processing and visual display of data are realized, and the intelligent level of mold management is improved.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to a multi-modal fusion-based fence crossing behavior detection method, which comprises the following steps of: firstly, judging whether a person in a video is overlapped with a fence or not through a semantic segmentation algorithm module so as to judge whether the person is in contact with the fence or not, and then acquiring distance information between the person and the fence by utilizing a monocular depth estimation algorithm module; the abnormal behavior detection algorithm module is used for analyzing whether the behavior of the current person deviates from a normal mode, and finally, the detection results of the three algorithm modules are fused to comprehensively judge whether the person is crossing the fence; according to the method, multiple information sources including semantics, depth, behaviors and the like are utilized, the spanning behaviors can be judged more comprehensively and accurately, meanwhile, the method can be achieved only through a monocular camera, the deployment and maintenance cost is lower, the method is more suitable for complex scenes such as shielding and angles, some tiny abnormal behaviors can be captured, and the method is suitable for popularization and application. All-weather automatic monitoring for efficiently and stably detecting the fence crossing behavior in a complex environment is realized.

The invention provides a product quality sampling inspection method and system based on an industrial internet of things, and a medium, and relates to the technical field of product quality management. The method comprises the following steps: acquiring model data, production data and equipment data, and determining a first preset condition, a first sampling parameter, a second preset condition and a second sampling parameter; obtaining production characteristic data, and responding to the condition that the production characteristic data meets a first preset condition: generating a first sampling inspection instruction based on the first sampling parameter, and obtaining a first quality inspection result; in response to the historical sampling inspection result and / or the historical model data meeting a second preset condition, generating a second sampling inspection instruction based on a second sampling parameter, and obtaining a second quality inspection result; and adjusting a first preset condition, a first sampling parameter, a second preset condition and a second sampling parameter based on the first quality inspection result and the second quality inspection result. According to the invention, the condition and / or strength of sampling inspection can be intelligently adjusted, and the quality inspection efficiency is improved.

The invention relates to the technical field of tower cranes, and provides a tower crane perpendicularity monitoring system and method.The monitoring system comprises a data acquisition module, a data processing module and a data processing module, and the data acquisition module comprises a perpendicularity related data acquisition device installed on the tower crane; the verticality related data acquisition device is used for acquiring and transmitting verticality related data of the tower crane; and the data processing module is used for calculating the perpendicularity according to the perpendicularity related data. According to the invention, the data acquisition module acquires the related data of the verticality of the tower crane in real time, and transmits the data to the data processing module for calculation, so that the monitoring of the verticality is more real-time and accurate, the limitation of the traditional manual measurement method is effectively overcome, and the safety and efficiency of the use of the tower crane are improved.

The invention relates to a network security situation awareness method and system based on big data analysis, and belongs to the technical field of network security. The method comprises the steps of collecting original network data which comprises original network data flow and original protocol data in broadband service, training a network security situation awareness model based on a preset network security situation awareness standard set, the network security situation awareness model comprises a network flow analysis model and a protocol analysis model, generating a network state label through the network security situation awareness model, and evaluating a network security level through a rating rule according to the network state label. And outputting a network security situation awareness instruction through an instruction control script according to the network security level, generating a network security situation report through an automatic reporting tool according to the original network data, the network state label and the network security level, and displaying the network security situation report through a data visualization tool.

The invention discloses an AI-based viral pneumonia auxiliary diagnosis method and system, and relates to the field of pneumonia diagnos.The method comprises the steps that abnormal state monitoring is conducted on target people in a target area, and when it is detected that a target human body is in the abnormal state within a preset time period and the occurrence frequency of abnormal behaviors exceeds a preset threshold value, the target human body is judged to be in the abnormal state; mapping the occurrence position, the moving track and the real-time people flow density into a three-dimensional building model to obtain space environment associated data; inputting the space environment associated data into a propagation prediction model to obtain a propagation risk probability of each target sub-region in the target region; and marking the target sub-region of which the propagation risk probability is greater than a preset risk threshold as a target risk region, and generating first early warning information containing the target risk region and the target human body. By implementing the method, the accuracy of the viral pneumonia early warning information can be improved.

The invention provides a self-adaptive operation and maintenance quality monitoring and management method, which comprises the following steps that: monitoring indexes are dynamically adjusted according to real-time data and business requirements so as to ensure the flexibility and adaptability of monitoring, and the monitoring indexes comprise monitoring frequency; the method comprises the following steps: acquiring a quality data set on line through data, wherein the quality data set comprises quality information data of a dimension service object, system service environment data, fault abnormal data and key element index data; analyzing the quality data set, detecting abnormity by adopting a high-dimensional algorithm, and triggering early warning if a detected abnormal value exceeds an abnormal threshold value; performing nonlinear mapping on the current actual operation data and the performance index reference value, and triggering a dynamic adjustment mechanism service to deal with abnormity; and the system calculates a quality problem score according to the operation and maintenance performance, and gives a qualified result according to the quality problem score. According to the invention, by optimizing resource configuration, the operation and maintenance quality is ensured to be matched with service requirements, and finally higher-level service quality and management effect are realized.

The invention relates to a method and a system for automatically upgrading and downgrading privacy number service, and relates to the technical field of communication, the method comprises the following steps of: storing privacy numbers, marking attribution information, and storing a call ticket in real time after any call is ended; the number ringing rate and the call completing rate are updated in real time; according to the number attribution information, the ringing rate and the call completing rate of the current and accumulated areas are updated; current and accumulated customer ringing rates and call completing rates are updated; updating current and accumulated platform ringing rates and call completing rates; performing calculation according to months, days, hours or minutes on the year-on-year basis and the year-on-year basis on the data; related statistical data is displayed in a charting manner; the ringing rate and the call completing rate, or the year-on-year-on-year-off ratio is provided with multiple threshold values for comparison according to the importance degree; and the private number distribution algorithm determines subsequent binding private number distribution according to the priority of the code number. According to the invention, diversified monitoring and judgment are provided, and automatic management of clients and numbers is realized, so that the security and reliability of private numbers of enterprise clients are improved.

The invention discloses a geotechnical engineering geological investigation system and method, relates to the technical field of geological investigation, and solves the problems of difficulty in multi-source data fusion, low geological anomaly intelligent identification precision and weak regional comprehensive evaluation and visual expression ability in the existing investigation method. According to the method, the unified comprehensive parameter vector is constructed, scattered drilling data, in-situ test parameters and real-time sensor monitoring data are subjected to standardized integration and collaborative analysis, the limitation that various types of data are independently stored in traditional exploration, and a data island is formed is broken through, efficient fusion of multi-source heterogeneous information is achieved, and the method has the advantages of being simple in structure and convenient to use. The problem of data fragmentation is solved, and deep cognition and judgment of complex geological conditions can be more comprehensively carried out.

The invention relates to the technical field of pipeline ground penetrating radar positioning and early warning, in particular to a ground penetrating radar positioning and early warning device for underground pipelines, which comprises a shell, a connecting plate is fixedly mounted at the top end of the shell, and a storage battery, a wireless communication module, a CPU control module, a circuit control board and a GPS positioning module are arranged in the fixed shell. The bottom end of the detection rod is connected with a vibration sensor, the outer surface of the fixed shell is connected with a plurality of groups of protection pipes, the plurality of groups of protection pipes are internally provided with a connection data line, and one end of the connection data line is connected with a soil pressure sensor. And tiny pressure change in the soil can be captured in real time. Once abnormal pressure change caused by leakage, damage and the like of the underground pipeline is monitored, the device can immediately trigger an early warning mechanism to quickly respond to potential safety hazards, and accidents are effectively prevented.

The invention relates to an elevator guide rail flatness anomaly detection method and a corresponding installation and maintenance judgment method and device in the technical field of elevator safety detection. According to the elevator guide rail flatness anomaly detection method, P, D1 and D2 of any position of a to-be-detected guide rail are obtained and subtracted from a target position parameter P0 and two target distances D10 and D20 respectively, then absolute values are obtained, corresponding deviation values are obtained, the deviation values are compared with a preset threshold value, and the flatness anomaly of the to-be-detected guide rail is detected. And whether the actual state of the position is the flatness abnormity of the guide rail to be detected caused by self factors can be directly judged. According to the method, the specific state of the flatness of the elevator guide rail can be directly reflected through collection and calculation of multiple sets of data, the state of the guide rail can be accurately distinguished into serious deformation, normal or local abnormal protrusion, and therefore complex operation of manual data rechecking is avoided.

This application discloses a vehicle diagnostic method, apparatus, device, and storage medium based on multiple network segments. Applied to diagnostic equipment, it relates to the field of vehicle diagnostic technology. The method includes: assigning a first IP address and a second IP address (located in a first network segment) to a target computer and the vehicle gateway of the vehicle to be diagnosed; determining a third IP address (located in a second network segment) for the target computer; if no network segment switching is required, performing vehicle diagnostics corresponding to a first entity node to be diagnosed located in the first network segment based on the first and second IP addresses; if a network segment switching is required, modifying the diagnostic communication architecture, determining a fourth and fifth IP address (located in the second network segment) for the second entity node to be diagnosed and the vehicle gateway, and performing vehicle diagnostics corresponding to the second entity node to be diagnosed based on the modified communication architecture, the third IP address, the fourth IP address, and the fifth IP address. This enables vehicle diagnostic communication in a multi-network segment environment.

This invention provides a target-controlled assisted drug delivery system for painless surgery anesthesia, belonging to the field of medical technology. It includes: an initial protocol generation module for determining the initial drug delivery protocol based on the type of painless surgery anesthesia and the patient's basic physiological information; a trial drug delivery control module for administering trial drugs to the patient based on the initial drug delivery protocol and recording the changes in the patient's electroencephalogram (EEG) signals and physiological signs during the trial drug delivery process; a resistance analysis module for analyzing the patient's resistance to the anesthetic drug in real time based on the EEG signal changes and physiological sign changes; and a drug delivery adjustment module for adjusting the initial drug delivery protocol in real time based on the resistance level and continuing drug delivery to the patient based on the adjusted protocol and medical intervention instructions until the drug delivery is completed. This improves the target control accuracy of anesthetic drug dosage and delivery rate during painless surgery.

The invention discloses a reclosing locking setting method and system considering transformer dynamic stability, and relates to the technical field of power transformer dynamic stability assessment, and the method comprises the steps: building a short-circuit current calculation model, and calculating a steady-state current amplitude and a transformer short-circuit impact current; establishing a transformer model, and calculating the radial force and the axial force of a wire cake; performing function fitting on the obtained data to obtain a corresponding function relationship; and calculating a check stress value, and carrying out reclosing locking detection action based on a reclosing locking criterion. According to the method, whether reclosing locking needs to be carried out or not can be more accurately judged by calculating the stress of the transformer under the short-circuit impact; through the self-defined correction coefficient, various factors are considered, the state of the transformer can be evaluated more comprehensively, and the safety is improved; the method has better effects in the aspects of judging whether reclosing endanger the dynamic stability of the transformer or not, evaluating the state of the transformer and judging reclosing locking.

A method for generating a production safety accident report of a hydroelectric power generation enterprise comprises the following steps: step 1, judging accident properties: constructing an accident classification standard library, and monitoring data in real time by using a sensor; step 2, accident parameter acquisition: establishing a data acquisition platform, and performing centralized acquisition and secure storage on data of various sensors; step 3, analyzing the severity and type of the accident; and step 4, outputting an accident report. By constructing an accident classification standard library and deploying a sensor network, automatic judgment and real-time monitoring are achieved, and it is ensured that preliminary submission is completed within a short time after an accident occurs.

The invention discloses a monitoring and protecting method and system for a speed reducer, which can extract micro-features strongly related to stator winding faults, such as fundamental frequency and side frequency components, specific sub-harmonic components and the like, by synchronously collecting three-phase current of a driving motor and performing spectral analysis. The characteristics are very sensitive to winding turn-to-turn short circuit, insulation aging and other early faults, effective coverage of traditional vibration monitoring blind areas is achieved, and fault discovery time is greatly advanced. By calculating the health degree index of the stator winding, the electrical characteristics are quantified into a visual and continuous evaluation index. Furthermore, the electrical health index is intelligently combined with the analysis result of the vibration signal. The fusion breaks the barrier between electrical and mechanical monitoring, so that the system can comprehensively consider electrical hidden dangers and mechanical conditions like experienced engineers, and make more comprehensive and more accurate judgment on the overall health state and risk level of the speed reducer system.

The invention relates to the technical field of chemical engineering system optimization, and discloses a planning design verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical engineering coupling system. The method comprises the following steps: acquiring first real-time operation parameters such as current density and cell pressure of an electrolytic manganese hydrogen production unit, and second real-time operation parameters such as alkali liquor concentration and hydrogen purity of a chlor-alkali chemical unit; screening a matching operation section matched with the current hydrogen production fluctuation characteristic from the historical coupling operation data; analyzing the bath pressure change on the time sequence of the matched operation section to determine a coupling response action point, and calculating the coupling delay amount from the starting point to the action point; then comparing the current density before and after the coupling response action point with the change trend of the hydrogen purity data sequence, and determining a parameter matching degree; determining a coupling weight coefficient by combining the coupling delay amount, the parameter matching degree and the fluctuation characteristic difference; and inputting the generated dynamic efficiency coefficient and the first real-time operation parameter into a system verification controller, and outputting a system adjustment instruction, so that accurate planning verification of the coupling system can be realized.

The invention discloses a second-hand vehicle performance detection method and system based on multi-source data analysis, and relates to the technical field of vehicle performance detection, and the main scheme is as follows: obtaining the basic information data of a second-hand vehicle, and obtaining the comprehensive information data of the second-hand vehicle from different detection channels according to the basic information data of the second-hand vehicle; analyzing and processing the comprehensive information data of the second-hand vehicle, and at least generating an automobile interior and exterior decoration comprehensive performance coefficient, a dynamic stability coefficient, a vehicle market price coefficient, an engine performance parameter, a brake system performance parameter and a core part condition coefficient; generating a second-hand vehicle performance detection report according to the basic information data of the second-hand vehicle and the analyzed data; the second-hand car performance detection report is translated into the selected target language through the integrated translation tool. According to the scheme, the second-hand car is comprehensively evaluated, various kinds of information are accurately presented, the transaction transparency is improved, multi-language translation is supported, and the internationalization level is improved.

The invention discloses an interface function detection method and device and a readable storage medium, and the method comprises the steps: applying a test signal to a control interface of a tested device; wherein the test signal is configured to simulate cooperative work between at least one external device and the device under test; collecting feedback information output by the tested equipment in response to the test signal through at least two different functional channels; and judging whether the function of the control interface is normal or not by analyzing the relationship between the feedback information and / or the logic association between the feedback information and the test signal. According to the invention, the system can achieve the integrated and automatic detection of the functions of a complex control interface through simulating a real cooperative working condition and carrying out the cross verification through a multifunctional channel.

This application proposes a method and system for determining pedestrian tailing at night based on video image analysis, relating to the field of video image analysis technology. The method includes: acquiring the motion state information of the device holder of a handheld device, and obtaining the device holder's motion intention and trajectory based on the motion state information; acquiring video image data from the handheld device, establishing a scene motion reference based on the video image data, and performing scene motion normalization processing on the video image data to obtain image data in the normalized scene; identifying and tracking moving objects, and obtaining the moving object's motion intention and trajectory based on the moving object's trajectory; evaluating the degree of synchronization to obtain a synchronization score; analyzing the degree of coordination to obtain a coordination score; and determining whether tailing behavior exists based on the synchronization score and coordination score, and issuing a warning. This application can more comprehensively and accurately determine potential tailing behavior.

The application discloses a multi-modal battery life estimation model training method, application method and device, and relates to the technical field of battery health management. The method comprises: in the training stage, constructing an attribute pool through battery-specific attributes and synthesizing multivariate battery time series data, and generating diversified battery life question and answer pairs based on the synthesized data using a large language model combined with an evolutionary algorithm; the large language model is pre-trained using the synthesized data, and instruction fine-tuning is performed using the battery life question and answer pairs to obtain a battery life estimation model. In the application stage, the real battery time series data is encoded for numerical preservation and then fused with natural language queries to input the model, multi-modal understanding and reasoning are performed, and a structured natural language diagnosis report is output. Through semantic alignment of attribute-driven battery time series data and a large language model, the application solves the problem that a large language model cannot directly understand battery time series data, and realizes high-precision, few-sample and interpretable battery life estimation.

A sound, vibration and temperature integrated sensor for inspection comprises a sensor base, a data acquisition integrated circuit board is connected in the sensor base, and a vibration sensor, a sound sensor and an infrared temperature sensor are integrated on the data acquisition integrated circuit board; the upper end of the sensor base is connected with the sensor upper cover, the data transmission integrated circuit board is connected in the sensor upper cover, and the input of the data transmission integrated circuit board is connected with the output of the data acquisition integrated circuit board; the sensor upper cover is connected with an aviation plug; the aviation plug is in output connection with the data transmission integrated circuit board; the lower end of the sensor base is connected with an annular magnet; the center of the lower end of the sensor base is connected with a plug or connected with an extension rod through a hand-screwed nut; the sound, vibration and temperature detector can detect sound, vibration and temperature at the same time, is convenient for hand-held operation, and can detect machinery equipment which is inconvenient to touch by a hand.

The invention discloses an unsafe behavior detection method and device for a flammable and explosive area based on a power plant unsafe behavior platform, a storage medium and a processor, relates to the technical field of operation safety management, and mainly aims at improving the accuracy of unsafe behavior detection of the flammable and explosive area and effectively preventing accidents. According to the main technical scheme, effective image frames of a target range are obtained, the target range is an entrance management range of an inflammable and explosive area, and the effective image frames are used for representing continuous image frames of pedestrian targets selected by boundary frames; detecting whether the pedestrian target triggers a non-safe behavior corresponding to the target range or not according to the effective image frame, wherein the non-safe behavior is set according to the safe operation requirement of the flammable and explosive area; and if yes, executing a corresponding alarm operation based on the behavior type of the pedestrian target triggering non-safety behavior. The method is used for non-safe behavior detection of flammable and explosive areas based on the power plant non-safe behavior platform.

The invention provides a micro-expression extraction method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring a face image sequence comprising a plurality of image frames; performing feature extraction on each image frame to obtain a plurality of face features corresponding to each image frame; the face features at least comprise at least two of a face expression feature, a face action feature, a face posture feature and a face sight line feature; merging the plurality of face features and the time sequence information corresponding to each image frame to obtain time sequence feature information; and performing feature fusion on the multiple pieces of time sequence feature information to generate fused feature information for acquiring micro-expression information corresponding to the face image sequence, and completing micro-expression extraction of the face image sequence. According to the method, through extraction and fusion of multiple face features, the micro-expression of the customer can be quickly and accurately captured in face examination scenes such as loan, account opening and underwriting in the financial industry, so that the fraud risk is effectively reduced, and the face examination efficiency and the service quality are improved.

The present disclosure relates to an automatic driving traffic regulation compliance judgment method and device based on OpenDrive map data, belonging to the technical field of automatic driving testing. The method comprises: constructing a road relationship connection graph based on OpenDrive map data; determining the relative position relationship between the test object and other scene participants based on the road relationship connection graph and the corresponding point coordinate values in the reference line coordinate system; obtaining the attribute information of the lane where the test object is located; obtaining the motion information of the test object and other scene participants; determining the traffic scene based on the relevant information of the test object, other scene participants and the lane, and judging whether the test object complies with the corresponding traffic rules of the traffic scene. The present disclosure helps to realize the all-round testing and verification of whether the automatic driving system complies with the traffic rules in the simulation environment, and enriches the testing and verification method of whether the automatic driving system complies with the traffic rules in the simulation environment.

The invention belongs to the technical field of laser radar detection, and particularly relates to a laser radar photomask smudginess detection method and device, and the method comprises the steps: S1, dividing a photomask detection region of a laser radar into a plurality of micro-regions; s2, emitting a first laser beam, and receiving a first echo signal; s3, according to the pulse width of the first echo signal, the smudginess condition of each micro-area is judged in sequence, and if smudginess possibly exists in the current micro-area, the step S4 continues to be executed; s4, emitting a second laser beam, and receiving a second echo signal; s5, according to the pulse width of the second echo signal, the smudginess condition of the current micro-area is judged, and if smudginess exists in the current micro-area, the smudginess type of the current micro-area is judged; and S6, processing the laser radar according to the smudginess type of each micro-area, and sending out an alarm prompt. The laser radar photomask smudginess detection method and device provided by the invention have the advantages of high detection precision and low misjudgment rate.