1084results about How to "Accurate collection" patented technology

The invention discloses a near infrared spectrum rapid detection system and a real-time analysis method, and the system comprises an environmental parameter and spectrum information collection module, a data processing module, an intelligent evaluation model construction and optimization module, a real-time analysis and evaluation module, and a result feedback and optimization module. The intelligent evaluation model construction and optimization module is used for constructing an intelligent evaluation model by fusing a neural network and a support vector machine, taking the characteristic vector information which is obtained after processing and represents environmental parameters and is associated with infrared spectrum information as input, and taking an expected evaluation result which is associated with the infrared spectrum information of a sample as output; training the intelligent evaluation model; and the real-time analysis and evaluation module is used for inputting the environment parameter information and the infrared spectrum information which are acquired in real time into the trained intelligent evaluation model to obtain an evaluation result of the infrared spectrum information. The system and the method have the advantages of high detection speed, high accuracy and the like.

The invention relates to the technical field of data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

The invention discloses a remote sensing surveying and mapping image enhancement processing method for an unmanned aerial vehicle, and relates to the technical field of image enhancement processing. During operation of a system, flight attitude and position information are recorded through GNSS and IMU equipment, the flight path of the unmanned aerial vehicle is controlled, and collected original images are primarily processed; calculating and obtaining a flight stability coefficient Fs, an image acquisition quality coefficient Iq and a topographic feature retention coefficient Tf, removing random noise in a remote sensing image by using an improved non-local mean NLM algorithm, optimizing and processing an edge region, improving edge definition and texture details of a topographic image through a Laplacian pyramid algorithm, strengthening landform features of mountains and hills, and obtaining a remote sensing image; according to the method, color distortion caused by complex mountain illumination conditions is corrected, image colors are enabled to truly restore topographic features, important landform information is prevented from being lost due to over-brightness or over-darkness, topographic feature extraction is optimized, a final index Zfinal is generated, and quality evaluation is performed on an enhanced and corrected image.

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention discloses a wireless communication network data secure transmission method and system, and relates to the technical field of secure transmission. During system operation, agricultural monitoring data are collected in real time through various sensor devices arranged in an agricultural environment, encryption processing is performed by using a symmetric encryption algorithm, and a dynamic key length adjustment mechanism is supported; a random number generator is integrated, unified management is performed on key generation, distribution, updating and destruction in a system, a timestamp or a random serial number is embedded into each data packet, the uniqueness of the timestamp or the serial number is verified at a receiving end, so that replay attacks are resisted, and the data are comprehensively analyzed and evaluated according to a set algorithm, so that the replay attack is prevented. The method comprises the following steps: acquiring a security data transmission performance index SDTP through calculation, generating a monitoring report, detecting abnormal conditions in a data transmission process, including channel quality reduction, data loss or verification failure, and providing a visual monitoring result.

The invention relates to the technical field of power dispatching, in particular to a power demand real-time sensing and responding system and method based on Internet of Things driving, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting power utilization power and power utilization time data at a user side, and carrying out the preprocessing of the data; constructing an electricity utilization power curve, and extracting electricity utilization characteristics; using a K-means clustering algorithm to carry out clustering analysis on the user power consumption characteristics, and dividing the users into k power consumption categories; a power consumption demand prediction model is constructed by training a long short-term memory (LSTM) network model, and the future power consumption power curve trend is predicted; and establishing a power utilization scheduling response mechanism based on the prediction result and the clustering-divided power utilization behavior categories in combination with a charging and discharging scheduling strategy of the power energy storage equipment, thereby realizing flexible scheduling response to the power utilization behaviors in different time periods in the future. According to the method, the power consumption management precision and efficiency can be improved, personalized power consumption requirements are met, and energy utilization is optimized.

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention belongs to the technical field of railway transportation safety, provides a railway contact net carrier cable tension online monitoring and early warning method and system, and aims at solving the technical problems that in the prior art, capturing is not comprehensive, and different environments are difficult to adapt. Acquiring a tension data stream by using a context awareness technique; analyzing the tension data flow by using a hidden Markov model to obtain a preliminary analysis result, processing by using a neural network, generating an abnormal mode recognition result, performing risk assessment by using a Bayesian network algorithm and a digital twin technology to obtain a preliminary fault estimation probability, and predicting a target fault estimation probability in combination with multi-context sensing data; a self-adaptive control algorithm is used for generating an adjusted alarm threshold value, when the monitored tension value exceeds the adjusted alarm threshold value, early warning information is generated in time, and processing is carried out through the block chain technology to obtain target early warning information; according to the technical scheme provided by the invention, the monitoring and early warning capabilities of the railway overhead line system carrier cable are comprehensively improved.

The invention relates to the technical field of grounding monitoring, and discloses a multipath grounding monitoring method for an intelligent station electrostatic monitor, and the method comprises the steps: carrying out the infrared scanning of a station region, comparing a station layout template, obtaining the station configuration data, and reading an electrostatic monitoring parameter; performing time division multiplexing matrix design on the multiple monitoring channels to obtain a time division multiplexing configuration scheme; performing on-off control time sequence compilation on the high-speed solid-state relay to obtain an on-off control time sequence table, and executing intelligent on-off control to realize monitoring channel switching; inputting the electrostatic signals of the plurality of monitoring channels into a high-precision analog-to-digital converter for parallel sampling and de-noising processing to obtain pre-processed electrostatic data; according to the invention, the monitoring strategy can be dynamically adjusted according to the station characteristics, the efficient collection and processing of multichannel data are realized, and an accurate static risk assessment and early warning mechanism is provided.

The invention discloses a self-adaptive music synthesis sleep aiding method and system based on physiological feedback. The method comprises the following steps: acquiring physiological data of a user; preprocessing the physiological data; evaluating the sleep state of the user based on the preprocessed physiological data; defining a music parameter space according to the sleep state; optimizing music parameters based on the sleep state and the music parameter space; generating and playing music according to the optimized music parameters; while music is played, physiological reactions of the user are continuously monitored, music parameters are adjusted in real time based on the physiological reactions, and feedback circulation is formed; analyzing a long-term sleep pattern of the user based on the data obtained in the feedback loop; according to the long-term sleep mode, updating a music parameter space and an optimization strategy; security monitoring is executed, and intervention is carried out when an abnormal condition is detected; according to the invention, a plurality of key indexes such as sleep time, deep sleep proportion, sleep quality and the like are obviously improved.

The invention discloses a new energy grid-connected electric energy quality monitoring method and device, a terminal and a medium, and the method comprises the steps: deploying a dual-mode sensor in a grid-connected node, and collecting the electric energy quality data; adjusting the frequency of data collection according to the operation mode of the current power grid; analyzing the power quality data, and identifying power quality abnormity; adjusting a grid-connected mode according to an electric energy quality data analysis result, and managing load response; according to the invention, real-time accurate acquisition of the electric energy quality data is realized by designing the bimodal sensor, electric energy quality abnormity can be identified more accurately in combination with a deep learning algorithm, a grid-connected mode and load response are optimized according to an analysis result, and the adaptability of a power grid to new energy fluctuation is enhanced.

The invention relates to the field of technical data acquisition, and particularly discloses a data acquisition method and device for an industrial Internet of Things and a storage medium, and the method comprises the following steps: S1, constructing a dynamic sensor network, and collecting the operation data of industrial equipment in real time through a multi-modal sensor; s2, dynamically adjusting a data acquisition frequency # imgabs2 # based on an environment noise coefficient # imgabs0 # and an equipment health index # imgabs1 #; s3, performing hierarchical compression and encryption on the acquired data, and allocating a compression rate # imgabs4 # and encryption strength # imgabs5 # according to data type importance # imgabs3 #; s4, executing load balancing through an edge computing node, and distributing a computing task to an optimal node; and S5, storing the processed data into a distributed database according to spatial-temporal correlation, and generating a multi-copy disaster recovery index. Through dynamic acquisition, intelligent processing, optimized storage and adaptive strategy adjustment, accurate and efficient data acquisition is realized, data security and processing performance are flexibly guaranteed, storage query efficiency and disaster tolerance capability are improved, and storage resources are utilized to the maximum extent.

The invention discloses an array type multichannel acoustic imaging device and an abnormal sound source positioning display method, and the device comprises a main microphone array which is used for collecting sound signals of a target region; the camera device is used for collecting an image of the target area; the thermal imaging device is used for collecting a thermal image of the target area; the first data processing and analyzing module is used for identifying an abnormal sound signal of the target area and preliminarily estimating the position of an abnormal sound source; acquiring sound field distribution of the abnormal sound source and generating a sound field image by using an acoustic imaging algorithm; the second data processing and analyzing module is used for inputting the change rate of the sound wave parameters of the unit area and the temperature into a second neural network model, obtaining the sound velocity of the unit area in the sound field image, calculating the temperature of the unit area, fusing the sound velocity of the unit area with the temperature of the corresponding unit area in thermal imaging and determining the abnormal temperature; and the display screen is used for displaying the sound field image marked with the abnormal temperature. The abnormal sound source can be effectively distinguished and positioned, and the abnormal temperature can be displayed.

The invention discloses a double face recognition area early warning safety protection method, and relates to the technical field of face recognition, a face recognition camera, a database, a background server and a client are included, and the method has the advantages that under strong light, an intelligent light adjustment algorithm can automatically reduce the exposure, improve the contrast and enhance the color saturation; the face features are highlighted, the overexposure phenomenon of the face is avoided, under the backlight condition, the backlight compensation algorithm is started, the overall brightness of the face is improved by increasing the edge brightness and adjusting the hue, the accuracy of feature extraction is ensured, and in addition, based on the face detection technology of the deep neural network, through deep training of massive face image data, the accuracy of feature extraction is improved. According to the invention, the camera can learn the face features in various complex environments, and even if the face is partially shielded and is located at different angles or under poor light conditions, the system can accurately carry out detection and recognition, so that the stability and accuracy of face recognition are greatly improved.

The invention belongs to the technical field of fuel cells, and particularly relates to a fuel cell centrifugal air compressor irreversible loss prediction method and system, and the method comprises the steps: collecting performance data based on a fuel cell centrifugal air compressor comprehensive performance test platform, and building and verifying a multi-physics coupling CFD simulation model; defining an irreversible loss type and related structure parameters, constructing an integrated CFD simulation framework, generating a parameter combination table through an orthogonal test, and performing simulation calculation; an RL-GHEM method is provided, the weight and combination strategy of a base model are optimized, linear and nonlinear combination modes are dynamically switched, and irreversible loss prediction models under different working conditions are constructed. The hybrid integrated model based on reinforcement learning guidance is provided, the model combination strategy is dynamically optimized through the reward mechanism, the base model with the optimal performance is optimized, the prediction precision, stability and robustness are improved, precise prediction of the irreversible loss of the air compressor is achieved, and the problem that in the prior art, the prediction precision is insufficient under variable working conditions is solved.

The invention relates to the technical field of photovoltaic power station inspection. The invention provides a photovoltaic inspection management method and system based on an unmanned aerial vehicle, and the method comprises the steps: constructing an inspection management database based on historical data; respectively constructing an inspection planning model and a data analysis and evaluation model based on the inspection management database; planning a flight path and an endurance strategy through the inspection planning model, and dynamically optimizing the flight path and the endurance strategy through the inspection planning model according to the real-time data; an inspection task is executed according to the dynamically optimized flight path and the endurance strategy, related data of the photovoltaic power station in the inspection process are collected in real time, and the related data are returned in real time; related data returned in real time is analyzed and evaluated through the data analysis and evaluation model, and corresponding operation and maintenance measures are formulated according to evaluation results, so that the potential problems that an existing inspection mode depends on manual inspection, the efficiency is low, each corner of the photovoltaic power station is difficult to comprehensively cover, and missing is likely to occur are solved.

The invention relates to the technical field of demolding control, and provides an anti-deformation demolding control method and system for a plastic mold. The method comprises the steps that temperature sensing is conducted on a mold, and the initial temperature is obtained; traversing according to the initial temperature, determining a to-be-preheated area, and performing temperature control calculation to obtain a temperature uniformity coefficient; activating a cooling channel based on the temperature uniformity coefficient, and performing staged cooling to obtain cooling state data; demolding analysis is conducted according to the cooling data, demolding parameters are generated, dynamic association is conducted in combination with environment temperature changes, and a demolding strategy is formulated; simulating and executing a demolding strategy, performing deformation monitoring, generating feedback data, optimizing the strategy and executing anti-deformation demolding operation. The technical problem of mold deformation caused by non-uniform temperature distribution in the demolding process of the plastic mold is solved, and the technical effects of avoiding mold deformation and improving the product quality and the production efficiency by accurately controlling the temperature distribution, the cooling process and the demolding parameters are achieved.

The invention discloses an intelligent tamper alarm and data self-destruction protection terminal equipment system, which relates to the technical field of data security, and comprises a sensor detection module, an environment sensing module, an operation recording module, a data summarization module, a data analysis module and a risk assessment module. In combination with the environment sensing module, the physical state and surrounding environment information of the equipment can be comprehensively and accurately collected, meanwhile, the operation recording module records all operation behaviors of the equipment in detail, the data analysis module carries out deep analysis on the data through an advanced algorithm, and the risk assessment module can calculate risk coefficients according to the comprehensive data. Therefore, the state of the current terminal equipment can be analyzed according to the risk coefficient, normal operation or environment change is effectively prevented from being misjudged as a disassembly or intrusion behavior, the false alarm rate is remarkably reduced, and unnecessary troubles brought to a user due to false alarm are reduced.

The invention discloses a photovoltaic power generation system optimization management system based on intelligent scheduling, and relates to the technical field of renewable energy sources and intelligent power grids. The system comprises the following components: a distributed cooperative control strategy, an information sharing and transaction platform supported by a block chain technology, an intelligent scheduling and optimization engine, intelligent power grid deep integration and a user interaction and visualization interface. Through an innovative distributed cooperative control strategy, comprehensive operation data of a photovoltaic power generation system, a wind energy system, an energy storage system and a micro-grid peripheral energy system can be accurately collected in real time, and an energy supply and demand trend is analyzed and predicted based on big data; therefore, the system can automatically adjust the power generation plan, the energy storage strategy and the load distribution of each system, and the optimal operation of the whole energy system is realized.

The invention relates to the technical field of industrial data acquisition and processing, and discloses a different industrial data optimal acquisition scheme analysis system and method based on an SCADA system, and the system comprises an industrial data acquisition information processing module, an industrial data acquisition scheme analysis module, and an industrial data acquisition optimal scheme generation module. According to client target industrial data acquisition scheme type information, in combination with an intelligent identification algorithm and industrial data acquisition scheme equipment composition information, industrial data acquisition scheme equipment composition information efficient retrieval is carried out, and equipment information used by different types of industrial data acquisition schemes is efficiently and scientifically analyzed; the industrial application applicability of the SCADA system is improved; the unit price information of the client target industrial data acquisition scheme composition equipment and the cost information of the client target industrial data acquisition scheme are scientifically and accurately counted, the cost information of different types of industrial data acquisition schemes is autonomously measured on line, and the scientificity of the industrial application of the SCADA system is improved.

The invention relates to the technical field of electricity transaction, in particular to an hour-level green electricity transaction matching method and system considering a green certificate transaction blockchain, and the method comprises the following steps: S1, data collection and preprocessing: collecting the power generation operation data of a green electricity supplier and the power utilization behavior data of a user, preprocessing the collected power generation operation data and power consumption behavior data; s2, constructing a green certificate block chain: constructing a green certificate transaction platform based on a block chain technology, and recording green certificate transaction information; s3, establishing a matching model: constructing a multi-objective optimization model, and integrating multiple factors to carry out transaction matching; s4, transaction execution monitoring: after successful matching, executing the transaction and performing real-time monitoring; s5, transaction settlement evaluation: performing settlement and evaluation after the transaction is completed; according to the invention, the efficiency of green electricity transaction and the satisfaction degree of users and suppliers are effectively improved, and efficient utilization of green electricity resources and vitality improvement of the energy market are realized.

The invention discloses an intelligent control system and method for a tea tender shoot picking robot, and the method comprises the steps: obtaining scene image information through a visual analysis module, and accurately recognizing a to-be-picked tea tender shoot region based on a preset neural network model; a task decomposition module is used for dividing a tea tender shoot area, each task in a large number of picking tasks is actually distributed to a corresponding mechanical arm, and a shortest safe working route of the mechanical arm is formulated; through the motion control module, it is ensured that the picking range of each mechanical arm of the robot can completely cover the area where the tea tender shoots suitable for being picked are located, and the missing picking rate of the tea tender shoots is effectively reduced. Multiple mechanical arms of the picking robot can be coordinated to pick tea tender shoots, mechanical picking has the advantages of being controllable in cost, accurate in collection and excellent in efficiency, data calculation and control of the whole system are completely conducted locally, smoothness and high efficiency of the system can be guaranteed, and popularization of the tea picking robot technology is facilitated.

The invention discloses a motion analysis system based on inertia-visual signal enhancement and fusion, and relates to the technical field of intelligent perception and fusion. In the system, an inertial sensor module is used for collecting an inertial signal of a moving object; the visual sensor module is used for collecting human body key point information; the signal processing module is used for performing enhancement processing on the inertial signals by using the improved deep learning model, and reconstructing outrange signals in the inertial signals based on the KAN; the inertia-visual information fusion module is used for carrying out fusion processing on the human body key point information and the inertia signal after signal processing based on a multi-modal feature alignment method of an optimal transport theory and an SINkhorn algorithm to obtain a motion track of the moving object; and the motion analysis module is used for analyzing the motion mode and posture change of the motion object based on the motion trail. According to the invention, the motion trail of the moving object can be accurately captured and accurately analyzed.

The embodiment of the invention provides a park energy carbon supervision method and system, and belongs to the technical field of energy carbon supervision. The method is executed by an energy-carbon management platform, the energy-carbon management platform is in communication connection with area control modules arranged on all energy sides based on an energy internet-of-things base, and the method comprises the steps that energy consumption data and equipment operation data which are collected in real time and correspond to all enterprises are recycled; updating a sub-energy grid of each enterprise based on the equipment operation data, and updating a main energy grid of each enterprise based on the energy consumption data of each enterprise; wherein the lowest carbon emission is taken as an optimization target, and real-time regulation and control of the energy scheduling scheme of each enterprise in the park are executed based on the updated main energy grid and the updated sub-energy grid; and monitoring the regulation and control process of each enterprise energy scheduling scheme, executing park data operation and maintenance based on a monitoring result, and generating a regulation and control report in real time. The scheme of the invention not only improves the accuracy of energy management, but also effectively promotes the realization of the carbon emission reduction target of the park.

The utility model relates to the technical field of wearable electrode materials, in particular to a signal acquisition structure and a patch detection device with the same. The signal acquisition structure provided by the utility model comprises a flexible printed circuit and an adhesive layer, the flexible printed circuit comprises a base material layer and a circuit layer located on the base material layer; the circuit layer comprises a plurality of conductive traces and a plurality of electrode parts, and the adhesive layer covers part of the flexible printed circuit and does not cover the plurality of electrode parts. According to the signal acquisition structure provided by the utility model, the adhesive layer is arranged outside the flexible printed circuit, so that the skin attaching performance when the signal acquisition structure is used can be improved, the user experience can be improved, meanwhile, one surface of the flexible printed circuit can be always fixed at a specific position on a human body, the stability of signal transmission is enhanced, and the service life of the flexible printed circuit is prolonged. And the reliability of detection data is improved.

The invention relates to the field of data acquisition, in particular to a multi-sensor array type equipment acoustic fingerprint self-adaptive acquisition device and method, and the device comprises a track which is arranged around a transformer body and is provided with a plurality of acoustic sensors; the controller is responsible for receiving operation state information and environment parameters of the transformer equipment and acquiring the operation state information and the environment parameters through an interface of the transformer equipment monitoring system and the environment monitoring sensor; the controller is provided with a communication module, sends an instruction to the acoustic sensor array through the communication module, and adjusts the layout and parameters of the acoustic sensor; and the server is used for monitoring, analyzing and storing the acoustic fingerprint signals in real time. When the device is used, the layout and parameters of the acoustic sensor are automatically adjusted, so that accurate acquisition of acoustic fingerprint signals of the transformer equipment is realized. Meanwhile, the advanced signal fusion technology is utilized to improve the signal strength and reliability, and more accurate and more reliable data support is provided for state monitoring and fault diagnosis of transformer equipment.

The invention relates to the technical field of water conservancy projects, particularly discloses a test device and method for simulating lateral water flow scouring of a slope protection circulating water tank, and aims to solve the problem that in the prior art, slope protection scouring of different gradients cannot be simulated at the same time, and erosion sediment particles cannot be effectively collected. According to the technical scheme, the device comprises a circulating water tank, a multi-stage flow guide box, a slope-adjustable gravel washing tank, a slope-adjustable plant maintenance tank, a sediment collecting area, a high-speed shooting device and the like; by adjusting the gradient of the multi-stage flow guide box and the gradient of the broken stone washing groove, accurate control over the water flow state and the slope of the slope protection is achieved; a multi-stage filter screen and water baffle technology is adopted in the sediment collecting area, and erosion sediment is effectively collected and analyzed; according to the invention, not only can the scouring process of the broken stone revetment and the ecological revetment be simulated, but also the erosion resistance of the revetment can be accurately reflected, and theoretical and technical support is provided for ecological revetment engineering design and disaster prevention and reduction; the device is simple and convenient to operate, accurate in data collection and high in test efficiency, and can meet various requirements in scientific research and engineering practice.

The embodiment of the invention discloses a testing device and a testing system. The testing device comprises a simulation load which is used for providing a heat source and is arranged adjacent to a to-be-tested radiator, and the to-be-tested radiator is used for cooling the simulation load; the temperature acquisition module is arranged in the simulation load and is used for acquiring temperature information of the simulation load; the sensing module is arranged inside or on the surface of the to-be-tested radiator and used for collecting heat exchange information of the to-be-tested radiator; and the control module is respectively connected with the simulation load, the temperature acquisition module and the sensing module, and the control module is used for testing the heat dissipation efficiency of the to-be-tested radiator according to the temperature information and the heat exchange information of the simulation load. In the testing device provided by the invention, the simulation load can simulate the heating characteristics of different loads, and the sensor can accurately measure various physical quantities, so that the accurate evaluation of the heat dissipation performance of the high-power chip is realized, and the automation and accuracy of the testing process are ensured.

The invention discloses a structural strength detection device for sealing ring production, which comprises a base and an auxiliary pressing plate, the base is provided with a water bath pool, the inner wall of the bottom of the water bath pool is provided with four heaters in rectangular distribution, the upper part of the base is provided with a U-shaped frame, the middle position of the upper part of the U-shaped frame is provided with a driving rod in a threaded manner, and the driving rod is connected with the auxiliary pressing plate. A connecting shaft is rotationally arranged at the bottom end of the driving rod, a first pressure sensor is arranged at the bottom end of the connecting shaft, a four-corner jacking mechanism is arranged at the bottom of the first pressure sensor, and the auxiliary pressing plate is arranged at the bottom of the four-corner jacking mechanism. According to the invention, data detection and collection can be carried out on the sealing ring body through comprehensive factor changes, and data collection can be carried out on the sealing ring body under single factor changes, so that more comprehensive structural strength data of the sealing ring body can be obtained; the structural strength analysis structure of the sealing ring body obtained through data analysis can be more accurate and fit the reality.

The invention relates to the technical field of image recognition, in particular to a quality detection and recognition method. The method comprises the steps of performing spatial mapping on an original image, performing feature extraction on the mapped image, performing step-by-step segmentation on an obtained image to be layered, extracting a required layer, forming color features by using color information of the layer, performing regression judgment through machine learning training, and obtaining a score. The color features are calculated through distribution of hue, saturation and brightness of each pixel in the image and comprise one or more combinations of a mean value, a standard deviation and a deviation. Through computer vision and image processing technologies, an optimal detection area in a sample is automatically identified and analyzed, stratified analysis is carried out, multi-dimensional quality data of the sample is detected, and rapid and accurate acquisition and quality evaluation of the multi-dimensional quality data of the sample are realized. For the evaluation standard difference of different groups, only the evaluation score Y value needs to be adjusted, the model does not need to be trained again, and the application range of the method is greatly widened.