1691results about How to "Avoid lost" patented technology

A headset antenna and a connector for the same are provided. The headset antenna includes an audio signal line, an antenna and a high impedance element in specified application frequency ranges. The audio signal line is adapted for transmitting an audio signal and the antenna is adapted for receiving an RF signal. The high impedance element is disposed on a transmission path of the audio signal and generates a high impedance at a specified frequency band of the RF signal, so that the audio signal line is equivalent to an open circuit and the antenna obtains a better receiving capability.

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention provides a photovoltaic station intelligent operation and maintenance simulation method and system based on digital twinning, and relates to the technical field of photovoltaic station operation and maintenance, and the method comprises the steps: constructing a digital twinning hierarchical structure through multi-modal detection signals through discrete wavelet packet transformation, tensor decomposition and a graph neural network, and determining structured data; extracting fault features by using a dynamic neural network, predicting a fault probability based on a Markov model, and triggering maintenance early warning; inputting the early warning signal into a maintenance strategy model based on multi-agent reinforcement learning, and generating a candidate maintenance time window set; the maintenance scheduling module optimizes sorting to obtain an optimal time window sequence, and the resource allocation module generates a personnel material allocation scheme by adopting a genetic algorithm; and iteratively optimizing the strategy parameters based on the maintenance cost, and outputting a final operation and maintenance implementation scheme.

The invention provides a multi-modal sentiment analysis method and system based on attention and graph enhanced text, and belongs to the technical field of multi-modal sentiment analysis. The method comprises the steps of obtaining text features, image features and voice features in video data and performing preprocessing; an adaptive cross-modal interaction module is adopted to calculate attention weights between the text features and the image features and between the text features and the voice features, and enhanced text features are obtained; inputting the enhanced text features into a hierarchical multi-modal graph fusion network, and constructing an adjacent matrix by using a self-attention mechanism; inputting the adjacent matrix and the enhanced text features into a jump connection graph convolutional network to obtain a final feature matrix; and combining the feature matrix with the adjacent matrix, and obtaining a prediction result of sentiment analysis by using an encoder and a classifier. The problem of deficiency in context alignment among different modes is solved, and emotion polarity prediction is performed more comprehensively and accurately.

The invention relates to the technical field of water gate maintenance, and discloses a water gate engineering safety risk assessment and early warning system which integrates five modules, namely a data analysis module, a real-time monitoring module, a state comparison module, a risk assessment module and an early warning notification module. The future safety state of the sluice project is predicted through a machine learning model, key safety parameters are monitored in real time, real-time data and predicted data are compared and analyzed, and a curve graph is drawn and visually displayed. And the risk assessment module calculates the difference degree of the two groups of curves and identifies risk points. And the early warning notification module comprehensively evaluates the safety state of the sluice project according to the risk point and the safety evaluation rule, and timely sends an evaluation result and early warning information to a manager so as to quickly take countermeasures. According to the system, the intelligent level of safety management of the water gate engineering is improved, early discovery and early warning of safety risks are realized, and safe operation of the water gate engineering is effectively guaranteed.

The invention relates to the technical field of panoramic video processing, in particular to a multi-modal fusion panoramic video intelligent content analysis method, which comprises the following steps of: acquiring a panoramic video frame and a synchronous audio thereof; performing spherical projection transformation on the panoramic image data, and converting the equidistant projection image into a spherical image; generating a plurality of plane images based on the spherical image; performing candidate target position detection based on the plurality of plane images; based on a candidate target position detection result, an image target detection model is utilized to realize instance segmentation of a target, and target features are extracted to generate a target embedded description vector sequence; based on the synchronous audio, an audio recognition processing module is used for carrying out time sequence information extraction, and an acoustic attention sequence is formed; a spectrum sensor is adopted to realize association fusion of the target embedded description vector sequence and the acoustic attention sequence, and an audio and video collaborative attention sequence is generated; and based on the audio and video collaborative attention sequence, establishing an audio and visual event identification model, and obtaining event content described by the panoramic content.

The invention provides a sunlight greenhouse intelligent environment prediction and regulation method comprising the following steps: collecting greenhouse data and meteorological data outside a greenhouse, combining to form a greenhouse environment data set, and carrying out data noise processing, screening, correlation analysis and normalization processing to obtain a prediction data set; constructing and training a greenhouse temperature and humidity prediction model comprising an input layer, a convolutional neural network layer and a gating circulation unit neural network layer; inputting the prediction data set into a greenhouse temperature and humidity prediction model, and training, verifying and adjusting hyper-parameters of the greenhouse temperature and humidity prediction model to obtain a greenhouse temperature and humidity prediction result; and constructing a greenhouse environment control module, and adjusting the greenhouse environment based on the optimal temperature and humidity information of crop growth and the greenhouse temperature and humidity prediction result. According to the intelligent prediction and control method, the greenhouse control precision and management efficiency can be remarkably improved, modernized and intelligent development of agricultural production is achieved, crop growth is promoted, and losses caused by extreme weather are reduced.

The invention relates to the technical field of electric power system safety early warning, and discloses a fault risk sensing early warning method for a high-low voltage switchgear assembly, and the method carries out the data preprocessing and feature extraction through collecting the state monitoring data, environmental parameters and basic attribute information of a device in historical operation. And based on the feature data sets, constructing a fault risk prediction model to predict the fault risk probability of the equipment in a period of time in the future, obtaining the operation state data and environmental parameters of the equipment in real time, and inputting the prediction model to obtain the fault risk probability. Meanwhile, the equipment is monitored in real time, original monitoring data is cleaned, abnormal values are removed, real-time monitoring data is compared with prediction data, whether the difference exceeds a threshold value or not is judged according to a preset risk threshold value, and risk points are marked. And evaluating the equipment fault risk according to the risk point and the evaluation strategy, and sending a result to related personnel to take prevention measures.

The invention discloses a park safety operation and maintenance management method based on a digital twinborn technology, and relates to the technical field of park safety operation and maintenance management, and the method comprises the following steps: S1, obtaining park information which comprises park structure information, environment information and real-time operation and maintenance data; and S2, constructing a digital twinborn model of the park based on the obtained park structure information. According to the park safety operation and maintenance management method based on the digital twin technology, the conditions after different emergency measures are implemented are simulated, and different evacuation routes and guide modes are simulated for multiple times to evaluate the advantages and disadvantages of the scheme, so that the optimal evacuation scheme is selected, the safety of personnel in an emergency is improved, and the safety of the personnel is improved. The response efficiency and effect when the security event occurs are improved, so that the security event can be timely and effectively controlled when the security event occurs, and greater loss is avoided.

The invention discloses an intelligent financial question and answer system based on a large language model, and relates to the technical field of financial question and answer, the system solves the problems of financial term ambiguity and context ambiguity through cooperative work of a plurality of modules, and the system comprises the following modules: a context recognition module, a query module and a query module, a natural language processing algorithm is combined with a context analysis technology in the financial field to automatically extract context clues in user questions; and a financial term knowledge base module. According to the method, context recognition and a financial term knowledge base are combined, and the accuracy of understanding the term ambiguity and the complex context is improved through the graph neural network. And the intelligent recommendation module dynamically provides personalized financial suggestions by using collaborative filtering and content recommendation algorithms in combination with user history and market data, and performs real-time adjustment. The risk assessment and prompt module assesses risks through a Bayesian network and a logistic regression model, and the adaptive learning module continuously optimizes risk prediction and helps users to avoid financial risks.

The invention belongs to the field of time sequence anomaly detection, and discloses a time sequence anomaly detection method and system based on frequency enhanced convolution Transform, and the method comprises the steps: carrying out the sequence-level preprocessing of an input sequence, including normalization and block generation; encoding the input sequence by adopting a multi-head self-attention mechanism; extracting interrelationships in the sequence by a full convolution module having a plurality of stacked 1D convolution layers of different kernel sizes; s2 and S3 are repeated, and multi-layer coding is carried out on the sequence; converting the encoded embedding into a frequency signal by using a Fourier transform method; the KL divergence and the absolute error are respectively used for calculating the consistency loss of the time domain and the frequency domain, calculating the point-by-point anomaly score, and identifying whether the point is abnormal or not. Wide experiments on five common data sets show that the method is superior to an existing method in the aspect of abnormal recognition.

The invention relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

The invention provides a high-performance all-solid-state sodium ion battery, and belongs to the technical field of power batteries, the sodium ion battery comprises a Prussian white positive electrode, a metal sodium negative electrode and an NASICON type solid electrolyte; preparation raw materials of the Prussian white positive electrode comprise Prussian white, the general formula of the Prussian white is NaxFe [Fe (CN) 6], and x is larger than or equal to 1.8 and smaller than 2.2; the composition of the NASICON type solid electrolyte is NaxZr2-yMySiuPvO12, x is more than 0 and less than or equal to 4, y is more than or equal to 0 and less than 2, u is more than 0 and v is less than 3; in the NaxZr2-yMySiuPvO12, M is at least one of a Zn element, a Mg element, a Cu element, an Al element, a Ni element, a Ga element, a Fe element and a K element. The sodium ion battery has excellent electrochemical performance at room temperature, is high in safety, and is beneficial to commercialized development and application of all-solid-state sodium ion batteries. The invention also provides a preparation method of the high-performance all-solid-state sodium ion battery.

The invention relates to the technical field of hardware fault early warning, in particular to a server hardware fault early warning and recovery method and system based on an intelligent optimization algorithm, and the method comprises the steps: obtaining hardware self-inspection information; acquiring load state information; acquiring historical operation data; constructing a preliminary mapping matrix based on the acquired hardware self-inspection information, load state information and historical operation data; according to the preliminary mapping matrix, through a topological space optimization algorithm, key modes are extracted, and an optimized mode matrix is generated; based on the optimized mode matrix, combining a fault-tolerant strategy group, and screening a fault-tolerant or backup strategy suitable for the current state; according to a fault-tolerant or backup strategy, evaluating the benefit, the cost and the success probability of the strategy, and generating an optimal decision scheme; outputting fault early warning information including early warning time, an early warning level and a trigger strategy; and outputting an optimal decision scheme, including a fault-tolerant strategy or a backup strategy, so that a more complex hardware abnormal mode can be captured, and the accuracy of early warning is greatly improved.

The invention relates to the technical field of photovoltaic modules, in particular to a photovoltaic module control method and device. The method comprises the following steps: obtaining an installation position according to design data of the photovoltaic module, carrying out illumination intensity change measurement, and further evaluating the luminous flux change of the photovoltaic module; on the basis of the luminous flux change data, calculating the generating capacity fluctuation of the photovoltaic module, further performing power quality attenuation evaluation, and extracting power grid transmission voltage fluctuation data; according to the power grid voltage fluctuation and power quality attenuation data, the battery overcharge condition and the battery life attenuation are evaluated, the battery capacity attenuation is further calculated, and the photovoltaic module system efficiency attenuation is evaluated; by analyzing system efficiency attenuation data, photovoltaic module degradation risk early warning is carried out, and risk early warning information is fed back to the terminal in real time; according to the invention, through early warning of power generation of the photovoltaic module, the deterioration prediction of the photovoltaic module is more accurate.

The invention discloses a negative-pressure tea-leaf picker and a tea-leaf picking method. The negative-pressure tea-leaf picker comprises a frame body, the air duct is obliquely arranged on the frame body and is used for transporting the sheared tea leaves; the shearing tool is arranged at the lower end of one side, close to the advancing direction of the frame body, of the air duct; an adsorption opening for adsorbing tea leaves is formed in the lower end of the air duct; an air blowing opening is formed in the lower end of one side, far away from the advancing direction of the frame body, of the air duct and is connected with a fan through an air pipe; the air blowing opening comprises an adjusting plate arranged on the side close to the adsorption opening, the adjusting plate is rotationally arranged on the air channel, and the adsorption force at the adsorption opening is adjusted by adjusting the inclination angle between the adjusting plate and the air channel. According to the tea picking machine, the cut tea leaves are adsorbed and conveyed by forming negative pressure at the adsorption opening, the effectiveness of adsorbing and collecting the cut tea leaves is improved, and waste and loss caused by the fact that the tea leaves fall out of an air duct are prevented.

The invention provides a red ginseng processing method capable of increasing the content of rare ginsenoside and reducing pesticide residues, and relates to the technical field of medicinal material processing, the red ginseng processing method comprises the following steps: A, steaming fresh ginseng by using atomized probiotic fermentation liquor; b, drying the steamed fresh ginseng to obtain red ginseng; the pH (Potential of Hydrogen) of the probiotic fermentation liquor is 3.0 to 4.0; the OD600 value of the probiotic fermentation liquor is 1.2-2.5, the probiotic fermentation liquor is obtained through probiotic fermentation, the fermentation liquor with the pH value being 3.0-4.0 is adopted as an acid catalyst to replace purified water, in the ginseng steaming process, steaming is conducted through the atomized probiotic fermentation liquor, conversion of ginsenoside is promoted, and meanwhile organophosphorus pesticide residues in ginseng are degraded. The method is safe, efficient, green and natural, and loss of nutritional ingredients and functional ingredients caused by cooking is avoided. The technical problems that red ginseng processed and prepared in the prior art is low in rare saponin content and poor in effect and pesticide residues exist are solved.

The invention provides a micro-nano impact press-in tester operation monitoring system based on digital twinning, and is suitable for the field of precise instrument maintenance. The system realizes multi-physical field coupling modeling in a micro-nano press-in test through a digital twinning technology, and can monitor the state of an instrument in real time with high precision, predict faults and perform dynamic optimization. The system is integrated with a high-precision sensor, data such as micro force, extremely high temperature and nanoscale displacement are collected in real time, a digital twin model is constructed, and the operation state of an instrument is mapped. Historical and real-time data are analyzed by means of a machine learning algorithm, potential faults can be accurately predicted, maintenance suggestions can be provided, and the precision retentivity and the test stability of the instrument under the material service working condition are ensured. The interaction center provides a visual interface, supports real-time monitoring, dynamic adjustment and early warning functions, and remarkably improves the intelligent maintenance level of the instrument. The invention is suitable for the fields of material science, micro-nano mechanical test and the like, and provides an innovative precise instrument intelligent management scheme.

The invention discloses a method for improving the cooling effect of a pole winding based on a mixed-flow fan, and belongs to the technical field of cooling of high-rotating-speed and high-capacity hydraulic generators and pumped storage generator motors. Mixed-flow fans are arranged at the two ends of a magnetic yoke, and cooling air is divided into radial cooling air for cooling coils at the ends of the magnetic poles and axial cooling air entering the outer surfaces between the magnetic poles. An interelectrode outer surface ventilation cooling space is formed between every two adjacent pole windings, interelectrode ventilation partition plates which are axially arranged are arranged in the interelectrode outer surface ventilation cooling spaces, and separated axial ventilation air channels are formed by the interelectrode ventilation partition plates and the outer surfaces of the pole windings; therefore, rotor pole winding cooling of a high-rotating-speed and high-capacity hydro-generator and a pumped storage generator motor in which radial ventilation cooling cannot be adopted in a pole winding interelectrode area is ensured, and the problem that the difference between the cooling effects of the windward side and the leeward side of a traditional structure is large is solved.

The invention discloses an asphalt viscosity testing device for road and bridge construction, and relates to the technical field of asphalt viscosity testing. Liquid asphalt reaching the detection temperature circularly flows between the heating mechanism and the viscosity testing mechanism, so that the asphalt can be continuously heated in the flowing process, the temperature of the asphalt is kept in a relatively stable state, and the inner walls of components such as a conveying pipe and a return pipe can be heated in the long-time flowing process of the asphalt, so that the viscosity of the asphalt is improved. Before the asphalt viscosity is detected, the temperature of the asphalt and the temperature of a component in contact with the asphalt can be kept consistent, so that the condition of heat loss before the asphalt is detected is avoided, and the accuracy of asphalt viscosity detection can be improved; the plurality of viscosity testing units are used for synchronously testing the viscosity of the asphalt at a plurality of positions at the same time, and the viscosity value of the asphalt at the testing temperature can be comprehensively evaluated, so that the condition that the testing data have relatively large deviation due to misoperation or equipment failure is avoided.

The invention relates to an aerosol moisture absorption and activation process observation system and an observation method thereof. The aerosol moisture absorption and activation process observation method comprises the following steps: collecting sample gases under different humidity conditions, and carrying out particle size screening on the sample gases to respectively obtain aerosols with different particle sizes; measuring scattering coefficients of the aerosols with different particle sizes under different humidity conditions; calculating scattering moisture absorption growth factors of the aerosols with different particle sizes according to the scattering coefficients, and performing inversion calculation on the scattering moisture absorption growth factors so as to obtain moisture absorption, water content, volume concentration and surface area concentration of the aerosols; according to the scattering coefficients corresponding to the aerosols with different particle sizes in the environment state, the dry state critical particle sizes of the aerosols with the dry state increased to 1 micrometer and 2.5 micrometers through water vapor condensation and the scattering moisture absorption growth factor of the aerosols with the environment state ranging from 1 micrometer to 2.5 micrometers are calculated in an inversion mode, and the effective supersaturation ratio of the cloud mist is obtained through inversion. And the aerosol moisture absorption and activation process observation system is arranged according to the aerosol moisture absorption and activation process observation method.

The invention provides a method and a system for regulating and controlling a raw material ratio and parameter combination in production of a nitrogen-phosphorus-potassium ternary fertilizer, and belongs to the technical field of automatic control of fertilizers. The production data is collected in real time through the sensor, the production model is established by using the convolutional neural network and the long-short-term memory network, the optimal parameter combination is calculated by using the particle swarm optimization algorithm, and real-time optimization and self-learning of the production process are realized. By adopting the method and the system for regulating and controlling the raw material ratio and the parameter combination in the production of the nitrogen-phosphorus-potassium ternary fertilizer, the real-time optimization of the production process can be realized, the product quality and the production efficiency can be improved, and the production cost can be reduced.

The invention discloses a fault prediction method and device for an intelligent operation and maintenance system, and relates to the technical field of fault prediction. The method comprises the following steps: receiving a prediction request sent by a user side, and determining first equipment according to the prediction request; determining preset first monitoring data according to a first model corresponding to the first equipment; judging whether the first monitoring data is consistent with preset first monitoring data or not; when the first monitoring data is inconsistent with preset first monitoring data, determining that the first equipment has a first fault; obtaining a target association degree between the first device and the second device; judging whether the target association degree is greater than or equal to a preset association degree; and if the target association degree is greater than or equal to the preset association degree, determining that the first equipment and the second equipment have fault states, and sending the fault states to the user side, so that a user corresponding to the user side checks the fault states. By implementing the technical scheme provided by the invention, the operation and maintenance personnel have enough time to repair, and unnecessary loss caused by fault expansion is avoided.

The invention provides a stable multivalent metal surface doped ruthenium oxide catalyst, a preparation method thereof and application of the stable multivalent metal surface doped ruthenium oxide catalyst in a proton exchange membrane water electrolysis device, and belongs to the technical field of catalysts and preparation thereof. According to the prepared multivalent metal such as molybdenum, niobium, tungsten, tantalum and chromium doped ruthenium oxide catalyst, the active center electronic structure is effectively stabilized due to the flexible valence change of the multivalent metal in the oxygen evolution process, meanwhile, a surface layer can be constructed through surface doping, separation of the active center Ru and lattice oxygen is effectively inhibited, and the stability of the catalyst is improved. The prepared catalyst shows excellent catalytic activity and electrochemical stability in an acidic oxygen evolution reaction, has the advantages of low cost and good catalytic stability, and in addition, can stably operate under relatively high current density as a proton exchange membrane electrolyzed water anode catalyst.

The invention relates to a tree painting projection data collection and analysis system and method, and the system comprises a data collection module, a data preprocessing module, a feature extraction module, a quantitative analysis module, a classification and prediction module, and a result evaluation module. The modules cooperate with each other to realize standardized processing, feature extraction, quantitative analysis, intelligent classification and prediction and result evaluation of data. According to the method, the holographic theory and the Ama pothole theory are combined, the drawing features are finely analyzed through the cross-modal comparative learning and graph neural architecture search combined algorithm, a scientific and objective psychological data acquisition and analysis method is provided, the diagnosis sensitivity and specificity are improved, a powerful tool is provided for clinical psychological assessment, and the method is suitable for clinical psychological assessment. The method has important clinical and research values, and has wide application prospects in the fields of psychological problem screening, social psychological services, judicial, public security, marriage matching, talent selection and the like.

The invention discloses a wafer film defect detection system and method, and relates to the technical field of wafer film defect detection.The wafer film defect detection method comprises the following steps that in the wafer film defect detection process, laser beams are emitted through a laser scanner according to the preset light beam coverage rate, and the surface of a wafer is covered for preliminary scanning; in the laser beam scanning process, the intensity and the consistency of laser reflection signals are analyzed in real time. According to the invention, preliminary scanning is carried out by presetting the light beam coverage rate, the significance of the potential flaw area is evaluated by using the convolutional neural network, accurate classification of the flaw obvious area and the flaw non-obvious area is realized, high-density secondary scanning is carried out by dynamically adjusting the light beam coverage rate for the flaw non-obvious area, and finer surface information is captured. The detection efficiency is improved, the risks of missing detection and false detection are effectively reduced, the accurate detection of tiny flaws is ensured, the quality control of wafer film pasting is remarkably improved, and the production loss is avoided.

The invention belongs to the technical field of information security, and relates to an Android malicious software detection method based on API semantic enhancement, and the method comprises the steps: obtaining a to-be-detected Android application; performing static analysis on the to-be-detected Android application, and constructing a function call graph of the to-be-detected Android application; optimizing the function call graph of the Android application; extracting a complete API context sequence of a corresponding function from the optimized function call graph by adopting a recursive algorithm; adopting Skpi-Gram to map the complete API context sequence into feature vectors, and adopting a clustering algorithm to divide the feature vectors with close distances into the same class; inputting the clustered feature vectors into a trained graph convolutional neural network model to obtain a detection result of the to-be-detected Android application; according to the method, the extracted function call graph nodes are optimized by adopting the graph convolutional neural network, so that information loss caused by feature structure conversion is avoided, and the efficiency of model training and the accuracy of a detection result are improved.

The invention discloses an intelligent temperature control system for a greenhouse, and relates to the technical field of greenhouse temperature control, the intelligent temperature control system comprises a data acquisition module, a crop growth database module, a data analysis decision module, a remote monitoring alarm module, an intelligent control module and a user interaction interface module, all the modules are connected through electric signals. The temperature environment in the greenhouse is monitored in real time, accurate regulation and control are performed according to the growth requirements of crops and the current environmental conditions, the crops are ensured to be in the most suitable growth temperature range all the time, healthy growth and development of the crops are promoted, compared with a traditional manual regulation and control mode, temperature fluctuation can be more accurately controlled, and the crop yield is improved. The problems of crop growth resistance, increase of plant diseases and insect pests and the like caused by inappropriate temperature are avoided, in addition, the temperature set value can be automatically adjusted according to different growth stages and types of crops, the growth environment is further optimized, and the yield and quality of the crops are improved.

The invention discloses a warehouse management method based on the industrial Internet of Things, and the method comprises the following steps: automatically collecting image data of goods through Internet of Things equipment, taking the image data as goods information, and storing the goods information in a central database for recognition; image data and temperature and humidity data of a warehouse shelf are obtained through Internet of Things equipment to serve as shelf information, and the shelf information is stored in a central database to be recognized; matching is carried out according to the goods information and the goods shelf information, the optimal goods shelf is selected to store goods on the basis of goods types, sizes, storage requirements and storage condition factors, and a unique classification number is distributed to each piece of goods; the goods are stored to the corresponding goods shelf positions according to the distributed classification numbers and the matching results; through automatic and intelligent warehouse management, the operation efficiency of the warehouse and the accuracy of inventory management are improved, and complex storage conditions and dynamically changing inventory requirements can be dealt with, so that the overall efficiency and precision of warehouse management are effectively improved.

The invention provides an intelligent data analysis technology of a lithium battery fire hazard detector, relates to the technical field of lithium battery safety early warning, predicts the occurrence of thermal runaway by detecting an extremely trace amount of characteristic gas separated out from a lithium battery, and provides an intelligent data analysis technology of a lithium battery fire hazard detector through a large number of experimental tests on different brands of battery cells. According to the technology, a lithium battery thermal runaway first-stage characteristic model database is established, an intelligent data analysis algorithm is adopted, detected gas data can be analyzed in real time and compared with the characteristic model database, and therefore whether the lithium battery is in a thermal runaway early stage or not is rapidly judged; the intelligent data analysis algorithm needs to consider various complex conditions and factors, such as ambient temperature, battery aging degree, charge and discharge states and the like, is integrated with a battery management system (BMS), immediately gives an alarm to the BMS once detecting the hidden danger of thermal runaway of the lithium battery, and suggests to take corresponding treatment measures, so that the safety of the lithium battery is ensured. And the efficiency and the automation level of lithium battery fire hazard treatment are greatly improved.