366 results about "Detailed data" patented technology

The invention belongs to the technical field of computer data protection, and particularly relates to a computer data hierarchical protection method and a dynamic protection system. According to the method, the to-be-protected data is graded and scored, dynamic adjustment of the data protection grade is realized in combination with the dynamic risk of the computer network environment, and more detailed data grading is performed in combination with the semantic risk value by identifying sensitive information in the to-be-protected data, so that the pertinence and effectiveness of data protection are ensured, and the data protection efficiency is improved. Meanwhile, multiple risk parameters of the computer network environment are comprehensively considered, the security state of the network environment is reflected in real time through calculation of the dynamic risk score value, the data protection grade is intelligently adjusted according to the grading score and the dynamic risk score value, and dynamic optimization of the data protection strategy is achieved; and through fusion of historical behavior records and current session contexts of operators, fine management of access permissions is realized, and the security of data access is further enhanced.

The invention relates to the technical field of meteorological disaster early warning, in particular to a lightning disaster early warning system and method based on multi-source data, and the system comprises an atmospheric electric field monitoring module, an early warning strategy customization module, a risk area recognition module and a real-time early warning release module. According to the invention, through multi-source data integration and analysis and in combination with the regional electric field intensity and lightning frequency, the lightning activity intensity is accurately evaluated, the monitoring sensitivity and accuracy are significantly improved, and detailed data support is provided for intensity change, so that a more targeted early warning strategy is formulated; the safety level and the response requirement are optimized through frequency and intensity analysis, resource allocation is more reasonable, response time is more efficient, potential risk areas are further identified through path analysis, thunder and lightning conduction paths are refined, the risk areas are accurately calibrated, the early warning level is dynamically adjusted, it is ensured that early warning information has timeliness and space adaptability, and the early warning efficiency is improved. Powerful support is provided for power grid safety and lightning protection management, and the intelligent disaster prevention level is comprehensively improved.

The invention provides a food information tracing method and system based on Internet data, and relates to the technical field of industrial Internet, and the method comprises the following steps: generating an initial batch identifier of a to-be-produced product according to a product production plan, obtaining real-time external data of an associated region, and storing the real-time external data in a database; when it is judged that a batch adjustment event occurs in the production process of the to-be-produced product, based on real-time state data and real-time external data of a production line, dynamic batch decision is carried out to generate at least two sub-batch identifiers, and a tree-shaped association relationship is established between the sub-batch identifiers and the initial batch identifier; and receiving sub-batch detailed data of each sub-batch identifier, uploading the sub-batch detailed data to a block chain network, storing the batch topological relation in a block chain main chain, storing the sub-batch detailed data in a corresponding sub-chain node, responding to a tracing request of a user terminal, and according to the tree-shaped association relation, tracing the sub-batch data of each sub-batch identifier. And retrieving the full life cycle data of the corresponding target batch in the block chain network. The method overcomes the problem of tracing information splitting caused by batch splitting.

The invention discloses an intelligent optimization system based on a self-adaptive control hydraulic cylinder, and relates to the technical field of hydraulic cylinder control, and the system comprises a monitoring data collection module which collects data of a flow field, a temperature field and a stress field in real time by means of various sensors arranged at different parts of the hydraulic cylinder, synchronously carries out the preprocessing and coding of the collected data, and transmits the data to a data processing module; the data is transmitted to the data processing and analyzing module in real time; detailed data of the hydraulic cylinder in different physical fields are acquired in real time through the monitoring data acquisition module, and the coupling coefficient is accurately calculated by using the multi-physical field coupling model, so that the system can deeply understand the specific influence of each physical field on the performance of the hydraulic cylinder; according to the method, the system can construct a more accurate hydraulic cylinder dynamic model in the model prediction control module, so that the future state of the hydraulic cylinder is accurately predicted, further, the system can obtain an optimal control input sequence through an objective function optimization algorithm, and the pertinence and effectiveness of a control strategy are remarkably improved.

The invention relates to the technical field of optic nerve detection, in particular to a plastic particle quality detection method and system based on optic nerves, and the method comprises the following steps: capturing a plastic particle image through an industrial camera, carrying out the balance processing of the image contrast, extracting the brightness and color features of the image, and removing the noise interference; and adjusting the image brightness and optimizing the particle edge to obtain a particle optimization image. According to the invention, the accuracy and automation level of plastic particle quality detection are greatly enhanced by using the visual neural network and the image processing technology, and the edge and shape features of the particles can be accurately extracted from a complex background by automatically adjusting the image contrast and brightness and applying an advanced edge detection algorithm. According to the method, the visual quality of the image is optimized, tiny flaws such as cracks and bubbles of the particles can be effectively recognized and analyzed, and more detailed data support can be provided compared with a traditional method by accurately calculating the surface roughness and texture uniformity of the particles.

The invention relates to the technical field of industrial park intelligent monitoring, and particularly discloses a data acquisition and monitoring control method based on an intelligent industrial park, which comprises the following steps: acquiring key state data of equipment in real time, including operation time, operation temperature and operation power; an advanced signal processing algorithm is used for calculating an operation temperature abnormal index and an operation power abnormal index, the system can comprehensively evaluate the state of equipment and accurately judge whether slight or serious abnormal conditions exist or not based on detailed data analysis results, and the system can not only give out instant early warning notifications but also give out alarm information according to the serious abnormal conditions. According to the method, a machine learning model is adopted to predict the future operation state of the equipment, the equipment state abnormal coefficient in each future monitoring period is predicted based on the equipment state data in the current period, and classification and dynamic regulation and control of the equipment state are carried out according to a preset threshold value.

The invention provides an urban low-altitude unmanned aerial vehicle instant distribution scheduling and path planning method considering demand prediction. The method comprises the following steps: establishing a three-dimensional environment map of an unmanned aerial vehicle distribution system by adopting a grid-topology hybrid modeling strategy based on three-dimensional urban environment map data; according to the three-dimensional environment map of the unmanned aerial vehicle distribution system, the starting point and the target point of the unmanned aerial vehicle, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost are obtained by using an unmanned aerial vehicle path planning algorithm of space-time improvement A *; and according to the order data, the unmanned aerial vehicle performance parameters, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost, using the CNN-LSTM-Attention order prediction model and the task allocation model to output a task allocation scheme including a virtual order and an actual order. Accurate spatial data support is provided for path planning of the unmanned aerial vehicle, the reasonability of low-altitude logistics distribution path planning is improved, and finer data support is provided for urban airspace management.

The invention relates to the technical field of self-adaptive starting, and discloses a self-adaptive starting method and system based on voltage and current monitoring, and the method comprises the steps that an MCU (Microprogrammed Control Unit) triggers an auxiliary silicon controlled rectifier and a main silicon controlled rectifier through preset starting logic to start a motor, and carries out the high-frequency sampling of current and voltage data in real time; once starting failure is detected, short-time voltage sequences and short-time current sequences before and after a failure occurrence point can be automatically captured and stored. Then, key features are extracted from the sequences, and failure causes are determined through decision tree model analysis. And based on the analysis result and the current retry times, the MCU outputs a corresponding retry strategy to ensure that the motor can be successfully started or enter a safety mode. Thus, the reliability of motor starting is improved, subsequent fault diagnosis is supported through detailed data recording, and the maintainability and safety of the system are greatly improved.

The invention provides a medical aid decision-making method and device, relates to the field of intelligent medical treatment, and adopts three models with different capabilities to cover different plates of a whole inquiry process, thereby effectively reducing common illusion problems of a large model. The triage route is responsible for a special medical logic reasoning model, and it is ensured that the patient is accurately guided to the needed medical service. The multi-modal model is responsible for collecting the complaint information of the patient through an efficient voice interaction technology, meanwhile, effective communication and recording are carried out, and detailed data support is provided for follow-up diagnosis. The medical expert model realizes semantic alignment on medical data through special training, can deeply understand slight differences in expression of patients, and further improves the accuracy and efficiency of diagnosis and treatment.

The invention relates to the technical field of geographic information data, in particular to an ecological remote sensing data processing system which comprises a remote sensing layer acquisition module, a boundary position checking module, a region coding integration module, a spatial change splicing module and an ecological trend drawing module. According to the method, by accurately matching the geographic identification information and the spatial boundary value, the coverage range and the positioning precision of the ground feature pattern spots are remarkably improved, the time information and the spatial information are effectively integrated, the continuity and the accuracy of the data are enhanced, the boundary resetting technology effectively reduces the errors of the geographic information, and the time-space continuity of the data is improved; the processing efficiency is greatly improved by constructing a continuous layer structure and optimizing a data retrieval process, the fine boundary splicing technology also allows fine tracking of ecological changes, more detailed data support is provided for ecological monitoring, the ability of ecological trend analysis is enhanced, and thus the overall effect and application value of ecological monitoring are improved.

The present invention discloses a BMS battery remote monitoring and data analysis system based on a cloud platform, which relates to the technical field of remote monitoring and is used to solve the problems in the prior art that due to the lack of detailed data monitoring, it is impossible to deeply analyze the performance and loss of each part of the battery, thereby affecting the overall health assessment and management of the battery. Moreover, the battery state of the battery is usually a fixed and static value, ignoring the dynamic impact of the management frequency and power loss of different functional parts on the battery state; by combining the management frequency and power loss, the battery state of each functional part can be accurately allocated, the battery usage strategy can be optimized according to the actual situation of different modules, and unnecessary energy loss can be reduced. Unnecessary interference with the battery can be reduced. Through an automated monitoring and adjustment mechanism, manual intervention in battery management can be reduced, making battery management more intelligent and efficient.

The invention discloses an intelligent data monitoring system and method based on an ultrasonic water meter, and the method comprises the steps: determining a data grading label through the comparison between the vibration features of the vibration data of an ultrasonic sensor in a preset time period and a recent historical feature baseline; therefore, the system can intelligently identify the abnormal degree of the current running state of the water meter. Furthermore, based on the data grading label, a data transmission strategy and a data compression strategy for the original vibration signal are specified, so that the data transmission and storage efficiency is optimized to the greatest extent on the premise of ensuring that key information is not lost. The self-adaptive strategy not only solves the problem of low data transmission and storage efficiency in a traditional scheme, but also ensures that enough detailed data can be obtained at a critical moment, and resources can be efficiently utilized during conventional operation, so that the optimal balance between data integrity and accuracy and transmission and storage cost is realized.

The embodiment of the invention discloses a test sample information updating method, device and equipment based on multi-dimensional data. A specific embodiment of the method comprises the following steps: obtaining a test sample detailed data list of each piece of test sample information in a test sample information list to generate a test sample detailed data list, and obtaining a test sample detailed data list set; generating a test sample information state list set according to the list access frequency set; in response to determining that the test sample information state representing the abnormal state exists in the test sample information state list set, performing abnormal operation processing on at least one piece of test sample information representing the abnormal state to generate a processed test sample information set; and updating the processed test sample information set to the test sample storage equipment. According to the embodiment, leakage of test sample information is avoided, and the integrity and safety of the test sample information are improved.

The invention relates to the technical field of wind power energy storage, and discloses a collaborative peak regulation method and system based on a wind power energy storage system, and the method comprises the steps: collecting the detail data and SCADA data of a power generation fan, carrying out the time sequence alignment, constructing a prediction error model, calculating a standardized prediction error, constructing a hybrid prediction model, and dynamically adjusting the model parameters; a time-function double-layer decomposition strategy is constructed to calculate an energy storage attenuation value, a multi-objective optimization model is constructed in combination with the energy storage attenuation value, cross-day balance is carried out, and a charging and discharging strategy is optimized; constructing a three-channel reward function, dynamically adjusting the weight coefficient of the reward function, and automatically adjusting the weight according to the real-time state of the power grid to realize multi-target dynamic balance; the method comprises the following steps: constructing a hybrid hierarchical control execution framework, optimizing an edge layer, obtaining a wind power fluctuation rate, judging whether to trigger super capacitor pre-charging, performing model prediction control MPC optimization, realizing credible synchronization of energy storage SOC data in combination with an intelligent contract, and performing health state early warning.

The invention relates to the technical field of frequency converter detection, in particular to a quality test board for frequency converter production and processing and a test method, an interface management module automatically identifies and manages interface connection, the problem of inaccurate data caused by misplug or misconnection is avoided, and the test safety is enhanced at the same time. The test execution module covers multiple aspects of performance test, protection function test, communication test, load simulation test and the like. The data acquisition module acquires key parameters in real time and provides detailed data support for subsequent analysis. And the data analysis module quickly identifies abnormal conditions and generates a test report through preprocessing, anomaly detection and performance evaluation. The fault diagnosis module can quickly identify fault types, accurately position fault positions, provide repair suggestions and shorten maintenance time. Therefore, the problem of inaccurate detection data of the frequency converter caused by misplug or misconnection when a tester repeatedly executes interface plugging operation is solved.

A communication self-inspection and emergency shutdown method for an offshore wind turbine generator comprises the following steps: acquiring heartbeat signal data between a fan main control unit and a looped network, analyzing frequency and intensity, and judging whether a signal is normal or not; if the signal intensity is low, the interference detection module is started to identify external interference or equipment faults, and a communication interruption timer is started accordingly. And once the communication interruption exceeds the preset duration, a fault is judged and a confirmation mechanism is triggered, and after the fault is confirmed, the main control unit sends an emergency shutdown instruction. And after shutdown, acquiring running state data of the fan to judge whether the fan is completely stopped, recording shutdown time and state change, generating a fault report covering fault types, interruption, shutdown time and fan state change, and uploading the fault report to a looped network monitoring system. According to the method, accurate self-inspection of the communication state of the offshore wind turbine generator is achieved, faults can be found in time, emergency shutdown measures can be taken, detailed data are provided for operation and maintenance personnel, and safe and stable operation of the wind turbine generator is guaranteed.

The invention relates to a soil state monitoring method and system based on a sensor network, and the method comprises the steps: reasonably arranging a plurality of sensor nodes in a target soil region to form a sensor network, and comprehensively and precisely collecting soil attribute data corresponding to each node in different time periods; and the original monitoring data set can reflect the soil state in all directions. According to the method, detailed data preprocessing is carried out on an original monitoring data set, abnormal data is cleaned, error information possibly interfering analysis is effectively removed, soil attribute data of different dimensions are normalized, and a uniform and accurate data basis is provided for subsequent processing. A fusion state feature set is generated through feature fusion processing, and the soil state can be deeply described from multiple dimensions. And determining a soil state category label according to the fusion state feature set, and generating an optimization strategy to be fed back to the monitoring terminal, so that the soil state can be accurately grasped, the strategy can be dynamically adjusted, and the accuracy and reliability of soil state monitoring can be improved.

The invention discloses a social governance risk early warning large model, which comprises a data acquisition unit, an event comparison unit, an early warning analysis table generation unit, an early warning research and judgment unit, an early warning disposal unit and an interaction unit, and is characterized in that the data acquisition unit is used for generating a detailed data set of key personnel; the event comparison unit is used for generating a comparison event data set; the early warning analysis table generation unit is used for generating a key personnel comparison early warning analysis table; the early warning research and judgment unit is used for constructing an early warning research and judgment agent; the early warning processing unit is used for building an early warning processing agent; and the interaction unit is used for displaying the key personnel risk research and judgment situation overview map and the risk disposal strategy in real time, and building an intelligent interaction question and answer window through the early warning research and judgment agent and the early warning disposal agent. According to the invention, by introducing big data analysis and artificial intelligence technologies, intelligent early warning, research and judgment and disposal management of key personnel are realized, and the efficiency and level of social governance are improved.

The invention discloses a satellite precipitation product improvement method, device and system based on a dual-time scale minimum error, and the method comprises the steps: carrying out the improvement of a first satellite precipitation product, a second satellite precipitation product and an interpolation precipitation product according to the obtained first satellite precipitation product, second satellite precipitation product and interpolation precipitation product; sequentially adopting a dual tool variable algorithm, a least square fusion algorithm and an inverse error weighting algorithm to respectively calculate first fusion rainfall products under a daily scale and a monthly scale; and fusing the first fused precipitation products under the daily scale and the monthly scale to generate a second fused precipitation product. Ground observation data are not needed, daily scale and monthly scale rainfall errors can be minimized at the same time, the thought of data fusion is incorporated, the performance of satellite rainfall products is enhanced, and detailed data support can be provided for natural disaster monitoring and early warning and regional water resource management.

The invention relates to the technical field of advertisement data analysis, in particular to a mobile advertisement effect analysis method and system based on user behaviors. According to the method, detailed data of user behaviors and advertisement putting are captured in real time through an SDA interface and an API interface of a mobile advertisement platform, users are subdivided into user groups with different activeness, activeness and consumption levels by calculating activeness parameters, advertisement activeness parameters and consumption level parameters, accurate description and classification of the user behaviors are achieved, and the user experience is improved. The method helps advertisers to more accurately position target users, can mine potential user demands, improves the precision and conversion rate of advertisements, and reduces the cost of the advertisements at the same time. The advertisement putting effect is comprehensively evaluated by calculating key indexes such as the advertisement revenue index, the advertisement adaptation degree index and the shopping willingness index, a scientific decision basis is provided for advertisers, the advertisement putting strategy is optimized, and the advertisement effect is improved.

The invention discloses an interest rate risk prevention and control method and device, electronic equipment and a storage medium, and the method and device are applied to the electronic equipment, specifically, multiple interest rate scenes based on business configuration are sequentially applied to full-amount detail data in a pre-configured financial tool table, and a detail data copy corresponding to each interest rate scene is generated; each detail data copy is stored in a financial tool table; a distributed task scheduling engine is adopted to distribute each detail data copy to a plurality of calculation nodes for parallel cash flow calculation and conversion, and cash flow data information of the financial tool table is obtained; calculating the cash flow data information to obtain a plurality of cash flow financial elements; and performing calculation according to cash flow financial elements to obtain right and interest value interest rate sensitivity. According to the scheme, the interest rate risk prevention and control information can be quickly and efficiently obtained, so that the user can perform interest rate risk avoidance operation in time according to the risk prevention and control information.

The invention relates to the technical field of power grid load prediction, in particular to a power grid load prediction method and system based on stochastic load flow calculation. The method comprises the following steps: obtaining regional power grid operation data; performing data preprocessing on the regional power grid operation data to generate standard regional power grid operation data; performing multi-level novel power grid energy power supply stage influence analysis on the regional power grid operation data to generate an energy power generation stage fluctuation influence factor; performing power transmission path analysis on the standard regional power grid operation data to generate power transmission path data; performing transmission distance calculation on the power transmission path data to obtain power transmission distance data; and comparing the power transmission distance data with a preset standard power transmission distance threshold value to generate long-distance power transmission path data. According to the invention, through detailed data integration and complex analogue simulation, the precision of power grid load prediction is improved.

The invention discloses a data comparison method based on metadata. The method specifically comprises the following steps: acquiring data structures of a source database and a target database to formulate corresponding meta-models; reading metadata of the source database and the target database according to the meta-model and the connection information of the database, and recording the metadata into an intermediate database for comparison; on the basis of the total table record number, the field data type and the like in the metadata, determining the size of a fragment modulo cardinal number, calculating a hash value of a unique identification key by adopting an XXHash algorithm, and determining the number of fragments, the serial number of each fragment and the hash value range corresponding to each fragment; the XXHash algorithm is adopted again to calculate the data of the corresponding fragments, and corresponding hash values are generated; and comparing detailed data in the corresponding fragments, and outputting a difference result. The method has the advantages that the data processing process is optimized, the high calculation speed is kept, the overall efficiency is effectively improved, and two or more data sets can be efficiently and accurately compared.

The invention discloses a PINN-based displacement efficiency analysis method for an eccentric annulus multiphase flow field, and relates to the technical field of oil-gas exploration and development, and the method comprises the following steps: S1, constructing an eccentric annulus model in a well cementation process, and determining the speed of a fluid in an eccentric annulus; s2, determining the upper boundary of the eccentric annulus model; s3, determining boundary conditions of the first fluid and boundary conditions of the second fluid; s4, obtaining the latest boundary condition of the first fluid and the latest boundary condition of the second fluid; s5, determining a kinematics formula of a material interface; s6, training the PINN network; and S7, determining the flow field distribution and displacement efficiency of the fluid in the eccentric annulus. According to the method, all physical constraints are ensured to be fully supervised, the detailed data sampling strategy enables the PINN model to comprehensively capture physical information in each region in the training process, and the robustness of overall solution is improved.

The invention relates to the technical field of functional group analysis, and discloses an adsorption material surface functional group analysis method and system based on a Gaussian function model.According to the method, proton binding isotherm data of an adsorption material under different pH conditions are obtained through an incremental titration method, and the proton binding isotherm data are input into a Gaussian function model; the method comprises the following steps: carrying out characterization analysis on different types of functional groups, fitting experimental data by using a nonlinear least square method to obtain optimized parameters, verifying the accuracy of the optimized parameters by using a preparatory test means, analyzing the proportions of various functional groups in the adsorption material based on the verified optimized parameters, and generating a surface functional group distribution map. According to the method, distribution of different functional groups is accurately analyzed through the Gaussian function model, the defect that quantitative analysis of a traditional method is not accurate is overcome, high accuracy of a result is ensured, detailed data support is provided for design and optimization of an adsorption material, and the problem that in the prior art, when multiple functional groups coexist, the detection effect is not accurate is solved.

The invention relates to the technical field of brain-computer interaction, in particular to a patient mental state assessment system for a clinician, which comprises a semantic manifold calibration module, a state trajectory projection module, a geometric curvature resolving module and an emotion transformation quantification module. According to the method, electroencephalogram data are collected and deeply analyzed, high-dimensional neural activity characteristics are converted into a visual three-dimensional state track in combination with titer and awakening degree information, continuous dynamic monitoring of the mental state of a patient is achieved, then the emotion conversion process is quantitatively analyzed according to the geometric curvature change rate of the state track, and therefore the mental state of the patient can be rapidly and accurately monitored. According to the method, rapid emotion fluctuation which is difficult to perceive in a traditional evaluation mode can be accurately recognized and marked, objective and fine data support is provided for clinical diagnosis, and the accuracy and instantaneity of mental state evaluation are remarkably improved.

The invention discloses a ground-air cooperative early warning system based on big data identification. The method comprises the following steps: firstly, dividing an area to be inspected by a cooperative system, enabling an unmanned aerial vehicle to enter a target area to start autonomous inspection after the unmanned aerial vehicle lifts off, completing data acquisition on the ground in an inspection process, carrying out primary processing on the acquired data, carrying out cross matching with a big data model which is trained in advance and deployed, and marking suspected points according to the matching degree; then the suspected point coordinates are sent to the ground intelligent auxiliary equipment, after the ground intelligent auxiliary equipment receives the suspected point coordinates, a path for going to the suspected point area is generated according to the position of the ground intelligent auxiliary equipment and an airborne map, and the ground intelligent auxiliary equipment goes to the suspected point through autonomous obstacle avoidance navigation. After the ground intelligent auxiliary equipment reaches the suspected point recognition area and within the suspected point recognition area, an airborne high-precision composite sensor is used for recognizing the suspected point and the surrounding area of the suspected point, and more detailed data are obtained; and after data processing is completed, the data is compared with a big data model, and after accurate identification of a suspected area is completed, a matching identification result is sent to the cooperative system and the unmanned aerial vehicle. And after receiving the identification result and the corresponding coordinate, the cooperative system sends a disposal instruction to the unmanned aerial vehicle according to the result type and the judgment of the worker, and the unmanned aerial vehicle disposes the suspected point. According to the method, the advantages of the unmanned aerial vehicle and the ground intelligent auxiliary equipment are maximized by using a big data model and air-ground cooperation, and guidance is provided for improving the inspection efficiency and the judgment accuracy.

The invention discloses a knowledge base construction and application method based on a large language model, and the method comprises the steps: obtaining external data, carrying out the feature extraction of the external data, obtaining entity features, and enabling the external data to comprise detailed data of intellectual property data content; the method comprises the following steps: acquiring instance entity feature associated data, matching entity features with the entity feature associated data to obtain matched associated data, and performing relationship generation on the entity features through a large model according to the matched associated data to obtain a relationship of the entity features; constructing a knowledge graph according to the entity features and the corresponding relationship, and constructing a knowledge base according to the knowledge graph; and obtaining knowledge retrieval information required by a user, performing retrieval in the knowledge base according to the knowledge retrieval information, and analyzing a retrieval result through the large model to obtain reply information of the knowledge retrieval information.

The invention provides a technical photographing system for calligraphy and painting cultural relics, and relates to the technical field of photographing and repairing of the calligraphy and painting cultural relics, and the system comprises an automatic light source adjustment unit which comprises a light source recognition module and a light source optimization module, and the light source recognition module is used for recognizing physical feature parameters of the calligraphy and painting cultural relics and optimizing the light source parameters of the calligraphy and painting cultural relics; the physical characteristic parameters comprise materials, sizes and surface characteristics of the calligraphy and painting cultural relics. According to the photographing system for the calligraphy, painting and cultural relic technology, a multi-spectral image sequence is cooperatively collected by combining multiple light sources, detailed data including an ultraviolet fluorescence image, an infrared reflection image, a light-transmitting image and a sidelight texture image can be obtained, and the light sources in the lamp set are automatically selected and combined; therefore, precise illumination adjustment for different types of calligraphy and painting cultural relics is realized, the acquisition quality of the multispectral image sequence is ensured, and the accuracy and definition of the image are improved.

The invention relates to the technical field of fault prediction, and particularly discloses a communication network fault early warning method and system based on artificial intelligence, and the method comprises the steps: firstly, deploying a distributed data collection unit at each edge node of a communication network, and monitoring the communication state of the edge nodes in real time; when the data acquisition unit detects frequency adjustment trigger conditions such as network topology change or burst traffic impact, the data acquisition unit adjusts the acquisition frequency correspondingly to obtain a more detailed data feature set. And after the collection frequency is adjusted, the edge computing node receives the data feature sets and evaluates the abnormal fluctuation feature value of each communication link, so that the network operation state of each communication link is judged. And then, the edge computing node uploads a judgment result to the central computing node, and the central computing node performs early warning response according to the result. Through the mode, the state of the communication network can be monitored and analyzed in real time, the network fault can be early warned in time, and the reliability and the stability of the communication network are improved.