554 results about "Fingerprint database" patented technology

The invention relates to a regulating valve opening / flow linear correction system and a control method, and the system comprises a flow deviation unit which is used for calculating a real-time deviation value between an actual control flow and a dynamic ideal flow of a regulating valve and comprises a flow prediction module based on an LSTM network, and the flow prediction module outputs a working condition self-adaptive ideal flow reference value; the manual / automatic undisturbed switching unit is used for tracking an ideal flow reference value in a manual mode, keeping a deviation value at the switching moment in an automatic mode and pre-judging the switching opportunity; the deviation correction unit is used for dynamically generating a nonlinear compensation amount by adopting Gaussian process regression, and solving an optimal opening correction value through an online convex optimization algorithm; the correction condition limiting unit is internally provided with a digital twinborn simulation module and a safety boundary prediction model, virtual verification is carried out before opening correction is executed, and the maximum opening limit value is dynamically adjusted; and the digital valve fingerprint database is used for storing valve mechanical wear characteristics, seasonal characteristic drift data and a medium influence matrix.

The invention belongs to the technical field of water pollutant traceability, and particularly discloses a water quality fingerprint database analysis method based on feature recognition, which relates to the field of water pollutant traceability, and comprises the following steps: cross-comparing feature curved surface information of different upstream and downstream under feature peak whole database data and sorting to obtain pollutant features under different intensities; the pollution source type is identified according to the pollutant difference; sorting the characteristic curved surface information of the water body samples reflecting different nodes, calling a chemical characteristic analysis result, outputting a chemical characteristic sequence, and selecting a first sequence sampling point position of the chemical characteristic sequence corresponding to the spectral characteristics of the identified pollution source type as a pollution source; according to the method, whether the identification result is met or not is analyzed according to the biological characteristics of the pollution source, efficient source tracing is carried out on water pollutants, an accurate pollutant transmission path is output, meanwhile, the corresponding pollutant type is evaluated, the corresponding pollutant diffusion trend is given, and powerful data is provided for follow-up pollutant treatment.

The invention relates to the technical field of image processing, and discloses a printed matter detection method and device. The method comprises the following steps: acquiring multispectral image data of a printed matter to be detected in different spectral bands; performing spectral feature extraction and multi-level fusion on the multispectral image data to obtain a comprehensive spectral feature map; acquiring sample spectral feature maps of a plurality of sample printed matters to perform frequent feature mode extraction, generating a spectral fingerprint database, and performing region division and matching degree search to obtain a reference spectral fingerprint map; carrying out multi-scale decomposition on the comprehensive spectrum feature map and the reference spectrum fingerprint map to obtain a plurality of scale layers, and carrying out local spectrum similarity calculation and adaptive threshold segmentation to obtain a binary mask map of a defect region; performing spectral feature clustering analysis to obtain defect categories; and performing defect root cause analysis and process adjustment on the printing process parameters to generate a process adjustment scheme, thereby improving the printing quality detection accuracy of the printed matter.

The invention discloses a private network data security detection method based on big data, and belongs to the field of security detection, and the method comprises the steps: obtaining a real-time data packet from private network traffic, encoding the data packet through a forward error correction code technology, generating redundancy check information, and obtaining an encoded data packet; if a bit error is detected in the transmission process of the coded data packet, requesting a sending end to retransmit a damaged data packet through an automatic retransmission request mechanism to obtain a retransmitted data packet; adjusting the redundancy of a forward error correction code and a trigger threshold of an automatic retransmission request according to the adaptive error correction parameter, generating an optimized error correction scheme, and obtaining a repaired data packet; and aiming at the alarm processing instruction, automatically adjusting a network flow control strategy, limiting the transmission bandwidth of an abnormal destination, updating the sensitive data fingerprint database, and obtaining optimized protection configuration.

The invention relates to the technical field of adaptive network shunting, and discloses an adaptive network shunting method and system based on deep reinforcement learning, and the method comprises the steps: obtaining the flow data of a heterogeneous network, building a protocol fingerprint database, and converting the data of different protocols into a unified network state feature vector; constructing a multi-agent reinforcement learning framework, including defining a state space, defining an action space and defining a reward function, and optimizing a strategy of each agent based on a differential game; adjusting the weight of each agent reward function by using a meta-learning model, training a multi-agent reinforcement learning framework by using deep reinforcement learning, and learning an optimal shunting strategy under different heterogeneous network conditions; and deploying the trained strategy to a heterogeneous network environment, and dynamically selecting an optimal network path according to the current network state. And the heterogeneous data processing capability is obviously improved.

The invention particularly relates to a water pollution intelligent traceability system based on a three-dimensional fluorescence spectrum, which relates to the technical field of environmental monitoring, and comprises a water sample collection and three-dimensional fluorescence data acquisition module, a data preprocessing and PARAFAC component decomposition module, a multi-source pollution characteristic fingerprint database module and an intelligent comparison and pollution source analysis module. According to the method, standard acquisition and preprocessing are adopted, and the original data quality is ensured by means of scattering rejection, instrument correction and the like; pure fluorescence components are decomposed through a PARAFAC algorithm, and feature extraction reliability is guaranteed through triple verification. In the comparison link, spectrum similarity, concentration proportion difference and space-time hydrological constraint multi-round screening are adopted, and irrelevant pollution sources are removed; and then the contribution proportion is quantified, and Monte Carlo simulation, water quality and specific pollutant cross validation are carried out, so that the crossing from suspected tracing to quantitative responsibility investigation is realized.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention belongs to the technical field of fault early warning, and discloses a heater fault early warning method and system based on current analysis. Comprising the steps that transient current signals of all heating wire loops in the copper bush heater are collected in real time, harmonic characteristic parameters are extracted, and a dynamic current fingerprint database and a multi-dimensional harmonic characteristic spectrum are generated respectively; dynamically calculating the contact impedance between each heating wire and the copper sleeve groove according to the dynamic current fingerprint database and the multi-dimensional harmonic characteristic spectrum; the current distribution path of each heating wire in the copper sleeve groove is restored in real time, the abnormal contact form of each heating wire is identified, the falling risk probability and falling risk strength of each heating wire are dynamically predicted by fusing the falling precursor signals of each heating wire detected in real time, and a grading early warning strategy report is generated in real time; according to the invention, fault early warning can be converted into preventive maintenance from post-maintenance, the intelligent level of operation and maintenance of the heater is improved, and the continuity and safety of production are guaranteed.

The invention discloses a data security monitoring and early warning method based on the Internet of Things, and the method comprises the steps: obtaining the real-time operation characteristics of target Internet of Things equipment, and constructing an equipment behavior fingerprint database based on the real-time operation characteristics; constructing a quintuple ontology model based on the equipment behavior fingerprint database, mapping the general tactical framework to an Internet of Things equipment operation scene according to the quintuple ontology model, and generating a cross-domain tactical association rule database; analyzing the multi-source interaction data according to the cross-domain tactical association rule base, and constructing an attack path atlas according to the obtained multi-source interaction features; inputting the attack path atlas into a pre-constructed graph neural network model to obtain a multi-stage attack chain, identifying a persistent threat attack chain in the multi-stage attack chain, and calculating a confidence score of the persistent threat attack chain; and performing anomaly detection on the original network traffic data based on the confidence score, and generating an anonymized early warning analysis report. According to the method provided by the invention, the anomaly detection accuracy of the Internet of Things data is improved.

The invention relates to the technical field of sound monitoring, in particular to a wild animal sound detection system, method and device based on voiceprint recognition, and the system comprises a multi-mode sound collection module, a voiceprint feature extraction engine, an acoustic fingerprint database, a species recognition decision module and an ecological behavior analysis unit, the multi-mode sound acquisition module integrates an array microphone and an infrasonic wave sensor, and supports full-band environmental acoustic signal acquisition; the voiceprint feature extraction engine extracts animal voiceprint biological features by adopting a self-adaptive frequency band segmentation algorithm; the acoustic fingerprint database stores a multi-species acoustic feature template and an environment interference noise sample; the species identification decision module constructs a voiceprint classification model based on a deep residual network; and the ecological behavior analysis unit generates a wild animal activity mode report through the acoustic event space-time distribution map. Therefore, the problems of serious environmental noise interference, low individual feature discrimination, difficulty and insufficiency in small sample species identification and the like in the prior art are solved.

The invention discloses a positioning method and system based on an RSSI fingerprint database and a generalized regression neural network, and relates to the technical field of indoor positioning. The method comprises the following steps: acquiring RSSI values and corresponding position information of a plurality of sampling points in a target space, and constructing a fingerprint database based on the RSSI values and the position information; establishing a generalized regression neural network prediction model based on the fingerprint database, and globally optimizing model parameters of the generalized regression neural network prediction model by adopting a quantum particle swarm algorithm to obtain preliminary model parameters; performing local optimization on the preliminary model parameters by adopting a simulated annealing algorithm to obtain target model parameters, and training based on the target model parameters to obtain a target generalized regression neural network prediction model; and collecting a real-time RSSI value of a to-be-positioned target, inputting the real-time RSSI value into the target generalized regression neural network prediction model, and outputting a target position coordinate of the to-be-positioned target. By implementing the technical scheme provided by the invention, parameter optimization can be prevented from falling into a locally optimal solution, and the positioning precision is improved.

The invention discloses a high-precision positioning method based on combination of a Beidou satellite and 5G-Mesh, and relates to the technical field of satellite positioning, and the method comprises the steps: receiving a navigation signal of the Beidou satellite and a communication signal of a 5G-Mesh network in real time; the topological structure change of the 5G-Mesh network is monitored in real time, and the connection relation between the nodes is dynamically adjusted in combination with network performance indexes; the receiver performs pseudo-range measurement on the signals of the visible satellites; a navigation message is demodulated from the received satellite signal, and the accurate position of each satellite at the current moment is calculated by using a satellite orbit calculation model; constructing a signal intensity fingerprint database; outputting a reference position having the highest similarity with the current signal strength characteristic to carry out error modeling on the Beidou satellite signal and the 5G-Mesh network signal; and fusing the two kinds of position information by adopting a weighted fusion algorithm. By fusing the technical advantages of the two positioning technologies, the positioning precision and reliability are improved, and the requirement for high-precision positioning in a complex scene is met.

The invention relates to the technical field of injection molding production process monitoring, and discloses an injection molding production monitoring method, device and system.The injection molding production monitoring method comprises the steps that multi-source sensing data is collected, and pressure time sequence data and material spectrum data in the injection molding process are collected; extracting pressure waveform features to obtain a complete pressure waveform feature set; performing material characteristic analysis, and forming a material characteristic fingerprint in combination with the pressure waveform characteristics and the spectral data; material batch similarity calculation: constructing a material batch fingerprint database, and calculating the similarity between the new batch of materials and the historical batch of materials; process parameter optimization: continuously improving the accuracy of parameter optimization through multi-objective tradeoff optimization and parameter verification and iterative optimization; quality problem root causes are analyzed, and material problems and process parameter problems are accurately distinguished; through fusion of pressure waveform analysis and a material characteristic monitoring technology, comprehensive monitoring and intelligent regulation and control of an injection molding production process are realized.

The invention provides a chemical ship cabin intelligent coating leakage positioning and early warning control method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: collecting data through a multi-class sensor array, constructing a parameter distribution map, extracting features through a convolutional neural network, carrying out the time sequence analysis through a long and short term memory network, and carrying out the early warning. The variational auto-encoder constructs a leakage feature fingerprint database, collects data in real time, then carries out feature extraction and fusion processing, matches the data with the fingerprint database, determines a leakage position and a leakage rate, divides a monitoring area, and executes prevention and control. The leakage monitoring precision and the response speed can be improved, and the chemical accident risk is reduced.

The invention discloses a method for tracing polluted water quality of a water outlet of a park based on Bayesian reasoning, and belongs to the technical field of data processing and management systems.The method comprises the steps that production management data of enterprises in the park is acquired to generate enterprise pollution feature vectors, and the enterprise pollution feature vectors are associated with enterprise geographic coordinates to construct a dynamic pollution fingerprint database; and acquiring water quality parameters of a water outlet monitoring point in real time, inputting the water quality parameters into the Bayesian probability model, calculating to obtain basic pollution probability distribution, calling the dynamic pollution fingerprint database and the pipe network topology coefficient to carry out space-time correction, generating a traceability probability matrix, and determining and outputting a target pollution enterprise list. According to the invention, production management data of an enterprise, real-time water quality monitoring data of a water outlet and pipe network physical constraint conditions are deeply fused, a probabilistic reasoning model is adopted for intelligent decision making, accurate and efficient management and positioning of a pollution source can be realized, compliance evidences are automatically generated, and the method is suitable for large-scale popularization and application. Therefore, the efficiency and the intelligent level of park environment management are improved.

The invention discloses a solid waste-based zinc chloride preparation process control method and system, and relates to the technical field of zinc chloride preparation, and the method comprises the following steps: obtaining a unified time base line, establishing a bubble phase reference field, collecting a sound-induced echo sequence, a micro-pressure waveform sequence and a light scattering sequence of a reaction liquid under the bubble phase reference field, and calculating the solid waste-based zinc chloride preparation process. Fusing to generate a bubble density space-time matrix; and performing reversible time-frequency rearrangement based on the bubble density space-time matrix, decomposing to obtain different frequency band distributions, extracting a narrow-band resident bubble group, calculating critical volume distribution and collapse precursor indexes, and constructing a dynamic risk fingerprint database. According to the method, a unified time base line and a bubble phase reference field are constructed, the bubble state is perceived in real time, collapse forebodes are recognized, a dynamic risk fingerprint database is established, directional intervention and reverse energy channel collapse nucleus extinguishing are achieved in combination with an energy aggregation node, finally closed-loop regulation is constructed through calibration write-back, the safety and stability of zinc chloride preparation are improved, and the method is suitable for large-scale popularization and application. And efficient utilization and clean production of solid waste are promoted.

The invention relates to an open source component multi-modal dependency risk tracing method and device, and the method comprises the steps: employing a mode of combining static analysis and dynamic analysis to analyze a component dependency relationship of software, and combining AST analysis and construction script analysis; function-level features are extracted, a binary fingerprint database is constructed, the similarity between different versions is analyzed through LSH, behavior patterns in binary codes are analyzed, and hidden dependencies or malicious code injection is detected; the version updating history of the dependent component is analyzed and monitored by using a time sequence, and the vulnerability security of the component is evaluated in combination with attack graph analysis; high-risk components on the path are calculated, potential supply chain attack points are identified, and risk points are subjected to cross validation; a time sequence diagram database is used for recording the component dependency relationship, and time backtracking query is supported. According to the method, a multi-mode dependency analysis method is adopted, potential dependency risks are rapidly identified, and potential supply chain attack risks are timely warned.

The invention discloses a three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality. The system comprises a water sample collection module, a three-dimensional fluorescence spectrum detection module, a pollution fingerprint database, a spectral analysis unit, a diffusion path simulation module based on a distributed sensor network and an automatic control execution module. The diffusion path simulation module is used for acquiring spatio-temporal data through a water quality spectrum sensor, a hydrological sensor and a meteorological sensor, and dynamically predicting a pollutant migration track by combining a water body flow field model constructed by a finite volume method and a first-order dynamic fluorescence attenuation model; the automatic control execution module is based on the pollution recognition result and track prediction, emergency operations such as gate opening and closing and medicament adding are linked through a fuzzy logic algorithm, and full-process automation from monitoring to disposal is achieved. The system completes pollution identification within 10 minutes through a spectral angle mapping algorithm, and the defects that a traditional method lags in response and lacks dynamic prediction and automatic disposal are overcome.

The invention provides an indoor positioning method and system based on dynamic mode switching and data fusion, and relates to the technical field of visible light indoor positioning, and the method comprises the following steps: deploying a VLC transmitting device and an auxiliary positioning point, constructing a VLC fingerprint database, carrying out dynamic signal detection and mode conversion, carrying out VLC fingerprint positioning, carrying out IMU data processing, and carrying out main positioning mode data fusion. VLC signal intensity distance estimation, ultrasonic ranging, IMU relative displacement estimation and standby positioning mode data fusion are carried out. According to the invention, by deploying the visible light communication transmitting device and the auxiliary positioning point, the strength and the stability of the VLC signal are monitored in real time, and dynamic positioning mode switching is realized through fuzzy logic reasoning. In the main positioning mode, VLC fingerprint matching is combined with IMU data, and accurate positioning is carried out through a Kalman filtering algorithm; and when the VLC signal is weak or unstable, a standby positioning mode is switched, and accurate positioning is carried out by fusing data through a particle filter algorithm according to VLC signal intensity distance estimation, ultrasonic ranging and I MU data.

The invention provides a specific unmanned aerial vehicle model rapid identification method and system based on a radio frequency fingerprint database. The method comprises the following steps: firstly, receiving a radio frequency signal generated by a target unmanned aerial vehicle during flight, carrying out dynamic noise reduction processing on the radio frequency signal to generate a noise-reduced radio frequency signal, then separating combined data comprising a signal strength fluctuation mode, a phase hopping frequency degree and a modulation distortion characteristic from the noise-reduced radio frequency signal, and finally, carrying out noise reduction processing on the combined data. The method comprises the following steps: acquiring combined data of a target unmanned aerial vehicle, performing layer-by-layer matching on the combined data and a pre-constructed radio frequency fingerprint database to generate a matching result, then extracting a model correlation parameter of the target unmanned aerial vehicle from the matching result, finally generating an identification decision containing a real-time environment adaptation factor based on the model correlation parameter, and outputting a target unmanned aerial vehicle model result corresponding to the identification decision; according to the technical scheme provided by the invention, the distinction degree of fine signal differences is greatly improved, and the stability and accuracy of unmanned aerial vehicle model identification in complex electromagnetic interference and multi-obstacle reflection scenes are realized.

The invention relates to a human body sensing detection method and system based on machine learning, and the method comprises the steps: extracting target micro-motion change, space trajectory and time sequence features through the collection and high-precision marking of original time-space behavior data of millimeter wave, infrared, pressure and sound multi-type sensors in combination with preprocessing, noise removal and normalization standards, and carrying out the detection of a human body. A human body space-time behavior fingerprint database is constructed in an off-line or on-line state through a machine learning algorithm, and regional difference behavior characteristic models are respectively established for different space regions and installation materials. And automatically triggering multiple rounds of redundancy detection and learning optimization. The scheme reduces the false alarm rate of the system and supports continuous evolution of the model.

The invention relates to the technical field of distribution network unmanned aerial vehicle autonomous tour-inspection navigation, in particular to a distribution network unmanned aerial vehicle tour-inspection autonomous navigation method based on GPS information and visual information, which comprises the following steps: acquiring a GPS and smoothing projection coordinates, constructing a Thiessen region random index, optimizing a route and acquiring a flight image, evaluating a risk and adjusting exposure parameters, and generating a light environment value. According to the method, an environment model is constructed by fusing GPS information, Gaussian projection is adopted to eliminate earth curvature errors, moving average filtering is adopted to suppress signal noise, Thiessen polygon is utilized to quantify routing inspection point and obstacle distribution, a tornado optimization algorithm is combined to generate an optimal route, the shortest path without repetition and obstacle avoidance are realized, and obstacles are identified based on a YOLOv12 model. A risk avoiding strategy is made according to the risk value, a route is dynamically optimized, a light environment fingerprint database is constructed, illumination change is predicted in combination with LSTM, aperture shutter sensitivity and white balance are adjusted in a self-adaptive mode, image brightness and definition are optimized, and inspection efficiency and data reliability are improved.

The invention discloses a visible light indoor adaptive positioning method fusing self-attention and a width learning network. The method specifically comprises the following steps: step 1, collecting pose information of received signal strength RSS and PD at different indoor space reference points; step 2, constructing a multi-dimensional feature fingerprint database containing reference point coordinates, RSS values and pose information; step 3, constructing an SA-BLS positioning model and carrying out training; 4, testing the positioning model trained in the step 3; and step 5, inputting the RSS and PD pose data at the target point to be positioned into the tested positioning model, calculating the coordinates of the target position, and outputting a positioning result. According to the invention, the problem that the existing positioning method is poor in precision due to the fact that the receiving end of the existing visible light positioning is in a fixed pose and the pose of the receiving end is dynamic and the RSS obviously fluctuates in practical application in the prior art is solved.

The invention relates to the technical field of wireless positioning, and provides a high-speed rail user positioning method and system based on trajectory compensation, and the method comprises the steps: recognizing high-speed rail users, carrying out the data preprocessing, constructing high-speed rail grids, and generating a signal fingerprint database; preliminarily screening out a plurality of candidate grids according to RSRP and TA signal features reported by high-speed rail users in real time; based on the service cell switching sequence, a hidden Markov model or particle filtering is adopted for motion trail modeling, and the advancing direction and speed of a train are recognized; introducing a multi-source information fusion and grid compensation strategy; and selecting a final positioning result from the candidate grids in a weighted voting mode, and outputting grid IDs and latitude and longitude coordinates. Through multi-source data fusion, track compensation and intelligent decision making, the positioning precision and reliability of the high-speed rail user in a complex mobile environment are remarkably improved, and the method has important technical popularization value.

The invention belongs to the technical field of industrial asset protection and positioning, and discloses an asset positioning monitoring method based on Wi-Fi and inertial navigation fusion and a large language model, and the method comprises the following steps: obtaining multi-modal data, and constructing a fingerprint database; the industrial assets are accurately positioned through the fusion positioning technology of Wi-Fi and inertial navigation in combination with the acquired signal strength, the inertial navigation path and the feature data in the fingerprint database; and performing semantic fusion and analysis on the multi-modal data by using a large language model, identifying potential risks, and generating a safety assessment report. According to the asset positioning monitoring method based on Wi-Fi and inertial navigation fusion and the large language model, accurate tracking of asset positions can be realized, asset operation states can be sensed and potential risks can be identified through a multi-modal data fusion technology and an anomaly detection algorithm, and real-time risk early warning can be provided.

The invention relates to the technical field of data processing, and discloses an advertisement putting state real-time monitoring method and system. The method comprises the following steps: collecting an advertisement putting event stream, and extracting an instantaneous index and a fluctuation rate; counting historical abnormal conduction time delay and intensity, and constructing a conduction matrix; when single-point abnormity is detected, downstream abnormity is predicted according to the conduction matrix, the deviation degree is calculated, and an abnormal conduction chain is searched; conducting chain features are extracted to form abnormal fingerprints, fingerprint database similarity is matched, high similarity is directly confirmed, and low similarity is injected into probe flow verification. By constructing the real-time monitoring method based on the abnormal conduction matrix, the problems that in the prior art, the conduction relation between indexes cannot be quantified, the downstream abnormal moment cannot be predicted, the false report and missing report rate is high, and the abnormal confirmation response is slow are solved, and the accuracy, the real-time performance and the intelligent level of advertisement putting abnormal detection are improved.

A system and method using a digital replica to populate large fingerprinting databases. A digital twin map is created ray-tracing simulations on a digital replica of the environment across several frequency bands and beamforming configurations. Online user equipment fingerprints are matched against this spatial database. A user equipment position measured in real-time and the digital twin map are used to compute the most probable location of the user equipment.

The invention provides an antibacterial ceramic glaze sintering process optimization and intelligent temperature control method and system. According to the method, the molecular vibration characteristics of the antibacterial agent at different temperatures are dynamically obtained, and a thermal decomposition dynamic fingerprint database is constructed according to the attenuation law of the molecular vibration characteristics; meanwhile, a three-dimensional temperature field of the glaze layer is reconstructed based on a coupling relation between sound wave propagation parameters and glaze thermal expansion. And carrying out transfer learning on the fingerprint database and historical process data, predicting a glaze thermal stability attenuation parameter, and calculating a temperature gradient and molecular chain fracture compensation coefficient. And fusing the temperature field and the attenuation parameter, and dynamically regulating and controlling the sintering temperature curve and preserving heat by combining the compensation coefficient. The adjusted parameters generate closed-loop feedback by verifying the matching degree of the glaze microstructure and the antibacterial indexes, the compensation coefficient is corrected, and it is ensured that the antibacterial performance of the glaze is stabilized within the process allowance. The temperature control precision and the antibacterial performance stability of antibacterial ceramic glaze sintering are improved.

The invention relates to the technical field of intelligent monitoring of power distribution systems, and discloses an intelligent digital electric box power utilization monitoring management system based on the Internet of Things. The system comprises a data acquisition module, a data processing and analysis module, an intelligent control module, an Internet of Things communication module and a user interaction and management module. The data acquisition module acquires electrical and environmental parameters in real time; the data processing and analyzing module constructs an electricity utilization fingerprint database of the equipment, calculates a real-time threshold value through a dynamic threshold value engine, and realizes equipment-level anomaly recognition in combination with an anomaly detection engine; the intelligent control module executes a control action according to a detection result; the Internet of Things communication module realizes two-way data transmission; and the user interaction and management module provides a visual operation interface. According to the invention, through a dynamic threshold mechanism, equipment fingerprint identification and multi-level early warning control, equipment-level hidden faults are accurately identified, and the power utilization safety and the intelligent management level are improved.