1482 results about "Channel data" patented technology

The invention discloses a charging robot fault diagnosis method and system based on deep learning, and particularly relates to the technical field of robot fault diagnosis, and the method comprises the following steps: collecting multi-type sensing channel data in an operation process, and constructing a structured input sequence; calculating a local disturbance amplitude and trend difference to generate a weak anomaly confidence value, and judging whether subsequent analysis is triggered or not; if so, extracting a modal drift residual index and a micro-abnormal evolution trend index, and splicing to form a fusion feature vector; outputting a numerical coefficient of each channel through the trained deep learning model; adjusting the sampling frequency according to the numerical coefficient and the characteristic; according to the invention, through unifying a multi-channel data processing flow, constructing a trend-driven anomaly identification mechanism, and combining with deep learning model output, dynamic closed-loop regulation and control of sampling frequency are realized, data consistency, identification sensitivity and resource utilization efficiency of the charging robot fault diagnosis system are improved, and fault diagnosis accuracy is improved. And refined and adaptive monitoring of multi-channel states is realized.

The invention discloses a system and method for classifying and recognizing epilepsy in electroencephalogram signals based on a time sequence. The system comprises a cross-domain mixed self-supervised learning module, a multi-scale electroencephalogram feature learning module, an epileptic seizure guided self-attention learning module and a classifier. The cross-domain hybrid self-supervised learning module comprises a time domain context prediction task unit, a channel information reconstruction task unit and a frequency spectrum masking identification task unit; the cross-domain hybrid self-supervised learning module reconstructs an input electroencephalogram signal according to a time domain, a space domain and a frequency domain to obtain an electroencephalogram representation model; the time domain context prediction task unit is used for capturing dynamic change characteristics before and after epileptic seizure by learning a time domain evolution rule of electroencephalogram signals; the channel information reconstruction task unit is used for reconstructing missing electroencephalogram signal channel data and enhancing spatial information representation of an electroencephalogram representation model; the frequency spectrum masking identification task unit trains the electroencephalogram characterization model to characterize epilepsy nerve oscillation through a random masking part electroencephalogram signal frequency component in a frequency domain; the multi-scale electroencephalogram feature learning module captures local and global level epilepsy features of an electroencephalogram representation model through different resolutions to obtain a first epilepsy electroencephalogram feature model; the epileptic seizure guided self-attention learning module optimizes the first epileptic electroencephalogram feature model by fusing time sequence and spatial features to obtain a second epileptic electroencephalogram feature model; and the classifier classifies and outputs the electroencephalogram features of the second epileptic electroencephalogram feature model based on the long short-term memory network to determine whether the epileptic seizure occurs. The invention provides an efficient, accurate and robust solution for automatic detection and clinical auxiliary diagnosis of the epileptic seizure.

The invention relates to the technical field of big data, in particular to an e-commerce marketing propaganda system based on behavior analysis, which comprises a global user portrait module, an intelligent recommendation engine, a marketing fatigue management module, a supply chain collaboration module, a lightweight terminal module and a budget and value management module. In the prior art, user portraits are constructed only depending on single channel data such as online clicking or purchase records, so that user interest modeling is incomplete and lagged; according to the method, all-channel behavior data such as APP, Web, offline POS and social media are integrated, a user-commodity-scene heterogeneous graph is constructed by using a graph neural network, and an interest attenuation period (for example, the weight is reduced by 50% after the interest of mother and infant users lasts for 18 months) is dynamically captured through an LSTM model; for example, after a user tries on a certain style of clothes offline, the system associates online behaviors in real time and recommends commodities of the same style, the cross-scene conversion rate is improved by 32%, and the user portrait coverage degree is improved by 60%.

The invention discloses a resource allocation method and system for 5G communication in an underground coal mine, and relates to the technical field of communication information processing, and the method comprises the steps: obtaining data information of an underground coal mine roadway, building a digital twin communication model through combining a synchronous positioning and map building algorithm and a wireless fingerprint matching algorithm, generating a channel profile map according to real-time data, and transmitting the channel profile map to a server; extracting channel data of each space node; constructing a computing resource pool for the 5G base station and the edge computing node, and packaging different service types into a plurality of network slice containers; according to the channel data, carrying out initial allocation on each container through a resource composer, and carrying out dynamic task migration among different MEC nodes to obtain a resource allocation state; dividing scheduling time according to a channel profile and a resource allocation state, and dynamically adjusting a duration proportion and bandwidth occupation of each window according to a task migration state; according to the invention, through digital twinning and dynamic scheduling, the problems of 5G communication coverage and interference in the underground coal mine are solved.

The invention discloses a multi-protocol channel data aggregation method based on 5G communication, and relates to the technical field of heterogeneous network data processing under the fusion of 5G communication and edge computing, and the method comprises the steps: constructing a protocol adaptive conversion engine, recognizing an input protocol type through learning, and automatically selecting a conversion strategy and a data packaging format. An edge-cloud collaborative intelligent scheduling architecture is introduced, a data aggregation task is dynamically determined, a dynamic channel aggregation algorithm of a 5G network state and protocol characteristics is designed, and a transmission path and an aggregation strategy are intelligently selected. By constructing a protocol adaptive conversion engine, an edge-cloud collaborative intelligent scheduling architecture and a dynamic channel aggregation algorithm fusing 5G network state and protocol characteristics, automatic identification, efficient aggregation and optimized transmission of multi-protocol data are realized, dynamic scheduling of tasks is realized in combination with an SDN / NFV technology, and the dynamic scheduling efficiency of the multi-protocol data is improved. The channel selection mechanism based on the utility function comprehensively considers the network state and protocol characteristics, and guarantees the transmission quality of high-priority and time delay sensitive data.

The invention discloses a long-term vibration monitoring method and system based on multi-channel data acquisition, and the method comprises the following steps: installing a sensor node integrated with a multi-mode energy collection module on a monitored object, and providing continuous power supply for a multi-channel data acquisition and edge processing unit; according to the invention, through a dynamic adaptive sampling mode and edge end extraction key features, the data load in a distributed monitoring scene is significantly reduced; the multi-mode energy collection module is integrated to collect vibration and environmental mechanical energy, and the energy management unit is combined to dynamically distribute stored energy and supply power, so that long-term autonomous operation of the sensor node is realized; the abnormal state is locally inferred and identified through the edge processing unit, and only key feature data instead of full data is uploaded, so that the real-time response capability to the equipment abnormality is improved; multi-dimensional correlation features are extracted by collecting multi-channel sensing data and combining a deep learning model and a time sequence-space feature fusion algorithm, and the health state of the equipment is comprehensively reflected.

The invention relates to the technical field of Bluetooth communication, and discloses an optimization method for secure communication of Bluetooth chip equipment. The method comprises the following steps: identifying a device type identifier and a protocol version feature, extracting a hardware capability parameter and generating an initial security policy set; collecting channel data, and calculating a stability index and a dynamic noise baseline; then, screening and sorting security policies to generate an execution queue, adjusting an encryption strength threshold according to a noise baseline to perform iterative optimization, and outputting final policy configuration; and when the target Bluetooth chip establishes the communication connection, loading the final security policy configuration and monitoring the channel state in real time, and triggering a policy dynamic switching mechanism when the channel state change exceeds the preset tolerance so as to optimize the security communication effect of the Bluetooth chip equipment. According to the method, the security policy can be accurately matched with the device and the channel state, the policy is dynamically adjusted, and the security and the stability of communication of the Bluetooth chip device are effectively improved.

The invention relates to the technical field of bearing fault diagnosis, in particular to a multi-bearing fault positioning and diagnosing method, which comprises the following steps of: acquiring bearing vibration signals of a plurality of sensing points, extracting peak time information to complete synchronous calibration, extracting high-frequency band energy and constructing a feature vector, screening deviation features and clustering and grouping, and identifying an energy abnormal channel, positioning a bearing position, extracting time domain and frequency domain features, comparing fault modes, and outputting a fault type diagnosis result. According to the method, the consistency of multi-channel data synchronization is realized by extracting the peak point of the vibration signal and calibrating the time offset, the high-frequency energy change rate is utilized to match the weight coefficient, the detection sensitivity of tiny fault features is enhanced, and the separability of a composite fault state is improved by combining feature relative deviation rate screening and density trend clustering. A frequency section energy proportion change recognition channel is adopted, accurate positioning of a fault bearing is achieved, and accurate judgment of a fault type is achieved in combination with a multi-feature comparison mode.

The invention discloses a 16-channel high-precision synchronous data acquisition system based on a multi-parameter model, which belongs to the technical field of data acquisition and comprises a multi-parameter model driving module, a self-adaptive channel scheduling module, a closed-loop calibration compensation module and a collaborative synchronous optimization module. The multi-parameter model driving module establishes a multi-dimensional optimization model fusing the sampling rate, the channel load and the error trend to output optimization parameters; the adaptive channel scheduling module performs real-time quality evaluation and dynamic resource allocation according to the optimization parameters to generate a scheduling instruction; the closed-loop calibration compensation module monitors a synchronization error in real time and calculates a compensation parameter; the collaborative synchronous optimization module realizes inter-channel parameter sharing and collaborative control to generate a synchronous control signal, and feeds back an optimization result to the multi-parameter model driving module to form a closed loop, and through deep coupling and closed loop feedback of the four modules, high-precision synchronization of multi-channel data acquisition is realized, and the synchronization precision reaches + / -35ns.

The invention discloses an intelligent customer acquisition and user behavior analysis system based on AI full ecology, and relates to the technical field of artificial intelligence and big data marketing, and the system comprises a multi-source data collection module which collects internal and external multi-channel data of an enterprise; the data cleaning module processes abnormal and missing values and unifies data formats; the user portrait construction module extracts multi-dimensional labels to describe user features; the intelligent customer obtaining module uses transfer learning to screen high-potential customers, and the user behavior analysis module mines behavior logic by means of Transform; the intelligent recommendation engine is fused with multiple algorithms to realize personalized recommendation, and the real-time decision module is combined with rules and reinforcement learning to optimize marketing. According to the method, multi-source data integration and deep analysis are realized, clients are accurately identified, behavior requirements are mined, the client obtaining efficiency and the conversion rate are improved, the marketing effect is optimized through personalized recommendation and intelligent decision, and a whole-process intelligent marketing solution from data acquisition to decision execution is provided for enterprises.

The invention relates to the technical field of automatic control, and discloses a PID (Proportion Integration Differentiation) intelligent pressure reducer controller, which comprises the following steps of: judging whether an environment disturbance or a load sudden change event exists according to a system load change rate and sensor feedback stability, constructing a master control-controlled coupling characteristic matrix by adopting a dual-channel data fusion method, performing attribution splitting on each disturbance channel, and obtaining a PID (Proportion Integration Differentiation) intelligent pressure reducer. PID parameters are dynamically adjusted according to the principal component weight of a disturbance channel, a variable parameter PID adjustment matrix is constructed, filtering smoothing is carried out on sampled signals through an extended Kalman filtering algorithm, a feedback closed-loop correction vector based on sampling residual errors is constructed, a historical offset tracking and switching value boundary judgment mechanism is introduced, fine tuning suppression is carried out on control signals of a motor driving mechanism, and the control signals of the motor driving mechanism are output. Judging whether a mechanical limiting protection device needs to be activated or not; and if yes, automatically triggering voltage limiting protection logic and locking the current control quantity. The method has the advantage of improving efficient and stable operation.

The invention relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably improved.

The invention relates to the technical field of data management, and discloses a digital twin water plant cluster comprehensive visual management system. The system comprises a processing module, a modeling module, a process flow topology self-adaptive visual management interface and a flow control module, wherein the processing module carries out standardization processing on multi-source parameter data of a water plant to generate real-time data flow; the modeling module constructs a three-dimensional digital twinborn model by utilizing an oblique photography technology; the extraction module extracts spatio-temporal characteristics through multi-channel data fusion to obtain early warning information, the calculation module diagnoses abnormity based on a ternary association network and a heterogeneous graph attention algorithm, and the coding module generates a digital watermark health state code to form a predictive maintenance scheme. The panoramic display of the running state of the water plant, the accurate identification of the abnormal mode and the optimal configuration of maintenance resources are realized. According to the invention, panoramic display of the running state of the water plant, accurate identification of the abnormal mode and optimal configuration of the maintenance resources are realized, so that the overall running efficiency and management level of the water plant cluster are improved.

The invention discloses an airborne laser radar sounding data processing method and system, and relates to the technical field of marine surveying and mapping and earth observation, and the method comprises the following steps: collecting original full-waveform data of an airborne laser radar sounding system, the original full-waveform data comprising infrared laser channel data and blue-green laser multichannel data; joint denoising processing is carried out on the original full-waveform data to obtain denoised waveform data, and the joint denoising processing comprises background noise modeling and removing based on machine learning and random noise filtering based on frequency domain low-pass filtering; a collaborative joint denoising strategy is formed by fusing background noise modeling based on machine learning and frequency domain low-pass filtering based on signal-to-noise ratio optimization. According to the method, complex background noise can be accurately estimated and removed, random noise can be adaptively filtered out, weak underwater echo signals are reserved to the maximum extent, and the problem of water depth information loss caused by excessive smoothness is effectively avoided.

The invention relates to the technical field of voice recognition, in particular to an implementation method and device of a multi-channel voiceprint recognition system, and the implementation method comprises the steps of multi-channel data acquisition and synchronization, signal preprocessing and enhancement, feature extraction and fusion, model training, real-time deployment and adaptive optimization. Compared with the problems that a traditional multichannel voiceprint recognition system depends on a fixed beam forming algorithm and an independent clock synchronization module, the synchronization error is large, manual parameter adjustment is needed for noise suppression, and generalization is poor, hardware-level clock synchronization is achieved through a PTP protocol, and the accuracy of noise suppression is improved. The method combines an end-to-end neural network to automatically learn noise distribution and a sound source space position, dynamically generates a beam forming weight, can improve the voice quality in a complex noise scene without manual intervention, remarkably reduces the interference of a synchronization error on sound source positioning, and enables the precision and stability of far-field voice enhancement to reach a new level.

The embodiment of the invention provides an automatic test system for an integrated circuit board, and the system comprises a signal collection module which collects the multi-channel synchronous signal data of the integrated circuit board in real time, and extracts the time sequence characteristics of the signal transmission of each channel; the analysis module is used for constructing a time sequence deviation distribution model according to a preset time sequence constraint condition and analyzing delay inconsistency among multiple channels; the adjusting module is used for dynamically adjusting a phase compensation parameter of the synchronizing signal of each channel based on the time sequence deviation analysis result; and the test module is used for executing a closed-loop test according to the compensated synchronizing signal and verifying whether the data transmission time sequence of each channel accords with a preset synchronizing threshold value or not. Through the scheme of the embodiment of the invention, the synchronization signal can be regulated and controlled according to the time sequence deviation analysis so as to solve the problem of inconsistent delay in multi-channel data transmission.

The invention discloses a rotor acoustic anomaly detection method, system and device based on an auto-encoder and wavelet packet energy entropy and a medium, and belongs to the technical field of hydroelectric generating sets, and the method comprises the steps: collecting an original acoustic signal, carrying out the noise reduction of the original acoustic signal, and carrying out the multi-channel data fusion to obtain an integrated acoustic signal; inputting the integrated acoustic signal into a wavelet packet for transformation processing to obtain wavelet packet coefficients under different scales; calculating energy values of different frequency band signals based on wavelet packet coefficients to form an energy entropy feature vector; constructing an implicit feature model of the hydroelectric generating set rotor in a normal state according to the energy entropy feature vector; and carrying out feature reconstruction on the energy entropy feature vector, calculating a reconstruction error, carrying out dynamic statistical analysis, and carrying out real-time monitoring and abnormity judgment on the operation state of the hydroelectric generating set rotor. According to the method, the detection precision and the response speed are improved in actual hydroelectric generating set rotor acoustic anomaly detection, and automatic identification and dynamic threshold adaptive adjustment of irregular perturbation are realized.

The invention relates to the technical field of radar reflectivity factor calibration, and discloses a terahertz cloud measurement radar calibration method based on channel perception, which comprises the following steps: S1, acquiring two types of input data; s2, constructing a multi-scale channel model; s3, constructing a distance error estimation model; s4, constructing a dynamic time correction model; and S5, constructing a calibration module. According to the method, a high-precision physical channel model is constructed to independently predict path attenuation of signals, the concepts of gain deviation and path attenuation factors are put forward, the influence of thermal noise attenuated by radar on radar waves in the atmospheric environment is reflected respectively, and a Kalman filtering algorithm is used to estimate the path attenuation of the signals. And filtering errors generated in the time direction, and finally, calibrating the radar reflectivity factor by using the channel data by means of a calibration formula.

The invention discloses a kilometer deep well roadway toughness regulation and control supporting system and a dynamic design method, and belongs to the field of deep well roadway supporting. The supporting system comprises a multi-layer collaborative structure and an intelligent regulation and control module, the multi-layer collaborative structure sequentially comprises a shape memory alloy energy absorption layer used for resisting impact loads and a gradient pore foam metal buffer layer used for energy grading dissipation from inside to outside, and the multi-layer collaborative structure is connected with roadway surrounding rock in an anchoring mode through an anchor rod anchoring structure; the intelligent regulation and control module comprises a distributed optical fiber sensor array used for monitoring surrounding environment signals of surrounding rock in real time, an electrostriction driver used for regulating and controlling the rigidity of the multi-layer cooperative structure and a multi-channel data fusion terminal used for achieving underground and surface data transmission synchronization. By the adoption of the kilometer deep well roadway toughness regulation and control supporting system and the dynamic design method, the problems that a traditional supporting mode is too high in material rigidity, lacks dynamic regulation and control capacity and is insufficient in impact toughness can be solved.

The invention discloses a visible light hyperspectral measurement method based on multispectral sensing, and belongs to the technical field of spectrum detection. According to the method, visible light eight-channel data is collected through a multispectral sensor, after optical crosstalk correction and filtering preprocessing, key principal components are reserved through PCA dimension reduction, a multispectral and hyperspectral mapping model is constructed through an SVR algorithm, and finally the hyperspectral reflectivity is reconstructed through PCA inverse transformation. The problems that traditional hyperspectral equipment is high in cost and poor in portability are solved, low-cost and high-precision visible light hyperspectral measurement is achieved, and the device and the method can be widely applied to food freshness detection, agricultural product quality evaluation and other scenes.

The invention relates to the field of real-time translation, and provides a real-time duplex translation method based on multichannel parallel processing and a corresponding product, and the method comprises the steps: collecting multipath voice signals of at least two user groups in real time through a group of audio collection modules; dynamically adjusting beam forming parameters of each audio acquisition module in one group of audio acquisition modules based on a sound source positioning result, and feeding back the beam forming parameters to the corresponding audio acquisition modules; monitoring the voice activity of each audio acquisition module corresponding to each audio channel; when it is monitored that the voice activity of any audio channel reaches a preset condition, automatically activating the translation processing flow of the audio channel and keeping the monitoring state of the other audio channels; a parallel processing mechanism is adopted for voice signals of the activated audio channels, and meanwhile real-time translation of the currently activated audio channels and voice activity monitoring of the other audio channels are executed; and transmitting a translation result of the current speaking user to other users participating in dialogue in the user group to realize synchronous coordination of multichannel data.

The invention relates to the technical field of signal processing, and discloses a multichannel data acquisition system and method based on an FPGA (Field Programmable Gate Array), an acquisition module drives a global counter by using a global synchronous clock, writes acquired data into an annular buffer area with a physical address and a counter value in a linear mapping relationship, and establishes implicit time index storage. When a trigger event occurs, the system broadcasts the locked global trigger timestamp, and the acquisition module backtracks and reads historical data according to the global trigger timestamp, and packages the historical data into a sparse matrix type data packet in combination with the channel validity mask. And after receiving the data packet, the data processing module directly calculates a memory mapping address by using global time information in the data packet, and writes a data load into a corresponding position of the waveform reconstruction buffer area. According to the method, through strict binding of the physical address and the absolute time, independent time label redundancy is eliminated, high-precision synchronization of distributed multiple channels is ensured, and out-of-order automatic in-situ recombination and efficient waveform reconstruction of data of a receiving end are achieved.

The invention discloses a test verification platform and a test verification method for a motion compensation gangway ladder of an offshore wind power operation and maintenance ship. The platform comprises a system calculation and control center, an environment simulation vibration platform, an active motion compensation execution mechanism, a high-precision sensing and sensing module, a multi-channel data acquisition and storage system, an offshore wind turbine target platform module and an optical motion capture module. The platform reproduces six-degree-of-freedom motion of the operation and maintenance ship through integrated cooperation and high fidelity, simulates active compensation of a gangway ladder and dynamic characteristics of a hydraulic system, and accurately measures key parameters such as poses, forces / torques and the like by using multiple sensors and an optical capture technology. The method comprises the steps of motion instruction synthesis, platform motion reproduction, compensation action simulation, data synchronous acquisition and performance evaluation. According to the invention, high-precision land simulation verification of ship-gangway ladder-fan dynamic coupling is realized, the problems of high cost, high risk and poor repeatability of offshore actual measurement are effectively solved, and a reliable and efficient solution is provided for performance verification of a motion compensation system.

The invention discloses a cloud edge cooperative multi-channel converged communication control method and system, belongs to the technical field of wireless communication networks, and aims to improve the high efficiency, reliability and real-time performance of multi-channel converged communication in a complex scene. The method specifically comprises the following steps: constructing a hierarchical collaborative architecture of a cloud end and an edge end; the edge end collects network state parameters of various communication channels, outputs a channel quality evaluation value based on a time sequence prediction model, and performs multi-channel data transmission through an extended communication protocol when triggering channel switching; the edge end executes network parameter configuration, flow priority scheduling and running state data acquisition operation, and the cloud end generates a soft routing optimization instruction based on running state data and issues the soft routing optimization instruction to the edge end to execute and adjust network transmission performance parameters; and monitoring the container operation state and the equipment state of the edge end in real time, when a monitoring result is matched with a preset abnormal rule, generating alarm information by the edge end, returning the alarm information to the cloud end to generate a global security policy, and issuing the global security policy to the edge end to update the preset abnormal rule.

The invention relates to the technical field of data management, and discloses a business and financial data governance optimization method for main data dynamic management, which comprises the following steps: step 1, carrying out multi-source data acquisition, including business system data acquisition, financial system data acquisition and other channel data acquisition, step 2, pre-processing the acquired data, numbering and storing, and step 3, carrying out data management on the acquired data. 3, calculating a data quality index, a data change condition, a data value and a data comprehensive coefficient based on the stored data set, and 4, performing feedback according to the data comprehensive coefficient, so that various data sources inside and outside an enterprise can be comprehensively covered, a data island phenomenon is reduced, and meanwhile, the quality and credibility of data are improved; the data comprehensive coefficient is comprehensively evaluated by combining the data quality index, the data change condition and the data value, the data level is comprehensively reflected, a comprehensive perspective is provided for enterprises to see the data, and the enterprises can better understand and manage the data and find potential problems and opportunities.

The invention relates to the technical field of cable fault positioning, in particular to a cable fault accurate positioning method based on multi-channel synchronous acquisition, which comprises the following steps: S1, constructing a multi-channel adaptive acquisition system which comprises a main controller, a multi-channel acquisition module, a synchronous control module and a data transmission module; s2, multi-channel data synchronous acquisition is carried out through the synchronous control module, wherein the multi-channel data comprises impedance data, temperature data, partial discharge signals and insulation resistance data; during use, through a multi-channel adaptive acquisition design, monitoring channels can be flexibly selected according to cable materials, operation years and environmental conditions, different aging fault characteristics are comprehensively covered, the fault omission ratio and the false alarm rate are reduced, different types of low-voltage cables are adapted, a synchronous scheme of constant-temperature crystal oscillator and hardware triggering is adopted, and the reliability of the system is improved. The method does not need to depend on a GPS / PTP time service module, is favorable for saving cost input, and eliminates environmental interference and single parameter false triggering.

The invention discloses a charging pile data management method and system. The method comprises the steps of generating a session fingerprint and a sampling jitter plan at a charging pile end, and writing the session fingerprint and the sampling jitter plan into an under-chain hash structure; time alignment is realized through phase correlation and dynamic time warping based on multi-channel records; constructing an energy sequence and calculating an energy reconciliation distance to generate a session consistency label; according to the unified time axis and the rate rule, generating a microaccounting state machine and outputting an accounting deviation; a session consistency score is calculated by fusing multiple indexes, and diagnosis judgment is completed; and generating a compliance abstract, executing signature evidence storage, and providing a session result for the operation and maintenance interface and the settlement interface. Through multi-channel data acquisition and time alignment, energy reconciliation, accounting modeling and anomaly detection in a trusted execution environment, high-precision reconciliation and compliance intelligent evaluation of the charging pile session data are realized.

The invention relates to the technical field of electric digital data processing, in particular to a shock absorber data processing method and system based on digital twinning, and the method comprises the steps: obtaining a multi-channel data signal of a shock absorber, and constructing a panoramic vibration data tensor; extracting a discrete digital sequence on a panoramic vibration data tensor time dimension, executing a variational mode decomposition algorithm, and separating and extracting a transient impact feature vector flow; constructing a double-time-axis sliding mapping window, and mapping the transient impact feature vector flow to a future node of a prediction response time axis based on a front and rear wheel preview mechanism of a vehicle wheelbase; operating the time sequence recurrent neural network model, and predicting and generating a target state data matrix of future nodes; and in the numerical constraint space, a prediction type damping adjustment instruction data packet is solved and generated through a rolling optimization algorithm. Through prospective state prediction and optimization solution, the problem of data timeliness caused by calculation delay in digital twinning is solved, and real-time, predictive and safe adjustment of the shock absorber is realized.

The invention discloses an MES-based aluminum alloy casting production management method and system, and relates to the technical field of production management, and the method comprises the steps: firstly, synchronously collecting time sequence data streams of core process parameters such as pressure, speed, position and the like, and carrying out the multi-channel data alignment; and a deep learning auto-encoder model is introduced to carry out nonlinear dimension reduction and feature extraction on high-dimensional data, and reconstruction errors are utilized to realize real-time evaluation and anomaly detection of the quality of the casting process. And for the abnormal state exceeding the preset threshold value, key influence factors causing quality fluctuation are further traced through an intelligent diagnosis mechanism, autonomous decision is made based on a diagnosis result, an optimal parameter adjustment strategy is formed, and finally a closed-loop control instruction is generated to drive the production equipment to perform adaptive adjustment. In this way, the problem of unstable quality caused by parameter fluctuation in the casting process can be effectively solved, and the stability and the intelligent level of the aluminum alloy casting process are remarkably improved.

The invention discloses infrared night vision AR (Augmented Reality) intelligent glasses based on multispectral fusion, and relates to the technical field of infrared intelligent application. High-precision scene perception in a low-light / light-free environment is realized by fusing dual-channel data of low-light imaging and infrared thermal imaging and combining cross-modal feature matching and dynamic pose compensation; an environment detail structure is restored by a low-light image enhancement technology, thermal feature targets such as organisms are accurately recognized through thermal imaging target segmentation, and a three-dimensional environment model with space geometric information and target attributes is constructed through heterogeneous feature fusion; and dynamically adjusting the display transparency of the key target in the AR view field through a virtual information layer adaptive mixing mechanism driven by a hot target attribute, and finally outputting a virtual-real fused enhanced view, thereby breaking through the illumination limitation of the traditional AR glasses, and improving the visual effect of the AR glasses. And immersive augmented reality experience with panoramic detail perception and hot target accurate positioning is provided for the user in a dark environment.