8064 results about "High density" patented technology

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

The invention discloses a high-density warehouse intelligent management system and method and application, and relates to the technical field of intelligent warehouse logistics, the system comprises a dynamic collaborative decision framework, the input end of the dynamic collaborative decision framework is connected with a multi-mode sensing module, and the output end of the dynamic collaborative decision framework is connected with a dynamic game scheduling module and a conflict autonomous resolution module. According to the high-density storage intelligent management system and method and the application, the real-time response capability and the resource utilization rate of the high-density storage system are improved through the dynamic collaborative decision framework and the multi-mode sensing mechanism. Based on an incremental state transition model and a local re-calculation strategy, the system can update equipment capability and environment constraint data in time, and calculation power consumption and response delay caused by traditional global optimization are avoided; through a task-equipment bidirectional matching game algorithm and a hierarchical conflict resolution mechanism, autonomous collaborative scheduling of heterogeneous equipment groups is realized, path cross deadlock and goods shelf scrambling conflicts are effectively reduced, and the system throughput fluctuation rate is reduced.

The invention relates to the technical field of printed circuit board assembly quality detection, and discloses a PCBA anomaly detection method and system based on three-dimensional modeling and AI fusion, and a medium. The method comprises the following steps: acquiring three-dimensional point cloud data of a PCBA board to be detected; generating a reference three-dimensional digital twin model according to a standard PCBA design drawing; carrying out spatial registration on the three-dimensional point cloud data and the reference three-dimensional digital twin model, obtaining the three-dimensional point cloud data, carrying out hierarchical processing on the obtained three-dimensional point cloud data, extracting geometric features of a welding spot region, contour features of an element region and surface features of a substrate region, and carrying out fusion to generate a feature vector group; constructing a generative adversarial model based on a preset semi-supervised learning framework and the normal PCBA sample vector group; and inputting the feature vector group into a generative adversarial model, and examining the abnormal vectors, the corresponding three-dimensional coordinates and the abnormal types in the feature vector group by the generative adversarial model to complete the abnormal detection of the PCBA board. The method is suitable for quality control of a high-density and miniaturized PCBA.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention discloses a power grid load prediction method and system based on a large model, and relates to the technical field of power grid load prediction. Comprising the following steps: S1, collecting multi-source load comprehensive data, and carrying out edge processing and data preprocessing; s2, monitoring a sudden change point in real time, constructing a behavior tag sequence, and identifying a high-frequency disturbance point; s3, constructing a prediction input vector of the multi-source load comprehensive data, performing load data prediction, and calculating a physical constraint regularization value; s4, performing dynamic credible interval evaluation, performing real-time judgment on a prediction result according to a credible boundary value interval, and feeding back a regulation and control instruction; and S5, performing error analysis and retraining triggering, comprehensively evaluating prediction accuracy and output stability, and performing model optimization, version management and feedback learning. The problem that the prediction model is easy to vibrate due to high load change speed in a high-density load scene is solved.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention discloses a multi-wave detection and imaging system for fish schools, which relates to the field of detection and imaging of fish schools and comprises a metamaterial acoustic emission module, a self-adaptive signal receiving module, a three-dimensional point cloud generation module, an image enhancement and generation module and an intelligent decision control module. According to the multi-wave detection and imaging system for the fish school, clustering parameters can be dynamically adjusted based on the real-time fish school density, the neighborhood radius is automatically expanded in a high-density area to avoid segmentation errors, and the core point judgment threshold is reduced in a low-density area to reduce missing detection; an acoustic attenuation model is constructed in combination with water body environment parameters, emission parameters are calibrated in real time, images can be enhanced, space and time dimension features are fused, the image resolution and definition are improved, accurate recognition of multiple fish species is achieved, the fish species can be better distinguished, meanwhile, a generative network is adopted to directly convert acoustic data into visual images, and the recognition accuracy is improved. Therefore, the generated image can reflect the actual distribution condition of the fish school more truly and accurately.

The invention relates to the technical field of civil engineering monitoring, and discloses a structure health monitoring system based on civil engineering. A high-density sensing network of the system collects strain field distribution, vibration spectrum and environmental corrosion parameters of a structure through a distributed multi-mode sensor array; performing clock drift compensation and space coordinate normalization on the asynchronous sampling data by a space-time alignment engine to generate an original feature tensor of a unified space-time reference; the semantic modeling unit is combined with a design drawing and a material parameter library, the original feature tensor is mapped to a component semantic space, and hierarchical structure features with topological marks are output; the adaptive fusion core executes dynamic weight distribution, eliminates sensor conflict data and generates an anti-interference fusion diagnosis index; the edge computing node operates a lightweight damage detection model according to the fusion diagnosis index, and outputs a local component health state level; and the cloud collaborative analyzer aggregates multiple edge node results and predicts the overall residual life of the structure in combination with historical degradation data.

The invention discloses a multichannel electrophysiological signal acquisition system and an acquisition method thereof, and the multichannel electrophysiological signal acquisition system comprises a power supply module electrically connected with a controller, an analog front-end chipset, a filter module and an analog-to-digital conversion module; the analog front-end chip set is used for acquiring an analog signal and preprocessing the analog signal; the pre-processed analog signal is converted into a digital signal through a filter module and an analog-to-digital conversion module, and the digital signal is transmitted to a controller; the filter module is used for filtering and denoising; the timer module connected with the controller generates a sampling frequency, transmits the sampling frequency to the analog-to-digital conversion module, and provides a time sequence signal for the analog-to-digital conversion module. The invention discloses a multi-channel electrophysiological signal acquisition system, which solves the problem of synchronous sampling time sequence control of a high-density electrode array and eliminates signal phase deviation caused by clock jitter among multiple channels under the constraint of maintaining the total power consumption of the system. And real-time power frequency interference suppression is realized.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention discloses an implementation method of a multi-chip high-density connection photoetching technology, an implementation method of a photoetching technology for multi-chip interconnection, a chip-level chip interconnection method, a manufacturing method of a mask plate line and photoetching equipment. The implementation method of the multi-chip high-density connection photoetching technology comprises the following steps: selecting a first mask plate to align and expose with a first original pattern unit in a first M * N chipset through a second alignment mark, and transferring a pattern of the first mask plate to an exposure area covering the first original pattern unit; longitudinally moving the substrate by M times of the longitudinal size of the original pattern or transversely moving the substrate by N times of the transverse size of the original pattern to execute alignment exposure, and completing exposure of corresponding areas of other M * N chip groups; and other masks are selected to repeatedly perform exposure on other corresponding pattern units of the first M * N chip set, and longitudinal movement or transverse movement is repeatedly performed to complete exposure of corresponding areas of the corresponding pattern units in other M * N chip sets. According to the scheme, high-density interconnection of multiple chips is realized.

The invention belongs to the technical field of machine room safety monitoring, and discloses an image review method fusing semantic understanding and visual recognition, which comprises the following steps: calculating visual / semantic feature dynamic credibility in real time through an exponential weighted moving average algorithm in combination with environment interference and equipment state parameters; resNet50 is adopted to extract visual features in global and local branches, and a BERT model is adopted to encode semantic features; correcting the feature correlation degree according to the scene, calculating a dynamic weight, carrying out heterogeneous calibration through an attention mechanism, and calling priority rules such as'physical security features are higher than behavior features' for arbitration during conflicts; the method is advantaged in that low-credibility feature interference fusion is avoided, abnormity identification accuracy in a complex scene of a machine room is improved, multi-modal data cooperation demands are adapted, scene labels are marked based on time, work orders and historical data dimensions, an exclusive sub-model is constructed for a high-density scene through DBSCAN density clustering, and low-frequency scene parameters are migrated to a similar model.

The invention discloses a small-sample high-density crowd detection method based on text cross-modal guidance, and belongs to the field of computer vision and cross-modal intelligent detection. The method aims at solving the problems that an existing method is high in labeling cost, few in sample, poor in generalization, lack of semantic guidance and insufficient in shielding scene adaptation. According to the method, the semantic guiding capacity of a CLIP text encoder and the feature extraction capacity of DINOv2 and SAM pre-training visual models are fused, three light-weight trainable modules are constructed, and text and visual feature fine-grained alignment is achieved. According to the technical scheme, detection is completed through data acquisition and labeling, feature preprocessing, model initialization, cross-modal feature fusion, mask generation and screening, module optimization and performance iterative optimization. According to the method, high-precision detection can be realized only by a small number of labeled samples, the labeling cost is remarkably reduced, the detection robustness of the high-density shielding scene is improved, and the method has the advantages of flexible scene adaptability and lightweight deployment, and is suitable for scenes such as monitoring video analysis and public safety early warning.

The invention discloses a cable optical fiber temperature measurement monitoring system based on big data AI fusion, particularly relates to the technical field of cable optical fiber temperature measurement monitoring, and aims to realize thermal-electric dynamic coupling monitoring through high-density distributed optical fiber temperature measurement and big data AI fusion modeling and introduction of current load synchronous acquisition, and realize high-density distributed optical fiber temperature measurement and big data AI fusion by means of time synchronization and space relocation processing. The method comprises the following steps: identifying a weak response signal of a thermal inertia hysteresis characteristic and an initial temperature rise stage, constructing a cross-scale time sequence recurrent neural network through multi-dimensional enhancement and vector fusion, outputting a continuous scoring sequence reflecting abnormal credibility, and constructing a risk factor group reflecting a fault evolution path in combination with a scoring fluctuation amplitude, a continuous period and a response delay characteristic, and carrying out positioning offset correction through correlation mapping of risk factors and along-cable temperature distribution and by utilizing a topological relation between optical fiber path coordinates and a cable space, and realizing space reverse projection of an abnormal peak value, thereby recovering an accurate position of a fault heat source and a latent hidden danger section.

The invention belongs to the field of aquaculture, and particularly relates to an underwater fish multi-target identification, tracking and motion analysis method, the method is based on a multi-target attitude tracking framework based on direction perception, and the framework comprises an improved YOLOv11 model (YOT model) and a directional bounding box attitude tracking module (OPBT module). The YOT model integrates directional bounding box detection and attitude estimation, adopts a task-aligned dynamic detection head structure, and combines a multi-task loss function to realize efficient direction perception detection and key point positioning. The OPBT module is based on an unscented Kalman filter, integrates rotation IoU, angle similarity and body length similarity, and improves identity continuity and occlusion recovery capability in multi-target tracking. The framework is suitable for complex scenes such as high-density fish schools, the real-time processing performance can be kept, meanwhile, the detection and tracking precision can be remarkably improved, and stable and reliable technical support is provided for individual behavior analysis.

The present invention relates to a contour accuracy compensation method and system for a precision hot-press forming mold. The method comprises: delineating grids on a three-dimensional model of a hot-press forming mold to obtain mold surface grid data; performing curvature analysis to obtain a high-curvature region and a low-curvature region; performing high-density sampling on the high-curvature region, and performing low-density sampling on the low-curvature region, so as to obtain a sampling point set; using a coordinate measuring machine to perform three-dimensional coordinate measurement on the sampling point set to obtain surface contour data; performing comparative analysis on the surface contour data and a theoretical model to obtain a contour error distribution; on the basis of the contour error distribution, using an inverse distance weighted interpolation algorithm to perform interpolation operation on an entire mold surface to obtain a mold surface compensation amount distribution; and on the basis of the mold surface compensation amount distribution, correcting the three-dimensional model of the hot-press forming mold to generate a compensated mold processing model. The implementation of the present invention realizes intelligent and automated contour accuracy compensation.

The invention belongs to the technical field of safety protection, and particularly relates to an energy storage equipment safety protection early warning system applied to an industrial park, which adopts a module-cluster-cabin three-level architecture to realize real-time monitoring and dynamic evaluation of thermal runaway risk, the architecture integrates multi-source data, and when the risk exceeds a preset threshold value, the energy storage equipment safety protection early warning system is used for early warning the thermal runaway risk. The system automatically starts graded fire extinguishing measures and a fire-fighting linkage mechanism, and meanwhile, secondary analysis is carried out on an early warning result through a cloud platform to optimize model parameters, so that the accuracy and the efficiency of the system are ensured; the system constructs an integrated monitoring, response and self-correction prevention and control process, and effectively overcomes the problems of early warning delay, high false alarm rate, single protection means, data islanding and the like in the prior art through a multi-parameter fusion early warning, accurate recognition, graded fire extinguishing and intelligent linkage mechanism. And a solid guarantee is provided for fire safety in a high-density energy storage environment.

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a computer vision-based hundred-million-pixel panoramic situation awareness method and system, and the method comprises the steps: synchronously collecting original local field-of-view images through multiple cameras; independently executing target detection and semantic segmentation in an original image domain; constructing local-to-global coordinate mapping based on the calibration parameters; projecting the target features to a global coordinate system and detecting a cross-view-field target; extracting multi-view-field texture, edge and color features, and performing weighted fusion according to a visible area; according to the method, through identification preposition and semantic level feature fusion, geometric splicing distortion is avoided, the cross-boundary identification precision is improved, the calculation load is reduced, target continuous tracking and situation real-time output in a high-density scene are realized, and the method has high engineering suitability and industrial popularization value.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

The invention relates to the technical field of building templates, and particularly provides a building template flatness threshold partition detection method based on normal vector constraint RANSAC, which comprises the following steps: performing three-dimensional laser scanning on a building template to obtain high-density point cloud data; preprocessing operations such as denoising and downsampling are carried out on the collected point cloud data, so that the data quality is improved; fitting a reference surface of the template based on the preprocessed point cloud data by using an improved RANSAC algorithm, and introducing a unit normal vector to ensure the correct direction of the reference surface; calculating the vertical distance from each point cloud point to the reference surface to obtain a deviation value; independently setting a deviation threshold value according to a building specification, dividing the deviation value into different grades, and distinguishing and displaying the deviation value on the three-dimensional point cloud model through colors; and a visual result containing the three-dimensional point cloud model and the color identification is output, the flatness condition of the template is visually displayed, and the method has the effects of automatic measurement, high efficiency and high precision.

The invention relates to the technical field of wireless communication, and discloses a communication equipment command and dispatch system based on artificial intelligence, the system maps the state of communication equipment into a standardized carrier phase disturbance mode, and after the disturbance mode is adaptively adjusted in combination with channel noise perception and conflict arbitration logic, the communication equipment is commanded and dispatched. Controlling the radio frequency front end to carry out injection; the adjacent communication equipment directly detects the disturbance mode to trigger the cooperative scheduling action by monitoring the physical error signal of the carrier synchronization loop of the adjacent communication equipment, and the implicit cooperative mode based on the physical layer beacon is constructed, so that the complex high-level signaling interaction required by the traditional scheduling is avoided, and the scheduling efficiency is improved. According to the method, the millisecond level, determined by a software protocol, of the collaborative response time between the devices is substituted into the microsecond level determined by the hardware physical layer response entscheid, the system can still maintain reliable transmission of scheduling instructions when facing high-density concurrent conflicts and strong channel noise, and a technical implementation path is provided for ultra-low-delay and high-reliability communication.

A high-density ferroelectric memory, and a preparation method therefor and an application thereof, belonging to the field of semiconductor memories. In the memory, multiple memory cells are arranged in an array, and the two sides of the array of the memory cells are connected to substantially orthogonal word lines and bit lines, the memory cell of the present invention adopts a stacked structure of a top electrode, a resistive switching dielectric layer, an intermediate metal layer, a ferroelectric dielectric layer, and a bottom electrode, which is electrically equivalent to a ferroelectric capacitor and a resistive switching selector connected in series; the voltage division of the distributed ferroelectric capacitor in the unselected cells is reduced by regulating the RC delay of the memory cell, so that its disturbance is reduced; and the capacitance value of the ferroelectric capacitor is stable, and the influence of the disturbance voltage can be effectively reduced by RC regulation. The storage window of the memory is improved and the bit error rate is reduced, without increasing additional area overhead.

The invention relates to the fields of computer graphic processing, industrial software technology and the like, discloses a Cocos engine-oriented pipeline loop perception linear decomposition method, and aims to solve the problem that a complex topological graph which cannot be effectively processed and comprises a high-density nested loop in the prior art. The structure is completely and accurately decomposed into a linear drawing instruction sequence which can be accepted by a Cocos engine, and lossless conversion of original topological information is ensured; comprising the steps that after undirected topological graph data are input, a ring detection algorithm is called to recognize and record all independent ring structures in an undirected topological graph and nodes and edges contained in the independent ring structures; judging whether at least one ring is detected in the previous step, and if the at least one ring exists, assigning a determined traversal direction for each detected ring according to a preset ring direction rule; and if the ring does not exist, skipping, and directly performing depth-first search traversal on the undirected topological graph data.

The invention relates to the technical field of injection molding pressure control, in particular to a multi-stage pressure closed-loop compensation system and method for high-precision double-color injection molding, and the system comprises an interface gradient positioning module, a flow resistance state recognition module, a fitting function reconstruction module, a time sequence deviation triggering module and a gain reverse correction module. According to the method, the high-density measuring point data is collected, the interface radial pressure gradient change is extracted through the finite difference method, the stably-arranged nodes at the material intersection can be accurately locked, and the flow resistance state of the flow channel is judged according to the slope difference value of the screw speed and the pressure intensity change. A linear boosting or index slow increasing strategy is automatically switched to reconstruct a pressure function form, local high-pressure accumulation characteristics of a product sensitive area are accurately matched, the coincidence degree of a time window drift trend and a slope sudden change window is monitored, historical displacement and pressure errors are converted into backstepping factors through an iterative learning model, and the backstepping factors are calculated. And a driving gain curve is initialized and revised, so that forming deviation caused by thermal drift of equipment is eliminated.

The invention discloses an end-side safe path planning method based on a small language model, which comprises the following steps: generating a task prompt by fusing the small language model SLM with an A * algorithm and a structured map token, storing the task prompt in a vector database, retrieving historical map information similar to the current task from the database when the path planning task is executed, and performing path planning according to the historical map information. The method comprises the following steps: constructing a COT logic chain prompt, guiding a small language model to gradually generate path key nodes and weights thereof in combination with the constructed COT logic chain prompt, then executing greedy path search by an A * algorithm by taking the key nodes as local targets, and finally generating a safe and efficient path. The method provided by the invention has end-side deployment capability, can be operated in scenes without network, resource limitation and the like, greatly reduces node traversal and improves planning efficiency compared with the existing A * algorithm, is particularly suitable for path planning tasks in a large-scale or high-density obstacle environment, gives consideration to intelligent reasoning and planning safety, and has wide application prospects. The method has good interpretability and expansibility, and has a wide application prospect.

The invention discloses a photoetching technology implementation method for multi-chip high-density connection, a chip-level chip interconnection method, a mask plate line manufacturing method and photoetching equipment, and the photoetching technology implementation method comprises the following steps: selecting a first mask plate, and aligning and exposing the first mask plate and a first original pattern unit through a second alignment mark, transferring the first mask pattern to a first exposure area; longitudinally moving the substrate by M times of the longitudinal size of the original pattern for alignment exposure, and transversely moving by 1 time of the transverse size of the original pattern for alignment exposure to complete exposure of other M * 1 chipset exposure areas; a second mask is selected to be aligned with the first original pattern unit for exposure through a second alignment mark, and a second mask pattern is transferred to a second exposure area; and transversely moving the substrate by N times of the transverse size of the original pattern for alignment exposure, and longitudinally moving by 1 time of the longitudinal size of the original pattern for alignment exposure, thereby completing exposure of other 1 * N chipset exposure areas. According to the scheme, high-density interconnection of multiple chips is realized.

The invention relates to the technical field of all-solid-state batteries, in particular to a halide sulfide double-layer coated positive electrode material and a preparation method and application thereof. The halide sulfide double-layer coated positive electrode material comprises a positive electrode material substrate, and a halide electrolyte coating layer and a sulfide electrolyte coating layer which sequentially coat the positive electrode material substrate. The halide electrolyte coating layer is located in the middle layer, compatibility with a positive electrode interface can be achieved by means of high oxidation resistance of the halide electrolyte coating layer, and the problem of the positive electrode interface occurring in sulfide electrolyte oxygenolysis is solved. The sulfide electrolyte coating layer is located on the outermost layer, air stability is excellent, processing in a dry environment can be met, and the problems that halide electrolyte is poor in chemical stability, cannot be processed and manufactured in an external environment and is high in density are solved. The halide sulfide double-layer coated positive electrode material has high interface stability and good chemical stability.