20398results about How to "Improve reliability" patented technology

The invention discloses a method and system for cooperatively positioning a vehicle through a door lock, and the method comprises the steps: obtaining and dynamically updating a neighbor door lock list through the detected door lock of the vehicle according to the broadcast and scanning data of a Bluetooth tag on a moving vehicle; according to the periodic detection report of the door locks on the vehicle and the global analysis of the management system, the cooperative timers of the related door locks are synchronized, and timing management for preventing repeated triggering is implemented; according to the response of the obtained neighbor door lock which initiates multicast query after the collaborative timer expires, the door lock which initiates the query verifies whether all responders are in the neighbor list, so as to judge whether the vehicle is stably parked; and according to the vehicle parking information confirmed by the door lock, the management system updates the global state and sends a parking position notification to the owner. According to the embodiment of the invention, accurate and low-misjudgment judgment of the parking state of the vehicle can be realized, and the reliability and real-time performance of vehicle positioning management are improved by means of global state synchronization.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention provides a high-power high-reliability voltage retainer used for an automobile starting and stopping system. The high-power high-reliability voltage retainer is higher in power and reliability and smaller in size and ripple. The high-power high-reliability voltage retainer comprises a through circuit module (1), a booster circuit module (2), a reversed polarity circuit module (3), a CAN transceiver (4), a CPU (5), a DSP (digital signal processor) (6) and a battery. A battery input port and a battery output port are formed in the through circuit module (1), a battery input port and a battery output port are formed in the booster circuit module (2), and the reversed polarity circuit module (3) is electrically connected with the booster circuit module (2); the CAN transceiver (4), the CPU (5) and the DSP (6) are sequentially and electrically connected; the CPU (5) is electrically connected with the through circuit module (1), and the DSP (6) is electrically connected with the through circuit module (1); the DSP (6) is electrically connected with the booster circuit module (2).

The invention belongs to the technical field of knowledge graph construction, and particularly relates to a multi-source heterogeneous data fusion and knowledge graph automatic construction system, which comprises a semantic extraction module for extracting minimum semantic fragments from multi-source data and performing cross-source alignment to generate a symbolized semantic framework; the data purification module converts multi-source data into symbolic predicates according to the framework, filters low-evidence data and outputs a purification set; the ternary generation module inputs the initial triad candidate set into a minimum rule grammar, and extracts entities, relationships and attributes to generate an initial triad candidate set; the disambiguation calibration module generates entity two-hop topological fingerprints based on the candidate set, completes disambiguation alignment and corrects conflicts, and outputs unambiguous structured knowledge; the mapping fusion module fuses the mapping information with the minimum connected ontology, and constructs and verifies an initial mapping knowledge domain; and the incremental updating module processes newly added data, performs incremental merging and maintains consistency, and forms a complete knowledge graph. According to the method, through full-link automatic construction, efficient fusion and high-quality atlas are realized.

Disclosed is a liquid crystal composition. The liquid crystal composition includes liquid crystal molecules including at least one of a liquid crystal compound having an alkenyl group and a liquid crystal compound having an alkoxy group, and a first stabilizer including a compound having a structure represented by Formula 1:where n is 0 or 1; m is 0 or 1; k is 0 or 1; Z1, Z2, and Z3 are each independently a single bond, —C═C—, —OCO—, —COO—, —CF2O—, or a C1 to C5 alkylene group; X1, X2, X3, and X4 are each independently hydrogen or halogen; Y is hydrogen, an alkylene group, an alkoxy group, a hydroxyl group, —NHCOCH3, or ═O; and A and B are cyclic compounds.

The invention discloses an intelligent tooling for paving a photovoltaic module and a control method thereof, the method is suitable for a robot end effector, and the method comprises the following steps: collecting three-dimensional space point cloud data of the photovoltaic module to generate pose information of the photovoltaic module, and controlling the end effector to carry out adaptive pose adjustment according to the pose information; the tooling is controlled to press down, and after it is judged that fitting is in place by monitoring the compression stroke of the suction cup, a vacuum adsorption instruction is started; the adsorption state is judged based on vacuum pressure change curve parallel monitoring and a dynamic balance algorithm, and a carrying permission signal is generated after a preset adsorption success condition is met; continuously carrying out anomaly detection in the carrying process, and if an anomaly is found, triggering a fault processing strategy; and releasing the negative pressure to place the photovoltaic module after the target position is reached, and rechecking the placement pose. Intelligent and self-adaptive picking and placing of the photovoltaic module by the tooling are realized, and the reliability, the safety and the automation level in the carrying process are improved.

The invention is suitable for the technical field of road construction, and provides a wind power plant road intelligent line selection method based on multi-dimensional constraint dynamic modeling, and the method comprises the steps: obtaining a multi-dimensional constraint condition of wind power plant road line selection, and constructing a constraint model; generating a slope grid map based on a digital elevation model, and fusing the slope grid map with a constraint map layer reflecting geological stability and a land cover type to generate a dynamic cost surface; adopting an improved heuristic search algorithm to perform parallel path search from the road starting point to each target fan position, generating a plurality of initial feasible paths, and calculating a comprehensive cost value; screening a candidate line set according to a comparison result of the comprehensive cost values; receiving real-time updating data in a construction process and environment monitoring, inputting the real-time updating data into the constraint model to trigger model updating, performing iterative optimization on the candidate line set based on the updated constraint model, and outputting a final line selection scheme; and the line selection efficiency, economy and reliability are effectively improved.

The invention provides a one-dimensional pneumatic design method and device for an axial flow compressor, and belongs to the field of axial flow compressor pneumatic design. The method comprises the following steps: taking values of a plurality of design variables as the state of a reinforcement learning agent; the intelligent agent outputs the adjustment amount of the design variable according to the state, and the adjustment amount serves as the action executed by the intelligent agent; applying the action to the value of the current design variable to obtain an updated design variable; utilizing a one-dimensional inverse problem design program to generate a one-dimensional pneumatic design scheme of the gas compressor; utilizing a one-dimensional normal problem analysis program to output performance indexes; constructing a reward function according to the performance indexes; based on feedback provided by the reward function, a depth deterministic strategy gradient algorithm is adopted to train the intelligent agent; and performing one-dimensional pneumatic design of the gas compressor by using the trained intelligent agent. According to the method and device provided by the invention, the problems that the one-dimensional design of the axial flow compressor depends on experience, the efficiency is low, automatic optimization is difficult and the response is slow can be solved.

The invention discloses a knowledge question-answering method and system based on a large language model and a semantic abstract. The system comprises a tree structure abstract generation subsystem which comprises a knowledge base configuration module, a file management module, a document analysis module and a knowledge block management module and is used for automatically generating tree structure hierarchical knowledge blocks for large manual documents or multi-chapter manual documents; the dual-channel retrieval engine subsystem comprises a question rewriting module, a semantic abstract retrieval module, a detail knowledge block retrieval module, a father-son retrieval module and a prompt word construction module, and all the modules work cooperatively to ensure that semantic abstract knowledge blocks and detail knowledge blocks related to the question of the user are retrieved. According to the method, by introducing the semantic abstract based on the tree structure and the two-channel retrieval, perception of global knowledge and accurate extraction of local fine-grained knowledge can be achieved at the same time, and then the ability of a knowledge base question-answering system in processing the general and inductive problems is improved.

The invention discloses an automatic causal structure generation method based on semantic representation and logical reasoning of a large language model. The method comprises the following steps: acquiring an input text; performing semantic coding and clustering on the obtained input text by utilizing a large language model, and establishing a candidate causal variable set; causal relationship detection is carried out on the established candidate causal variable set based on anti-fact intervention and do-calculation; performing causal direction judgment, and generating a directed acyclic causal graph meeting logic consistency; and on the basis of the generated directed acyclic causal graph, natural language interpretation is generated by using a large language model, and logic consistency closed-loop verification is carried out. According to the method, automatic generation from the natural language to the causal structure is realized, the causal variable set is automatically extracted and constructed from the unstructured natural language text, the defects that variables need to be manually defined and modeling depends on field experts in the existing causal modeling process are avoided, and the labor cost and professional threshold of causal structure construction are remarkably reduced.

The invention discloses a deep tunnel surrounding rock mechanical parameter inversion method based on a three-dimensional brittle failure zone contour and sparrow optimization algorithm-information vector machine (SSA-IVM) combined optimization algorithm. The engineering technical problem that due to the fact that excavation instantaneous displacement is difficult to monitor, deep tunnel surrounding rock mechanical parameters are difficult to reasonably obtain through displacement back analysis is solved. The method comprises the following steps: firstly, constructing a tunnel FLAC3D numerical simulation model with the same ground stress condition at the occurrence position of a three-dimensional brittle failure zone; secondly, taking an absolute error between the total number of computational grid units in the actually measured brittle failure zone and the total number of computational grid units entering a plastic state in the range of the actually measured brittle failure zone after calculation of the FLAC3D numerical model as an optimization objective function; and then, by taking the tunnel surrounding rock mechanical parameters as optimization variables and taking a target function reaching a global minimum value as a target, performing global optimization by combining a tunnel FLAC3D numerical model and adopting an SSA-IVM joint optimization algorithm, thereby obtaining reasonable tunnel surrounding rock mechanical parameters.

The invention discloses a user track privacy protection method and system fusing a generative model and three-stage personalized differential privacy, and relates to the technical field of data security, and the method comprises the steps: obtaining real user track data, and carrying out the preprocessing of the real user track data to form standardized track representation; training a depth generation model to generate synthetic trajectory data; performing density clustering on the synthetic trajectory to obtain a feature region and calculating a region centroid; point-level privacy budgets are distributed according to three stages of the area proportion, the centroid distance and the global density; constructing an adaptive elliptical disturbance domain based on the regional covariance, sampling candidate confusion points in the disturbance domain and applying road feasibility screening; and calculating a score based on the privacy budget and the utility function, and sampling replacement points by adopting an index mechanism to output a privacy protection trajectory. According to the technical scheme, the track structure continuity and the road semantic consistency are remarkably improved under the condition that the differential privacy constraint is met, and the privacy intensity and the data availability are both considered.

The invention discloses a farmland furrow three-dimensional point cloud data correction method and system based on a laser radar, and relates to the technical field of three-dimensional image processing based on the laser radar, in particular to a three-dimensional image geometric correction technology. The problems that original point cloud data directly obtained under the dynamic operation condition of an existing agricultural machine has geometric distortion, and the shape of a furrow cannot be truly reflected are solved; meanwhile, the problems that furrow quality evaluation seriously depends on low-efficiency manual means, the adaptability of an existing automatic technical scheme to an unstructured field environment is insufficient, and robust, real-time and high-precision perception of a real three-dimensional shape of a furrow cannot be achieved are solved. According to the method, dynamic tilt correction is carried out through real-time acquisition of point cloud and agricultural machine attitude information, boundary feature points are extracted, boundary lines are fitted, and a three-dimensional point cloud set of a single furrow is reconstructed by fusing a self-adaptive clustering algorithm of spatial prior. The method is suitable for the fields of farmland tillage and soil preparation quality detection, precision agricultural management, agricultural machinery intelligent operation and the like under the dynamic operation condition of agricultural machinery.

The embodiment of the invention provides an anti-blocking device and an anti-blocking method for a fuel loading and unloading system of a high-temperature gas cooled reactor, and relates to the field of high-temperature gas cooled reactors. The anti-blocking device of the high-temperature gas cooled reactor fuel loading and unloading system comprises a driving module, a transmission module, an execution module and a control module, the transmission module is connected with the driving module, the execution module is connected with the transmission module, and the transmission module is driven by the driving module to drive the execution module to do linear reciprocating motion. And the control module is connected with the driving module and the execution module at the same time. A cooperative structure of the driving module, the transmission module and the execution module is arranged, a driving signal is converted into linear reciprocating motion of the execution module, mechanical disturbance is applied to a fuel ball stack in a blocked area, the lap joint balance or a bridge junction structure of the fuel ball stack is damaged, and active bridge breaking and jam releasing are achieved. And the jam releasing efficiency and success rate are obviously improved.

The invention relates to the technical field of deep foundation pit monitoring, and discloses a deep foundation pit micro-deformation InSAR remote sensing method and system and a storage medium. The method comprises the following steps: synchronously acquiring data through a ground radar and an InSAR satellite, and obtaining a point target set and an original scattering characteristic sequence; obtaining a net deformation phase sequence without influence through registration residual correction, phase deduction and weighted fusion processing, and carrying out phase consistency quality control and unwrapping processing to obtain an accumulated deformation quantity sequence; constructing a deformation mechanism correlation characteristic matrix based on correlation analysis of the cumulative deformation quantity sequence and radar reflection intensity amplitude attenuation characteristics; and fusing the deformation mechanism correlation feature matrix and the real-time differential interference image group, extracting deformation rate and phase gradient change, and further generating a deep foundation pit local micro-deformation risk early warning index set. According to the invention, the tiny deformation of the surrounding area of the foundation pit can be effectively detected, potential risks are warned in advance, and the safety and monitoring efficiency in the construction process are improved.

The invention discloses an industrial measurement data intelligent analysis report generation method and system based on a large language model, and relates to the technical field of industrial measurement, and the method comprises a multi-source data and abstraction module which carries out the structural processing of original data DAT output by measurement analysis software; and the large language model service and interface module is used for submitting the PCT to a selected large language model LLM through a multi-model adaptation interface to execute semantic reasoning and output a structured text TXT. Structured analysis and semantic association modeling are carried out on original measurement data through multi-source data and an abstract module, when overall alignment deviation or local feature anomaly exists in the original data, a hierarchical dependency relationship between the data can be established through a semantic graph structure, a deviation transmission path is revealed, and the accuracy of the measurement data is improved. The large language model is analyzed in a unified data context, and the utilization depth of measurement data can be effectively improved, so that the accuracy and integrity of report analysis are improved.

The invention provides a BIM-fused port infrastructure digital twinborn operation and maintenance management system and method. The method comprises the steps of collecting a port infrastructure BIM full-life-cycle multi-source heterogeneous data set, constructing a port infrastructure real-time three-dimensional digital twinborn body, constructing an equipment health prediction model and generating a fault diagnosis result; simulating tide influence to form a stress distribution prediction result; a three-level linkage optimization model is constructed, a scheduling scheme is generated, a multi-energy collaborative optimization engine optimization scheme is used, a three-dimensional safety matrix is constructed through unmanned aerial vehicle inspection and visual recognition, risks are monitored in real time, and safety early warning is generated. The prediction model is constructed, faults are accurately diagnosed, the tide influence is quantified, and the prediction precision is improved; multi-target collaborative optimization is realized through a linkage optimization model and an engine, and the problem of single-target optimization is solved; and on the basis of a monitoring and early warning system, rapid identification and hierarchical response are realized, a closed-loop link is formed, and the port operation and maintenance adaptability is improved.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention relates to the technical field of multi-beam sounding calibration, and discloses a multi-beam sounding online real-time calibration method. The method comprises the following steps: acquiring original water depth data and corresponding sensor attitude data in real time; generating a data quality evaluation result containing a data credibility index and a data abnormal distribution feature based on the original water depth data; triggering a dynamic calibration mechanism containing a calibration period adaptive adjustment strategy and a calibration parameter optimization rule according to the data credibility index; optimizing the target calibration rule parameters in combination with the sensor attitude data to generate an optimized calibration parameter set, and correcting the original water depth data in real time by using the parameter set to generate primary calibration data; and performing topographic feature consistency check on the primary calibration data, if the primary calibration data does not pass the topographic feature consistency check, starting a re-calibration process containing a rule base updating mechanism and a parameter iteration mechanism, and analyzing a deviation trend based on historical calibration records to generate a system error prediction model.

The invention provides a multi-process-mode intelligent switching method and system for vehicle-mounted sludge treatment, and the method comprises the steps: carrying out the time-space alignment and standardization of multi-dimensional sensing data such as sludge characteristics, environmental parameters and equipment states, carrying out the real-time reasoning of a process switching critical value through combining with a lightweight incremental regression model, generating a self-adaptive threshold value band with a confidence interval, and carrying out the real-time reasoning of the process switching critical value. Dynamic monitoring and mode switching pre-judgment of actual operation parameters are realized; in the switching process, a weighted fusion progressive transition control strategy is adopted, the transfer rate is adaptively adjusted according to real-time feedback, the operation stability is guaranteed, and the intelligence, reliability and switching smoothness of process switching of the vehicle-mounted sludge treatment system are remarkably improved.

A kind of power device parallel type power module and capacitor charging and discharging method, power module includes two or more than pressure type power device, the two or more than pressure type power device adopts the physical form of half bridge group string upper and lower parallel;The half bridge group string of the pressure type power device is connected in parallel by silicon pile composite busbar;Each power module is provided with pneumatic short-circuit switch, in the case of multiple power module series connection, the pneumatic short-circuit switch is connected in series with all DC capacitors, and charging and discharging is carried out through the ground loop of flexible AC transmission equipment AC, DC valve side;The pneumatic short-circuit switch is connected with gas driving pipeline, and the gas driving pipeline is connected with valve-based gas pressurizing device.The power module can effectively improve the capacity of flexible AC transmission equipment, so that it has short-time frequency modulation capacity output, provides technical scheme support for new power system construction, and guarantees system safety and stability;The provided capacitor charging and discharging method can improve maintenance efficiency and enhance product reliability.

The invention discloses an oligopeptide from undaria pinnatifida and application thereof. The amino acid sequence of the oligopeptide is YRKD, and the molecular weight is 580.63 g / mol. The polypeptide is identified from undaria pinnatifida through a high performance liquid chromatography technology for the first time, and an unreported oligopeptide capable of inhibiting tyrosinase and cooperating with vitamin C to synergistically inhibit tyrosinase is screened out. The oligopeptide derived from undaria pinnatifida is synthesized by a solid phase method, has the characteristics of safety, no toxicity, good water solubility and good stability, and is combined with vitamin C to generate a synergistic effect and synergistically increase the inhibition effect on tyrosinase, so that the oligopeptide can achieve a better tyrosinase inhibition effect at a lower and more stable concentration; the use concentration of the oligopeptide can be greatly reduced, the stability is enhanced, and the oligopeptide can be widely applied to preparation of cosmetics, medicines, health-care products or food additives, and has important economic value and social significance.

The invention discloses a multi-modal sensor abstract description and packaging method and system based on software definition. Firstly, a unified sensor abstract description model based on software definition is introduced, so that hardware independence is realized, and the differences of a bottom-layer multi-mode sensor in the aspects of interfaces, protocols and data formats can be effectively shielded; secondly, abstract packaging of data is achieved through an autonomous information processing module, and high-bandwidth and high-redundancy original data is converted into low-bandwidth and high-value structured information; in addition, by means of automatic registration, discovery, scheduling and control mechanisms of the sensor management control module on sensor resources, the system realizes a real plug-and-play function. According to the invention, by deploying the autonomous information processing modules at various sensor nodes, the original sensing data is preprocessed, and standardized data packaging with semantic consistency is output, so that the operation load of the central controller is reduced, and the information processing efficiency and the system response capability are also improved.

The utility model relates to a high-voltage cable buffer layer ablation characteristic gas detection device which comprises a gas acquisition unit, a gas detection unit, a data processing unit and an alarm unit, the gas collection unit comprises a circulating pump, a vacuum pump, a sampling gas cylinder, a pressure sensor and a plurality of valves, the gas detection unit comprises two detection modules, the gas inlet pipe is connected with a first electromagnetic valve, then is connected with the sampling gas cylinder and is connected with the circulating pump through a second electromagnetic valve, and the circulating pump is connected with the two detection modules through an electromagnetic three-way switching valve. An outlet of the sampling gas cylinder is connected with a third electromagnetic valve, then connected with an inlet of a vacuum pump, connected with a fourth electromagnetic valve and then connected with two detection modules at the same time; the first detection module is composed of a gas concentration sensor, a temperature sensor and a humidity sensor, and the second detection module is a gas sensor array. The device can perform field detection on gas generated in the ablation process of the buffer layer of the high-voltage cable, so that potential faults of the cable can be found and early warned in time.

The invention relates to the technical field of creep age forming, in particular to an online monitoring and repairing method for creep age forming, which realizes closed-loop intelligent control of a creep age forming process by integrating acoustic emission real-time monitoring and electric pulse online repairing. According to the invention, real-time acoustic emission characteristic parameters are collected and extracted in real time, and damage initiation activities in the material can be captured immediately; the real-time acoustic emission characteristic parameters are compared with the creep aging damage information base in real time, and the current damage can be subjected to early recognition and accurate quantitative diagnosis according to historical damage evolution data, so that the damage type and the damage degree are accurately judged; and an optimal electric pulse parameter combination predetermined for the damage type and the damage degree of the target damage is called as a repair parameter, and electric pulse repair is immediately applied, so that damage self-adaptive accurate intervention is realized. And after repairing is completed, the process automatically circulates and returns to the monitoring step, and whole-process closed-loop control of damage monitoring, damage diagnosis and damage repairing is formed.

The invention relates to the field of electrical power grid operation and maintenance, in particular to a differentiated operation and maintenance method and system based on real-time weak links of equipment. According to the method, online monitoring data and power supply reliability operation data of power distribution network equipment are collected in real time to form an equipment state multi-source data set; based on the data set, calculating an equipment health degree evaluation result by adopting a state evaluation model, and obtaining an importance degree evaluation result through an analytic hierarchy process in combination with the position and load importance of the equipment in the network topology; and inputting the two results into a preset operation and maintenance strategy matrix to match a corresponding operation and maintenance strategy type, and finally generating a differentiated operation and maintenance strategy instruction containing specific operation and maintenance measures, an execution time window and a resource allocation scheme. According to the method and the device, the problems of lack of equipment-level pertinence and unreasonable resource allocation of an operation and maintenance strategy in the prior art are effectively solved, and the operation and maintenance accuracy and the resource utilization efficiency are improved.

The invention provides an equipment health monitoring method and device, equipment and a storage medium. The method comprises the following steps: acquiring working condition parameters of target equipment to obtain monitoring data; calculating a reference datum according to the monitoring data through a pre-trained baseline generation model, and comparing the monitoring data with the reference datum to obtain difference data; performing feature extraction and distribution analysis on the difference data to obtain health feature representation; and performing health state judgment according to the health feature representation to obtain a monitoring result. According to the method, the reference benchmark is dynamically calculated through the baseline generation model according to the real-time working condition, a traditional fixed threshold value method is replaced, a depth feature extraction network is adopted to capture equipment degradation features, a probability distribution divergence value is utilized to quantify the health state, and the problem that an existing method is poor in adaptability under the dynamic working condition is solved; and the accuracy and reliability of equipment health monitoring are improved.

The invention discloses a shallow reinforcement mesh echo suppression method and system based on deep learning, and the method comprises the steps: employing a ground penetrating radar to detect a shallow reinforcement mesh shielding scene, obtaining corresponding data information, and processing the data information to obtain a training data set; based on a U-net network structure, combining a convolution unit, a pooling unit, an attention mechanism, interpolation operation and a jump connection mechanism, constructing a shallow reinforcement mesh echo suppression initial model, and training to obtain a shallow reinforcement mesh echo suppression model; and adopting the obtained shallow reinforcing mesh echo suppression model to perform shallow reinforcing mesh echo suppression in the actual ground penetrating radar detection process. According to the method, echo suppression of the shallow reinforcing mesh based on deep learning is realized, the reliability is higher, and the accuracy is better.

The invention discloses a multi-modal condition-driven closed-loop sensing and generation optimization method and device and a medium, which are applied to an automatic driving long-tail scene, firstly, multi-modal conditions such as text description, a semantic map and a 3D layout are uniformly represented, a causal inference network is introduced, a causal structure between scene elements is explicitly inferred from multi-modal input, and a multi-modal condition-driven closed-loop sensing and generation optimization model is obtained. Generating structured causal embedding to realize causal perception enhancement of generation conditions; in a diffusion generation stage, a causal consistency mechanism is deeply fused into a condition control and denoising process, and a diffusion model is guided to effectively inhibit generation of unreasonable or common sense violating scenes in a generation process through an anti-fact condition constructed by a causal inference network, so that semantic reasonability, spatial consistency and dynamic credibility of long-tail data are remarkably improved. According to the method, the problem that long-tail scene data is deficient is solved, causal constraints are introduced to a generation source, and the reliability, robustness and cross-domain generalization ability of an automatic driving system in an extreme scene are remarkably improved.

The invention provides a firmware online upgrading method, embedded equipment, a medium and a product, and relates to the technical field of intelligent terminals. The method comprises the following steps: after the embedded equipment is powered on, running a primary bootstrap program, and checking a first mark state: if the state is a state of updating a secondary bootstrap program, executing online updating of the program and resetting a system; and if the state is the default state, loading and running the secondary bootstrap program. Checking a second mark state; if the second mark state is an updating mirror image state, executing firmware mirror image online updating and resetting; and if the state is the default state, determining a target mirror image, copying the target mirror image from the nonvolatile memory to the random access memory, and skipping to run. And the equipment simultaneously monitors the running state of the target mirror image in real time, and executes successful confirmation or abnormal recovery operation according to the state. In this way, quick response of faults is achieved through running state feedback, the stability and reliability of firmware upgrading of the embedded device are effectively improved, and the risk of device paralysis in the upgrading process is reduced.