16results about How to "Achieve objectification" patented technology

The application discloses a kind of ship intelligent navigation algorithm simulation verification system and test method, the system uses four-layer modular structure, including: virtual navigation environment module, standardization data interaction module, algorithm execution module, quantitative evaluation module;Between each module, standardization data interaction module is realized two-way data transmission, forms the complete simulation verification system capable of independent test and closed-loop operation.The method, using the system, the steps are: step 1: build inland virtual navigation scene, step 2: select test mode and configure algorithm, step 3: start simulation and data acquisition, step 4: evaluation index calculation, step 5: generate verification report.The algorithm unit of the application can be independently accessed to test, without overall reconstruction, perception, decision-making, control algorithm can be quickly adapted, greatly reduce the difficulty of integration debugging, improve algorithm development and verification efficiency, effectively shorten the intelligent ship development cycle.

The invention discloses a necking detection method based on geometric feature self-adaption. The necking detection method comprises the following steps that S1, the longest main axis of a simulation graph is determined; s2, selecting N feature points on the longest main shaft; s3, obtaining the minimum key size of the simulation graph at each feature point; and S4, judging whether the simulation graph has a necking risk or not based on each minimum key size. By identifying the geometric centroid and the longest principal axis of a graph, adaptively selecting feature points and performing omnidirectional scanning, the measured minimum critical dimension is compared with a set safety threshold, and a risk point which is most likely to generate necking on the graph is accurately positioned, so that the automation and objectification of risk detection are realized, and the risk detection efficiency is improved. Subjectivity and experience dependence of manual marking are avoided, and consistency of detection results is guaranteed; meanwhile, the method has good adaptability to complex or deformed contours, missing detection and misjudgment are effectively avoided, and the yield and reliability of chip manufacturing are improved.

The application relates to the field of microbial treatment, and particularly relates to a metal inoculation ring capable of testing impedance by electrification, which comprises a metal working end used for contacting and absorbing a sample, a mounting part connected with the metal working end, and a control module electrically connected with the metal working end, wherein the control module comprises a mode switching unit used for switching between a heating mode and a detection mode, a power driving module used for applying an electric current to the metal working end in the heating mode so that the metal working end generates heat to achieve sterilization, and an impedance detection module used for applying a detection electric signal to the metal working end in the detection mode to detect the absorption condition of the sample; and the control module is configured to judge the state of the sample absorbed by the metal working end according to the change of the electric characteristic parameter relative to a reference value in the detection mode. The metal ring has the functions of heating sterilization and impedance detection, and can objectively judge whether the absorption amount of the inoculation ring reaches a preset standard.

The invention discloses a base station deployment method, system and device based on reinforcement learning, and a medium. The method comprises the following steps: modeling a sequential deployment process of a base station as a decision process of reinforcement learning; in response to the reinforcement learning agent executing an action of deploying a base station in a current state to enter a next state, determining a coverage performance representing a network coverage range change; determining a quality performance representing a change in network communication quality; based on the coverage performance and the quality performance, constructing a comprehensive reward function to generate a comprehensive reward value used for guiding agent decision making; on the basis of the comprehensive reward value and the maximum future expected reward obtained according to the next state, the decision basis of action execution of the intelligent agent in the current state is updated; and repeatedly executing the steps of deploying the base station, generating the comprehensive reward value and updating the decision basis until a final base station deployment scheme meeting a preset condition is obtained.

The application provides a method and device for dividing a propagation mechanism area of a super large-scale MIMO system. The method provided by the application comprises: acquiring channel delay domain data corresponding to a dual polarization mode of a MIMO system in a dynamic indoor scene with a receiving end moving; constructing a dynamic time delay heat map with a transmitting end antenna serial number and a measurement time as two-dimensional dimensions based on the channel delay domain data, wherein the dynamic time delay heat map represents delay distribution differences in different polarization modes; performing noise smoothing and edge preserving processing on the dynamic time delay heat map, and then performing area segmentation, boundary positioning of different propagation mechanisms, edge detection and morphological optimization; extracting delay distribution features of the segmented areas for unsupervised quantitative clustering to obtain different time delay levels; and based on a combination mode of the time delay levels, combining the physical nature of the propagation mechanism, dividing an occluded line of sight into a transition state subclass to obtain a classified propagation mechanism.

This invention discloses a method and platform for evaluating the resilience of towns and villages on the outskirts of large cities. The method includes steps such as constructing a multi-level evaluation index system, acquiring and processing evaluation data, calculating index weights, and obtaining evaluation results. It establishes a multi-level index system encompassing economic resilience, social resilience, ecological resilience, and infrastructure resilience. Relevant literature data is acquired using web crawling technology and semantic analysis models. The final indicators are selected using the TF-IDF algorithm, and the index weights are calculated using the information entropy method. Finally, the evaluation results are obtained using the fuzzy comprehensive evaluation method. This method automates the evaluation process, enabling rapid and accurate assessment of the resilience level of towns and villages on the outskirts of large cities, providing a scientific basis for urban planning and management decisions.

This invention discloses an intelligent detection system for residual coal unloading in skips based on image recognition, comprising the following modules: a data acquisition module, using an intrinsically safe mining camera positioned at the skip unloading location to acquire unloading video sequences; a calibration and segmentation module, used to perform region calibration and frame-by-frame image segmentation to form a residual feature sequence; a temporal modeling module, including a neural oscillator network, used for rhythm modeling and phase discrimination, outputting temporal discrimination results; a fusion and judgment module, used to combine spatial parameters and temporal results to generate a fused residual score; an alarm recording module, used to trigger an alarm and generate snapshot and video recordings when residual anomalies occur; and a linkage control module, used to transmit the judgment status and output linkage signals to the hoisting system safety loop. This invention achieves accurate detection and safe control of residual coal unloading in skips through camera recognition and neural oscillator network modeling.

The invention relates to the technical field of defect detection, in particular to a 4D-STEM material defect automatic detection method and system, and the method comprises the steps: obtaining 4D-STEM data, carrying out the structural processing, and generating low-dimensional features inputted by a student network and privileged modal data inputted by a teacher network; an asymmetric teacher-student network is constructed, the teacher network processes the privilege mode to obtain physical semantic features, and the student network processes low-dimensional features; a compound loss function formed by DINO distillation and the like is used for pre-training a student network, and teacher parameters are updated through EMA; after the teacher network is removed, the decoder is accessed for fine tuning; and the deployment network inputs the to-be-detected data output result. By means of cross-modal distillation, students can learn physical priori in a network mode, the detection precision is improved, automatic detection of defects such as dislocation is achieved, and efficiency and robustness are both considered.

This invention discloses a manual valve opening and closing device and operating method, belonging to the field of industrial valve safety and energy isolation technology. The opening and closing device includes: a transmission cylinder having a three-section stepped inner hole consisting of an upper inner hole, a middle inner hole, and a lower inner hole from top to bottom; a transmission block with a transmission tooth groove on its upper end face, the transmission block being fitted into the upper inner hole; a closing device located above the transmission block; the closing device includes a movable blocking part and a lever, wherein the movable blocking part blocks the transmission tooth groove when the lever is moved in a first direction, and exposes the transmission tooth groove when the lever is moved in the opposite direction; a sliding sleeve; a transmission head with transmission teeth at its lower end, the transmission head being axially slidably disposed within the sliding sleeve; and a handle detachably connected to the sliding sleeve. This invention effectively prevents unauthorized personnel from operating the valve using external tools.

The utility model relates to a kind of battery thickness screening device, to solve the current according to the thickness of battery screening requirement by manually adjusting the distance between the upper pressing roller assembly of battery and the lower slide plate of battery, and then the precision of battery screening is lower, the technical problem of the greater error of battery thickness screening, including: base, the top side of base is equipped with mounting plate;Detection component, set in the side of mounting plate, for detecting the thickness of battery;The detection component includes: connecting plate, the bottom end of the connecting plate is vertically fixedly connected with sleeve;Connecting column, the top of the connecting column is slidably inserted into the bottom of the sleeve, and the bottom thereof is vertically fixedly connected with detection plate, the utility model fundamentally solves the problem of low screening precision caused by human operation error in traditional manual adjustment mode by physical quantity conversion and automation control, and realizes the high precision of battery thickness screening.

The invention discloses a digital culture creative content intelligent generation method based on reinforcement learning, relates to the technical field of content intelligent generation, and solves the technical problem that culture compliance depends on posterior auditing and is lack of embedded constraints in the generation process. The data and entities are embedded and stored in the cultural knowledge graph in a combined mode, and a reasoning engine is supported to verify compliance in real time; meanwhile, the rule weight and the graph structure are dynamically adjusted based on the generated feedback, so that the system has the self-evolution ability of cultural cognition, an action mask dynamically generated by an activation rule set is introduced into a strategy network output layer, illegal atomic operation is forbidden from the source, culture misuse is fundamentally prevented, the credibility and the safety of generated content are improved, and the method is suitable for popularization and application. And three core indexes, namely a culture element accuracy score, an innovative index and cross-modal consistency loss, are proposed, and objectification, quantifiability and interpretability of quality evaluation are realized.

The application provides an image measurement method and system of a left atrial appendage, an electronic device and a storage medium, and relates to the technical field of image measurement of a left atrial appendage. The image measurement method comprises: transforming three-dimensional heart image data according to a spatial position transformation relationship to determine transformed three-dimensional heart image data; transforming the transformed three-dimensional heart image data according to a left atrial appendage motion change function to determine four-dimensional heart image data; and analyzing the four-dimensional heart image data based on a preset AI algorithm to determine a target dynamic parameter of the left atrial appendage in a heartbeat cycle. The application introduces a dynamic parameter change curve of the left atrial appendage in the entire heartbeat cycle (including the systole and diastole) based on the spatial position transformation relationship and the left atrial appendage motion change function, provides dynamic change information of the left atrial appendage morphology in the heartbeat cycle, and thus makes the image measurement of the left atrial appendage more reliable and stable, and improves the measurement accuracy.

The application relates to the technical field of computer vision and environment monitoring, and discloses a microplastic component rapid identification method based on multi-modal data driving, which comprises the following steps: firstly, acquiring morphological sequence images and chemical component maps of microplastics, and constructing a standardized three-dimensional voxel dataset through metadata alignment; then, constructing a non-coupling training set and performing three-dimensional data enhancement based on spatial transformation; training a component identification network by using the enhanced data, and establishing a mapping relationship between the three-dimensional morphological characteristics of the microplastics and the chemical components; finally, collecting morphological images of samples to be measured and inputting the images into the network, and outputting three-dimensional segmentation masks with chemical component codes. The application realizes rapid and accurate identification of the chemical components and three-dimensional distribution of microplastics only according to low-cost morphological images by mining the deep connection between the morphology and the components, solves the problems of high cost and slow speed of traditional chemical imaging, and is suitable for efficient monitoring of environmental microplastic pollution.

This invention relates to the field of energy consumption monitoring technology, specifically to a building energy conservation assessment system and method based on energy consumption monitoring. The system includes an energy consumption sequence construction module, an energy efficiency status quantification module, an abnormal energy consumption element location module, and an energy conservation scheme evaluation module. By collecting building energy consumption data and driving data such as external environment and internal operation, a contextualized data sequence is constructed. A dynamic energy consumption benchmark model is established based on historical data. The energy efficiency health level is calculated and compared with a diagnostic threshold determined through nonparametric kernel density estimation to determine energy consumption anomalies. When anomalies are present, the contribution of each element in the context vector to the energy consumption deviation is quantified to locate the main anomaly source. An energy conservation scheme is generated based on the anomaly elements. The energy conservation effect is predicted through dynamic model simulation and compared with the threshold to evaluate the effectiveness of the scheme, thereby achieving accurate diagnosis and energy conservation optimization of building energy consumption.

The invention provides an automatic virtual coronary intervention planning method and device based on deep reinforcement learning, and the method comprises the following steps: obtaining a geometric model and physiological parameters of a target coronary artery, and generating a state vector representing a blood vessel environment based on a state encoder; according to the state vector, outputting an action vector based on an Actor network in an Actor-Critic model of Bayesian optimization; calculating a corresponding hemodynamic index based on the coronary artery virtual implantation simulation environment according to the action vector and the geometric model, and calculating a reward value according to the index; according to the reward value, the action vector is evaluated based on a Critic network, and parameters of an Actor-Critic network model are updated; and iteratively executing the steps of state vector generation, action vector output, reward value calculation and model parameter updating until a preset convergence condition is reached, and outputting a final stent implantation planning scheme. Through interaction of a state-action-reward-update circulation mechanism of reinforcement learning, output of an optimal implantation scheme of the coronary stent is realized.

This application relates to the field of suspension bridge cable strand processing technology, specifically disclosing a method for anchoring ultra-high strength cable strands. The method includes the following steps: collecting structural parameters of the anchor cup, wire separator, and wire bundle entities and constructing a digital twin model; collecting process parameters of the cable strand anchoring process and synchronously mapping them to the digital twin model; comparing the collected parameters with preset safety ranges, marking parameters that do not meet preset safety thresholds as process parameters to be adjusted; calculating the actual injection amount by collecting the alloy weight difference before and after anchoring; comparing the reverse displacement curve and injection amount of each anchor with historical qualified data intervals; if the range is exceeded, marking the anchor as a defective anchor and triggering an alarm; retrieving parameters of the defective anchor and analyzing them to obtain an optimized safety threshold range for the process parameters. The purpose of this patent is to solve the problem of lacking effective monitoring and control methods during the cable strand anchoring process, making real-time process monitoring and adjustment impossible.