309 results about "Intelligent modeling" patented technology

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention discloses a high-accuracy threat intelligence assisted network threat tracing method, which comprises the following steps: S1, collecting and preprocessing multi-source network security data, and constructing a time-marked event sequence set; s2, constructing an optimized Transform network model, and processing an attack event sequence by using position coding and time embedding; s3, a black swan optimization algorithm is initialized, and a Transform structure hyper-parameter is dynamically optimized; s4, outputting an attack event semantic vector, and constructing an attack path semantic map; s5, the intelligence information vector is embedded into a Transform hidden space; s6, calculating semantic similarity and dependency intensity, and generating an attack source candidate set and a traceability path; s7, outputting an attack traceability path, a starting point node and an information label, and generating a structured traceability report; and S8, according to the traceability result feedback, updating the black swan algorithm and the Transform model. The method is used for realizing intelligent modeling of multi-source network attack events and high-accuracy traceability analysis of attack source nodes.

The invention relates to the technical field of machining precision control, in particular to a laser carving precision control method and system.The laser carving precision control system comprises a feature collecting unit, a model building and analyzing unit, a dynamic threshold value adjusting unit and an online incremental learning unit, and the feature collecting unit collects vibration, current and temperature data through a multi-source sensor array; the model construction analysis unit realizes dynamic prediction of processing parameters by combining a bidirectional long-short-term memory network with an attention mechanism, and the dynamic threshold adjustment unit dynamically updates parameters of a numerical control system based on a material hardness real-time detection and thermal coupling model. The online incremental learning unit automatically generates training samples through error data, continuously optimizes model parameters and constructs a'data acquisition-intelligent modeling-dynamic compensation-model evolution 'closed loop, so that accurate prediction and adaptive adjustment of machining parameters are realized, and the adaptability of the manufacturing process to multi-variety and small-batch working conditions is remarkably improved.

The invention discloses a multi-target task and resource intelligent modeling method, particularly relates to the field of complex adversarial simulation, is used for solving the problems of dynamic constraint optimization and robustness improvement in multi-dimensional task planning, and aims at realizing multi-dimensional coupling of space-time resource parameters by constructing a three-dimensional hypergraph model, mining a parameter association rule by means of tensor decomposition, and realizing multi-dimensional optimization of the space-time resource parameters. Dynamic constraint quantization is supported, multi-dimensional index priority evaluation is fused in a constraint layered injection stage, hard constraints are recognized, a solution domain is compressed, a hybrid optimization strategy regulates and controls balance between global exploration and local optimization, and after annealing is simulated to jump out of a local extreme value, a multi-target particle swarm algorithm is used for screening a space-time resource equilibrium solution in a trimming solution domain. Digital twinborn verification promotes physical and virtual space interaction data closed loop, a parameter correlation degree matrix is corrected, scheme robustness is enhanced, efficient generation and adaptive optimization of a task planning scheme under complex constraints are realized, and system stability and multi-target cooperation capability under sudden disturbance are improved.

According to the distributed clustering model training device based on the insurance big data and the application thereof, the distributed clustering model training device based on the insurance big data and the application thereof are combined with distributed computing, cloud computing optimization and a big language model auxiliary strategy, and efficient data processing, low-cost computing resource scheduling and intelligent modeling are achieved. The device adopts a security mechanism to ensure data compliance, introduces man-machine collaborative optimization, improves model suitability and prediction precision, and meets the requirements of the insurance industry for large-scale data analysis.

The invention relates to the technical field of communication, and discloses a multi-parameter automatic detection system and method for laser communication equipment, and the method comprises the following steps: data collection and forward scattering compensation, feature extraction and modeling, virtual debugging and enhanced simulation, parameter optimization and noise filtering, and result verification and feedback adjustment. Through deep fusion of multi-mode sensing, intelligent modeling and closed-loop optimization, leap-over upgrade of laser communication parameter detection is realized, under the core breakthrough of signal-to-noise ratio improvement, the bit error rate of the system is reduced to a small magnitude, the transmission distance is synchronously extended to 182km, and through collaborative innovation of a federated learning framework and a digital twinning technology, the communication efficiency is greatly improved. The training efficiency is improved, the extreme working condition verification passing rate is improved, and meanwhile the ultrahigh energy efficiency ratio and the low annual failure rate are achieved.

The invention discloses a lithium battery industry MES system production scheduling method based on deep learning. The method comprises the following steps: S1, collecting and preprocessing lithium battery production data; s2, constructing a double-tower Siamese network to encode the standardized sequence, and generating a similarity matrix; s3, modeling long-term dependence by utilizing Transform-XL, and extracting a cross-cycle production scheduling relationship; s4, constructing the production scheduling relation into an association weight tensor, inputting the association weight tensor into a neural Turing machine, and generating an original production scheduling instruction stream through read-write operation; s5, performing conflict detection and dependency rearrangement based on a controller memory comparison mechanism, and iteratively updating the state of the neural Turing machine; and S6, feeding back the candidate scheme to an MES execution layer and updating model parameters. According to the method, a multi-model structure is fused, intelligent modeling and dynamic optimization of lithium battery scheduling are realized, and the scheduling efficiency, the response speed and the system adaptive capacity are effectively improved.

The invention discloses a wireless network high-speed switching automatic test optimization method based on edge computing, and relates to the field of wireless network switching. An improved gravitational search algorithm is combined with a GIS deployment node, multi-dimensional data is collected through an intelligent heterogeneous sensor and reinforcement learning, preprocessing is carried out through a denoising auto-encoder based on a GAN, modeling is carried out through space-time diagram convolution and an LSTM mixed model, a test case is generated through reinforcement learning Monte Carlo tree search, and testing is carried out on edge nodes in a distributed mode. And an optimization strategy is formulated through multi-agent deep reinforcement learning, and strategy implementation and feedback are realized through SDN and a block chain. According to the method, multi-dimensional acquisition, intelligent modeling, automatic testing and deep reinforcement learning optimization are realized, the high-speed switching performance is improved, the time delay and failure rate are reduced, the testing efficiency is improved, resource allocation is optimized, the network security is enhanced, and stable and efficient operation of the wireless network is guaranteed in an omnibearing manner.

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

The invention belongs to the field of industrial thermal engineering and intelligent modeling, and discloses a heat exchanger heat exchange coefficient prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring multi-dimensional operation data through a signal acquisition system, cleaning abnormal and blank values, standardizing, and segmenting into time sequence samples by adopting a sliding window method; a double-layer physical information long-short-term memory network is constructed, and a time sequence feature and a physical equation residual error are combined to generate a space-time fusion feature matrix. And a composite loss function including data loss, physical equation loss and physical consistency loss is designed, physical and data driving influences are balanced through hyper-parameter tuning, and accurate prediction of the heat exchange coefficient is achieved based on a gradient descent optimization model. The method combines field physical laws and data features, improves the reliability and physical interpretability of prediction, and is suitable for operation optimization of the heat exchanger of the desulfurization wastewater treatment system of the thermal power plant.

The invention relates to the technical field of laser frequency stabilization control, and discloses a multi-mode laser frequency stabilization error feedback correction system driven by machine learning. The system comprises a multi-modal data acquisition module for acquiring various data to generate a multi-modal time sequence data set; the error feature extraction module is used for extracting an error feature tensor by using a kernel principal component analysis algorithm; the dynamic compensation modeling module is used for constructing a support vector regression model to generate a dynamic compensation strategy matrix; and the feedback control optimization module is used for designing a self-adaptive model prediction control framework to generate a closed-loop correction instruction sequence. In addition, a self-adaptive correction execution module, an error traceability analysis module and an abnormal mode recognition model are further arranged. Through multi-modal data acquisition and analysis and intelligent modeling and control, high-precision laser frequency stabilization is realized, the system can effectively adapt to a complex environment, the stability and reliability of the system are improved, and the system has a wide application prospect in the fields of laser processing, optical communication and the like.

The invention relates to the technical field of electric power system digitization, in particular to an automatic efficient modeling method and system for an electricity utilization inspection scene. The automatic efficient modeling method for the electricity utilization inspection scene comprises the four steps of multi-source data collection, dynamic semantic modeling, intelligent modeling decision making and augmented reality rendering, the electricity utilization inspection scene modeling system comprises a data collection module, an intelligent modeling engine, an augmented reality terminal and edge computing equipment, and data interaction is achieved among the modules through a gRPC protocol. According to the method, an L4-level multi-physical field model is generated through unmanned aerial vehicle cluster collaborative collection and multi-source data millisecond-level fusion in combination with a dynamic semantic network and reinforcement learning resource allocation; edge calculation and dynamic LOD rendering are adopted to realize equipment internal perspective and multi-user collaborative labeling, quality closed-loop verification is matched, the bottlenecks of low modeling efficiency, multi-physical field data missing, high interaction delay and the like in the prior art are overcome, and the overall efficiency is improved by 80%.

The invention provides a universal 3D intelligent modeling method for multi-view coupling constraint, belongs to the technical field of three-dimensional simulation modeling, and solves the problems that an existing intelligent three-dimensional modeling generation method is poor in generalization ability, a generation result is easy to over-fit, and multi-view geometric inconsistency is easy to occur. Comprising the following steps: optimizing learnable three-dimensional representation through a pre-trained text generation image diffusion model and a fine-adjusted multi-view generation diffusion model, and generating initial three-dimensional representation which is completely consistent with text semantics; according to the transparency prediction information of each point in the preliminary three-dimensional representation and the multi-view rendering graph of the preliminary three-dimensional representation, extracting and converting into a rough three-dimensional grid representation containing geometric structure and texture information; a geometry and texture decoupling optimization method is adopted, multi-view coupling constraints are combined, the geometrical shape and the surface texture of the triangular mesh are finely adjusted and optimized respectively, and a three-dimensional triangular mesh model with high-quality geometrical morphology and realistic texture is generated.

The invention discloses a big data-based marine disaster risk prevention and control early warning system and method, and belongs to the technical field of marine disaster monitoring and early warning. The invention discloses a big data-based marine disaster risk prevention and control early warning system and method. The system comprises a multi-source data fusion module, an intelligent modeling module and a real-time early warning module, the method comprises the following steps: cleaning and storing multi-source data after wavelet transform downsampling through an edge computing node; a space-time Transform network is adopted to extract space-time features, a Coriolis force correction equation is combined to calibrate a predicted value, and the disaster risk probability and tide level and wind speed key parameters in the next 24 hours are generated; graded early warning is triggered based on a dynamic risk map, and second-level pushing is carried out through multiple channels such as a mobile network and emergency broadcast. The tide level prediction error is reduced to be smaller than or equal to 0.2 m, the early warning response delay is smaller than 10 seconds, and the problems that a traditional system is high in false alarm rate and updating lags are effectively solved.

The invention discloses an environment perception and command control intelligent deduction platform based on bionic unmanned equipment, and relates to the field of intelligent deduction, and the platform comprises the steps: collecting environment data through a multi-mode sensor, encrypting the environment data through an anti-interference communication relay module, and transmitting the encrypted environment data to a central control platform; the data fusion preprocessing module is used for processing multi-source data and outputting a structured data stream, and the environment intelligent modeling module is combined with a geographic information system to construct a three-dimensional dynamic environment model; an intelligent deduction module carries out multi-scene deduction based on the model, and a self-adaptive control strategy generation module converts a decision tree into an anti-disturbance control sequence; and the man-machine collaborative decision-making module fuses operator intervention to generate a final instruction, and the instruction distribution and equipment collaborative control module issues the instruction to the bionic unmanned equipment to realize intelligent collaborative control. The method has the advantages that bionic perception, anti-interference communication and intelligent deduction are integrated, efficient cooperation and adaptive control of unmanned equipment in a complex environment are achieved, and the intelligence and robustness of task execution are remarkably improved.

The invention discloses a three-dimensional fine geological modeling method for conventional and unconventional oil and gas reservoirs, which comprises the following steps of: firstly, performing multi-source data integrated management: integrating heterogeneous data such as drilling data (lithology and logging curves), a geological profile map, seismic exploration data (seismic waves and induced polarization), construction dynamic data (tunnel face sketches and laser point clouds) and the like; unified storage and dynamic calling are realized; by integrating multi-source data of earthquake, logging, geochemistry and the like, a high-precision three-dimensional model is established, such as a drilling data regularization processing algorithm and a point cloud hole filling algorithm, the problems of inconsistent data formats and space missing are effectively solved, and the geometric precision and topological integrity of the model are improved. Therefore, the problems of difficulty in multi-source heterogeneous data fusion and insufficient model precision in existing oil and gas reservoir three-dimensional geological modeling are solved; through the multi-dimensional data fusion and intelligent modeling technology, the precision of the three-dimensional geologic model of conventional and unconventional oil and gas reservoir development is improved.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention relates to the field of environment management, and discloses a watershed digital twinning environment real-time simulation and interaction platform fusing cloud edge collaboration and intelligent modeling, and the platform comprises the steps: carrying out the dynamic collection of the Yangtze River and Yellow River watershed, and generating environment state information organized according to the temporal-spatial resolution; associating the generated environment state information with multi-source environment data to form a multi-source knowledge basis of the drainage basin pollution field; establishing a rule knowledge base covering pollution identification, early warning and prevention and control business processes by utilizing a generative knowledge representation method, and generating a pollution event candidate identification result; carrying out fusion calculation on the pollution event candidate recognition result and multi-source heterogeneous data, constructing a digital twin simulation model, and eliminating data islands between modules and data sources; and executing linkage calculation on the pollution area and the risk node calibrated in the digital twinborn simulation model, generating environment state interaction information and synchronously updating the environment state interaction information to the digital twinborn environment. The method has the advantage that the regional water environment risk prevention and control capability is improved.

The invention relates to the technical field of CAD modeling, in particular to a ship CAD structural member intelligent modeling and parameter completion method, system and equipment, and the method comprises the steps: obtaining user voice information, and converting the voice information into text information; matching the user text information with ship structural member parameterized modeling instruction set knowledge base information to obtain instruction information expressed in the user voice text information, and outputting a required parameter list and parameter meanings corresponding to the instruction information in the knowledge base according to the matched instruction information; performing parameter extraction on the text information of the user according to the output required parameter list and parameter meaning, outputting a parameter list, integrating an instruction matching result and a parameter extraction result, combining to generate a complete parameterized modeling instruction, and outputting the parameterized modeling instruction in a standardized JSON (JavaScript Object Notation) format; and inputting the parameterized modeling instruction generated by combination into the rear end of CAD software, analyzing the parameterized modeling instruction, executing a corresponding parameterized modeling interface, and completing a corresponding modeling operation.

The invention provides a smart home system, and the system comprises a user behavior data collection module which is used for collecting and processing the daily behavior data of a user, including a position, an activity track, and the frequency and time of using home equipment; the shadow intelligent modeling module is used for creating a digital twin model of the user based on the user behavior data, analyzing user habits through a machine learning algorithm and generating a prediction rule; the home control module is used for executing home operation in advance based on the shadow intelligence prediction result, and the home operation comprises light adjustment, temperature control, home appliance starting and stopping and curtain opening and closing; and the augmented reality interaction module is used for generating a virtual image of a user, displaying a home decision of the shadow intelligence through AR equipment or a terminal, and allowing the user to modify or optimize the home decision. The method has the following beneficial effects: intelligent prediction: the shadow intelligence can predict user behaviors in advance and execute corresponding operation at a proper time point; the air conditioner is turned on in advance, water is boiled and tea is made, and light is adjusted.

The invention relates to a science and technology project evaluation method and system, and relates to the technical field of multi-index decision and intelligent modeling. Comprising the steps of collecting multi-dimensional data of finance, intellectual property, teams, historical projects and the like of science and technology projects; constructing an evaluation index system; constructing a cognitive map by taking the indexes as nodes and combining statistical correlation and expert rules; a graph attention network (GAT) is utilized to train the graph, and structural causal weights among indexes are extracted; fusing the structure weight and the original fuzzy relation matrix to form a structure enhanced fuzzy relation matrix; and in combination with a weight vector determined by a fuzzy analytic hierarchy process (FAHP), performing fuzzy weighted comprehensive evaluation, and outputting a project grade and an interpretable result. According to the method, the accuracy, self-adaptability and interpretability of science and technology project evaluation are effectively improved, and the method is suitable for complex project evaluation tasks in a multi-source heterogeneous data environment.

The invention provides a potential safety hazard analysis method and system based on underground cavern three-dimensional geological intelligent modeling, and the method comprises the steps: firstly, based on underground cavern three-dimensional model data, cutting the underground cavern three-dimensional model data into a plurality of sub-models according to a geological condition segmentation rule, and enabling the geological condition segmentation rule to dynamically adjust the cutting spacing according to the gradient change characteristics of a rock stratum; performing gradient analysis on the longitudinal and axial rock stratum attributes of each sub-model to generate rock stratum transition characteristic data containing the adjacent rock stratum attribute change rate and the transition area space distribution, and dynamically identifying the surrounding rock type of the sub-model according to the rock stratum transition characteristic data; and mapping and matching the surrounding rock identification result with a preset protected object space coordinate, collecting a sub-model geological parameter set in real time, finally triggering a safety early warning signal according to a geological parameter set numerical value interval, and generating an emergency disposal scheme set associated with the safety early warning signal, so that the potential safety hazard of the underground cavern can be effectively analyzed.

The invention relates to the technical field of point cloud processing, in particular to an intelligent modeling method based on point cloud data. The method comprises the following steps: scanning the interior of the transformer substation to obtain vision field overlapping graph data; performing point cloud density distribution field construction on the view field overlapping graph data to obtain point cloud density distribution field data; performing occlusion region extraction on the point cloud density distribution field data to obtain occlusion region data; performing structure connectivity calculation on the point cloud density distribution field data according to the occlusion region data to obtain occlusion region connectivity data; performing hole residual mapping on the occlusion area communication data to obtain hole residual data; performing graph neural network reasoning on the hole residual data to obtain category reasoning data; and performing physical rule fusion according to the category reasoning data to obtain point cloud complementation data, and performing point cloud model construction to obtain a point cloud model. According to the invention, the spatial coverage integrity of the point cloud data and the recognition precision of the occlusion area in the occlusion environment are improved, so that the accuracy of point cloud modeling is improved.

Disclosed herein are methods and systems for using artificial intelligence modeling techniques to train and execute an artificial intelligence model to analyze camera feed received from an ego to generate an occupancy data indicating whether different voxels within the ego's surroundings are occupied by an object having mass. A method comprises inputting, using a camera of an ego object, image data of a space around the ego object into an artificial intelligence model; predicting, by executing the artificial intelligence model, an occupancy attribute of a plurality of voxels; and generating a dataset based on the plurality of voxels and their corresponding occupancy attribute.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The invention discloses a side type water inlet and outlet hydraulic numerical simulation intelligent calculation method which comprises the following steps: S1, according to shape parameters of a side type water inlet and outlet, creating a parameterization construction method based on multi-level intelligent modeling; s2, intelligent positioning of a key boundary surface is completed by designing a DesignModeler geometric feature recognition algorithm; s3, constructing a water inlet and outlet distributed data transmission architecture based on a TCP / IP protocol, and realizing high-performance real-time data exchange of calculation data; s4, establishing a simulation mechanism module based on PyFluent to realize intelligent calculation of a water inlet and a water outlet and result feedback; according to the method, automatic and efficient calculation of the hydraulic characteristics of the side water inlet and outlet is realized.

The invention provides a human body motor function decline risk prediction method based on multi-modal data evaluation. The method comprises the following steps: S1, collecting multi-modal data; s2, data preprocessing; s3, multi-dimensional feature extraction is carried out; s4, labeling and quantifying; s5, feature coding and fusion; s6, model training; s7, evaluating and verifying the model; and S8, outputting the model application. Through multi-modal data fusion and intelligent modeling, the defects in the prior art are effectively overcome.

The invention discloses a building updating design method based on digital integrated modeling and a virtual reality interaction platform, which forms a point cloud model by means of a digital information acquisition technology for indoor and outdoor scanning of an old building, and assists professional modeling software in rapid modeling, so as to improve the modeling efficiency. The generated model can be effectively in butt joint with building design and transformation and related technical data analysis software in an ideal city building intelligent modeling and VR display experiment platform, follow-up research work or deepening creation work is carried out, finally, finished product resources in various forms of pictures, videos, panorama, roaming and VR are published, building site live-action experience is created, and the development and development of the ideal city building intelligent modeling and VR display experiment platform is facilitated. Multi-party intelligent interaction is realized; through the assistance of a digital information acquisition technology, more intelligent surveying and mapping and more efficient modeling are realized; on the basis of a reality model, a design model and technical data, a whole-process optimization drilling platform under building updating and reconstruction is formed; multi-party intelligent interaction with multi-element interaction and real-time sharing is realized, and the directness and high efficiency of public communication and multi-party conversation are improved.

The invention discloses an intelligent path optimization control system for an industrial robot. The intelligent path optimization control system comprises a task input and analysis module, an environment sensing module, an intelligent modeling and path planning module, a feedback and execution module and a robot execution unit, the task input and analysis module sends a task instruction to the environment sensing module; the environment sensing module collects environment data, forms a dynamic semantic map and sends the dynamic semantic map to the intelligent modeling and path planning module. The intelligent modeling and path planning module sends a robot joint track instruction to the feedback and execution module; the robot motion unit drives the robot to execute trajectory tracking according to a motion control instruction issued by the feedback and execution module, and meanwhile, the feedback and execution module is connected with the weight coefficient input end of the intelligent modeling and path planning module to dynamically optimize the weight coefficient. Robot path planning is automatically adjusted to avoid collision, and it is ensured that the robot efficiently completes tasks.

The invention discloses a synchronous detection system for surface density and wall thickness uniformity of a composite bulletproof helmet, which belongs to the technical field of bulletproof helmet detection and comprises a detector cooperative control module, a two-parameter synchronous acquisition module, a data preprocessing module, a two-parameter cooperative constraint module and an intelligent modeling output module. Through the integrated design that the detection unit integrates the 3D scanning detector, the microwave thickness measuring probe and the beta-ray density detector, and in cooperation with the synchronous triggering and double-channel signal acquisition mode of the double-parameter synchronous acquisition module, synchronous acquisition of the surface density and wall thickness data of the helmet is realized; the problems that traditional step-by-step detection is low in efficiency and poor in data relevance are solved, the detection efficiency and the accuracy of quality judgment are greatly improved, meanwhile, a detection report containing process optimization suggestions is output, the problems that traditional detection results are not visual, and connection with the production process is not smooth are effectively solved, and the product quality is improved. And accurate guidance can be provided for mass production quality control and process adjustment.