99 results about "Parametric transformation" patented technology

The invention discloses a robot grabbing posture generation method and a related device, and the method comprises the steps: obtaining an RGB image, a depth image and a position coordinate of a target object, and generating a spatial calibration matrix through data preprocessing; target object segmentation is carried out on the calibrated RGB matrix according to the target point coordinate sequence, a segmentation mask is generated, and object mass center coordinates are calculated; generating a three-dimensional point cloud by using the calibrated depth matrix, the segmentation mask and the camera parameters, extracting a plane point set and a non-plane point set through an RANSAC algorithm, and analyzing to obtain an axial feature vector and a plane normal vector; finally, grabbing parameters are calculated according to the feature vectors and the centroid coordinates, and a grabbing posture transformation matrix is generated through vector operation. According to the technical scheme, under the condition of not depending on a preset model library, the grabbing posture of an unknown object is generated by analyzing the geometrical characteristics of the object, the accuracy problem in the process of converting two-dimensional image information into three-dimensional grabbing parameters is solved, and the adaptability and reliability of a robot grabbing task are improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention discloses an assembly type composite floor slab cast-in-place section span beam joint design method based on digital twinning, and relates to the technical field of intelligent construction, the method comprises the following steps: adopting a least square optimization algorithm to obtain a material performance correction parameter and a geometric compensation parameter; based on the historical material performance correction parameters and the geometric compensation parameters, training the structure model through a genetic algorithm to generate a digital twin reference model; inputting the material performance correction parameters and the geometric compensation parameters into the digital twin reference model, performing multi-scale finite element analysis, and outputting a multi-scale node performance evaluation report; identifying a multi-scale node performance influence factor and a sensitive area by adopting a response surface method, outputting a design parameter sensitivity analysis result, and converting the multi-scale node performance influence factor into a preliminary node design scheme through a design parameter conversion algorithm; according to the method, the real-time accuracy of node design is remarkably improved through least square dynamic parameter correction.

The invention discloses a quantum circuit compiling method and system, a terminal and a computer readable storage medium. The quantum circuit compiling method comprises the steps of obtaining target unitary transformation and hardware constraints; converting a unitary transformation space parameter corresponding to the target unitary transformation into a Riemannian manifold; calculating a geodesic path using a Lagrange action amount based on the Riemannian manifold and the hardware constraint; synthesizing a gate sequence of the geodesic line path; a compiling circuit with authenticated minimum depth and maximum fidelity is output based on the synthesized gate sequence. The compilation circuit obtained by the invention has circuit optimality mathematical guarantee.

The embodiment of the invention provides a DCS man-machine picture conversion method and system, electronic equipment and a medium, and the method comprises the steps: firstly, responding to a conversion request, locking a target DCS system and a required target picture conversion parameter, and then carrying out the locking of the target picture conversion parameter based on a preset general mapping relation and a unique preset extension mapping relation of the target system, and converting the parameters into an intermediate execution file compatible with the target system. Wherein the problem of cross-system common compatibility is solved through general mapping, and it is ensured that icon layout parameters do not fail due to system differences; extended mapping gives consideration to uniqueness of a target system, so that personalized functions of icons are accurately converted. The generated intermediate file can be directly driven to generate a corresponding picture after being imported into a target system, and a design process does not need to be repeated. Therefore, only one conversion request is needed, and the target system and the expansion mapping relation are adjusted, so that the same set of picture requirements can adapt to different systems, the phenomenon that pictures with the same function are redrawn for each system is avoided, and mutual conversion of man-machine pictures among different DCS systems is effectively realized.

The invention belongs to the field of artificial intelligence and financial science and technology, and relates to a model calling method, system and device based on an API engine and a storage medium, and the method comprises the steps: transmitting a request parameter to an API task engine through a large model, and converting the request parameter based on a conversion rule to obtain a first target request parameter; packaging the access authority based on an API task engine to verify the first target request parameter; and if verification succeeds, calling a target API through an API task engine to obtain target data according to the first target request parameter, and returning the target data to the large model after conversion based on a conversion rule. In addition, the invention also relates to a block chain technology, and the called data can be stored in a block chain. The method can be applied to an API interface calling scene in the financial field, and the API task engine is used for converting and verifying the request parameters, so that the safety and reliability of data calling and the interface calling efficiency can be improved.

The invention discloses a GIS software interaction implementation method and system based on a large language model. The method comprises the steps that S1, a GIS task demand text input by a user is obtained; s2, on the basis of a large language model, performing text preprocessing on the required text, performing intention classification and parameter extraction on the preprocessed text, and converting the extracted intention and parameters into an operation instruction which can be executed by GIS software; and S3, the obtained operation instruction is sent to GIS software to be executed, and an execution result of the GIS software is obtained and fed back to a user. According to the method, the user is helped to complete GIS related operation in a natural language interaction mode, the user does not need to have a deep technical background, and the use difficulty of GIS software is remarkably reduced.

The invention discloses a tool calling method and device, electronic equipment, a medium and a program product, and relates to the technical field of computers. And converting a first parameter output by the large language model based on the data description corresponding to the function tool into a second parameter capable of being input into a target function corresponding to the function tool, calling the target function based on the second parameter to obtain a calling result output by the function tool, and then returning the calling result to the large language model. The big language model can output the first parameter according to the input parameter structure of the target function, so that the function tool can automatically convert the first parameter into the second parameter, and the effect that a developer does not need to process parameter analysis and mapping is achieved; the code quantity is greatly reduced, the development efficiency is greatly improved, and adaptation of complex parameter types can be supported.

The invention discloses a category-level 6D attitude estimation method and system, a medium and a program product, and the method comprises the steps: obtaining a real enclosing rectangle and a real contour point of each assembly according to an image of a target object; respectively inputting the rectangular parameters of the real external rectangle and the real contour points into the corresponding common mapping models for branch mapping to obtain standard rectangular parameters and potential contour features; the standard rectangular parameters are converted into external rectangular features, the external rectangular features and the potential contour features are input into a pose estimation model, and the center rotation angle and depth of the target object are obtained; and calculating the horizontal offset, the vertical offset, the offset rotation matrix and the real rotation angle of the target object based on the camera internal reference matrix, the depth and the central point position of the target object on the image. According to the method, the target object in the image is processed through the neural network without depending on a point cloud or setting a key point in advance, so that the attitude reasoning process is effectively accelerated.

The invention discloses an industrial autonomous mobile robot control method, device and equipment and a medium, is applied to a robot control system based on a hierarchical architecture, and relates to the field of automation, and the method comprises the steps: analyzing an input operation text instruction, and generating a navigation task and a grabbing task of a robot based on an obtained structured subtask sequence; calling preset laser equipment according to the navigation task to generate a three-dimensional semantic map of a target end point corresponding to the operation text instruction, and performing path planning and obstacle avoidance through the three-dimensional semantic map to obtain a target path; calling a preset image acquisition device to acquire a material image of a preset material area according to the grabbing task, and processing the material image to generate a material grabbing parameter; and converting the target path and the material grabbing parameter into a robot scheduling instruction corresponding to the robot, and controlling the robot to execute a navigation task and a grabbing task through the robot scheduling instruction. Therefore, autonomous operation of the industrial robot can be realized in combination with semantic understanding.

The invention discloses an accelerated rehabilitation data processing method and device based on electrophysiological data, and relates to the technical field of medical rehabilitation, and the method comprises the steps: calculating a full-mode phase locking value based on a noise reduction matrix, and generating a fusion feature vector through a gating circulation network; microcosmic entropy, mesoscopic entropy, macroscopic entropy and psychological entropy are extracted from the fusion feature vector, and an entropy state vector is constructed; according to the entropy state vector, generating personalized rehabilitation parameters under security constraints and exploration constraints; the personalized rehabilitation parameters are converted into exoskeleton control instructions and virtual reality scene parameters, and feedback data are collected and executed; and dynamically updating parameters of signal noise reduction, feature fusion and strategy optimization based on execution feedback data. According to the method, the quality and precision of multi-mode electrophysiological signal processing are improved, optimization of personalized rehabilitation parameters is achieved, the key problems in the prior art are solved through the innovations, and wide application prospects are shown in the cross field of biomedical engineering and artificial intelligence.

The application relates to an AI large model access method and system, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: if an AI large model selection instruction sent by a terminal is not received, determining the intention complexity corresponding to a target prompt, and selecting a target AI large model adapted to the intention complexity from all to-be-called AI large models; adding a target Token label in the target prompt to obtain a labeled target prompt; based on a preset standard metadata structure, converting the labeled target prompt into a standard parameter, converting the standard parameter into specific manufacturer parameters corresponding to the target AI large model; forwarding the specific manufacturer parameters to the target AI large model, receiving an output result of the target AI large model, processing the output result to obtain a processed result, and sending the processed result to the terminal. The application has the effect of reducing the AI large model access difficulty.

The present invention relates to a system and method for realizing SAS data analysis capability in a python environment, wherein the system comprises the following modules: a SAS log analysis module, configured to periodically extract SAS execution logs from SAS, and perform statistics, preprocessing and analysis on the SAS program execution statement information therein; a newly added operator module, configured to use the result of periodic analysis of the SAS execution log to screen the operator list that needs to be stored; a python conversion module, configured to convert and encapsulate statements and parameters with a frequency of occurrence that meet the standard into python operators according to the screened operator list, and test and encapsulate the python operators into a sastopy library; a sastopy library, configured to convert statements in SAS into operator forms of python language when encapsulating the python operators into the sastopy library, and store them; an operator operation and maintenance module, configured to monitor and maintain the sastopy library. The present invention enables business personnel to complete data analysis tasks more intuitively and conveniently, thereby improving work efficiency and flexibility.

The invention relates to the field of artificial intelligence, in particular to a model deployment method and device, equipment and a program product. The method comprises: using an operator and / or a structure adapted to a neural network processor, training a to-be-deployed model through a predetermined first training framework to obtain a trained model in a first format, the first training framework being different from a second training framework adapted to the neural network processor, and the second training framework being different from the second training framework; the model in the first format is a model obtained after training of a first training framework; performing parameter conversion and / or operator combination reconstruction on the model in the first format, and converting the model in the first format into a model in a second format which is a format supported by a neural network processor; and compiling the model in the second format, and deploying the compiled model to the edge device. As excessive computational nodes or intermediate conversion layers are not introduced in the method, the operator redundancy can be reduced, and the reasoning speed and the quantization precision can be improved.

The invention discloses a model deployment method, system and device, electronic equipment and a medium, and belongs to the technical field of electronics. The method comprises the following steps: determining a target hardware parameter of first hardware equipment based on a performance operation result fed back by the first hardware equipment for N times; wherein the performance operation result fed back by the first hardware equipment for the ith time is obtained by performing a performance operation test after the first hardware equipment deploys a model execution file sent by the electronic equipment for the ith time, N is a positive integer, and i belongs to [1,..., N]; determining target model configuration parameters matched with the target hardware parameters; and sending the neural network model obtained by converting the target model configuration parameters to the first hardware equipment.

The invention provides an intelligent question and answer and text portable processing engine based on a large model miniaturization technology, which is characterized in that a large model miniaturization processing unit converts floating-point number parameters in a model into low-precision integer parameters, and controls model loading and occupied resource management as required; the large-model miniaturization processing unit is connected with the large-model miniaturization application, and the large-model miniaturization application comprises an artificial intelligence question and answer module which enables a user to ask questions through natural languages and obtain accurate answers. And the natural language to SQL module is used for converting the natural language request of the user into a structured query language and returning a query result. And the RAG retrieval module is used for training private knowledge and providing retrieval questions for an internal knowledge base. And an auxiliary reading module based on a large model provides intelligent text understanding and abstract generation functions. According to the method, the problem that efficient question answering and text processing cannot be carried out by fully utilizing a large model due to the fact that computing resources are limited on traditional portable equipment is solved.

The invention relates to the technical field of network security, in particular to a WEB application mimicry judgment system and method.The WEB application mimicry judgment method based on runtime data is characterized in that a parameter collection module is used for implanting instrumentation codes in key functions of WEB application programs and collecting runtime parameters of the WEB application programs in real time; the event conversion module is used for converting the acquired runtime parameters of the plurality of heterogeneous execution bodies into structured runtime events; the sequence alignment module is used for carrying out time sequence alignment on runtime events from different heterogeneous executors according to the business operation labels; and the risk judgment module is used for vectorizing the aligned text data of the runtime events, calculating a plurality of judgment factors and performing weighted fusion to generate a final confidence score, and performing feedback control to judge whether to trigger an execution body rotation operation or not according to the confidence score. According to the method, the WEB application runtime parameters are collected, through event conversion, sequence alignment and multi-factor weighted decision, the WEB application abnormal attack recognition accuracy is greatly improved, and the defect of traditional flow decision is effectively overcome.

The invention provides a geological analysis parameter protection method and system based on logarithm mapping, and relates to the technical field of geological exploration data protection, and the method comprises the steps: obtaining an original R parameter, building a linear mapping relation g = aR + b, building logarithm transformation mapping based on the linear mapping relation, converting the original parameter into a transformed parameter value, and obtaining a parameter value; and the transformed parameter values are provided for the data processing party for geological exploration operation, and the data owner recovers the original parameters through an inverse transformation formula after receiving the data obtained by exploration of the data processing party. According to the method, through logarithm transformation, an exploration company cannot identify the stratum type through conventional linear fitting, and sensitive geological information is effectively protected; meanwhile, irreversibility of the transformation process is ensured by adopting random parameters, and a data owner can completely recover original geological parameters through an inverse transformation formula; on the premise of protecting the data security, the normal exploration operation flow and the data processing precision are not influenced.

The invention provides an assembly pose prediction method and device, electronic equipment, a medium and a slave program product, and relates to the technical field of industrial intelligent assembly. The method comprises the steps of determining a target entity from a plurality of to-be-selected entities based on the matching degrees corresponding to the plurality of to-be-selected entities by determining the matching degrees between a specific entity and the plurality of to-be-selected entities, and determining the target entity from the plurality of to-be-selected entities based on first position information of the specific entity and second position information of the target entity. Determining an optimized pose parameter of the target entity, converting the optimized pose parameter into an affine transformation matrix, and controlling the target entity to perform pose adjustment according to the affine transformation matrix so as to realize corresponding assembly with the specific entity; according to the method, automatic identification and high-precision pose adjustment of the target entity in the entity assembly process are realized through a collaborative process of intelligent matching, optimization solution and precise transformation execution, and the efficiency and accuracy of assembly modeling are improved.

The invention discloses a robot dexterous operation synthetic data generation method and system based on reinforcement learning, and the method comprises the steps: carrying out the preprocessing of motion capture data containing hand joint angles and object poses, and converting the posture parameters of a hand parameterized model into a unified control space; generating an action instruction through an accumulated residual action control strategy; performing strategy training on the action instruction by utilizing a reinforcement learning strategy, wherein an observation state space comprises a hand joint state, relative position information and an object pose; in the strategy execution process, a hand object contact event is detected, and contact force information containing multi-coordinate system representation is generated; operation track data is generated based on the original motion capture data, synthetic operation data containing complete contact force information is generated through a hybrid strategy, and data quality screening is executed; the invention provides a technical scheme capable of efficiently generating robot flexible operation synthetic data containing contact force information with high quality.

The methods and systems for model parameter compression includes selecting a total number of bits in which to store a plurality of parameters wherein the number of bits is divided into a signal bit, an exponent set of bits and a fractional set of bits. Additionally, a maximum parameter and a minimum parameter of the plurality of parameters is received. The method maximizes the number of bits in the fractional set of bits subject to two constraints: 1) the fractional set of bits and the exponent set of bits is less than or equal to one less than the total number of bits, and 2) a relational model of the number bits in the exponent set of bits to a range of parameters. Once the fractional set of bits and the exponent set of bits are thus determined, the system may convert the plurality of parameters into a plurality of converted values utilizing a mapping function.

This application provides a method, apparatus, device, and storage medium for driving the action of a target object, relating to the field of artificial intelligence technology. The method includes: acquiring source speech and a target video, wherein the target video includes a target object; performing facial parameter conversion processing on the speech parameters of the source speech at each moment to obtain source parameters of the source speech at the corresponding moment; extracting parameters from the target video to obtain target parameters; reconstructing the target object in the target video based on a combination parameter obtained by combining the source parameters and the target parameters to obtain a reconstructed image; and generating a synthesized video from the reconstructed image, wherein the synthesized video contains the target object, and the action of the target object corresponds to the source speech. This application enables a smoother and more realistic synthesized video with speech-driven target object action, improving the visual effect of video synthesis.

The invention discloses a kettle-based multi-library-table data automatic filing method, device and equipment and a medium, and relates to the technical field of computers, and the method comprises the following steps: carrying out multi-dimensional verification on configured filing strategy parameters; if the multi-dimensional verification is passed, acquiring a target driver and a target adaptive plug-in from the multi-data source adaptive library by using the archiving strategy parameter, and establishing a connection relationship based on the target driver and the target adaptive plug-in; performing analysis and logic legality verification on the archiving strategy parameter, if the verification is passed, extracting target to-be-archived data from the source database table based on a generated archiving extraction instruction, and performing data conversion on the target to-be-archived data; and determining a target archiving storage mode based on the target archiving library table information and the converted data, generating an archiving task in combination with the archiving strategy parameters, the converted data and the connection relationship, executing the archiving task, and generating a corresponding archiving report. The multi-library table data archiving cost is reduced, and the universality is improved.

The invention discloses a parameter conversion method and device, equipment, a computer storage medium and a program product, and relates to the technical field of big data. The method comprises the steps that a first target parameter to be converted and a configuration modification instruction corresponding to a target conversion code are received, and a mapping relation exists between a target service type of the first target parameter and the target conversion code; the configuration modification instruction is used for indicating to modify a parameter conversion mode defined by the target conversion code from an initial conversion mode to a target conversion mode; in response to the configuration modification instruction, adjusting a conversion type and / or a conversion parameter set by a first conversion configuration rule in the N conversion configuration rules to obtain a second conversion configuration rule; and converting the first target parameter according to the second conversion configuration rule and the residual conversion configuration rules which are not subjected to configuration modification in the N conversion configuration rules to obtain a second target parameter. According to the embodiment of the invention, configuration modification and parameter conversion of the parameter conversion mode can be efficiently realized.

This invention discloses an artificial intelligence-based method for recommending buried pipeline layout, belonging to the field of intelligent recommendation technology. The method includes: converting the laying environment, construction purpose, and specific parameters of the buried pipeline into input vectors and inputting them into a pre-trained buried pipeline layout recommendation model to obtain an initial recommended layout scheme; mining historical new problems and solutions for each historical pipeline project during the pipeline laying process, forming several problem-strategy pairs; using a hierarchical clustering algorithm to perform cluster analysis on all problem-strategy pairs under all historical pipeline projects; calculating the similarity between the current buried pipeline laying environment and construction purpose and each historical pipeline project based on an autoencoder similarity algorithm to obtain the required retained problem-strategy pairs; and improving the initial recommended layout scheme according to the layout improvement type. The pipeline layout scheme is optimized and improved to generate a layout scheme that meets actual needs while also considering potential risk prevention.

A method for determining positions of a real moulding material front during a process to be carried out with a moulding machine, whereina simulation progression (SV) of a variable characteristic of the processa is calculated,positions of a simulated moulding material front are determined from the simulation,the real process is carried out, and at least one measurement progression (MV) of the at least one characteristic variable is measured directly or indirectly,a transformation is chosen, which has at least one parameter (ΔV, kp, Vunknown),the transformation is applied to the at least one simulation progression (SV), with the result that a transformed simulation progression (tSV) is formed, anda parameter value is determined for the parameter (ΔV, kp, Vunknown) such that a deviation between the measurement progression (MV) and the transformed simulation progression (tSV) is minimized according to a predetermined error measure or according to an operator input.

The application discloses a kind of patent retrieval statistical engine based on Elasticsearch, comprising: standard request parameter acquisition module, for obtaining standard request parameter;Elasticsearch query statement conversion module, for converting standard request parameter into Elasticsearch query statement;Patent retrieval module, for carrying out patent retrieval in Elasticsearch using Elasticsearch query statement;Standard response result obtains module, for converting Elasticsearch retrieval result into standard response result output;The application defines an interface specification of patent retrieval statistics, implements patent retrieval statistical engine based on Elasticsearch, can handle a variety of patent retrieval statistical needs including complex condition retrieval, multidimensional data statistical analysis, patent deduplication document merging and asynchronous patent retrieval.

A sensory control method includes a step of receiving, a step of converting, and a step of outputting. In the step of receiving, a sensitivity parameter is received. In the step of converting, the received sensitivity parameter is converted into, among a plurality of physical parameters included in physical properties relating to a sensory presentation, a physical parameter correlated with the sensitivity parameter. In the step of outputting, a sensory presentation signal based on the physical parameter obtained as a result of the conversion is output.

This application provides a seven-parameter transformation geometric quality inspection method and system for building components, belonging to the field of quality inspection technology. The method includes: constructing a point cloud dataset; performing semantic partitioning of the CAD model using the parsing results; establishing partition identification features of the semantic partitions using the CAD model, and performing global search matching of the point cloud dataset using the partition identification features as matching features, configuring the partition mapping using the global search matching results; performing partition feature matching of the CAD model based on the partition mapping using the point cloud feature set, and establishing initial values ​​for the seven parameters; performing recursive iterative optimization to complete fine registration; calculating local residuals based on the fine registration results, and generating quality inspection results using the local residual calculation results. This application can solve the technical problem of insufficient registration accuracy between point cloud data and CAD models in key functional areas in the prior art, achieving the technical effect of improving the registration accuracy of building component quality inspection.

The application discloses a parameter conversion method and device, equipment, computer storage medium and program product, and relates to the technical field of big data. The method comprises the following steps: receiving a first target parameter to be converted and a configuration modification instruction corresponding to a target conversion code, a target service type of the first target parameter and the target conversion code having a mapping relationship, and the configuration modification instruction being used for indicating that a parameter conversion mode defined by the target conversion code is modified from an initial conversion mode to a target conversion mode; in response to the configuration modification instruction, adjusting a conversion type and / or a conversion parameter set by a first conversion configuration rule in N conversion configuration rules to obtain a second conversion configuration rule; and converting the first target parameter according to the second conversion configuration rule and the remaining conversion configuration rules in the N conversion configuration rules which have not been subjected to configuration modification to obtain a second target parameter. According to the embodiment of the application, the configuration modification of the parameter conversion mode and the parameter conversion can be efficiently realized.