430 results about "Model matching" patented technology

The invention relates to the technical field of artificial intelligence and enterprise decision making, and discloses an intelligent decision making system and method based on an enterprise life index large model, and the system comprises a multi-source heterogeneous data collection module which is used for obtaining and associating various data in real time, and generating target enterprise multi-source heterogeneous data; the dynamic index system generation module is used for sequencing feature importance through a graph neural network and generating a dynamic evaluation index set containing survival, risk and growth indexes; the enterprise life index large model building module is used for fusing index sets, capturing enterprise state evolution, embedding risk factors and outputting multi-dimensional enterprise life indexes; the intelligent decision dynamic generation module is used for inputting the indexes into the decision strategy tree model and matching a strategy library to generate a decision instruction set; and the closed-loop feedback optimization module is used for realizing closed-loop optimization by acquiring execution data and combining Bayesian optimization dynamic correction parameters. According to the invention, the accuracy and timeliness of enterprise intelligent decision making can be improved.

The invention relates to the technical field of high-pressure dredge pump simulation calculation, and discloses a construction and application method of a high-pressure efficient dredge pump simulation calculation model. A model is established based on dynamic fluid-solid coupling parameters (mud pulsating pressure, impeller dynamic stress and the like), soil multiphase rheological parameters (particle phase volume fraction, viscous resistance coefficient and the like) and pipeline transient resistance parameters (transient pressure drop gradient and the like), and factors such as rotating speed-power matching and the like are incorporated to calculate an operation envelope and a critical failure threshold. A multi-physics field coupling analysis system comprising parameter configuration, dynamic simulation (integrating a flow field and a structural vibration solver) and a safety evaluation module is developed, after actually measured data and self-defined parameters are input, dynamic flow velocity distribution, pressure pulsation characteristics and the like are obtained through fluid-solid bidirectional coupling calculation, and an optimal configuration scheme is generated. The system supports database management, multi-rheological model matching and three-dimensional visualization, the simulation precision and the engineering efficiency are improved, and support is provided for performance optimization and safety evaluation of the high-pressure working condition.

The invention discloses an anomaly detection model selection method and system based on index portraits, and relates to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: collecting historical data of a target monitoring index, extracting multi-dimensional features to construct an index portrait, and classifying the index portrait; screening candidate anomaly detection models from the matching rule base, performing adaptation degree scoring in combination with a model compatibility evaluation mechanism, determining an optimal anomaly detection model to perform anomaly detection, and outputting an anomaly judgment result; when a plurality of models exist, generating a final abnormal result through a confidence-driven arbitration mechanism; for multi-index abnormity, causal reasoning is carried out in combination with an electric power knowledge graph, main alarm indexes are determined, and secondary indexes are processed according to a delay strategy; meanwhile, incremental updating of index portrait features, adaptive adjustment of model parameters and dynamic optimization of matching rules are supported, and a whole-process closed-loop mechanism covering'portrait construction-model matching-result fusion-alarm decision-feedback updating 'is constructed.

The invention discloses a power enterprise intelligent collaborative office method and system based on artificial intelligence, and relates to the technical field of power informatization. According to the intelligent collaborative office method and system for the power enterprise based on artificial intelligence, request data is received from a user terminal through a deep learning model, a service demand type, an emergency degree and task complexity are identified, a service priority index is generated, classification and priority distribution are carried out, and based on the service priority, the user terminal is subjected to intelligent collaborative office. The method comprises the following steps of: matching associated tasks and resource requirements through a model, retrieving related historical data to optimize a resource configuration path, dynamically tracking task progress, execution state and resource consumption, identifying a bottleneck in an execution process and generating an optimization instruction, and finally, dynamically adjusting model parameters and resource configuration rules through an adaptive learning algorithm. And a closed-loop feedback mechanism is formed. Information flow and resource configuration are effectively integrated, the flexibility and response speed of collaborative office are improved, and the development requirements of smart grids and smart cities are met.

The invention particularly relates to a laser point cloud over-break and under-break volume calculation method for tunnel engineering, and relates to the technical field of tunnel engineering construction quality detection. Modeling a tunnel design contour; matching and aligning the point cloud with the design model; carrying out over-break and under-break area division and volume calculation; and result visualization and report output. According to the method, point cloud preprocessing parameter optimization and high-precision registration are achieved through multi-dimensional quantitative analysis, and the reliability of an over-break and under-break calculation benchmark is remarkably improved; in a point cloud denoising link, a voxel evaluation coefficient is introduced to ensure that the filtered point cloud not only keeps key details such as tunnel vault radian and side wall broken lines, but also can simplify the data volume; in a model registration stage, outlier interference is eliminated and a strict iteration stop condition is set, so that a reliable reference is provided for back break deviation calculation.

The invention discloses a large model scheduling system and method, a server, a medium and a product. The large model scheduling system comprises a model management module, a computing power scheduling module and a model scheduling module, the model management module is used for performing corresponding model management on a target large model in response to a management operation of the client for the target large model; the computing power scheduling module is used for determining a target computing power resource node matched with the large model according to model parameter information of the large model for each large model, and mounting the large model to a container on the target computing power resource node; and the model scheduling module is used for acquiring the container parameter information of each container, and performing model scheduling processing on the large model mounted on the abnormal container when the container parameter information is abnormal. According to the technical scheme, the computing power resource utilization rate is improved, and efficient computing power resource selection and large model scheduling are achieved.

The invention provides a BIM-based building engineering three-dimensional modeling method and system, and relates to the technical field of building information modeling. The method comprises the following steps: generating a point cloud model through multi-source data acquisition and preprocessing; semantic annotation is carried out on the model, and a structure complexity coefficient and a component association degree parameter are introduced; constructing a three-dimensional topological relation and optimizing the grid model; construction management information is embedded into the model, and dynamic updating and multi-dimensional data integration are achieved; and finally, verification and output are carried out through point cloud and geometric model matching. According to the method, the modeling efficiency and precision are improved, the data integration and dynamic adaptability of the model are enhanced, and the storage performance and the calculation burden in a complex scene are remarkably optimized.

The present application relates to a network data analysis model scheduling method and apparatus, a computer device, a storage medium, and a computer program product. The method comprises: in response to a task request initiated by a service requester for network data analysis tasks, determining the request type of the task request (S210), wherein the task request is used for requesting a network data analysis service network element to allocate models to process the network data analysis tasks; performing matching from a pre-updated prediction model matching library to obtain a target prediction model corresponding to the request type (S220), wherein the target prediction model is used for outputting an operation parameter prediction result for the network data analysis tasks, and the operation parameter prediction result represents resource occupation information required for completing the network data analysis tasks; and scheduling the network data analysis tasks according to the operation parameter prediction result (S230).

The invention provides an artificial intelligence routing method. The method comprises the following steps: S100) an AI routing agent receives and analyzes at least one piece of prompt information; s200) the AI routing agent analyzes at least one request intention of the user according to the prompt information, decomposes and splits the request intention into a plurality of sub-tasks, and plans an interdependence relationship among the sub-tasks to form a task list; s300) the AI routing agent selects an AI model agent interface registered on an AI gateway according to the characteristics of an artificial intelligence model so as to match the requirements of the task list, and the selected AI model matching the requirements of the task list is the selected AI model; s400) calling and executing each selected AI model, and returning a result to the AI routing agent; and S500) integrating the prediction results of all the selected AI models by the AI routing agent, and generating a response.

The invention discloses a rotor punching and stacking welding defect identification system based on edge calculation, and relates to the technical field of rotor welding identification, and the rotor punching and stacking welding defect identification system comprises a data acquisition module which is used for carrying out real-time acquisition on smoke dust data generated in the welding process, and the smoke dust data comprises smoke concentration, suspended particulate matter concentration and smoke infrared thermal imaging temperature; the model matching module is used for comparing the smoke concentration, the suspended particulate matter concentration and the smoke infrared thermal imaging temperature with corresponding preset reference models respectively to obtain abnormal data with an abnormal trend; and the judgment module is used for performing time sequence analysis on the abnormal data, calculating a standard deviation and a mean deviation rate based on a sliding window, and generating a corresponding early warning growth value. According to the method, a dynamic alarm value generation and attenuation mechanism is constructed, so that false alarm caused by continuous influence of historical data is avoided, and the real-time response accuracy of the integrated value is improved. And through a pre-judgment mechanism, it is ensured that alarm value attenuation is executed only on the premise that data are stable or no early warning growth trend exists.

The invention relates to the field of artificial intelligence, in particular to an answer matching method and system for an artificial intelligence large model, and the method comprises the steps: obtaining a question text of the nth question asked by a user, carrying out the feature extraction of the question text through a natural language processing technology, and constructing a question feature set; inputting the question feature set into a difficulty evaluation model, and outputting a difficulty score of the question text; based on the difficulty score of the question text, performing matching degree calculation on all the artificial intelligence large models in the large model library by adopting a large model matching degree formula to obtain the matching degree of each artificial intelligence large model; and analyzing the matching degree of each artificial intelligence large model according to a preset large model matching strategy to obtain a selection scheme. By adopting the method, the matching degree between the question and various large models can be accurately and quantitatively measured, so that the professionalism and accuracy of question answering are improved; and meanwhile, the continuous dialogue processing capability of the large model can be improved.

Embodiments of this application provide a model matching method and a communication apparatus. The method includes: receiving an identifier of a first artificial intelligence AI model from a first communication apparatus; and determining, based on the identifier of the first AI model, whether a second AI model is deployed on a second communication apparatus, where the second AI model is an AI model that matches the first AI model. In this way, an identifier of an AI model can be exchanged to determine, based on the identifier of the AI model, an AI model that matches the AI model.

The invention relates to the technical field of radar detection, and provides a multi-model filtering target positioning method, system, device, product and medium, and the method comprises the steps: determining a model matching parameter, carrying out the initialization assignment, obtaining an initialization parameter, and obtaining a sampling point set and a weight coefficient through the initialization parameter; performing state one-step prediction to obtain a state one-step prediction value, and performing secondary sampling and measurement one-step prediction to obtain an autocorrelation covariance and a mixed covariance; carrying out SVD operation to obtain a decomposition value set, detecting a target by a target radar to obtain a radar detection value, obtaining a Kalman gain matrix according to the mixed covariance, and calculating state estimation and an initial covariance; updating the model probability to obtain a Markov transition probability, and normalizing the Markov transition probability to obtain a normalized probability; and state estimation and target covariance are obtained through state fusion, so that the target orientation is determined. According to the invention, accurate tracking of the high maneuvering target is realized.

The invention provides a part free edge visual matching method and system based on model matching, and the method comprises the steps: analyzing an assembly three-dimensional model or a cutting two-dimensional model of a ship part, and building a part model database; determining a visual matching template database according to the two-dimensional contour of the part and the part features; collecting an image of a to-be-detected part; determining part features of the to-be-detected part; matching the part features of the to-be-tested part with the visual matching template database, and determining a matching result; if the matching result is successful matching, determining pixel coordinates of the to-be-detected part in the image of the to-be-detected part according to a visual matching template corresponding to the image of the to-be-detected part; and converting the pixel coordinates of the to-be-detected part in the image of the to-be-detected part into actual coordinates of a physical space, and determining the free edge of the to-be-detected part on the machine tool of the ship. According to the method and the device, the free edges of various parts of the ship are positioned, and the efficiency and the quality of ship manufacturing are improved.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving text submitted with a request. Token values are automatically extracted for selected token types from the text using a machine learning model. Tokens are identified in the text, and for each token, features and a token type of the token are determined, along with a score that indicates a likelihood that the token has the token type. A token value is extracted for the token from the text. Extracted token values are compared to request data values. A request value for the selected token type is identified in the data values for each selected token type. The extracted token value for each selected token type is compared to the request value. An audit alert is generated in response to determining that an extracted token value does not match a corresponding request value.

The invention belongs to the field of model matching, and discloses a three-dimensional model matching method and device and electronic equipment, and the method comprises the steps: converting a to-be-matched three-dimensional model into first point cloud data, carrying out the preprocessing of the first point cloud data, setting a registration parameter according to a preprocessing result, extracting a first feature of the first point cloud data, and carrying out the matching of the first feature and the second feature; and registering the first feature with a second feature of the historical three-dimensional model according to the registration parameter to determine a matching result. Three-dimensional model matching is converted into feature matching, and the automation degree and efficiency of the matching process are improved. According to the method, feature extraction is performed on the first point cloud data to obtain the first feature, and then the first feature is registered with the second feature in the preset feature library, so that the similarity between the to-be-matched model and the historical model can be quickly and accurately evaluated. In the whole process, the dependence on professional modeling software is reduced, the consumption of manpower and computing resources is reduced, and meanwhile, the efficiency and accuracy of three-dimensional model matching are improved.

The invention relates to the technical field of bolt length detection data processing, in particular to a bolt length detection system, which is characterized in that a three-dimensional data acquisition module generates a high-density point cloud and extracts a thread curvature extreme point, and a dynamic model matching module screens an optimal lead angle parameter based on a Hausdorff distance; and constructing an axial expansion model carrying phase offset gradient features. The projection mapping correction module adopts a genetic algorithm to optimize a compensation coefficient matrix, and generates a dynamic weight correction parameter to suppress geometric distortion; and the phase error correction module combines Fourier spectrum decomposition and an exponential decay weight coefficient to construct a frequency domain transfer function to eliminate an axial accumulative error. According to the method, the problems of projection distortion and phase deviation caused by the lead angle are effectively solved through geometric-phase double-domain correction, dynamic parameter iteration and an error collaborative suppression mechanism, and the spatial positioning precision of the feature points at the tail end of the thread is improved.

The invention discloses a virtual power plant automatic response tracking method based on an energy storage EMS system. The method comprises the following steps: S1, acquiring real-time load consumption data of the lower end of an energy storage system; s2, acquiring historical load consumption data; s3, obtaining an order demand of the virtual power plant in a future prediction time period; s4, obtaining a historical order demand of a virtual power plant; s5, predicting future load demand data according to the historical load consumption data and the prediction model; s6, predicting future order influence factors of the virtual power plant according to historical virtual power plant orders and execution results, determining future order response feasibility, formulating a control strategy, and enabling an energy storage system to execute; and S7, predicting future load demand data according to the historical load consumption data and the prediction model, cleaning the historical load consumption data, deleting abnormal data in the historical load consumption data, obtaining the cleaned historical load consumption data, importing the cleaned historical load consumption data into the prediction model, and outputting the future load demand data. According to the virtual power plant automatic response tracking method and device based on the energy storage EMS system, model matching derivation can be continuously carried out on the acquired data, a scheduling strategy is corrected, the complex workload of manual analysis is effectively reduced, the order response efficiency of the virtual power plant is improved, the prediction accuracy is improved, the total power consumption cost is reduced, and the method and device are suitable for popularization and application. And the benefit effect of total grid connection is improved.

The invention discloses a deformed workpiece twin modeling method based on measurement point cloud and three-dimensional CAD model matching. The deformed workpiece twin modeling method comprises the steps that an improved GFO-ICP registration algorithm is adopted to solve the initial coarse registration problem of the measurement point cloud and a CAD model in combination with geometric feature constraint and self-adaptive step length optimization; extracting a key geometric curved surface of the point cloud by adopting a clustering-based plane segmentation algorithm, and extracting a semantic feature vector of a segmented region in combination with a Pointnet + + network; a cosine similarity matching mechanism is constructed by integrating mixed descriptors of geometric features (areas and normal vectors) and semantic features, and accurate binding of a point cloud curved surface and a curved surface of a CAD model is achieved. According to the method, the problem of assembly geometric deviation caused by workpiece deformation due to manufacturing errors in compressor and fan rotor assembly is solved, multi-link error accumulation is effectively processed, digital twin modeling of high-precision deformed workpieces is achieved, and the assembly simulation effect and intelligent manufacturing application are improved.

The invention discloses an automatic auditing method and system based on ship-shore model matching fusion, and belongs to the technical field of data processing.The method comprises the steps that ship-shore matching related documents of a user are interacted, and the ship-shore matching related documents support multiple formats; activating a multi-model fusion processing module to perform preprocessing and multi-modal analysis on the ship-shore matching related document, extracting a text, a visual element and structured data, and generating a structured data object; a preset rule engine and the large language model are called to process the structured data object in parallel, the rule engine executes deterministic rule verification, and the large language model processes uncertainty semantic recognition determination; and fusing output results of the rule engine and the large language model to generate an audit result and a risk prompt. The problems that in the prior art, ship-shore matching auditing depends on labor, the efficiency is low, errors are large, multi-format documents are difficult to process through a single technology, rule and semantic coverage is incomplete, information fragmentation is caused, and auditing accuracy and efficiency are difficult to consider at the same time are solved.

The invention discloses a workpiece grabbing and releasing system and method based on multi-modal data fusion and Kalman filtering tracking. Comprising a hardware grabbing and releasing module, a mechanical arm control module, a target recognition module, a dynamic tracking module and a pose estimation module. Firstly, a depth camera collects RGB images and depth point cloud data of the surface of a conveying belt, then the position and the type of a workpiece are recognized, and three-dimensional point cloud of a target workpiece is extracted from the point cloud data according to the position of the workpiece. And the pose estimation module searches the stored model library for the point cloud of the template model matched with the target workpiece, takes the point cloud as an initial iteration value of the ICP algorithm, and outputs the optimal grabbing pose of the target workpiece. And the dynamic tracking module dynamically compensates the position of the target workpiece in the pose estimation process through a Kalman filtering algorithm. And the mechanical arm control module drives the mechanical arm to perform position adjustment according to a prediction result of the dynamic tracking module, and calculates an instruction for controlling the mechanical arm to complete the grabbing action according to the optimal grabbing pose.

The invention discloses an intelligent task allocation method and related equipment. The method comprises the steps of receiving multi-format task description information, extracting multi-dimensional attributes to generate a task vector, constructing an executive vector containing multiple capability dimensions, matching an optimal main executive through a multi-layer perception model, and performing real-time monitoring and adjustment. According to the scheme, the existing defects are specifically solved: multi-format information is analyzed to extract multi-dimensional attributes, so that a system comprehensively understands the context of a task, and the understanding insufficiency of a traditional method is avoided; constructing a multi-dimensional executive capacity vector and combining a scoring model to realize accurate matching of a task and an executive, and solving the problem of unreasonable distribution caused by asymmetry of man-machine capacity; the execution state is monitored in real time, redistribution is triggered, a dynamic feedback closed loop is formed, and the defect that the dynamic performance of a labor division mechanism is insufficient is overcome, so that the task execution flexibility and reliability are improved, resource waste and decision errors are reduced, and the execution efficiency is improved.

The invention discloses a laser point cloud-based industrial equipment digital twin model rapid reconstruction method and system, and the method combines a model matching mode and a point cloud reconstruction mode, and effectively solves the problem of twin model reconstruction in different scenes. According to the first mode, on the basis of an existing three-dimensional model, through shape matching, feature extraction and model retrieval methods, registration of point cloud data and the three-dimensional model is combined, and rapid reconstruction of the equipment twinborn model is successfully achieved. The mode is suitable for equipment with an existing standard three-dimensional model, the repeated utilization rate of the model can be improved, and the reconstruction time and cost are reduced. In the second mode, the reverse grid model construction method based on the point cloud data is provided for equipment which cannot be retrieved through an existing model. Through an improved greedy projection triangulation algorithm, point cloud data is converted into a grid model, and the precision and authenticity of the model are improved in combination with a grid optimization algorithm and a surface texture mapping technology. According to the method, the digital twinborn model of the industrial equipment can be efficiently and accurately reconstructed, the modeling difficulty in an industrial scene is relieved, the actual demand of field modeling is met, and the method has high application value and practicability.

The invention belongs to the technical field of data processing, and discloses a deep learning optimization system and method for intelligent equipment. Comprising the steps of collecting data and performing data cleaning to obtain accurate equipment parameters; identifying a target intelligent device resource level based on the accurate device parameters; performing energy consumption scheduling on the target intelligent device based on the resource level to obtain a scheduled intelligent device; performing disturbance simulation on the accurate equipment parameters, and outputting a disturbance stability index; appropriate parameter screening is carried out on the accurate equipment parameters based on the disturbance stability index, and stable equipment parameters are output; performing risk assessment on the stable equipment parameters to obtain parameter risk indexes; performing model matching on the scheduling intelligent equipment based on the parameter risk index to generate a model adjustment strategy; performing scene simulation based on the model adjustment strategy, performing loading optimization on the model adjustment strategy based on a simulation effect, and outputting an optimization model strategy; the efficient deep learning optimization method for the intelligent equipment is realized.

The invention relates to the technical field of intelligent recruitment management, and discloses an intelligent matching recruitment management system based on a large model. The system comprises a talent database construction module which collects multi-dimensional ability characteristics of job seekers and enterprise post demand data and generates a three-dimensional recruitment data set containing ability, post and time dimensions; the personnel post adaptation field modeling module performs dynamic integrating degree analysis on the data set, constructs a three-dimensional personnel post adaptation field, and identifies adaptation field critical point distribution and a flowing path; the key feature screening module extracts core adaptive features meeting a threshold value; the large model matching engine module inputs the core adaptive features into a pre-training large model generator, introduces adaptive field topology and feature continuity constraints, and outputs optimized dynamic adaptive field distribution; the environment variable compensation module fuses the dynamic indexes of the labor market to generate compensation parameters, and corrects the core adaptation features; and the strategy generation module generates a post matching strategy set and a talent optimization path to adapt to the requirements of the recruitment market.

The invention discloses a high-altitude track AGV accurate positioning and map building method and system. The method comprises the following steps: collecting data through multiple sensors; constructing a fusion model, and dynamically distributing fusion weights of laser and visual SLAM based on visual image information entropy and laser point cloud geometric feature distribution; semantic features such as track planes and edge lines are extracted and matched with the prior model; and jointly inputting the weight fusion pose, the UWB constraint and the semantic constraint into a back-end graph for optimization, solving an optimal pose and updating the map. The system comprises corresponding modules. Through cooperation of dynamic weight distribution and semantic auxiliary optimization, the problems of low AGV positioning precision and poor robustness in a high-altitude orbit environment are solved, dynamic closed-loop updating of a map can be realized, and the automation and intelligence level of an intelligent detection system is remarkably improved.

The invention relates to the technical field of multi-cloud operation and maintenance, in particular to a cloud platform operation and maintenance method and system and a storage medium, and the method comprises the steps: collecting multi-modal data of a plurality of cloud platforms; a multi-objective optimization decision is generated through an analysis decision model, the analysis decision model comprises a deep learning model, a pre-training language model, a neural network model and an evolutionary algorithm model, and a resource demand range in a future preset time period is predicted through the deep learning model; matching a historical fault library through a pre-training language model to generate a similar case recommendation list; calculating a fault propagation probability through a graph neural network model; generating a multi-objective optimization decision by the evolutionary algorithm model in combination with a resource demand, a similar case recommendation list and a fault propagation probability; and executing a repair operation according to the multi-objective optimization decision. According to the method, global optimization decision and closed-loop operation and maintenance can be realized, and efficiency and stability are improved.

The invention discloses a man-machine interaction enhancement method based on intelligent driving. The man-machine interaction enhancement method comprises the following steps: S1, acquiring identity information of a current driver; s2, finding a corresponding target interaction model based on identity information matching; s3, matching and searching a corresponding optimal cockpit state; s4, adjusting the current state of the cockpit; s5, monitoring the behavior state of the driver in real time to obtain monitoring data; the behavior state comprises a face behavior and a head behavior; s6, predicting the fatigue state of the driver based on the monitoring data and a fatigue judgment model; and S7, based on the predicted driver fatigue state, automatically adjusting a human-computer interaction interface and an interaction mode. The man-machine interaction enhancement system comprises a monitoring module, an analysis module, an interaction module and the like. According to the method, the cockpit can be quickly adjusted to the optimal adaptive position to adapt to individual differences, and the man-machine interaction comfort is improved; the accuracy of fatigue judgment can be improved, the man-machine interaction mode and interface can be accurately adjusted, and the trust degree of man-machine interaction is enhanced.

The invention relates to the technical field of computers, and provides a server state monitoring management system and method based on data analysis, and the system comprises a monitoring management middle platform, a data collection module, an operation mode determination module, a model matching module, a model prediction module and a state monitoring module. The data acquisition module acquires operation data of the target server at the current time; the operation mode determination module determines a current operation mode of the target server based on the network flow data and the process information; the model matching module is used for matching a target fault prediction model based on the current operation mode; the model prediction module inputs the target CPU usage rate, the target memory usage rate and the target disk read-write speed into a target fault prediction model to obtain a fault prediction result output by the target fault prediction model; the state monitoring module monitors the operation state of the target server based on the fault prediction result. According to the invention, the continuity of enterprise businesses is guaranteed, and the reliability and stability of server management are improved.

The present application describes methods of data-driven AI / ML model signaling for AI / ML based pairable model search signaling, where techniques for pre-configuring and signaling about searching model pairing of machine learning model operations in wireless mobile communication systems including base stations (e.g., gNB) and mobile stations (e.g., UE) are disclosed. The minimum model information set is provided to facilitate the search for pairable model(s) to match through a model pairing search process.