306 results about "Empirical data" patented technology

The present application discloses a data processing method, device and system, and a storage medium. The method of the present application comprises: constructing a quality problem dictionary on the basis of an experience database, the quality problem dictionary comprising data quality problems and corresponding problem identifiers; acquiring data to be processed, and identifying a target data problem present in the data to be processed; querying the quality problem dictionary for the target data problem to obtain a corresponding target problem identifier; adding the target question identifier into target data by means of a predefined field; reading a plurality of target fields in the target data, determining the plurality of target fields according to a preset fusion rule, and on the basis of a determination result, determining whether there is conflict data; if there is conflict data, identifying a service application scenario of the current data, and selecting an authority source determination strategy on the basis of an identification result; and processing the credible source authority level of each field in the conflict data according to the authority source determination strategy, and updating the database.

The invention discloses a multi-agent collaborative scheduling method and system based on maximum entropy reinforcement learning, and the method comprises the steps: generating an environment state vector according to the first environment data of a production line; generating an initial production task according to the environment state vector; performing task scheduling operation on the initial production task, and generating a path plan; storing the environment state vector, the initial production task and the path planning to obtain empirical data; according to the empirical data, training an initial strategy model through a maximum entropy reinforcement learning algorithm to obtain a target strategy model; and obtaining a multi-agent collaborative target scheduling strategy according to the target strategy model. The multi-agent cooperation efficiency can be improved, so that the production efficiency is improved, and the method can be widely applied to the technical field of agent scheduling.

The invention provides a man-machine cooperation method and system based on robot process automation, and relates to the technical field of automation, and the method comprises the steps: building a data perception processing network, collecting a user operation instruction, a system operation state and task environment data, obtaining a task feature vector through semantic analysis, and matching a business model to generate a task processing strategy. A man-machine cooperative processing framework is constructed based on the strategy, a robot unit processes a structured task, a manual operation unit processes an unstructured task and is connected through an information interaction channel, and transmission of a data analysis result and decision suggestions and updating of processing regulation information when deviation exceeds a threshold value are achieved. Besides, a knowledge migration processing network is constructed, business rules and expert experience data features are extracted, a scene migration model is trained, business feature vectors are mapped to a preset scene feature space, and after validity verification, parameters of the data perception processing network and the man-machine cooperative processing framework are updated.

The invention provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention discloses an artificial intelligence robot path planning method and system, and the method comprises the steps: 1, carrying out the comprehensive perception of an environment geometric structure, object semantics and dynamic obstacles, eliminating scene differences, and further constructing the topological graph representation of an environment; 2, pre-training a path planning model, designing a scene context encoder, encoding a scene specific rule into a low-dimensional vector, and outputting a scene context code and updated planning model parameters to provide a planning capability adapted to a new scene for the step 3; 3, dynamically adjusting a track according to real-time sensor data by adopting a hierarchical decision-making architecture; step 2, recording success / failure path segments in the new scene, regularly updating a local planning module, regularly feeding back empirical data in the new scene to the step 2, and triggering a conservative obstacle avoidance mode when the confidence coefficient is lower than a threshold value; and 4, constructing a quantitative evaluation system, building an automatic test platform, simulating diversified scenes and dynamic interference, and automatically generating a test case.

The invention relates to the field of traffic transportation information engineering, and discloses a group division method and system based on individual movement mode dynamic hypergraph clustering, and the method comprises the steps: constructing an individual movement mode feature matrix under each time slice; constructing an individual movement mode hypergraph under each time slice, and modeling a'many-to-many 'complex high-order association relationship among individual movement modes under the whole time period; proposing a dynamic hypergraph clustering model based on a moving mode, and performing category division of individual associated groups; the provided model is applied to a series of empirical data sets for training and testing, and it is proved that the model can obtain good group division performance superior to that of a baseline model.

The invention relates to a dynamic ontology construction method based on a large model, and belongs to the field of artificial intelligence, large models and knowledge maps. According to the method, expert knowledge, historical experience, a database and industrial standard data are comprehensively utilized, entity types, attributes and relationships are defined, and an initial ontology O1 is constructed; by means of rich knowledge contained in a large model, extension from a large class to a subclass is achieved, on this basis, extraction of subclass knowledge is further achieved through a large model technology, and an ontology O2 is formed; for structured, semi-structured and non-structured data updated in real time in the business data, forming an ontology set O3 based on small-sample prompt learning of a large model; the merging and updating of the domain ontology are realized through a Function Calling technology based on LLM (Logical Language Model); a function is defined through word similarity calculation based on a'known network ', mathematical calculation is carried out on a large model, merging and updating of domain ontologies are achieved, and an ontology set O4 is formed. According to the invention, the quality of ontology construction is improved.

The invention discloses an efficient medicine logistics order scheduling and distribution method and system, and relates to the technical field of medicine logistics, and the method comprises the steps: constructing a multi-modal medicine feature extraction module, and outputting an order constraint matrix; establishing an adaptive space-time distribution network according to the order constraint matrix, and performing dynamic grid division on a distribution area to generate a multi-level distribution area model; constructing a double-layer heterogeneous graph neural network, and generating a distribution path planning candidate scheme set considering drug characteristics; defining a state space and an action space, constructing a dual-target reward function, and carrying out iterative training to obtain a distribution scheduling strategy model; and aggregating multi-region distribution empirical data by adopting a federated learning framework, and periodically updating the scheduling strategy model based on a differential privacy mechanism. According to the method, the multi-modal drug feature extraction module is constructed, and the image, text and storage condition information of the drug are fused, so that the drug storage requirement and the distribution limitation condition can be identified more accurately, and the identification accuracy of the distribution constraint is improved.

The invention provides a process knowledge graph construction method, which comprises the following steps of: acquiring original process data, establishing a process field dictionary by combining expert knowledge of a process production field, and respectively preprocessing structured original process data and unstructured original process data based on the dictionary to obtain standard process data; classifying the standard process data according to six dimensions of workpiece data, feature data, process equipment data, process method data, material characteristic data and process history and experience data so as to establish an ontology model of the process knowledge graph and form a mode layer of the process knowledge graph; inputting the various standard process data into a pre-trained deep learning model to identify and extract entities and relationships thereof so as to construct a data layer of the process knowledge graph; and integrating the mode layer and the data layer to form a complete process knowledge graph. The process knowledge graph constructed by the method is wide in applicable process range and high in intelligent degree.

The invention provides a filling process prediction method and device based on a discrete element method and data driving, and relates to the technical field of bulk material forming, and the method combines the physical modeling advantage of the discrete element method and the powerful prediction capability of a data driving method. The filling process of the granular material under different process parameters is simulated through a discrete element method, key quality indexes are obtained to serve as input of a data driving model, and the process parameters serve as output for training. The model not only can accurately predict the filling quality, but also can adjust the process parameters in real time according to the target quality index, so that the intelligent optimization of the filling process is realized. According to the method, the calculation efficiency is greatly improved, the generalization ability of the model is enhanced, the dependence on empirical data is reduced, a more efficient and accurate solution is provided for the filling process in the industries of building materials, pharmacy, powder metallurgy and the like, the product quality is improved, the production cost is reduced, and the research, development and application of novel materials and equipment are accelerated.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

The invention discloses a sewage treatment drug delivery control method based on reinforcement learning and particle swarm optimization, and the method achieves the precise control of turbidity and the optimization of drug cost through the dynamic adjustment of particle swarm parameters and the fusion of a reinforcement learning strategy. According to the technical scheme, the method comprises the following steps: combining a PSO algorithm with a DDPG algorithm to construct a drug control model; designing a reward function; the empirical data with the state coverage rate higher than a first preset threshold value are stored in a full-reservation empirical pool, the empirical data with the current policy access larger than a second preset threshold value are reserved in a first-in first-out mode through a policy empirical pool, and mixed sampling is carried out according to a preset proportion; training a drug control model; and inputting the current turbidity value, the drug delivery amount of the last time step, the water inlet flow, the parameters of the water quality detection camera and the future turbidity value into a trained drug control model, and outputting the current drug delivery amount. Through prediction-control closed-loop integration, a raw water turbidity prediction result is fed back to the optimizer, and the method is suitable for real-time adaptive control of a sewage treatment system.

The invention relates to the technical field of parameter optimization, in particular to an automatic kernel network parameter optimization method, which comprises the steps of constructing an enhanced deep Q network model, and integrating the enhanced deep Q network model with a priority playback buffer area, a meta learning module, a Bayesian optimizer and a neural architecture search module; using performance index data to train an enhanced deep Q network model, the training process including using a priority playback buffer to store and sample empirical data, using a meta-learning module to perform task adaptation, and monitoring training indexes of multiple dimensions to evaluate the convergence state of the model; selecting a kernel parameter adjustment action according to the current state through the trained enhanced deep Q network model; executing the selected kernel parameter adjustment action, and evaluating a parameter adjustment effect based on the multi-target reward function; and updating the enhanced deep Q network model according to an evaluation result, wherein the priority playback buffer area and the Bayesian optimizer are utilized in the updating process.

The invention relates to the technical field of traffic transportation environment engineering and carbon emission evaluation, in particular to a construction method and equipment of a plateau gradient zone vehicle CO2 emission factor calculation model and a medium. Through the method, a route-level CO2 emission factor distribution map is obtained, and different quantitative calculation models are formed for different altitude intervals. And a specific and quantitative design basis is provided for low-carbon highway design. It is revealed for the first time that the CO2 emission rate of the plateau gradient zone has a remarkable altitude segmentation effect through empirical data, and a method for constructing a calculation model in a partition mode is innovatively provided. According to the method, the defects that a traditional single model is poor in adaptability and misaccurate in prediction in a complex plateau environment are overcome, and differentiated quantitative models (such as a low-altitude quadratic function and a high-altitude S-type function) are established for different altitude intervals; the finally formed partition model provides a core theoretical tool and decision basis for plateau highway carbon emission accurate accounting, low-carbon route optimization and green traffic construction.

The invention relates to the technical field of image processing, in particular to an endoscope image processing method based on photodynamic imaging and an endoscope imaging system.The endoscope image processing method based on photodynamic imaging comprises the steps that a focus image is collected through a photodynamic imaging endoscope, and preprocessing, three-dimensional reconstruction, combined reaction and curative effect database analysis are conducted; configuring an image processing module according to the request, and docking three-dimensional display; according to the method, the technical problem that the imaging request cannot be quickly and accurately responded due to the fact that the speed of endoscopic image processing through empirical data is low is solved, and the effects that the endoscopic image is finely processed, the photodynamic reaction features are extracted and the imaging request cannot be quickly and accurately responded are achieved. The technical effects that the optimal image enhancement parameter is adaptively searched, the imaging request is quickly responded, and the image targeting enhancement is performed are achieved, so that the focus area is accurately and visually displayed in a three-dimensional manner, and the output quality of the endoscope image is remarkably improved are achieved.

The invention relates to the technical field of operation and maintenance of new energy electric field fan equipment, in particular to a fan fault decision-making method based on region, model and knowledge graph dynamic evolution, which comprises the following steps: S1, constructing a maintenance experience database according to historical data, and updating data according to a newly added maintenance work order; s2, carrying out quantitative modeling according to regional features and model features in the maintenance experience database, constructing a feature matrix, and carrying out dynamic updating according to a newly added maintenance work order; s3, constructing a knowledge graph according to the maintenance experience database, fusing the feature matrix and the knowledge graph, and performing dynamic knowledge graph updating through real-time data driving and closed-loop feedback optimization double-introduction; and S4, adopting a self-optimization aided decision, predicting a fault occurrence probability through a reinforcement learning strategy network, and matching a solution in the knowledge graph. The method is suitable for wind generating set fault auxiliary decision making, and the wind power operation management efficiency is greatly improved.

The invention relates to a 5GC fault diagnosis root cause analysis method for realizing intelligent decision and iterative closed loop, and belongs to the field of 5G mobile communication.The method comprises the steps that experience of various fault analysis steps is extracted and analyzed, and a diagnosis model is designed according to entity nodes and entity relations; empirical data is collected for preprocessing, converted according to a model structure and then imported into a graph database, and a diagnosis model is generated; carrying out dynamic planning on the current decision path; establishing a local knowledge base by adopting historical data and knowledge documents, training a large model, and updating a diagnosis model; traversal analysis of the whole decision path is completed based on the diagnosis model, and a final fault analysis conclusion and a treatment scheme are generated; obtaining the diagnosis result of each node by using the atomic power of the 5GC system; and displaying a return result of each step node and a final fault analysis conclusion and a disposal scheme. According to the method, the current network fault can be quickly identified, positioned and processed, meanwhile, processing experience is accumulated, and the network monitoring and fault processing capacity is continuously improved.

The invention discloses an intelligent agent optimization method and device, equipment, a storage medium and a product, relates to the technical field of artificial intelligence, and discloses a method for recording a task execution track of an intelligent agent in a process of executing a task based on a target large model in the intelligent agent; extracting execution empirical data from the task execution track, wherein the execution empirical data comprises problems existing in the task execution process, generation reasons of the problems and solutions; and generating a target training sample based on the execution experience data, the target training sample being used for training the target large model to optimize the agent. According to the method, the efficiency of obtaining the training sample can be remarkably improved, so that the optimization efficiency of the intelligent agent is improved, and the ability of the intelligent agent to deal with rapidly changing task requirements and complex application scenes is enhanced.

The embodiment of the invention provides an unmanned ship cooperative hunting control method, and belongs to the technical field of control, and the method specifically comprises the steps: distributing a main hunting target for each chasing unmanned ship; for each chasing unmanned ship, an original observation vector containing a variable-length interaction sequence is formed according to the distributed main hunting target of the chasing unmanned ship; a variable-length interaction sequence in the original observation vector is converted into a standardized state vector of a fixed dimension through a two-stage attention mechanism; inputting the standardized state vector into a strategy network of the unmanned ship, and outputting a continuous action control instruction; and calculating an instant reward based on empirical data obtained after executing an action control instruction, and performing iterative updating on a strategy neural network and a corresponding value neural network by adopting a centralized training and decentralized execution mode based on a multi-agent depth deterministic strategy gradient framework to obtain a control model to generate a cooperative control scheme. Through the scheme disclosed by the invention, the control precision and adaptability are improved.

A system is described that combines outputs of network simulations, which are used for capacity planning, with empirical data of network element failure probabilities, to produce what traffic utilization is needed for each network device interface to achieve a specified availability target. Utilization of each interface can be captured for each failure case simulated. From this failure data, the utilization distribution for each link can be produced, across all failure cases. This utilization-by-failure data from simulation is combined with the empirical mean-time between failure (MTBF) and mean-time to recovery (MTTR) of each failure from the span reliability data set. This allows for a prediction of what percentage of time the interface is utilized. Thus, a relationship is established between the capacity provisioned and the percentage of time that the capacity will be uncongested. Network devices can then be added to the network to establish the availability target.

In an automated insulin delivery device, fuzzy logic may be applied to the responding to the possibility of a user taking additional action that may affect the blood glucose concentration. Fuzzy sets may be defined for empirically derived different likelihoods of the user taking such additional action based on correlated factors. A membership function may be provided for each fuzzy set. The membership function may provide a probability of membership in the set based on a parameter. Each fuzzy set may have a response that is reflective of the likelihood of additional user action associated with the fuzzy set. The responses by the AID device to each of these cases may reflect the probability of each such case occurring as evidenced by empirical data.

The invention relates to the technical field of artificial intelligence, in particular to a gynecological traditional Chinese medicine data processing method and system based on four diagnosis data weight analysis. The method comprises the following steps: acquiring four diagnosis data; map mapping is carried out according to the four-diagnosis data to obtain four-diagnosis map mapping data; obtaining expert experience data and historical case data according to the four-diagnosis map mapping data; performing decision tree construction according to the expert experience data to obtain an expert experience judgment tree model; performing hierarchical weighted regression according to the historical case data to obtain historical case hierarchical weighted data; performing four-diagnosis feature weighting processing according to the expert experience judgment tree model and the historical case level weighting data to obtain four-diagnosis data weight analysis data. According to the method, accurate butt joint of four diagnosis features and a traditional Chinese medicine knowledge system is realized, and a complete link from four diagnosis original feature collection, semantic mapping and rule data collaborative modeling to context adaptive reasoning is constructed.

The invention discloses a power control method for an energy harvesting wireless communication system, which belongs to the technical field of communication, and comprises the following steps: reasoning a power distribution formula based on a Bellman equation and an empirical state value function; state value neural network parameters in the reinforcement learning framework are defined as calculation parameters in an empirical state value function; calculating consumed power based on the battery electric quantity, the channel coefficient, the dynamic average capacity ratio, the state value neural network parameters and a power distribution formula of the current time slot; sending and observing the data volume according to the consumed power of the current time slot, recording the battery electric quantity of the next time slot, and generating an empirical data item containing the battery electric quantity and the data volume of the current time slot and the battery electric quantity of the next time slot; according to the empirical data items, the differential rewards are updated, and state value neural network parameters are updated; and calculating the ratio of the charged battery energy to the maximum chargeable battery energy in the current time slot, and updating the dynamic average capacity ratio. Therefore, the calculation complexity is reduced while the communication performance is optimized.

The invention provides a mine video semantic communication transmission method and system based on deep learning, which can realize the recovery of observable data and the execution of pragmatic tasks. According to the method, a semantic communication system model under a mining working face is constructed, the semantic communication system model comprises a semantic encoder and a semantic decoder which are realized based on a deep neural network, and the semantic encoder is located at a transmitting end and used for extracting data containing observable data and semantic information; the semantic decoder is located at the receiving end and used for reconstructing data related to empirical data and pragmatic tasks. According to the method, a semantic encoder and a semantic decoder are trained based on a DL-SC algorithm, image restoration and pragmatic task execution are considered in the aspect of design of a loss function, appropriate distortion measure functions are selected for the image restoration and pragmatic task execution respectively, and then the weighted sum of the distortion measure functions is solved to form a semantic distortion measure function to serve as the loss function. According to the invention, more efficient information interaction can be realized with lower data transmission quantity.

The invention belongs to the technical field of precision equipment assembly, and discloses a human-in-the-loop precision equipment assembly precision regulation and control method and related device.The method comprises the steps that parameter input and precision output are obtained according to data obtained through actual measurement and adjustment of precision equipment, and a high-precision sample set database is established in combination with geometric parameters and physical parameters; obtaining empirical data, and constructing an empirical model and a preference learning model based on the empirical data; bayesian optimization is carried out based on a high-precision sample set database, a digital twinborn model is established in combination with a human-in-the-loop, optimization is carried out through the digital twinborn model, an empirical model and a preference learning model are combined, and an assembly adjustment scheme meeting the assembly precision and performance is selected. According to the method, empirical data are introduced into optimization regulation and control of complex precision equipment assembly, an effective method is provided for achieving rapid and accurate regulation and control of complex precision equipment, and the method has great significance in greatly improving the assembly precision of the complex precision equipment.

The invention relates to the technical field of artificial intelligence, and provides a training method and device of a reinforcement learning model, equipment and a medium, and the method comprises the steps: obtaining experience data of an intelligent agent interacting with an environment through employing a historical strategy provided by a to-be-trained reinforcement learning model; determining an original action probability ratio of the reinforcement learning model to be trained between a historical strategy and a current strategy by using empirical data, and performing shear constraint on the original action probability ratio according to a current shear coefficient to obtain a shear action probability ratio; the current shearing coefficient is obtained by updating the difference value between the strategy divergence and the target divergence, the strategy divergence is determined through the shearing action probability ratio, the training loss is determined according to the empirical data and the shearing action probability ratio, and the current strategy of the reinforcement learning model to be trained is updated and trained by using the training loss. According to the invention, by optimizing the shear constraint mechanism, the dynamic adjustment of the current shear coefficient is realized, the training efficiency of the model is improved, and the scene adaptability of the model is improved.

The invention relates to the technical field of computer risk control, in particular to a big-data-based collaborative self-optimization method and system for a self-growth risk control system, and the method comprises the steps: obtaining the operation data of the risk control system, so as to construct a state vector; inputting the state vector into a reinforcement learning agent, and selecting one coordination action from a preset coordination action space for output; the coordination action is executed in the risk control system, and a scalar reward value used for evaluating the effect of the coordination action is obtained through calculation; and forming an empirical data tuple by using the state vector, the coordination action, the scalar reward value and a new state of the system after the action is executed, and training the reinforcement learning agent to update a decision strategy of the reinforcement learning agent. According to the risk control system, the rule engine and the model engine in the risk control system can be considered as a unified whole, and respective parameters are dynamically and cooperatively adjusted, so that the overall comprehensive efficiency of the system is maximized, and the self-adaptive capability and the iteration efficiency of the system are improved.

The invention discloses a multi-agent collaborative decision-making method based on federal reinforcement learning, and the method comprises the steps: firstly, enabling a central server to be initialized, then randomly selecting a client, enabling the client to initialize a local model into a global model, training and updating parameters, and then uploading the parameters; and the central server aggregates and updates the global model parameters until the model training is completed, and the multi-agent collaborative decision-making is completed. According to the method, the reinforcement learning model is trained in a federated learning mode, and similarity weighted staggering of two types of empirical data is combined, so that the effectiveness of the reinforcement learning model can be ensured, and possibility is provided for multiple parties to jointly train the same model; and the data sample richness and the model training efficiency are improved while the data privacy security is ensured.

The embodiment of the invention provides an enterprise information architecture (4A)-based AI Agent planning enhancement system, a control device and equipment, and the system comprises a Prompt construction module which is used for analyzing an unstructured demand to obtain a structured demand, and cooperating with other modules to generate a standardized Prompt comprising a business process, a data entity and a tool strategy; the knowledge matching module is used for matching related knowledge based on a knowledge graph employment number fusion knowledge base and sorting the related knowledge into a knowledge list containing business processes, practical experience, a data list and a tool list; and the context reasoning module is used for generating a reasoning result based on the historical data and the knowledge list and feeding back the reasoning result to the Prompt construction module so as to assist in generating the normalized Prompt. According to the system, the integration capability of the AI Agent on business logic and data resources in the task planning process is remarkably improved, so that the AI Agent can more accurately understand enterprise-level business scenes, and standardized and intelligent planning support is provided for practical application of the AI technology in enterprise digital transformation.

The invention discloses a government affair material auditing method and system based on historical case analysis, and relates to the technical field of intelligent government affair platforms, and the method comprises the steps: obtaining archived historical case data and a to-be-audited target government affair material in a government affair auditing system, carrying out the structural modeling of the historical case data, and obtaining a to-be-audited target government affair material; forming a case database containing content features, audit conclusions and business categories, and realizing computable representation of empirical data; based on the multi-dimensional feature extraction of the target material, combining the reliability weight of the business category obtained by clustering, the similarity weight between the target material and the historical case and the importance weight of each auditing dimension; a multi-layer weighted voting mechanism is adopted, a material comprehensive auditing score is generated through fusion calculation integrating three types of weights, and a machine auditing conclusion is automatically output according to a score threshold value. Therefore, the conversion from artificial experience to data-driven decision is realized, and the accuracy, consistency and intelligent level of government affair material auditing are effectively improved.