60 results about "Human intelligence" patented technology

The invention provides a formation cooperative task allocation method based on a hierarchical mixed auction algorithm, and the method comprises the steps: carrying out the tactical decision of a manned aerial vehicle through quantifying the attribute values of tasks in a battlefield, determining the priorities of different types of tasks, and carrying out the task auction allocation solution through the corresponding types of unmanned aerial vehicles according to the task types, the solution rate of the algorithm and the rationality of the allocation scheme are improved, a target function is improved by introducing a coverage factor and a penalty term, the rationality of task allocation is optimized, and a task reallocation mechanism is formulated, that is, the manned aerial vehicle performs real-time dynamic reallocation on tasks according to the remaining resources of the unmanned aerial vehicle and the execution cost. According to the method, the advantage of human intelligence of manned aircraft pilots is exerted, the unmanned aerial vehicle most suitable for the selected task can be quickly found, dynamic task allocation is realized in a complex and changeable environment, the task load of the unmanned aerial vehicles is balanced, and the unmanned aerial vehicles in an unmanned aerial vehicle cluster are used as reasonably as possible.

The invention discloses a method and an apparatus in a node used for wireless communication. The node receives the first request; generating a first result through the first model as a response to receiving the first request; the first model is used for at least one of training or reasoning; the identifier of the first model is a first identifier, and the first identifier is a non-negative integer; the first identifier is associated with a first index, and the first index is one of a plurality of indexes included in a first index set; the first index set is associated to a first sub-identifier set; the plurality of types of data indicated by the plurality of sub-identifiers included in the first sub-identifier set comprise at least one of channel quality information data, codebook data and position auxiliary information data; the sub-identifier associated with the first identifier depends on the first index. According to the method, the design of the model identifier and the matching of the data in the system adopting human intelligence and machine learning are optimized, so that the overall performance of the system is optimized.

The invention provides an AR-HUD display control method based on human intelligence and related equipment. The method comprises the following steps: acquiring physiological feature data and identity information of a driver; determining the driving attention state of the driver according to the physiological feature data; wherein the driving attention state represents the attention degree of the driver when the driver drives the target vehicle; generating display parameters corresponding to the vehicle information according to the identity information and the driving attention state, and generating a corresponding display picture according to the vehicle information and the display parameters; and controlling the AR-HUD to project the display interface to the target area.

The invention discloses a human factor intelligence-based near-infrared brain imaging system and method and edge device.The human factor intelligence-based near-infrared brain imaging system comprises a plurality of near-infrared brain imaging devices, each near-infrared brain imaging device comprises a head wearing device, a light source and a detector, the light source and the detector are arranged on the head wearing device, and the head wearing device is connected with the near-infrared brain imaging devices; under the condition that a testee wears the head wearing device and receives stimulation of brain region stimulation information, the light source provides an input signal of a brain region, and the detector receives an output signal of the brain region and determines brain blood oxygen concentration change data based on the output signal; the plurality of host devices are synchronized through a time synchronization mechanism, the plurality of host devices are in one-to-one correspondence with the plurality of near-infrared brain imaging devices, and each host device is in communication connection with the corresponding near-infrared brain imaging device; each host device is configured to output brain region stimulation information, obtain brain blood oxygen concentration change data and perform data processing.

The invention belongs to the field of ship engineering design, and discloses a ship cabin layout method and system based on three-dimensional knowledge gene layer dynamic fusion. The system comprises a cabin layout reasoning module based on priori knowledge, an intelligent cabin layout optimization algorithm module based on computational knowledge, a human intelligence knowledge cabin layout scheme online design module and a three-dimensional knowledge fusion solving cabin layout design module. Three-dimensional knowledge individuals of the priori knowledge scheme, the algorithm knowledge scheme and the human intelligence knowledge scheme which are obtained respectively are dynamically fused on the evolution algorithm gene level to form an evolution group, and a cabin layout scheme is obtained through operation in a replacement mode, a crossing mode and a mode of combining crossing and a multi-objective function tradeoff method; according to the method, an optimization target and constraint conditions which are difficult to be expressed by mathematical languages in the cabin layout process can be considered in the algorithm solving process, and a more practical ship cabin layout scheme is obtained.

The invention provides a human factor intelligence-based personnel stress state detection method, device and equipment and a medium, and the method comprises the steps: obtaining a PPG signal of a to-be-tested person, the PPG signal of the to-be-tested person comprising first resting state data and to-be-detected state data; data slicing is conducted on the first resting state data and the to-be-detected state data, corresponding first fragment data and second fragment data are obtained, and the length of the first fragment data is larger than that of the second fragment data; the first fragment data and the second fragment data are input into a trained stress state detection model for prediction, and a first EDA (Electrocortical Activity) prediction result is obtained; and judging the stress state of the to-be-tested person based on the first EDA prediction result. According to the method provided by the invention, the problem that the detection precision of the short-time stress state is not high when the PPG data is utilized to detect the stress state of the personnel can be solved.

The embodiment of the present application provides a training method, a physiological state recognition method, a device and an apparatus for a human factor intelligent physiological state recognition model, and the model training method includes: obtaining a first physiological signal, a first state label of the first physiological signal, a second physiological signal corresponding to the first physiological signal and a second state label of the second physiological signal, wherein the second physiological signal is a physiological signal generated according to the first physiological signal; the first physiological signal is a physiological signal obtained by collection; the first state label is used to indicate the physiological state corresponding to the first physiological signal; the second state label is used to indicate the physiological state corresponding to the second physiological signal; and the physiological state recognition model is trained according to the first physiological signal, the second physiological signal, the first state label and the second state label. The embodiment of the present application can realize the physiological state recognition of personnel more accurately.

The present invention belongs to the field of artificial intelligence, machine intelligence and intelligent robots. Provided is a method for using human intelligence to compensate for inherent defects of machine intelligence. By means of manipulating multi-perspective visual representations, the respective worlds perceived and constructed by machine intelligence and human intelligence in human-machine inconsistency, respective event narratives of the machine intelligence and the human intelligence in their respective constructed worlds, and spatial-temporal information in the respective event narratives are identified, and are integrated in a unified manner into a fact chain and a logic chain that reflect the complementary nature of the human intelligence and the machine intelligence under the apparent human-computer inconsistency. The application of machine intelligence in human society is evolving, as a whole, from the current imperfect unstable state to a mature and stable state that meets expectations in various aspects of society; In the mature and stable state, predictions can be performed in advance, adjustment can be performed in real time, refusal can be performed post hoc, facts can be confirmed, logic can be clarified, efficient intervention can be performed, correction can be performed in a timely manner, the truth can be understood, efficient communication can be implemented, group consensus can be achieved, the source can be traced post hoc, the rationality can be self-proved, responsibility can be distinguished, responsibility can be determined at each stage, cross-examination can be performed in court, loss can be calculated, and cases can be accumulated.

The present invention discloses a software development system based on artificial intelligence technology, and relates to the field of software development technology. A software development system based on artificial intelligence technology includes: a user demand analysis module, a self-awareness module, an automatic code generation module and an intelligent feedback optimization module. The present invention simulates human intelligence through the self-awareness module, perceives changes in the development process in real time, and performs self-learning and adjustment according to historical data and real-time feedback; this adaptive capability enables the system to automatically optimize the development strategy according to different needs, technical changes and development progress, ensuring that the software development process can flexibly respond to various dynamic changes, avoiding frequent manual intervention in the traditional development process due to demand changes or technical bottlenecks.

The embodiment of the invention provides a business change processing method and device, equipment and a program product, and relates to the technical field of human intelligence. The method comprises the steps that an object change request is received, the object change request comprises an object change list, and the object change request is used for indicating a service program for updating a target service; according to the object change request, text extraction processing is carried out on the object change list to obtain change information, and the change information comprises a change cut-off moment, a change problem of a business program, a program identifier and a business department identifier; according to the change information, request information is sent to a preset device, and the request information is used for instructing a development department to update the service program; and receiving response information corresponding to the request information, and updating a service program in the service system according to the response information. According to the method provided by the invention, the business change processing efficiency is improved.

The invention discloses a light environment ergonomics visual comfort evaluation method based on human intelligence, an edge computing device and a medium. The light environment visual comfort evaluation method comprises the steps of obtaining light environment data for a light environment scene where a user is located; inputting the light environment data into at least one trained visual prediction model for visual comfort correlation analysis to obtain a visual comfort correlation structure corresponding to the light environment data; determining a target visual prediction model based on the visual comfort correlation structure; the light environment scene where the user is located is analyzed through the target visual prediction model, the visual comfort evaluation result is output, and by applying the method, the reliability, accuracy and effectiveness of light environment ergonomics visual comfort evaluation can be improved.

Augmented human intelligence using artificial intelligence (AI) with a human in the loop is disclosed. The AI is trained with data sets with brain-muscle patterns. Brain and muscle signals are obtained for a subject by EEG and EMG sensors to input into the AI. The AI augments human intelligence by overlapping natural human responses with real-time, goal-directed adjustments to instantaneous brain-muscle reactions. The AI enables the human to repeat such adjustments in order to re-wire the brain to adopt new reactions.

The present application relates to a kind of human intelligence collaborative generation type interaction methods of coal mining scene, belong to coal mine intelligent technology field.The present application includes: through coal mine multi-modal intention decoupling-dynamic Agent evaluation and fusion reasoning engine algorithm, the intention of coal mining process scene instruction is identified, subtask division and dependency analysis, obtain structured semantic intention, executable subtask single and task scheduling result and submit to orchestrator, select coal mine professional big model, external tool integrated Agent and decision model integrated Agent are routed by language model empowerment intention analysis database routing technology and are retrieved, obtain the preliminary search result of each Agent;Determine the reliability score of preliminary search result, generate and visual display final search result.The present application makes search result fully fuse multi-modal data, cooperatively consider the search result of different Agent, search result is richer, more accurate, improves the intelligence of human intelligence interaction.

The invention discloses a human factor intelligent state real-time evaluation method, platform and system and an edge computing device. The human factor intelligent state real-time evaluation method comprises the steps that human factor data flow collected for a subject in real time is acquired; the method comprises the following steps: performing real-time online evaluation on the state of a tested person based on a human factor data stream acquired in real time to obtain a person state online evaluation result, and complementing current missing data based on historical acquisition data acquired in the human factor data stream in response to determining that data missing occurs in the human factor data stream in the person state online evaluation process to obtain a person state online evaluation result; obtaining first human factor data; performing offline evaluation on the state of the tested person based on the first human factor data to obtain a first evaluation result; and correcting the personnel state online evaluation result based on the first evaluation result to obtain a state evaluation result of the tested personnel. According to the human factor intelligent state real-time evaluation method, when the data is interrupted or lost, supplementary evaluation can be carried out on the missing data, and the integrity and accuracy of evaluation are guaranteed.

The present invention relates to a fast and efficient human-computer collaborative program translation method, comprising: 1. When a program translation model translates a source program, a software engineer corrects the output code and feeds back the editing results to the program translation model in the form of a prefix; 2. The subsequent retranslation process is divided into two parts: 1). For the generation of prefixes, the cached attention information is used to remove repeated prefixes in the next round. 2). For the remaining generation parts, at each time step, a two-stage method is proposed to determine whether the position can be spliced by a known suffix. If so, the reasoning is terminated in advance. This interaction will be repeated until the software engineer is satisfied with the result. Through the above method, human intelligence and machine intelligence are effectively combined, the accuracy of the final result of program translation is improved, the workload of software engineers is reduced, and the response delay in the collaborative process caused by the excessive length of the program is greatly reduced.

The invention provides an unmanned aerial vehicle combat intention recognition method based on causal characterization, and aims to solve the technical problems of prediction and decision making under non-independent identically distributed data. And the output of human intelligence and machine intelligence is fused by using the Bayesian neural network, so that the robustness and decision-making ability of the system in a complex environment are improved. The model framework designed by the invention is divided into three levels: the first level is independent processing of machine intelligence and human intelligence, the second level is used for converting features into potential causal variables through CE-VAE, and the third level is used for fusing the potential variables through a Bayesian neural network, generating joint causal representation and outputting a final decision.

According to the multi-modal data synchronization edge computing device based on human intelligence and the synchronization system provided by the embodiment of the invention, the behavior and / or voice of the testee can be recognized through the AI module, and the event marking information is sent out when the recognition result is matched with the target behavior and / or the target voice; according to the event marking method and device, the tested person can accurately trigger the event marking as long as making a predetermined target behavior or sending a predetermined target voice under the condition that the tested person is stimulated in vision, hearing, touch, taste and the like, so that the inaccuracy of test analysis caused by manually triggering the event marking by a person implementing an experiment is avoided, the objectivity of the event marking is effectively improved, and the test efficiency is improved. Therefore, the influence of the stimulation event on the multi-modal data can be accurately analyzed.

This application discloses a method, apparatus, and equipment for adjusting navigation tasks based on human intelligence function allocation, belonging to the field of ship control technology. The method includes: performing cross-band dynamic functional connectivity analysis on physiological signals to extract neural state trajectories; performing task semantic parsing on navigation operation logs to extract behavioral log streams corresponding to the ship's sub-task types; constructing positive and negative sample pairs based on the synchronization state of the neural state trajectories and behavioral log streams; mapping the neural state trajectories, behavioral log streams, and environmental pressure vectors to a maritime context-aware latent space with the optimization objectives of maximizing the mutual information of positive sample pairs, minimizing the distance of negative sample pairs, and maximizing the correlation between negative sample pairs and the environmental pressure vector, thereby obtaining a comprehensive contextualized common representation of the crew; and calculating metacognitive alignment loss based on the comprehensive contextualized common representation to adjust the crew's navigation tasks. This method can improve the contextual sensitivity of task adjustment.

The invention discloses a regression test case generation method based on a code large language model, and belongs to the technical field of computer programs. The method comprises the following steps: a step of automatically generating and evaluating a test case oriented to a C language program, a step of generating a test expectation by dialogue with a code large model and using a voting tool, a step of constructing a customized prompt (prompt) template oriented to a C language program type, and a step of generating a test case oriented to a natural language description test requirement. According to the method and the device, the problems of searching in a huge test case space to obtain a high-quality test case and obtaining a reliable test expectation (Oracle) are solved. By combining human intelligence with the generation capability of the code large model, the accuracy, coverage and effectiveness of the test case can be effectively improved, and the problem that a traditional test case generation tool cannot generate test expectation (Oracle) can be solved, so that the software development process is accelerated, and the quality assurance level of software is improved.

1. Name of the product of this design: Electronic device with a graphical user interface for the human intelligence digital platform interface. 2. Purpose of this design product: for electronic equipment. 3. The design highlights of this appearance design product are the graphical user interface on the screen. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Purpose of the graphical user interface: A graphical user interface used by human resource management personnel to view human resource related indicators. 6. Human-computer interaction mode of the graphical user interface: In the interface shown in the main view, the user clicks the human resource indicator function item to enter the interface shown in interface change state diagram 1; in the interface shown in interface change state diagram 1, the user slides the interface downward to enter the interface shown in interface change state diagram 2; in the interface shown in interface change state diagram 2, the user slides the interface downward to enter the interface shown in interface change state diagram 3.

The present disclosure relates to transparent and controllable human-AI interaction via chaining of machine learning language models. The present disclosure provides transparent and controllable human-AI interaction via chaining of machine learning language models. In particular, while existing language models (e.g., so-called “large language models” (LLMs)) demonstrate impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans with more complex tasks. In response, the present disclosure introduces the concept of chaining instantiations of machine learning language models (e.g., LLMs) together, where the output of one instantiation becomes the input of the next instantiation, and so on, thereby aggregating gains at each step.

The application provides a UAV combat intention judgment method based on a Bayesian model and human-machine complementarity, and the technical scheme is as follows: a human-machine hybrid intelligent framework combining Bayesian inference and human-machine complementarity theory is constructed, real-time situation information of enemy UAVs is collected and analyzed, situation data of the enemy UAVs are subjected to feature extraction, independent intention judgment is carried out by machine intelligence and human intelligence respectively, and relevant parameters are dynamically obtained through Bayesian inference; a confidence feedback mechanism is used to fuse the prediction results of human and machine by combining a human-machine complementarity coefficient; and through dynamic weight distribution and parameter adjustment, more robust and accurate intention recognition results are generated. The application has the beneficial effect that in a complex and dynamic battlefield environment, the complementary advantages of the intuitive judgment of human intelligence and the high-speed calculation of machine intelligence are effectively fused.

The present invention provides a multi-agent system human-in-the-loop elastic control method based on event-triggered integral reinforcement learning, which belongs to the field of artificial intelligence collaborative control technology. The method innovatively adopts an integral reinforcement learning method based on an event-triggered mechanism to reduce the frequency of information exchange between agents, reduce the burden of agents, and solve the optimal control problem of multi-agent systems under false input injection attacks; in addition, by integrating human intelligence and decision-making, when a human operator sends a command signal to a non-autonomous leader agent, the system security is greatly improved.

The invention relates to a coal mining autonomous decision-making and human intelligence symbiotic multi-agent system, and belongs to the technical field of coal mine intellectualization. Comprising the steps that an equipment control layer is configured with a reflection type program Agent, a distribution decision-making layer is configured with an inference type model Agent, a collaborative decision-making layer is configured with a planning type organization Agent and a planning type arbitration Agent, a capability evolution layer is configured with a learning type reality ring Agent, a learning type simulation ring Agent and a learning type double-ring Agent, and a human-intelligent interaction layer is configured with a generation type task Agent and a generation type investigation Agent. According to the invention, the spanning of an industrial agent from single-machine automation to system autonomy is realized, an Agent community organization mode covering a full coal mining process is formed, systematic design is carried out on multi-Agent role positioning, hierarchical relationship and cooperation rules, and the requirement of real-time cooperative decision making of Agent communities can be met.

The present disclosure provides transparent and controllable human-intelligent interaction via a chain of machine learning language models. In particular, although existing language models (e.g., so-called "Large Language Models" (LLM)) exhibit impressive potential on simple tasks, their breadth of range, lack of transparency and insufficient controllability can make them less effective when assisting humans in performing more complex tasks. In response, the present disclosure introduces the concept of linking instantiations of machine learning language models (e.g., LLMs) together, where the output of one instantiation becomes the input of the next instantiation, and so on, thereby aggregating gains per step.

A method for generating artificially-intelligent, synthetic responses to a natural language conversational survey is provided. Methods create a synthetic persona that reflects an authentic human. Methods store the synthetic persona as vectors within a vector database. Methods initiate a survey. Methods select the synthetic persona from the vector database. The selection is based on a correspondence between data points input by the researcher and vectors included in the synthetic persona. Methods initiate the survey with the selected synthetic persona as a participant. Methods generate a first question for the survey. Methods augment, at the vector database, the first question with vectors that correspond to data points relevant to the first question. Methods transmit the augmented first question to a large language model. Methods receive a response to the augmented first question. Methods process the response at the survey system. Methods enable the researcher to analyze the survey.

The invention discloses an electroencephalogram signal processing method and device based on human intelligence, edge computing equipment and a medium, and belongs to the technical field of signal processing. The method comprises the steps of collecting electroencephalogram signals of a target object; decomposing the electroencephalogram signal into a plurality of statistically independent signal components through blind source separation; inputting the plurality of signal components into a preset neural network model to obtain the category of each signal component output by the neural network model; and combining the signal components of the effective electroencephalogram signals to obtain the electroencephalogram signals after noise reduction. According to the embodiment of the invention, blind source separation and machine learning algorithms are fully utilized, and the accuracy and efficiency of electroencephalogram signal processing are greatly improved.

The invention relates to the technical field of cognitive feature mining, and particularly discloses a cognitive feature mining method for enhancing a human-intelligent cooperation mechanism. The method comprises the following steps: collecting physiological and psychological characteristics of a user to construct a cognitive preference database; constructing a symbol sequence through a segmented aggregation approximation dimension reduction and symbol approximation theory; extracting an optimized population based on a multi-strategy enhanced escape algorithm, constructing an elite pool to adjust a cognitive boundary, reversely mapping a feature vector into a symbol sequence and labeling an interaction intention to form a symbol data set; and training an LARA model based on the symbol data set, and obtaining the human-intelligent cooperation mutual reliability and evaluating the human-intelligent cooperation mutual reliability in combination with the model recognition accuracy. According to the method, the comprehensiveness of cognitive features is guaranteed through the multi-modal data, the symbol screening accuracy is improved, and the human-intelligent synergy effect is enhanced.

The present invention provides a human intelligence assessment method based on Bayesian deep learning, which belongs to the technical field of UAV intention recognition; it solves the technical problem that the intention of the enemy UAV cannot be accurately judged in an uncertain environment. Its technical solution is: establish a Bayesian deep learning network suitable for intention recognition and collect situation information of the enemy UAV; use the situation information of the enemy UAV as input, and use the established Bayesian deep learning network and human intelligence to independently judge the intention of the enemy UAV; integrate the judgment results of machine intelligence through the reasoning mechanism, so as to infer and verify the judgment results of human intelligence. The beneficial effects of the present invention are: in a complex and uncertain environment, combining the computational advantages of machine intelligence and the empirical judgment of human intelligence, providing more accurate enemy UAV intention recognition, and enhancing the decision-making ability and tactical advantage of our UAV on the battlefield.

The invention provides an emotion prediction method and system based on human intelligence, an edge computing device and a medium, and the method comprises the steps: obtaining original video stream data, extracting an image of a key frame from the original video stream data according to a preset frame rate, and carrying out the preprocessing of the extracted image of each key frame; wherein the image of the key frame comprises a plurality of face images; performing emotion recognition on the preprocessed image of each key frame, recognizing the emotion category of each face in the image of the key frame, and counting the occurrence frequency of different emotion categories in the image of the current key frame; according to the time sequence of the key frames, the occurrence frequency of each emotion category in the images of all the key frames is integrated and counted in a time sequence data mode, and a comprehensive group emotion prediction result is weighted and calculated according to a preset weight.