32 results about "Target reaching" patented technology

The invention discloses a self-adaptive software test case generation optimization method, which comprises the following steps of: carrying out structured analysis on service requirements of a target system, extracting multi-dimensional test requirements of performance, resources, risks and coverage rates, and carrying out tagging and normalization processing to form a quantifiable index system; based on a meta-strategy layer decision engine, fusion reinforcement learning and fuzzy reasoning are carried out to realize multi-target weight distribution and dynamic compromise; generating and optimizing a preferred case set by means of a trade-off adaptive algorithm, performing conflict marking and constraint cooperative adjustment, collecting feedback data through automatic testing, correcting weight distribution and conflict detection rules according to actual results, and continuously iterating case generation strategies; the objective achievement degree is periodically evaluated, the strategy model is reconstructed or switched to adapt to scene changes, the accuracy of test requirement expression and the self-adaptability of the use case generation strategy are improved, and efficient collaborative optimization of multi-objective testing and dynamic adaptation of service scenes are achieved.

The invention provides an intelligent recruitment process control method and system based on a task state and an action type, and the method comprises the following steps: obtaining multi-dimensional input data of a recruitment process, converting the multi-dimensional input data into a standardized task state feature vector, the task state feature vector at least comprises a process progress quantized value, candidate interaction feedback parameters, a resource occupancy rate and a target achievement degree; constructing a collaborative decision-making system of a preset decision-making rule and a reinforcement learning model; according to the method, the multi-dimensional task state features are collected in real time, the execution strategy is dynamically adjusted in combination with the adaptation logic of the candidate action types, various changes in the recruitment scene can be flexibly responded, and accurate matching of the process and the real-time service scene is achieved.

1. The name of the design product: the graphical user interface of the electronic device for recording continuous learning. 2. The use of the design product: an electronic device. 3. The design points of the design product: the graphical user interface. 4. The picture or photo that best shows the design points: design 1 front view. 5. The product carrier is the conventional design, and the rear view, left view, right view, top view, and bottom view of each design are omitted. 6. Design 1 is designated as the basic design. 7. The use of the graphical user interface: for recording continuous learning. 8. The human-computer interaction mode of the graphical user interface: in design 1 front view, click on any position to enter design 1 change state diagram 1 continuous learning main interface (no learning record interface yet), start continuous learning to enter design 1 change state diagram 2 learning record interface, the module in the lower left corner of the interface is "task standard module", when any standard in the module is reached, enter design 1 change state diagram 3, and the task has a "standard reached" status identifier. In design 2 front view, click on any position to enter design 2 change state diagram 1 continuous learning main interface (no learning record interface yet), start continuous learning to enter design 2 change state diagram 2 learning record interface, when the user reaches the target, enter design 2 change state diagram 3 target reached interface. In design 3 front view, click on any position to enter design 3 change state diagram 1 continuous learning main interface (no learning record interface yet), start continuous learning to enter design 3 change state diagram 2 learning record interface, during continuous learning, if the task is not completed all day, enter design 3 change state diagram 3 interface of still not learning all day.

The invention belongs to the technical field of talent management development, and relates to a staged progressive talent growth path planning method and system, and the system collects and integrates the static and dynamic data of talents through a talent portrait construction module to generate an initial talent portrait; the growth stage division module divides progressive growth stages and sets capability thresholds based on the initial talent portraits and the organization development objectives; the stage target setting module is used for disassembling a stage target into quantifiable sub-target schemes with time limit; the resource matching and pushing module is used for matching resources according to the target and the portrait and automatically pushing the resources to the talent end; the capability evaluation feedback module is used for evaluating a stage target achievement condition and generating an evaluation report; the path dynamic adjustment module progressively or triggers path adjustment based on the evaluation report judgment stage; and the data storage and analysis module is used for storing the whole-process data, carrying out multi-dimensional analysis and optimizing parameters. According to the invention, personalized planning, dynamic adjustment and whole-process closed-loop management of talent growth paths can be realized.

The application discloses a kind of based on unmanned aerial vehicle's laser cleaning and cladding collaborative control method, device, electronic equipment, readable storage medium and computer program product.The method comprises: determining operation target;When the surface rust depth of operation target exceeds preset depth threshold, the current operation mode of unmanned aerial vehicle is adjusted to cleaning mode;Laser cleaning operation is carried out to operation target in cleaning mode;When detecting that the target metal ion characteristic peak intensity of the surface of operation target reaches preset intensity threshold, the current operation mode of unmanned aerial vehicle is switched from cleaning mode to cladding mode;Cladding operation is carried out to operation target in cladding mode.The method can solve the problem that in the prior art, cleaning and cladding step operation lead to long exposure time of metal surface, easy to occur secondary oxidation, thereby affecting cladding layer combination quality and operation efficiency.

The application relates to a method for intelligently stringing and parallelizing videos through target labels, which comprises the following steps: extracting a label string keyword and a structured feature attribute of a target in a main video; performing algorithm matching weight on the label string keyword and the structured feature attribute; and performing batch matching of the structured feature attribute and the label string keyword similarity in a target information table. As long as the two dimensions of the structured feature attribute similarity and the label string keyword matching degree of any target except a current target reach a certain percentage or more, the target is added to a similar target pool and waits for further inspection. The method can solve the problem that, in video work, because target features cannot be effectively utilized for intelligent comparison and stringing and parallelizing, omission occurs, thereby leading to incomplete stringing and parallelizing videos.

An object of the system according to the embodiment is to provide progress management and feedback for a user to achieve a goal.SOLUTION: A system according to an embodiment includes an objective setting section, a progress tracking section, an advice providing section, and a reward giving section. The goal setting unit uses the generated AI to find out what the user wants to achieve or improve, and sets an appropriate goal and deadline. The progress tracker tracks the user's progress and provides real-time feedback. The advice providing unit provides advice based on the progress status. The reward giving section gives a reward when the objective is achieved.SELECTED DRAWING: Figure 1

The application discloses a high-speed camera testing device for a movable target, which comprises a movable target simulation unit, a camera testing unit and an illumination unit. The movable target simulation unit comprises a rotating disc, a calibration plate and a resolution plate as a movable target, and a rotating mechanism. The calibration plate and the resolution plate are arranged on the rotating disc, and the rotating mechanism drives the rotating disc to rotate. The rotating mechanism can be steplessly speed-regulated, so that the movable target reaches a preset linear velocity. The camera testing unit comprises a high-speed camera and a linear motion mechanism. The high-speed camera is mounted on the linear motion mechanism, and the distance between the high-speed camera and the movable target is adjusted through the linear motion mechanism. The high-speed camera is used for recording dynamic images of the movable target, and detecting resolution levels and tailing amounts. The illumination unit comprises first and second light sources with adjustable illumination intensity, which respectively provide sufficient exposure brightness for the camera testing unit and the movable target simulation unit. The application is used for imaging quality evaluation of the high-speed camera under different environmental conditions, and provides guidance for parameter matching in practical application.

The invention discloses a personal holographic self-consistent management method and a servo system based on artificial intelligence, belongs to the crossing field of artificial intelligence, Internet of Things and life science, and is an intelligent self-consistent support system for achieving an autonomous target of a natural person. Physiological, psychological, living environment, life demands and daily behavior data are collected through multi-modal holography, a personal digital twinborn model (quantifying senescence markers and predicting health risks) is constructed based on deep learning, a dynamic personalized intervention scheme is generated in combination with a multidisciplinary knowledge graph, and user compliance is improved in cooperation with an motivation incentive and behavior guide module. And closed-loop servo feedback is formed. The system comprises a data acquisition layer, a model analysis layer, an external tool connection layer and an intervention execution layer, solves the problem that ordinary people cross a digital gap to manage life, overcomes the defects of single data acquisition and the like, improves the accuracy and continuity of target achievement, and can also retain the life process in a holographic digitization manner.

The present disclosure provides an automatic obstacle avoidance method and system for a surgical robot. The method comprises: during the process of controlling the surgical robot to move to a target position, if the surgical robot meets the obstacle avoidance condition, determining a direction vector of the surgical robot to the target position, and determining a unit vector of the shortest distance point of the surgical robot to the target obstacle; calculating the obstacle avoidance direction and speed of the surgical robot according to the direction vector and the unit vector; calculating the attractive speed of the surgical robot according to the direction vector and the pose information of the target position; the attractive speed refers to the speed component of the surgical robot pointing to the target position; calculating the resultant speed of the surgical robot according to the obstacle avoidance speed and the attractive speed, and controlling the surgical robot to realize automatic obstacle avoidance motion and target reaching through the resultant speed. The embodiment of the present disclosure can enable the surgical robot to have autonomous obstacle avoidance capability, and can safely and efficiently realize surgical pose alignment and complete the specified surgical task.

An electronic device, a user terminal, and an operating method for guiding goal setting and goal achievement based on body indicators are disclosed. The operating method of the electronic device may include selecting, from among a plurality of clusters based on body indicators, a target cluster that a target user aims for, based on a user input, determining, based on time series data of body indicators pre-obtained from a plurality of users, a target route that guides the target user from a starting cluster including a body indicator of the target user to the target cluster, determining, based on the time series data, a difficulty level and expected time to be taken for the body indicator of the target user to move from the starting cluster to a most adjacent cluster on the target route, and providing the target route, the time, and the difficulty level to the target user.

The present application relates to the technical field of robot control, in particular to a kind of unmanned mobile target vehicle travel control method and system, comprising the following steps: obtaining path segment information determination acceleration and direction deviation generation uniform instruction sequence execution record and archive control path state completion mark.In the present application, by introducing path length and setting value comparison determination acceleration condition, the angle relationship of current position and path direction is combined to identify motion deviation state, and the unified control instruction is constructed by integrating speed and direction adjustment requirements, which can realize dynamic adaptive scheduling at path segment level, and through execution state record and time mark tracking actual operation process, ensure that each path segment execution condition is real-time controllable, control process has serialization, refinement and closed-loop feedback capability, improve the response speed, running stability and target achievement efficiency of path execution, and have stronger autonomous adaptation and execution guarantee capability when facing environmental uncertainty and path diversity.

This invention relates to a dialogue goal optimization device, method, and program for optimizing goal achievement in user interactions. Based on user utterances and actions, it estimates user states, including emotions, resistance, comprehension, and signs of detachment, as well as goal progress indicating the degree of achievement of primary and sub-goals, and maps these to a state space. After restricting the set of candidate response strategies based on constraints such as compliance and cost applied to the dialogue, it selects a response strategy that maximizes the objective function. Furthermore, it performs a self-correction process that dynamically updates the parameters of the response strategy in response to signs of failure detected during the dialogue. This makes it possible to suppress user detachment while adhering to practical constraints and continuously improve the business goal achievement rate.

A system is provided.SOLUTION: A system comprising: means for a user to set a goal; means for a AI to generate a message in accordance with the goal; means for managing and displaying a countdown to goal achievement; means for setting an intermediate goal and providing a minor achievement award; means for providing a reward upon achieving a final goal; and means for reporting user progress to family and friends.SELECTED DRAWING: Figure 1

The invention discloses an operation vehicle-mounted unmanned aerial vehicle multi-target path planning method based on reinforcement learning, and relates to the technical field of unmanned aerial vehicles. Comprising the following steps: step 1, executing strategy judgment based on medical task analysis; 2, high-precision environment perception and map construction are carried out; step 3, generating a time optimal path of emergency rescue; 4, multi-target path optimization of conventional rescue is carried out; according to the method, medical condition intelligent judgment, environment insight and multi-objective optimization are fused, and the aim of fast, safe and efficient operation vehicle is achieved.

The application discloses a surgical vehicle-mounted unmanned aerial vehicle multi-target path planning method based on reinforcement learning, and relates to the technical field of unmanned aerial vehicles; the method comprises the following steps: step one, execution strategy determination based on medical task analysis; step two, high-precision environment sensing and map construction; step three, time-optimal path generation for emergency rescue; and step four, multi-target path optimization for regular rescue; the method combines medical condition intelligent discrimination, environment insight and multi-target optimization, and realizes the achievement of the fast, safe and efficient target of the surgical vehicle.

We provide the system. [Solution] A data collection means for collecting and processing target data, A learning method that generates a learning model using artificial intelligence based on collected data, An evaluation means that receives target information entered via a user terminal and evaluates said target information, A progress management system that tracks user progress in real time and provides feedback based on evaluation results, A recovery plan generation method that proposes a recovery plan to the user when goal achievement is delayed, A system that includes this.

The invention relates to a dynamic priority object reaching method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring an initial path priority strategy about a target newly-added object; based on the strategy correction reference data and the to-be-corrected path priority strategy, a current corrected path priority strategy obtained through current iteration is obtained through a strategy correction model, the to-be-corrected path priority strategy obtained through first iteration is an initial path priority strategy, and the to-be-corrected path priority strategy obtained through second iteration is an initial path priority strategy; the to-be-corrected path priority strategy not iterated for the first time is a previous corrected path priority strategy obtained by previous iteration; taking the current corrected path priority strategy as a to-be-corrected path priority strategy of the next iteration, returning to the step of determining the strategy correction reference data of the to-be-corrected path priority strategy, and continuing to execute until an iteration stop condition is met; and the target reaching path is determined based on the last-time path correction priority strategy of the last-time iteration, so that the accuracy of reaching path determination is improved.

The application is suitable for the field of computers and provides a supervision method and system based on image recognition. The method comprises the following steps: acquiring an acquisition image of a critical region in a target region, wherein the target region comprises a target water area, and the critical region comprises a feature sub-region from a side bank to the target water area; when an activity target appears in the feature sub-region according to the acquisition image, judging a trend of the activity target in the feature sub-region; when the trend of the activity target is close to the target water area, acquiring a change of height information of the activity target, wherein the height information comprises height information in a non-crouching state; and if it is judged that a height information drop value of the activity target reaches a preset ratio according to the change of the height of the activity target, reporting a prompt information to a supervision end and alarming in the feature sub-region. The application has the beneficial effect that dangerous behaviors entering the target water area can be effectively supervised.

We provide the system. [Solution] In an information processing device, there is a means for receiving target information from a user via an input means, A generation model means that analyzes the aforementioned target information to generate specific target settings and deadlines, A notification means for notifying the user of the generated goal setting information, A progress management means for managing progress information toward the goal provided by the generative model means, An advice provision means that generates advice information based on the aforementioned progress information and notifies the user, A reward calculation method that calculates and notifies reward information when a goal is achieved, A system that includes this.

The invention provides a power marketing data intelligent processing method and system, and belongs to the technical field of data processing, and the method comprises the steps: obtaining energy target parameters, constructing a target and data mapping model, outputting a differential data collection dimension set, and updating the collection frequency and priority; the equipment operation behavior sequence is converted into a behavior semantic tag, and the weight of the behavior semantic tag is dynamically adjusted along with the equipment operation frequency; a target deviation correction model is adopted to calculate a deviation value, and if the deviation value exceeds a preset threshold value, marketing strategy generation logic is triggered; and generating a response result containing a target achievement path and a marketing scheme, collecting a response behavior of a user to a marketing strategy in real time, and reversely iterating parameters of the target data mapping model and the target deviation correction model. According to the electric power marketing data intelligent processing method provided by the invention, accurate matching of data and user demands is ensured by dynamically collecting dimensions, behavior motivation deep interpretation is realized by semantic labels, and user responsiveness and target achievement rate are improved by deviation linkage and model self-evolution.

The invention discloses a system and method for enabling a target to reach a preset position by controlling multiple paths of stepping motors, and belongs to the field of stepping motors, and the system comprises a stepping motor sub-control unit, a stepping motor driver, a plurality of stepping motors, a plurality of target preset position monitoring units, a plurality of target objects, and a control, self-inspection, fault and information prompting unit. The plurality of stepping motors, the plurality of target preset position monitoring units and the plurality of target objects form a plurality of paths of selected paths, and each path of selected path is composed of one stepping motor, one target preset position monitoring unit and one target object. According to the invention, a plurality of stepping motors can be controlled to respectively drive so that a target object reaches a preset position, that is, any one stepping motor in the system can be controlled to start, rotate forwards or reversely, stop and drive to the target object, and a target preset position detection unit is utilized to identify whether the target reaches the preset position; finally, the purpose of controlling the plurality of stepping motors to respectively drive so as to enable the target object to reach a preset position is achieved.

The application discloses a robot navigation control method and device and electronic equipment, the method comprises the following steps: generating an initial moving route according to a preset target position; obtaining environment information in a preset range of the initial moving route, the environment information comprising obstacle information and pedestrian information; calculating the predicted position and predicted moving information of each pedestrian according to the current position and historical moving information of each pedestrian; using a first machine learning model to calculate the influence degree of the predicted position of each pedestrian on the initial moving route; determining the pedestrian with an influence degree greater than a preset threshold as a target pedestrian, and constructing potential field information comprising attractive potential field and repulsive potential field according to the predicted position, predicted moving information of the target pedestrian and the current state of the robot; using a second machine learning model to calculate the response action of the robot according to the potential field information and the current state of the robot; controlling the movement of the robot according to the response action, and updating the local motion trajectory. The application can improve the navigation stability, control rationality and target reaching efficiency of the robot in a dynamic crowd environment.

The invention relates to the technical field of robot control, in particular to an unmanned moving target vehicle advancing control method and system, and the method comprises the following steps: obtaining path section information, judging acceleration and direction deviation, generating a unified instruction sequence execution record, and archiving a control path state completion mark; according to the method, the acceleration condition is judged by introducing the comparison of the path length and the set value, the motion offset state is recognized by combining the included angle relation between the current position and the path direction, and the unified control instruction is constructed by integrating the speed and direction adjustment requirements, so that the dynamic adaptive scheduling of the path section level can be realized; the actual operation process is tracked through execution state recording and time stamps, it is ensured that the execution condition of each path section is controllable in real time, the control process has serialization, refinement and closed-loop feedback capabilities, the response speed, operation stability and target achievement efficiency of path execution are improved, and the path execution efficiency is improved. And the method has stronger self-adaption and execution guarantee capability in the face of environment uncertainty and path diversity.

The invention discloses a distributed moving target passive positioning method and system based on deep sea multipath time delay combination, and belongs to the technical field of deep sea target detection and positioning. In order to solve the problems that Doppler compensation and multipath time delay combined processing cannot be considered at the same time and a special positioning scheme for a moving target is lacked in the prior art, two hydrophone nodes horizontally arranged near the seabed are utilized; relative Doppler and multipath delay inequality information of radiation signals of a moving target reaching two hydrophone nodes through different propagation paths are combined, a multipath propagation geometrical relationship positioning model of the moving target is constructed by combining a spatial position relationship between the moving target and the hydrophone nodes, and the model is optimized and solved, so that the positioning precision of the moving target is improved. And continuous high-precision positioning of a moving target track is realized. According to the method, the huge calculated amount of matching field processing is avoided, the positioning precision is high, simple, efficient and robust positioning is realized, and the engineering applicability is remarkably improved.

This invention provides an intelligent recruitment process control method and system based on task status and action type. The method includes the following steps: acquiring multi-dimensional input data of the recruitment process and converting it into a standardized task status feature vector, wherein the task status feature vector includes at least a process progress quantification value, candidate interaction feedback parameters, resource occupancy rate, and target achievement degree; constructing a collaborative decision-making system of preset decision rules and reinforcement learning model; this invention dynamically adjusts the execution strategy by collecting multi-dimensional task status features in real time and combining them with the adaptation logic of candidate action types, which can flexibly respond to various changes in the recruitment scenario and achieve accurate matching between the process and the real-time business scenario.

The invention discloses a lightweight federated learning method based on a pulse neural network and gradient quantization cutting, and belongs to the technical field of machine learning. The method comprises the following steps: a central server determines a global task and broadcasts generated global model parameters to a local client; the local client performs training according to the local private data and the global model parameters and updates the local model parameters to obtain a gradient; performing element-by-element cutting and quantization on the gradient, and uploading a quantized gradient index to a central server; the central server reconstructs gradients through inverse quantization, and aggregates the gradients according to the data volume weight of each local client; performing element-by-element cutting on the global aggregation gradient, and updating global model parameters by using the cut global aggregation gradient; and iteration is repeated until a global target is achieved. According to the method, error compensation is realized while the communication overhead is reduced, the convergence speed and precision of the model are improved, the robustness of the model is enhanced, and the method is suitable for edge equipment scenes with limited resources.

The invention discloses an intelligent switching control system and method for multi-class fluid output, belongs to the technical field of intelligent control, and aims to solve the problems that according to a traditional fluid output system, invalid scheduling and single-dimensional decision are easily triggered due to micro parameter fluctuation, so that environment adaptation is poor, requirements are mismatched or equipment exceeds the limit, and fluid quality and operation safety are difficult to cooperate. The method comprises the following steps: collecting processing environment, demand interaction and equipment processing parameters of target equipment, grading and marking according to a preset adaptation interval, and constructing a reference database; identifying parameter grading change based on a database, judging whether scheduling is triggered or not, generating an output pipeline priority sequence after triggering, calculating a real-time demand gap and an equipment constraint index, and quantifying a target achievement degree to obtain a comprehensive score to screen a target pipeline; a machining instruction is generated according to the fluid quality of the target pipeline and the required notch, and the instruction is dynamically updated by outputting anomaly detection; accurate scheduling, stable processing and safe operation of multi-class fluid output are achieved, and the output adaptability and the overall efficiency are effectively improved.

This invention provides a method and apparatus for positioning and correcting a mobile camera based on long-distance shooting. The method automatically obtains feature point sets from two sets of image samples through a feature point model, and obtains the corresponding transformation matrix based on the feature point sets. Then, the transformation matrix is ​​used as the basis for adjusting the position coordinates of the mobile camera. Through feedback adjustment, the deviation between the adjusted position of the mobile camera and the target position coordinates can be effectively reduced. In addition, the mobile camera is adjusted according to the transformation matrix, the first position coordinates, the first gimbal angle, the second position coordinates, and the second gimbal angle until the target reaches the preset position, ensuring the reliability of the mobile camera positioning and correction results.

We provide the system. [Solution] Means for obtaining individual user information, A means for generating an exercise plan based on the individual information of the user, Means for providing visual content corresponding to the aforementioned movement plan, A means of recording the user's exercise progress and evaluating the degree of goal achievement, A means of offering rewards according to the degree of achievement of the aforementioned goals, A system that includes this.