165 results about "Time estimation" patented technology

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention provides a software development project progress prediction management method based on artificial intelligence, and the method comprises the steps: obtaining a project module dependency graph, analyzing the dependency intensity and influence range between modules, and determining the weight of a key module and the weight of a dependency module; according to the skill level of developers, assigning task priorities and combining technical complexity, calculating development time estimation of each module, and generating an initial progress plan; the development progress of the key module is monitored in real time, if lagging is found, a resource allocation mechanism is triggered, the task priority is evaluated again, and distribution of developers is adjusted; and if the deviation between the output of the progress prediction model and the actual situation exceeds a preset threshold value, triggering a model self-learning mechanism, retraining the progress prediction model, and updating the Bayesian network and the adaptive filtering parameters.

Methods, systems, and devices for predicting a departure time of a user from a labeled place. In some embodiments, the location sharing system accesses historical location data of the user and extracts, for one or more labeled location of the user, an attendance record of the user at the labeled place. Then, when the location sharing system receives current location data of the user, and the system determines that the user is currently at the labeled place, the user predicts a departure time of the user from the labeled place based on the attendance record of the user at the labeled places. Some embodiments share the predicted departure time of the user with the user's friends via a map GUI.

The invention discloses a foundation pit support deformation real-time monitoring method based on edge calculation, and aims to solve the problems of multi-node time synchronization and clock drift compensation caused by unavailability or poor quality of satellite navigation and precise time protocols in an underground or shielded environment. According to the method, through event dictionary learning, partition time slot scheduling and safety coding micro excitation time synchronization, matched filtering decoding, multi-scale arrival time estimation, paired time difference robust fusion, frequency priority and phase speed limit correction and network disconnection time holding, the time synchronization efficiency is improved; the technical effects that multi-source data alignment, deformation field reconstruction and graded early warning are completed on the unified virtual reference time scale, and high-precision synchronization and online drift compensation are obtained without depending on the external time service condition are achieved.

The invention relates to the technical field of power generation, and discloses a wind turbine generator fault early warning method and system based on multi-model combination, and the early warning method comprises the steps: 1, data collection; step 2, establishing an early warning model; 3, real-time monitoring and early warning are carried out; step 4, analyzing a fault source; step 5, estimating fault existence time; and step 6, fault subsequent condition prediction. According to a fault source analysis result, historical fault data and current operation data are combined through the system, the fault existence time is estimated, the prediction model is utilized, the system can not only predict the fault expansion speed, but also predict the influence degree of the fault on the unit performance, and therefore a more effective coping strategy is formulated, and the unit performance is improved. The beneficial effects of early warning the fault of the wind turbine generator and analyzing the fault source are achieved.

The invention discloses an SLAM-oriented multi-sensor space-time synchronization and online calibration method, and relates to the technical field of computer vision and robot perception. Comprising the steps that S100, initialization is carried out, and an initial pose and an external parameter transformation matrix initial value are obtained through coarse alignment; s200, data acquisition and preprocessing: acquiring IMU data, LiDAR data and Odom data in real time, and performing data filtering, distortion removal, time sequence correction and point cloud downsampling processing on the acquired data; and S300, performing time synchronization processing, estimating the time offset of the LiDAR and Odom data in real time, and performing unified correction to generate a unified timestamp. According to the method, an online time offset estimation and compensation mechanism with an IMU as a main clock is established, accurate synchronization of sensor data of different frequencies is achieved, and the problem of data alignment caused by sampling frequency difference and clock drift is effectively solved; and meanwhile, tight coupling fusion is realized by using an IMU pre-integration factor, a LiDAR residual factor and an Odom constraint factor, so that the space-time consistency of multi-sensor data is remarkably improved.

The embodiment of the invention discloses a printing time estimation method and device for additive manufacturing, equipment and a medium. The printing time estimation method for additive manufacturing can comprise the steps that reference printing time based on a reference piece is decomposed into reference energy output time and reference additive time; determining target energy output time according to the reference energy output time, the geometric attribute ratio of the reference piece to the target piece and the quantity ratio of the energy input devices; determining the target additive time of the target piece according to the reference additive time and the physical structure size ratio of the reference piece to the target piece; and the target printing time of the target piece is determined according to the target material increasing time and the target energy output time.

The invention provides an air conditioner starting duration estimation model construction method and device, equipment and a medium. The method comprises the steps that historical data of target parameters are obtained; the target parameters comprise the air conditioner number, the space temperature, the air conditioner horsepower, the target closed area, the air conditioner set temperature and the air conditioner air speed of the target closed area; based on the historical data of the target parameter, training a duration estimation model to obtain a training error; if the training error meets a preset error requirement, adjusting parameters of the duration estimation model according to an optimization function to obtain a duration estimation model with updated parameters; and if the training error does not meet the preset error demand, repeating the step of training the duration estimation model based on the historical data of the target parameter to obtain the training error until the training error meets the preset error demand. The starting time of the air conditioner can be accurately calculated, the automation degree is high, and manpower resources are saved.

The present invention relates to a sitting time estimation system, a sitting time estimation program, and a sitting time estimation method that can estimate sitting time according to the user's lifestyle. [Solution] The sedentary time estimation system according to the present invention is a sedentary time estimation system that estimates the time a user spends sitting, and comprises a lifestyle information acquisition unit, a sedentary time calculation unit, and a sedentary time presentation unit. The lifestyle information acquisition unit acquires the user's lifestyle schedule and the activity intensity for each lifestyle scene in the lifestyle schedule. The sedentary time calculation unit calculates the user's sedentary time based on the activity intensity for each lifestyle scene. The sedentary time presentation unit presents the user's sedentary time to the user.

The invention provides a traffic time estimation method based on deep learning in a complex environment, and the method comprises the steps: firstly, obtaining a track data set comprising a plateau environment region and a mountain environment region; the trajectory data set is then divided into a training data set, a verification data set, and a test data set. Next, constructing a transit time estimation model, and training and optimizing the transit time estimation model according to the training data set and the verification data set to obtain an optimized transit time estimation model; and testing the optimized transit time estimation model according to the test data set, and determining a model evaluation index value. And finally, under the condition that the model evaluation index value meets the index threshold value, inputting to-be-estimated data into the optimized passing time estimation model, and outputting to obtain a passing time estimation result. According to the method, the adaptability of the traffic time estimation model to modeling of complex environmental terrains and weather factors is improved, so that the accuracy of estimating the traffic time in a complex environment can be effectively improved.

In general, disclosed herein are systems and methods of using a monocular camera to determine an estimated time from encounter between an object a region of interest, including receiving an image from a camera, identifying an object from the image, calculating a scaled distance between the object and the region of interest based on the image, calculating a scaled velocity based on the scaled distance, and calculating an estimated time from encounter until the object will encounter the region of interest based on the scaled distance and the scaled velocity.

A computing device having a host system coupled to a memory sub-system. The host system runs a garbage collection manager and a plurality of storage space tenants. The garbage collection manager can: write, to the memory sub-system according to a protocol of flexible direct placement (FDP), data of the plurality of storage space tenants to a reclaim unit in the memory sub-system; identify, after a first portion of the data stored in the reclaim unit is invalidated, first storage space tenants having a second portion of the data remaining valid in the reclaim unit; communicate with the first storage space tenants to identify estimates of expiration time of the second portion; and select, based on the estimates, the reclaim unit for acceleration of invalidation of the second portion of the data.

By a communication device, a line delay time estimation method, a non-transitory computer-readable storage medium storing a line delay time estimation program, a communication packet is accumulated, a data amount of the communication packet is monitored, and a residence time, which is a duration and for which the communication packet remains in a transmission buffer, is estimated as an upstream buffer delay time from the communication device to a counterpart device.

The invention belongs to the technical field of power distribution network energy efficiency analysis, and provides a real-time estimation method and system for additional loss of a distribution transformer based on quantile envelope and dynamic characteristics. According to the method, real-time electrical data and rated electrical data of a transformer are fused, a reference loss envelope model is accurately fitted in a strong noise environment by adopting a low-quantile envelope regression technology, and an optimal nonlinear mapping relation of a self-adaptive learning load rate of a mechanism is constructed in combination with dynamic adjustable index features; and continuously tracking the state change of the distribution transformer based on a sliding window retraining trigger mechanism, and finally realizing real-time stripping and quantification of an additional loss component, namely dynamically calculating instantaneous non-negative additional loss and time cumulant thereof. And a high-precision, self-adaptive and landing quantitative decision basis is provided for harmonic pollution treatment and energy-saving optimization of core energy consumption equipment.

The invention discloses an unmanned aerial vehicle positioning method and device, a medium and a product, and relates to the field of unmanned aerial vehicle positioning. An electromagnetic wave signal emitted by the unmanned aerial vehicle is received at each reconnaissance site, and an IQ signal with a timestamp is obtained through digital signal processing; then estimating the absolute time of signal arrival at each reconnaissance site by using a time estimation method based on a Zadoff-Chu sequence; and finally, the absolute time estimated by each reconnaissance site is uploaded to an upper computer system, and the position of the unmanned aerial vehicle is calculated in the upper computer system by using a TDOA method. According to the method, the Zadoff-Chu sequence contained in the unmanned aerial vehicle signal frame is used for positioning, and the method is simple and efficient; and meanwhile, the specific CP sequence characteristic of the OFDM signal segment is utilized, so that the time delay estimation precision is improved, and the positioning precision of the unmanned aerial vehicle is further improved.

The invention discloses an ammunition automatic filling robust control method based on singular system IL disturbance estimation, and aims to solve the problems of flexible transmission, parameter uncertainty, periodic external disturbance, residual vibration and the like in an automatic filling system. According to the method, a fast-slow dual-time-scale cooperative control architecture is constructed, a slow subsystem adopts a disturbance self-adaptive PD controller, low-frequency periodic disturbance is estimated and compensated online in combination with an iterative learning mechanism, and the trajectory tracking precision is improved; the fast subsystem carries out real-time estimation and feedforward suppression on high-frequency elastic vibration based on an extended state observer (ESO); and filtering and amplitude limiting are further carried out on slow and fast control inputs, a self-adaptive scheduling strategy based on tracking errors is designed, and the slow and fast control inputs are dynamically fused to generate a final control instruction. According to the method, only motor end position / speed measurement is needed, a load end sensor is not needed, high precision, high robustness and excellent vibration suppression capability are achieved, and the dynamic performance and complex condition adaptability of automatic ammunition filling are remarkably improved.

This processing time estimation method includes: allocating a plurality of tasks in an automated warehouse to each of a plurality of transport devices; generating a plurality of divided travel routes for each transport device by dividing, at a predetermined first time interval, an entire travel route during processing of the tasks for each transport device into N sections; and, for each of the N sections, executing input, to a trained model capable of outputting an estimated travel time for a divided travel route in a predetermined section for each transport device, of a divided travel route in an n-th section for each transport device, a map, and the first time interval.

In some embodiments, a method receives a file for transcoding. A timing requirement is associated with the transcoding of the file. The method generates a prediction of a timing estimation based on characteristics of the file and a set of target configurations. A target configuration in the set of target configurations is associated with a transcoding setting. The timing estimation is analyzed for the set of target configurations and the timing requirement to determine a transcoding configuration of a transcoding system. The transcoding configuration of the transcoding system is output to configure the transcoding system to transcode the file. The file is to be transcoded using at least a portion of the set of target configurations based on configuring of the transcoding system.

The invention discloses a path planning method, a path planning system and a transportation controller. The path planning method comprises the following steps: S1, acquiring a starting point and an ending point of a path to be planned; s2, determining a plurality of passing lines from the starting point to a plurality of demarcation points in the neighbor area and first estimation time consumption of moving on each passing line through a neighbor area path planning model; s3, determining second estimation time consumption from each demarcation point to an end point through a trained far and adjacent region time estimation model; and S4, determining which passing line is selected as the optimal path according to the determined first estimation time consumption corresponding to each passing line and the second estimation time consumption from the demarcation point to the end point of the passing line. According to the method, low-efficiency blind searching in the far and adjacent areas when the Dijkstra algorithm is executed can be avoided, the path planning time and the calculation cost can be reduced under the condition that the path planning accuracy is ensured, the efficiency of the transportation controller is improved, the crown block can be guided to be away from the congestion area in time, and the carrying efficiency is improved.

The invention discloses a ship navigation time estimation method, system and device, a medium and a product, and relates to the field of marine shipment.The method comprises the steps that trajectory data, ship geometric features and meteorological data are obtained; the trajectory data is obtained based on ship AIS data; the geometric features of the ship comprise the length, width and maximum draft of the ship and the cargo type of the ship; the meteorological data comprises wind speed, wind direction, wave height and visibility; a VDTEE model is constructed; the VDTEE model comprises a space-time module, an attribute module and a multi-task module; inputting the trajectory data into the space-time module, and inputting the geometric features of the ship and the meteorological data into an attribute module; and transmitting the output data of the time-space module and the attribute module to the multi-task module, and determining a ship navigation time estimation result, thereby improving the time navigation estimation accuracy.

The application discloses a time delay control method and system based on an error compensation type extended state observer, which comprises the following steps: a time delay controller acquires input signals and each output state of a tracking differentiator, and outputs a first control signal; an estimation error model of a displacement is constructed for an extended state observer; based on the first control signal, a total disturbance signal and an estimation error of the extended state observer, a controlled object is converted into an integrator series type form, and a displacement signal is output based on the controlled object; the displacement signal is input into the tracking differentiator to obtain real-time estimation values of each state of a nano-positioning stage; and the real-time estimation values of each state are fed back to the time delay controller. By introducing the extended state observer, the application solves the problem that the stability of a control system is easily affected by the uncertainty of a control input matrix; by introducing the tracking differentiator, the application solves the difficult problem that high-order system states and their derivatives required by time delay control are not easy to obtain.

The present application belongs to the technical field of water rescue, and particularly relates to an air-water collaborative full-autonomous anti-drowning rescue method, system and readable storage medium. The air-water collaborative full-autonomous anti-drowning rescue method plans the paths of the water surface and the flying life buoy at the same time, compares the time consumption, and selects the fastest rescue mode, thereby solving the poor collaboration and low efficiency of the traditional single rescue mode and maximizing the rescue time. The intelligent path planning can adapt to complex environments by using sensing information as the basis for path planning, thereby improving the accuracy of time estimation and the efficiency of time estimation and further shortening the rescue time.

The invention discloses an aircraft view field limited terminal guidance method with specified flight time, and relates to the field of aircraft guidance system design, and the method comprises the following steps: building a mathematical model of intersection of an aircraft and a stationary target in a two-dimensional plane; obtaining a relative motion differential equation by using the mathematical model, and designing a polynomial of an auxiliary variable based on a field angle as a relative distance profile; combining the mathematical model, the relative motion differential equation and the relative distance profile to obtain an analytical guidance law; and by limiting the field angle in the step S2 to monotonically decrease to 0 along with time, the field angle constraint of the aircraft is realized, and a flight time reachable domain is obtained. The design method is an analytical method based on relative distance profile shaping, does not need to linearize the model, does not depend on remaining time estimation, is suitable for guidance tasks with flight time constraints and limited aircraft view fields, and has the advantages of being easy to design and use, high in applicability and the like.

A method comprises identifying an area of worksite at which an operation is to be performed, identifying a historical rate of work metric, obtaining a model, identifying a number of work machines that are to perform the operation, identifying a crop type corresponding to the operation to be performed, identifying an ETC metric based on the area of the worksite, the historical rate of work metric, the number of work machines, the crop type, and the model. The method further comprises identifying, once the operation is initiated, a first weight metric, a second weight metric, a live estimate metric indicative of a first time until the work will be completed in the operation, and identifying an ETR metric, indicative of a second time until the work will be completed in the operation, based on the first weight metric, the ETC metric, the second weight metric, and the live estimate metric.

The invention relates to the technical field of audio loading, and discloses a USB media file loading method and system, a storage medium and equipment, and the method comprises the steps: detecting equipment connection and initializing a pre-scanning process when USB storage equipment is inserted into the system; the method comprises the following steps: quickly pre-scanning a USB storage device, acquiring basic information of media files through a lightweight file system traversal algorithm, and counting the number and types of the media files; based on a pre-scanning result and historical loading data, establishing a time prediction model to estimate loading time, and formulating a loading priority strategy; in the loading process, multi-level progress information including the overall progress, the classification progress and the real-time state is displayed to the user, and dynamically updated remaining time estimation is provided; according to the method, the loading of the media file is executed according to the loading priority strategy, and result verification and cache management are performed after loading is completed, so that intelligent pre-scanning and optimized loading of the USB media file are realized, and the practicability and user satisfaction of the system are improved.

The invention relates to the technical field of vehicles, in particular to a charging remaining time estimation method and device, a vehicle, a storage medium and a program. The method comprises the steps that battery characteristic parameters and charging pile characteristic parameters of a vehicle are obtained, and the battery characteristic parameters comprise the current battery temperature and the current state of charge; on the basis of a preset optimal charging temperature interval, the charging heat management duration of the vehicle is calculated according to the current battery temperature, and the battery characteristic parameters and the charging pile characteristic parameters are input into a pre-established charging time prediction model to obtain estimated charging duration; and according to the charging heat management duration and the estimated charging duration, obtaining the final residual charging duration of the vehicle. Therefore, the problems that in the prior art, estimation deviation is large, user experience and credibility are damaged, complex application scenes are difficult to deal with, and user anxiety is aggravated are solved.

The present invention provides a collision time estimation method based on an event camera, an electronic device and a storage medium, which estimate the collision time based on the event stream collected by the event camera, overcome the problem of very large delay in the prior art, and improve the accuracy of collision time estimation, as well as the robustness in autonomous driving scenarios. At the same time, the low energy consumption feature enables the method to be better applicable to embedded scenarios, reducing procurement and operating costs. The TTC output frequency of the method in this embodiment can reach 200 Hz, which has great application potential in real-time TTC tasks and is more suitable for real-time TTC tasks with sudden changes in relative speed.

The invention provides a CNN reasoning task time slice segmentation method and system based on a divisible tuple, and relates to the technical field of data processing. The CNN reasoning task is decomposed into sequentially executed BMO sequences, and continuous BMO is combined into tuples based on data dependence; based on the time estimation model of the tuple, segmenting the tuple according to the length of the target time slice to generate sub-tuples; wherein the segmentation is divided according to data dimensions and is rounded down to a minimum divisible unit; tuples or sub-tuples are selected from the tuple list to be combined into the time slice, the total execution time does not exceed the length of the target time slice, memory allocation is managed in a unified mode before the time slice is executed, and the memory is released in a unified mode after the time slice is executed. Performance modeling and time prediction are carried out according to CNN model operation characteristics, the task is divided into time slices to support time division multiplexing scheduling, and the real-time performance, the resource utilization rate and the hardware universality are improved.

The present application belongs to the technical field of power distribution network energy efficiency analysis, and provides a real-time estimation method and system for distribution transformer additional loss based on quantile envelope and dynamic characteristics. The present application fuses real-time electrical and rated electrical data of the transformer, adopts low quantile point envelope regression technology to accurately fit the benchmark loss envelope model in a strong noise environment, combines a dynamic adjustable exponential characteristic construction mechanism to adaptively learn the optimal nonlinear mapping relationship of the load rate, and continuously tracks the state change of the distribution transformer based on a sliding window retraining trigger mechanism, finally realizes real-time stripping and quantification of the additional loss component, that is, through dynamic calculation of the instantaneous non-negative additional loss and its time cumulative amount, provides high-precision, adaptive and implementable quantitative decision basis for harmonic pollution control and energy saving optimization of core energy-consuming equipment.

The invention belongs to the field of TOA tracking, and discloses a 5G signal arrival time robust tracking method and system based on a challenge environment, and the method comprises the steps: carrying out the synchronization and pilot frequency information extraction of a 5G downlink broadcast channel (PBCH); cSI measurement and denoising are carried out; and estimating the time of arrival. According to the invention, based on a dual noise reduction mechanism of CSI, time domain smoothing and frequency domain processing are combined, so that the quality of low-signal-to-noise-ratio signals is remarkably improved. An SAGE algorithm and delay phase-locked loop collaborative architecture is introduced, accurate separation of multipath components and dynamic phase-locked optimization are achieved, complex environment interference is effectively inhibited, and TOA tracking precision is improved by more than 30%. According to the invention, a reliable solution is provided for high-precision positioning in a complex environment.