15 results about "Time efficiency" patented technology

The invention relates to the technical field of scheduling management, in particular to a dynamic production scheduling management system based on artificial intelligence, which comprises a task compression coefficient generation module, a task priority remapping module, a resource conflict index extraction module, a path broken link node identification module and an alternative task sequence construction module. According to the method, quantitative evaluation is performed on the task timeliness urgency degree through the time buffer region constructed by the latest starting time and the predicted execution duration of the task, the key task influencing the delivery node can be dynamically identified, so that the task priority relationship is not statically solidified any more, and potential link breaking points in the technological process are screened in combination with the path structure dependency relationship; a prediction trigger condition is constructed through a judgment node with the minimum structural sensitivity, a task replacement path is predicted by matching a process parameter group with a convolutional neural network model, a node with the minimum interference degree is screened to form a repair scheme path, and the plan failure risk caused by abnormal nodes is reduced.

Provided is a printer that improves the time efficiency of printing. A heating device 110 of a printer 10 comprises a heating device body 111, a heater 120, and a media guide 130. The media guide 130 is disposed so as to face a recording medium 5 transported by a medium transport device 50. The media guide 130 is a plate-like member that guides the recording medium 5 toward a heating chamber 113. A plurality of holes 131 are formed in the media guide 130 so as to pass through the thickness direction thereof.

A non-volatile memory improves read time efficiency by combining hard and soft reads into a single effective soft read sequence using two senses per state level. Instead of a standard hard read, which performs two soft reads offset above and below the hard read value, the hard reads are shifted so that they reliably sense one state but less reliably sense the other, and soft data is determined only for the less reliably sensed state. This reduces the amount of soft data. The effective soft-sensing sequence can be used as the default read mode, providing soft-sensing information for ECC correction without triggering read error handling. Combining soft and hard sensing into a single sequence avoids the additional overhead for read sequence operations.

A system for increasing the speed and reducing the time to obtain a required amount of data, from a secondary storage device, for a digital computer, based upon measures to improve the time efficiency of I / O request processing by improving the timing and sequence of transfers to improve the efficiency of mass storage devices.

To improve time efficiency and user-friendliness in connection with the setting of parameters used in a three-dimensional manufacturing device.A three-dimensional fused deposition modeling (FDM) manufacturing device comprises: a parameter setting unit that sets the type of user-replaceable part and the value of a manufacturing parameter of an object; a manufacturing data generation unit that creates manufacturing data for producing a three-dimensional manufactured object based on the value of the manufacturing parameter; a storage unit that stores allowable range reference data specifying a allowable range of the manufacturing parameter value according to each type of replacement part; and a guidance unit that guides the manufacturing parameter value to fall within the allowable range based on the type of replacement part, the value of the manufacturing parameter, and the allowable range reference data stored in the storage unit.

The present invention aims to provide a rotating body article visual inspection device and a rotating body article visual inspection method that can improve inspection accuracy and shorten cycle time to increase the time efficiency of inspection. [Solution] The O-ring appearance inspection device 1 of the present invention is an appearance inspection device for inspecting the appearance of an O-ring K, comprising: a plurality of line sensor cameras 20 whose positions and orientations are set in advance and which image the O-ring K; and a holding device 10 which holds the O-ring K and has a head portion 14 that is rotatable around an axis C1, wherein the holding device 10 sequentially moves the O-ring K to a plurality of imaging positions P corresponding to each of the plurality of line sensor cameras 20, and rotates the head portion 14 around the axis C1 at the imaging positions P to rotate the O-ring K in the circumferential direction.

The invention discloses a portable intelligent power first-aid repair metering method, system, equipment and medium, and belongs to the technical field of power distribution network maintenance, and the method comprises the steps: receiving and integrating the position and type information of a fault point and the real-time state data of each first-aid repair device, dynamically predicting the first-aid repair time of each device based on the historical efficiency and the real-time efficiency, and calculating the first-aid repair time of each device; the total scheduling time is calculated in combination with the path distance and the distribution speed; and through intelligent comparison, predicting a matching relationship between the first-aid repair time and the total scheduling time, selecting a device with the optimal comprehensive scheduling time efficiency from the devices meeting the time efficiency conditions as a supply source, generating a distribution instruction and issuing the distribution instruction to distribution personnel. According to the method, the historical efficiency data, the real-time working condition information and the multi-device synergistic effect are integrated, the first-aid repair time is predicted, and the optimal device is dynamically selected, so that the timeliness of first-aid repair response and the scientificity of resource scheduling are improved, and the whole-process intelligence from first-aid repair operation to equipment maintenance is realized while the continuous and reliable operation of first-aid repair equipment is ensured.

The present application relates to the technical field of intelligent manufacturing and production scheduling, in particular to a multi-variety variable-batch flexible job shop intelligent scheduling method based on enhanced genetic algorithm and dynamic time window, comprising the following steps: step S1: obtaining a production order list to be scheduled, workshop resource information and a current material inventory state, splitting the order into a work order set with process constraints according to the process route of the order product, and performing a material completeness pre-check on the split work order; step S2: constructing a machine idle time period management model based on a dynamic time window, and maintaining available time segments of each production device in real time through a multi-value mapping data structure; step S3: taking the minimization of the maximum completion time, the maximization of the weighted average device utilization rate and the maximization of the material completeness just-in-time start rate as optimization objectives; the present application realizes accurate maintenance of the device idle time period by introducing a dynamic time window management mechanism, and significantly improves the time efficiency in the decoding process of the genetic algorithm.

The invention provides a method for optimizing a vacuum system in a magnesium reduction process, which comprises the following steps of: S4, judging whether a starting condition of a jet vacuum pump is met or not according to a stable medium temperature value, and calculating a cooperative operation time point of a mechanical vacuum pump and the jet vacuum pump as a pump group linkage moment through a time sequence arrangement mechanism; s6, according to the smooth vacuum degree transition curve, whether the process stability reaches the expectation after the system is started or not is judged, and optimized operation parameters are stored through a data recording module to serve as an optimization template; s7, according to the optimization template, obtaining improvement data of the vacuumizing efficiency in the magnesium reduction process, and determining a final vacuum system operation scheme as a standardized operation protocol by comparing historical operation records with real-time efficiency indexes; and S8, according to the final vacuum system operation scheme, the operation states of the mechanical vacuum pump and the jet vacuum pump are monitored in real time through a continuous monitoring mechanism, and long-term stable process parameters are obtained to serve as lasting and reliable indexes.

The present application relates to the technical field of aero-engine fault data isolation processing, and in particular to an aero-engine fault data processing method and system which can achieve a good balance between time efficiency and generation quality and has good versatility. First, the original data is preprocessed through the MAW workflow, and then the fine semantic segmentation of the text and the efficient vectorization index construction are completed through the domain adaptive large language model and the dynamic code execution mechanism. Second, the two-stage progressive retrieval enhancement generation architecture TSP-RAG is proposed. At the same time, the adaptive HNSW retrieval module dynamically adjusts the search breadth according to the obtained ef weight, performs vector retrieval, and removes the results at the content level. Finally, the nodes retrieved by the FIL-knowledge graph and the HNSW retrieval results are semantically fused and reordered, and are input into the large language model together with the reasoning tree to generate answers. Compared with the prior art, the present application has the advantages of high time efficiency, high generation quality, and good versatility.

A method and system for quickly emptying a plurality of substantially identical target items from a transport structure with a picking tool for placement into a transport container are provided. The method includes the step of providing a plurality of equivalence classes which partition all possible sequences of legal picks. Each equivalence class containing a subset of items from a ranked configuration. The method also includes ranking the equivalence classes in order of expected time efficiency and selecting the best ranked equivalence class for picking.

The invention discloses a material guarantee task scheduling system based on a double-population genetic algorithm. The system comprises a node model, a task demand and capability model, a material guarantee task scheduling model, constraint conditions of the model and a model solution guarantee task scheduling algorithm based on double-population heredity. The node model comprises a demand node, a guarantee node, various material reserves capable of being provided by the guarantee node and guarantee equipment of the guarantee node; the task demand and capability model comprises the demand quantity of a certain material guarantee task of the demand node, the expected completion time of the demand task and the quantity of materials needed by the guarantee task. According to the system, the limitation that a traditional single population optimization algorithm is easy to fall into a local optimal solution in material guarantee task scheduling and is difficult to consider multi-target conflicts is effectively overcome, and the time efficiency, the resource utilization rate and the overall feasibility of a scheduling scheme of the material guarantee task are remarkably improved.

The application discloses a time series prediction method and device, electronic equipment and medium, and relates to the technical field of big data. The method comprises the following steps: acquiring historical time series data; constructing a prediction model based on a Transformer network, wherein the prediction model comprises an attention layer, and the attention layer comprises a linear mapping autocorrelation attention module, which is used for acquiring autocorrelation features of input data of the prediction model; inputting the historical time series data into the prediction model as the input data, obtaining autocorrelation features of the historical time series data based on the linear mapping autocorrelation attention module of the prediction model, and obtaining a prediction sequence in a preset time period after a current time based on the autocorrelation features of the historical time series data. The method can capture context correlation information of the historical time series data, is more accurate in prediction, reduces spatial and temporal complexity, and improves memory efficiency and time efficiency.

Embodiments of the present application provide a kind of based on distributed cluster partition method.The method comprises: based on the initial sampling period of current round, the operating parameter of each node in power system is collected, and initial operating parameter is obtained;According to initial operating parameter, determine failure index;Wherein, failure index represents the time efficiency adaptation degree of cluster partition according to initial operating parameter;If it is determined that the period adjustment condition is satisfied according to failure index, then determine the actual sampling period according to failure index and initial sampling period;Based on actual sampling period, the actual operating parameter of each node in current round is collected, and each node is divided into multiple distributed clusters according to the actual operating parameter of current round.The method is used to improve the timeliness and accuracy of distributed cluster partition, to improve the stability and resource utilization efficiency of power system.

The application provides a weapon target allocation method based on a neural network, and mainly solves the problem that the prior art cannot be applied to the continuous combat scene of unmanned aerial vehicle group attacking ground targets which has extremely high time efficiency requirement.The scheme is as follows: threat evaluation is carried out on the discovered targets, the targets are sorted according to the threat degree, and the targets are allocated with weapons one by one; the damage probability estimation is used to obtain the attack suitability of each weapon on the current target to be allocated, the commander adjusts part of the suitability according to the actual combat experience, and a training data set and a test data set are formed; the full connection network model is trained through the training set, the best full connection network model is selected through the test data set, and the selected best full connection network model is used to allocate the weapon target for each target one by one.The application not only can utilize the historical combat information and the combat experience of the commander, but also can reduce the allocation time, and can be applied to the continuous combat scene of unmanned aerial vehicle group attacking ground targets which has extremely high time efficiency requirement.