The system (1) comprises a plurality of products stored in dedicated product containers (RC), a computer server (S RC) hosting a product stock management software module (GSP, GS) and connected to an extended data communication network (IP), a weighing device (BC) interconnected with the computer server (S-RC) through a wireless data communication link (RWF, L5) and through the extended data communication network, and a user computer device (UD) connected to the computer server and hosting a software application (APPLI), the product containers each comprising an identification tag storing an identification number, and the weighing device comprising an identification reader module. According to the invention, the reader module is a radio-identification reader module and the tag is a radio-identification tag having a location distance from a placement surface of the container which is at most equal to a radio detection height of the reader module from a weighing surface (SP) of the weighing device, and the weighing device comprises an accelerometer and means arranged so as ensure that the weighing device is put on standby, or woken up, on the basis of movement information supplied by the accelerometer.

A system (102) includes a digital information repository (106) configured to store information about performances of individuals, including performances of an individual of interest. The system further includes a computing apparatus (103). The computing apparatus includes a memory (110) configured to store instructions for a performance benchmarking engine trained to learn factors of the performances that impact key performance indicators independent of the individuals’ performance. The computing apparatus further includes a processor (108) configured execute the stored instructions for the performance benchmarking engine to determine a key performance indicator of interest for the individual of interest based at least in part on the information in the digital information repository about the performances of the individual of interest and the learned factors that impact the key performance indicator of interest.

A device for providing charging information for a charging process is configured to determine a total set of N data tuples for N different times of a charging time period for the charging process. A data tuple includes values of one or more characteristic variables relating to electrical energy that can be provided in the charging process. Furthermore, the device is configured to reduce the total set of N data tuples to a reduced set of M data tuples, with M<N, and to provide the reduced set of M data tuples for the determination of a charging plan for the charging process.

Provided is a patent transaction prediction method, comprising the following steps: obtaining to-be-predicted patent data(S1); constructing a prediction model, which is executed by a computer to predict a transaction probability of to-be-predicted patent data(S2); and displaying the transaction probability in a data attribute of the to-be-predicted patent data(S3). According to the method, the probability of patent transactions is displayed in the attribute of the patent data, improving the probability of patent transaction. Also provided are a patent transaction prediction system and a patent transaction platform, which also have the above-mentioned advantages.

A required time prediction apparatus (2000) acquires utilization facility information and assumed date and time information. The utilization facility information is information enabling to determine a departure facility and a destination facility. The assumed date and time information is information enabling to determine an assumed departure date and time assuming departure from the departure facility or an assumed arrival date and time assuming arrival at the destination facility. The apparatus (2000) estimates a waiting time at each facility being present between the departure facility and the destination facility, regarding a case of assuming departure from the departure facility at the assumed departure date and time, or a case of assuming arrival at the destination facility at the assumed arrival date and time. Then, the apparatus (2000) computes, based on the estimated waiting time, a predicted required time from departure from the departure facility to arrival at the destination facility.

A function to optimize factory management using information on operation ability and capability of an operator and a machine is provided.

Provided is a factory management device which makes a plan for a factory, the device including a storage unit which stores operator measurement information obtained by measuring a movement of an operator and machine measurement information obtained by measuring a predetermined value which indicates an operating state of a machine that is production equipment of the factory, an operator ability prediction unit which predicts changes in operation ability of the operator using the operator measurement information, a machine capability prediction unit which predicts changes in operation capability of the machine using the machine measurement information, a production capacity prediction unit which predicts a production capacity of the factory using prediction of changes in the operation ability and capability of the operator and the machine, and a planning unit which makes a plan for the factory that satisfies a predetermined productivity index using the predicted production capacity of the factory.

A management system includes a course data generation unit that generates course data for each of a plurality of unmanned vehicles such that loading work for the plurality of unmanned vehicles by a loader is sequentially performed on a work site where a plurality of the loaders operates; and a priority determination unit that determine a passage order at an intersection on the work site of the plurality of unmanned vehicles traveling according to the course data so as to reduce a total loading loss indicating a total of loss amounts in operation of each of the plurality of the loaders.

A data collection apparatus (1) according to the present disclosure includes: instruction data collection means (31a) for collecting instruction data representing an instruction that a leader has given to a subordinate, and first time information indicating a time at which the instruction was given, the subordinate being a person who works at a site, the leader being a person who leads the subordinate, and the instruction data and the first time information being data and information transmitted from a leader terminal (20) operated by the leader; site work data collection means (31b) for collecting site work data representing work performed by the subordinate at the site, and second time information indicating a time at which the site work data was detected, the site work data and the second time information being data and information transmitted from a subordinate terminal (11) carried by the subordinate.

The present invention relates to an aggregation method for dispatching the wind and solar power plants. The primary technical solutions include: introducing the power output complementarity indexes to characterize the average effect of the degree of power output complementarity between different power stations, using cohesive hierarchical clustering to identify the optimal cluster division under different division quantities, and introducing the economic efficiency theory to determine the optimal cluster quantity, which avoids the randomness and irrationality that may result from relying on the subjective determination of the number of clusters. According to the analysis of dozens of real-world wind and solar power cluster engineering in the Yunnan Power Grid, the results show that the invention can effectively reduce the number of directly dispatched power stations, and the uncertainty of wind and solar power output can be more accurately described in a cluster manner, presenting better reliability, concentration, and practicality.

The disclosure belongs to the technical field of furniture processing and system planning, and in particular, to an order grouping and processing method for personalized custom furniture. The implementations of multi-order personalized custom furniture can be processed and produced efficiently through the manners of order sorting into sets, parts marking, parts grouping, parts sorting by order, and parts packaging by order in sequence.