564results about How to "Reduce randomness" patented technology

A system and method for routing telephone calls is based on identifying caller goals, and maximizing the benefit of routing the call to the appropriate call center. The appropriate call center is identified as the call center best equipped to respond to the needs of the caller. The cost or benefit is based on the fastest and least expensive way to answer a query posed by a caller. A probabilistic model of the caller's goals or call topics based on a response to a top-level prompt is used, along with a set of functions associating a utility or benefit with routing those call topics to destinations within the center. Maximum benefit routing allows for the separation of the caller's goals from those of the call center, thereby optimizing use of available resources.

The invention discloses a mobile robot path planning method based on an improved RRT* algorithm. The method introduces a target biasing strategy into a standard RRT* algorithm so as to reduce the randomness of sampling points; provides an avoidance step length extension method in order that a random tree can reasonably stay away from an obstacle area and avoids falling into a local minimum; and smoothes a path obtained by the improved RRT* algorithm by using a reverse sequence connection method smoothing strategy, so as to reduce the direction-changing operations of the robot and achieve the stable movement of the robot. Compared with an original standard RRT* algorithm, the improved RRT* algorithm has a better planned path and takes less time.

The present invention provides a unique system and method that facilitates obtaining high performance and more secure HIPs. More specifically, the HIPs can be generated in part by caching pre-rendered characters and / or pre-rendered arcs as bitmaps in binary form and then selecting any number of the characters and / or arcs randomly to form a HIP sequence. The warp field can be pre-computed and converted to integers in binary form and can include a plurality of sub-regions. The warp field can be cached as well. Any one sub-region can be retrieved from the warp field cache and mapped to the HIP sequence to warp the HIP. Thus, the pre-computed warp field can be used to warp multiple HIP sequences. The warping can occur in binary form and at a high resolution to mitigate reverse engineering. Following, the warped HIP sequence can be down-sampled and texture and / or color can be added as well to improve its appearance.

The invention relates to a method for logistics dispensing center work task processing and scheduling. The method comprises steps that, a first step, according to correlation of items in historical orders, storage location assigning for in-stockroom items is carried out; a second step, batch setting for out-stockroom orders in an order pool is carried out; a third step, sorter assignment for the out-stockroom orders after batch setting is carried out; and a fourth step, optimal walking paths are set for sorters. According to the method, storage optimization for items of new orders is realized according to a shipment trend of the historical orders, sorted task assigning for the sorters is carried out, optimization of the walking paths of the sorters is carried out, complex sorting work is made to be simple, work intensity is reduced, the work flow is standardized, and work efficiency is improved.

The invention relates to an optimal control method for a ground source heat pump refrigerating system and belongs to the field of energy-saving optimal control for air conditioning systems of buildings. Multiple models are optimally combined, as along as a prediction model contains contrasting information of the system, the prediction model can be combined with multiple good prediction models for predicting even if the prediction model is poor in effect, and a predicting character of the system can still be improved; in order to improve final predicting precision, a control system operates a combined predicting method to comprehensively utilize information provided by various methods, loss of useful information caused by a single prediction module is avoided, randomness is reduced, and predicting precision is increased. The optimal value provided by the optimal control method is ensured to meet tail end load requirements and capable of minimizing energy consumption of the system. After the optimal set value is determined, a central refrigerating system of the ground source heat pump refrigerating system is controlled to operate in an optimal set working status.

The present invention provides a unique system and method that facilitates obtaining high performance and more secure HIPs. More specifically, the HIPs can be generated in part by caching pre-rendered characters and / or pre-rendered arcs as bitmaps in binary form and then selecting any number of the characters and / or arcs randomly to form a HIP sequence. The warp field can be pre-computed and converted to integers in binary form and can include a plurality of sub-regions. The warp field can be cached as well. Any one sub-region can be retrieved from the warp field cache and mapped to the HIP sequence to warp the HIP. Thus, the pre-computed warp field can be used to warp multiple HIP sequences. The warping can occur in binary form and at a high resolution to mitigate reverse engineering. Following, the warped HIP sequence can be down-sampled and texture and / or color can be added as well to improve its appearance.

The invention discloses audible noise measuring circuit and method of a power capacitor. The audible noise measuring method comprises the following steps of: selecting a noise-eliminating test area; mounting a power capacitor to be measured in the noise-eliminating test area; setting a power capacitor to be measured in accompany; building the power capacitor to be measured and the power capacitorto be measured in accompany into a bridge-type power capacitor loading circuit; respectively loading an external high-voltage alternating current power source and an external harmonic power source attwo ends of the bridge-type power capacitor loading circuit, wherein the whole bridge-type circuit achieves resonance state under the common action of power frequency voltage generated by the high-voltage alternating current power source and various time harmonic voltage generated by the harmonic power source; and obtaining various noise values generated by the power capacitor to be measured in an actual work state according to data which are measured by a noise sensor arranged around the power capacitor to be measured and are related to the noise. The invention reduces the random of the noise measuring operation of the power capacitor, so that a measuring result has more comparability and a set of noise measuring method of the power capacitor with systematicness, completeness, science and reasonability is formed.

The invention relates to the technical fields of image processing and video processing and provides a multi-scale cylindrical projection panorama image generating method for video monitoring. The method comprises the following steps: intercepting a preset overlapping region of a to-be-spliced image, thereby acquiring a cylindrical projection image; establishing sub-images of the image under various scales, searching feature points by adopting an improved Harris method, and solving feature vectors under the scales; searching the matched feature point pairs, filtering, solving the optimized homography matrix, coordinates of the splicing joints and the actual overlapping region of to-be-spliced images; and adjusting the chroma so as to eliminate the splicing joints, thereby obtaining the final panorama image. The method disclosed by the invention has the great advantages that the image edge distortion caused by the shooting angle of a camera is eliminated, the processing speed and the algorithm stability are improved by adopting a mode of presetting the overlapping region, the solved matching points are filtered, the image color difference is adjusted by adopting a gradual coefficient adjustment mode, and the final joint-free panorama image with consistent chroma is formed.

The present invention provides a spunbond nonwoven fabric made of a specific crystalline resin composition having a melt flow rate of 25 to 80 g / 10 min and a melting endotherm ΔH of 65 to 100 J / g. More particularly, the invention provides a polypropylene spunbond nonwoven fabric having a very small fiber diameter and providing an excellent feel to the touch, and a polypropylene spunbond nonwoven fabric exhibiting high softness.

The invention discloses a full-automatic aluminum cutting device which aims at solving the problem of low manual feeding efficiency of the existing aluminum cutting device and provides a safe and convenient full-automatic aluminum cutting device which can ensure the quality. The device of the invention includes: a feed device, a bracket and a cutting saw arranged on the bracket; the feed device includes an upper clamping part, a lower clamping part, a horizontal feed part and a locating part. The device fully uses machines to replace manual operation, thus not only enhancing the accuracy, increasing the efficiency, reducing the cost, but also saving the human labor and materials as well as enhancing the safety performance.

The invention discloses a discrimination algorithm for aviation fault arc detection and belongs to a field of aviation fault arc detection. The algorithm specifically includes collecting fault arc current signals in conditions with different loads on a test platform; judging whether the fault arc current signals are DC fault arc current or AC fault arc current and extracting characteristic quantities with both time domain and frequency domain; aiming at wavelet energy of DC fault arcs, information entropy and current change rate and wavelet energy of AC fault arcs, training a support vector machine predication model by taking the information entropy and a fourth eigenmode function value obtained through empirical mode decomposition; and distinguishing fault and normal states of arcs by utilizing two support vector machine predication models. According to the invention, a plurality of characteristic quantities are selected, fault characteristic contingency is reduced and discrimination accuracy is increased. Intelligent discrimination is performed on characteristics in a fault and normal critical range and randomness is reduced.

The invention provides an electromagnetically-assisted laser drilling method. The method includes the steps of 1, clamping a workpiece to be processed onto an electromagnetically-assisted laser drilling device; 2, driving a fixture base to rotate by a motor so that a permanent magnet clamped on the fixture base rotates at high speed and forms a rotational magnetic field; meanwhile, allowing a mouse cage in the rotational magnetic field to produce electromagnetic force whereby the workpiece rotates in sync with the mouse cage; 3, after the speed of the motor stabilizes to a preset value, starting a laser, which generates a laser beam acting on the surface of the workpiece via a focusing lens so as to finish the electromagnetically-assisted laser drilling process. According to the method, the application of the operating method of rotating the workpiece enhances the assistance of the magnetic field in laser drilling; meanwhile, the rotational magnetic field can control motion of plasma and molten metal generated in the laser drilling process, so that laser drilling effect is optimized. The invention further provides the electromagnetically-assisted laser drilling device.

The invention discloses a method for visualizing multi-dimensional time sequence information, which aims to solve the problem that the same view cannot disclose the multi-dimensional distribution and the time-evolution characteristics of a data set. The technical scheme comprises the following steps: firstly, establishing a multi-dimensional time sequence coordinate system; secondly, calculating a related coefficient of any two dimensions in a multi-dimensional time sequence data set; thirdly, regulating the dimensional axis of the multi-dimensional time sequence coordinate system according to the related coefficient, and demarcating the positive direction of the dimensional axis of the multi-dimensional time sequence coordinate system; fourthly, calculating the Descartes coordinate representation, of the multi-dimensional time sequence data set, recorded in the multi-dimensional time sequence coordinate system; fifthly, establishing an objective function, solving an optimum value of the objective function, positioning all record entries of the multi-dimensional time sequence data set; and finally, rendering a point cloud formed by the multi-dimensional time sequence record to obtain a visualization result of the multi-dimensional time sequence data set. By adopting the method for visualizing the multi-dimensional time sequence information, the multi-dimensional distribution character and the time-evolution characteristics of the multi-dimensional time sequence data set can be visualized in the same view at the same time, so that the complex degree and the randomicity of dimensional axis configuration are lightened effectively and the time complex degree of dimensional operation is reduced.

The invention provides a method and system for calculating retail asset risk. The method comprises the following steps of: collecting source data, wherein the source data comprises loan and credit card transaction data and customer asset data collected from each business system of a bank, customer and account data collected from a personal credit system and a bank card system in real time during application and customer credit information data collected from a personal credit information system in real time during application; receiving the source data, and performing credit scoring and credit rating to generate credit scoring result data; storing the credit scoring result data into a data storage device for a user to invoke; and invoking the credit scoring result data to calculate the retail asset risk according to a preset rule. The method and the system can be used for controlling the retail asset risk of the bank, can greatly reduce the dependence of bank credit personnel on the assessment criteria not objective and stable enough in the credit business, can effectively reduce randomness in decision and can reduce manual operation cost.

The invention provides a method of product process programming simulation for intelligent manufacturing of aeronautical machined products. The method constructs the whole process of intelligent manufacturing of machined products, including the definition of design features and modeling of specification standards, and the realization of automatic programming of process features driven. The method establishes a collaborative process simulation and inspection platform, realizes the connection and transmission between the process simulation platform and the standard libraries, constructs the programming feature library and operation library suitable for intelligent NC programming, establishes the NC programming standard integration method for intelligent manufacturing, realizes the NC programautomatic programming and simulation operation environment driven by machining features, and then establishes the omni-directional intelligent manufacturing.

Production MDI-base polyurethane micro-porous elastomer is prepared by performed polymer, reacting excess polyisocyanate with polyhydroxy compound at 120-140degree to from NCO-base performed polymer, pouring, mixing performed polymer with chain extender proportionally, injecting reactive liquid into mould at 80-95degree, pre-curing, de-molding, and after-curing for de-molded product at 110degree for 13-16hrs. It can be used for buffering and shock-absorbing components.

The invention discloses a multi-time-scale optimal scheduling method for a power distribution company containing large-scale renewable energy sources. The method comprises steps as follows: optimal scheduling on the day-ahead market: the minimum day-ahead operating cost is taken as an optimal target, and the optimal scheduling is performed according to operating cost constraint conditions, load demand per hour in the next day, renewable energy source output and real-time electrovalence prediction; the intraday rolling optimal scheduling: according to predication for intraday short-term renewable energy source output, the optimal scheduling is performed again on the period after the moment with larger deviation, and deviation of the optimal scheduling on the day-ahead market is corrected; real-time market optimization: the optimal power flow optimization on a power distribution network is performed for a certain intraday moment according to super-short-term load and predication for renewable energy source output. With the adoption of the method, adverse effects of volatility and randomness of the large-scale renewable energy sources on the power distribution company can be reduced, the optimal scheduling for distributed power sources and interruptible loads accessed to the power distribution company in the electricity market environment is realized, and the safety and the stability of operation of the power distribution network are improved.

The invention discloses a hen house environment ammonia gas concentration intelligent monitoring system which is composed of two parts of a hen house environment parameter acquisition platform and a hen house environment ammonia gas concentration intelligent prediction model. The hen house environment ammonia gas concentration intelligent monitoring system based on a CAN field bus is designed in view of difficulties that hen house environment ammonia gas concentration change has characteristics of non-linearity, high time lag, high inertia and time variation and hen house area is relatively large and ammonia gas concentration is difficult to accurately predict. The system is the intelligent monitoring system which realizes detection, intelligent prediction and management of the monitored hen house environment ammonia gas concentration by the hen house environment parameter acquisition platform and the hen house environment ammonia gas concentration intelligent prediction model, and the basis of performing high-quality and high-efficiency purification on hen house environment ammonia gas concentration is provided. The system has wide application prospect and great popularization value.

The invention provides a pilotless vehicle path planning and controlling system, the pilotless vehicle path planning and controlling system comprises a route information acquiring module for acquiringinformation of a starting point and a terminal point of a route, a master control module for generating a path of a pilotless vehicle through a path planning module and transmitting the path to a locating control module through a communication module, the locating control module for controlling the driving of the pilotless vehicle, locating the running pilotless vehicle and feeding back a locating result and the vehicle driving state to the master control module through the communication module, so that the pilotless vehicle drives automatically according to the path generated by the path planning module. Meanwhile, a path planning and controlling method is provided. The pilotless vehicle path planning and controlling system can respond to the operation demand of the pilotless vehicle onany site. The driving path of the pilotless vehicle is designed and the pilotless vehicle is efficiently operated through inputted locating coordinates of the starting point and the terminal point. The operation efficiency of the pilotless vehicle is greatly improved, the comfort level of the user is improved, and the labor cost is reduced.

The invention relates to the electric power system load prediction field and especially relates to a grey load prediction method based on exponential smoothing. Aiming at conditions of randomness and uncertainty possessed by an original date sequence of a traditional grey model, the invention provides an improved method combining the exponential smoothing. Through using the exponential smoothing, weighting is performed on the original date sequence to generate a new sequence. Historical data which has large volatility and easily generates a large error is smoothed so that the sequence becomes a sequence which has a high regularity and shows an exponential function. Simultaneously, a background value of a grey model is optimized and a prediction error of the model is further reduced. Through combining the two prediction models, prediction precision is greatly increased; and the method is suitable for correlation departments of an electric power system and the like and is used to solve a load prediction problem in an electric power program.

The invention provides a method for forecasting the crop yield of per unit area in an irrigation district. Respective forecasting models of economic yield and environmental yield are respectively built, and then a combined forecasting model of crop field is obtained through the weighted sum of weight coefficients. The method weakens the respective defects of different models to enable the two models to mutually and self-adaptively complement each other, and also weakens the system analysis limitations brought by unilaterally forecasting the crop yield according to the socio-economic factors or natural environment factors simultaneously.

The invention discloses an inventory material demand predicting method and device. The method comprises: according to an initial investment plan, a planned investment volume of each project and an engineering attribute corresponding to each project are obtained; on the basis of the engineering attribute corresponding to each project, material subclasses included by each project are obtained, a preset correspondence relationship list of material subclasses, engineering attributes, and prediction models is searched, and a prediction model corresponding to each material subclass is obtained; the planned investment volumes are inputted into the prediction models corresponding to the material subclasses and calculation is carried out to obtain a predicted demand quantity of each material subclass in each project; and the predicted demand quantities of the material subclasses in different projects are added and calculation is carried out to obtain a total predicted demand quantity of each material subclass. According to the predicting method and device disclosed by the invention, the corresponding prediction models are constructed in advance for different material subclasses of each project, so that the efficiency and accuracy of inventory material prediction can be improved.

The invention discloses a method for predicting the main indexes of flue-cured tobacco smoke, which comprises the following steps: firstly, establishing a model for the main indexes of the flue-cured tobacco smoke; secondly, detecting the main chemical compostions and the main indexes of the smoke of a training flue-cured tobacco sample and a testing flue-cured tobacco sample; thirdly, using a gray function clustering method to cluster the training flue-cured tobacco sample; fourthly, determining the variable of the sample entering the model; fifthly, establishing a gray prediction model between the main indexes and the main chemical compositions of the smoke of each type of the sample; sixthly, establishing a BP neural network for each type of the training sample; and seventhly, performing a test and an adjustment by using the main indexes and the main chemical compositions of the smoke of the testing flue-cured tobacco sample. In the process of detection, the main chemical components of the flue-cured tobacco sample to be tested are clustered; and then the model of the type of the sample is applied to performing prediction so as to obtain the main indexes of the smoke. The method overcomes defects that the prior art needs to detect and operate a large number samples, achieves the prediction of the main indexes of the flue-cured tobacco smoke by using a small amount of the chemical composition samples, lightens the workload caused by smoke panel tests for formulating personnel, and can be widely used in the tobacco industry.

A method for evaluating the benefit of source-load complementation across time and space in consideration of the randomness of both sides of a system is characterized by comprising the following steps: constructing a typical power output feature database of different clean power sources across time and space; building a load prediction model based on a data mining technology, and predicting the load characteristics of different regions in the future by considering multiple influence factors; and building a cross-regional source-load time-space complementation characteristic evaluation system based on the statistical characteristics of big power data by considering the natural conditions of different regions, the regional differences and the external influence factors of energy structure differences. By taking into full consideration the complementation optimization configuration of renewable energy and loads in a wide area and across time and space in the global scope, the benefit of cross-national, cross-regional or cross-continental source-load time-space complementation can be evaluated from three dimensions (source-source complementation, load-load complementation and source-load complementation) based on the broad, multi-source, polymorphic and heterogeneous data differences and the statistical characteristics of big power data. The method can be widely applied to the reasonable evaluation of the benefit of source-load complementation across time and space.

The invention relates to the technical field of teaching management systems, in particular to a server with a teaching quality evaluation function. The server comprises a data storage module, an evaluation management module, a data analysis module and a data evaluation module. The evaluation management module is used for controlling a data collection device to perform collection and sending of classroom data; the data analysis module is used for analyzing the classroom data; the data evaluation module includes an attendance evaluation module and an evaluation statistics module, wherein the attendance evaluation module is used for generating student attendance data according to a recognition result of a human face recognition sub-module and basic data of a course, and the evaluation statistics module is used for generating a course evaluation result for the course and updating the course evaluation result in the storage module according to classroom evaluation data records. The server with the teaching quality evaluation function can solve the problems that there are great randomness and subjectivity in existing teaching quality evaluation processes, and the statistics process is cumbersome and time consuming.

A low temperature polycrystalline silicon device and techniques to manufacture thereof with excellent performance. Employing doped poly-Si lines which we called a bridged-grain structure (BG), the intrinsic or lightly doped channel is separated into multiple regions. A single gate covering the entire active channel including the doped lines is still used to control the current flow. Using this BGpoly-Si as an active layer and making sure the TFT is designed so that the current flows perpendicularly to the parallel lines of grains, grain boundary effects can be reduced. Reliability, uniformityand the electrical performance of the BG poly-Si TFT are significantly improved compared with the conventional low temperature poly-Si TFT.

The invention belongs to the technical field of short term power load forecasting, and discloses a power load forecasting method based on improved exponential smoothing and a gray model. The method includes the following steps: inputting original power load real-time data, and conducting a single exponential smoothing on the original power load real-time data, weakening the randomness of the original power load real-time data, such that the original power load real-time data approaches exponential development trend; predicting a smoothed sequence by using a gray forecasting model which optimizes background value; conducting inverse exponential smoothing on the forecasting result and returning the result to original power load data and a forecasting value at a next forecasting moment; determining whether the result reaches the requirements of knitting fitting errors, and outputting a forecasting result. According to the invention, the method expands the application range of the gray forecasting model, shortens search intervals, has higher forecasting reliability as high as 97%, can the meet requirements for maintaining the average error of short term power load forecasting at approximately 3% so as to address the problem of short term power load forecasting in future development of intelligent power grids.

The invention discloses a method for predicting the wind speed of a wind power plant at a short term. The method includes the following steps that wind speed data in a period of time are acquired and reduced into a time sequence for analytical prediction; a carpet traversal search method is adopted, and based on a dynamic cubic exponential smoothing prediction method, dynamic smoothing coefficients are determined according to the criterion of the minimum error sum of squares; the determined dynamic smoothing coefficients and the dynamic cubic exponential smoothing method are utilized to conduct one-step or multi-step prediction; the rest is conducted in the same way, new historical data are acquired, the smoothing coefficients are updated, and the later wind speed prediction continues to be conducted. According to the method for predicting the wind speed of the wind power plant at the short term, the characteristics of local historical wind speed are comprehensively considered, and the prediction effect is good.

A distribution transformer fault diagnosis device based on multi-index comprehensive evaluation includes a sound acquisition unit, an oil temperature acquisition unit, a unit for analyzing gas dissolved in oil of a transformer, a fault diagnosis unit and a battery power supply module; the fault diagnosis unit includes an FM receiver module (7) whose output end is connected with an AD converter (8) and an ARM single-chip microcomputer (9) in sequence; and the sound acquisition unit includes a fixed support (2) on which a strong magnet (1) and a pickup head (3) are fixed, the pickup head is connected with a pickup (4), an output end of the pickup is connected with a signal conditioning circuit (5) and an FM transmitter module (6) in sequence, and the FM transmitter module is connected with the FM receiver module through FM electromagnetic waves. The distribution transformer fault diagnosis device based on multi-index comprehensive evaluation can satisfy requirements for performing fault detection on a distribution transformer in operation on site and judging the fault type, does not need a power supply to be provided on site, and is convenient to carry, low in signal acquisition and transmission cost, fast in diagnosis speed and convenient to use.