155results about How to "Achieve automatic optimization" patented technology

The invention discloses a driver seat position automatic adjusting system based on distance images. The driver seat position automatic adjusting system comprises an image collecting device, an image processing device and a seat adjusting device, and is characterized in that the image collecting device at least comprises a distance image sensor, a CMOS camera and two infrared cameras and is used for collecting strength images and the distance images of a driver in real time and transmitting the collected strength images and distance images to the image processing device; the image processing device is used for processing the collected strength images and distance images of the driver to obtain the position information of the driver, set parameters of adjusting a seat position are generated according to the position information of the driver, and set parameters of adjusting the seat position by the driver are transmitted to the seat adjusting device according to instructions of the driver; the seat adjusting device optimizes and adjusts a vehicle driver seat according to the instructions of the driver and the received set information of adjusting the seat position by the driver. The system detects the positions of the eyes of the driver, the driver seat position is automatically optimized and adjusted by combining vehicle parameters, and the problem that the driver seat position is manually adjusted is solved.

The invention relates to a method for network coverage optimization processing, which comprises the steps: in step S102, collecting first coverage optimization data related to a local party and secondcoverage optimization data related to other base stations by a base station, judging whether coverage optimization is carried out or not in the local party according to a predetermined decision rule,if so, carrying out step S104, or else, carrying out step S106 and step S108, in step S104, if so, carrying out adjustment by the base station according to a first predetermined coverage optimizationrule, in step S106, or else processing the first coverage optimization data and the second coverage optimization data, reporting the data to a network administration node, determining an adjusting result by the network administration node according to local configuration data, the first coverage optimization data, the second coverage optimization data and a second predetermined coverage optimization rule, informing the base station and / or other base stations of the adjusting result, and in step S108, carrying out corresponding adjustment by the base station and / or other stations according tothe adjusting result. In addition, the invention also discloses a system for network coverage optimization processing.

The invention discloses an electronic file storage structure of mobile terminal equipment and a management method, belonging to a field for wireless mode management file of mobile terminal equipment. Currently file managing and storing mode of computers are not suitable for mobile terminal equipment and not compatible. So the invention adopts a series of software and hardware technology, converts through material format, fills memory area block, fills data bits, produces catalogue and label, fills copyright data, compresses file data and encrypts electronic content before encrypted target file UMD file comprising original data, index structure and copyright information compression is finally generated; the file is copied to the mobile terminal and edit interface is arranged at terminal of users. The invention has the advantages of simple structure, less operative steps, high compatibility, flexibility, safety and expansibility.

Embodiments of the invention provide neighbor relation optimization methods and a device. One of the neighbor relation optimization methods comprises determing neighbor regions to be processed through centralized equipment according to a default rule, generating optimization suggestion according to the determined neighbors, and issuing the optimization suggestion to a radio network controller (RNC) so that the RNC performs optimization on the processed neighbors according to the optimization suggestion. The embodiments of the invention realize an automatic neighbor relation.

The invention provides a vehicle gear shift control method and device. A gear shift parameter corresponding to each gear shift mode is taken as a character value, a membership degree value of each gear shift mode is calculated with a neural network algorithm, weight occupied by each gear shift mode in a current work condition is calculated, and after each gear shift speed in each gear shift mode under the current work condition is determined, the optimal gear shift speed under the current work condition is calculated according to the weight occupied by each gear shift mode in the current work condition and each gear shift speed in each gear shift mode under the current work condition, so that the gear shift speed is automatically optimized. The invention provides a flexible and variable gear shift strategy. The gear shift strategy can be timely adjusted according to the current vehicle condition, the driving state and the road condition change, so that a gear shift wire of a vehicle is more reasonable, and the driving experience of a driver is enhanced.

The invention discloses a method and a system for optimizing database parameters. The method comprises the following steps: selecting a plurality of sample values of database performance parameters, and carrying out typical application pressurization operation on a database to obtain server performance index parameter values as teacher values corresponding to the sample values; carrying out fuzzification processing operation on each sample value and the corresponding teacher value through a fuzzy subordinating degree function so as to form a plurality of input samples; constructing a fuzzy neural network, and using the input samples to train the fuzzy neural network; selecting the server performance index parameter values with a preset numeric value of the database as the input of an adjusting and optimizing model according to the adjusting and optimizing model corresponding to the trained fuzzy neural network, and computing the database performance parameter values as optimization results so as to adjust the database performance parameters of the database. Through the technical scheme provided by the invention, automatic optimization of the database is realized, and complicated operation steps caused by empirical value depending on an engineer and manual adjusting and optimizing implementation in the prior art are prevented.

The invention discloses a method, a system and equipment for optimizing cell reference symbols. The method comprises the following steps that: a network node receives a measurement event from UE; the network node determines the information of the UE according to the measurement event; and the network node optimizes the cell reference symbols of the UE according to the information of the UE. The coverage condition of the cell reference symbols is acquired by using the measurement event of the UE, and the cell reference symbols are optimized and the transmitting power of the cell reference symbols is adjusted according to the coverage condition so as to fulfill the purpose of automatically optimizing the cell reference symbols; and the cell reference symbols can be accurately optimized without modifying a protocol.

The application provides a method and system for sorting search. The method comprises following steps: obtaining a search word and obtaining texts matched with the search word by means of static sorting index, wherein the obtained texts are used as mass-selection recall texts; obtaining rough sorting feature weight by fitting the pre-counted fine sorting score of the mass-selection recall texts by means of the machine learning method, and calculating the rough sorting score of the mass-selection recall texts by means of the rough sorting feature weight; selecting first M texts which are sorted at front according to the rough sorting score, wherein the selected texts are used as rough sorting texts; calculating the user behavior probability of the rough sorting texts corresponding to the search word and calculating the fine sorting score of the rough sorting texts by means of the machine learning method using the user behavior probability; sorting the rough sorting texts according to the fine sorting score. The application can increase search quality and improve the user search experience.

The invention discloses a servo system inertia identification method adopting a genetic algorithm for optimization. Based on a model reference self-adaption theory, the self-adaption rule of inertia identification is designed according to a Landau discrete time recursive algorithm. The global searching capability of the genetic algorithm is utilized, a model reference self-adaption system is used as a control object, an output difference between a motor practical angular speed and an estimated angular speed is used as a control error, the integration of the product of time and an error absolute value is used as an optimization target, the self-adaption gain [beta] in the inertia identification is dynamically adjusted, and the online optimization of control parameters is realized. According to the invention, both the higher convergence speed and the higher identification precision are realized in inertia identification, and the relatively high self-adaption capability to the change of rotary inertia is achieved.

This invention discloses an automatic optimization method for radio access parameters including: an auto-networking service unit gets optimized reference information of current radio access parameter information and its optimized reference information to adjust the radio access parameters automatically based on the information and informs the background to update them to realize automatic optimization of radio access parameters in local areas.

The invention relates to a breakout prediction method for continuous casting, belongs to the technical field of metallurgical continuous casting, and in particular relates to a breakout prediction method in plate blank continuous casting process. In terms of temperature monitoring-based breakout prediction algorithm, the self-adapting genetic algorithm is introduced into the BP (Back Propagation) neural network to achieve automatic optimization of the network structure and improve identification accuracy of a temperature monitoring model. In terms of friction monitoring-based breakout prediction algorithm, a friction monitoring model based on logical decision and neural network is established. Based on the prediction results of the two models, the method achieves combination of temperature monitoring accuracy and friction monitoring sensitivity, provides the prediction mechanism based on temperature monitoring in addition with friction monitoring and couples the temperature monitoring model to the friction monitoring model, so as to ensure the accuracy of final prediction and effectively reduce the false prediction rate.

The invention discloses a method for data migration, service system and server equipment. Data is migrated among different pieces of storage equipment at different access speed ranks according to the concerned degree of the data. According to the scheme by the invention, file storage can be automatically optimized, data is migrated among different pieces of storage equipment at different access speed ranks, hot spot data is immediately migrated to the storage equipment with the highest access speed, non hot spot data is migrated to the storage equipment at low access speed in time, so that the user can more quickly access the hot spot data, and the utilization ratio and the system service quality of the storage equipment are improved.

The invention discloses a method and a device for optimizing a multidimensional cube model. The method for optimizing a multidimensional cube model comprises the following steps: building an all-dimension multidimensional cube model based on a dimension set, wherein the dimension set includes all data dimensions in a service model; updating the dimension set based on acquired user behavior data and according to preset updating rules; and regenerating a multidimensional cube model according to the updated dimension set. According to the technical scheme of the invention, the dimension set is updated based on the acquired user behavior data, and then, a multidimensional cube is regenerated. The problem that the updating of a multidimensional cube at present requires a large amount of calculation and depends on the experience of the designer is solved. A multidimensional cube can be updated automatically. Updating of the dimension set based on the operation data in a database is closer to the actual need of users. Thus, the updated multidimensional cube model is smarter and more practical.

The invention discloses a machine vision cutting method applied to a laser cutting machine. The machine vision cutting method comprises an image distortion correction module, an image contour recognition module, a parameter optimization module based on supervised machine learning and a cutting path generation module; the image distortion correction module processes an acquired image to obtain an original image; the image contour recognition module performs contour recognition on the original image; the cutting path generation module generates a cutting path of the laser cutting machine according to a contour recognition result; and the parameter optimization module based on the supervised machine learning optimizes the image contour recognition module, so that the recognition contour extraction of the image contour recognition module is more accurate. The method of the invention has the advantages of being easy to operate, high in recognition precision, high in recognition speed and the like, can be adapted to a complex production environment, and improves the generality of the laser cutting machine.

The invention discloses an updating method for adjacent area list which comprises following steps: self-organized network server searches the related base station having the adjacent area relation with the abnormal base station under the situation that the self-organized network server understand the abnormality of the base station of the management; after the related base station receives the notice, the abnormality in the abnormal base station is deleted from the adjacent area and the adjacent area searching is processed to confirm whether there are the other adjacent areas entering the adjacent area list; and the related base station executes the deletion and updates the adjacent area list according to the result after the searching for the adjacent area, and sends the updated adjacent area list to the self-organized network server. The invention achieves the automated optimization for the adjacent area list via the abnormal detection and report of the subdistrict and base station, and reduces the resource consumption of the network null-port and the UE power source consumption, and ensures the mobile function for the users.

The invention relates to a noise simulating calculation method for an automotive alternating current generator. The method comprises the following steps of initializing basic data, performing setting and grid division on a simulating calculation area, calculating an unsteady aerodynamic characteristic equation set of the generator at specific rotating speed by adopting finite volume method-based commercial CFD (computational fluid dynamics) software, verifying correctness, verifying the correctness of a simulating numerical value calculation result, testing and measuring the aerodynamic performance and the noise of the alternating current generator, and performing noise reduction structure optimization design on the alternating current generator according to a comparison result of the simulating numerical value calculation result and a practical verification result. According to the method, the far-field radiation aerodynamic noise characteristics of a main aerodynamic noise source part, different turbulence model for predicting the nose of the alternating current generator, aerodynamic noise time-frequency characteristics under different working conditions and the like are researched and analyzed; an experimental research method is adopted for verifying the aerodynamic noise characteristics of the alternating current generator; on such a basis, the noise reduction structure of the alternating current generator is optimized.

The present invention relates to the mobile communication field, and provides a mobile terminal mode switching method. The method comprises accessing into a wireless fidelity WIFI network through an access point AP, acquiring a media access control MAC address of the AP, and switching a scene mode of a mobile terminal to a scene mode corresponding to the MAC address according to the acquired MAC address. A mobile terminal used for realizing the above method is also provided. According to the method, according to the MAC address of the current network access point AP of the mobile terminal, an application environment of the mobile terminal is judged, and corresponding scene mode switching is performed, thereby adapting to the current environment, realizing automatic optimization of user parameters, and improving user experience.

The invention discloses an electric vehicle non-contact charging automatic guiding and control system and a method. The system comprises a non-contact charging device, a non-contact charging positioning device and a charging monitoring backstage, wherein the charging monitoring backstage communicates with the electric vehicle and the non-contact charging device respectively; the non-contact charging device comprises a vehicle-mounted electric energy receiving device and a ground electric energy transmitting device; the ground electric energy transmitting device carries out charging electric energy transmission and transmits the electric energy to the vehicle-mounted electric energy receiving device; the vehicle-mounted electric energy receiving device transmits the received non-contact charging electric energy to a power battery of the electric vehicle; and the non-contact charging positioning device is provided with the ground electric energy transmitting device. Rough charging vehicle positioning and accurate charging position are carried out via a vehicle charging guiding device and a vision precision positioning device, and a basis is provided for high-efficiency non-contact charging.

The invention discloses a method and a device for optimizing an application server. The structured query language (SQL)-related data processed by the application server can be acquired in real time, the related data is compared with a preset reference value, and the maximum thread number of an application server connection pool is correspondingly changed according to a comparison result so as to automatically optimize the performance of the application server.

Provided is a rice plant type quantitative design method integrating a crop virtual growth model. The crop virtual growth model is constructed, effective correlation and combination of the main plant physiological process, morphological structures and a light environmental model are achieved, and visual virtual growth of crop plants can be achieved; in the crop virtual growth model, morphologies of crops at different growth periods are set up through a structure model, a physiological model is used for realizing dynamic prediction of production, distribution and final yield of crop assimilates, and the light environmental model is used for computing crop canopy light radiation quantity and crop individual fractional interception of photosynthetic active radiation; the crop plant type structure is continuously changed through an optimization algorithm, so that simulation results of different adaption degrees are obtained, and the optimal crop plant type based on different targets can be obtained; the different adaption degrees are used as optimization results of the targets and set as morphological parameters of the virtual growth model, analog operation is carried out, horizontal and longitudinal comparison is carried out, and the optimal plant type data of crops are verified and determined.

The invention provides a signal control time period dividing method based on ordered clustering. The signal control time period dividing method utilizes regular fluctuations of actual sequences of a traffic flow, and performs division and clustering on time sequences with high similarities, so as to obtain reasonable division of traffic time periods and define a traffic control time period dividing scheme. The signal control time period dividing method based on ordered clustering realizes automatic optimization of number of intersection signal control time periods, thereby achieving reasonable grouping of the traffic flow.

The invention relates to a cold load prediction method based on support vector machine parameters optimized with the cat swarm algorithm. The method includes the following steps: (1) selecting prediction features of a cold load, (2) pre-processing historical data of the cold load, (3) analyzing the historical data of the cold load, (4) automatically optimizing the support vector machine parameters with the cat swarm algorithm, and (5) performing cold load prediction with an optimized support vector machine. The parameters of the support vector machine are optimized through the local searching capability and the global searching capability possessed by the cat swarm algorithm, so that the prediction capability of the support vector machine is promoted, and the effect of promoting prediction accuracy is achieved. Due to the cat swarm algorithm is applied to the optimization process of the support vector machine parameters, automatic optimization of the support vector machine parameters is achieved, and finally the prediction accuracy of the cold load prediction is promoted. The cold load prediction method based on the support vector machine parameters optimized with the cat swarm algorithm is high in practicality and strong in popularization capacity.

The invention discloses a method for optimizing network coverage and capacity, which comprises that: a base station judges whether the acquired coverage and capacity optimization data are normal and determines whether coverage and capacity problems exist; and if existing, the base station determines a resolution strategy according to the existing coverage and capacity problems and the coverage and capacity optimization data causing the problems. The method finishes an optimization process through the base station, realizes automatic optimization of network coverage and capacity, saves a large amount of manpower and material resources required by the traditional optimization method, reduces the network operation and maintenance costs, can effectively make up missed matching and mismatching phenomena of parameters in network planning in dynamic network variation at the same time, effectively compensates and optimizes the network coverage and capacity, improves the network performance, reduces the service dropped call rate, and totally improves the satisfaction of users.

The invention refers to a kind of flow media data management method of electricity digital data processing on border buffer equipment of flow media net, according to the heat and fresh of flow media data, it manages the flow data on border buffer equipment, realizes the automatic optimization; determines the reasonable existing period ªñT of heat and fresh degree according to the user journal file, flow media data file attribute and the statistic data.

The invention provides a service choreography method and device, and a computer readable storage medium. The method comprises the following steps: a big data platform collects data of a service operation support system and stores the data in a local database; an AI engine of the big data platform extracts the data of the service operation support system in the set time quantum from the local database and inputs the data into a preset deep learning model to perform model training, thereby generating a service choreography model; the service choreography model is respectively deployed in the service operation support system; the resource corresponding to a resource use request is applied from the service operation support system according to the resource use request sent by an external SDN application, and the service choreography is performed on the resource corresponding to the resource use request through the service choreography model, a service path is generated and issued to a network element through a SDN cloud network controller. By combining the artificial intelligence and the big data platform, the method disclosed by the invention can realize the automatic choreography ofthe network service and the automatic optimization of the service path, and the resource scheduling efficiency is improved.

The invention relates to a data stream optimization device for realizing the multi-ISP (internet service provider) access in a local area network. The data stream optimization device comprises a flow acquisition module, a flow analysis module, a network detection module, a detection result analysis module and a configuration management module which are connected sequentially, wherein the flow acquisition module is respectively connected with a DNS (domain name server) arranged outside the data stream optimization device and a network interface of a routing device, and the configuration management module is respectively connected with the DNS and a configuration management interface of the routing device. The invention also relates to a data stream optimization method for realizing the multi-ISP (internet service provider) access in a local area network, which comprises the following steps: firstly, acquiring network data streams, and judging whether the network data streams are required to be optimized; then, detecting the network data streams required to be optimized, and analyzing and determining an optimal ISP access port according to the obtained detection results; and finally, configuring the DNS and the routing device. By using the device and method provided by the invention, an ISP access port with optimal connection performance can be selected, and the automatic optimization of data streams can be realized.

The invention provides a RACH self-optimization method, a device, a system and a base station for a LTE system. Access parameter configuration is checked during the starting procedure of the base station; conflict checking and processing are performed on access parameter configuration of adjacent base stations during the operation process of the base station; adaptive adjusting is performed on each PRACH configuration parameter based on the start-up status of an optimization switch which performs adaptive adjusting corresponding to each PRACH configuration parameter during the process of normal access to user terminals; and therefore RACH automatic optimization is realized according to continuously changing network situation.

The invention relates to a data processing method and device and a computer readable storage medium, and relates to the technical field of computers. The method comprises the steps of determining a to-be-trained machine learning model and a corresponding first hyper-parameter search space according to-be-processed data; sampling candidate hyper-parameters in the first hyper-parameter search space;training a to-be-trained machine learning model by utilizing the first training data set according to the candidate hyper-parameters; re-sampling the candidate hyper-parameters, and repeating the training process according to the re-sampled candidate hyper-parameters until the trained to-be-trained machine learning model meets an iterative stop condition, so as to obtain a trained machine learning model; and processing the to-be-processed data by utilizing the trained machine learning model and the corresponding candidate hyper-parameters.

The invention discloses an evolutionary digital twinborn drainage basin construction method, which comprises the following steps of S1, constructing an initial digital twinborn drainage basin based on related parameters of a drainage basin, meteorological data, hydrological data, soil data, topographic data and hydraulic engineering data; and S2, optimizing and verifying the model in the initial digital twinborn drainage basin, and performing digital twinborn integration when an iterative optimization condition is met to obtain a final digital twinborn drainage basin. The method has the advantages that the twinborn technology is applied to drainage basin construction, the targets of intelligent management, perceptual analysis, analogue simulation, automatic optimization, real-time updating and reasonable prediction are achieved, and meanwhile the requirement for combination of digitization and intelligentization is met. Technical support can be provided for follow-up high-precision hydrological forecasting, flood control dispatching and simulation prediction.