83results about How to "Reduce decision time" patented technology

The invention discloses a flight ground-guaranteed method for vehicle optimal scheduling. The method comprises the following steps: a mobile car-mounted terminal reports the information on the current position of the vehicle to a central server, and the central server correspondingly stores and timely updates the information; the central server receives a scheduling request and then calculates priority level of all the spare vehicles with the same type and draws out a priority level list, according to the guaranteed place and the guaranteed vehicle type appointed by the scheduling request and the formula pi is equal to Sigma i multiplied by (dmax / di) multiplied by di multiplied by (1 / (ni plus 1)), the vehicle with the highest priority level is the vehicle best suits for executing the request; the scheduling instruction is sent automatically by the central sever or by a dispatcher; the central server marks the state information of the vehicle which executes the request as a busy state; and the completion information is reported when the execution is finished; and the central server marks the vehicle as a spare state, and adds the complements of the vehicle on the current day. The method has the advantages of simple and convenient operation steps and good optimal scheduling effect, and can solve the practical problems of the existing flight guaranteed and site guaranteed vehicle that the scheduling efficiency is lower and the scientificity is poorer.

The invention discloses a near-distance air combat automatic decision-making method based on single-step prediction matrix gaming. The method comprises the following steps: step 1, erecting a six-degree-of-freedom nonlinear unmanned combat air vehicle control law structure; step 2, initializing a matrix gaming chessboard; step 3, according to the gaming chessboard, carrying out single-step prediction calculation; step 4, calculating a payment function matrix; step 5, carrying out strategy selection through a minimax algorithm; step 6, updating a six-degree-of-freedom plane kinetic and dynamic equation; and step 7, determining whether an air combat termination condition is reached. The method has the following advantages: compared to a three-degree-of-freedom particle model, the actual application value is higher. At the same time, a conventional matrix gaming method based on a maneuver library is changed to be based on a maneuver library of an instruction model, what is needed is only prediction of a step length of a single step, the decision-making time is effectively reduced, the requirement for real-time performance of air verification is satisfied, the method can be better adapted to complex dynamic baffle field environment change, and the combat capability of an unmanned combat air vehicle in a near-distance combat is improved.

The invention discloses a forest and grassland fire prevention and early warning monitoring system and relates to the fire hazard monitoring technology field. The system comprises static monitoring points and dynamic monitoring points. The plurality of static monitoring points and at least one dynamic monitoring point monitor monitoring data used for determining a fire probability and a fire condition in a set area. And then the monitoring data is transmitted to a monitoring center via a signal transmission network. The monitoring center analyzes and generates corresponding alarm data information, and sends to the mobile terminal of a firefighter through an alarm information sending network. The static monitoring points and the dynamic monitoring points are combined to realize the networkcoverage of the monitoring area and simultaneously emphasize a key point. The intensity of fire monitoring is flexibly adjusted according to the fire probability in the monitoring area, and accurate assessment and early warning are performed on the fire and a fire risk in a targeted mode. Because various factors influencing the fire probability are continuously changed, the above scheme can be adaptively adjusted and monitoring accuracy is high.

The invention discloses an Internet-of-Vehicles data-driven task unloading system and method based on mobile edge computing. The system comprises an application layer, a vehicle layer, an MEC layer and a cloud layer. The application layer comprises data perception management, ITS service management and road safety management; and the vehicle layer includes a sensing vehicle and a computing vehicle. The sensing vehicle collects various traffic data and unloads the traffic data to the computing server for computing, and the computing vehicle provides computing service for sub-tasks in the V2V communication range; the MEC layer comprises an MEC server and an RSU, and the MEC server serves as a computing server and a local scheduler to be deployed near the RSU; and the cloud layer comprises acloud server and a backbone network, and the vehicle can unload its subtasks to the cloud server through a cellular interface. The system and method have the advantages that the service delay and theservice cost of the data-driven task are reduced, the task unloading architecture is distributed, the decision time of task unloading scheduling can be effectively shortened, and the utilization rateof resources is increased.

The invention relates to an intelligent video monitoring system for electric power transmission and transformation. A star-topology three-level networking architecture is constructed based on an IP network; the first level of the star-topology three-level networking architecture is monitoring sites, a plurality of monitoring points are correspondingly arranged in each transformer substation or along each high voltage line, and corresponding monitoring data is obtained by installing collecting devices; the monitoring data comprises video image information obtained by monitoring the monitoring points; the second level of the star-topology three-level networking architecture is branch monitoring centers, each branch monitoring center monitors and controls monitoring points of multiple transformer substations or monitoring points of multiple high voltage lines arranged in the controlled area and manages the monitoring data in a centralized way; and the third level of the star-topology three-level networking architecture is a scheduling and monitoring center of an electric head corporation, and the scheduling and monitoring center of the electric head corporation uniformly manages, regulates and controls the monitoring data uploaded by each branch monitoring center. According to the invention, scattered and independent monitoring collecting points are connected to a network, so that cross-region large-range uniform monitoring, uniform storage, uniform management and resource sharing are realized.

The invention discloses an active distribution network (ADN) voltage real-time fuzzy control method. The method comprises steps: according to the maximum and minimum values of feeder voltage uploaded by each feeder, the maximum voltage and the minimum voltage of the distribution system are put forward, if certain feeder voltage exceeds a threshold, a reactive power fuzzy controller FQC for DG (distributed generation) is started, and reactive power is injected or absorbed through the DG; and if the reactive power fuzzy controller FQC for the DG can not restore the voltage to a normal level and deltaVsys< / =deltaVmax-deltaalpha, a transformer on-load tap changer fuzzy controller FOC is started, the transformer on-load tap changer OLTC completes a voltage regulating task, or otherwise, an FPC is started, and the deltaVsys is reduced, wherein the deltaalpha is an adjusting value of the tap changer. The main control unit of the invention has a simple control algorithm, the needed communication data are few, the calculation amount is small, the decision-making time is short, problems of over voltage and low voltage caused due to mutual action between the OLTC and the DG can be lessened, and the consumptive ability towards the DG by the ADN can be improved.

The invention discloses a mixed multiattribute group decision making method for network frame reconstruction scheme evaluation. The mixed multiattribute group decision making method comprises the steps that the quantitative attribute and the qualitative attribute of each subsystem in power system subareas are determined, a decision matrix is built, and the object weight of the attributes is determined according to the decision matrix; different decision makers in the group decision making process judge the relative importance of the attributes by using a pairwise comparison matrix or a fuzzy complementary judgment matrix respectively, the subjective weight is obtained, the subjective weight and the objective weight are combined to form a combination weight, a network frame reconstruction scheme is evaluated based on the combination weight and the decision matrix, and an optimal scheme and an alternative scheme are obtained. The method has the advantages that certain variables incapable of being given precisely and variables incapable of being precisely quantized in the restoring process are fully considered, the reliability and flexibility of the decision are improved, and restoring delay or even restoring failures caused by immature quantification methods and attribute precision are avoided.

A hierarchical battery-management system mainly comprises a monitoring and equalizing module, an intermediary module, and a decision and communication module. The monitoring and equalizing module electrically couples with the battery cells, the intermediary modules electrically couple with the monitoring and equalizing module and the decision and communication module. The decision and communication module electrically couples with a power system or an electronic / electrical apparatus, and a hierarchical management structure constructed by the intermediary module to screen data and to transmit meaningful cell data to meet real time managing requirements of the large battery set.

The invention relates to a six-degree-of-freedom unmanned combat aerial vehicle short-range dogfight method based on a simplified model machine game. The method comprises the steps that 1, a six-degree-of-freedom non-linear unmanned combat aerial vehicle Simulink model is established; 2, a six-degree-of-freedom non-linear unmanned combat aerial vehicle control law is designed; 3, an unmanned combat aerial vehicle simplified model is established; 4, an airborne cannon model is established; 5, an unmanned combat aerial vehicle control input instruction library is designed; 6, input instruction switching is controlled; 7, an unmanned combat aerial vehicle short-range dogfight machine game model based on the simplified model is established; S8, simulation verification is conducted. The method aims at providing an unmanned combat aerial vehicle aerial warfare autonomous decision-making method which has actual application value, the decision accuracy and scientificity are guaranteed, and meanwhile the decision time is effectively shortened, so that the fight power of an unmanned combat aerial vehicle is improved.

The invention provides an AGV charging management method. The method includes the following steps that a charging threshold value a, a target electric quantity value b and a lowest electric quantity threshold value c are set; a total real-time electric quantity value e of AGVs in a transport system is monitored; an expected number g of to-be-charged AGVs in the transport system is calculated; theg AGVs are selected as the to-be-charged AGVs, whether the AGVs marked with execution of charging tasks exist in the expected number g of the to-be-charged AGVs or not is judged, according to the charging threshold value a and the lowest electric quantity threshold value c, the precedence levels of the to-be-charged AGVs are judged, and the to-be-charged AGVs are sequentially moved to operating charging piles to be charged according to the precedence levels; and an operation in a new round is started after one to-be-charged AGV is charged. The provided transport system applies the above AGV charging management method. By the adoption of the AGV charging management method, the charging precedence levels of the AGVs can be arranged quickly; and the transport system can assess the total electric quantity of the AGVs in the transport system and then execute charging management, so that AGV charging management decision making time is shortened.

The invention relates to the field of power grid planning, in particular to an urban medium-voltage power grid planning and designing platform based on a three-dimensional real scene. The platform realizes the line design of the whole design period of the power distribution engineering and the three-dimensional visual display of the design process and result, and also has the functions of three-dimensional live-action line design, cable three-dimensional design, three-dimensional distance verification, tower model analysis and space analysis. The platform has the following beneficial effects that various existing resources are integrated, and informatization management and data sharing are promoted; the platform serves power planning and design integrated services, and the working efficiency is improved; all service links are integrated, and cooperation and connection of the whole service process and content are achieved; application and research tools are integrated, the technical content of planning and design is improved, and lean planning and design are achieved.

The invention provides a flight plan adjustment method and device and a computer readable storage medium. The flight plan adjustment method comprises the following steps: acquiring a flight plan and airport traffic capacity in a predetermined adjustment time period, wherein the flight plan comprises flight number information, route information, planned takeoff time and planned arrival time of a plurality of flights; predicting flight operation condition of the flight plan based on the airport traffic capacity, wherein the flight operation condition comprises the predicted takeoff time, the predicted arrival time and the predicted point-passing time of each flight included in the predicted flight plan; calculating at least one flight operation index based on the flight operation condition,and judging whether the flight operation index meets a preset index threshold value or not; and outputting a flight plan when the flight operation index meets a preset index threshold, and adjusting the flight plan based on the flight operation condition and the airport traffic capacity when the flight operation index does not meet the preset index threshold.

The embodiment of the invention provides a recommendation method, device and equipment and a readable storage medium, and the method comprises the steps: responding to a recommendation request initiated by a user, and obtaining a historical consumption commodity of the user; obtaining real-time supply commodities of the user within a preset distance range; extracting preset questions and answers matched with the historical consumption commodities and the real-time supply commodities from a preset question database; generating a question and answer path of a preset question and answer purpose according to the commodity knowledge graph and the preset question and answer questions; according to the question and answer path, proposing the preset question and answer questions to the user in a multi-round question and answer mode, and obtaining preset answers selected by the user according to the preset questions; obtaining real-time consumption preference commodity attributes of the user according to a preset answer; and extracting the commodity with the matched real-time consumption preference commodity attribute from the real-time supply commodities as a candidate commodity, and recommending the candidate commodity to the user. The problem that in the prior art, data recommendation based on historical data is poor in pertinence is solved.

The embodiment of the invention provides a drainage system scheduling method and device. The method comprises the steps that according to the precipitation of each area in a drainage system in a preset current time period and the water inlet flow of each area in the preset current time period, the trained neural network model is utilized to predict the water outlet flow of each area in a preset future time period; taking the safe water outlet flow range of each pump station in the drainage system and the maximum sewage treatment capacity of each sewage treatment plant in the drainage system asconstraint conditions; and taking the minimum sum of energy consumption of all pump stations in the drainage system as a target, and obtaining a scheduling scheme of each pump station in each regionaccording to the water outlet flow of each region in the preset future time period. According to the embodiment of the invention, the method employs the machine learning, enables the neural network tobe used in the scheduling of the drainage system, shortens the decision-making time in a control process, and provides data support for the optimal scheduling operation of the drainage system.

The invention belongs to the technical field of weapon equipment scheduling management, and discloses a maintenance task dynamic scheduling method, which comprises the following steps: taking secondary combat total time obtained after equipment repair, the number of obtained executable tasks and the importance contribution degree to combat after combat damage equipment maintenance as a multi-objective function model of an objective, and dynamically scheduling maintenance tasks under the constraint of the total time. A non-dominated sorting genetic algorithm with an elitist strategy is adopted,MATLAB software is used for solving instances, three objective function weight distribution schemes are set, three accompanying maintenance interruption conditions are obtained through solving, and different multi-objective function values are generated respectively. According to the method, a multi-objective optimization model is constructed, and the maintenance task of the battle damage equipment is dynamically scheduled within the specified time; an NSGAII algorithm is adopted to solve an optimization problem, chart display and comparative analysis are carried out on three scheduling interruption conditions, and scheduling result analysis has positive assistance significance in shortening decision time and reducing man-made decision risks in wartime.

Embodiments of the invention provide a method for preprocessing a rule set, and a method for performing decision-making on an operation request according to a preprocessing result. In the preprocessing process, firstly, according to executive main bodies of rules in the rule set, the rules are divided into a plurality of main body strategy sets; and further, for each main body strategy set, according to the similarity between the rules comprised in the main body strategy set, the rules are clustered into rule class clusters, and feature rules corresponding to the rule class clusters are determined, so that the processing result of the preprocessing is formed. In the decision-making process, for the obtained operation request is obtained, firstly the request is distributed to a decision-making processing part with the same executive main body; the feature rule most proximate to the request is determined from the feature rules; the request is distributed to the most proximate rule classcluster according to the feature rule; the matched rule is quickly determined; and a decision is made.

The present invention relates to a device capable of easily and reliably selecting and determining parameters such as the optimum maximum number of cuboids and the like corresponding to an analytical target model. For this purpose, in the present invention, two or more kinds of parameter kits each including a maximum number of cuboids and parameters for division control for producing cuboids are previously stored in a library, at least one of the two or more kinds of parameter kits stored in the library is selected, and the analytical target model is divided, based on the selected parameter kit and the three-dimensional CAD data, into cuboids of less than or equal to a maximum number of cuboids included in the selected parameter kit. Such present invention is preferably applied to automatic conversion software for converting three-dimensional CAD data obtained by Pro / E, I-DES, Parasalid, AutoCAD, VPS, or the like.

A system, method and computer program product for generating well location plans and field development plans assessing and ranking the potential of the different plans with a small number of parameters or initial conditions, thus considerably reducing the decision time for taking a particular strategy when compared with the techniques described in the art.

The invention provides power station monitoring equipment and a power station monitoring system. The power station monitoring equipment comprises a power supply module, an external environment monitoring module, a data interaction module and a transformer substation operation condition monitoring module. The power station monitoring system is provided with a multilevel management center. The IP network and the 4G network act as the core, multiple advanced technologies of computer, communication, internet, digital multimedia, storage and information security are integrated, accessing of the monitored resources through the optical network, the ethernet, the wireless network and multiple other modes is supported and the discrete and independent monitoring acquisition points are networked so that cross-regional and wide-range unified monitoring, unified storage, unified management and resource sharing can be realized. The clear and visual management mode can be provided for the user of various industries so that the decision time can be shortened, the working efficiency can be enhanced and the security level of the monitoring area can be enhanced.

The embodiment of the invention, which relates to the technical field of the internet, discloses a method and apparatus for processing service information, so that the cost-benefit ratio of the intelligent hardware is improved. The method comprises: order information sent by an intelligent hardware device is received and a commodity corresponding to the order information is determined; an updating record of the service data corresponding to the commodity is obtained and limit updating information is obtained by using the updating record, wherein the service data include price information of the commodity; and on the basis of the limit updating information, transaction document data corresponding to the user account are updated, wherein the transaction document data include deposited amount information and / or coupon data. The method and apparatus are used for online shopping.

The invention provides a robust single machine scheduling method based on interval uncertainty, and belongs to the field of production scheduling and operations research. The method includes: establishing a robust optimization model RSMSP of single machine scheduling, wherein an optimization target is to search an optimal workpiece machining sequence to enable the maximum waiting time of the sequence to be minimum in a poorest scene; during solution, converting the model RSMSP to a mixed linear integer programming model P; and solving the model P by employing a two-stage heuristic solution algorithm to obtain the optimal machining sequence, namely an optimal scheme of robust single machine scheduling. According to the method, uncertain parameters are expressed by employing the mode of interval estimation, a method for recognizing finite possible poorest scenes in an infinite scene set, which is in more accordance with the production reality, is firstly proposed, and decision risks canbe reduced to the maximum and the system performance can be guaranteed in the condition of poor information.

The embodiment of the invention discloses a log level prediction method and device and a storage medium. The method comprises the steps of obtaining a code block of a to-be-inserted log record statement, wherein the log record statement is used for forming a record log after triggering execution; according to a preset feature model, performing feature extraction on the code block and the file to which the code block belongs to obtain code block features and file features; predicting a log level according to the code block features and the file features, the log level being used for describingthe detail degree of information recorded in the record log; and inserting a log record statement into the code block according to the log level. According to the embodiment of the invention, log level prediction can be carried out on the to-be-inserted log record statement, the log level decision time is reduced, and the development efficiency is improved.

The invention discloses a long term voltage stability prevention and control method based on a time sequence optimal power flow method; the long term voltage stability prevention and control method comprises the following steps: using a load prediction start point as a prevention point, carrying out line fault screening pretreatment, building a grey load prediction model, and obtaining a predicted load curve in the time period; carrying out preconception accident set screening according to the predicted load curve; employing a system power flow equation minimum modulus characteristic value, in a maximum load Lmax portion of the predicted load curve, to carry out node classification for load node injection variable sensitivity, determining a node voltage to constrain an upper boundary and a lower boundary, and using transmission loss as a target function to do optimal power flow calculation; carrying out super-short term load forecast in the time period of h1-h2 at a new prevention point h1 when the lower time reaches the new prevention point h1. The beneficial effects are that the method provides a voltage state and tendency following control concept, so key load nodes can be monitored, thus making more effective idle balance and voltage safety control measures under heavy load conditions.

The invention discloses a Monte Carlo tree search and convolutional neural network-based Landlords strategy research method in the technical field of machine learning. The method comprises the following steps that a game is started randomly, and when each player plays cards, the current state of the player serves as a root node, and actions possibly adopted by the player according to the Landlordsrule serve as direct child nodes of the root node; starting from a root node of the game tree, a Monte Carlo tree search algorithm is used to carry out continuous simulation sampling learning; when enough data is obtained by using the Monte Carlo tree search algorithm, a convolutional neural network CNN learning network is continuously trained by taking (states and possible playing cards and earnings corresponding to the possible playing cards in the current state) as data samples until the network is stable; for possible errors of the CNN network during learning, a strategy improvement algorithm is further used to correct and improve the learning result of the CNN network.

The invention provides a method for dynamically generating a public opinion brief report. The method comprises the steps that: a system management user establishes a public opinion brief report template, configures and defines basic elements, issues by one key after the public opinion brief report template is completed, and thereby a public opinion brief report model is formed; in a downstage business process, a business operating user selects public opinion brief report business, also selects the type and time condition of the brief report, and invokes the background public opinion brief report template and business data in a database to generate the public opinion brief report. According to the method, effective inheritance and fusion of original data analysis result are achieved, data interaction during a public opinion brief report producing process is greatly enhanced, repeated data organizing time in the public opinion brief report is reduced, and reusability is achieved.

The invention belongs to the technical field of circuit protection and discloses a protection device and a method of a self-powered circuit. The protection device of the self-powered circuit is provided with a measuring circuit system. The measuring circuit system adopts a special line section. The current acquisition and analysis system uses intelligent digital ammeter to measure the current of the measurement circuit. The electric energy consumption acquisition and analysis system uses an electric energy meter to measure the electric energy consumed by the measurement circuit, and the temperature detection system uses a temperature sensor and a single chip microcomputer to measure the real-time temperature of the circuit, intelligently processes the collected data through an intelligentprocessing system and transmits the collected data to an alarm system, and displays the terminal. The invention measures each index of the measuring circuit and intelligently monitors and processes, and indirectly protects the self-powered circuit.

The invention discloses an intelligent agile discovery system and model for idle transportation business, and belongs to the technical field of logistics. The system comprises a service data input module, a service matching scheduling control module and a result visualization module, and the service data input module, the service matching scheduling control module and the result visualization module are connected with a database module. Precise service discovery is achieved, the preset matching degree threshold value and the attribute of the service are used as the control target, and only user triggering is needed; intermediate scheduling calculation is completely and automatically completed without user intervention, user interaction time is greatly reduced, irrelevant services which donot meet requirements are greatly reduced by controlling service attribute matching, excessive decision interference of service decision makers is avoided, and therefore decision time of the service decision makers is saved. Through the combination of the vehicles and the goods, transportation business operation is optimized, the timely utilization rate of resources is increased, and then the overall logistics cost is reduced.

The invention provides an intelligent fire coal scheduling system for a thermal power plant. The system comprises an on-line coal inventory unit, a material piling and taking machine unit, a coal conveying unit, a coal bin unit and a system server, wherein the system server comprises an intelligent material piling calculation module, an intelligent coal distributing and feeding calculation module, and a material taking decision module; the intelligent material piling calculation module determines the optimal coal piling position to guide the material piling and taking machine unit to complete coal piling; the intelligent coal distributing and feeding calculation module is used for receiving coal inventory information of the on-line coal inventory unit and the real-time state of a boiler unit, determining a coal distribution scheme according to the coal inventory information and the real-time state of the boiler unit and further determining a coal feeding scheme according to boiler characteristics and the coal distribution scheme; and the material taking decision module determines a material taking path according to the coal inventory information and the coal feeding scheme to guide the material piling and taking machine unit and the coal conveying unit to cooperatively complete material taking. The fire coal of the thermal power plant is subjected to full-life cycle management and intelligent scheduling, and the boiler unit is ensured to have high safety, economy and environment friendliness under the condition of co-combustion.

The invention discloses a method for reducing a fire false alarm rate based on a GA-BP neural network algorithm, and relates to the technical field of fire alarm; the method includes obtaining sensordata related to a fire; using the sensor data as input of a BP neural network, determining the structure of the BP neural network, determining the weight w and bias b of the BP neural network througha genetic algorithm, training the BP neural network according to the weight w and bias b, obtaining a GA-BP neural network model, and enabling the GA-BP neural network model to judge whether a fire disaster is early warned or not according to real-time sensor data. Through joint judgment of multiple sensors, the error probability is reduced; a BP neural network is adopted as a model trained by known sample data by performing classifier and fire sample data training, and the robustness is improved; a GA algorithm is adopted to initialize the weight w and the bias b of the BP neural network, sothat the BP neural network is prevented from falling into a local optimal solution.