45results about How to "Realize advance prediction" patented technology

The invention discloses a vehicular access synergism anti-collision pre-warning system based on V2X. Aiming at judgment links of an anti-collision pre-warning decision in the beyond visual range of anintelligent connected vehicle, in combination with a V2X communication technology and an inertial navigation technology, an improved safe distance model is designed, and an anti-collision pre-warningsystem of the intelligent connected vehicle is constructed. A V2X communication module is mainly used for being responsible for data communication, including data broadcasting of the vehicle, detection of data of surrounding vehicles, and receiving of roadside end road traffic information; a positioning module comprises a GPS/BD sensor for obtaining position data of the vehicle, including the data of longitude and latitude, course angle, speed, elevation and the like; the sensors such as a posture data module, an accelerometer and a gyroscope are mainly used for obtaining posture data of thevehicle, including the data of vehicle acceleration, rotation angle and the like; and an inertial navigation module has the main functions of reducing a data cycle and improving the accuracy by usingan inertial navigation algorithm by utilizing the data of the sensors such as a GPS/BD module, the accelerometer and the gyroscope.

The invention discloses an electromobile battery protecting circuit which comprises a main circuit, a voltage detecting module, a battery loop current detecting module, a battery temperature detecting module, an electromobile inertia detecting module, an electromagnetic relay switch module and a singlechip control module, wherein the detecting modules are used for detecting the battery monomer voltage, the battery loop current and the battery temperature, transmitting signals to the singlechip control module, and analyzing the variation trend of the current, the voltage and the temperature. With the adoption of the circuit, the external voltage and current of an electromobile battery are detected and the the electromobile battery under abnormal states such as over-voltage, over-current, under-voltage and the like is cut off to be protected, the output features of the battery are predicated in advance, the charge abnormity of the battery is detected and protected, and furthermore, due to the inertia detection on an electromobile, a power supply circuit is cut off under the condition that the battery is in good condition when the electromobile has various faults, therefore, the the power supply circuit is prevented against battery combustion and explosion.

The invention discloses a service system running state intelligent research and judgment system based on logs, flow and service access, which reconstructs a comprehensive network management system technical architecture, and comprises the following steps of: constructing a distributed data storage platform by utilizing a plurality of databases; the kafka distributed deployment environment is utilized to realize unified collection and centralized pushing of data in the distributed data storage platform, and the data is sent to the comprehensive network management system; the comprehensive network management system constructs an application system port monitoring and physical storage monitoring system and a display system; and summarizing and integrating key service system operation logs, middleware operation logs and service process operation information according to the acquired data, and mining association relationships between different log modes and service system faults and low performance phenomena by utilizing a big data intelligent analysis method to form a key service system operation state intelligent research and judgment model. The method can effectively improve the guarantee capability of an information network and key services, and reduces the operation risk of a service system.

The invention provides a management method and platform of a virtualization system. The method is applied to a cloud management platform of the virtualization system, and comprises the following steps: firstly monitoring at least two host nodes corresponding to the virtualization system through a monitor system in the cloud management platform to acquire the current state of each host node in at least two host nodes, and then determining the second host node is to be in a normal state from at least two host nodes when the monitor system predictively determines a fact that the first host node with the current state as the normal state in at least two host nodes is to be in abnormal state; and finally migrating at least one logic partition on the first host node to the second host node through a partition management system in the cloud management platform. Through the management method provided by the invention, the ahead prediction and migration of the possibly abnormal node can be realized, the service partition service interruption time is reduced, or the effect of not interrupting the service is achieved.

The invention discloses a hidden hazard management system and method of an electric system. The hidden hazard management system comprises a hidden hazard troubleshooting module, an alarming module anda rectifying module, wherein the hidden hazard troubleshooting module is used for utilizing running state information of the electric system to judge whether the electric system has hidden safety hazards or not; the alarming module is used for conducting alarming when the hidden hazard troubleshooting module judges that the electric system has the hidden safety hazards, and selecting a rectifyingscheme corresponding to the hidden safety hazards from a prestored rectifying scheme set. According to the hidden hazard management system and method of the electric system, by detecting the runningstate information of the electric system, whether the electric system has the hidden safety hazards or not is judged, and if yes, alarming can be conducted; meanwhile, the rectifying scheme corresponding to the hidden safety hazards is queried from the rectifying scheme set for user reference, advanced predicting of safety accidents is achieved, alarming is timely conducted to provide the corresponding rectifying scheme, it is ensured that a user can complete rectification timely aiming at the hidden safety hazards, the occurrence of the safety accidents is avoided, and the safety degree of the electric system is increased.

The invention relates to a heuristic routing method utilizing position information in a shipborne adhoc network. The heuristic routing method is characterized by comprising three main modules, namely a geographical position based routing strategy, a topologic void crossing method and a topologic void prediction model, and two auxiliary modules, namely a geographical position service model and a wireless network constraint strategy. According to the heuristic routing method, the existence of the topologic void can be predicted in advance, and the topologic void can be crossed by a geographical information based heuristic algorithm, so that the core technical problem of high-efficiency data forwarding in the shipborne adhoc network is solved.

The embodiment of the invention discloses an information processing method and a mobile terminal. The mobile terminal comprises a data acquisition unit, a data processing unit and an operation unit, wherein the data acquisition unit is used for obtaining first signal strength data and auxiliary data of the mobile terminal and sending the first signal strength data and the auxiliary data to the data processing unit, wherein the auxiliary data comprises information of a position, where the mobile terminal is, and information of a base station, which the mobile terminal belongs to; the data processing unit is used for performing classified processing of the first signal strength data according to a preset classification manner so as to obtain a first class corresponding to the first signal strength data, inquiring pre-stored historical data corresponding to the first class based on the first class, and determining second signal strength data of the mobile terminal in a first time range based on the historical data; and the operation unit is used for correspondingly operating based on the second signal strength data determined by the data processing unit.

The invention discloses a coal mining accident prediction model determination and monitoring method, a storage medium and electronic equipment. The method comprises the steps of analyzing acquired historical accident feature information to generat an accident feature set corresponding to a coal mining accident type, wherein the accident feature set comprises historical accident feature informationand accident feature value information; according to the accident feature value information and pre-stored feature word frequency threshold information, performing association analysis on the historical accident feature information to generate accident key factor information; analyzing the accident key factor information to generate key factor value information; according to the key factor valueinformation and a pre-stored association rule, performing association analysis on the accident key factor information to generate a key factor frequent item set; and according to the key factor frequent item set, generating an accident prediction mathematic model by using a BP neural network. According to the method, medium and equipment provided by the invention, through analyzing and performingassociation analysis on the historical accident feature information, the accident prediction mathematic model is generated, and the secure production of the coal mine is monitored in real time.

The invention relates to a wafer transmission monitoring method and a wafer transmission monitoring device. The wafer transmission monitoring method comprises the following steps of acquiring the transmission track data of a plurality of standard wafers; obtaining a reference distribution interval of the wafer transmission track offset according to the plurality of pieces of standard wafer transmission track data; acquiring the transmission track data of a plurality of wafers to be tested; acquiring a to-be-measured distribution interval of the wafer transmission track offset according to theplurality of pieces of to-be-measured wafer transmission track data; and judging whether the median of the to-be-detected distribution interval deviates from the median of the reference distribution interval, and if so, determining that the wafer transmission is abnormal. According to the invention, the method achieves the prediction of the severe wafer transmission fault is predicted in advance,and the severe abnormal transmission fault caused by the accumulation of small wafer transmission faults is avoided.

The invention relates to a resource delivery data processing method and device, a server, a system and a medium. The method comprises the following steps: predicting a delivery result of a target resource according to features of the target resource to obtain a prediction result; monitoring the prediction result, and generating prediction information corresponding to the prediction result; and sending the prediction information to the resource delivery client, so that the resource delivery client displays the prediction information. Therefore, the predictability and interpretability of the delivery result are improved, and the resource delivery person can conveniently adjust the delivery plan in a targeted manner and make a reasonable resource delivery decision in time, so that the experience effect of the resource delivery person is improved.

The invention discloses a rolling bearing residual life prediction method based on LSTM and TDNN, and the method comprises the steps: (1) extracting the time domain features of an original vibration signal of a training bearing, forming a time domain feature set, and carrying out the minimum-maximum normalization processing of each feature; (2) constructing a health factor by using at least two nonlinear functions, then constructing a mapping relation between training features and the health factor and a mapping relation between the health factor and the residual life percentage by using LSTM and TDNN in sequence, and constructing an LSTM-TDNN residual life prediction model; and (3) inputting test bearing vibration data, after feature extraction and normalization processing, obtaining a residual life estimation value according to the LSTM-TDNN prediction model, and giving out comprehensive evaluation of prediction accuracy. The rolling bearing residual life prediction result is more accurate, unification of high precision and strong conservative is achieved, the method can be effectively applied to rolling prediction maintenance tasks, and the maintenance cost is reduced.

The invention provides a method and a device for processing dirty data in data exchange operation. The method comprises the following steps: receiving dirty data information uploaded by any one or more working nodes in a working node set; adding the dirty data information into a dirty data sample set, wherein the dirty data sample set comprises the dirty data information received from the workingnode set; and analyzing the dirty data sample set to obtain a detection rule for a destination database, the detection rule being used for being issued to the working node set, so that the working nodes in the working node set predict dirty data information in the data information to be provided to the destination database according to the detection rule. By means of the technical scheme, the dirty data information can be rapidly checked, even if dirty data occurs, interruption of data exchange operation cannot be caused, continuous operation of data exchange operation of the working nodes canbe achieved, the dirty data processing efficiency in the data exchange process can be improved, and the data exchange operation efficiency is effectively improved.

The invention discloses a method and a device for predicting data of a carrier rocket, a storage medium and an electronic device. The method comprises the following steps: step S101, determining a weighting coefficient vector K; 102, calculating a weighted prediction value set ZP_final based on that weight coefficient vector K; Step S103: based on the comparison result of the weighted prediction value set ZP_final and the actual value of the rocket test data corresponding thereto, all weighted prediction values in the weighted prediction value set ZP_final are outputted. The present application combines the AR model as a stochastic model with the regression analysis model as a deterministic model, and is applied to the prediction of rocket test data, so as to not only measure the influenceof a certain factor, but also predict the upcoming data according to the already tested data.

The invention discloses a point cloud model plant and an analysis algorithm for identifying and early warning plant diseases and insect pests thereof, and relates to the technical field of agricultural prevention and control. The analysis algorithm comprises the following steps: performing closed culture on common pests in agricultural production at the present stage by taking mollusks, crustaceans, bees and aphids as basic experiment objects in a data laboratory, and performing control variable experiment research on temperature, humidity and illumination intensity of a culture environment; observing the survival rate change of the agricultural pests in different environment factors, and establishing an information table through a table. According to the method, an environmental factor three-dimensional database is constructed during a data experiment, and algorithm basic data is constructed by using a relationship between environmental factors and the survival rate of agricultural pests; and then, a predicted value is provided according to environmental factor data of the current year determined by fitting environmental factor data of previous years, so that advanced prediction of plant diseases and insect pests is realized.

The present invention relates to the technical field of signal processing, and discloses a method and system for obtaining music tempo information in real time. The method includes: tracking and receiving music signals in real time, and performing discrete sampling on the music signals to obtain a sampling sequence; The sequence determines the historical path set of the beat starting point of the music signal prediction segment; predicts the beat period of the newly received music signal; determines the beat starting point of the subsequent music signal according to the historical path set of the beat starting point and the predicted beat period. The invention can realize real-time tracking and prediction of music signals, and provide accurate information for real-time operation of various beats.

The embodiment of the invention discloses a request response state determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring request information; performing vectorization processing on the request information, and determining a target feature vector sequence; inputting the target feature vector sequence into a predetermined target state prediction model, and determining the response state of the request information according to the output of the target state prediction model; the problem of difficulty in fault analysis and positioning is solved, the target state prediction model is trained in advance, the request information is vectorized to obtain the target feature vector sequence, the target feature vector sequence is input into the target state prediction model for response state prediction, and the response state of the request information is predicted in advance. The situation that the fault cannot be accurately positioned due to the fact that the number of transaction data is large after the fault occurs is avoided, the response state of each piece of request information is automatically predicted, the workload of workers is reduced, and the working efficiency and the system reliability are improved.