85results about How to "Realize online update" patented technology

The invention provides a content updating method and system for a Docker container, and a server. When a Docker mirror image of a new version of a network product needs to be released, a developer can perform corresponding triggering operation at a client; the client initiates a corresponding mirror image version updating request to the server; and the server determines that the Docker container needs an updated target Docker mirror image according to the updating request, obtains a differential mirror image layer of the target Docker mirror image and a source Docker mirror image, and calls differential mirror image data corresponding to the differential mirror image layer from a mirror image warehouse. According to the method and the system, the source Docker mirror image corresponding to the Docker container is directly updated to the target Docker mirror image according to the differential mirror image layer data, the Docker container does not need to be restarted, namely, memory data in the Docker container is not lost, and normal external services of businesses are not influenced, so that online updating of the Docker container is realized.

The invention relates to the technical field of network security, in particular to an enterprise internal user abnormal behavior detection method and device, discloses an enterprise internal user abnormal behavior detection method, and further discloses an enterprise internal user abnormal behavior detection device. The enterprise internal user abnormal behavior detection device comprises a behavior log acquiring and preprocessing module, a behavior detail modeling module, a service state transfer prediction module and a malicious behavior score discrimination module. According to the enterprise internal user abnormal behavior detection method and device, an unsupervised machine learning method is used, a user behavior model is built by fully utilizing unmarked unhistorical behavior log data in an enterprise, the accurate rate of abnormal behavior detection is increased, the false reporting rate and the missing reporting rate are decreased, and an effective means is provided for detecting enterprise internal threats.

The invention discloses a method for automatically acquiring multi-source heterogeneous data knowledge, and aims to provide a method which has better integrity, universality and convenience and is beneficial to knowledge transmission. The method of the invention is realized through the following technical scheme: the method comprises the following steps: 1, processing; a concept-entity-attribute-relation-label is defined from top to bottom or from bottom to top, a knowledge model of an entity object is obtained, then data is obtained through direct data storage and crawler software, OCR and other recognition software, knowledge data is obtained, and conversion from a heterogeneous data source to a heterogeneous knowledge source is completed; obtaining entity-attribute-relationship triad instantiation under a known knowledge mode through a structured knowledge generation method; and updating knowledge and knowledge models by using a long-short-term memory network model (LSTM model) anda publisher-accomplisher cooperation mode to obtain a workflow for expanding and supplementing new knowledge, and obtaining a data flow accommodating concept, entity, relationship and attribute valueinstantiation triples by using the knowledge model formed by knowledge modeling.

This invention provides an intelligent synthesized electronic gauge. It integrates the storage oscillograph, digital frequency meter, signal generator, RLC detecting device, two-circuits stabilized voltage supply which are commonly used for electric experiment with the technology of microprocessor, EDA and flat display. It also adopts the colorful liquid crystal display (LCD) and the grade menu operation of touch screen to realize intelligent detecting. It has the merits of integrated function, an integrated unit of storage oscillograph, digital frequency meter, signal generator, RLC detecting device, two-circuits stabilized voltage supply which are commonly used for electric experiment, on-line update of the device via programmable logic device which is convenient for the production update. It also supports TFT high- resolution LCD with the technology of flush-bonding MPU providing touching screen control to make the device screen of high-definition and simple operation.

The invention provides a high speed data transmission receiver and aims to provide the data transmission receiver which is powerful in function and strong in universality and is designed modularly. The technical scheme provided by the invention is that in a simulating part, a DDS (Direct Digital Synthesizer) generates a variable clock to an ADC (Analog to Digital Converter) for sampling; high speed data stream after ADS sampling is sent to a data distributor DMUX (Demultiplexer) in a digital part to reduce the speed, and parallel ADC data stream is sent to a demodulating FPGA1 (Field Programmable Gate Array) to demodulate; demodulated data is decoded through a decoding FPGA 2 and output to a PFGA3 for protocol processing and packaging; the PFGA3 is sent to a data recording device to store through a photovoltaic conversion module, and the packaged data is output to a remote control center; a DSP (Digital Signal Processor) receives various control commands issued by a host computer monitoring program and dynamically loads the programs of the demodulating FPGA1 and the decoding FPGA2 online to write frequency control words into a DDS frequency control word register corresponding to the FPGA3; and the FPGA3 converts an updating command sent by the DSP into a control time sequence corresponding to the DDS to generate a corresponding sampling clock which is sent to the ADC for sampling.

The invention discloses an embedded software on-line updating method which comprises the following steps that: S11: upper layered software and embedded software communicate, and the parameter information of embedded software communication interaction is obtained; S21: the upper layered software divides a file into a plurality of data packets, and sends the packets to the embedded software; S31: the embedded software replies to the data receiving confirmation information; S41: if the upper layered software does not receive the data receiving confirmation information within preset threshold time, the packets are sent again; if the embedded software still does not receive the packets sent by the upper layered software within the preset threshold time, an instruction for requesting to re-sendis sent; S51: after the upper layered software receives the data receiving confirmation information of all the packets, data transmission is ended; S61: a target embedded system processes the file; and S71: the embedded system runs the latest received software. The embedded software on-line updating method realizes the on-line updating of the embedded software, and sets a re-sending mechanism of up and down interaction.

The invention discloses a method of a global sliding mode control of a neural network of a micro-gyroscope, which comprises the following steps of establishing a global sliding mode control system of the neural network, designing a control law and taking as a control input of the micro-gyroscope, designing a self-adaptive law based on an Lyapunov function theory, and verifying the stability of a closed-loop system. The global sliding mode control is realized by designing a dynamic nonlinear sliding mode surface, the defect of no robustness in a reach movement stage of the sliding mode control is eliminated, the system has robustness in the whole process of response, and the respective defects of the global sliding mode control and the neural network are reduced by utilizing the intelligent control function of global sliding mode and the neural network. According to the method, the selection of sliding mode coefficients is simplified, the transient performance and the robustness of the sliding mode control system are improved, so that the closed-loop control system has global robustness, buffeting in the sliding mode control is eliminated, and thus a powerful foundation is provided for expansion of an application range of the micro-gyroscope.

The invention discloses a USB (universal serial bus) compound device integrated with a U (USB) disc and a data acquisition module, belonging to the technical field of electronics. The USB compound device comprises a USB interface module, a USB controller module, a main controller module, at least one storage medium module, at least one data acquisition module, a display screen module and a keyboard module in a circuit structure. According to the USB compound device, the integration of the U disc and the data acquisition module is realized, and the compound device is enumerated to be the U disc and the data acquisition module after being connected with a computer. Related data and collected data of the device are stored in the U disc, and a user can directly install a driver of the data acquisition module, search data and read the collected data through the U disc, or can be directly in communication with the data acquisition module. The USB compound device integrated with the U disc and the data acquisition module avoids the resource waste caused by the data stored in the compact disc and the paper manual, facilitates the management of the data, the search and reading of the manual and the installation of the USB driver by the user, realizes the updating of the data in the U disc, ensures that the user can obtain the latest data and timely updates the errors and leaks of the data.

The invention relates to a predicating method for TLE (transfer line exchanger) outlet temperatures and operation cycles of ethylene cracking furnaces. The deducing of TLE heat transfer processes of industrial ethylene cracking furnaces is simplified, and is combined with empirical models of TLE coking and depositing of the industrial ethylene cracking furnaces so as to deduce parameterized prediction models of the TLE outlet temperatures and the operation cycles of the industrial ethylene cracking furnaces. Actual production data are utilized to estimate and identify model parameters, time factor corrections are performed on the identified models additionally, and online and real-time correction updates can be carried out on the TLE outlet temperature parameterized prediction models as required to expand adaptability and accuracy of the models. When TLE maximum permission upper limits and maximum operating cycles are given, the TLE outlet temperature parameterized prediction models can be used for predicating follow-up operating time of TLE systems before next decoking online and predicating the TLE operating cycles. The predicating method is simplified, reasonable in deducing, high in construction applicability, simple to operate, easy to copy, and wide in adaptability.

The invention provides a user portrait scoring method, a user portrait scoring device and a storage medium, which are used for improving the accuracy and timeliness of user portrait evaluation. The user portrait scoring method comprises the steps that a current behavior data sequence of a target user is collected, and the current behavior data sequence comprises a plurality of behavior data; basedon the current behavior data sequence, a behavior classifier set obtained through pre-training is used for determining an abnormal score corresponding to the target user, the behavior classifier setcomprises a plurality of behavior classifiers, and each behavior classifier is obtained through training by using the historical single-class behavior data sequence of the target user; searching a first hidden state corresponding to the current behavior data sequence according to an observation state corresponding to the current behavior data sequence; and determining a portrait score corresponding to the target user according to the transition probability from the first hidden state to the second hidden state and the abnormal score.

The invention provides an online diagnosis method for a mechanical fault of a high-voltage circuit breaker, which comprises the steps of S1, initializing an online diagnosis model; S2, for a new sample, acquiring a feature vector by using a pretreatment method, putting the feature vector into the online diagnosis model to calculate a discrimination function value and a fault diagnosis value; S3, inputting the discrimination function value and the fault diagnosis value of the new sample into an update triggering module to carry out calculation, judging whether an update triggering condition is satisfied or not, switching to a step S4 if the update triggering condition is satisfied, and resetting an update triggering counter; outputting a model diagnosis result directly if the update triggering condition is not satisfied so as to act as a final diagnosis result; and S4, updating the online diagnosis model, and switching to the step S2 so as to carry out diagnosis again on the current sample. According to the online diagnosis method, a historical training result can be fully utilized, the storage space requirement and the calculation time of the model can be greatly reduced, the calculation efficiency is improved, and online update of the fault diagnosis model is realized, thereby being better applied to mechanical fault diagnosis for equipment.

The invention discloses a master-slave distributed control inspection robot which comprises a robot body, a detection device, a master-slave distributed body control system based on industrial ethernet, and a multi-medium monitoring system. The detection device is used for detecting movement information of the robot body, environment information of a power distribution room and cabinet informationof the power distribution room; the master-slave distributed body control system based on the industrial ethernet receives the movement information of the robot body, the environment information of the power distribution room and the cabinet information of the power distribution room detected by the detection device, and carries out movement control on the robot body according to the movement information of the robot body; and the multi-medium monitoring system receives the movement information of the robot body, the environment information of the power distribution room and the cabinet information of the power distribution room so as to monitor the power distribution room. The master-slave distributed control inspection robot provided by the invention realizes the functions such as automatic movement, accurate positioning, information acquisition, wireless communication, automatic charging and real-time monitoring of the robot.

The invention relates to a preschool education smart interaction system and method. The system comprises a remote controller, a projection lens and a main control module, wherein the remote controller is used for being wirelessly connected with the main control module and sending a corresponding control instruction to the main control module, the projection lens is used for conducting projection display on a functional interface according to a projection instruction sent by the main control module, the main control module is used for sending the projection instruction to the projection lens and starting a corresponding function application module according to the control signal sent by the remote controller, starting a camera at a preset moment in the function application running process, and controlling the camera to take pictures after camera taken pictures and a camera virtual key are projected through the projection module. Projection is adopted as the course display mode, and therefore the effect of protecting vision of children can be achieved; the AR technology is applied to the preschool course education, and therefore the real site scene can be simulated; the Unity technology is applied to the preschool course education, and therefore interaction and participation of courses are enhanced, and the learning efficiency of children is improved.

The invention discloses a motion control system and method capable of supporting cognition. The system comprises an Ethernet interface unit, an Ethernet control unit, an SRAM storage unit, a main control unit, a control unit, an indicating lamp unit, a power supply unit, a motion control function library module, a control module, an EtherCAT bus communication module, and a program monitoring module. The main control unit is in star-shaped connection with the Ethernet control unit, the SRAM storage unit, a communication interface conversion unit, a key unit, the indicating lamp unit and the power supply unit. The power supply unit supplies power to all units. The system can carry out the real-time collection and transmission of data needed by a servo driver, and also can carry out the unified management of a CANopen application layer model defined by the data. The invention also can proposes a solution capable of supporting cognitive learning.

The invention discloses an offline voice recognition model updating method, a household appliance, a server and related equipment, wherein the method comprises the following steps: collecting voice data of an external environment of a household appliance; uploading the collected voice data to a server, wherein the voice data is used for indicating the server to carry out model training so as to obtain an updated voice recognition model; and receiving the updated voice recognition model sent by the server. According to the method in the invention, the server is used for collecting the voice data sent by the household appliance as training data, and model training is carried out according to the training data so as to obtain the updated voice recognition model, which is sent to the household appliance, so that the household appliance receives the updated voice recognition model sent by the server, the local offline voice recognition model can be replaced with the updated voice recognition model, so that online updating of the offline voice recognition model in the household appliance is realized, the replacement of the new and old models is completed, and the workload of offline model training of the household appliance is reduced.

The invention discloses a method and a system for online cloud updating of an AR application program. The method for the online cloud updating includes: enabling a client to obtain the version number of the AR application program of a server, comparing with the version number of the local AR application program, if the version numbers are inconsistent, sending an updating request to the server; sending the map matching relation between the identification data of the AR application program of the server and AR resource to the client in the form of data table; enabling the client to analyze the AR application program from the bottom layer, and constructing a simulative programmable environment in a buffer memory space; compiling and packaging the AR application program again according to the data table sent from the server. By means of the method and the system for the online cloud updating of the AR application program, the AR application program is analyzed through simulating the programmable environment in the client, the re-compiling and re-packaging are realized according to the mapping relation data table obtained from the server, and then the AR application program realizes the online updating; the current method of downloading an updated installation package to install again is replaced, and accordingly the flow rate is saved, and the operation is facilitated and is effective.

The invention discloses a parking lot intelligent control system based on a programmable map. The structure and the parking places of a parking lot are changed, a parking lot map PM file is imported through a map drawing subsystem, the map data are analyzed by the map drawing subsystem and then the parking lot map is drawn again. Then the new map data are exported to a U-disk through the map drawing subsystem. The U-disk is accessed to a node central control subsystem, the FAT32 file format is analyzed by the a node central control subsystem to acquire the map data PM file in the U-disk, and the node central control subsystem updates the map data. Front-end equipment acquires the parking place data in real time and transmits the parking place data to the node central control subsystem. The node central control subsystem converts the data transmitted by the front-end equipment into the data which can be saved by a cloud platform through data analysis and then performs data interaction with the cloud platform. The operation efficiency of the parking lot can be effectively enhanced and the system maintenance difficulty and the relevant cost can be reduced.

The invention discloses a detecting method for abnormal intrusion of large high-dimensional data of a network. The detecting method for abnormal intrusion of the large high-dimensional data of the network comprises the following steps of learning stage: establishing SST (signal sustain technology) space comprising fixed SST subspace, unsupervised SST subspace and supervised SST subspace; and detecting stage: updating synoptic PCS (personal communication service) of each SST subspace with data so as to acquire information of new arrived data after data of the network arrive, determining that the subspace is abnormal separated subspace if PCS of cells belongs to at least one SST subspace with a predefined threshold value, and feeding back abnormal PCS values of the cells and all abnormal values or a specific amount of abnormal values comprising abnormal separated subspace to a user and the SST space. By the detecting method, the detecting efficiency and the accuracy of network abnormal intrusion can be improved further under the conditions that the network data size is large, the dimension is increased and correlation of the data is reduced.

The invention provides a neural network increment-type feedforward algorithm based on sample increment driving. According to characteristics of input samples, input weights and hidden layer thresholds of a neural network are obtained by a least square method, and initialization of model parameters is completed; and the input weights and hidden layer thresholds of the model are updated according to increments between new samples and old samples, function relations between the model parameters and input samples are established, and functions of sample self-adaptability and online feedforward adjustment are realized. The algorithm provided by the invention has the advantages that the prediction precision is high, the generalization capability is high, and online feedforward adjustment is available.

The invention discloses an online trajectory prediction method based on particle filtering. The method comprises the steps: 1) building a trajectory prediction model, taking a longitudinal position x,a lateral position y, a vehicle speed v and an acceleration a of a historical time domain [-T, 0] as the input, and outputting the longitudinal position x, the lateral position y and the vehicle speed v of a future time domain [0, T]; and 2) correcting and updating the state of the trajectory prediction model by using the current measurement quantity. When the track is predicted, the informationof one time sequence can be input, the influence of the information of the previous moment on the information of the next moment is considered, and the prediction accuracy of the model in the dynamicenvironment is improved; in a nonlinear system in the presence of interference and noise, a statistical method is used to solve the prediction filtering problem, and an accurate solution of a state can be approached.

The invention provides a switchgear reliability evaluation method which comprises the following steps: (S1) pretreating and initializing the basic data and operation data generated by a switchgear respectively, and establishing corresponding vector comparison tables; (S2) establishing a tree structure of the basic data, and modeling by use of a recurrent neural network; (S3) establishing a time sequence structure of the operation data, and modeling by use of a circular neural network; (S4) establishing a loss function according to the models established in the step (S2) and step (S3), and performing model training; and (S5) in combination with the vector comparison tables, performing model-based reasoning to obtain final risk assessment. According to the invention, by combining the recurrent neural network and the circular neural network and introducing an attribute vector and an event vector, the problem of low-dimensional coding of nonnumeric data is solved; and in combination with the basic data and the operation data, the fusion of multi-source information can be realized so as to provide accurate quantitative evaluation and dynamic evaluation of the switchgear reliability.

The invention discloses a control method for a neural network global sliding mode of a micro gyroscope based on linearization feedback. The control method comprises the following steps of S101, establishing an ideal dynamical equation of the micro gyroscope; S102, establishing a dimensionless dynamical equation of the micro gyroscope according to Newton's law in a rotation system; S103, establishing a neural network global sliding mode control system based on the linearization feedback according to the ideal dynamical equation and the dimensionless dynamical equation, designing a control law, and taking the control law as the control input of the micro gyroscope; S104, designing a self-adaptive law based on the lyapunov function theory, thereby enabling the established control system to perform online update. The control method disclosed by the invention has the advantages that the universality is realized, and transient characteristic, robustness and stability of the system are improved.

The invention discloses a power equipment temperature prediction method based on PSO-LSSVM online learning, and the method carries out the online evaluation and screening of a training sample: firstlytransmitting the collected real-time temperature data of power equipment to a background system, and carrying out the preprocessing of the sampling data; then judging whether incremental learning iscarried out on newly added samples or not according to KKT conditions of an LSSVM model, Meanwhile, rejecting the samples with the maximum characteristic difference in the current training sample setthrough an adjusdesk cosine similarity method; and then, predicting the temperature of the power equipment after the moment t by using an online trained PSO-LSSVM model, and carrying out different levels of high-temperature early warning on the power equipment which may exceed a temperature threshold. According to the method, the contribution of the newly added samples is more emphasized, and thetraining sample set capable of best reflecting the current state is obtained, so that the generalization ability of the model is improved, the prediction operation efficiency is improved, the temperature at the next moment can be effectively predicted, and the safety of using electrical equipment is improved.

The invention discloses a Gaussian process trajectory tracking control method for a space robot for carrying out online identification on a kinetic model of a space robot system and dynamically adjusting and controlling a gain by utilizing the confidence coefficient of the model. Furthermore, a sparsification method based on guide data points is adopted for online training of the Gaussian processkinetic model, and meanwhile, an edge particle filter is adopted for online estimation of hyper-parameters of the Gaussian process model, so that the computational burden of model training and prediction is effectively reduced.

The invention discloses a programmable logic device configurator. The device comprises a microcontroller interface module (1), a working register array module (2), a program memory interface module (3) and a configuration time sequence generator module (4), an x80 series microcontroller can be realized; wherein the general Flash type program memory and the American Xilinx Company Artix7 / Kintex7 / Virtex7 series programmable logic devices are seamlessly connected together; a parallel high-speed programmable logic device slave SelectMap configuration circuit is formed, and after a user system is powered on and reset, the power-on reset program loading function of a corresponding programmable logic device of the user system can be directly completed; and in the normal working process of the user system, the user system autonomously controls and completes the functions of on-line program loading and function resetting of the corresponding programmable logic device, so that the target code on-line updating function of the corresponding programmable logic device is realized.

The invention discloses a portable battery management system diagnosis apparatus, which comprises a control module. The control module comprises a micro-control unit for receiving information, processing data and sending signals. The micro-control unit is connected with a TF storage card for storing battery information, complete vehicle information and fault information through a serial peripheral interface; the micro-control unit is also connected with a first path of CAN communication for carrying out information interaction with a BMS (battery management system), and a second CAN communication for carrying out information interaction with a vehicle control unit; and the TF storage card is connected with a USB interface for connecting with an external computer. The portable battery management system diagnosis apparatus has the advantages of being small in size, convenient to hold and convenient for on-site diagnosis and debugging. The invention also discloses a working method of the portable battery management system diagnosis apparatus.

The invention discloses a self-adaptive quality forecasting method based on incremental support vector regression, which is used for adapting to a complex and changeable multi-working-condition production process, realizes the differential learning of a newly added sample by utilizing the KKT condition of the self-adaptive quality forecasting method based on incremental support vector regression on the basis of the original support vector regression model, carries out the incremental learning on samples with information, keeps the model unchanged for samples without new information, and can reduce the updating frequency of the model while ensuring the generalization capability of the model. The self-adaptive quality forecasting method based on incremental support vector regression not onlycan effectively face the nonlinearity in the actual industrial process, but also can continuously update the model aiming at the time-varying characteristic in the process, and the problem of the updating efficiency of the model is improved to a certain extent, so that the purpose of self-adaptive quality prediction is achieved.

The invention discloses a vehicle intelligent product remote service system and an operation method thereof. The vehicle intelligent product remote service system comprises an adapter, a client, and anetwork server, wherein one end of the adapter is connected with a vehicle through a vehicle CAN bus with an OBDII port, the vehicle CAN bus is connected with vehicle electronic elements through a CAN module to form a local area network, and the other end of the adapter is connected with the client through a Bluetooth and / or a serial port; the client is used for processing and analyzing data, andsending the result to a network server through the network; the network server is used for data link and database management, the network server is connected with a remote client through the networkto form a vehicle intelligent product remote service system; the data monitoring, the parameter configuration, the fault diagnosis and the program write on the vehicle electric-control product are realized through the CAN bus technology and the Bluetooth transmission technology, and the remote operation of the above four functions can be realized through the network communication technology, thereby providing remote expert guidance service for the on-site staff, and the aftersales service level for the vehicle electric-control product is greatly improved.

The invention discloses a topological structure updating system and method for stream processing. The system comprises a task manager used for detecting a first state of a first state machine and generating a control message according to the state, wherein the first state is used for triggering a topological structure for stream processing to be managed; The task executor used for receiving the control message; And triggering a second state of a second state machine under the triggering of the control message, and executing an action corresponding to the second state on the topological structure according to the second state. The technical problem that an existing big data stream processing architecture is not suitable for online stream topology updating is solved.