75results about How to "Adapt to dynamic changes" patented technology

The invention discloses a decision-making method for large-scale unmanned aerial vehicle (UAV) cluster dynamic confrontation. The method comprises the steps that UAVs conduct situation assessment according to environment information of a battlefield and make multi-UAV cooperation and multi-target distribution decisions with maximizing attack benefits as the target; a cluster motion model based onthe society is established, the UAVs select corresponding behavior rules according to the target distribution result to update the speeds and locations; the UAVs make attack decisions and judge whether distributed targets meet the attack conditions or not, if yes, the UAVs attack the corresponding targets and update the number of weapons and the survival probabilities of the enemy targets, and ifnot, the UAVs judge whether the military operation is ended or not. According to the decision-making method, the UAVs conduct target distribution and negotiation through communication in real time; the cluster motion model is established to achieve updating the speeds and locations of the UAVS; and accordingly, dynamic confrontation between UAV clusters is further realized.

The invention relates to a distributed buffer memory strategy adaptive switching method based on machine learning and a system thereof. The method comprise the following steps: carrying out evaluation on each buffer memory strategy based on a reference test, and confirming scene elements influencing buffer memory strategy performance appearance; collecting data set of each buffer memory strategy under different scene elements conditions; training the data set to obtain a buffer memory performance model; and deciding an optimal buffer memory strategy based on buffer memory performance model and on-line monitoring data in cluster environment, and executing buffer memory strategy switching when present buffer memory strategy is inconsistent with the optimal strategy. In the invention, machine learning method is employed to establish the buffer memory performance model which can accurately describes present scene, the model is updated periodically through constructing a performance data warehouse to improve the precision of the model further. Thus the distributed buffer memory strategy adaptive switching method in the invention is adapted to environmental dynamic change well to enhance flexibility and adaptability of buffer memory service.

The invention relates to an urban expressway on-ramp and main line collaborative signal control system and method. The system comprises an on-ramp upstream detector, an on-ramp downstream detector, a main line upstream detector, a main line downstream detector, an on-ramp, a main line signal lamp and a processing control center. The processing control center judges whether a main line is blocked or not according to the detection speeds and the time occupation rates of the main line upstream detector and the main line downstream detector, and starts on-ramp and main line collaborative signal control if the main line is blocked. The processing control center confirms a timing plan for control according to flows detected by a ramp detector and the main line and ramp demands. After a period is finished, the processing control center judges whether the main line blockage is finished or not according to the detection speed and the time occupation rate of the main line upstream detector, continues to perform signal control if the blockage occurs and stops signal control if the blockage does not occur. The system releases main line and ramp vehicles in turn in an expressway blockage period through the collaborative signal, changes two orderless conflict flows into an order saturate flow, improves the bottleneck point passing capacity of the main line and greatly improves the convergence safety.

The invention discloses a wireless communication method which comprises the following steps of: transmitting one or more communication frames between network equipment and terminal equipment, wherein each communication frame comprises a downlink subframe and an uplink subframe, and a guard interval is formed between the downlink subframe and the uplink subframe; and dynamically configuring structures of the downlink subframe and the uplink subframe in each frame in a process of transmitting the one or more communication frames, wherein a dynamically configured downlink subframe at least comprises a leading sequence, a system information channel, a control channel and a downlink transmission channel, and a dynamically configured uplink subframe at least comprises an uplink transmission channel. The invention also discloses a wireless communication system. The frame structure is dynamically configured, so that uplink and downlink wireless transmission resources can be dynamically allocated based on service requirements, and future data service requirements with rich kinds and different characteristics can be well dynamically met.

Disclosed in present invention is a wireless communication system, comprising: a CAP, for determining a structure of a current physical frame according to scheduled transmission resources, and sending information for indicating the structure of the current physical frame in the current physical frame; and at least one STA communicating with the CAP, for determining the structure of the current physical frame according to the information for indicating the structure of the current physical frame, the length of each physical frame depending on the structure of the current physical frame and being not fixed. Also disclosed are two devices for wireless communication.

The present invention discloses a fine-grain multi-target passive positioning method on the basis of a RFID. The method provided by the invention is able to take the disturbance of multipath information as fingerprint information, and a multi-target positioning method in the prior art remove multipath or neglect multipath information, so that the method provided by the invention is able to realize the calculation of fine grain position, and other methods can only indicate the substantial position of an object. The method provided by the invention is able to accurately perform fine grain multi-target positioning and fill up defects of the multi-target positioning algorithm in the aspect of positioning precision, and has good dynamic adaptability of the environment and low economic cost.

The invention discloses a wireless sensor network route ant colony algorithm optimization method. A wireless sensor network route is solved by utilizing an ant colony optimization algorithm, on the basis of improvement and optimization of the basic ant colony algorithm, improvement of parameters in a probability selection formula is carried out, a pheromone regular broadcast mechanism related with the algorithm is introduced, the ant colony algorithm is realized in a special-purpose wireless sensor network operation system TinyOS, the algorithm enables the wireless sensor network route to realize selection of a shortest path as well as network energy consumption balance, a whole network life period is prolonged, and dynamic change of a network structure can be well adapted.

The invention discloses a wireless communication system, a network device and a terminal device. The network device is used for dynamically configuring the structure and the length of a current communication frame and transmitting frame structure configuration information in the current communication frame, and the terminal device is used for acquiring the structure and the length of the current communication frame according to the frame structure configuration information of the current communication frame. By dynamically configuring the frame structure, different service features and requirements of future rich and varied data services are considered, the flexible variable frame structure simultaneously meeting link adaptation and service requirements is designed for adaptive dynamic resource allocation, and the requirements of devices with different processing capacities can be met. By dynamically configuring the frame length, and control overhead, scheduling overhead and implementation complexity can be reduced.

The present invention discloses a method for terminal access, and this method includes: receiving an access request sent by a terminal; analyzing the access request to obtain a service type of the terminal; determining a number of residual equivalent users according to a difference between an access equivalent user number threshold corresponding to the service type of the terminal currently and a number of accessed terminals; and determining whether to respond to the access request according to the number of the residual equivalent users and a preset minimum residual equivalent user threshold. The present invention further discloses an apparatus for accessing the terminal. With the present invention, dynamic variation of the wireless environment can be adapted, QoS with higher quality can be provided for the users, and the experience and the feeling of the users are improved.

The invention provides a channel and observation domain combination multi-path restraining method in a GPS receiver, and belongs to the technical field of satellite navigation. The channel and observation domain combination multi-path restraining method includes the steps of firstly, expanding channel correlator branches of a standard receiver, improving the delay resolution ratio, extending a delay coverage range, then using a multi-path channel to output the characteristics causing distortion of a correlation function, building a multi-path balancer in a receiver channel, estimating multi-path signal components, compensating a multi-path tracking error, and finally using the wavelet-threshold-based filtering algorithm to build a multi-path filter for the receiver observed quantity to restrain the multi-path error in the pseudo-range and carrier phase observed quantity. The channel and observation domain combination multi-path restraining method does not depend on the assumption related to multi-path parameter prior probability distribution to be estimated and can be well suitable for dynamic changes of the multi-path environment, and the multi-path errors in GPS receiver codes, a carrier wave tracking loop and the pseudo-range and carrier phase observed quantity are eliminated through the multi-path processing comprehensive method.

The invention provides a safety domain control method based on a virtual network. The safety domain control method and system based on the virtual network comprises the steps that a safety domain controller receives a data package head which is based on a virtual exchange standard OVS and is transmitted by an OVS exchanger, wherein the date package head is a data package head of a data package in a data stream flowing into the OVS exchanger; the safety domain controller indicates the OVS exchanger to release the data stream when the safety domain controller confirms that the data stream received by the OVS is a safe data stream according to information carried by the data package head and a preset white list in a safety domain. In addition, the invention further provides a safety domain control system based on the virtual network.

The invention discloses an RFID-based multi-target passive-type indoor activity identification method, which takes perturbation of RFID tag phases caused by human activities as fingerprint information and obtains activity situations of multiple targets by using perturbation of different tag phases caused by activities of many persons. However, current indoor activity identification methods are mainly for a single target, and cannot be applied or lead to big activity identification errors in multi-target indoor scenes. The method of the invention realizes multi-target activity identification without the requirement that targets carry devices, solves the defects of indoor activity identification in terms of target quantity, achieves good dynamic adaptability to the environment, and is low in economic cost.

The invention relates to a method for on-line monitoring of the safety state of an energy storage battery. The method comprises the following steps: determining the temperature for an early warning of thermal runaway of the battery; and early warning of the thermal runaway of the battery. This method is based on the research on the reaction between the internal materials of the battery and the electrolyte, and through the research results of the temperature of the exothermic reaction between the internal materials of the battery and the electrolyte at different decay stages of the battery, it is determined when the battery is in different stages of capacity decay. Out-of-control early warning temperature, on the basis of the research results, use the method of detecting the internal temperature of the battery with a miniature wireless sensor. Early warning of loss of control.

The invention provides a signal capturing method under high-dynamic low signal-to-noise ratio (SNR) environment. The method is suitable for a frequency spectrum or frequency-hopping spread system, andcomprises the following steps: S1, decomposing long-time data into N sub-data sections, performing coherent integral on the sub-data sections and the local code with the corresponding length througha correlator to obtain N groups of integral values, wherein N is power of 2; S2, determining K groups of time domain interpolations according to a frequency analysis range of the FFT and the requiredfrequency domain analysis range, performing interpolation operation on N groups of integral values of the correlator on the time domain to acquire K groups of segmented coherent interpolation data foreliminating time domain sequential feature; and S3, performing coherent accumulation on each group of segmented coherent interpolation data to obtain the FFT operation required count form the obtained K groups of data, performing K-times FFT operation according to the accumulation result to obtain the frequency domain information. The resource consumption is greatly reduced through the signal capturing method provided by the invention.

The invention belongs to the technical field of ITS intelligent traffic systems, and particularly relates to an adaptive full-chain urban area network signal control optimization method. Traffic flow parameters are collected by utilizing a machine vision technology, traffic flow prediction is carried out based on a pre-trained traffic flow prediction algorithm by using obtained data, a microscopic traffic simulation model is constructed according to predicted traffic flow data, an original signal timing scheme and traffic network basic data, and a network-level signal optimization model is constructed. Active optimization is carried out on the network-level signal optimization model by adopting a Bayesian optimization algorithm so as to obtain an optimal signal timing scheme of the target area network. The method has good integration, an inner and outer circulation feedback closed loop is formed, an inner and outer circulation feedback mechanism can achieve interaction of a network signal optimization model and a microscopic traffic simulation model, it can be guaranteed that an optimization result scheme adapts to dynamic changes of the external environment, and then instantaneous dynamic optimization and long-term steady-state optimization are achieved.

The invention provides a single beacon locating method based on rotary RSS (received signal strength). According to the single beacon locating method based on the rotary RSS, the distance and the direction constraint of sending nodes relative to receiving nodes are obtained according to rotary RSS information, collected when a person rotates around a wireless receiving device, of a plurality of sending devices, so that the positions of all sending nodes relative to the receiving nodes are obtained; as long as the absolute positions of the receiving nodes all are known, the absolute positions of all the sending nodes can be obtained. The single beacon locating method based on the rotary RSS, which is disclosed by the invention, does not need to depend on special devices, and is an un-discussed new locating method in the field of wireless location; the location of a plurality of unknown nodes is finished in a single beacon scene, the limitation of a basic trilateral location algorithm to the minimum beacon number is broken through, the dynamic adaptability to the environment is good, and the communication overhead is lower.

The invention provides a method for optimizing an artificial fish school in a clustering analysis mode, and relates to the method for optimizing the artificial fish school. The method includes a first step of setting an original value of an artificial fish parameter, and calculating the food concentration of a current position by means of an objective function, a second step of executing a food searching module, a tailgating module, a clustering module and the like on each artificial fish according to an execution condition of a fish school module and advancing principles of artificial fishes, a third step of making the state of each artificial fish represent a decision variable and evaluating the optimizing degree of the fishes by means of calculation of the objective function, and a fourth step of choosing an input parameter of a K-center point according to bulletin board information and the position of the fish school and ultimately obtaining clear cluster division. According to the method, an artificial fish school algorithm is improved, optimization of data aggregation is carried out, needed time is reduced, and decision precision is high.

The invention proposes a distributed battlefield electromagnetic environment dynamic simulated building method. Before test execution, the stress of a test environment is pre-drilled using a pre-drilling execution mode of a piece of 'unattended timetable' software to get test data meeting requirements, and a simulated electromagnetic environment adapting to dynamic change is constructed. Multiple pieces of equipment are programmed for a long time in a continuous, automatic and distributed manner using a task action type library and a task time sequence of the 'unattended timetable' software, test data of electronic equipment in the process of electromagnetic environment simulation experiment collected on the basis is accurate and valuable, and sufficient reliable basic data can be provided for follow-up information integration, information interaction, analog simulation and information fusion. The method is advantaged in that the method has a high degree of automation control as a whole, can better adapt to the dynamic change of analog electromagnetic signals, and can adapt to the diversity and complexity of test equipment in a simulated electromagnetic environment.

The invention discloses a grid index-based dynamic top-k query method. The method comprises the steps of dividing a data region into a plurality of grid units same in size, wherein each grid unit comprises data falling in the region; establishing a TTI tree index; inserting a grid into the index; from a top trunk node of the TTI index, calculating a k dominating capability of the grid through existing summary information in the grid by left-right traversal, until a judgment unit is found; dividing an influence region and a free region by utilizing the judgment unit; calculating a top-k result set in the influence region; and if the data with changed attribute values falls in the free region, not updating the top-k result set. If the data change occurs in the free region, the top-k result set is re-calculated from the step 3 through data insertion and deletion steps. The method has the beneficial effects that quick increment of a data amount and dynamic change of the data can be adapted; and the query efficiency is high.

The invention discloses an MSWI process dioxin emission prediction method based on multi-window concept drift detection, and belongs to the field of urban solid waste incineration. Dioxin (DXN) is a very toxic pollutant discharged in an MSWI (municipal solid waste incineration) process. The actual industrial process adopts a soft measurement model to realize DXN prediction, but the prediction precision of the soft measurement model is reduced due to the time-varying characteristic of the industrial process. The method comprises the following steps: firstly, establishing a random forest (RF) soft measurement model and a principal component analysis (PCA) drift detection model based on historical data; secondly, performing drift detection on the new sample through a multi-window concept drift detection strategy to determine whether the new sample is a drift sample; and finally, performing redundancy removal processing on the drift samples and judging whether the number of the drift samples meets a set threshold value or not, if so, re-training the PCA model and the RF model, and otherwise, continuing to predict a new sample by adopting a historical model. The validity of the method is verified by adopting industrial process data.

The invention provides a robot-oriented deep learning model segmentation method under a cloud-edge-terminal framework, and belongs to the field of deep learning and distributed computing. According tothe method, firstly, a deep learning model is modeled into a directed acyclic graph, nodes of the directed acyclic graph represent deep learning model layers, and edges between the nodes represent data transmission between the deep learning model layers. Secondly, values are assigned to the nodes according to the processing time of the model layers on clouds, edges and terminals, and values are assigned to the edges between the nodes according to the transmission time of data between the model layers between the cloud edges, the edge terminals and the cloud terminals. Then, the nodes in the graph are layered by adopting a directed acyclic graph longest distance algorithm, and the nodes are processed layer by layer. For each node in one layer, a heuristic strategy is adopted to perform dynamic segmentation according to the input edge weight and the node weight of the node, the segmented deep learning model is distributed to cloud-edge-terminal computing equipment, and therefore cloud-edge-terminal distributed collaborative reasoning without precision losses is achieved.