730results about How to "Cope well" patented technology

A method for estimating a target object properties, including location, in an environment. A topology model indicates permissible locations and transitions and a data model models a location-dependent physical quantity which is observed by the target object's sensing device. Motion models model specific target object types, obeying the permissible locations and transitions. The target object is assigned a set of particles, each having a set of attributes, including location in relation to the topology model. The attributes estimate the target object properties The particles' update cycles include: determining a degree of belief for each particle to estimate the target object properties; determining a weight for each particle based on at least the determined degree of belief and generating new particles for update cycle n+1 in an evolutionary process.

The invention discloses a self-organizing method for a cooperative scouting and hitting task of a heterogeneous multi-unmanned-aerial-vehicle system. A heterogeneous multi-unmanned-aerial-vehicle system is decomposed into two isomorphic sub systems and a corresponding cooperative way between the two sub systems is also designed; and the two sub systems carry out task planning respectively and also carry out mutual cooperation. For an isomorphic reconnaissance type unmanned aerial vehicle system and an isomorphic scouting and hitting unmanned aerial vehicle system, a cooperative searching task self-organizing method and a cooperative scouting and hitting autonomous task planning method are designed respectively. According to the cooperative searching task self-organizing method, problem decomposition is carried out by using a method based on distributed model prediction control; and then solution is carried out by using a particle swarm algorithm. And according to the cooperative scouting and hitting autonomous task planning method, a normal flight mode without any detection of a threat and a threat avoiding mode with threat detection are designed during a local problem construction process of each unmanned aerial vehicle based on a distributed ant colony algorithm. Therefore, an on-line requirement can be met well.

A multi-stage event detector for monitoring a system, the multi-stage event detector including: a process generator operable to generate main and sub-processes, each main and sub-process being operable to generate and initiate a detection agent each of which is operable to be triggered by detecting the occurrence of a trigger event and to report back to its generating process or sub-process upon being so triggered. Each process or sub-process is operable to respond to receipt of a report from a triggered detection agent by reporting the detection of a multi-stage event to an overall controller.

The invention belongs to the field of communication technology and relates to a smart old-people caring system. The main structure of the system comprises an information collection subsystem, an information processing subsystem, a cloud platform and a command center, wherein the cloud platform is a data storage system and is used for storing information collected by the information collection subsystem and transmitting the information to the information processing subsystem for analytical processing; the information processing subsystem serves as an information processing pivot, is provided with an intelligent AI analytical algorithm module, performs intelligent analytical sort-out on collected big data and sends the processed information to the command center; and the command center can store all the information and provide information query and transmission services. Through the smart old-people caring system, 24-hour old people behavior analysis in videos is provided, a reasonable and effective judgment is made on suspicious abnormal behaviors of old people through bidirectional linkage in combination with sign data of old people, and abnormal behavior categories, confidence ofabnormal behaviors, risk levels, abnormal staff identities and first-aid projects are provided.

A multi-stage event detector for monitoring a system to detect the occurrence of multistage events in the monitored system, the multi-stage event detector includes: one or more event detecting detector units (142, 144) for detecting observable events occurring on the monitored system; one or more parameter generating detector units (152, 154) for generating parameter values which vary over time dependent on the behaviour of the monitored system; a hidden state determiner (120) for determining a likely sequence of states of interest of the system based on the outputs of the one or more event detecting detector units; and a transition determiner (130) for determining a likely transition occurrence based on a comparison of a set of values of a parameter or set of parameters generated by one or more of the one or more parameter generating detector units with a plurality of pre-specified functions or sets of values of a corresponding parameter or set of parameters associated with different transition occurrences.

The invention provides a method and device for identifying a phishing website, aiming to improve the judgment accuracy rate of the phishing website. The method comprises the following steps: analyzing the page information of a target website to which a user wants to access so as to obtain a text content to be analyzed; carrying out sentence and word segmentation processing on the text content to obtain the sentences in the text content and the words in each sentence; searching a preset semantic element knowledge base, wherein the semantic element knowledge base comprises words and attributes corresponding to the words, and acquiring the attributes of the words in the text content; taking a sentence as a unit, matching the sentence with the acquired each logical relation in the semantic element knowledge base, wherein the content to be matched in each logical relation at least comprises the sequence of the words, the attribute of each word and the content of at least one word; determining the sentence to hit the logical relation if the sentence is matched with the logical relations in the semantic element knowledge base; and calculating the hitting rate of the text content, and determining the target website to be a phishing website if the hitting rate is greater than or equal to a preset hitting threshold.

The invention discloses a variable-speed wind generating set maximum wind energy capturing method based on effective wind speed estimation. The method comprises an effective wind speed estimating model and a maximum wind energy capturing controller. In order to acquire an effective wind speed estimated value, a training set of an SVR model is formed by normalized unit historical output data and historical wind speed measured values, penalty parameters and kernel function parameters are selected by a GA algorithm to obtain the trained SVR model, and the model gives out the wind speed estimatedvalue in an online manner; and when the maximum wind energy capturing controller is design, the real-time optimal wind wheel rotating speed estimated value is obtained according to effective wind speed which is given out by the effective wind speed estimating model, nonlinear characteristics and parameter uncertainty of the system are responded by robust factors and a neural network, and therefore, boundedness of rotating speed tracking errors and stability of a wind generating set system are realized. By the method, a mathematical model and parameters of the unit do not require to be used, adesign process is simple, an implementing cost is low, and the capacity of the unit and the economic benefit of a wind power plant can be improved.

The invention provides an inertia-assisted multi-frequency multi-mode GNSS cycle slip repair method and system. The method comprises the steps that cycle-slip observation is conducted on all satellites to judge the satellites with the existence of cycle slip; after cycle-slip parameters are determined, a pseudo space and phase position non-difference uncombined epoch difference observation equation on frequencies and systems is formed, and a clock difference variation parameter is introduced; according to the activity degree of an ionosphere layer, fitting functions with different orders in a sliding window are used to model and forecast the time-varying ionosphere layer; inertia-assisted cycle slip calculation is conducted, and posteriori residuals of the cycle slip repair equation are tested; a three-step method is used to fix cycle slip values and carry out the cycle slip repair, and finally phase position observation values are repaired. The inertia-assisted multi-frequency multi-mode GNSS cycle slip repair method and system can accurately repair cycle slip values on different frequencies of different GNSS systems in a dynamic and complicated environment, and provide clean and pollution-free observation data for subsequent positioning and calculating treatments.

The invention discloses a photovoltaic power generation output power tracking algorithm based on a genetics algorithm improved RBF-BP neural network. By building an RBF-BP neural network, an error absolute value between predicted output and expected output of photovoltaic power generation output power is taken as the fitness, and then a genetics algorithm is adopted for selecting, intersecting and mutating data acquired by photovoltaic power generation equipment in order to find out an individual corresponding to the optimal the fitness. The photovoltaic power generation output power tracking algorithm disclosed by the invention combines the advantages that an RBF neural network is high in rate of convergence, good in heap sort performance and the BP neural network is high in self-learning and self-adaptive capabilities, and has the characteristics of better generalization performance, higher rate of convergence, higher prediction precision and the like.

The invention relates to a control method of a plug-in hybrid power bus, belonging to the technical field of the control of hybrid power buses. The method comprises the following steps of: obtaining present vehicle position information by using a GPS (Global Position System) navigation device and a digital map stored in a vehicle-mounted information unit, judging road congestion levels at the temporal position by combining vehicle speed-time history counting information of an urban fixed line stored in the vehicle-mounted information unit; and carrying out real-time optimization and adjustment for the energy distribution of the vehicle according to the information including different congestion levels, the present vehicle position, and power battery electric quantity and the like. The method disclosed by the invention can effectively and reasonably distribute the energy of the plug-in hybrid power bus running on fixed lines, so that the fuel oil consumption and the pollutant emission are reduced and the fuel oil economy is enhanced.

The invention belongs to the technical field of unmanned driving, and discloses a target following and dynamic obstacle avoidance control method for a speed difference slip steering vehicle, and the method comprises the steps: building four neural networks through employing a depth determinacy strategy in reinforcement learning; constructing a cost range of the obstacle so as to determine a single-step reward function of the action; determining continuous action output through an actor-critic strategy, and updating network parameters continuously through gradient transmission; and training a network model for following and obstacle avoidance according to the current state. According to the method, the intelligence of vehicle following and obstacle avoidance is improved, and the method canbetter adapt to an unknown environment and well cope with other emergencies. the complexity of establishing a simulation environment in the reinforcement learning training process is reduced. By utilizing a neural network prediction model trained in advance, the position and posture of each step of the target vehicle and the obstacle can be obtained according to the initial position and posture ofthe target and the obstacle and the action value of each step, so that the simulation accuracy and efficiency are improved.

The invention discloses a multi-ground-clearance self-steering intelligent sprayer and a control method. The sprayer comprises a vehicle platform, two self-steering traveling mechanisms, a controllable lifting spraying device, a gasoline power generation and supply device and a control device, and each self-steering traveling mechanism is connected with the vehicle platform by adopting a rotation structure; the controllable lifting spraying device is fixed at the tail of the vehicle platform and comprises a sprayer lifting mechanism, a spraying rod device and a drug mixing device, the spraying rod device is arranged on the sprayer lifting mechanism and connected with the drug mixing device, and a spraying rod can move up and down along with traction of the sprayer lifting mechanism; the drug mixing device is fixed in the middle of the vehicle platform and provides drug to spraying equipment for spraying, the gasoline power generation and supply device is fixed on the front portion of the vehicle platform and serves as a stable voltage power source of the equipment, and the control device is connected with driving equipment, a control motor and multiple sensors. Four hub motors are controlled by the control device to realize self-steering of wheels at differential speeds, and the sprayer is high in control accuracy and small in turning radius.

The embodiment of the invention provides a Webshell detection method and system based on machine learning and dynamic and static analysis and relates to the technical field of Webshell detection. Themethod comprises the steps of obtaining sample files; extracting static characteristics and dynamic characteristics of the sample files; and obtaining a classification model according to the static characteristics, the dynamic characteristics and a machine learning algorithm, wherein the classification model analyzes to-be-detected files and obtains detection results. According to the method and the system, through adoption a dynamic and static combined analysis method, the characteristics are extracted relatively comprehensively; a great number of Webshell samples and normal webpage samples are learned to form the classification model through adoption of various classification algorithm combined machine learning algorithm; the classification model is high in stability and accurate in classification; through adoption of the classification model, Webshells and variants thereof can be effectively detected; new-type Webshells are predicted; a text mixing method can be coped with well; andthe deficiency resulting from traditionally adopting a characteristic code matching detection mode can be compensated.

The invention discloses a powdered carbon backflow and carbon / sand filter combined process for treating polluted water, which belongs to the field of water treatment. A device comprises a mixing tank, a flocculating tank, a settling pond and a carbon / sand filter which are connected in series in turn, wherein powdered active carbon and a coagulant are added in raw water to be treated, and the raw water is mixed with the coagulant in the mixing tank; the mixed raw water enters the flocculating tank to undergo a flocculation reaction and then enters the settling pond to undergo solid-liquid separation, active carbon mud settled in the settling pond is concentrated in a mud storage chamber and flows back to the mixing tank or flocculating tank by a reflux pump, the concentrated active carbon mud flows back to the mixing tank when the coagulant is added and flows back to the flocculating tank when the coagulant is not added; and water after the settlement enters the carbon / sand filter, the granular active carbon is filled on the upper layer of the carbon / sand filter, and quartz sand is filled in the lower layer. In the invention, a powdered carbon backflow process is adopted, so that over 50 percent of medicinal preparation can be saved; and the physical absorption effect, biological degradation effect and strengthening coagulation effect of the active carbon act in coordination, and the carbon / sand dual-layer filter has the physical absorption and biological degradation effects at the same time.

The invention provides a global-local hybrid unmanned ship path planning method based on dynamic constraints. The global-local hybrid unmanned ship path planning method comprises the steps of: S1, constructing an unmanned ship kinematics model, and conducting kinetic analysis; S2, considering a safety range of an unmanned ship, realizing the planning of a global path of the unmanned ship by adopting a storage node mode, and constraining the safe driving of the unmanned ship by optimizing the generated path points; S3, for a dynamic unpredictable environment, constructing a hierarchical structure based on an FDM fuzzy decision module and an FDW precise dynamic window module, and realizing the planning of a local path of the unmanned ship, precisely taking drivable forward, heading angular velocities and acceleration into account in the planning of the local path, and generating a traceable path that enables real-time local collision avoidance; S4, and fusing a global path planning scheme and a local path planning scheme of the unmanned ship to form a global-local hybrid obstacle avoidance strategy. According to the global-local hybrid unmanned ship path planning method, virtual pathpoints are inserted into the global optimal path, so that global and local path planning is perfectly combined, and the whole path tracking task is completed.

The invention discloses an intelligent on-line liquid level monitoring and early warning system. The intelligent on-line liquid level monitoring and early warning system is composed of a data center and at least one liquid level monitoring and early warning subsystem, and is characterized in that the liquid level monitoring and early warning subsystem comprises a liquid level monitoring point, a liquid level monitoring host and a monitoring repeater; the liquid level monitoring host is used for carrying out continuous monitoring on the liquid level monitoring point, generating monitoring data and transmitting the monitoring data to the monitoring repeater according to a changeable transmission time interval, and the monitoring repeater is used for receiving the monitoring data and transmitting the monitoring data to the data center according to the changeable transmission time interval; the data center is used for processing the received monitoring data to obtain status data and various curves of the liquid level monitoring point and displaying and releasing the status data of the liquid level monitoring point in real time. By means of the intelligent on-line liquid level monitoring and early warning system, on-line liquid level monitoring, short-message warning, waterlogging early warning, short-time forecasting, monitoring data public release and the like can be carried out on points such as a drainage pipeline, a drainage channel, a drainage inspection well, a drainage exit, an urban sunken road, an urban underground parking garage and urban receiving water.

The invention relates to system and method for controlling artery green wave traffic signals based on an Internet of Things and belongs to road traffic technical field. The method comprises a signal control central server capable of acquiring vehicle information and flow information of correlative roads in real time from road monitoring systems and database servers which are arranged at various intersections. Thus, by means of the above information, the method may determine speed and time that a current vehicle flow passes two adjacent intersections. Further, the initial time and the duration time of green lights at various intersections in a green wave direction are optimized. Therefore, the setting of the green wave is more real-time and accurate. As a result, various emergent events can be well treated and a road passing efficiency can be greatly increased. The system for controlling artery green wave traffic signals based on an Internet of Things is simple in structure and low in cost. The control method is easy to implement and has a wide application range.

The invention discloses a human face emotion identifying method. The method includes pretreatment, image division, feature extraction, classification and classification result fusion of human face expression pictures. The method is characterized in that a complete human face expression picture is divided into four sub images (corresponding to the forehead, the eyes, the nose and the mouth) according to the distribution of the five sense organs; a sparse representation classifier is utilized for classifying the sub pictures and the original picture so as to obtain five possible classification results; finally the weighing Bayes fusion decision theory is utilized for adjusting the weight distribution of the five sense organs and the similarity and difference between expressions are both taken into consideration. The method has advantages of being simple to implement, good in noise prevention robustness and being capable of handling complicated human face expression identification well and improving human face expression identification accuracy and the like.

A detection system comprising a plurality of taut wire panels having vertical detection / sensor wires. The sensor wires are tensioned to position trigger plates associated one or more of the wires. Trigger plate movement causes an actuating means to indicate a sensor wire has been moved. In one embodiment, sensor wires and portions of the panel frame have similar coefficients of thermal expansion to substantially eliminate environmental expansion effects that may result in false alarms. Linked sensor wires on adjacent panel may signal movement of entire panels. Panels are monitored by panel controllers reporting to sector controllers that report to a central command computer that automatically numbers sector and panel controllers at start-up. Bi-directional communication enables alarms and system faults to be precisely located.

The invention discloses an adaptive configuration method for short-time variable big data job cluster scheduling. The method is a self-adaptive cluster scheduler configuration optimization method provided by aiming at the characteristics of cloud platform isomerism, cluster scheduler configuration optimization of dynamic load, isomerism of cloud platform load and short-time variability. Cloud platform loads can be divided into service applications and analysis applications, and different classifications are different in resource consumption and time requirements. According to the method, the configuration of the cluster scheduler is adjusted according to the state information of the job and the information of the cluster environment; the optimal scheduling configuration is always kept; therefore, the operation performance is improved, the operation delay is reduced, the heterogeneous load of the cloud platform can be better adapted, and the optimal configuration item corresponding to the current cluster state can be better found, so that the waiting time of the cluster operation is close to the minimum, the operation efficiency is improved, and the short-time variable big data operation is effectively scheduled.

The invention discloses an exoskeleton robot which comprises an exoskeleton robot body, a sensing system, a control system and a drive system. The exoskeleton robot body comprises an upper part support structure, a hip rod piece, thigh rod pieces, shank rod pieces, foot components, a hip joint, knee joints and ankle joints; the sensing system comprises a thin film strain gage sensor for detectingthe sole pressure, an encoder for detecting a joint angle, a force sensor for detecting a force / moment of the joint part, an attitude sensor for detecting the kinematic velocity / the accelerated speed,a capacitance-type sensor for detecting the muscle tension, and a connecting buckle fastener; the capacitance-type sensor non-contacting with a human skin is fixed onto the connecting buckle fastenerand comprises at least one electrode patch; and the control system comprises a full-communication network computing module formed based on a CNN machine learning algorithm, is connected with the sensing system and can receive data acquired by the sensing system, and is connected with the drive system capable of driving the exoskeleton robot body to move.

The invention relates to a channel estimation and data detection method for an OFDM (Orthogonal Frequency Division Multiplexing) system, in particular to a time delay domain base resolution based channel estimation and data detection method of the OFDM system in a high-speed moving scene, belonging to the field of wireless communication. The channel estimation and data detection method comprises the following steps of: resolving a channel frequency domain response matrix into two parts, i.e. a surface feature time delay domain and a surface feature ICI (Inter-Carrier Interference); and realizing the channel estimation of the OFDM system in the high-speed moving scene by simplifying the two parts. According to the channel estimation and data detection method disclosed by the invention, the ICI problem caused due to orthogonality misadjustment between subcarriers of the OFDM system under a time-varying channel can be effectively solved, and therefore influences caused by parameter time variant is overcome in a certain sense; bit error rate can be greatly reduced, the high-speed moving scene can be very well coped, and properties can not be reduced progressively with the increase of mobile carriers and base stations relative to mobile speed.

The invention relates to a signal control system and signal control method based on traffic simulation. The signal control system comprises a data acquisition module, a simulation and forecast merging module, an intelligent traffic signal lamp control module and a traffic guidance module. The data acquired by a last intersection acquisition module are sent to the simulation and forecast merging module, the traffic flow data of a current intersection and the data acquired at the last intersection are sent to the intelligent traffic signal lamp control module to carry out self-adaptation traffic control, accordingly, a detector set at the current intersection is replaced, half detectors in a road section can be replaced and removed, the purchasing and maintaining cost are greatly saved, the intersections of upstream and downstream are corelated, the signal timing dial is uniformly coordinated and adjusted, the intercommunication of a road network is enhanced, a scheme capable of improving the traffic capacity of the road section can be designed, and the problem of traffic jam in cities at present can be relieved.

The invention relates to a topological optimization design method for a cold plate liquid flow channel of an electric-electronic integration module. The method comprises the following steps: firstly establishing a finite element module, setting a growing initial point and finishing parameter initialization of self-adaption growth; then protocoling a self-adaption growth strategy of the cold plate liquid flow channel and giving out a method for determining growth direction and furcation of the cold plate liquid flow channel; finally performing step-by-step iteration growth of the cold plate liquid flow channel. By means of the topological optimization design method disclosed by the invention, optimized cold plate liquid flow channel distribution can be directly obtained; meanwhile, constraint of ground structure nodes is got rid, and the optimized cold plate liquid flow channel distribution is closer to the optimal result; an excessive demand that a designer has a lot of design experience is avoided, work of simulation, improvement and repeated simulation is prevented from being repeated, and work efficiency and design quality are obviously improved.

The invention relates to the field of urban rainwater treatment systems, in particular to a region sectioning rainwater treatment system based on a separate-system pipe network.The region sectioning rainwater treatment system comprises multiple sections divided according to area and a sewage manifold communicated with a sewage treatment plant, a sewage branched pipe, a rainwater branched pipe and at least one rainwater treatment facility are arranged in each section, each rainwater treatment facility is communicated with a discharging end of the corresponding rainwater branched pipe, a discharging end of each rainwater treatment facility is communicated with a natural water body, and a discharging end of each sewage branched pipe is communicated with the sewage manifold.The region sectioning rainwater treatment system is simple in structure, an urban water treatment system is divided into independent sections, and each rainwater treatment facility which is independent is arranged in the corresponding section, so that the delay problem in the process of long-distance delivery of early-stage rainwater in the prior art is solved, treatment pressure of the sewage treatment plant is reduced, the early-stage rainwater is avoided causing pollution to natural water, discharging treatment of rainwater is more reasonable and more efficient, and the region sectioning rainwater treatment system has high popularization value.

The invention discloses a short-time prediction method for future parking space demand quantity in a closed area, which is used for predicting the parking space demand quantity of a certain closed area in future hours, so that managers can effectively manage and schedule facilities of parking lots on roads in the area. The invention provides a deep learning theory-based recurrent neural network parking space demand prediction model of an LSTM (Long Short-Term Memory) unit, and the parking space demand corresponding to a subsequent time point is predicted according to the parking space demand variations of previous hours. Certain Hangzhou University is taken as an experimental object, and data of two random days and two specific days are adopted for practical verification. Results show thatthe LSTM recurrent neural network module is adopted to predict the parking demand in the area, the result is closer to the actual value than that of a traditional method, the precision is satisfactory, and it is indicated that the prediction method is feasible and effective.

The invention belongs to the technical field of optoelectronic devices, relates to a thermal imager, and particularly relates to a helmet type thermal imager system. The technical problems that the design in the prior art is not reasonable enough and the like are solved. The helmet thermal imager system comprises at least one helmet thermal imager; each helmet thermal imager comprises a helmet, a shell is arranged on one side of each helmet, an infrared optical lens is arranged at the front end of each shell, an infrared imaging component is disposed in each shell, an image displaying device is disposed at the front end of each helmet, each shell is connected with the corresponding helmet via a clamping structure, each image displaying device is connected with the corresponding helmet via an adjustable connecting structure, and each infrared imaging component is electrically connected with the corresponding image displaying device; and each infrared imaging component is further connected with a wireless and / or wired communication interface, each communication interface is connected with a receiving device in a wireless and / or wired communication mode, and the receiving device is connected onto a background management system. The helmet type thermal imager system has the advantages that load of equipment of the head of a user can be relieved, and the image displaying devices can be adjusted to monitor according to actual environments; and networking can be realized, so that various emergency problems can be solved conveniently.