159results about How to "The processing result is accurate" patented technology

The invention discloses an intelligent electrical equipment environment monitoring system based on cloud computing and big data analysis. The system comprises a cloud server, an intelligent master control device, monitoring devices and communication equipment communicating with the cloud server, wherein the monitoring devices are used for acquiring related data of an electrical equipment environment and transmitting the data to the intelligent master control device; the intelligent master control device is used for controlling the monitoring devices and transmitting the data received from the monitoring devices to the cloud server; the cloud server is used for receiving and storing the related data of the electrical equipment environment; a user accesses the cloud server through a mobile phone or a computer and then controls the master control device and the monitoring devices remotely. The system depends on cloud computing to perform big data analysis on online meteorological information, real-time data and historical data, intelligently monitors the monitoring devices of all nodes, evaluates the monitoring effectiveness, analyzes potential risks effectively and solves problems by which people are puzzled for many years about outdoor electrical equipment microenvironments, online micrometeorology monitoring, real-time intelligent control and the like.

The embodiment of the invention provides an image foreground and background segmentation network model training method, an image foreground and background segmentation method, an image processing method and device, and a terminal device. The image foreground and background segmentation network model training method comprises the following steps: obtaining the eigenvectors of a sample image to be trained; performing convolution on the eigenvectors to obtain the convolution results of the eigenvectors; magnifying the convolution results of the eigenvectors; determining whether the magnified convolution results of the eigenvectors satisfy the convergence conditions or not; if so, completing the training of the convolutional neural network model used for segmenting the foreground and background of the image; and if not, adjusting the parameters of the convolutional neural network model according to the convolution results of the amplified eigenvectors and performing iteration training on the convolutional neural network model according to adjusted parameters of the convolutional neural network model until the convolution results satisfy the convergence conditions. By means of the image foreground and background segmentation network model training method, the training efficiency of the convolutional neural network model is improved, and the training time is shortened.

A packet switching system includes a forwarding processing unit determining a destination of an input packet by analyzing the input packet and outputting it as an output packet, the forwarding processing unit comprises an ingress interface card checking if the input packet has a sequential cyclic number and an egress interface card creating a sequential cyclic number and assigning it to the output packet.

The embodiment of the invention provides a geological resource environment current situation evaluation method suitable for an ecological region, which is characterized by comprising the following steps: respectively establishing a geological environment evaluation system, an underground water resource evaluation system and a mineral resource evaluation system by using a geological environment, anunderground water resource and a mineral resource; And evaluating the current situation of the geological resource environment of the ecological region.

A plasma processing apparatus includes: a processing chamber; a state detector for detecting a state of plasma in the processing chamber; an input unit for inputting process result data of a specimen processed in the plasma processing chamber; and a controller including a prediction equation forming unit for forming a prediction equation of a process result in accordance with plasma state data detected with the state detector for the plasma process simulating a specimen existing state in the processing chamber in a specimen non-placed state and process result data of the specimen input with the input unit and processed by the plasma process in a specimen placed state, and storing the prediction equation, wherein the controller predicts the process result of a succeeding plasma process in accordance with plasma state data newly acquired via the state detector in the specimen non-placed state and the stored prediction equation.

The invention discloses a preceding start processing method, device and system. The method is used for the preceding start processing system, and the system comprises a video sensor and a gravity sensor. The method comprises the steps: collecting a vehicle head front image of a current vehicle through the video sensor, and collecting the acceleration information of the current vehicle through the gravity sensor; determining the driving state of the current vehicle based on the image features and/or acceleration information of the vehicle head front image; obtaining a movement track of a target object in the vehicle head front image when the current vehicle is in a driving state; judging whether to generate prompt information for reminding the current vehicle from starting according to the movement track of the target object. According to the invention, the method solves a problem that the start prompt of a preceding vehicle cannot be carried out accurately in a driving process in the prior art, and can timely remind the current vehicle of operating when a preceding vehicle of the current vehicle moves a certain distance.

The invention discloses an automatic sorting correction method based on shape characteristics for analytical processing of a flow cytometer. The method comprises the following steps: classifying and processing by an automatic sort algorithm to obtain a boundary curve of each cell according to scatter diagram data analyzed by flow cytometry; selecting a curve model equation to fit the boundary curves of each blood cell; computing standard curve parameters obtained by fitting; and comparing the standard parameters with pre-counted parameter empirical scope, judging whether the sort algorithm result is right and prompting. In the automatic sorting correction method based on the shape characteristics, a judgment mode of fitting curve parameters is adopted to compare with the pre-counted parameter empirical scope for correction, thus facilitating counting and graphic processing of cell detection data in the process of flow cell detection with more accurate processing result, and also facilitating the elimination and alarm processing of wrong detection results.

The invention belongs to the signal and information processing technology field and relates to a target detection method of combining a curvelet transformation theory, statistical parameter estimation, Wiener filtering and a synthetic aperture radar image characteristic. The method is characterized by through curvelet transformation decomposition, acquiring a plurality of decomposition scales; on each decomposition scale, providing different direction information and setting a direction of each scale; making an influence of noise reach a minimum through sampling filtering twice; taking a low false alarm probability to detect abundant target information. Compared to the prior art, by using the method, there are the following advantages that a defect that a small target, a weak scattering target and a hidden target is not easy to detect is effectively overcome through double filtering; an influence of speckle noise is reduced; a signal to noise ratio of a SAR image is increased; the curvelet transformation is selected to carry out processing on the SAR image so that a processing result is accurate; de-noise processing is performed on the image and enhancement processing is performed on a characteristic so that a detection rate of a target is increased; a false alarm probability of the target is low and a high anti-false-alarm capability is possessed.

The invention discloses a piece goods blemish detecting method based on morphological analysis. The piece goods blemish detecting method comprises the steps that a line-scan digital camera is used for conducting image collection on piece goods in motion, the distance of adjacent tissue points of the piece goods is obtained by conducting basic brightness stretching and binaryzation processing on gray level images of the piece goods, and the distance serves as the reference value of the size of structural elements; then, morphological close operation is conducted, filling is conducted on common weaving point gaps, the number of the weaving points at a blemish position is small and the weaving points at the blemish position are irregular, so that the weaving points stand out in brightness; at least, binaryzation processing is conducted on the images, determining of the threshold of the images is controlled through gamma training selecting of gray level power transform, blemished binary images are counted and marked, blemish information is output, and the blemish detecting process of the piece goods is completed. The piece goods blemish detecting method based on morphological analysis is more accurate and simpler in structure, and improves the calculation speed; can weaken the interference of background brightness unevenness; brings convenience to realization of automatic detection.

The invention provides an image processing method and a device. The method comprises a step of obtaining the edge intensity value of each edge direction of a pixel to be processed to determine the enhancement value of the pixel to be processed, wherein the enhancement value is associated with an edge intensity maximum value and a corresponding gain value, a step of determining the noise reduction value of the pixel to be processed based on at least one filtering result in a first pixel set and a second pixel set, wherein the first pixel set is the set of pixels on the minimum edge direction of the pixel to be processed in a preset area, and the second pixel set is the set of pixels on the orthogonal direction of the maximum edge direction of the pixel to be processed in the preset area, a step of combining the enhancement value of the pixel to be processed and the noise reduction value to determine the pixel value to the pixel to be processed after processing. The noise reduction and enhancement processing of an image can be realized at the same time, while the image edge is enhanced, the noise amplification is inhibited, the visual sensory quality of the image is effectively improved, and the method is simple.

The embodiment of the invention provides a method and a device for inter-process communication. The method applied to a second process specifically comprises the following steps: receiving a calling request from a first process, wherein the calling request comprises a unique identifier, the business information of the first process and a window handle of the first process; according to the unique identifier and the business information of the first process, processing the calling request to obtain a corresponding processing result; and sending a window message to the window handle of the first process, wherein the window message carries the processing result or the address information of the processing result. The embodiment of the invention can realize a purpose that the second process accurately processes the calling request under the situation that the first process is uniquely identified, and in addition, the first process can be guaranteed to receive the accurate processing result.

The invention discloses a Viterbi-BMC algorithm automatic speed analysis method. The method comprises the steps that under a certain t0, a short arrangement CMP gather is used; regular speed analysis is carried out to acquire an initial dynamic correction speed Vnmo; Vnmo and t0 are put into a non-hyperbolic time difference formula; large arrangement seismic data are scanned to acquire an initial eta value, and the eta value is put into the non-hyperbolic time difference formula; the large arrangement seismic data are scanned, and the dynamic correction speed Vnmo is redefined; repeated iteration is carried out on Vnmo and eta two to three times to acquire stable Vnmo and eta; a next t0 is scanned; a previous iteration process is carried out until all t0s are scanned; and a speed Vnmo spectrum and an eta spectrum are output. The Viterbi-BMC algorithm automatic speed analysis method provided by the invention has the advantages that the large arrangement seismic data are processed and a processing result is accurate.

A position-detecting device includes a body pressure distribution sensor including a plurality of pressure sensors that outputs pressure signals corresponding to pressure values respectively and are arranged in a matrix pattern, a specific pattern storage unit storing a range that pressure sensors can occupy and a condition of pressure values relating to the pressure sensors within the range as a specific pattern by sleeping posture and body part of a sleeper, a scanning unit applying the specific pattern to body pressure distribution information, which is a set of the pressure values, from a scan starting position to an adjacent position in sequence and to determine whether the condition is satisfied, and a body part position determining unit determining the position of the body part in the sleeping posture corresponding to the position of the specific pattern in the body pressure distribution information when the condition is satisfied.

The embodiment of the invention discloses an identification information association method and a device, electronic equipment and a storage medium. The identification information association method comprises the following steps: obtaining image identification information according to image information; obtaining identity identification information; and associating the image identification information meeting the preset matching rules with the identity identification information.

In view of the problem that speech recognition is of low recognition rate in big data analysis, the invention discloses a speech recognition system for big data analysis. The system comprises a speech information classification module, an original speech processing module, an acoustic feature recognition and extraction module, a basic metadata set, an acoustic feature matching module, a recognition error correction module, and a result output module. The ability of the computer to clean and correct data in each stage of speech recognition is improved, and the processing result of speech recognition is more accurate.

The invention provides an image processing method, a device, an electronic device and a computer-readable medium, which relate to the technical field of image analysis. The method comprises the following steps: obtaining an image to be processed, labeling a face of a target portrait in the image to be processed, and obtaining the face position information of the target portrait. Based on the faceposition information, the image to be processed is segmented to obtain the hair segmentation image of the target portrait. The hair attributes of the target portrait is analyzed by using the face position information, the image to be processed and the hair segmentation image to analyze, and the attribute analysis results are obtained. The image processing result determined by the method of the invention is more accurate, the classification accuracy is improved, and the technical problem that the existing method for directly classifying and processing the whole image is poor in accuracy and badin effect is alleviated.

The invention relates to a data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target object; obtaining to-be-processed data represented by a node graph according to the target object; obtaining target data according to the to-be-processed data represented by the node graph and a graph neural network, wherein the graph neural network is a trained graph neural network obtained by training the to-be-processed data represented by the node graph. By adopting the data processing method and device, a more accurate data processing result can be obtained.

The invention discloses a data processing method and device based on a graph convolutional neural network, equipment and a medium. The method comprises the steps of obtaining first graph structure data to be processed and inputting the first graph structure data to the pre-trained graph convolutional neural network; calling the graph convolutional neural network to perform neighbor node sampling operation and neighborhood information aggregation operation on each first node in the first graph structure data so as to update each first node; and calling the graph convolutional neural network toobtain a processing result of the first graph structure data based on the updated first nodes. According to the method, the difference of the relationship between the nodes is mined by the graph convolutional neural network, so that a more accurate processing result can be made according to mined more sufficient information.

The invention relates to the field of agricultural equipment and particularly discloses primary sorting and grading equipment for apples. The equipment comprises a feeding device, a conveying device,an image acquisation device and a grading device, wherein the image acquisition device comprises a camera obscura which is fixed on a belt conveyer rack; through holes through which a conveyer belt ofa belt conveyer penetrates are respectively formed in side walls on both sides of the camera obscura; a plurality of halogen lamps in annular direction and a plurality of first CCD (Charge Coupled Device) cameras are arranged on the top wall inside a camera obscura cavity; a plurality of first CCD cameras in annular distribution and second CCD cameras in a middle position are positioned on the top wall inside the camera obscura cavity; the grading device comprises an image acquisition card, a computer and a plurality of hydraulic cylinders for executing grading instructions of the computer; telescopic ends of the hydraulic cylinders all face to the conveyer belt direction of the belt conveyer; push plates for pushing out apples on the belt conveyer are fixedly distributed at the telescopic ends. By adopting the equipment, harvested apples can be rapidly and accurately graded, then additional values of the apples can be increased, and furthermore the market competitiveness can be improved.

The embodiment of the invention provides a mineral resource evaluation method suitable for an ecological region. The method comprises the steps of collecting mineral resource data of a target area, wherein the mineral resource data comprises at least one of the following: at least one of: mineral resource data; surrounding environment geological survey data of the mineral resource development areaand the survey area; mineral resource planning, green mining industry development planning, regional construction planning data, mineral development and economic benefit files of corresponding years,national economy and social development report data, local national economy reform and development, annual statistics annual investigation and annual investigation data of population, energy, territory resources and environment; Screening the collected historical geological data to determine a mineral resource evaluation index system; and performing background evaluation of mineral resource quantity and state evaluation of mineral development scale and strength according to the evaluation indexes.

Provided is a building structure safety monitoring system. The building structure safety monitoring system is characterized in that the building structure safety monitoring system is composed of a stress collecting sensor and a data analysis module; the stress data of a building structure are collected through the stress collecting sensor, and the collected data are transmitted to the data analysis module; an RTI curve, an MSI curve and an AEMA curve are respectively drawn through the data analysis module according to the data collected by the stress collecting sensor, and safety monitoring is carried out on the building structure through the data analysis module for a short period, a medium period and a long period respectively according to the RTI curve, the MSI curve and the AEMA curve. The building structure safety monitoring system has the advantages that a structural state can be analyzed only according to the data, various assumed conditions and models are not needed, complex and changeable factors do not need to be considered, and a processing result is relatively accurate and objective.

The invention discloses a distributed intelligent monitoring method and device; the method comprises the steps: obtaining first intelligent monitoring information; analyzing to obtain a first analysis result; matching a first distributed processing unit; inputting the first analysis result into a first distributed processing unit to obtain a first output result; judging whether the first output result meets a first preset abnormal threshold value or not; obtaining a first time interval when the condition is satisfied; collecting wave recording signals in a first time interval; inputting the first analysis result and the recording signal into a first expert system to obtain a second output result; judging whether the second output result has a first abnormal signal or not; and when the first abnormal signal position is subjected to power-off processing, generating a first abnormal log. The invention solves the technical problem that in the prior art, accurate and reliable distributed monitoring cannot be carried out on a complex circuit environment, so that a nodal circuit fault can be evolved into a regional circuit fault, and the influence range of a fault circuit is further expanded.

The embodiment of the invention discloses an image processing method, and the method comprises the steps: determining a first sub-image in a first image based on fixation point information, wherein the first image comprises the first sub-image and a second sub-image except the first sub-image; extracting first subimage information corresponding to the first subimage, the first subimage informationcomprising a first subimage position and a first subimage feature parameter; based on the first sub-image information, processing the first sub-image according to a first processing strategy and processing a second sub-image according to a second processing strategy to obtain a second image; extracting a target parameter corresponding to the second image; and processing the second image accordingto a third processing strategy based on the target parameter to obtain a third image. According to the embodiment of the invention, different regions in the image are differentially processed, so that the high efficiency of the image processing method is improved, and the power consumption in the image processing process is reduced.

The invention relates to a method utilizing the deep packet detection technology to carry out P2P network identification. According to the method, connection characteristics are firstly determined, a pertinent identification mode is further carried out, network identification modes under different conditions are effectively identified, and an identification result can be conveniently acquired, moreover, DPI processing is realized through a finite-state machine formed by a regular expression, searching is carried out through utilizing a binary tree, searching efficiency is improved, and a precise processing result is acquired. Through the method, the identification result in flow identification can be more accurately and rapidly acquired, the identification flow is greatly optimized, and the method can be more easily realized in equipment in the prior art.