206 results about "Information science" patented technology

The invention provides a method for assimilating remote sensing data of soil humidity in a watershed scale. The method comprises the following steps of: improving a watershed runoff producing calculation module and developing a distributed hydrological model which is suitable for assimilating remote sensing soil humidity information and describes a soil hydrodynamic process; introducing a particle filtering sequence data assimilation method of information science, and continuously merging and assimilating new remote sensing observation data in a dynamic operation process of distributed hydrological process numerical simulation so as to acquire updated watershed soil humidity assimilated data during sequential assimilation; feeding the updated watershed soil humidity assimilated data back to a distributed hydrological model platform; and gradually estimating the time and space distribution pattern of watershed soil moisture content. Practices prove that by the method, not only high-precision and physically consistent watershed soil humidity data can be provided for research on hydrology, zoology, environment and agriculture, but also the foundation is laid for performing four-dimensional data assimilation processing on soil humidity data of an upper soil layer acquired by using remote sensing retrieval, and improving the precision of the model.

The invention discloses an automatic analysis method of a tongue color and a coating color in traditional Chinese medicine based on image retrieval, and belongs to the field of interdiscipline of information science and traditional Chinese medicine. In view of the characteristics that a result of tongue diagnosis in traditional Chinese medicine varies with different individuals, has strong subjectivity and lacks of normalization and standardization, the automatic analysis method of the tongue color and the coating color in the traditional Chinese medicine based on the image retrieval is designed. The automatic analysis method is characterized by comprising the following steps of inputting a tongue image to be analyzed by a user, firstly, segmenting a tongue body region through a tongue body segmentation technology, carrying out regional division on a tongue surface, distinguishing a tongue nature region and a coated tongue region, distinguishing a visual feature of the image in each region so as to form a feature vector, secondly, computing similarity of the feature vector and the feature of a tongue picture of a marked tongue image in a feature library, returning a most similar image as a retrieved result, and lastly, judging the retrieved result by utilizing a method of statistical decision, and respectively giving classification suggestions to the tongue color and the coating color in the tongue picture. According to the automatic analysis method, a classification result is more accurate and has a certain practical value.

The invention discloses an intelligent monitoring system for elevator safe operation based on IOT (Internet of Things) technology. The intelligent monitoring system is mainly designed for government regulation departments, maintenance units, manufacturers, property management companies and the like, and provides the above with a set of intelligent, platform-based total management system which supports wireless access and remote access, so that safe operation, management, maintenance and detection of an elevator can be more effectively realized. While daily needs are met, monitoring on the elevator is more normalized, institutionalized and scientized, so that elevator accidents are further reduced, and speed and quality for solving problems after an accident occurs are improved; by utilizing information for driving, the informatization management level of the intelligent monitoring system is improved through advanced and applicable technology and exploitation and construction pattern innovation by comprehensively utilizing information science and technology as well as decision making and management methods, and an emergency response system of elevator safety is favorably established.

The invention belongs to the technical field of zoology and remote sensing information science and relates to a distributed remote sensing data organization query method based on the NoSQL database. The method includes the steps: preprocessing an original remote sensing data file, and outputting an initial remote sensing image file according to an appointed format, extracting data of each pixel and data element information in the initial remote sensing image file; reconstructing the extracted data of each pixel and data element information as unstructured text data; storing the unstructured text data in the distributed NoSQL database, and setting up query indexes; and returning corresponding remote sensing data to users according to query conditions inputted by the users, and regenerating image files. The distributed remote sensing data organization query method based on the NoSQL database can be used for effectively storing and managing massive remote sensing data and can be applied to correlated fields of remote sensing ecological monitoring, species distribution forecast, remote sensing data inversion and the like.

The invention belongs to biology technology field, which concretely refers to controlled release tissue factor path inhibitor (LTFPI) and the manufacturing method, and the application. The invention analyzes and experiments the biology information science and structure molecular biology science of tissue factor path inhibitor (TFPI) and its acceptor low density lipoprotein acceptor correspondent protein (LRP), it ascertains the part where the TFPI carboxy end combines with LRP and is eliminated. It designs TFPI carboxy end mutant, which is recombined with primary nucleus or eukarya expressing carrier after constructing LTFPI gene through PCR location, it converts bacillus coli or bici yeast, sifts the high expression project fungus. The primary project fungus are yeasted and expanded, crushed, then they are centrifugated and collects the inclusion body, purifies the LTFPI through molecular sift and ion interchanging two-step method; the eukarya project fungus are carried on with two-step purifying directly. The half-life are prolonged, it has good pour-depressant function.

The invention belongs to the field of alternating-current motor control teaching instruments, and relates to the teaching of an advanced control algorithm of a permanent magnet synchronous motor and an asynchronous motor aiming at undergraduate students or postgraduate students, aiming to provide an alternating-current motor experiment teaching platform which is designed for undergraduate students or postgraduate students of the relevant majors, such as electrical engineering and automation of electrical and information science and technology, mechanical and electrical integration of colleges and universities of engineering courses. The teaching platform is tightly matched with teaching contents, can deepen the understanding of an alternating-current control theory and method for students, and simultaneously the operation ability of the students is strengthened. The experiment system comprises a power supply conversion module, a central control module, a power driving module, a control parameter set module, a control algorithm selection module, a parameter measuring and display module, a motor loading module, an upper computer module, a serial port, a data acquisition card, a hall sensor, a photoelectric encoder and a motor.

The invention relates to an Android unknown malicious software detection method based on dynamic behavior characteristics and belongs to the technical field of computers and information science. The method comprises the steps that to-be-detected software is input into a system; the system uncompresses and decompiles a software package and extracts static characteristics in a result file; at the same time, the system operates the software package in an Android simulator and monitors dynamic behavior of the software by using a behavior monitoring system based on an LKM (Loadable Kernel Module); the dynamic behavior is recorded in logs and the dynamic behavior characteristics of the software are extracted from the logs; the extracted dynamic and static characteristics are subjected to normalization processing and the results are input into a trained classification algorithm classification detection module; the module can automatically judge whether the detected software is malicious software or not according to the input dynamic and static characteristic data. The method has high detection efficiency and accuracy and can be applied to security detection of software platforms such as the Android application market.

The invention relates to a big data-based collage students' psychological behavior abnormal change system scheme design method. According to the method, multidisciplinary theories such as system science, information science, medicine and psychology are comprehensively applied; based on multi-source heterogeneous information fusion, virtual-reality space psychological behavioral representation information is pooled; by means of big data association analysis, a mapping relationship model between college students' psychological behaviors and behavioral representation information is established; through pooling smart campus big data, appropriate image processing, text mining, deep learning, incremental learning and other algorithms are selected to mine and establish a psychological behavior abnormal change monitoring and early warning model; and based on a Hadoop and Tensorflow big data analysis platform, college students' psychological behavior abnormal change monitoring and early warningsoftware is researched and developed. Compared with an existing system, the system of the invention scientifically detects individual students with psychological behavior abnormal changes, and has the advantages of high accuracy, good dynamic performance and the like.

The invention discloses a sci-tech expert information aggregation method based on a semantic relation of knowledge organization, and relates to the technical fields of information science and knowledge engineering. The method provided by the invention has the advantages that the semantic aggregation, automatic discovery and dynamic updating of expert information are realized in a semantic relation framework of knowledge organization tools by semantically associating expert information with the knowledge organization tools, and the selection requirements for expert academic authority, professional correlation, research activity, dynamic update and peer avoidance are met, so that the experts can be selected more objectively and accurately.

The invention relates to a firewall rule set optimization method based on rule matching hit rate and distribution variance, belonging to the technical field of computer and information science. The method comprises the following steps: firstly preprocessing a firewall initialization rule set, wherein content of preprocessing comprises rule set exception detection, exception processing and rule combination, and obtaining a simplest firewall rule set without exceptional rules after preprocessing; then, collecting firewall logs of a period of time in real time, calculating rule weight according to the firewall log information, wherein rule weight calculation is divided into three parts including rule hit rate frequency statistic, rule hit time distribution statistic and a rule weight value calculated according to the two statistic data; finally, according to the calculated rule weight value, reordering the firewall rule set, and placing the rules with relatively higher weight values at positions with higher priority. Compared with the conventional firewall rule set optimization method, the method provided by the invention has better optimization effect, can enable a firewall to keep relatively higher data packet filtering rate and has relatively excellent flexibility and transportability.

The invention relates to an Android malicious behavior dynamic detection method based on binary dynamic instrumentation, and belongs to the technical field of computer and information science. The method comprises the following steps: firstly, triggering all potential malicious behaviors of tested software through an Android dynamic detection framework; then, through a dynamic binary instrumentation technology, constructing a calling sequence of a program to a system API, using an N-Gram model to extract call timing relationship characteristics of a function; finally, inputting the generated time sequence relation characteristics into a trained GBDT (Gradient Boosting Decision Tree, Gradient Boosting Decision Tree) multi-classification algorithm detection model, identifying malicious software, and carrying out fine-grained classification on malicious behaviors of the software. According to the invention, a dynamic binary instrumentation technology is used.A system function calling timesequence feature of the software is extracted without knowing a program source code. Compared with the prior art, the Android malicious behavior detection method has high accuracy for Android malicious behavior detection, malicious behaviors of the software can be divided into six classes. More detailed detection conclusion granularity is achieved, and the detection efficiency of the Android malicious software is effectively improved.

The invention relates to a sentiment word extension-based short text sentiment classifying method, and belongs to the technical field of computers and information science. The sentiment word extension-based short text sentiment classifying method comprises the following steps: first, segmenting a comment text into a sentence set, and dividing words and labelling parts of speech by utilizing a jieba word dividing tool to obtain a pre-processed result; then, aiming at each short text comment, acquiring the word vector of each word by using Wikipedia corpus training Glove, calculating the semantic similarity of other words and the primary sentiment features with the parts of speech of N, V, Adj and Adv by utilizing the word vectors, expanding the words with similar semantics to a primary sentiment feature set; next, proposing DF-TF-MI, performing feature dimension reduction by improving a conventional feature dimension reduction method by utilizing interlexical statistical features to obtain a low-dimension feature set, and weighting the sentiment features; finally, performing sentiment tendency classification on the obtained feature vectors through an RADA algorithm formed by weak classifier weighting. According to the sentiment word extension-based short text sentiment classifying method, the problem that unregistered words exit in a sentiment dictionary is solved, meanwhile, the problem of sparse sentiment features caused by few effective sentiment words of the short text comment is solved, and the performance and accuracy of sentiment tendency analysis are improved.

The invention discloses a traditional Chinese medicine constitution optimized classification method based on an improved CART decision-making tree and a fuzzy naive Bayes combined model, and belongs to the field of engineering technology mixing and merging traditional Chinese medicine constitution science and information science technology. According to the method, a multi-dimensional human body face skin index system is built; a quite highly related index set is screened out; and according to different contribution degrees of each index, the original attribute is remained in a focused way so that redundant attributes are removed. By aiming at the classification requirement of needing to consider the interpretability and the accuracy at the same time, the advantages of the good induction characteristic of the decision-making tree on the attributes and the inter-class relationship under the condition of few training samples and of highest accuracy of the naive Bayes classification under the condition of many training samples are brought into full play; the traditional Chinese medicine constitution optimized classification method can be used for quantificationally studying the potential relationship between the skin and the traditional Chinese medicine constitution, and provides a certain scientific basis, data and a method support for the health care, conditioning and treatment of the skin.

The invention relates to an interior threat figure risk prediction method based on personal behavior sequential characteristics, and belongs to the technical field of computer and information science.The method comprises the steps: carrying out the preprocessing and feature extraction of the accumulative historical behavior information of a figure, which comprises the quantitative sampling of figure information, pre-emphasis and windowing, and the extraction of the multi-domain heterogeneous figure data into corresponding digital characteristics; carrying out the synthesis training of an interior threat figure risk prediction model, and constructing an interior threat figure risk prediction model based on LSTM; and finally carrying out the risk prediction, estimation and early warning ofthe interior threat figure risk prediction model based on LSTM. Compared with other common methods, the method is higher in accuracy, provides a basis for the quantification of the interior threat foran enterprise, and provides a layered interior threat figure early-warning mode.

The invention discloses a land use change modeling method and system implemented in combination with spatial filtering and relates to the field of geographical information sciences. The method focuses on eliminating the spatial autocorrelation influence of data in the process of land use change modeling, and comprises the following steps: collecting original data, and preprocessing the original data so as to generate a data set sequence; determining the optimum distance of spatial filtering by using a variation function, and then splitting variable factors by using a Getis principle based a spatial filtering method; constructing a logistic regression model according to a filtered sample, and assessing the model by using methods such as fitting accuracies and ROC (receiver operating characteristic) curves; and finally, providing a Markov chain and a customized change pattern for forecasting the trend of land use change. According to the invention, based on a statistics mathematical model, a spatial filtering mode is adopted for making up the shortcomings and defects of the mathematical model in geo-spatial calculation, so that through the combination of the two, the fitting accuracy of the model is improved, and the model is more conform to the time and space characteristics of land use change.

The invention discloses a multi-AP adaptive switching method based on a Markov model. The method comprises the following steps: 1) collecting a real-time passenger flow volume, predicating the passenger flow volume in combination with a BP neural network and the Markov state model, and rolling for predication in real time; and 2) establishing an offline query table and a control query table through a membership function, and carrying out fuzzy control and real-time site control on AP allocation to realize multi-AP adaptive switch. The multi-AP adaptive switching method based on the Markov model disclosed by the invention has the beneficial effects that: the multi-AP adaptive switch is a complicated system project that involves information science, geographical science, control science and system science and the like, the Markov model is introduced in the method to establish a passenger flow volume prediction model, and the AP having the least time consumption and the best quality of service is automatically switched in combination with the intelligent scheduling technology, a strict mathematical model is established, and senseless internet experience of users is realized.

The invention relates to a network security technology competition dynamic FLAG management method, relates to network security technology competition, and belongs to the technical field of computer and information science. According to the method, a network security technology competition system is configured and managed through a virtualized cloud platform, virtual machines needed by competition are built in a cloud platform environment to deploy a competition management system and examination questions, and a virtual network is constructed for competition as needed for competitors to access and answer questions. On the basis, serial interface communication equipment is added to a virtual machine to realize a special data interaction interface between the virtual machine and a real machine, communication between a question virtual machine and a management virtual machine is realized via the interface under the condition of not affecting the data flow of the competition virtual network, and thus, dynamic flag update and configuration is realized.

The invention relates to the field of geographic information science, in particular to a boundary interaction-based area combination topological similarity measurement method. The method comprises the following steps of obtaining a boundary interaction record of complex entities by analyzing a topological relation between the complex entities based on a topological relation of area boundary interaction, and converting the interaction record into a binary sequence, thereby converting similarity assessment into a sequence calibration process; and representing a reference area combination and a candidate area combination by using CR and CP respectively, and then calculating topological similarity between the reference area combination CR and the candidate area combination CP by setting a weight of the similarity between a dimension sequence and a detail sequence. The method has the following advantages: 1), the topological relation between composite space objects, and the topological similarity difference can be well distinguished; and 2), various possible results are provided for users more visually.

The invention relates to an Android malware detection method based on depth learning, belonging to the field of computer and information science and technology. The invention firstly extracts featuresof Android application software, and then extracts relevant security features by decompressing and decompressing Android application files. The extracted features include three aspects: file structure feature, security experience feature and N-Gram statistic characteristic. Then the extracted features are numerically processed to construct feature vectors. Finally, a DNN (Deep Neural Network) model is constructed based on the above extracted features. The new Android software is classified and identified by the constructed model. This method combines the analysis of instruction set and has the function of anti-malware confusion. At the same time, malware detection based on depth model can enhance the feature learning, can express the abundant information of big data, and can adapt to theevolving malware more easily.

The invention relates to a method for hiding mass information by using a remote sensing image and belongs to the field of information hiding in pattern recognition and data encryption of information sciences. The method is characterized in that mass confidential files are stored in a remote sensing image file to have strong concealment; data is only stored in characteristic areas of the image file; and the hidden data is subjected to random encryption in advance and the encryption sites of an encrypted image still reserve the characteristics of an original image, so that the encrypted image has little appearance change to avoid being identified and decrypted and provide a security guarantee for the storage and transmission of private information. In addition, the invention also provides a method for extracting remote sensing image hidden information based on the method for hiding the mass information, which can effectively extract the hidden information and is simple and easy to implement.

The invention relates to the field of information science, and provides a detection method based on a heterogeneous social network, which maps the heterogeneous social network into a multidimensional matrix; determines the transition probability of the nodes and the transition probability of the relationship in the multidimensional matrix; uses random walk algorithm to obtain the balanced distribution of nodes and the balanced distribution of relationships; according to the balanced distribution of nodes and the balanced distribution of relationships, a weighted single-relationship social network is obtained; and using a single-relationship social network detection algorithm, based on the weighted single-relationship social network to obtain detection results of heterogeneous social networks. The present invention makes full use of the mutual influence of nodes and relationships in the heterogeneous social network, and fuses the heterogeneous network into a single-relationship social network with weights, and then utilizes the traditional single-relationship social network community detection method to Weighted single-relational networks for community detection.