50results about How to "Strengthen excavation" patented technology

The invention discloses a multi-dimensionality content labeling method based on a semaneteme label database. The multi-dimensionality content labeling method based on the semaneteme label database comprises the steps of establishing the semaneteme label database, configuring extensible resource types, establishing a multi-level configurable content labeling dimensionality, dividing dimensionalities of resources according to content features to establish a multi-level content dimensionality, establishing corresponding relationship between the configurable and revisable resource types and the content labeling dimensionality, conducting resource content labeling based on the semaneteme label database, processing temporary labels, conducting resource search based on the semaneteme label database, inputting index words by a user, and conducting automatic match in the extensible database by the system. If the match is successful, the system searches corresponding images according to corresponding label labeling codes. If the match is unsuccessful, the system can match the index words with resource description information, and at the same time the system saves the index words into a temporary label database. Precision and efficiency of resource labeling are effectively improved, and good base is founded for resource search and data analysis.

The invention belongs to the technical field of computers, and discloses an intelligent question-answer system and an analysis method. The analysis method comprises the following steps of: collectingrelated questions of a data set spring and analyzing the questions; after data analysis, carrying out preprocessing such as word segmentation and keyword extraction on the data set; expressing relevancy between the questions and answers by using an association rule; automatically searching answers in a database according to answers of users; and expressing relevancy between questions and relevancybetween answers. The intelligent question-answer system comprises a crawler unit, a word segmentation unit, a keyword extraction unit, a semantic similarity detection unit, a cluster unit and an association rule unit. According to the system and the method, intelligent question-answer of various forms of questions is supported, so that convenience is brought to users, the work efficiency is improved and the corporate image is enhanced.

The invention relates to an urban venous industrial park. Municipal solid waste is divided into old home appliances, construction waste, household waste, food waste, sludge, faeces and other biomass waste by a household waste separating system; the household waste is divided into dry waste and wet waste by a dry and wet separation device; the old home appliances, the construction waste and the dry waste are treated by a fossil waste processing system so as to form glass, plastic, metal, refuse derived fuel and recycled materials, and additionally, the fossil waste processing system is respectively connected with a generating system and a heating system; and the wet waste, the food waste, the sludge, the faeces and the other biomass waste are treated by a biomass waste processing system so as to form recycled water, biological carbon soil, methane gas and carbon dioxide. According to the urban venous industrial park, a resource-product-renewable resource closed-loop mode is realized, and the waste is changed into valuables and is recycled.

The invention provides a three-dimensional point cloud data instance segmentation method and system in an automatic driving scene. The method comprises steps of carrying out the preliminary recognition and division of an outdoor street scene through the spatial position information of a target object, and forming a point cloud visual column of an interested region; visual column point clouds containing objects and negative sample visual column background point clouds distributed in the same way are extracted from the point cloud visual columns of the region of interest to form a visual columnpoint cloud data set; and extracting high-dimensional semantic feature information of an object contained in each visual column point cloud in the visual column point cloud data set, and meanwhile, introducing a multi-classification focus loss function with a weight to obtain a category to which each point cloud in the visual column belongs, thereby realizing instance segmentation of the point cloud data. According to the three-dimensional point cloud data instance segmentation method in the automatic driving scene, target detail feature expression can be effectively enhanced so that the prediction capability of point cloud difficult samples can be enhanced and the performance of point cloud instance segmentation in the automatic driving scene can be enhanced.

The invention discloses a cross-modal Hash retrieval method based on deep learning. Assuming that the pixel feature vector set of the image modality of n objects is shown in the specification, the method is characterized by comprising the following steps: (1) obtaining binary Hash codes B shared by an image mode and a text mode, deep neural network parameters theta gamma and theta i of the image mode and the text mode, and projection matrixes Pv and Pi of the image mode and the text mode by using an objective function designed based on a deep learning technology; (2) solving unknown variablesB, theta gamma, theta i, Pv and Pi in the objective function in an alternative updating mode; (3) based on the obtained deep neural network parameters theta gamma and theta i of the image modality andthe text modality, and the projection matrixes Pv and Pi, generating the binary Hash codes; (4) calculating a Hamming distance from the query sample to each sample in the retrieval sample set based on the generated binary hash code; and (5) finishing retrieval of the query sample by using a cross-mode retriever based on approximate nearest neighbor search. According to the method, the cross-modalHash retrieval performance is effectively improved.

The invention provides a water conservancy portal information recommendation method based on a multi-layer attention mechanism and fused with a map. For the characteristics of collected water conservancy information data, Doc2vec and clustering are utilized to enrich a feature set; when a recommendation model is constructed, firstly, a feature-level attention mechanism is formed by means of softtention to fuse all features, meanwhile, a water conservancy information graph is constructed, and after potential interests of a user are mined, a final representation vector of each piece of water conservancy information is formed; then, a behavior-level attention mechanism is formed by using a self-attention with a position code to generate a representation vector of a user behavior; a representation vector of the interest of the user is generated by means of soft-attention; and finally, the probability that the user clicks the water conservancy information is calculated by using a multi-layer perceptron to generate a final TOP-N recommendation list. According to the method, the problem that personnel engaged in the water conservancy industry cannot read interested water conservancy information in a one-stop mode can be solved, and recommendation can be more accurate and higher in interpretability by means of a multi-layer attention mechanism and atlas.

The invention relates to a variance-based firewall abnormal log detection method. The variance-based firewall abnormal log detection method comprises the following steps: (1) extracting a log set of all time values in a time range n from a firewall log according to the time range n; (2) classifying all different values of an analysis target A in the log set involved in the step (1) to generate a set {a1, a2, a3 ellipsis am} according to the analysis target A, wherein A is one of the four parameters, i.e., an IP (Internet Protocol), a source port, a destination IP address and a destination port; (3) extracting an element from the set {a1, a2, a3 ellipsis am}, counting in the unit of day according to the log set involved in the step (1) to obtain the frequency of occurrence of the element every day: x1, x2, x3 ellipsis xn; (4) performing analysis of variance on the x1, x2, x3 ellipsis xn and performing alarm processing if a variance value exceeds a variance threshold; (5) repeating the steps (3) and (4) until each element in the set {a1, a2, a3 ellipsis am} is processed. The variance-based firewall abnormal log detection method provides favorable technical support for a security management system and a network monitoring analysis system.

The invention provides an agricultural product safety evaluation method and system based on risk entropy, and the method comprises the steps: constructing a monitoring matrix, and preprocessing the monitoring matrix to obtain a sample matrix; constructing a judgment matrix, and converting the sample matrix into a risk matrix based on the judgment matrix; splitting the risk matrix into a pluralityof sub-matrixes according to varieties, converting each sub-matrix into a risk entropy vector of each variety according to a risk entropy method, and longitudinally splicing the risk entropy vectors of all the varieties to obtain a risk entropy matrix; calculating the weight of each safety index based on the risk entropy matrix; and calculating a safety evaluation index of each variety based on the risk entropy vector of each variety and the weight of each safety index, thereby performing safety evaluation on the agricultural product based on the safety evaluation index of each variety of theagricultural product. The agricultural product safety risk assessment key technical problems of multi-source risk monitoring data fusion, high sparsity monitoring data conversion and utilization, macroscopic quantitative assessment and risk sorting can be effectively solved.

The invention relates to a drum-type continuous excavator, which comprises a power system, a control system and an excavation system, wherein the excavation system comprises a bucket. The invention is characterized in that the bucket is a drum-type bucket, the oblique lower part of the drum-type bucket is connected with the low end of a chain-type closed-loop loading system formed by a driving chain wheel installed at the upper part of a frame, a guide chain wheel installed at the lower part of the frame and a plurality of loading buckets and chains. The invention has the advantages that the continuous excavation can be realized, the operating speed is fast and the work efficiency is high. The drum-type continuous excavator is particularly suitable for construction in sandy soil fields and muddy fields, removal and transportation of construction wastes on construction sites and the like.

The invention provides an image processing method and device, a computer storage medium and electronic equipment, and relates to the field of artificial intelligence. The method comprises the steps ofobtaining an original image, and performing feature extraction on the original image to obtain a feature map corresponding to the original image; performing first pooling processing on the feature map to obtain feature vectors with different spatial scales corresponding to the feature map; and performing second pooling processing on the feature vectors with different spatial scales, performing fusion and normalization processing on the feature vectors with different spatial scales to obtain an image fingerprint corresponding to the original image, and determining a similar image correspondingto the original image according to the image fingerprint. According to the method of the invention, while the global information description of the image is considered, the mining of the local features of the image is enhanced, the key information of the image is better understood, the core content of the image is mined in a data driving mode, the noise content is suppressed, and the image processing efficiency and precision and the image matching accuracy are improved.

The invention discloses a traditional Chinese medicine intelligent diagnosis and treatment auxiliary system and relates to the technical field of computers. A data acquisition device is used for crawling target data from a traditional Chinese medicine website, anda deep learning device is used for performing fusion deep learning on the target data according to a preset knowledge graph relationship, generating triple data, storing the triple data into a graph database, generating mapping information, and storing the mapping information into a relational database; and a terminal is used for searching the mapping information of the corresponding disease from the relational database according to the search instruction, and calling and displaying the associated triple data. The system can realize auxiliary decision making of traditional Chinese medicine diagnosis and treatment, improve the accuracy and efficiency of traditional Chinese medicine diagnosis and treatment, improve the sharing, accumulation and mining of traditional Chinese medicine knowledge and experience, assist clinical front-line doctors in intelligent diagnosis and treatment, reduce the misjudgment rate, better analyze disease phenomena, integrally understand the disease phenomena and search disease nodes for the system, search disease nodes, accurately analyze the corresponding relation between the medicine and the disease phenomenon and improving the scientific and practical basis, and have important valuein improving scientific and practical basis.

The invention relates to a national defense mobilization information system. The national defense mobilization information system comprises a data processing center, a guarantee situation plotting module and an electronic map, wherein the data processing center comprises a central processing unit, a data storage module, a map measurement and calculation module, a data analysis module and a data exchange interface module; the central processing unit is connected with the data storage module and the map measurement and calculation module respectively; the map measurement and calculation module can achieve distance, elevation and area measurement and calculation on the electronic map; the data analysis module and the data exchange interface module are connected with the map measurement and calculation module respectively; the guarantee situation plotting module comprises a data engine, a Beidou engine and a communication module; the communication module is used for receiving video signaldata and positioning data of each peripheral monitoring device and a Beidou respectively, and visually plotting into the electronic map through the data engine. The national defense mobilization information system can accurately and efficiently acquire monitoring and positioning data and improve the mobilization efficiency, and the data are relatively high in mining and analysis values and are relatively strong in pertinence.

The invention discloses a biological organic bacterial fertilizer. The biological organic bacterial fertilizer is composed of, by weight, 1000 parts of a fungi bag, 5 to 10 parts of an activator, 1 to 2 parts of zymocyte, 5 to 15 parts of trace element, and 10 to 30 parts of seaweed liquid. The invention also discloses a production method of the biological organic bacterial fertilizer. The production method comprises following steps: 1, waste fungi bag is unpacked and smashed into powder with a fineness of 220 meshes or higher, the activator, seaweed liquid, trace element, and zymocyte are added successively with stirring until uniform mixing is realized and water content is 40% or higher; 2, an obtained mixture is subjected to stirring continuously for 24h so as to control the temperature of the mixture to be 80 DEG C or lower; 3, the mixture is sealed, and is stored at a dark environment for 140 to 160h so as to obtain the biological organic bacterial fertilizer with bacteria unit content of two billion. The formula and the production method are simple, and the biological organic bacterial fertilizer is extremely suitable for large scale popularization and application.

The invention discloses a test flight data file automatic distribution method, and aims to search a more effective test flight data file distribution realization scheme, and the method comprises the following steps: obtaining a test flight sortie report based on a test flight sortie and a test point; reading a test flight data file according to the test flight frame number; confirming a test flight subject through the test point number, and searching a test parameter list in a test flight outline database corresponding to the test flight subject; and intercepting the test flight data file by using the test parameter list, the test point execution starting time and the test point execution ending time to obtain intercepted test flight data, packaging the intercepted test flight data, and storing the packaged test flight data in a test flight subpackage result cache database. Through the associated test flight data, test flight sortie report and test flight outline, the sub-packaging andunpackaging processes of all test flight data are realized at the server side, so that a large amount of useless data is filtered before distribution, and the communication efficiency is improved.

The invention discloses an SEM (Search Engine Marketing) putting data classification system. The system comprises a database module, a data putting simulation module, a feature extraction module, a feature processing module, a classification module and a marking module, wherein the database module is used for storing putting data information; the data putting simulation module is used for simulating a data putting process and collecting putting feedback information; the feature extraction module is used for performing feature extraction in the collected feedback information; the feature processing module is used for performing association processing on extracted features; the classification module is used for classifying data according to the processed features; and the marking module is used for marking the classified data and establishing a classification mapping relation. With the SEM putting data classification system, the defect in the prior art can be overcome, and the accuracy rate and speed of data classification are improved.

The invention provides an attention contribution degree-based entity relationship extraction method, which comprises the following steps of: performing word segmentation on an original sentence in a data set through spaCy to obtain a word list, and storing the word list and a label into an input data set D in a dictionary form; sampling the input data set D to obtain an entity sample set and a relation sample set of each sentence in the input data set D; a BERT model pre-trained on a large-scale biomedical corpus, a judicial database and a tourism database is selected, interaction information between entities is calculated through an attention contribution degree algorithm, then the interaction information is transmitted to downstream entity extraction and relation extraction tasks, and a span-based entity relation extraction model is formed; and finally, putting the entity sample set and the relationship sample set into a span-based entity relationship extraction model for training, thereby greatly improving the F1 value of an entity extraction task and the F1 value of a relationship extraction task.

The embodiment of the invention discloses a data processing method and device. The method comprises the steps of receiving collection information of candidate wireless communication points which are collected by a candidate terminal and located around the candidate terminal, wherein the collection information comprises collection time; in the candidate wireless communication points, identifying atarget wireless communication point of which the acquisition time is within the same preset time period and information is not acquired before the preset time period; and if the acquisition frequencyof the acquisition information of the target wireless communication point is greater than a preset threshold after the preset time period, determining attribute information of a target point of interest POI corresponding to the target wireless communication point based on the acquisition information of the target wireless communication point. According to the invention, the mining comprehensiveness, mining accuracy and mining timeliness of online POIs can be improved.

There is provided a telescoping boom system for an excavating apparatus which includes a linkage member having a pivot segment and lever segment pivoted about a rotating fixed boom. A first hydraulic cylinder is coupled from the fixed boom to the pivot segment. A second hydraulic cylinder is coupled from the lever segment to a retractable outer boom. The first hydraulic cylinder extends to rotate the pivot segment away from a proximate end of the fixed boom, thus forcing the lever segment to rotate towards the proximate end of the fixed boom. The second hydraulic cylinder retracts and combines with the extension force of the first hydraulic cylinder across the linkage member to provide a retraction force to the outer boom.

The invention discloses a supervised commodity intelligent recommendation method and system in bulk commodity transaction. The method comprises four parts: data processing of bulk commodity transaction data and market data, market data associated commodity discovery, transaction data associated commodity discovery, and construction of an associated commodity set for intelligent recommendation and supervision of commodities. According to the invention, the problem that the commodity supervision range is fuzzy and uncertain in bulk commodity transaction supervision is solved. Firstly, data preprocessing operation is performed on historical market information and transaction information of commodities; secondly, the correlation of different commodity market data (price fluctuation and the like) is analyzed to obtain price fluctuation related commodities; then, correlation of different commodity transaction data (transaction behaviors and the like) is analyzed, and transaction event related commodities are obtained; and finally, the associated commodities are formed through integration of the two steps, finally, an associated commodity set is constructed, and intelligent recommendation is performed on the supervised commodities according to the supervised task information.

The embodiment of the invention discloses a knowledge graph updating method, device and equipment and a computer readable storage medium, which can be applied to scenes such as cloud technology. To-be-reasoned entities can be obtained; performing entity relationship reasoning on the to-be-reasoned entity through the first target sub-model to obtain an entity relationship sub-graph between the to-be-reasoned entity and the result entity; path relation matching is conducted on the entity relation sub-graph through a second target sub-model, a matching score set is obtained, and the matching score set comprises matching scores of the path relation between each result entity and the to-be-reasoned entity; determining a target matching score with the maximum score from the matching score set, and determining a target result entity corresponding to the to-be-reasoned entity according to the target matching score; and establishing a target entity relationship between the to-be-reasoned entity and the target result entity, and updating the target entity relationship to the knowledge graph. Therefore, the matching degree when new knowledge entities are mined is improved, a large number of knowledge entity samples are not needed, and the accuracy of the knowledge graph is ensured.