A classification method of scientific and technological intelligence based on big data

Abstract

The present application proposes a method for classifying scientific and technological intelligence based on big data, which belongs to the field of information technology. The method refines the granularity of the initial information, extracts the first detection information with a first probability, obtains the information granularity of the initial information based on the first detection information, and then dynamically adjusts the probability of extracting the second detection information for matching the category according to the different granularity of the initial information. This method optimizes the data processing efficiency and improves the accuracy of classification. For information with high details, the classification is more accurate. The present application also proposes a scientific and technological intelligence classification system based on big data and related electronic equipment.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a scientific and technological intelligence classification method based on big data. Background Art

[0002] With the rapid development of information technology, big data has become an important resource in today's society. Using big data to classify scientific and technological intelligence is an efficient method that can obtain a large amount of complex data through big data and classify and organize it according to specific standards and attributes, so that users can quickly and accurately obtain appropriate information when using big data servers to obtain information.

[0003] Traditional scientific and technological intelligence classification methods mainly rely on manual classification and keyword or key feature matching. In the process of matching using keywords or key features, since keywords or key features may appear in other categories and the actual information scale of keywords or key features in different articles or information is different, if matching is performed directly, the matching process is prone to errors and has low accuracy. Summary of the invention

[0004] The embodiments of the present application provide a method and system for classifying scientific and technological intelligence based on big data to improve the above-mentioned problems.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In the first aspect, the embodiment of the present application proposes a scientific and technological intelligence classification method based on big data, which is applied to a cloud server. The scientific and technological intelligence classification method based on big data includes:

[0007] Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information;

[0008] Matching the extracted first detection information with the data in the database, and determining the overall granularity information corresponding to each initial information according to the matching result;

[0009] Based on the plurality of overall granularity information, the plurality of initial information is divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group;

[0010] Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ;

[0011] Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result;

[0012] According to the classification result of the second detection information, the initial information corresponding to the second detection information is classified.

[0013] In combination with the first aspect, in some implementations, multiple initial information is obtained, and each initial information is analyzed according to a first probability Extracting first detection information, wherein the first detection information is part of initial information, including:

[0014] Divide the initial information into multiple sub-information according to the smallest granularity;

[0015] According to the first probability Part of the sub-information is extracted and used as the first detection information.

[0016] In combination with the first aspect, in some implementations, the extracted first detection information is matched with data in a database, and overall granularity information corresponding to each initial information is determined according to the matching result, including:

[0017] Matching the plurality of first detection information with the data in the database, and determining first target data matching the first detection information from the data in the database according to the matching result;

[0018] Acquire, according to the first target data, upper-level data corresponding to the first target data, wherein the upper-level data is data in a database, the upper-level data includes the first target data, and the granularity corresponding to the upper-level data is greater than the granularity corresponding to the first target data;

[0019] The granularity information of the upper-level data is obtained, and the overall granularity information corresponding to the initial information is determined based on the granularity information of the upper-level data.

[0020] In combination with the first aspect, in some implementations, obtaining granularity information of upper-level data, and determining overall granularity information corresponding to the initial information based on the granularity information of the upper-level data includes:

[0021] Obtaining the proportion of the first target data in the upper-level data;

[0022] Obtain the granularity information of the upper-level data, and determine the overall granularity information corresponding to the initial information based on the proportion value and the granularity information of the upper-level data;

[0023] Based on the proportion value, the information scale of the granularity information of the upper-level data is expanded to obtain the overall granularity information corresponding to the initial information, satisfying:

[0024]

[0025] in, is the overall granularity information corresponding to the initial information, is the granularity information of the parent data, Is the percentage value.

[0026] In combination with the first aspect, in some implementations, the multiple initial information is divided into at least two groups based on the multiple overall granularity information, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group, including:

[0027] The multiple initial information are divided into multiple groups based on multiple overall granularity information, and the granularity information corresponding to the initial information in the multiple groups decreases in sequence.

[0028] In combination with the first aspect, in some implementations, based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ,include:

[0029] The second detection information is extracted from each initial information based on different probabilities, wherein the larger the granularity information corresponding to the initial information in the plurality of groups is, the smaller the corresponding extraction frequency is when extracting the second detection information.

[0030] In combination with the first aspect, in some implementations, the extracted second detection information is matched with data in a database, and the second detection information is classified according to the matching result, including:

[0031] Divide the initial information into multiple sub-information according to the smallest granularity;

[0032] According to the second probability or the third probability Part of the sub-information is extracted and used as the second detection information.

[0033] In combination with the first aspect, in some implementations, the extracted second detection information is matched with data in a database, and the second detection information is classified according to the matching result, including:

[0034] Matching the plurality of second detection information with the data in the database, and determining second target data matching the second detection information from the data in the database according to the matching result;

[0035] Based on the category of the second target data, a category corresponding to the second detection information is determined.

[0036] A second aspect of an embodiment of the present invention provides a scientific and technological intelligence classification system based on big data, the system being configured as follows:

[0037] Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information;

[0038] Matching the extracted first detection information with the data in the database, and determining the overall granularity information corresponding to each initial information according to the matching result;

[0039] Based on the plurality of overall granularity information, the plurality of initial information is divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group;

[0040] Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ;

[0041] Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result;

[0042] According to the classification result of the second detection information, the initial information corresponding to the second detection information is classified.

[0043] In conjunction with the second aspect, in some feasible embodiments, the system is configured as follows:

[0044] Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information, including:

[0045] Divide the initial information into multiple sub-information according to the smallest granularity;

[0046] According to the first probability Part of the sub-information is extracted and used as the first detection information.

[0047] In conjunction with the second aspect, in some feasible embodiments, the system is configured as follows:

[0048] The extracted first detection information is matched with the data in the database, and the overall granularity information corresponding to each initial information is determined according to the matching result, including:

[0049] Matching the plurality of first detection information with the data in the database, and determining first target data matching the first detection information from the data in the database according to the matching result;

[0050] Acquire, according to the first target data, upper-level data corresponding to the first target data, wherein the upper-level data is data in a database, the upper-level data includes the first target data, and the granularity corresponding to the upper-level data is greater than the granularity corresponding to the first target data;

[0051] The granularity information of the upper-level data is obtained, and the overall granularity information corresponding to the initial information is determined based on the granularity information of the upper-level data.

[0052] In conjunction with the second aspect, in some feasible embodiments, the system is configured as follows:

[0053] Obtaining the granularity information of the upper-level data, and determining the overall granularity information corresponding to the initial information based on the granularity information of the upper-level data, including:

[0054] Obtaining the proportion of the first target data in the upper-level data;

[0055] Obtain the granularity information of the upper-level data, and determine the overall granularity information corresponding to the initial information based on the proportion value and the granularity information of the upper-level data;

[0056] Based on the proportion value, the information scale of the granularity information of the upper-level data is expanded to obtain the overall granularity information corresponding to the initial information, satisfying:

[0057]

[0058] in, is the overall granularity information corresponding to the initial information, is the granularity information of the parent data, Is the percentage value.

[0059] In conjunction with the second aspect, in some feasible embodiments, the system is configured as follows:

[0060] Based on the plurality of overall granularity information, the plurality of initial information are divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group, including:

[0061] The multiple initial information are divided into multiple groups based on multiple overall granularity information, and the granularity information corresponding to the initial information in the multiple groups decreases in sequence.

[0062] In conjunction with the second aspect, in some feasible embodiments, the system is configured as follows:

[0063] Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ,include:

[0064] The second detection information is extracted from each initial information based on different probabilities, wherein the larger the granularity information corresponding to the initial information in the plurality of groups is, the smaller the corresponding extraction frequency is when extracting the second detection information.

[0065] In conjunction with the second aspect, in some feasible embodiments, the system is configured as follows:

[0066] Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result, including:

[0067] Divide the initial information into multiple sub-information according to the smallest granularity;

[0068] According to the second probability or the third probability Part of the sub-information is extracted and used as the second detection information.

[0069] In conjunction with the second aspect, in some feasible embodiments, the system is configured as follows:

[0070] Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result, including:

[0071] Matching the plurality of second detection information with the data in the database, and determining second target data matching the second detection information from the data in the database according to the matching result;

[0072] Based on the category of the second target data, a category corresponding to the second detection information is determined.

[0073] A third aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0074] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method proposed in the first aspect of the embodiment of the present invention.

[0075] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0076] In summary, the above method and device have the following technical effects:

[0077] The present application proposes a scientific and technological intelligence classification method based on big data, firstly obtaining multiple initial information, and classifying each initial information according to a first probability Extracting first detection information, then matching the extracted first detection information with data in a database, determining overall granularity information corresponding to each initial information according to the matching result, then dividing the multiple initial information into at least two groups based on the multiple overall granularity information, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group, then, based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > Then, the extracted second detection information is matched with the data in the database, and the second detection information is classified according to the matching result. Finally, according to the classification result of the second detection information, the initial information corresponding to the second detection information is classified. The present application proposes a method for classifying scientific and technological intelligence based on big data, which extracts the first detection information with a first probability by refining the granularity scale of the initial information, obtains the information granularity of the initial information based on the first detection information, and then dynamically adjusts the probability of extracting the second detection information for matching the category according to the different granularity scales of the initial information, which optimizes the data processing efficiency and improves the accuracy of classification. For information with higher details, the classification is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A flowchart of a scientific and technological intelligence classification method based on big data proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] In the information field, the concept of granularity is crucial. Granularity refers to the degree of refinement of data, information or knowledge. In different application scenarios, the division criteria and processing methods of granularity are also different. For example, in data storage and processing, granularity can refer to the level of detail of data records; in data analysis, granularity may refer to the level of refinement of the analysis object; and in knowledge representation, granularity involves the division and organization of knowledge units.

[0081] This application embodiment proposes a scientific and technological intelligence classification method based on big data, please refer to Figure 1 , including the following steps:

[0082] S101: Acquire multiple initial information, and analyze each initial information according to a first probability Extract first detection information, wherein the first detection information is part of initial information.

[0083] In the new data collection technology of the big data cloud server, in this embodiment, scientific and technological intelligence information technology is taken as an example. In the process of collection, various different information is the initial information in this application. For example, an initial information can be an article, a paragraph of text, multiple images or other information resources of information granularity. For the server, it will consume a lot of computing power to fully identify the information. Therefore, in this embodiment, a partial extraction method is used for detection.

[0084] Specifically, in this embodiment, each initial information is analyzed according to the first probability Extract the first detection information. Exemplarily, in this embodiment, the initial information can be divided according to the minimum granularity and form multiple sub-information. The minimum granularity standard can be determined manually or by other technical means, which is not limited in this embodiment. Taking the minimum information granularity as a field as an example, in this embodiment, in an initial information formed by an article, the article is divided into multiple fields, and the multiple fields are multiple sub-information. In some other embodiments, for example, the initial resource is a video, and the multiple sub-information can be several consecutive frames or each frame. The specific scale is not limited in this embodiment.

[0085] In this embodiment, firstly according to the first probability Extracting first detection information, for example, from a plurality of fields according to a first probability Part of the fields are randomly extracted, and these part of the fields can be used as the first detection information.

[0086] S102: Match the extracted first detection information with the data in the database, and determine the overall granularity information corresponding to each initial information according to the matching result.

[0087] It is understandable that in this embodiment, during the matching process, the system will use relevant algorithms to analyze and process the extracted first detection information. First, the system will preprocess the information, including steps such as removing noise and standardizing the format to ensure data quality. Subsequently, the system will compare the preprocessed information with the data in the database item by item to find the closest match. In order to improve the accuracy of the match, the system may adopt a variety of matching strategies. For example, it can use fuzzy matching technology to deal with problems such as incomplete consistency and spelling errors in the data. Based on the matching results, the system will determine the overall granularity information corresponding to each initial information. For example, if the initial information is a description of a certain science and technology, the system may determine the detail scale of the description based on the matching results, and determine the overall granularity information corresponding to the initial information based on the detail scale. The smaller the scale of the overall granularity information, the more detailed the description process of the science and technology.

[0088] In this embodiment, as an implementation method, multiple first detection information can be matched with data in a database, and based on the matching results, first target data matching the first detection information can be determined in the data in the database. These target data can be data that is substantially the same as the first detection information, for example, similar description methods, similar images, or similar information.

[0089] Then, the superior data corresponding to the first target data is obtained according to the first target data, wherein the superior data is the data in the database, the superior data includes the first target data, and the granularity corresponding to the superior data is greater than the granularity corresponding to the first target data. It can be understood that the data in the database is complete and classified. For example, in this embodiment, after matching a certain paragraph of text with the data in the database, the similar paragraph of text is the first target data, the first target data is extracted from the database, and a certain article corresponding to the first target data is also the superior data. In this way, the granularity information of the superior data can also be obtained at the same time, and based on the granularity information of the superior data, the overall granularity information corresponding to the initial information can be determined.

[0090] Specifically, as an implementation mode, the overall granularity information corresponding to the initial information may be determined by the proportion of the first target data in the upper-level data.

[0091] As an example, when the matched first target data, that is, a certain field occupies a% of an article, since the first detection information is also extracted from the initial information according to a specific probability, the information granularity corresponding to the initial information can be roughly calculated based on the occupancy relationship. In this embodiment, it can meet the following conditions:

[0092]

[0093] in, is the overall granularity information corresponding to the initial information, is the granularity information of the parent data, Of course, in other embodiments, other calculation methods may be used, which are not limited in this application.

[0094] S103: Based on multiple overall granularity information, multiple initial information is divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group.

[0095] It can be understood that after obtaining the overall granularity information corresponding to the multiple initial information, the granularity scale of all the initial information is roughly determined. Therefore, in order to more accurately identify the degree of detail in the data, in this embodiment, a part of the initial information with a larger granularity scale and a part of the initial information with a smaller granularity scale are extracted again, and the data extraction at this time is used to classify the data. In other embodiments, the multiple initial information is divided into multiple groups based on the multiple overall granularity information, and the granularity information corresponding to the initial information in the multiple groups decreases in sequence.

[0096] S104: Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ;

[0097] It can be understood that in this embodiment, when the granularity is larger, the detail level is lower. Therefore, its content can be extracted with a smaller probability, while information with a smaller granularity can be extracted with a larger probability, thereby improving the accuracy of data matching.

[0098] In some other embodiments, as an implementation method, the second detection information is extracted from each initial information based on different probabilities, wherein the larger the granularity information corresponding to the initial information in multiple groups is, the smaller the corresponding extraction frequency is when extracting the second detection information.

[0099] For example, an adaptive threshold can be applied to determine the algorithm, and the algorithm can dynamically adjust the extraction probability according to the size of the granularity, so as to find the best balance point among information of different scales. Specifically, when the granularity is large, the algorithm will automatically reduce the extraction probability to avoid excessive redundant information interfering with the accuracy of data matching. On the contrary, for information with a smaller granularity, the algorithm will increase the extraction probability to ensure that these detailed information can be fully captured, thereby improving the accuracy of overall data matching.

[0100] S105: Match the extracted second detection information with the data in the database, and classify the second detection information according to the matching result.

[0101] It can be understood that the initial information is divided according to the minimum granularity to form a plurality of sub-information, and then, the plurality of sub-information is divided according to the second probability or the third probability Part of the sub-information is extracted and used as the second detection information.

[0102] S106: Classify the initial information corresponding to the second detection information according to the classification result of the second detection information.

[0103] It can be understood that after the second detection information is actually extracted, the second detection information is used as a feature to match the data in the database. After a certain data in the database is matched, the classification of the data can be determined, that is, the classification category of the initial information corresponding to the second detection information is obtained.

[0104] The present application proposes a scientific and technological intelligence classification method based on big data, firstly obtaining multiple initial information, and classifying each initial information according to a first probability Extracting first detection information, then matching the extracted first detection information with data in a database, determining overall granularity information corresponding to each initial information according to the matching result, then dividing the multiple initial information into at least two groups based on the multiple overall granularity information, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group, then, based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > Then, the extracted second detection information is matched with the data in the database, and the second detection information is classified according to the matching result. Finally, according to the classification result of the second detection information, the initial information corresponding to the second detection information is classified. The present application proposes a method for classifying scientific and technological intelligence based on big data, which extracts the first detection information with a first probability by refining the granularity scale of the initial information, obtains the information granularity of the initial information based on the first detection information, and then dynamically adjusts the probability of extracting the second detection information for matching the category according to the different granularity scales of the initial information, which optimizes the data processing efficiency and improves the accuracy of classification. For information with higher details, the classification is more accurate.

[0105] Based on the same inventive concept, the embodiment of the present invention also proposes a scientific and technological intelligence classification system based on big data, which is configured as follows:

[0106] Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information;

[0107] Matching the extracted first detection information with the data in the database, and determining the overall granularity information corresponding to each initial information according to the matching result;

[0108] Based on the plurality of overall granularity information, the plurality of initial information is divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group;

[0109] Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ;

[0110] Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result;

[0111] According to the classification result of the second detection information, the initial information corresponding to the second detection information is classified.

[0112] In some possible embodiments, the system is configured to:

[0113] Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information, including:

[0114] Divide the initial information into multiple sub-information according to the smallest granularity;

[0115] According to the first probability Part of the sub-information is extracted and used as the first detection information.

[0116] In some possible embodiments, the system is configured to:

[0117] The extracted first detection information is matched with the data in the database, and the overall granularity information corresponding to each initial information is determined according to the matching result, including:

[0118] Matching the plurality of first detection information with the data in the database, and determining first target data matching the first detection information from the data in the database according to the matching result;

[0119] Acquire, according to the first target data, upper-level data corresponding to the first target data, wherein the upper-level data is data in a database, the upper-level data includes the first target data, and the granularity corresponding to the upper-level data is greater than the granularity corresponding to the first target data;

[0120] The granularity information of the upper-level data is obtained, and the overall granularity information corresponding to the initial information is determined based on the granularity information of the upper-level data.

[0121] In some possible embodiments, the system is configured to:

[0122] Obtaining the granularity information of the upper-level data, and determining the overall granularity information corresponding to the initial information based on the granularity information of the upper-level data, including:

[0123] Obtaining the proportion of the first target data in the upper-level data;

[0124] Obtain the granularity information of the upper-level data, and determine the overall granularity information corresponding to the initial information based on the proportion value and the granularity information of the upper-level data;

[0125] Based on the proportion value, the information scale of the granularity information of the upper-level data is expanded to obtain the overall granularity information corresponding to the initial information, satisfying:

[0126]

[0127] in, is the overall granularity information corresponding to the initial information, is the granularity information of the parent data, Is the percentage value.

[0128] In some possible embodiments, the system is configured to:

[0129] Based on the plurality of overall granularity information, the plurality of initial information are divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group, including:

[0130] The multiple initial information are divided into multiple groups based on multiple overall granularity information, and the granularity information corresponding to the initial information in the multiple groups decreases in sequence.

[0131] In some possible embodiments, the system is configured to:

[0132] Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ,include:

[0133] The second detection information is extracted from each initial information based on different probabilities, wherein the larger the granularity information corresponding to the initial information in the plurality of groups is, the smaller the corresponding extraction frequency is when extracting the second detection information.

[0134] In some possible embodiments, the system is configured to:

[0135] Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result, including:

[0136] Divide the initial information into multiple sub-information according to the smallest granularity;

[0137] According to the second probability or the third probability Part of the sub-information is extracted and used as the second detection information.

[0138] In some possible embodiments, the system is configured to:

[0139] Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result, including:

[0140] Matching the plurality of second detection information with the data in the database, and determining second target data matching the second detection information from the data in the database according to the matching result;

[0141] Based on the category of the second target data, a category corresponding to the second detection information is determined.

[0142] The present application proposes a scientific and technological intelligence classification method based on big data, firstly obtaining multiple initial information, and classifying each initial information according to a first probability Extracting first detection information, then matching the extracted first detection information with data in a database, determining overall granularity information corresponding to each initial information according to the matching result, then dividing the multiple initial information into at least two groups based on the multiple overall granularity information, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group, then, based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > Then, the extracted second detection information is matched with the data in the database, and the second detection information is classified according to the matching result. Finally, according to the classification result of the second detection information, the initial information corresponding to the second detection information is classified. The present application proposes a method for classifying scientific and technological intelligence based on big data, which extracts the first detection information with a first probability by refining the granularity scale of the initial information, obtains the information granularity of the initial information based on the first detection information, and then dynamically adjusts the probability of extracting the second detection information for matching the category according to the different granularity scales of the initial information, which optimizes the data processing efficiency and improves the accuracy of classification. For information with higher details, the classification is more accurate.

[0143] Based on the same inventive concept, an embodiment of the present application further proposes an electronic device, the electronic device comprising:

[0144] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the scientific and technological intelligence classification method based on big data of an embodiment of the present application.

[0145] In addition, to achieve the above-mentioned purpose, an embodiment of the present application also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the scientific and technological intelligence classification method based on big data of the embodiment of the present application.

[0146] The following is a detailed introduction to the various components of electronic equipment:

[0147] The processor is the control center of the electronic device, which can be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0148] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0149] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0150] Optionally, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor, or may exist independently and be coupled to the processor through an interface circuit of the electronic device, which is not specifically limited in the embodiments of the present invention.

[0151] A transceiver is used to communicate with a network device or a terminal device.

[0152] Optionally, the transceiver may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0153] Optionally, the transceiver may be integrated with the processor, or may exist independently and be coupled to the processor via an interface circuit of the router, which is not specifically limited in the embodiment of the present invention.

[0154] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method in the above method embodiment, which will not be repeated here.

[0155] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0156] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0157] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0158] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0159] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0160] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0161] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Claims

1. A scientific and technological intelligence classification method based on big data, characterized in that: Applied to cloud servers, the scientific and technological intelligence classification methods based on big data include: Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information; Matching the extracted first detection information with the data in the database, and determining the overall granularity information corresponding to each initial information according to the matching result; Matching the plurality of first detection information with the data in the database, and determining first target data matching the first detection information from the data in the database according to the matching result; Acquire, according to the first target data, upper-level data corresponding to the first target data, wherein the upper-level data is data in a database, the upper-level data includes the first target data, and the granularity corresponding to the upper-level data is greater than the granularity corresponding to the first target data; Obtaining granularity information of upper-level data, and determining overall granularity information corresponding to the initial information based on the granularity information of the upper-level data; Obtaining the proportion of the first target data in the upper-level data; Obtain the granularity information of the upper-level data, and determine the overall granularity information corresponding to the initial information based on the proportion value and the granularity information of the upper-level data; Based on the proportion value, the information scale of the granularity information of the upper-level data is expanded to obtain the overall granularity information corresponding to the initial information, satisfying: ,in, is the overall granularity information corresponding to the initial information, is the granularity information of the parent data, is the percentage value; Based on the plurality of overall granularity information, the plurality of initial information is divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group; Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ; Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result; According to the classification result of the second detection information, the initial information corresponding to the second detection information is classified.

2. A scientific and technological information classification method based on big data according to claim 1, characterized in that: Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information, including: Divide the initial information into multiple sub-information according to the smallest granularity; According to the first probability Part of the sub-information is extracted and used as the first detection information.

3. A scientific and technological information classification method based on big data according to claim 1, characterized in that: Based on the plurality of overall granularity information, the plurality of initial information are divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group, including: The multiple initial information are divided into multiple groups based on multiple overall granularity information, and the granularity information corresponding to the initial information in the multiple groups decreases in sequence.

4. A method for classifying scientific and technological information based on big data according to claim 3, characterized in that: Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ,include: The second detection information is extracted from each initial information based on different probabilities, wherein the larger the granularity information corresponding to the initial information in the plurality of groups is, the smaller the corresponding extraction frequency is when extracting the second detection information.

5. A method for classifying scientific and technological information based on big data according to claim 1, characterized in that: Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result, including: Divide the initial information into multiple sub-information according to the smallest granularity; According to the second probability or the third probability Part of the sub-information is extracted and used as the second detection information.

6. A method for classifying scientific and technological information based on big data according to claim 5, characterized in that: Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result, including: Matching the plurality of second detection information with the data in the database, and determining second target data matching the second detection information from the data in the database according to the matching result; Based on the category of the second target data, a category corresponding to the second detection information is determined.

7. A scientific and technological information classification system based on big data, characterized in that: For executing the method according to any one of claims 1 to 6, the system is configured as follows: Get multiple initial information, and calculate each initial information according to the first probability Extracting first detection information, wherein the first detection information is part of initial information; Matching the extracted first detection information with the data in the database, and determining the overall granularity information corresponding to each initial information according to the matching result; Matching the plurality of first detection information with the data in the database, and determining first target data matching the first detection information from the data in the database according to the matching result; Acquire, according to the first target data, upper-level data corresponding to the first target data, wherein the upper-level data is data in a database, the upper-level data includes the first target data, and the granularity corresponding to the upper-level data is greater than the granularity corresponding to the first target data; Obtaining granularity information of upper-level data, and determining overall granularity information corresponding to the initial information based on the granularity information of the upper-level data; Obtaining the proportion of the first target data in the upper-level data; Obtain the granularity information of the upper-level data, and determine the overall granularity information corresponding to the initial information based on the proportion value and the granularity information of the upper-level data; Based on the proportion value, the information scale of the granularity information of the upper-level data is expanded to obtain the overall granularity information corresponding to the initial information, satisfying: ,in, is the overall granularity information corresponding to the initial information, is the granularity information of the parent data, is the percentage value; Based on the plurality of overall granularity information, the plurality of initial information is divided into at least two groups, namely a first information group and a second information group, wherein the overall granularity information corresponding to the initial information in the first information group is greater than the overall granularity information corresponding to the initial information in the second information group; Based on the second probability Extracting second detection information from the initial information in the first information group based on the third probability The second detection information is extracted from the initial information in the second information group, wherein: > ; Matching the extracted second detection information with the data in the database, and classifying the second detection information according to the matching result; According to the classification result of the second detection information, the initial information corresponding to the second detection information is classified.

8. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the method as claimed in any one of claims 1 to 6.

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