CN117785868A - Data storage method and system applied to preparation of glass sand inclusion pipe - Google Patents
Data storage method and system applied to preparation of glass sand inclusion pipe Download PDFInfo
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Abstract
The invention relates to the technical field of data storage, and discloses a data storage method and a data storage system applied to preparation of a glass sand inclusion pipe, wherein the data storage method comprises the following steps: extracting time series data and non-time series data in the preparation data; calculating the data association degree between each data in the time sequence data, performing data classification processing on the time sequence data to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data; respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in a distributed database to obtain a first access interface and a second access interface; and mining characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data, and executing data storage of the time series data and the non-time series data to obtain a storage result. The invention aims to improve the data storage efficiency of the glass sand inclusion pipe under the preparation.
Description
Technical Field
The invention relates to the technical field of data storage, in particular to a data storage method and system applied to preparation of a glass sand inclusion pipe.
Background
The glass sand inclusion tube is a device commonly used in laboratories and is mainly used for filtering, drying, vacuum extraction and other operations, and consists of a glass tube and two glass joints.
The existing preparation data storage method of the glass sand inclusion pipe mainly comprises the following steps: the method comprises the steps of obtaining a preparation flow of a glass sand inclusion tube, creating a file storage system according to the preparation flow, packaging preparation data of each flow in the preparation flow, sequentially storing the packaged data into the file storage system, analyzing the data when abnormal data or additional added data are encountered, so that the storage position of the data is determined, the data are stored in a region, sequentially traversing the data when the data are read subsequently, and determining the data finally needed, so that the storage performance of the storage method is lower, the storage efficiency of the preparation data is reduced, and a method capable of improving the storage efficiency of the data under the preparation of the glass sand inclusion tube is needed.
Disclosure of Invention
The invention provides a data storage method and a data storage system applied to preparation of a glass steel sand inclusion pipe, and mainly aims to improve data storage efficiency of the glass steel sand inclusion pipe.
In order to achieve the above object, the invention provides a data storage method applied to the preparation of a glass sand inclusion pipe, comprising the following steps:
acquiring preparation data in a preparation scene of the glass sand inclusion tube, and extracting time sequence data and non-time sequence data in the preparation data;
calculating the data association degree between each data in the time sequence data, carrying out data classification processing on the time sequence data according to the data association degree to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data;
respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface, and carrying out parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database;
And mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data according to the characteristic information, and respectively executing data storage of the time series data and the non-time series data by combining the target database and the storage database to obtain a storage result.
Optionally, the extracting the time-series data and the non-time-series data in the preparation data includes:
performing data noise reduction processing on the preparation data to obtain noise reduction preparation data, and identifying time index information of each data in the noise reduction preparation data;
determining a preparation time point of each data in the noise reduction preparation data according to the time index information;
constructing a time scatter diagram corresponding to the noise reduction preparation data according to the preparation time points, and performing fitting treatment on data points in the time scatter diagram to obtain a fitting curve;
calculating a curve slope corresponding to the fitted curve, and analyzing the linear relation between each data in the noise reduction preparation data and the preparation time point according to the curve slope;
and extracting time sequence data and non-time sequence data from the noise reduction preparation data according to the linear relation.
Optionally, the calculating the curve slope corresponding to the fitted curve includes:
calculating the slope of the curve corresponding to the fitted curve by the following formula:
wherein A represents the slope of the curve corresponding to the fitted curve, N t1 And M t1 Represents the corresponding point coordinates, N, in the fitting curve when the preparation time point is t1 t2 And M t2 Represents the corresponding point coordinates in the fitted curve when the preparation time point is t2,represents the slope of the curve point at the preparation time point t2 and the preparation time point t1, N tβ And M tβ And (3) representing corresponding point coordinates in the fitting curve when the preparation time point is tβ, wherein β represents the number of curve points in the fitting curve.
Optionally, the calculating the data association degree between each data in the time series data includes:
calculating the data association degree between each data in the time series data by the following formula:
wherein B represents a degree of data correlation between each of the time-series data, D b Representing a data vector corresponding to the b-th data in the time-series data, D b+1 Represents the data vector corresponding to the (b+1) th data in the time series data, mu represents the data dimension, min b min b+1 |D b -D b+1 The expression represents the second-order minimum difference, max, representing the b-th data and the b+1th corresponding data vector in the time-series data b max b+1 |D b -D b+1 The i represents the second-order maximum difference between the b-th data and the b+1-th corresponding data vector in the time-series data.
Optionally, the constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data includes:
respectively extracting structural features corresponding to the associated sequence data and the non-associated sequence data to obtain a first structural feature and a second structural feature;
performing feature screening on the first structural feature and the second structural feature to obtain a first target feature and a second target feature;
respectively constructing feature matrixes corresponding to the first target feature and the second target feature to obtain a first feature matrix and a second feature matrix;
respectively carrying out matrix fusion on the first feature matrix and the second feature matrix to obtain a first fusion matrix and a second fusion matrix;
generating fusion structural features corresponding to the associated sequence data and the non-associated sequence data according to the first fusion matrix and the second fusion matrix to obtain a first fusion feature and a second fusion feature;
extracting data parameters corresponding to the associated sequence data and the non-associated sequence data to obtain a first data parameter and a second data parameter;
Setting storage requirements corresponding to the associated sequence data and the non-associated sequence data according to the first fusion feature, the second fusion feature, the first data parameter and the second data parameter;
and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data according to the storage requirement.
Optionally, the configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database respectively to obtain a first access interface and a second access interface includes:
performing attribute extraction on the associated sequence data and the non-associated sequence data respectively to obtain a first data attribute and a second data attribute;
calculating a support coefficient between each attribute in the first data attributes to obtain a first support coefficient;
calculating a support coefficient between each attribute in the second data attributes to obtain a second support coefficient;
according to the first support coefficient and the second support coefficient, key attributes in the first data attribute and the second data attribute are extracted respectively to obtain a first key attribute and a second key attribute;
According to the first key attribute and the second key attribute, interface types corresponding to the associated sequence data and the non-associated sequence data are respectively determined, and a first interface type and a second interface type are obtained;
respectively analyzing service logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first service logic and a second service logic;
according to the first service logic and the second service logic, interface logic corresponding to the associated sequence data and the non-associated sequence data is formulated to obtain first interface logic and second interface logic;
and respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database by combining the first interface type, the second interface type, the first interface logic and the second interface logic to obtain a first access interface and a second access interface.
Optionally, the calculating a support coefficient between each attribute in the first data attribute, to obtain a first support coefficient includes:
the support coefficient between each of the first data attributes may be calculated by the following formula:
Wherein F represents a support coefficient between each attribute in the first data attribute, q represents the attribute number of the first data attribute, e represents the attribute serial number of the first data attribute, H e Attribute probability, H, representing the e-th attribute of the first data attributes e+1 Representing attribute probability of (e+1) th attribute in first data attribute, G e，e+1 Representing the e-th attributeVector ratio of the (e+1) th attribute.
Optionally, the mining the feature information corresponding to the non-time series data includes:
performing information mining on the non-time sequence data by using a preset decision tree mining model to obtain initial data information;
performing information cleaning on the initial data information to obtain target data information;
extracting the text of the target data information to obtain an information text, and calculating text weight corresponding to the information text;
and extracting characteristic information in the target data information by combining the information entropy value and the text weight.
Optionally, the calculating an information entropy value corresponding to each piece of information in the target data information includes:
calculating an information entropy value corresponding to each piece of information in the target data information through the following formula:
Wherein U represents the information entropy value corresponding to each piece of information in the target data information, i represents the information serial number corresponding to the target data information, delta represents the information quantity corresponding to the target data information, E i Represents the ith information, W (E i ) The occurrence probability of the i-th information in the target data information is represented.
A data storage system for use in the preparation of glass sand inclusion tubes, the system comprising:
the data processing module is used for acquiring preparation data in a preparation scene of the glass sand inclusion tube and extracting time sequence data and non-time sequence data in the preparation data;
the database construction module is used for calculating the data association degree between each piece of data in the time sequence data, carrying out data classification processing on the time sequence data according to the data association degree to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data;
the database adjustment module is used for respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface, and carrying out parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database;
The data storage module is used for mining the characteristic information corresponding to the non-time sequence data, constructing a storage database corresponding to the non-time sequence data according to the characteristic information, and respectively executing data storage of the time sequence data and the non-time sequence data by combining the target database and the storage database to obtain a storage result.
According to the invention, the time sequence data and the non-time sequence data in the preparation data are extracted, so that the preparation data can be divided, the data with time dependency relationship and the data without time dependency relationship are obtained, further, the preparation condition of related time trend under the glass sand inclusion scene can be known through the time sequence data, the related description information under the glass sand inclusion scene can be known through the non-time sequence data, the data association degree between each data in the time sequence data can be calculated, the association relationship between each data in the time sequence data can be known through the data association degree, the subsequent data classification processing is facilitated, and the guarantee is provided for the construction of the subsequent distributed database. Therefore, the data storage method and the data storage system applied to the preparation of the glass sand inclusion pipe can improve the data storage efficiency of the glass sand inclusion pipe.
Drawings
FIG. 1 is a schematic flow chart of a data storage method applied to the preparation of a glass sand inclusion pipe according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a data storage system for use in the preparation of glass sand inclusion tubes according to one embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the data storage method applied to the preparation of a glass sand inclusion tube according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a data storage method applied to preparation of a glass sand inclusion pipe. In the embodiment of the present application, the execution body of the data storage method applied to the preparation of the glass sand inclusion pipe includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided in the embodiment of the present application. In other words, the data storage method applied to the preparation of the glass sand inclusion pipe can be executed by software or hardware installed on a terminal device or a service end device, wherein the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a data storage method applied to a glass sand inclusion tube according to an embodiment of the invention is shown. In this embodiment, the data storage method applied to the preparation of the glass sand inclusion pipe comprises steps S1-S4.
S1, acquiring preparation data of a glass sand inclusion tube preparation scene, and extracting time sequence data and non-time sequence data in the preparation data.
According to the invention, the preparation data can be divided by extracting time sequence data and non-time sequence data in the preparation data, so that the data with time dependency and the data without time dependency are obtained, further, the preparation condition of relevant time trend under the scene of the glass sand inclusion tube can be known through the time sequence data, the relevant description information under the scene of the glass sand inclusion tube can be known through the non-time sequence data, wherein the glass sand inclusion tube is made of glass, the glass sand inclusion tube is generally composed of two sections of glass tubes, fine sand particles are clamped in the middle, the glass sand inclusion tube can be used for operations such as liquid separation, filtration and drying in a laboratory by controlling the pressure difference between the upper section of tube and the lower section of tube, the rising or falling speed of the sand inclusion tube can be controlled by adjusting the upper position and the lower position of the sand inclusion tube, the glass sand inclusion tube has the characteristics of corrosion resistance and high temperature resistance, the glass sand inclusion tube is convenient to use, the preparation data is the relevant data in the scene of glass sand inclusion tube, the time sequence data, the relevant data such as the data in the preparation process of materials are different from the data in the time sequence, the relevant data, the data in the preparation process of the material is different from the data, and the relevant data in the time sequence data, and the relevant data can be displayed in the data, and the data are relevant data in the time sequence, and relevant data are different from the data.
As an embodiment of the present invention, the extracting time-series data and non-time-series data in the preparation data includes: performing data noise reduction processing on the preparation data to obtain noise reduction preparation data, identifying time index information of each data in the noise reduction preparation data, determining preparation time points of each data in the noise reduction preparation data according to the time index information, constructing a time scatter diagram corresponding to the noise reduction preparation data according to the preparation time points, performing fitting processing on data points in the time scatter diagram to obtain a fitting curve, calculating a curve slope corresponding to the fitting curve, analyzing a linear relation between each data in the noise reduction preparation data and the preparation time points according to the curve slope, and extracting time sequence data and non-time sequence data from the noise reduction preparation data according to the linear relation.
The time index information is timestamp information of each data in the noise reduction preparation data, such as heating time information about materials in a glass sand inclusion tube preparation scene, the time scatter diagram is constructed by taking the preparation time point and data variables of the noise reduction preparation data as coordinate axes, and the linear relation is a direct relation between each data in the noise reduction preparation data and the preparation time point.
Optionally, the data noise reduction processing of the preparation data may be implemented by a low-pass filter, the time index information may be obtained by identifying log data of each data in the noise reduction preparation data, and the construction of a time scatter diagram corresponding to the noise reduction preparation data may be implemented by a drawing tool, where the drawing tool is constructed by a scripting language, the fitting processing of data points in the time scatter diagram may be implemented by a linear fitting function, a ratio between adjacent slopes of the curve may be calculated, a linear relationship between each data in the noise reduction preparation data and the preparation time point may be analyzed according to a degree of change of the ratio, and the extraction of the time series data and the non-time series data from the noise reduction preparation data may be implemented by an extraction function, such as a left function.
Further, as an optional embodiment of the present invention, the calculating a curve slope corresponding to the fitted curve includes:
calculating the slope of the curve corresponding to the fitted curve by the following formula:
wherein A represents the slope of the curve corresponding to the fitted curve, N t1 And M t1 Represents the corresponding point coordinates, N, in the fitting curve when the preparation time point is t1 t2 And M t2 Represents the corresponding point coordinates in the fitted curve when the preparation time point is t2,represents the slope of the curve point at the preparation time point t2 and the preparation time point t1, N tβ And M tβ And (3) representing corresponding point coordinates in the fitting curve when the preparation time point is tβ, wherein β represents the number of curve points in the fitting curve.
S2, calculating the data association degree between each piece of data in the time sequence data, carrying out data classification processing on the time sequence data according to the data association degree to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data.
According to the invention, through calculating the data association degree between each data in the time series data, the association relation between each data in the time series data can be known through the data association degree, so that the subsequent data classification processing is facilitated, and the guarantee is provided for the construction of the subsequent distributed database, wherein the data association degree represents the association degree between each data in the time series data.
As an embodiment of the present invention, the calculating the data association degree between each data in the time-series data includes:
Calculating the data association degree between each data in the time series data by the following formula:
wherein B represents a degree of data correlation between each of the time-series data, D b Representing a data vector corresponding to the b-th data in the time-series data, D b+1 Represents the data vector corresponding to the (b+1) th data in the time series data, mu represents the data dimension, min b min b+1 |D b -D b+1 The expression represents the second-order minimum difference, max, representing the b-th data and the b+1th corresponding data vector in the time-series data b max b+1 |D b -D b+1 The i represents the second-order maximum difference between the b-th data and the b+1-th corresponding data vector in the time-series data.
According to the method, the time series data are subjected to data classification processing according to the data association degree, so that the data with association and the data without association in the time series data can be separated, the management efficiency of the time series data is improved, wherein the associated sequence data are the data with association in the time series data, the non-associated sequence data are the data without association in the time series data, optionally, the data classification processing of the time series data can be carried out by comparing the data association degree with a preset threshold value, if the data association degree is larger than the preset threshold value, the data are the associated sequence data, and if the data association degree is not larger than the preset threshold value, the data are the non-associated sequence data.
The invention can store the data in a distributed mode by constructing the distributed database corresponding to the associated sequence data and the non-associated sequence data, thereby improving the flexibility of data storage and being capable of fast reading and responding during data storage, wherein the distributed database is a virtual memory for storing the associated sequence data and the non-associated sequence data.
As one embodiment of the present invention, the constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data includes: extracting structural features corresponding to the associated sequence data and the non-associated sequence data respectively to obtain a first structural feature and a second structural feature, screening the first structural feature and the second structural feature to obtain a first target feature and a second target feature, constructing feature matrixes corresponding to the first target feature and the second target feature respectively to obtain a first feature matrix and a second feature matrix, performing matrix fusion on the first feature matrix and the second feature matrix respectively to obtain a first fusion matrix and a second fusion matrix, generating fusion structural features corresponding to the associated sequence data and the non-associated sequence data according to the first fusion matrix and the second fusion matrix to obtain a first fusion feature and a second fusion feature, extracting data parameters corresponding to the associated sequence data and the non-associated sequence data to obtain a first data parameter and a second data parameter, setting the non-associated sequence data to correspond to the associated sequence data and the non-associated sequence data according to the first fusion feature, the second fusion feature, the first data parameter and the second data parameter, setting the associated sequence data and the non-associated sequence data to store the non-associated sequence data according to a distribution requirement, and building a storage requirement database.
The first structural feature and the second structural feature are data structural features corresponding to the associated sequence data and the non-associated sequence data, the first feature matrix and the second feature matrix are square matrixes constructed by feature values corresponding to the first target feature and the second target feature, the first fusion feature and the second fusion feature are data structural comprehensive features corresponding to the associated sequence data and the non-associated sequence data, the first data parameter and the second data parameter are parameter information corresponding to the associated sequence data and the non-associated sequence data, such as data memory capacity, and the storage requirement is a requirement in terms of performance, capacity, reliability, safety and the like of a data storage area corresponding to the associated sequence data and the non-associated sequence data.
Optionally, the structural features corresponding to the associated sequence data and the non-associated sequence data may be implemented by a SURF feature extraction method, feature screening may be performed according to feature values corresponding to the first structural feature and the second structural feature, feature matrices corresponding to the first target feature and the second target feature may be implemented by matrix functions, such as zero matrix functions, matrix fusion of the first feature matrix and the second feature matrix may be implemented by matrix fusion algorithms, such as matrix addition algorithms, fusion structural features corresponding to the associated sequence data and the non-associated sequence data may be implemented by feature generators, data parameters corresponding to the associated sequence data and the non-associated sequence data may be implemented by parameter extractors, storage requirements corresponding to the associated sequence data and the non-associated sequence data may be set by conditional restriction functions, and a distributed database corresponding to the associated sequence data and the non-associated sequence data may be constructed by script languages.
S3, respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface, and carrying out parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database.
According to the invention, the access interfaces corresponding to the associated sequence data and the non-associated sequence data are respectively configured in the distributed database, so that the subsequent data access efficiency can be improved through the access interfaces, and the related data can be rapidly retrieved, wherein the first access interface and the second access interface are the interfaces corresponding to the associated sequence data and the non-associated sequence data, respectively, used for accessing the distributed database.
As an embodiment of the present invention, the configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface includes: and respectively extracting the key attributes in the first data attribute and the second data attribute to obtain a first key attribute and a second key attribute, respectively determining interface types corresponding to the associated sequence data and the non-associated sequence data to obtain a first interface type and a second interface type, respectively analyzing service logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first service logic and a second service logic, respectively according to the first service logic and the second service logic, formulating interface logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first interface and a second interface logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first interface type and a second interface type, respectively according to the first key attribute and the second key attribute, respectively determining interface types corresponding to the associated sequence data and the non-associated sequence data to obtain a first interface type and a second interface type, respectively analyzing service logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first service logic and a second service logic, respectively according to the first service logic and the second service logic, formulating interface logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first interface type and the second interface type, and the interface type.
The first data attribute and the second data attribute are respectively characteristics for describing aspects of the associated sequence data and the non-associated sequence data, the first support coefficient represents the influence degree of each attribute in the first data attribute on other attributes, the key attribute is an important attribute in the first data attribute and the second data attribute, the interface type is an interface type corresponding to the associated sequence data and the non-associated sequence data, such as RESTfulAPI, SOAP, graphQL, and the service logic is a service requirement and a rule corresponding to the associated sequence data and the non-associated sequence data, and the interface logic is an interface access rule corresponding to the associated sequence data and the non-associated sequence data.
Optionally, attribute extraction of the associated sequence data and the non-associated sequence data may be implemented by an attribute extraction tool, where the attribute extraction tool is compiled by a Java language, the second support coefficient is the same as the first support coefficient, and is not described in detail herein, key attributes in the first data attribute and the second data attribute may be extracted according to the numerical values of the first support coefficient and the second support coefficient, interface types corresponding to the associated sequence data and the non-associated sequence data may be determined according to attribute types corresponding to the first key attribute and the second key attribute, a service function corresponding to the associated sequence data and the non-associated sequence data may be determined, a service logic may be obtained by analyzing the service function, and access interfaces corresponding to the associated sequence data and the non-associated sequence data may be implemented by configuration files, such as XML, JSON, YAML, in the distributed database, respectively configured according to the service function.
Optionally, as an optional embodiment of the present invention, the calculating a support coefficient between each attribute in the first data attribute, to obtain a first support coefficient includes:
the support coefficient between each of the first data attributes may be calculated by the following formula:
wherein F represents a support coefficient between each attribute in the first data attribute, q represents the attribute number of the first data attribute, e represents the attribute serial number of the first data attribute, H e Attribute probability, H, representing the e-th attribute of the first data attributes e+1 Representing attribute probability of (e+1) th attribute in first data attribute, G e，e+1 Representing the vector ratio of the e-th attribute and the e+1th attribute.
According to the invention, the first access interface and the second access interface are combined to perform parameter adjustment processing on the distributed database, so that a database with high adaptability is obtained, and a guarantee is provided for subsequent data storage.
S4, mining characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data according to the characteristic information, and respectively executing data storage of the time series data and the non-time series data by combining the target database and the storage database to obtain a storage result.
According to the invention, the characteristic information corresponding to the non-time series data is mined, so that the existing characteristic information of the data in the non-time series data can be found, the accuracy of a subsequent storage database is improved, and a guarantee is provided for improving the storage efficiency of the non-time series data, wherein the characteristic information is the characteristic description information about the non-time series data.
As one embodiment of the present invention, the mining feature information corresponding to the non-time-series data includes: and performing information mining on the non-time series data by using a preset decision tree mining model to obtain initial data information, performing information cleaning on the initial data information to obtain target data information, performing text extraction on the target data information to obtain an information text, calculating text weight corresponding to the information text, and extracting characteristic information in the target data information by combining the information entropy value and the text weight.
The decision tree mining model is used for extracting information in data, the initial data information is all relevant description information of each piece of data in the non-time sequence data, the target data information is information obtained by removing total invalid information and repeated information of the initial data information, the information entropy value represents the amount of information contained in each piece of information in the target data information, and the text weight represents the importance degree of the information text.
Optionally, the information cleaning of the initial data information may be implemented by a cleaning tool, where the cleaning tool is compiled by a programming language, the text extraction of the target data information may be implemented by an OCR text technique, a weight value of a corresponding text character in the information text is calculated, and a text weight of the information text is determined according to the weight value.
Optionally, as an optional embodiment of the present invention, the calculating an information entropy value corresponding to each piece of information in the target data information includes:
calculating an information entropy value corresponding to each piece of information in the target data information through the following formula:
wherein U represents the information entropy value corresponding to each piece of information in the target data information, i represents the information serial number corresponding to the target data information, delta represents the information quantity corresponding to the target data information, E i Represents the ith information, W (E i ) The occurrence probability of the i-th information in the target data information is represented.
According to the invention, the data storage of the time series data and the non-time series data is respectively executed by combining the target database and the storage database, so that the distributed storage of the data is realized, the storage efficiency of the data is improved, and convenience is provided for subsequent data reading.
According to the invention, the time sequence data and the non-time sequence data in the preparation data are extracted, so that the preparation data can be divided, the data with time dependency relationship and the data without time dependency relationship are obtained, further, the preparation condition of related time trend under the glass sand inclusion scene can be known through the time sequence data, the related description information under the glass sand inclusion scene can be known through the non-time sequence data, the data association degree between each data in the time sequence data can be calculated, the association relationship between each data in the time sequence data can be known through the data association degree, the subsequent data classification processing is facilitated, and the guarantee is provided for the construction of the subsequent distributed database. Therefore, the data storage method applied to the preparation of the glass sand inclusion pipe can improve the data storage efficiency of the glass sand inclusion pipe.
FIG. 2 is a functional block diagram of a data storage system for use in the preparation of glass sand inclusion tubes according to one embodiment of the present invention.
The data storage system 100 applied to the preparation of the glass sand inclusion pipe can be installed in electronic equipment. Depending on the functions implemented, the data storage system 100 applied to the preparation of the glass sand inclusion pipe may include a data processing module 101, a database construction module 102, a database adjustment module 103, and a data storage module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data processing module 101 is configured to obtain preparation data in a preparation scene of a glass sand inclusion tube, and extract time sequence data and non-time sequence data in the preparation data;
the database construction module 102 is configured to calculate a data association degree between each piece of data in the time series data, perform data classification processing on the time series data according to the data association degree to obtain associated sequence data and non-associated sequence data, and construct a distributed database corresponding to the associated sequence data and the non-associated sequence data;
The database adjustment module 103 is configured to configure access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database respectively to obtain a first access interface and a second access interface, and perform parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database;
the data storage module 104 is configured to mine feature information corresponding to the non-time series data, construct a storage database corresponding to the non-time series data according to the feature information, and perform data storage on the time series data and the non-time series data in combination with the target database and the storage database respectively to obtain a storage result.
In detail, each module in the data storage system 100 applied to the preparation of the glass sand inclusion tube in the embodiment of the present application adopts the same technical means as the data storage method applied to the preparation of the glass sand inclusion tube in fig. 1, and can produce the same technical effects, which are not described herein.
Fig. 3 is a schematic structural diagram of an electronic device 1 according to an embodiment of the present invention for implementing a data storage method applied to a glass sand inclusion tube.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a data storage method program applied in the preparation of glass sand inclusion tubes.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects the respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes a data storage method program applied to the preparation of a glass sand inclusion tube, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various data, for example, a code applied to a data storage method program under the preparation of a glass sand inclusion tube, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
One data storage method program stored in the memory 11 of the electronic device 1 and applied to the preparation of the glass sand inclusion tube is a combination of a plurality of instructions, and when running in the processor 10, the method can be realized:
acquiring preparation data in a preparation scene of the glass sand inclusion tube, and extracting time sequence data and non-time sequence data in the preparation data;
Calculating the data association degree between each data in the time sequence data, carrying out data classification processing on the time sequence data according to the data association degree to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data;
respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface, and carrying out parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database;
and mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data according to the characteristic information, and respectively executing data storage of the time series data and the non-time series data by combining the target database and the storage database to obtain a storage result.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring preparation data in a preparation scene of the glass sand inclusion tube, and extracting time sequence data and non-time sequence data in the preparation data;
calculating the data association degree between each data in the time sequence data, carrying out data classification processing on the time sequence data according to the data association degree to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data;
Respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface, and carrying out parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database;
and mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data according to the characteristic information, and respectively executing data storage of the time series data and the non-time series data by combining the target database and the storage database to obtain a storage result.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A data storage method applied to preparation of a glass sand inclusion pipe, which is characterized by comprising the following steps:
acquiring preparation data in a preparation scene of the glass sand inclusion tube, and extracting time sequence data and non-time sequence data in the preparation data;
calculating the data association degree between each data in the time sequence data, carrying out data classification processing on the time sequence data according to the data association degree to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data;
Respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface, and carrying out parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database;
and mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data according to the characteristic information, and respectively executing data storage of the time series data and the non-time series data by combining the target database and the storage database to obtain a storage result.
2. The method for storing data applied to the preparation of a glass sand inclusion tube according to claim 1, wherein the extracting time series data and non-time series data in the preparation data comprises:
performing data noise reduction processing on the preparation data to obtain noise reduction preparation data, and identifying time index information of each data in the noise reduction preparation data;
determining a preparation time point of each data in the noise reduction preparation data according to the time index information;
Constructing a time scatter diagram corresponding to the noise reduction preparation data according to the preparation time points, and performing fitting treatment on data points in the time scatter diagram to obtain a fitting curve;
calculating a curve slope corresponding to the fitted curve, and analyzing the linear relation between each data in the noise reduction preparation data and the preparation time point according to the curve slope;
and extracting time sequence data and non-time sequence data from the noise reduction preparation data according to the linear relation.
3. The method for storing data applied to the preparation of a glass sand inclusion tube according to claim 2, wherein the calculating the curve slope corresponding to the fitted curve comprises:
calculating the slope of the curve corresponding to the fitted curve by the following formula:
wherein A represents the slope of the curve corresponding to the fitted curve, N t1 And M t1 Represents the corresponding point coordinates, N, in the fitting curve when the preparation time point is t1 t2 And M t2 Represents the corresponding point coordinates in the fitted curve when the preparation time point is t2,represents the slope of the curve point at the preparation time point t2 and the preparation time point t1, N tβ And M tβ And (3) representing corresponding point coordinates in the fitting curve when the preparation time point is tβ, wherein β represents the number of curve points in the fitting curve.
4. The method for storing data applied to the preparation of a glass sand inclusion tube according to claim 1, wherein the calculating the data association degree between each data in the time series data comprises:
calculating the data association degree between each data in the time series data by the following formula:
wherein B represents a degree of data correlation between each of the time-series data, D b Representing a data vector corresponding to the b-th data in the time-series data, D b+1 Represents the data vector corresponding to the (b+1) th data in the time series data, mu represents the data dimension, min b min b+1 |D b -D b+1 The expression represents the second-order minimum difference, max, representing the b-th data and the b+1th corresponding data vector in the time-series data b max b+1 |D b -D b+1 The i represents the second-order maximum difference between the b-th data and the b+1-th corresponding data vector in the time-series data.
5. The method for storing data applied to preparation of glass sand inclusion tubes according to claim 1, wherein the constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data comprises:
respectively extracting structural features corresponding to the associated sequence data and the non-associated sequence data to obtain a first structural feature and a second structural feature;
Performing feature screening on the first structural feature and the second structural feature to obtain a first target feature and a second target feature;
respectively constructing feature matrixes corresponding to the first target feature and the second target feature to obtain a first feature matrix and a second feature matrix;
respectively carrying out matrix fusion on the first feature matrix and the second feature matrix to obtain a first fusion matrix and a second fusion matrix;
generating fusion structural features corresponding to the associated sequence data and the non-associated sequence data according to the first fusion matrix and the second fusion matrix to obtain a first fusion feature and a second fusion feature;
extracting data parameters corresponding to the associated sequence data and the non-associated sequence data to obtain a first data parameter and a second data parameter;
setting storage requirements corresponding to the associated sequence data and the non-associated sequence data according to the first fusion feature, the second fusion feature, the first data parameter and the second data parameter;
and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data according to the storage requirement.
6. The method for storing data applied to preparation of glass sand inclusion pipe according to claim 1, wherein the step of respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface comprises the steps of:
performing attribute extraction on the associated sequence data and the non-associated sequence data respectively to obtain a first data attribute and a second data attribute;
calculating a support coefficient between each attribute in the first data attributes to obtain a first support coefficient;
calculating a support coefficient between each attribute in the second data attributes to obtain a second support coefficient;
according to the first support coefficient and the second support coefficient, key attributes in the first data attribute and the second data attribute are extracted respectively to obtain a first key attribute and a second key attribute;
according to the first key attribute and the second key attribute, interface types corresponding to the associated sequence data and the non-associated sequence data are respectively determined, and a first interface type and a second interface type are obtained;
respectively analyzing service logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first service logic and a second service logic;
According to the first service logic and the second service logic, interface logic corresponding to the associated sequence data and the non-associated sequence data is formulated to obtain first interface logic and second interface logic;
and respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database by combining the first interface type, the second interface type, the first interface logic and the second interface logic to obtain a first access interface and a second access interface.
7. The method for storing data in the preparation of a glass sand inclusion tube according to claim 6, wherein the calculating the support coefficient between each of the first data attributes to obtain the first support coefficient comprises:
the support coefficient between each of the first data attributes may be calculated by the following formula:
wherein F represents a support coefficient between each attribute in the first data attribute, q represents the attribute number of the first data attribute, e represents the attribute serial number of the first data attribute, H e Attribute probability, H, representing the e-th attribute of the first data attributes e+1 Representing attribute probability of (e+1) th attribute in first data attribute, G e，e+1 Representing the vector ratio of the e-th attribute and the e+1th attribute.
8. The method for storing data applied to preparation of glass sand inclusion pipe according to claim 1, wherein the mining of the characteristic information corresponding to the non-time series data comprises:
performing information mining on the non-time sequence data by using a preset decision tree mining model to obtain initial data information;
performing information cleaning on the initial data information to obtain target data information;
extracting the text of the target data information to obtain an information text, and calculating text weight corresponding to the information text;
and extracting characteristic information in the target data information by combining the information entropy value and the text weight.
9. The method for storing data applied to the preparation of a glass sand inclusion tube according to claim 8, wherein the calculating the information entropy value corresponding to each piece of the target data information comprises:
calculating an information entropy value corresponding to each piece of information in the target data information through the following formula:
wherein U represents the information entropy value corresponding to each piece of information in the target data information, i represents the information serial number corresponding to the target data information, delta represents the information quantity corresponding to the target data information, E i Represents the ith information, W (E i ) The occurrence probability of the i-th information in the target data information is represented.
10. A data storage system for use in the preparation of glass sand inclusion tubes, the system comprising:
the data processing module is used for acquiring preparation data in a preparation scene of the glass sand inclusion tube and extracting time sequence data and non-time sequence data in the preparation data;
the database construction module is used for calculating the data association degree between each piece of data in the time sequence data, carrying out data classification processing on the time sequence data according to the data association degree to obtain associated sequence data and non-associated sequence data, and constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data;
the database adjustment module is used for respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain a first access interface and a second access interface, and carrying out parameter adjustment processing on the distributed database by combining the first access interface and the second access interface to obtain a target database;
The data storage module is used for mining the characteristic information corresponding to the non-time sequence data, constructing a storage database corresponding to the non-time sequence data according to the characteristic information, and respectively executing data storage of the time sequence data and the non-time sequence data by combining the target database and the storage database to obtain a storage result.
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